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@@ -57,3 +57,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+GCI/pubmed/experimental_evidence.csv filter=lfs diff=lfs merge=lfs -text
+VCI/erepo.tabbed_2025-02-25.txt filter=lfs diff=lfs merge=lfs -text
+VCI/pubmed_id_to_text.csv filter=lfs diff=lfs merge=lfs -text

GCI/SOP/experimental_evidence/SOP10.json

@@ -0,0 +1,56 @@
+{
+  "EvidenceCategories": [
+    {
+      "title": "Biochemical Function A",
+      "description": "Evidence showing the gene product performs a biochemical function shared with other known genes in the disease of interest. NOTE: The biochemical function of both gene products must have been proven experimentally, and not just predicted. When awarding points in this evidence category, the other known gene(s) should have compelling evidence to support the gene-disease relationship. Consider increasing points based on the strength of the evidence and number of other proteins with the same function that are involved in the same disease."
+    },
+    {
+        "title": "Biochemical Function B",
+        "description": "Evidence showing the gene product performs a biochemical function consistent with the phenotype. NOTE: The biochemical function of both gene products must have been proven experimentally, and not just predicted. When awarding points in this evidence category, the other known gene(s) should have compelling evidence to support the gene-disease relationship. Consider increasing points based on the strength of the evidence and number of other proteins with the same function that are involved in the same disease."
+    },
+    {
+      "title": "Protein Interaction",
+      "description": "Evidence showing the gene product interacts with proteins previously implicated in the disease of interest. Typical examples of this data include, but are not limited to, physical interaction via Yeast-2-Hybrid (Y2H) or co-immunoprecipitation (coIP). NOTE: The interaction of the gene products must have been proven experimentally, and not just predicted. Proteins previously implicated in the disease of interest should have compelling evidence to support the gene-disease relationship. NOTE: Some studies provide evidence that a variant in the gene of interest disrupts the interaction of the gene product with another protein. In these cases, the positive control (showing interaction between the two wild-type proteins) can be counted as evidence of protein interaction. Points can also be awarded to case-level (variant) evidence or functional alteration for the variant that disrupts the interaction."
+    },
+    {
+      "title": "Expression A",
+      "description": "Evidence showing the gene is expressed in tissues relevant to the disease of interest and/or is altered in expression in patients who have the disease. This category is specific to methods to detect RNA transcripts (RNAseq, microarrays, qPCR, qRT-PCR, Real-Time PCR). An example scenario to consider for altered expression in patients includes studies in which expression of the gene of interest (and possibly additional genes) is examined in tissue and/or cell samples obtained from individuals with the disease of interest in which the molecular etiology is unknown."
+    },
+    {
+      "title": "Expression B",
+      "description": "Evidence showing the gene is expressed in tissues relevant to the disease of interest and/or is altered in expression in patients who have the disease. This category is specific to methods to detect protein expression (western blot, immunohistochemistry). An example scenario to consider for altered expression in patients includes studies in which expression of the gene of interest (and possibly additional genes) is examined in tissue and/or cell samples obtained from individuals with the disease of interest in which the molecular etiology is unknown."
+    },
+    {
+      "title": "Functional Alteration Patient cells",
+      "description": "Evidence showing that cultured cells, in which the function of the gene has been disrupted, have a phenotype consistent with the human disease process. This category is specific to experiments conducted in patient cells. Examples include experiments involving expression of a genetic variant, gene knock-down, or overexpression."
+    },
+    {
+      "title": "Functional Alteration Non-patient cells",
+      "description": "Evidence showing that cultured cells, in which the function of the gene has been disrupted, have a phenotype consistent with the human disease process. This category is specific to experiments conducted in non-patient cells. Examples include experiments involving expression of a genetic variant, gene knock-down, or overexpression."
+    },
+    {
+      "title": "Model System Non-human model organism",
+      "description": "A non-human model organism with a disrupted copy of the gene shows a phenotype consistent with the human disease state. NOTE: Cell culture models should recapitulate features of the diseased tissue (e.g., engineered heart tissue or cultured brain slices)."
+    },
+    {
+      "title": "Model Systems Cell culture model",
+      "description": "A cell culture model with a disrupted copy of the gene shows a phenotype consistent with the human disease state. NOTE: Cell culture models should recapitulate features of the diseased tissue (e.g., engineered heart tissue or cultured brain slices)."
+    },
+    {
+      "title": "Rescue Human",
+      "description": "Evidence showing that the phenotype in humans (i.e., patients with the condition) can be rescued. For example, successful enzyme replacement therapy for a lysosomal storage disease. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype. While the default points and point range are the same for human and non-human model organism, consider awarding more points if the rescue was in a human."
+    },
+    {
+      "title": "Rescue Patient Cells",
+      "description": "Evidence showing that the phenotype in patient cells can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    },
+    {
+      "title": "Rescue Non-human model organism",
+      "description": "Evidence showing that the phenotype in non-human model organisms can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    },
+    {
+      "title": "Rescue Cell culture model",
+      "description": "Evidence showing that the phenotype in cell culture models can be rescued (i.e. a cell culture model engineered to express the variant of interest). If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    }
+  ]
+}

GCI/SOP/experimental_evidence/SOP11.json

@@ -0,0 +1,56 @@
+{
+  "EvidenceCategories": [
+    {
+      "title": "Biochemical Function A",
+      "description": "Evidence showing the gene product performs a biochemical function shared with other known genes in the disease of interest. NOTE: The biochemical function of both gene products must have been proven experimentally, and not just predicted. When awarding points in this evidence category, the other known gene(s) should have compelling evidence to support the gene-disease relationship. Consider increasing points based on the strength of the evidence and number of other proteins with the same function that are involved in the same disease."
+    },
+    {
+        "title": "Biochemical Function B",
+        "description": "Evidence showing the gene product performs a biochemical function consistent with the phenotype. NOTE: The biochemical function of both gene products must have been proven experimentally, and not just predicted. When awarding points in this evidence category, the other known gene(s) should have compelling evidence to support the gene-disease relationship. Consider increasing points based on the strength of the evidence and number of other proteins with the same function that are involved in the same disease."
+    },
+    {
+      "title": "Protein Interaction",
+      "description": "Evidence showing the gene product interacts with proteins previously implicated in the disease of interest. Typical examples of this data include, but are not limited to, physical interaction via Yeast-2-Hybrid (Y2H) or co-immunoprecipitation (coIP). NOTE: The interaction of the gene products must have been proven experimentally, and not just predicted. Proteins previously implicated in the disease of interest should have compelling evidence to support the gene-disease relationship. NOTE: Some studies provide evidence that a variant in the gene of interest disrupts the interaction of the gene product with another protein. In these cases, the positive control (showing interaction between the two wild-type proteins) can be counted as evidence of protein interaction. Points can also be awarded to case-level (variant) evidence or functional alteration for the variant that disrupts the interaction."
+    },
+    {
+      "title": "Expression A",
+      "description": "Evidence showing the gene is expressed in tissues relevant to the disease of interest and/or is altered in expression in patients who have the disease. This category is specific to methods to detect RNA transcripts (RNAseq, microarrays, qPCR, qRT-PCR, Real-Time PCR). An example scenario to consider for altered expression in patients includes studies in which expression of the gene of interest (and possibly additional genes) is examined in tissue and/or cell samples obtained from individuals with the disease of interest in which the molecular etiology is unknown. For instance, tissue samples from 10 individuals diagnosed with hypertrophic cardiomyopathy were examined by western blot analysis and found that gene X was reduced in the heart cells of all patients. Expert reviewers may specify appropriate uses of this category in the context of their particular disease domain. For example, groups may choose to award points based on the specificity of expression in relevant organs."
+    },
+    {
+      "title": "Expression B",
+      "description": "Evidence showing the gene is expressed in tissues relevant to the disease of interest and/or is altered in expression in patients who have the disease. This category is specific to methods to detect protein expression (western blot, immunohistochemistry). An example scenario to consider for altered expression in patients includes studies in which expression of the gene of interest (and possibly additional genes) is examined in tissue and/or cell samples obtained from individuals with the disease of interest in which the molecular etiology is unknown. For instance, tissue samples from 10 individuals diagnosed with hypertrophic cardiomyopathy were examined by western blot analysis and found that gene X was reduced in the heart cells of all patients. Expert reviewers may specify appropriate uses of this category in the context of their particular disease domain. For example, groups may choose to award points based on the specificity of expression in relevant organs."
+    },
+    {
+      "title": "Functional Alteration Patient cells",
+      "description": "Evidence showing that cultured cells, in which the function of the gene has been disrupted, have a phenotype consistent with the human disease process. This category is specific to experiments conducted in patient cells. Examples include experiments involving expression of a genetic variant, gene knock-down, or overexpression."
+    },
+     {
+       "title": "Functional Alteration Non-patient cells",
+      "description": "Evidence showing that cultured cells, in which the function of the gene has been disrupted, have a phenotype consistent with the human disease process. This category is specific to experiments conducted in non-patient cells. Examples include experiments involving expression of a genetic variant, gene knock-down, or overexpression."
+    },
+    {
+      "title": "Model System Non-human model organism",
+      "description": "A non-human model organism with a disrupted copy of the gene shows a phenotype consistent with the human disease state. NOTE: Cell culture models should recapitulate features of the diseased tissue (e.g., engineered heart tissue or cultured brain slices)."
+    },
+    {
+      "title": "Model Systems Cell culture model",
+      "description": "A cell culture model with a disrupted copy of the gene shows a phenotype consistent with the human disease state. NOTE: Cell culture models should recapitulate features of the diseased tissue (e.g., engineered heart tissue or cultured brain slices)."
+    },
+    {
+      "title": "Rescue Human",
+      "description": "Evidence showing that the phenotype in humans (i.e., patients with the condition) can be rescued. For example, successful enzyme replacement therapy for a lysosomal storage disease. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype. While the default points and point range are the same for human and non-human model organisms, consider awarding more points if the rescue was in a human."
+    },
+    {
+      "title": "Rescue Patient Cells",
+      "description": "Evidence showing that the phenotype in patient cells can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    },
+    {
+      "title": "Rescue Non-human model organism",
+      "description": "Evidence showing that the phenotype in non-human model organisms can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    },
+    {
+      "title": "Rescue Cell culture model",
+      "description": "Evidence showing that the phenotype in cell culture models can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    }
+  ]
+}

GCI/SOP/experimental_evidence/SOP5.json

@@ -0,0 +1,56 @@
+{
+  "EvidenceCategories": [
+    {
+      "title": "Biochemical Function A",
+      "description": "Evidence showing the gene product performs a biochemical function shared with other known genes in the disease of interest. NOTE: The biochemical function of both gene products must have been proven experimentally, and not just predicted. When awarding points in this evidence category, the other known gene(s) should have compelling evidence to support the gene-disease relationship. Consider increasing points based on the strength of the evidence and number of other proteins with the same function that are involved in the same disease."
+    },
+    {
+      "title": "Biochemical Function B",
+      "description": "Evidence showing the gene product performs a biochemical function consistent with the phenotype. NOTE: The biochemical function of both gene products must have been proven experimentally, and not just predicted. When awarding points in this evidence category, the other known gene(s) should have compelling evidence to support the gene-disease relationship. Consider increasing points based on the strength of the evidence and number of other proteins with the same function that are involved in the same disease."
+    },
+    {
+      "title": "Protein Interaction",
+      "description": "Evidence showing the gene product interacts with proteins previously implicated in the disease of interest. Typical examples of this data include, but are not limited to, physical interaction via Yeast-2-Hybrid (Y2H) or co-immunoprecipitation (coIP). NOTE: The interaction of the gene products must have been proven experimentally, and not just predicted. Proteins previously implicated in the disease of interest should have compelling evidence to support the gene-disease relationship. NOTE: Some studies provide evidence that a variant in the gene of interest disrupts the interaction of the gene product with another protein. In these cases, the positive control (showing interaction between the two wild-type proteins) can be counted as evidence of protein interaction. Points can also be awarded to case-level (variant) evidence or functional alteration for the variant that disrupts the interaction."
+    },
+    {
+      "title": "Expression A",
+      "description": "Evidence showing the gene is expressed in tissues relevant to the disease of interest and/or is altered in expression in patients who have the disease. This category is specific to methods to detect RNA transcripts (RNAseq, microarrays, qPCR, qRT-PCR, Real-Time PCR). Expert reviewers may specify appropriate uses of this category in the context of their particular disease domain.  For example, groups may choose to award points based on the specificity of expression in relevant organs."
+    },
+    {
+      "title": "Expression B",
+      "description": "Evidence showing the gene is expressed in tissues relevant to the disease of interest and/or is altered in expression in patients who have the disease. This category is specific to methods to detect protein expression (western blot, immunohistochemistry). Expert reviewers may specify appropriate uses of this category in the context of their particular disease domain.  For example, groups may choose to award points based on the specificity of expression in relevant organs."
+    },
+    {
+      "title": "Functional Alteration Patient cells",
+      "description": "Evidence showing the gene and/or gene product function is demonstrably altered in cultured patient cells carrying candidate variants. This category is specific to experiments conducted in patient cells. For instance, does disrupting the gene in cells have a phenotype similar to that in human patients?  Examples include experiments involving gene knock-down, overexpression, etc."
+    },
+    {
+      "title": "Functional Alteration Non-patient cells",
+      "description": "Evidence showing the gene and/or gene product function is demonstrably altered in cultured non-patient cells carrying candidate variants. This category is specific to experiments conducted in non-patient cells. For instance, does disrupting the gene in cells have a phenotype similar to that in human patients?  Examples include experiments involving gene knock-down, overexpression, etc."
+    },
+    {
+      "title": "Model System Non-human model organism",
+      "description": "A non-human model organism with a disrupted copy of the gene shows a phenotype consistent with the human disease state. NOTE: Cell culture models should recapitulate features of the diseased tissue (e.g., engineered heart tissue or cultured brain slices)."
+    },
+    {
+      "title": "Model Systems Cell culture model",
+      "description": "A cell culture model with a disrupted copy of the gene shows a phenotype consistent with the human disease state. NOTE: Cell culture models should recapitulate features of the diseased tissue (e.g., engineered heart tissue or cultured brain slices)."
+    },
+    {
+      "title": "Rescue Human",
+      "description": "Summarize evidence showing the phenotype in humans (i.e. patients with the condition) can be rescued by exogenous wild-type gene or gene product. For example, successful enzyme replacement therapy for a lysosomal storage disease."
+    },
+    {
+      "title": "Rescue Patient Cells",
+      "description": "Summarize evidence showing the phenotype in patient cells can be rescued by exogenous wild-type gene or gene product."
+    },
+    {
+      "title": "Rescue Non-human model organism",
+      "description": "Summarize evidence showing the phenotype in non-human model organisms can be rescued by exogenous wild-type gene or gene product. Note: While the default points and range of points are the same for human and non-human model organism, consider awarding more points if the rescue was in a human."
+    },
+    {
+      "title": "Rescue Cell culture model",
+      "description": "Summarize evidence showing the phenotype in cell culture models (i.e. a cell culture model engineered to express the variant of interest) can be rescued by exogenous wild-type gene or gene product."
+    }
+  ]
+}

GCI/SOP/experimental_evidence/SOP6.json

@@ -0,0 +1,56 @@
+{
+  "EvidenceCategories": [
+    {
+      "title": "Biochemical Function A",
+      "description": "Evidence showing the gene product performs a biochemical function shared with other known genes in the disease of interest. NOTE: The biochemical function of both gene products must have been proven experimentally, and not just predicted. When awarding points in this evidence category, the other known gene(s) should have compelling evidence to support the gene-disease relationship. Consider increasing points based on the strength of the evidence and number of other proteins with the same function that are involved in the same disease."
+    },
+    {
+      "title": "Biochemical Function B",
+      "description": "Evidence showing the gene product performs a biochemical function consistent with the phenotype. NOTE: The biochemical function of both gene products must have been proven experimentally, and not just predicted. When awarding points in this evidence category, the other known gene(s) should have compelling evidence to support the gene-disease relationship. Consider increasing points based on the strength of the evidence and number of other proteins with the same function that are involved in the same disease."
+    },
+    {
+      "title": "Protein Interaction",
+      "description": "Evidence showing the gene product interacts with proteins previously implicated in the disease of interest. Typical examples of this data include, but are not limited to, physical interaction via Yeast-2-Hybrid (Y2H) or co-immunoprecipitation (coIP). NOTE: The interaction of the gene products must have been proven experimentally, and not just predicted. Proteins previously implicated in the disease of interest should have compelling evidence to support the gene-disease relationship. NOTE: Some studies provide evidence that a variant in the gene of interest disrupts the interaction of the gene product with another protein. In these cases, the positive control (showing interaction between the two wild-type proteins) can be counted as evidence of protein interaction. Points can also be awarded to case-level (variant) evidence or functional alteration for the variant that disrupts the interaction."
+    },
+    {
+      "title": "Expression A",
+      "description": "Evidence showing the gene is expressed in tissues relevant to the disease of interest and/or is altered in expression in patients who have the disease. This category is specific to methods to detect RNA transcripts (RNAseq, microarrays, qPCR, qRT-PCR, Real-Time PCR). Expert reviewers may specify appropriate uses of this category in the context of their particular disease domain.  For example, groups may choose to award points based on the specificity of expression in relevant organs."
+    },
+    {
+      "title": "Expression B",
+      "description": "Evidence showing the gene is expressed in tissues relevant to the disease of interest and/or is altered in expression in patients who have the disease. This category is specific to methods to detect protein expression (western blot, immunohistochemistry). Expert reviewers may specify appropriate uses of this category in the context of their particular disease domain.  For example, groups may choose to award points based on the specificity of expression in relevant organs."
+    },
+    {
+      "title": "Functional Alteration Patient cells",
+      "description": "Evidence showing that cultured cells, in which the function of the gene has been disrupted, have a phenotype consistent with the human disease process. This category is specific to experiments conducted in patient cells. Examples include experiments involving expression of a genetic variant, gene knock-down, or overexpression."
+    },
+    {
+      "title": "Functional Alteration Non-patient cells",
+      "description": "Evidence showing that cultured cells, in which the function of the gene has been disrupted, have a phenotype consistent with the human disease process. This category is specific to experiments conducted in non-patient cells. Examples include experiments involving expression of a genetic variant, gene knock-down, or overexpression."
+    },
+    {
+      "title": "Model System Non-human model organism",
+      "description": "A non-human model organism with a disrupted copy of the gene shows a phenotype consistent with the human disease state. NOTE: Cell culture models should recapitulate features of the diseased tissue (e.g., engineered heart tissue or cultured brain slices)."
+    },
+    {
+      "title": "Model Systems Cell culture model",
+      "description": "A cell culture model with a disrupted copy of the gene shows a phenotype consistent with the human disease state. NOTE: Cell culture models should recapitulate features of the diseased tissue (e.g., engineered heart tissue or cultured brain slices)."
+    },
+    {
+      "title": "Rescue Human",
+      "description": "Evidence showing that the phenotype in humans (i.e., patients with the condition) can be rescued. For example, successful enzyme replacement therapy for a lysosomal storage disease. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype. While the default points and point range are the same for human and non-human model organism, consider awarding more points if the rescue was in a human."
+    },
+    {
+      "title": "Rescue Patient Cells",
+      "description": "Evidence showing that the phenotype in patient cells can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    },
+    {
+      "title": "Rescue Non-human model organism",
+      "description": "Evidence showing that the phenotype in non-human model organisms can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    },
+    {
+      "title": "Rescue Cell culture model",
+      "description": "Evidence showing that the phenotype in cell culture models can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    }
+  ]
+}

GCI/SOP/experimental_evidence/SOP7.json

@@ -0,0 +1,56 @@
+{
+  "EvidenceCategories": [
+    {
+      "title": "Biochemical Function A",
+      "description": "Evidence showing the gene product performs a biochemical function shared with other known genes in the disease of interest. NOTE: The biochemical function of both gene products must have been proven experimentally, and not just predicted. When awarding points in this evidence category, the other known gene(s) should have compelling evidence to support the gene-disease relationship. Consider increasing points based on the strength of the evidence and number of other proteins with the same function that are involved in the same disease."
+    },
+    {
+      "title": "Biochemical Function B",
+      "description": "Evidence showing the gene product performs a biochemical function consistent with the phenotype. NOTE: The biochemical function of both gene products must have been proven experimentally, and not just predicted. When awarding points in this evidence category, the other known gene(s) should have compelling evidence to support the gene-disease relationship. Consider increasing points based on the strength of the evidence and number of other proteins with the same function that are involved in the same disease."
+    },
+    {
+      "title": "Protein Interaction",
+      "description": "Evidence showing the gene product interacts with proteins previously implicated in the disease of interest. Typical examples of this data include, but are not limited to, physical interaction via Yeast-2-Hybrid (Y2H) or co-immunoprecipitation (coIP). NOTE: The interaction of the gene products must have been proven experimentally, and not just predicted. Proteins previously implicated in the disease of interest should have compelling evidence to support the gene-disease relationship. NOTE: Some studies provide evidence that a variant in the gene of interest disrupts the interaction of the gene product with another protein. In these cases, the positive control (showing interaction between the two wild-type proteins) can be counted as evidence of protein interaction. Points can also be awarded to case-level (variant) evidence or functional alteration for the variant that disrupts the interaction."
+    },
+    {
+      "title": "Expression A",
+      "description": "Evidence showing the gene is expressed in tissues relevant to the disease of interest and/or is altered in expression in patients who have the disease. This category is specific to methods to detect RNA transcripts (RNAseq, microarrays, qPCR, qRT-PCR, Real-Time PCR). Expert reviewers may specify appropriate uses of this category in the context of their particular disease domain.  For example, groups may choose to award points based on the specificity of expression in relevant organs."
+    },
+    {
+      "title": "Expression B",
+      "description": "Evidence showing the gene is expressed in tissues relevant to the disease of interest and/or is altered in expression in patients who have the disease. This category is specific to methods to detect protein expression (western blot, immunohistochemistry). Expert reviewers may specify appropriate uses of this category in the context of their particular disease domain.  For example, groups may choose to award points based on the specificity of expression in relevant organs."
+    },
+    {
+      "title": "Functional Alteration Patient cells",
+      "description": "Evidence showing that cultured cells, in which the function of the gene has been disrupted, have a phenotype consistent with the human disease process. This category is specific to experiments conducted in patient cells. Examples include experiments involving expression of a genetic variant, gene knock-down, or overexpression."
+    },
+    {
+      "title": "Functional Alteration Non-patient cells",
+      "description": "Evidence showing that cultured cells, in which the function of the gene has been disrupted, have a phenotype consistent with the human disease process. This category is specific to experiments conducted in non-patient cells. Examples include experiments involving expression of a genetic variant, gene knock-down, or overexpression."
+    },
+    {
+      "title": "Model System Non-human model organism",
+      "description": "A non-human model organism with a disrupted copy of the gene shows a phenotype consistent with the human disease state. NOTE: Cell culture models should recapitulate features of the diseased tissue (e.g., engineered heart tissue or cultured brain slices)."
+    },
+    {
+      "title": "Model Systems Cell culture model",
+      "description": "A cell culture model with a disrupted copy of the gene shows a phenotype consistent with the human disease state. NOTE: Cell culture models should recapitulate features of the diseased tissue (e.g., engineered heart tissue or cultured brain slices)."
+    },
+    {
+      "title": "Rescue Human",
+      "description": "Evidence showing that the phenotype in humans (i.e., patients with the condition) can be rescued. For example, successful enzyme replacement therapy for a lysosomal storage disease. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype. While the default points and point range are the same for human and non-human model organism, consider awarding more points if the rescue was in a human."
+    },
+    {
+      "title": "Rescue Patient Cells",
+      "description": "Evidence showing that the phenotype in patient cells can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    },
+    {
+      "title": "Rescue Non-human model organism",
+      "description": "Evidence showing that the phenotype in non-human model organisms can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    },
+    {
+      "title": "Rescue Cell culture model",
+      "description": "Evidence showing that the phenotype in cell culture models can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    }
+  ]
+}

GCI/SOP/experimental_evidence/SOP8.json

@@ -0,0 +1,56 @@
+{
+  "EvidenceCategories": [
+    {
+      "title": "Biochemical Function A",
+      "description": "Evidence showing the gene product performs a biochemical function shared with other known genes in the disease of interest. NOTE: The biochemical function of both gene products must have been proven experimentally, and not just predicted. When awarding points in this evidence category, the other known gene(s) should have compelling evidence to support the gene-disease relationship. Consider increasing points based on the strength of the evidence and number of other proteins with the same function that are involved in the same disease."
+    },
+    {
+        "title": "Biochemical Function B",
+        "description": "Evidence showing the gene product performs a biochemical function consistent with the phenotype. NOTE: The biochemical function of both gene products must have been proven experimentally, and not just predicted. When awarding points in this evidence category, the other known gene(s) should have compelling evidence to support the gene-disease relationship. Consider increasing points based on the strength of the evidence and number of other proteins with the same function that are involved in the same disease."
+    },
+    {
+      "title": "Protein Interaction",
+      "description": "Evidence showing the gene product interacts with proteins previously implicated in the disease of interest. Typical examples of this data include, but are not limited to, physical interaction via Yeast-2-Hybrid (Y2H) or co-immunoprecipitation (coIP). NOTE: The interaction of the gene products must have been proven experimentally, and not just predicted. Proteins previously implicated in the disease of interest should have compelling evidence to support the gene-disease relationship. NOTE: Some studies provide evidence that a variant in the gene of interest disrupts the interaction of the gene product with another protein. In these cases, the positive control (showing interaction between the two wild-type proteins) can be counted as evidence of protein interaction. Points can also be awarded to case-level (variant) evidence or functional alteration for the variant that disrupts the interaction."
+    },
+    {
+      "title": "Expression A",
+      "description": "Evidence showing the gene is expressed in tissues relevant to the disease of interest and/or is altered in expression in patients who have the disease. This category is specific to methods to detect RNA transcripts (RNAseq, microarrays, qPCR, qRT-PCR, Real-Time PCR). An example scenario to consider for altered expression in patients includes studies in which expression of the gene of interest (and even additional genes) is examined in tissue and/or cell samples obtained from individuals with the disease of interest in which the molecular etiology of the individual is unknown. For instance, tissue samples from 10 individuals diagnosed with hypertrophic cardiomyopathy were examined by western blot analysis and found that gene X was reduced in the heart cells of all patients. Expert reviewers may specify appropriate uses of this category in the context of their particular disease domain. For example, groups may choose to award points based on the specificity of expression in relevant organs."
+    },
+    {
+      "title": "Expression B",
+      "description": "Evidence showing the gene is expressed in tissues relevant to the disease of interest and/or is altered in expression in patients who have the disease. This category is specific to methods to detect protein expression (western blot, immunohistochemistry). An example scenario to consider for altered expression in patients includes studies in which expression of the gene of interest (and even additional genes) is examined in tissue and/or cell samples obtained from individuals with the disease of interest in which the molecular etiology of the individual is unknown. For instance, tissue samples from 10 individuals diagnosed with hypertrophic cardiomyopathy were examined by western blot analysis and found that gene X was reduced in the heart cells of all patients. Expert reviewers may specify appropriate uses of this category in the context of their particular disease domain. For example, groups may choose to award points based on the specificity of expression in relevant organs."
+    },
+    {
+      "title": "Functional Alteration Patient cells",
+      "description": "Evidence showing that cultured cells, in which the function of the gene has been disrupted, have a phenotype consistent with the human disease process. This category is specific to experiments conducted in patient cells. Examples include experiments involving expression of a genetic variant, gene knock-down, or overexpression."
+    },
+    {
+      "title": "Functional Alteration Non-patient cells",
+      "description": "Evidence showing that cultured cells, in which the function of the gene has been disrupted, have a phenotype consistent with the human disease process. This category is specific to experiments conducted in non-patient cells. Examples include experiments involving expression of a genetic variant, gene knock-down, or overexpression."
+    },
+    {
+      "title": "Model System Non-human model organism",
+      "description": "A non-human model organism with a disrupted copy of the gene shows a phenotype consistent with the human disease state. NOTE: Cell culture models should recapitulate features of the diseased tissue (e.g., engineered heart tissue or cultured brain slices)."
+    },
+    {
+      "title": "Model Systems Cell culture model",
+      "description": "A cell culture model with a disrupted copy of the gene shows a phenotype consistent with the human disease state. NOTE: Cell culture models should recapitulate features of the diseased tissue (e.g., engineered heart tissue or cultured brain slices)."
+    },
+    {
+      "title": "Rescue Human",
+      "description": "Evidence showing that the phenotype in humans (i.e., patients with the condition) can be rescued. For example, successful enzyme replacement therapy for a lysosomal storage disease. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype. While the default points and point range are the same for human and non-human model organism, consider awarding more points if the rescue was in a human."
+    },
+    {
+      "title": "Rescue Patient Cells",
+      "description": "Evidence showing that the phenotype in patient cells can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    },
+    {
+      "title": "Rescue Non-human model organism",
+      "description": "Evidence showing that the phenotype in non-human model organisms can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    },
+    {
+      "title": "Rescue Cell culture model",
+      "description": "Evidence showing that the phenotype in cell culture models can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    }
+  ]
+}

GCI/SOP/experimental_evidence/SOP9.json

@@ -0,0 +1,56 @@
+{
+  "EvidenceCategories": [
+    {
+      "title": "Biochemical Function A",
+      "description": "Evidence showing the gene product performs a biochemical function shared with other known genes in the disease of interest. NOTE: The biochemical function of both gene products must have been proven experimentally, and not just predicted. When awarding points in this evidence category, the other known gene(s) should have compelling evidence to support the gene-disease relationship. Consider increasing points based on the strength of the evidence and number of other proteins with the same function that are involved in the same disease."
+    },
+    {
+        "title": "Biochemical Function B",
+        "description": "Evidence showing the gene product performs a biochemical function consistent with the phenotype. NOTE: The biochemical function of both gene products must have been proven experimentally, and not just predicted. When awarding points in this evidence category, the other known gene(s) should have compelling evidence to support the gene-disease relationship. Consider increasing points based on the strength of the evidence and number of other proteins with the same function that are involved in the same disease."
+    },
+    {
+      "title": "Protein Interaction",
+      "description": "Evidence showing the gene product interacts with proteins previously implicated in the disease of interest. Typical examples of this data include, but are not limited to, physical interaction via Yeast-2-Hybrid (Y2H) or co-immunoprecipitation (coIP). NOTE: The interaction of the gene products must have been proven experimentally, and not just predicted. Proteins previously implicated in the disease of interest should have compelling evidence to support the gene-disease relationship. NOTE: Some studies provide evidence that a variant in the gene of interest disrupts the interaction of the gene product with another protein. In these cases, the positive control (showing interaction between the two wild-type proteins) can be counted as evidence of protein interaction. Points can also be awarded to case-level (variant) evidence or functional alteration for the variant that disrupts the interaction."
+    },
+    {
+      "title": "Expression A",
+      "description": "Evidence showing the gene is expressed in tissues relevant to the disease of interest and/or is altered in expression in patients who have the disease. This category is specific to methods to detect RNA transcripts (RNAseq, microarrays, qPCR, qRT-PCR, Real-Time PCR). An example scenario to consider for altered expression in patients includes studies in which expression of the gene of interest (and even additional genes) is examined in tissue and/or cell samples obtained from individuals with the disease of interest in which the molecular etiology of the individual is unknown. For instance, tissue samples from 10 individuals diagnosed with hypertrophic cardiomyopathy were examined by western blot analysis and found that gene X was reduced in the heart cells of all patients. Expert reviewers may specify appropriate uses of this category in the context of their particular disease domain. For example, groups may choose to award points based on the specificity of expression in relevant organs."
+    },
+    {
+      "title": "Expression B",
+      "description": "Evidence showing the gene is expressed in tissues relevant to the disease of interest and/or is altered in expression in patients who have the disease. This category is specific to methods to detect protein expression (western blot, immunohistochemistry). An example scenario to consider for altered expression in patients includes studies in which expression of the gene of interest (and even additional genes) is examined in tissue and/or cell samples obtained from individuals with the disease of interest in which the molecular etiology of the individual is unknown. For instance, tissue samples from 10 individuals diagnosed with hypertrophic cardiomyopathy were examined by western blot analysis and found that gene X was reduced in the heart cells of all patients. Expert reviewers may specify appropriate uses of this category in the context of their particular disease domain. For example, groups may choose to award points based on the specificity of expression in relevant organs."
+    },
+    {
+      "title": "Functional Alteration Patient cells",
+      "description": "Evidence showing that cultured cells, in which the function of the gene has been disrupted, have a phenotype consistent with the human disease process. This category is specific to experiments conducted in patient cells. Examples include experiments involving expression of a genetic variant, gene knock-down, or overexpression."
+    },
+    {
+      "title": "Functional Alteration Non-patient cells",
+      "description": "Evidence showing that cultured cells, in which the function of the gene has been disrupted, have a phenotype consistent with the human disease process. This category is specific to experiments conducted in non-patient cells. Examples include experiments involving expression of a genetic variant, gene knock-down, or overexpression."
+    },
+    {
+      "title": "Model System Non-human model organism",
+      "description": "A non-human model organism with a disrupted copy of the gene shows a phenotype consistent with the human disease state. NOTE: Cell culture models should recapitulate features of the diseased tissue (e.g., engineered heart tissue or cultured brain slices)."
+    },
+    {
+      "title": "Model Systems Cell culture model",
+      "description": "A cell culture model with a disrupted copy of the gene shows a phenotype consistent with the human disease state. NOTE: Cell culture models should recapitulate features of the diseased tissue (e.g., engineered heart tissue or cultured brain slices)."
+    },
+    {
+      "title": "Rescue Human",
+      "description": "Evidence showing that the phenotype in humans (i.e., patients with the condition) can be rescued. For example, successful enzyme replacement therapy for a lysosomal storage disease. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype. While the default points and point range are the same for human and non-human model organism, consider awarding more points if the rescue was in a human."
+    },
+    {
+      "title": "Rescue Patient Cells",
+      "description": "Evidence showing that the phenotype in patient cells can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    },
+    {
+      "title": "Rescue Non-human model organism",
+      "description": "Evidence showing that the phenotype in non-human model organisms can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    },
+    {
+      "title": "Rescue Cell culture model",
+      "description": "Evidence showing that the phenotype in cell culture models can be rescued. If the phenotype is caused by loss of function, summarize evidence showing that the phenotype can be rescued by exogenous wild-type gene, gene product, or targeted gene editing. If the phenotype is caused by a gain-of-function variant, summarize evidence showing that a treatment which specifically blocks the action of the variant (e.g., siRNA, antibody, targeted gene editing) rescues the phenotype."
+    }
+  ]
+}

GCI/pubmed/experimental_evidence.csv

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VCI/erepo.tabbed_2025-02-25.txt

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+3,ClinGen Monogenic Diabetes Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines Version 1.1,HNF1A,1.1.0,6/5/2021,https://cspec.genome.network/cspec/ui/svi/doc/GN017?version=1.1.0,ClinGenMonogenicDiabetesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesVersion1.1_version=1.1.0.csv
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+1,ClinGen Brain Malformations Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines Version 1,"AKT3, MTOR, PIK3CA, PIK3R2",1.0.0,5/15/2021,https://cspec.genome.network/cspec/ui/svi/doc/GN018?version=1.0.0,ClinGenBrainMalformationsExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesVersion1_version=1.0.0.csv
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+1,ClinGen Glaucoma Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines Version 1.1,MYOC,1.1.0,11/19/2021,https://cspec.genome.network/cspec/ui/svi/doc/GN019?version=1.1.0,ClinGenGlaucomaExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesVersion1.1_version=1.1.0.csv
+2,ClinGen Glaucoma Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines Version 1,MYOC,1.0.0,10/12/2021,https://cspec.genome.network/cspec/ui/svi/doc/GN019?version=1.0.0,ClinGenGlaucomaExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesVersion1_version=1.0.0.csv
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+2,"ClinGen Hereditary Breast, Ovarian and Pancreatic Cancer Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for ATM Version 1.1",ATM,1.1.0,2/25/2022,https://cspec.genome.network/cspec/ui/svi/doc/GN020?version=1.1.0,"ClinGenHereditaryBreast,OvarianandPancreaticCancerExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforATMVersion1.1_version=1.1.0.csv"
+3,"ClinGen Hereditary Breast, Ovarian and Pancreatic Cancer Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for ATM Version 1",ATM,1.0.0,1/19/2022,https://cspec.genome.network/cspec/ui/svi/doc/GN020?version=1.0.0,"ClinGenHereditaryBreast,OvarianandPancreaticCancerExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforATMVersion1_version=1.0.0.csv"
+0,ClinGen ACADVL Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines Version 1,ACADVL,1.0.0,11/9/2021,https://cspec.genome.network/cspec/ui/svi/doc/GN021?version=1.0.0,ClinGenACADVLExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesVersion1_version=1.0.0.csv
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+0,ClinGen DICER1 and miRNA-Processing Gene Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for DICER1 Version 1.3.0,DICER1,1.3.0,1/30/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN024?version=1.3.0,ClinGenDICER1andmiRNA-ProcessingGeneExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforDICER1Version1.3.0_version=1.3.0.csv
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+3,ClinGen DICER1 and miRNA-Processing Gene Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for DICER1 Version 1,DICER1,1.0.0,5/5/2022,https://cspec.genome.network/cspec/ui/svi/doc/GN024?version=1.0.0,ClinGenDICER1andmiRNA-ProcessingGeneExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforDICER1Version1_version=1.0.0.csv
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+1,ClinGen Cerebral Creatine Deficiency Syndromes Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for GAMT Version 1.1.0,GAMT,1.1.0,9/14/2022,https://cspec.genome.network/cspec/ui/svi/doc/GN026?version=1.1.0,ClinGenCerebralCreatineDeficiencySyndromesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforGAMTVersion1.1.0_version=1.1.0.csv
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+2,ClinGen Cerebral Creatine Deficiency Syndromes Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for SLC6A8 Version 1,SLC6A8,1.0.0,3/21/2022,https://cspec.genome.network/cspec/ui/svi/doc/GN027?version=1.0.0,ClinGenCerebralCreatineDeficiencySyndromesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforSLC6A8Version1_version=1.0.0.csv
+0,ClinGen Rett and Angelman-like Disorders Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for TCF4 Version 4.0.0,TCF4,4.0.0,2/28/2025,https://cspec.genome.network/cspec/ui/svi/doc/GN032?version=4.0.0,ClinGenRettandAngelman-likeDisordersExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforTCF4Version4.0.0_version=4.0.0.csv
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+1,ClinGen Epilepsy Sodium Channel Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for SCN1A Version 1.0.0,SCN1A,1.0.0,3/19/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN067?version=1.0.0,ClinGenEpilepsySodiumChannelExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforSCN1AVersion1.0.0_version=1.0.0.csv
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+1,ClinGen Epilepsy Sodium Channel Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for SCN2A Version 1.0.0,SCN2A,1.0.0,3/19/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN068?version=1.0.0,ClinGenEpilepsySodiumChannelExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforSCN2AVersion1.0.0_version=1.0.0.csv
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+0,"ClinGen Hereditary Breast, Ovarian and Pancreatic Cancer Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for PALB2 Version 1.1.0",PALB2,1.1.0,11/28/2023,https://cspec.genome.network/cspec/ui/svi/doc/GN077?version=1.1.0,"ClinGenHereditaryBreast,OvarianandPancreaticCancerExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforPALB2Version1.1.0_version=1.1.0.csv"
+1,"ClinGen Hereditary Breast, Ovarian and Pancreatic Cancer Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for PALB2 Version 1.0.0",PALB2,1.0.0,3/17/2023,https://cspec.genome.network/cspec/ui/svi/doc/GN077?version=1.0.0,"ClinGenHereditaryBreast,OvarianandPancreaticCancerExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforPALB2Version1.0.0_version=1.0.0.csv"
+0,ClinGen VHL Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for VHL Version 1.1.0,VHL,1.1.0,1/10/2025,https://cspec.genome.network/cspec/ui/svi/doc/GN078?version=1.1.0,ClinGenVHLExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforVHLVersion1.1.0_version=1.1.0.csv
+1,ClinGen VHL Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for VHL Version 1.0.0,VHL,1.0.0,2/29/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN078?version=1.0.0,ClinGenVHLExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforVHLVersion1.0.0_version=1.0.0.csv
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+0,ClinGen Platelet Disorders Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for GP9 Version 1.0.0,GP9,1.0.0,2/11/2025,https://cspec.genome.network/cspec/ui/svi/doc/GN083?version=1.0.0,ClinGenPlateletDisordersExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforGP9Version1.0.0_version=1.0.0.csv
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+1,ClinGen Monogenic Diabetes Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for GCK Version 1.3.0,GCK,1.3.0,8/11/2023,https://cspec.genome.network/cspec/ui/svi/doc/GN086?version=1.3.0,ClinGenMonogenicDiabetesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforGCKVersion1.3.0_version=1.3.0.csv
+2,ClinGen Monogenic Diabetes Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for GCK Version 1.2.0,GCK,1.2.0,6/7/2023,https://cspec.genome.network/cspec/ui/svi/doc/GN086?version=1.2.0,ClinGenMonogenicDiabetesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforGCKVersion1.2.0_version=1.2.0.csv
+3,ClinGen Monogenic Diabetes Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for GCK Version 1.1.0,GCK,1.1.0,3/23/2023,https://cspec.genome.network/cspec/ui/svi/doc/GN086?version=1.1.0,ClinGenMonogenicDiabetesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforGCKVersion1.1.0_version=1.1.0.csv
+4,ClinGen Monogenic Diabetes Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for GCK Version 1.0.0,GCK,1.0.0,11/16/2022,https://cspec.genome.network/cspec/ui/svi/doc/GN086?version=1.0.0,ClinGenMonogenicDiabetesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforGCKVersion1.0.0_version=1.0.0.csv
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+2,ClinGen RASopathy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for MRAS Version 1.2.0,MRAS,1.2.0,12/2/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN087?version=1.2.0,ClinGenRASopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforMRASVersion1.2.0_version=1.2.0.csv
+3,ClinGen RASopathy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for MRAS Version 1.1.0,MRAS,1.1.0,9/17/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN087?version=1.1.0,ClinGenRASopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforMRASVersion1.1.0_version=1.1.0.csv
+4,ClinGen RASopathy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for MRAS Version 1.0.0,MRAS,1.0.0,8/3/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN087?version=1.0.0,ClinGenRASopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforMRASVersion1.0.0_version=1.0.0.csv
+0,ClinGen InSiGHT Hereditary Colorectal Cancer/Polyposis Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for APC Version 2.1.0,APC,2.1.0,11/24/2023,https://cspec.genome.network/cspec/ui/svi/doc/GN089?version=2.1.0,ClinGenInSiGHTHereditaryColorectalCancerPolyposisExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforAPCVersion2.1.0_version=2.1.0.csv
+1,ClinGen InSiGHT Hereditary Colorectal Cancer/Polyposis Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for APC Version 2.0.3,APC,2.0.3,7/24/2023,https://cspec.genome.network/cspec/ui/svi/doc/GN089?version=2.0.3,ClinGenInSiGHTHereditaryColorectalCancerPolyposisExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforAPCVersion2.0.3_version=2.0.3.csv
+2,ClinGen InSiGHT Hereditary Colorectal Cancer/Polyposis Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for APC Version 1.0.0,APC,1.0.0,1/10/2023,https://cspec.genome.network/cspec/ui/svi/doc/GN089?version=1.0.0,ClinGenInSiGHTHereditaryColorectalCancerPolyposisExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforAPCVersion1.0.0_version=1.0.0.csv
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+0,ClinGen Lysosomal Diseases Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for IDUA Version 1.0.0,IDUA,1.0.0,12/5/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN091?version=1.0.0,ClinGenLysosomalDiseasesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforIDUAVersion1.0.0_version=1.0.0.csv
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+1,ClinGen RASopathy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for LZTR1 Version 1.2.0,LZTR1,1.2.0,12/2/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN094?version=1.2.0,ClinGenRASopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforLZTR1Version1.2.0_version=1.2.0.csv
+2,ClinGen RASopathy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for LZTR1 Version 1.1.0,LZTR1,1.1.0,9/17/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN094?version=1.1.0,ClinGenRASopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforLZTR1Version1.1.0_version=1.1.0.csv
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+0,ClinGen Cardiomyopathy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for ACTC1 Version 1.0.0,ACTC1,1.0.0,4/22/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN101?version=1.0.0,ClinGenCardiomyopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforACTC1Version1.0.0_version=1.0.0.csv
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+0,ClinGen Severe Combined Immunodeficiency Disease Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for FOXN1 Version 1.0.0,FOXN1,1.0.0,7/29/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN113?version=1.0.0,ClinGenSevereCombinedImmunodeficiencyDiseaseExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforFOXN1Version1.0.0_version=1.0.0.csv
+0,ClinGen Severe Combined Immunodeficiency Disease Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for ADA Version 1.0.0,ADA,1.0.0,10/9/2023,https://cspec.genome.network/cspec/ui/svi/doc/GN114?version=1.0.0,ClinGenSevereCombinedImmunodeficiencyDiseaseExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforADAVersion1.0.0_version=1.0.0.csv
+0,ClinGen InSiGHT Hereditary Colorectal Cancer/Polyposis Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for MLH1 Version 1.0.0,MLH1,1.0.0,8/9/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN115?version=1.0.0,ClinGenInSiGHTHereditaryColorectalCancerPolyposisExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforMLH1Version1.0.0_version=1.0.0.csv
+0,ClinGen Severe Combined Immunodeficiency Disease Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for DCLRE1C Version 1.0.0,DCLRE1C,1.0.0,10/9/2023,https://cspec.genome.network/cspec/ui/svi/doc/GN116?version=1.0.0,ClinGenSevereCombinedImmunodeficiencyDiseaseExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforDCLRE1CVersion1.0.0_version=1.0.0.csv
+0,ClinGen Severe Combined Immunodeficiency Disease Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for IL7R Version 1.0.0,IL7R,1.0.0,10/9/2023,https://cspec.genome.network/cspec/ui/svi/doc/GN119?version=1.0.0,ClinGenSevereCombinedImmunodeficiencyDiseaseExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforIL7RVersion1.0.0_version=1.0.0.csv
+0,ClinGen Leber Congenital Amaurosis/early onset Retinal Dystrophy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for RPE65 Version 1.0.0,RPE65,1.0.0,10/24/2023,https://cspec.genome.network/cspec/ui/svi/doc/GN120?version=1.0.0,ClinGenLeberCongenitalAmaurosisearlyonsetRetinalDystrophyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforRPE65Version1.0.0_version=1.0.0.csv
+0,ClinGen Severe Combined Immunodeficiency Disease Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for JAK3 Version 1.0.0,JAK3,1.0.0,10/9/2023,https://cspec.genome.network/cspec/ui/svi/doc/GN121?version=1.0.0,ClinGenSevereCombinedImmunodeficiencyDiseaseExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforJAK3Version1.0.0_version=1.0.0.csv
+0,ClinGen Severe Combined Immunodeficiency Disease Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for RAG1 Version 1.0.0,RAG1,1.0.0,10/9/2023,https://cspec.genome.network/cspec/ui/svi/doc/GN123?version=1.0.0,ClinGenSevereCombinedImmunodeficiencyDiseaseExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforRAG1Version1.0.0_version=1.0.0.csv
+0,ClinGen Severe Combined Immunodeficiency Disease Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for RAG2 Version 1.0.0,RAG2,1.0.0,10/9/2023,https://cspec.genome.network/cspec/ui/svi/doc/GN124?version=1.0.0,ClinGenSevereCombinedImmunodeficiencyDiseaseExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforRAG2Version1.0.0_version=1.0.0.csv
+0,ClinGen Pulmonary Hypertension Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for BMPR2 Version 1.1.0,BMPR2,1.1.0,4/6/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN125?version=1.1.0,ClinGenPulmonaryHypertensionExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforBMPR2Version1.1.0_version=1.1.0.csv
+1,ClinGen Pulmonary Hypertension Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for BMPR2 Version 1.0.0,BMPR2,1.0.0,3/1/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN125?version=1.0.0,ClinGenPulmonaryHypertensionExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforBMPR2Version1.0.0_version=1.0.0.csv
+0,ClinGen RASopathy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for RRAS2 Version 1.3.0,RRAS2,1.3.0,12/3/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN127?version=1.3.0,ClinGenRASopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforRRAS2Version1.3.0_version=1.3.0.csv
+1,ClinGen RASopathy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for RRAS2 Version 1.2.0,RRAS2,1.2.0,12/2/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN127?version=1.2.0,ClinGenRASopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforRRAS2Version1.2.0_version=1.2.0.csv
+2,ClinGen RASopathy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for RRAS2 Version 1.1.0,RRAS2,1.1.0,9/17/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN127?version=1.1.0,ClinGenRASopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforRRAS2Version1.1.0_version=1.1.0.csv
+3,ClinGen RASopathy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for RRAS2 Version 1.0.0,RRAS2,1.0.0,8/3/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN127?version=1.0.0,ClinGenRASopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforRRAS2Version1.0.0_version=1.0.0.csv
+0,ClinGen RASopathy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for PPP1CB Version 1.3.0,PPP1CB,1.3.0,12/3/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN128?version=1.3.0,ClinGenRASopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforPPP1CBVersion1.3.0_version=1.3.0.csv
+1,ClinGen RASopathy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for PPP1CB Version 1.2.0,PPP1CB,1.2.0,12/2/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN128?version=1.2.0,ClinGenRASopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforPPP1CBVersion1.2.0_version=1.2.0.csv
+2,ClinGen RASopathy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for PPP1CB Version 1.1.0,PPP1CB,1.1.0,9/17/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN128?version=1.1.0,ClinGenRASopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforPPP1CBVersion1.1.0_version=1.1.0.csv
+3,ClinGen RASopathy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for PPP1CB Version 1.0.0,PPP1CB,1.0.0,8/3/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN128?version=1.0.0,ClinGenRASopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforPPP1CBVersion1.0.0_version=1.0.0.csv
+0,ClinGen Severe Combined Immunodeficiency Disease Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for IL2RG Version 1.0.0,IL2RG,1.0.0,10/9/2023,https://cspec.genome.network/cspec/ui/svi/doc/GN129?version=1.0.0,ClinGenSevereCombinedImmunodeficiencyDiseaseExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforIL2RGVersion1.0.0_version=1.0.0.csv
+0,ClinGen Hereditary Hemorrhagic Telangiectasia Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for ACVRL1 Version 1.1.0,ACVRL1,1.1.0,3/20/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN135?version=1.1.0,ClinGenHereditaryHemorrhagicTelangiectasiaExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforACVRL1Version1.1.0_version=1.1.0.csv
+1,ClinGen Hereditary Hemorrhagic Telangiectasia Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for ACVRL1 Version 1.0.0,ACVRL1,1.0.0,3/5/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN135?version=1.0.0,ClinGenHereditaryHemorrhagicTelangiectasiaExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforACVRL1Version1.0.0_version=1.0.0.csv
+0,ClinGen Hereditary Hemorrhagic Telangiectasia Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for ENG Version 1.1.0,ENG,1.1.0,3/20/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN136?version=1.1.0,ClinGenHereditaryHemorrhagicTelangiectasiaExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforENGVersion1.1.0_version=1.1.0.csv
+1,ClinGen Hereditary Hemorrhagic Telangiectasia Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for ENG Version 1.0.0,ENG,1.0.0,3/5/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN136?version=1.0.0,ClinGenHereditaryHemorrhagicTelangiectasiaExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforENGVersion1.0.0_version=1.0.0.csv
+0,ClinGen InSiGHT Hereditary Colorectal Cancer/Polyposis Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for MSH2 Version 1.0.0,MSH2,1.0.0,8/9/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN137?version=1.0.0,ClinGenInSiGHTHereditaryColorectalCancerPolyposisExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforMSH2Version1.0.0_version=1.0.0.csv
+0,ClinGen InSiGHT Hereditary Colorectal Cancer/Polyposis Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for MSH6 Version 1.0.0,MSH6,1.0.0,8/9/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN138?version=1.0.0,ClinGenInSiGHTHereditaryColorectalCancerPolyposisExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforMSH6Version1.0.0_version=1.0.0.csv
+0,ClinGen InSiGHT Hereditary Colorectal Cancer/Polyposis Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for PMS2 Version 1.0.0,PMS2,1.0.0,8/9/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN139?version=1.0.0,ClinGenInSiGHTHereditaryColorectalCancerPolyposisExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforPMS2Version1.0.0_version=1.0.0.csv
+1,ClinGen InSiGHT Hereditary Colorectal Cancer/Polyposis Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for PMS2 Version 1.0.0,PMS2,1.0.0,8/9/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN139?version=1.0.0,ClinGenInSiGHTHereditaryColorectalCancerPolyposisExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforPMS2Version1.0.0_version=1.0.0.csv
+0,ClinGen Congenital Myopathies Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for NEB Version 1.0.0,NEB,1.0.0,8/7/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN146?version=1.0.0,ClinGenCongenitalMyopathiesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforNEBVersion1.0.0_version=1.0.0.csv
+0,ClinGen Congenital Myopathies Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for ACTA1 Version 2.0.0,ACTA1,2.0.0,8/27/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN147?version=2.0.0,ClinGenCongenitalMyopathiesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforACTA1Version2.0.0_version=2.0.0.csv
+1,ClinGen Congenital Myopathies Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for ACTA1 Version 1.0.0,ACTA1,1.0.0,8/7/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN147?version=1.0.0,ClinGenCongenitalMyopathiesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforACTA1Version1.0.0_version=1.0.0.csv
+0,ClinGen Congenital Myopathies Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for DNM2 Version 1.0.0,DNM2,1.0.0,8/7/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN148?version=1.0.0,ClinGenCongenitalMyopathiesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforDNM2Version1.0.0_version=1.0.0.csv
+0,ClinGen Congenital Myopathies Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for MTM1 Version 1.0.0,MTM1,1.0.0,8/7/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN149?version=1.0.0,ClinGenCongenitalMyopathiesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforMTM1Version1.0.0_version=1.0.0.csv
+0,ClinGen Congenital Myopathies Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for RYR1 Version 2.0.0,RYR1,2.0.0,12/12/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN150?version=2.0.0,ClinGenCongenitalMyopathiesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforRYR1Version2.0.0_version=2.0.0.csv
+1,ClinGen Congenital Myopathies Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for RYR1 Version 1.0.0,RYR1,1.0.0,8/7/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN150?version=1.0.0,ClinGenCongenitalMyopathiesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforRYR1Version1.0.0_version=1.0.0.csv
+0,ClinGen Leber Congenital Amaurosis/early onset Retinal Dystrophy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for GUCY2D Version 1.0.0,GUCY2D,1.0.0,1/22/2025,https://cspec.genome.network/cspec/ui/svi/doc/GN167?version=1.0.0,ClinGenLeberCongenitalAmaurosisearlyonsetRetinalDystrophyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforGUCY2DVersion1.0.0_version=1.0.0.csv
+0,ClinGen Congenital Myopathies Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for ACTA1 Version 1.0.0,ACTA1,1.0.0,8/7/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN169?version=1.0.0,ClinGenCongenitalMyopathiesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforACTA1Version1.0.0_version=1.0.0.csv
+0,ClinGen Congenital Myopathies Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for RYR1 Version 2.0.0,RYR1,2.0.0,12/12/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN179?version=2.0.0,ClinGenCongenitalMyopathiesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforRYR1Version2.0.0_version=2.0.0.csv
+1,ClinGen Congenital Myopathies Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for RYR1 Version 1.0.0,RYR1,1.0.0,8/7/2024,https://cspec.genome.network/cspec/ui/svi/doc/GN179?version=1.0.0,ClinGenCongenitalMyopathiesExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforRYR1Version1.0.0_version=1.0.0.csv
+0,ClinGen Limb Girdle Muscular Dystrophy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for DYSF Version 1.0.0,DYSF,1.0.0,1/7/2025,https://cspec.genome.network/cspec/ui/svi/doc/GN180?version=1.0.0,ClinGenLimbGirdleMuscularDystrophyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforDYSFVersion1.0.0_version=1.0.0.csv
+0,ClinGen Limb Girdle Muscular Dystrophy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for SGCB Version 1.0.0,SGCB,1.0.0,1/7/2025,https://cspec.genome.network/cspec/ui/svi/doc/GN184?version=1.0.0,ClinGenLimbGirdleMuscularDystrophyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforSGCBVersion1.0.0_version=1.0.0.csv
+0,ClinGen Limb Girdle Muscular Dystrophy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for SGCG Version 1.0.0,SGCG,1.0.0,1/7/2025,https://cspec.genome.network/cspec/ui/svi/doc/GN185?version=1.0.0,ClinGenLimbGirdleMuscularDystrophyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforSGCGVersion1.0.0_version=1.0.0.csv
+0,ClinGen Limb Girdle Muscular Dystrophy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for SGCD Version 1.0.0,SGCD,1.0.0,1/7/2025,https://cspec.genome.network/cspec/ui/svi/doc/GN186?version=1.0.0,ClinGenLimbGirdleMuscularDystrophyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforSGCDVersion1.0.0_version=1.0.0.csv
+0,ClinGen Limb Girdle Muscular Dystrophy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for CAPN3 Version 1.0.0,CAPN3,1.0.0,1/7/2025,https://cspec.genome.network/cspec/ui/svi/doc/GN187?version=1.0.0,ClinGenLimbGirdleMuscularDystrophyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforCAPN3Version1.0.0_version=1.0.0.csv
+0,ClinGen Limb Girdle Muscular Dystrophy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for ANO5 Version 1.0.0,ANO5,1.0.0,1/7/2025,https://cspec.genome.network/cspec/ui/svi/doc/GN188?version=1.0.0,ClinGenLimbGirdleMuscularDystrophyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforANO5Version1.0.0_version=1.0.0.csv
+0,ClinGen Limb Girdle Muscular Dystrophy Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines for SGCA Version 1.0.0,SGCA,1.0.0,1/7/2025,https://cspec.genome.network/cspec/ui/svi/doc/GN189?version=1.0.0,ClinGenLimbGirdleMuscularDystrophyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforSGCAVersion1.0.0_version=1.0.0.csv

VCI/parsing_csr_criteria/get_versions.py

@@ -0,0 +1,66 @@
+from selenium import webdriver
+from selenium.webdriver.chrome.service import Service
+from selenium.webdriver.common.by import By
+from selenium.webdriver.chrome.options import Options
+import time
+import random
+import pandas as pd
+
+def extract_variant_guideline_data(url: str) -> pd.DataFrame:
+    chrome_driver_path = "/usr/local/bin/chromedriver"  # ← Update this to your actual path
+
+    # Chrome options to minimize detection
+    options = Options()
+    options.add_experimental_option("excludeSwitches", ["enable-automation"])
+    options.add_experimental_option('useAutomationExtension', False)
+    options.add_argument("--disable-blink-features=AutomationControlled")
+    options.add_argument("--headless=new")
+    options.add_argument("--disable-gpu")
+    options.add_argument("--no-sandbox")
+    options.add_argument("--log-level=3")
+
+    service = Service(executable_path=chrome_driver_path)
+    driver = webdriver.Chrome(service=service, options=options)
+
+    driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument", {
+        "source": "Object.defineProperty(navigator, 'webdriver', {get: () => undefined})"
+    })
+
+    driver.get(url)
+    time.sleep(random.uniform(1, 2))  # Just enough delay for JS to render
+
+    rows = driver.find_elements(By.CSS_SELECTOR, "div.row.version")
+    data = []
+
+    for row in rows:
+        try:
+            link_elem = row.find_element(By.CSS_SELECTOR, "div.title a")
+            genes_elem = row.find_element(By.CSS_SELECTOR, "div.genes")
+            version_elem = row.find_element(By.CSS_SELECTOR, "div.version-num")
+            date_elem = row.find_element(By.CSS_SELECTOR, "div.release-date")
+
+            data.append({
+                "Title": link_elem.text.strip(),
+                "Genes": genes_elem.text.strip(),
+                "Version": version_elem.text.strip(),
+                "Released Date": date_elem.text.strip(),
+                "Link": link_elem.get_attribute("href").strip()
+            })
+
+        except Exception:
+            continue  # Skip bad rows silently
+
+    driver.quit()
+    return pd.DataFrame(data)
+
+
+
+# 🧪 Test block
+if __name__ == "__main__":
+    test_url = "https://cspec.genome.network/cspec/ui/svi/doc/GN037/versions"
+    df = extract_variant_guideline_data(test_url)
+
+    print(df.head())  # Show first few entries
+    import ipdb; ipdb.set_trace()  # Debugging breakpoint
+    #df.to_csv("variant_guideline_output.csv", index=False)
+    print("📄 Data saved to 'variant_guideline_output.csv'")

VCI/parsing_csr_criteria/parse_on_date.py

@@ -0,0 +1,215 @@
+import pandas as pd
+import numpy as np
+from datetime import date, datetime
+import re
+from tqdm import tqdm
+from dateutil.parser import parse
+
+RYR1_VCEP_MAP = {
+    "Malignant Hyperthermia Susceptibility VCEP": "",
+    "Congenital Myopathies VCEP": "",
+}
+
+def extract_doc_id(url: str) -> str:
+    """
+    Extract the *GN…* document identifier from a ClinGen CSPEC URL.
+
+    Parameters
+    ----------
+    url : str
+        A URL like
+        'https://cspec.genome.network/cspec/ui/svi/doc/GN006?version=1.0.0'
+
+    Returns
+    -------
+    str
+        The identifier after `/doc/`, e.g. 'GN006'.
+
+    Raises
+    ------
+    ValueError
+        If the pattern `/doc/<id>` is not found in the URL.
+    """
+    match = re.search(r"/doc/([^/?]+)", url)
+    if not match:
+        raise ValueError(f"No document id found in: {url}")
+    return match.group(1)
+
+
+def closest_before(d: date, dates: list[date]) -> date | None:
+    # keep only the dates that are earlier than d
+    earlier = [x for x in dates if x <= d]
+    # the latest of those is the closest before d
+    return np.argmax(earlier) if earlier else None
+
+def parse_date_CVG(date_str):
+    """
+    Convert date string in MM/DD/YYYY format to datetime date object.
+    """
+    try:
+        return datetime.strptime(date_str, '%m/%d/%Y').date()
+    except (ValueError, TypeError):
+        return None
+
+def parse_erepo_date(date_str):
+    """
+    Convert date string in YYYY-MM-DD format to datetime date object.
+    
+    Parameters:
+    -----------
+    date_str : str
+        Date string in YYYY-MM-DD format
+        
+    Returns:
+    --------
+    datetime.date or None
+        Parsed date as a datetime date object, or None if parsing fails
+    """
+    try:
+        return datetime.strptime(date_str, '%Y-%m-%d').date()
+    except (ValueError, TypeError):
+        return None
+
+def map_gene_disease_to_index(df, cd):
+
+    map_gd = {}
+
+    problem_types = {
+        "no gene, disease": 0,
+        "multiple overlaps": 0,
+        "no gene": 0,
+    }
+    
+    for i, row in df.iterrows():
+        gene, mondo_id = row["hgnc_gene"], row["mondo_id"]
+
+        disease_presence = (cd["disease_id"] == mondo_id)
+        gene_presence = (cd["gene"] == gene)
+        overlap = gene_presence & disease_presence
+
+        if overlap.sum() == 0:
+            # Means we need to search:
+            # Search in gene-centric way:
+            if gene_presence.sum() == 0:
+                problem_types["no gene"] += 1
+                continue
+                
+            df_consider = cd[gene_presence & cd["disease_id"].isna()]
+            if df_consider.shape[0] == 0:
+                problem_types["no gene, disease"] += 1
+                continue
+                #raise ValueError
+            map_gd[(gene, mondo_id)] = df_consider.index[0]
+
+        elif overlap.sum() == 1:
+            # Means we have a unique match
+            map_gd[(gene, mondo_id)] = cd[overlap].index[0]
+
+        else:
+            # Means we have multiple matches
+            problem_types["multiple overlaps"] += 1
+            #raise ValueError
+        
+    return map_gd, problem_types
+
+map_vcep = {
+    #'Mitochondrial Diseases': "MITO", 
+    'Severe Combined Immunodeficiency Disease ': 'Severe Combined Immunodeficiency Disease', 
+    'von Willebrand Disease ': 'von Willebrand Disease',
+}
+
+map_vcep_from_cvg = {
+    "Lysosomal Storage Disorders Variant Curation": "Lysosomal Diseases",
+    "PAH": "Phenylketonuria",
+}
+
+# Skip: LRRC56, KLLN
+
+def main():
+
+    df = pd.read_csv("../clingen_vci_pubmed_fulltext.csv")
+
+    for i, row in df.iterrows():
+        vcep_name = row["expert_panel"]
+        if vcep_name in map_vcep.keys():
+            df.loc[i, "expert_panel"] = map_vcep[vcep_name]
+
+    # Load df with dates:
+    df_full = pd.read_csv("../erepo.tabbed_2025-02-25.txt", sep="\t")
+
+    cd = pd.read_csv("../csr_criteria/cspec_directory.csv")
+    cvg = pd.read_csv("cspec_version_guide_processed.csv")
+
+    for i, row in cvg.iterrows():
+        vcep_name = row["Title"]
+        if vcep_name in map_vcep_from_cvg.keys():
+            cvg.loc[i, "Title"] = map_vcep_from_cvg[vcep_name]
+
+    cvg["unifying_code"] = cvg["Link"].apply(extract_doc_id)
+    cd["unifying_code"] = cd["criteria_link"].apply(lambda x: x.split("/")[-1])
+
+    # Convert dates in version guide:
+    #cvg["date"] = cvg["Released Date"].apply(lambda x: parse(x).date())
+    cvg["date"] = cvg["Released Date"].apply(parse_date_CVG)
+    def map_gene_vcep_to_index(vcep, gene):
+
+        gene_mask = (cvg["Genes"] == gene)
+        vcep_mask = (cvg["Title"] == vcep)
+
+        if (gene_mask.sum() == 0) and ("Mitochondrial" in vcep):
+            # This is probably mitochondrial disease
+            return cvg[(cvg["Title"] == "Mitochondrial Disease Nuclear and Mitochondrial") & cvg["Genes"].isna()]
+        else:
+            return cvg[gene_mask & vcep_mask]
+
+    df_full["Approval Date"] = df_full["Approval Date"].apply(parse_erepo_date)
+    df_full["Published Date"] = df_full["Published Date"].apply(parse_erepo_date)
+
+    counter_invalid = 0
+    row_lists = []
+    path_list = []
+    for i, row in tqdm(df.iterrows(), total=len(df)):
+
+        # if (row["hgnc_gene"] == "GAA"): # CHECKED
+        #     # Use Published as exception:
+        #     app_date = df_full["Published Date"].iloc[row["entry_index"]]
+        #else:
+        app_date = df_full["Approval Date"].iloc[row["entry_index"]]
+            #app_date = parse_erepo_date(app_date)
+
+        out = map_gene_vcep_to_index(row["expert_panel"], row["hgnc_gene"])
+
+        if out.shape[0] == 0:
+            print(f"No VCEP found for {row['hgnc_gene']} in {row['expert_panel']}")
+            # Skip this one entirely
+            path_list.append(None)
+            continue
+
+        # Now compute date lower bound:
+        cbefore = closest_before(app_date, out["date"])
+        if cbefore is None:
+            
+            if (row["hgnc_gene"] == "GAA") or (row["hgnc_gene"] == "MYH7") or (row["hgnc_gene"] == "LDLR"):
+                cbefore = 0
+            elif (row["expert_panel"] == "RASopathy") or (row["hgnc_gene"] == "PTEN"):
+                # This problem needs to be moved to Published date
+                app_date = df_full["Published Date"].iloc[row["entry_index"]]
+                cbefore = closest_before(app_date, out["date"])
+                assert cbefore is not None
+
+            else:
+                counter_invalid += 1
+                row_lists.append(i)
+
+        # Now get the cbefore:
+        out_use = out.iloc[cbefore] 
+        path_list.append(out_use["path"])
+
+    df["path"] = path_list
+    df = df.loc[df["path"].notna()]
+
+    df.to_csv("../clingen_vci_pubmed_fulltext_vceps.csv", index=False)
+
+
+if __name__ == "__main__":
+    main()

VCI/parsing_csr_criteria/scrape_criteria.py

@@ -0,0 +1,166 @@
+from selenium import webdriver
+from selenium.webdriver.chrome.service import Service
+from selenium.webdriver.common.by import By
+import pandas as pd
+import time
+import random
+import os
+from bs4 import BeautifulSoup
+
+# === Configuration ===
+chrome_driver_path = "/usr/local/bin/chromedriver"
+target_url = "https://cspec.genome.network/cspec/ui/svi/doc/GN016"
+
+# === Setup ChromeDriver ===
+options = webdriver.ChromeOptions()
+options.add_argument("--headless=new")
+options.add_argument("--disable-gpu")
+options.add_argument("--no-sandbox")
+options.add_argument("--log-level=3")
+prefs = {
+    "profile.managed_default_content_settings.images": 2,
+    "profile.managed_default_content_settings.fonts": 2
+}
+options.add_experimental_option("prefs", prefs)
+
+service = Service(executable_path=chrome_driver_path)
+driver = webdriver.Chrome(service=service, options=options)
+driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument", {
+    "source": "Object.defineProperty(navigator, 'webdriver', {get: () => undefined})"
+})
+
+# === Open page ===
+driver.get(target_url)
+time.sleep(2)
+driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
+time.sleep(1)
+
+expand_buttons = driver.find_elements(By.CSS_SELECTOR, ".panel-title a[data-toggle='collapse']")
+print(f"🧬 Expanding {len(expand_buttons)} gene rule sections...")
+for btn in expand_buttons:
+    try:
+        driver.execute_script("arguments[0].click();", btn)
+        time.sleep(random.uniform(0.3, 0.6))
+    except Exception as e:
+        print(f"⚠️ Error expanding section: {e}")
+time.sleep(2)
+
+html = driver.page_source
+driver.quit()
+
+# ... (unchanged setup) ...
+
+# === Parse HTML ===
+soup = BeautifulSoup(html, "html.parser")
+records = []
+
+rulesets = soup.find_all("div", class_="panel-body")
+for ruleset in rulesets:
+    gene_tag = ruleset.find("span", class_="gene")
+    gene_name = gene_tag.find("span", class_="value").get_text(strip=True) if gene_tag else "Unknown"
+
+    table = ruleset.find("table", class_="criteria-codes")
+    if not table:
+        continue
+
+    all_rows = table.find_all("tr", class_=["criteria-code", "criteria-code parent", "criteria-code child"])
+    i = 0
+    while i < len(all_rows):
+        row = all_rows[i]
+        if "parent" in row.get("class", []):
+            code = row.get("data-cspec-cc-label")
+            code_id = row.get("data-cspec-cc-id")
+
+            if i + 1 < len(all_rows) and "child" in all_rows[i + 1].get("class", []):
+                child_row = all_rows[i + 1]
+
+                # print(f"\n--- Code: {code} ---")
+                # not_applicable_div = child_row.find("div", class_="cc-item not-app")
+                # print(f"Found .not-app section: {not_applicable_div is not None}")
+
+                # strength_divs = child_row.find_all("div", class_="row strength")
+                # print(f"Total strength rows found: {len(strength_divs)}")
+
+                # for idx, s in enumerate(strength_divs):
+                #     s_class = s.get("class", [])
+                #     print(f"  Strength {idx + 1} class: {s_class}")
+                #     if "hide" not in s_class:
+                #         print("  ✅ This strength is VISIBLE")
+                #     else:
+                #         print("  ❌ This strength is HIDDEN")
+
+                # # Then compute visibility
+                # visible_strengths = [div for div in strength_divs if "hide" not in (div.get("class") or [])]
+                # print(f"Visible strengths found: {len(visible_strengths)}")
+
+                # is_not_applicable = not_applicable_div is not None and len(visible_strengths) == 0
+                # mod_type_value = "NA" if is_not_applicable else ""
+
+                # print(f"FINAL DECISION for 'is_not_applicable': {is_not_applicable}")
+
+                # ✅ Correctly locate the TD block
+                td_block = child_row.find("td", class_="strength-specs")
+
+                # ✅ Find .not-app from inside TD
+                not_applicable_div = str(td_block).find("cc-item not-app") if td_block else None
+                #not_applicable_div = td_block.find("div", class_="cc-item not-app") if td_block else None
+
+                # ✅ Find strength rows from inside TD
+                strength_divs = td_block.find_all("div", class_="row strength") if td_block else []
+
+                visible_strengths = [
+                    div for div in strength_divs
+                    if "hide" not in (div.get("class") or [])
+                ]
+
+                # ✅ Determine applicability
+                is_not_applicable = not_applicable_div is not None and len(visible_strengths) == 0
+                #if code == "PM3":
+                #    import ipdb; ipdb.set_trace()
+                mod_type_value = "NA" if is_not_applicable else ""
+
+
+                # === Original ACMG Summary
+                summary_div = child_row.find("div", class_="acmg-summary")
+                if summary_div:
+                    html_div = summary_div.find("div", class_="html")
+                    if html_div:
+                        summary_text = BeautifulSoup(html_div.decode_contents(), "html.parser").get_text(separator="\n").strip()
+                        records.append({
+                            "Gene": gene_name,
+                            "Code": code,
+                            "Strength": "Original ACMG Summary",
+                            "Description": summary_text,
+                            "Modification Type": mod_type_value
+                        })
+
+                # === Visible Strengths
+                if not is_not_applicable:
+                    for spec_div in child_row.find_all("div", class_="strength-spec"):
+                        strength_row = spec_div.find("div", class_="row strength")
+                        if not strength_row or "hide" in strength_row.get("class", []):
+                            continue
+
+                        strength = strength_row.find("div", class_="strength-label").get_text(strip=True)
+
+                        desc_div = spec_div.find("div", class_="html")
+                        description = BeautifulSoup(desc_div.decode_contents(), "html.parser").get_text(separator="\n").strip() if desc_div else ""
+
+                        mod_tag = spec_div.find("div", class_="modification-type")
+                        mod_type = mod_tag.find("span", class_="value").get_text(strip=True) if mod_tag else ""
+
+                        records.append({
+                            "Gene": gene_name,
+                            "Code": code,
+                            "Strength": strength,
+                            "Description": description,
+                            "Modification Type": mod_type
+                        })
+        i += 1
+
+# === Save Results ===
+df = pd.DataFrame(records)
+output_file = "criteria_specifications_final.csv"
+#import ipdb; ipdb.set_trace()
+df.to_csv(output_file, index=False)
+print(f"\n✅ Fixed detection script complete! Data saved to: {os.path.abspath(output_file)}")

VCI/parsing_csr_criteria/scrape_criteria_fn.py

@@ -0,0 +1,127 @@
+from selenium import webdriver
+from selenium.webdriver.chrome.service import Service
+from selenium.webdriver.common.by import By
+from bs4 import BeautifulSoup
+import pandas as pd
+import time
+import random
+import os
+
+def extract_criteria_specifications(url, chrome_driver_path="/usr/local/bin/chromedriver", output_file="criteria_specifications_final.csv"):
+    # Setup headless Selenium driver
+    options = webdriver.ChromeOptions()
+    options.add_argument("--headless=new")
+    options.add_argument("--disable-gpu")
+    options.add_argument("--no-sandbox")
+    options.add_argument("--log-level=3")
+    prefs = {
+        "profile.managed_default_content_settings.images": 2,
+        "profile.managed_default_content_settings.fonts": 2
+    }
+    options.add_experimental_option("prefs", prefs)
+
+    service = Service(executable_path=chrome_driver_path)
+    driver = webdriver.Chrome(service=service, options=options)
+    driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument", {
+        "source": "Object.defineProperty(navigator, 'webdriver', {get: () => undefined})"
+    })
+
+    print(f"🌐 Navigating to {url}")
+    driver.get(url)
+    time.sleep(2)
+    driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
+    time.sleep(1)
+
+    expand_buttons = driver.find_elements(By.CSS_SELECTOR, ".panel-title a[data-toggle='collapse']")
+    print(f"🧬 Expanding {len(expand_buttons)} gene rule sections...")
+    for btn in expand_buttons:
+        try:
+            driver.execute_script("arguments[0].click();", btn)
+            time.sleep(random.uniform(0.3, 0.6))
+        except Exception as e:
+            print(f"⚠️ Error expanding section: {e}")
+    time.sleep(2)
+
+    html = driver.page_source
+    driver.quit()
+
+    # === Parse HTML ===
+    soup = BeautifulSoup(html, "html.parser")
+    records = []
+
+    rulesets = soup.find_all("div", class_="panel-body")
+    for ruleset in rulesets:
+        gene_tag = ruleset.find("span", class_="gene")
+        gene_name = gene_tag.find("span", class_="value").get_text(strip=True) if gene_tag else "Unknown"
+
+        table = ruleset.find("table", class_="criteria-codes")
+        if not table:
+            continue
+
+        all_rows = table.find_all("tr", class_=["criteria-code", "criteria-code parent", "criteria-code child"])
+        i = 0
+        while i < len(all_rows):
+            row = all_rows[i]
+            if "parent" in row.get("class", []):
+                code = row.get("data-cspec-cc-label")
+                code_id = row.get("data-cspec-cc-id")
+
+                if i + 1 < len(all_rows) and "child" in all_rows[i + 1].get("class", []):
+                    child_row = all_rows[i + 1]
+                    td_block = child_row.find("td", class_="strength-specs")
+
+                    # Custom not-applicable check via string search
+                    not_applicable_div = str(td_block).find("cc-item not-app") if td_block else None
+                    strength_divs = td_block.find_all("div", class_="row strength") if td_block else []
+
+                    visible_strengths = [
+                        div for div in strength_divs
+                        if "hide" not in (div.get("class") or [])
+                    ]
+
+                    is_not_applicable = not_applicable_div is not None and len(visible_strengths) == 0
+                    mod_type_value = "NA" if is_not_applicable else ""
+
+                    # Original ACMG Summary
+                    summary_div = child_row.find("div", class_="acmg-summary")
+                    if summary_div:
+                        html_div = summary_div.find("div", class_="html")
+                        if html_div:
+                            summary_text = BeautifulSoup(html_div.decode_contents(), "html.parser").get_text(separator="\n").strip()
+                            records.append({
+                                "Gene": gene_name,
+                                "Code": code,
+                                "Strength": "Original ACMG Summary",
+                                "Description": summary_text,
+                                "Modification Type": mod_type_value
+                            })
+
+                    # Visible Strengths
+                    if not is_not_applicable:
+                        for spec_div in child_row.find_all("div", class_="strength-spec"):
+                            strength_row = spec_div.find("div", class_="row strength")
+                            if not strength_row or "hide" in strength_row.get("class", []):
+                                continue
+
+                            strength = strength_row.find("div", class_="strength-label").get_text(strip=True)
+
+                            desc_div = spec_div.find("div", class_="html")
+                            description = BeautifulSoup(desc_div.decode_contents(), "html.parser").get_text(separator="\n").strip() if desc_div else ""
+
+                            mod_tag = spec_div.find("div", class_="modification-type")
+                            mod_type = mod_tag.find("span", class_="value").get_text(strip=True) if mod_tag else ""
+
+                            records.append({
+                                "Gene": gene_name,
+                                "Code": code,
+                                "Strength": strength,
+                                "Description": description,
+                                "Modification Type": mod_type
+                            })
+            i += 1
+
+    # === Save and Return ===
+    df = pd.DataFrame(records)
+    #df.to_csv(output_file, index=False)
+    #print(f"\n✅ Extraction complete! Data saved to: {os.path.abspath(output_file)}")
+    return df

VCI/parsing_csr_criteria/scrape_criteria_versions.py

@@ -0,0 +1,60 @@
+import pandas as pd
+from tqdm import tqdm
+
+from get_versions import extract_variant_guideline_data
+from scrape_criteria_fn import extract_criteria_specifications
+
+def transform_url(url_original):
+
+    return url_original.replace("api/SequenceVariantInterpretation/id/", "ui/svi/doc/")
+
+def main():
+    cspec_df = pd.read_csv("../csr_criteria/cspec_directory.csv")
+
+    unique_urls = cspec_df["criteria_link"].unique()
+
+    # Take all url's, iterate over + "/versions"
+
+    df_versions_list = []
+
+    IND = 1
+    for url in tqdm(unique_urls, total = len(unique_urls)):
+        url_version = transform_url(url) + "/versions"
+
+        df_versions = extract_variant_guideline_data(url_version)
+
+        #import ipdb; ipdb.set_trace()
+        
+        version_cspec = {}
+        version_fname_list = []
+
+        # Go over all versions:
+        for index, row in df_versions.iterrows():
+            link = row["Link"]
+
+            # Extract the criteria specifications
+            criteria_specifications = extract_criteria_specifications(link)
+
+            version_cspec[index] = criteria_specifications
+        
+            # Save each version_cspec in a dictionary
+            fname = "{}_version={}.csv".format(row["Title"].replace(" ", "").replace("/", ""), row["Version"])
+            criteria_specifications.to_csv(f"version_csv_individual/{fname}", index=False)
+            version_fname_list.append(fname)
+
+
+        df_versions["path"] = version_fname_list
+
+        df_versions_list.append(df_versions)
+
+        IND += 1
+        if (IND % 10) == 0:
+            df_whole = pd.concat(df_versions_list)
+            df_whole.to_csv("cspec_version_guide.csv", index=True)       
+
+    df_whole = pd.concat(df_versions_list)
+    df_whole.to_csv("cspec_version_guide.csv", index=True)
+
+
+if __name__ == "__main__":
+    main()

VCI/parsing_csr_criteria/tests/.ipynb_checkpoints/examine_vci_cspec-checkpoint.ipynb

@@ -0,0 +1,430 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ef833d9f-1199-4e29-83ba-2d7e47fa90ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "DATA_PATH = \"/Users/owenqueen/Desktop/stanford_research/SHERLOCK_HOME/clingen_benchmark/data\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1ba8d447-e0a0-4c4a-8422-cfbc14a1f773",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"../../clingen_vci_pubmed_fulltext_vceps.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "910cfb99-64b2-4a54-98ba-9de34c2dff07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# /Users/owenqueen/Desktop/stanford_research/SHERLOCK_HOME/clingen_benchmark/data/VCI/parsing_csr_criteria/version_csv_individual\n",
+    "\n",
+    "def get_criteria_per_row(row):\n",
+    "    base_path = row[\"path\"]\n",
+    "    criteria = pd.read_csv(os.path.join(DATA_PATH, \"VCI/parsing_csr_criteria/version_csv_individual\", base_path))\n",
+    "    criteria_trim = criteria.loc[criteria[\"Gene\"].apply(lambda x: x.split(\" \")[0]) == row[\"hgnc_gene\"],:]\n",
+    "    return criteria, criteria_trim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "255c35dc-dd04-40c7-b233-40a6637da7a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clist = []\n",
+    "ctrim_list = []\n",
+    "for i, row in df.iterrows():\n",
+    "    c, ctrim = get_criteria_per_row(row)\n",
+    "    clist.append(c)\n",
+    "    ctrim_list.append(ctrim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "5e3c0fde-55d7-4bd0-a9b1-b4bcc5702e75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "size = []\n",
+    "for c in ctrim_list:\n",
+    "    size.append(c.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "467a6806-3778-4a1d-a159-fb49b77f464b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 17,  18,  19,  62,  63,  64,  65,  67,  68,  69,  70,  71,  72,\n",
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+       "        131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n",
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+       "        212, 213, 216, 217, 218, 219, 220, 223, 224, 225, 226, 227, 228,\n",
+       "        229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 306, 307,\n",
+       "        308, 309, 310, 311, 313, 325, 326, 327, 332, 339, 340, 341, 342,\n",
+       "        351, 352, 353, 354, 362, 363, 364, 388, 390, 391, 392, 393, 394,\n",
+       "        406, 436, 447, 455, 456, 457, 458, 459, 460, 461, 462, 513, 514,\n",
+       "        515, 516, 517, 521, 522, 523, 524, 532, 533, 562, 563, 581, 582,\n",
+       "        583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595,\n",
+       "        596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608,\n",
+       "        609, 610, 611, 612, 613, 614, 615, 619, 620, 639, 651, 654, 655,\n",
+       "        656, 658, 659, 677, 678, 681, 682, 683, 711, 713, 736, 738, 758]),)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(np.array(size) == 0).nonzero()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "29fdb42d-ee60-49c7-ae8f-c570b9bd41d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ucount = []\n",
+    "for ind in (np.array(size) == 0).nonzero()[0]:\n",
+    "    ucount.append(clist[ind].Gene.unique().shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "2513b7b7-44ca-4e1f-89f8-abf4e85a7abb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "entry_index                                                       141\n",
+       "variant                     NM_004004.5(GJB2):c.35delG (p.Gly12Valfs)\n",
+       "hgnc_gene                                                        GJB2\n",
+       "disease                                 nonsyndromic genetic deafness\n",
+       "mondo_id                                                MONDO:0019497\n",
+       "assertion                                                  Pathogenic\n",
+       "mode_inheritance                      Autosomal recessive inheritance\n",
+       "expert_panel                                             Hearing Loss\n",
+       "pub_date                                                   2019-07-17\n",
+       "evidence_code                                                     PS4\n",
+       "met_status                                                        met\n",
+       "pmid                                                  PubMed:26969326\n",
+       "comments            3.2% (72/2238) of alleles reported in this stu...\n",
+       "summary             In 1 large study (Sloan-Heggen 2015) and in 1 ...\n",
+       "summary_comments    In 1 large study (Sloan-Heggen 2015) and in 1 ...\n",
+       "path                ClinGenHearingLossExpertPanelSpecificationstot...\n",
+       "Name: 17, dtype: object"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[17]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "b1e2c9a2-bc9c-452c-8930-62f6776059c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_criteria_per_row_FULL(row):\n",
+    "    base_path = row[\"path\"]\n",
+    "    criteria = pd.read_csv(os.path.join(DATA_PATH, \"VCI/parsing_csr_criteria/version_csv_individual\", base_path))\n",
+    "    if criteria[\"Gene\"].unique().shape[0]:\n",
+    "        # Aggregate codes:\n",
+    "        criteria[\"aggregate_code\"] = [f\"{row['Code']}_{row['Strength'].replace(\" \", \"\")}\" for i, row in criteria.iterrows()]\n",
+    "        return criteria\n",
+    "    else:     \n",
+    "        criteria_trim = criteria.loc[criteria[\"Gene\"].apply(lambda x: x.split(\" \")[0]) == row[\"hgnc_gene\"],:]\n",
+    "        return criteria_trim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "23ed83e5-3991-409a-9826-c325b161a521",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Base operation:\n",
+    "clist = []\n",
+    "for i, row in df.iterrows():\n",
+    "    c = get_criteria_per_row_FULL(row)\n",
+    "    clist.append(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "1313a1bc-05d1-49e4-9857-1980d8d3e5a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "size = []\n",
+    "for c in clist:\n",
+    "    size.append(c.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "ba8aafaa-cd48-457b-a934-3ba451092f1e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get unique criteria:\n",
+    "path_unique = df[\"path\"].unique().tolist()\n",
+    "all_codes = []\n",
+    "clist_unique = []\n",
+    "for p in path_unique:\n",
+    "    row = df[df[\"path\"] == p].iloc[0]\n",
+    "    c = get_criteria_per_row_FULL(row)\n",
+    "    clist_unique.append(c)\n",
+    "    all_codes.append(df[\"evidence_code\"].loc[df[\"path\"] == p].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "a4937bd0-b56a-40fd-8db2-4a78227da190",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gene</th>\n",
+       "      <th>Code</th>\n",
+       "      <th>Strength</th>\n",
+       "      <th>Description</th>\n",
+       "      <th>Modification Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>PVS1</td>\n",
+       "      <td>Original ACMG Summary</td>\n",
+       "      <td>Null variant (nonsense, frameshift, canonical ...</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>PVS1</td>\n",
+       "      <td>Very Strong</td>\n",
+       "      <td>Applicable as described in Tayoun et al. 2018....</td>\n",
+       "      <td>Disease-specific</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>PVS1</td>\n",
+       "      <td>Strong</td>\n",
+       "      <td>Use PVS1_strong with:\\n\\n\\n\\n\\nAny nonsense or...</td>\n",
+       "      <td>Disease-specific</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>PS1</td>\n",
+       "      <td>Original ACMG Summary</td>\n",
+       "      <td>Same amino acid change as a previously establi...</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>PS1</td>\n",
+       "      <td>Strong</td>\n",
+       "      <td>Same predicted splicing impact as a previously...</td>\n",
+       "      <td>Disease-specific</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>BP5</td>\n",
+       "      <td>Supporting</td>\n",
+       "      <td>Applicable as described</td>\n",
+       "      <td>No change</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>BP6</td>\n",
+       "      <td>Original ACMG Summary</td>\n",
+       "      <td>Reputable source recently reports variant as b...</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>BP7</td>\n",
+       "      <td>Original ACMG Summary</td>\n",
+       "      <td>A synonymous variant for which splicing predic...</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>BP7</td>\n",
+       "      <td>Strong</td>\n",
+       "      <td>Applicable as described by Walker et al. (PMID...</td>\n",
+       "      <td>Strength</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>BP7</td>\n",
+       "      <td>Supporting</td>\n",
+       "      <td>Per SVI recommendations (PMID: 36865205), use ...</td>\n",
+       "      <td>Gene-specific,None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>62 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Gene  Code               Strength  \\\n",
+       "0   PAH (HGNC:8582)  PVS1  Original ACMG Summary   \n",
+       "1   PAH (HGNC:8582)  PVS1            Very Strong   \n",
+       "2   PAH (HGNC:8582)  PVS1                 Strong   \n",
+       "3   PAH (HGNC:8582)   PS1  Original ACMG Summary   \n",
+       "4   PAH (HGNC:8582)   PS1                 Strong   \n",
+       "..              ...   ...                    ...   \n",
+       "57  PAH (HGNC:8582)   BP5             Supporting   \n",
+       "58  PAH (HGNC:8582)   BP6  Original ACMG Summary   \n",
+       "59  PAH (HGNC:8582)   BP7  Original ACMG Summary   \n",
+       "60  PAH (HGNC:8582)   BP7                 Strong   \n",
+       "61  PAH (HGNC:8582)   BP7             Supporting   \n",
+       "\n",
+       "                                          Description   Modification Type  \n",
+       "0   Null variant (nonsense, frameshift, canonical ...                 NaN  \n",
+       "1   Applicable as described in Tayoun et al. 2018....    Disease-specific  \n",
+       "2   Use PVS1_strong with:\\n\\n\\n\\n\\nAny nonsense or...    Disease-specific  \n",
+       "3   Same amino acid change as a previously establi...                 NaN  \n",
+       "4   Same predicted splicing impact as a previously...    Disease-specific  \n",
+       "..                                                ...                 ...  \n",
+       "57                            Applicable as described           No change  \n",
+       "58  Reputable source recently reports variant as b...                 NaN  \n",
+       "59  A synonymous variant for which splicing predic...                 NaN  \n",
+       "60  Applicable as described by Walker et al. (PMID...            Strength  \n",
+       "61  Per SVI recommendations (PMID: 36865205), use ...  Gene-specific,None  \n",
+       "\n",
+       "[62 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clist_unique[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "710da496-c9a2-4d4a-a375-4c2e637fed93",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['PM3-Very Strong', 'PM3', 'PP4-Moderate', 'PM3-Strong', 'PP4',\n",
+       "       'PP1', 'PM3-Supporting', 'PS3-Supporting'], dtype=object)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Are the codes in the DF?\n",
+    "all_codes[0]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

VCI/parsing_csr_criteria/tests/examine_vci_cspec.ipynb

@@ -0,0 +1,945 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ef833d9f-1199-4e29-83ba-2d7e47fa90ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "DATA_PATH = \"/Users/owenqueen/Desktop/stanford_research/SHERLOCK_HOME/clingen_benchmark/data\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1ba8d447-e0a0-4c4a-8422-cfbc14a1f773",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"../../clingen_vci_pubmed_fulltext_vceps.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "945374a9-6b56-4fdb-9e2d-a3a6c24b648b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>entry_index</th>\n",
+       "      <th>variant</th>\n",
+       "      <th>hgnc_gene</th>\n",
+       "      <th>disease</th>\n",
+       "      <th>mondo_id</th>\n",
+       "      <th>assertion</th>\n",
+       "      <th>mode_inheritance</th>\n",
+       "      <th>expert_panel</th>\n",
+       "      <th>pub_date</th>\n",
+       "      <th>evidence_code</th>\n",
+       "      <th>met_status</th>\n",
+       "      <th>pmid</th>\n",
+       "      <th>comments</th>\n",
+       "      <th>summary</th>\n",
+       "      <th>summary_comments</th>\n",
+       "      <th>path</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8</td>\n",
+       "      <td>NM_000277.2(PAH):c.331C&gt;T (p.Arg111Ter)</td>\n",
+       "      <td>PAH</td>\n",
+       "      <td>phenylketonuria</td>\n",
+       "      <td>MONDO:0009861</td>\n",
+       "      <td>Pathogenic</td>\n",
+       "      <td>Autosomal recessive inheritance</td>\n",
+       "      <td>Phenylketonuria</td>\n",
+       "      <td>2019-04-09</td>\n",
+       "      <td>PM3-Very Strong</td>\n",
+       "      <td>not_met</td>\n",
+       "      <td>PubMed:26322415</td>\n",
+       "      <td>Detected in 13 patients with: c.611A&gt;G, p.E280...</td>\n",
+       "      <td>Detected with: p.R158Q (P, 11 submitters); p.R...</td>\n",
+       "      <td>Detected with: p.R158Q (P, 11 submitters); p.R...</td>\n",
+       "      <td>ClinGenPhenylketonuriaExpertPanelSpecification...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>23</td>\n",
+       "      <td>NM_000277.2(PAH):c.158G&gt;A (p.Arg53His)</td>\n",
+       "      <td>PAH</td>\n",
+       "      <td>phenylketonuria</td>\n",
+       "      <td>MONDO:0009861</td>\n",
+       "      <td>Uncertain Significance</td>\n",
+       "      <td>Autosomal recessive inheritance</td>\n",
+       "      <td>Phenylketonuria</td>\n",
+       "      <td>2019-04-08</td>\n",
+       "      <td>PM3</td>\n",
+       "      <td>met</td>\n",
+       "      <td>PubMed:26322415</td>\n",
+       "      <td>Patient genotype: c.[158G&gt;A];[728G&gt;A],p.[R53H]...</td>\n",
+       "      <td>Detected in trans with p.R243Q (P, 7 submitter...</td>\n",
+       "      <td>Detected in trans with p.R243Q (P, 7 submitter...</td>\n",
+       "      <td>ClinGenPhenylketonuriaExpertPanelSpecification...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>23</td>\n",
+       "      <td>NM_000277.2(PAH):c.158G&gt;A (p.Arg53His)</td>\n",
+       "      <td>PAH</td>\n",
+       "      <td>phenylketonuria</td>\n",
+       "      <td>MONDO:0009861</td>\n",
+       "      <td>Uncertain Significance</td>\n",
+       "      <td>Autosomal recessive inheritance</td>\n",
+       "      <td>Phenylketonuria</td>\n",
+       "      <td>2019-04-08</td>\n",
+       "      <td>PP4-Moderate</td>\n",
+       "      <td>not_met</td>\n",
+       "      <td>PubMed:26322415</td>\n",
+       "      <td>c.158G&gt;A p.R53H identified on 8 alleles. All p...</td>\n",
+       "      <td>Detected in multiple patients with mild hyperp...</td>\n",
+       "      <td>Detected in multiple patients with mild hyperp...</td>\n",
+       "      <td>ClinGenPhenylketonuriaExpertPanelSpecification...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>26</td>\n",
+       "      <td>NM_000277.2(PAH):c.500A&gt;T (p.Asn167Ile)</td>\n",
+       "      <td>PAH</td>\n",
+       "      <td>phenylketonuria</td>\n",
+       "      <td>MONDO:0009861</td>\n",
+       "      <td>Likely Pathogenic</td>\n",
+       "      <td>Autosomal recessive inheritance</td>\n",
+       "      <td>Phenylketonuria</td>\n",
+       "      <td>2019-04-06</td>\n",
+       "      <td>PP4-Moderate</td>\n",
+       "      <td>not_met</td>\n",
+       "      <td>PubMed:26666653</td>\n",
+       "      <td>364 hyperphenylalaninemic patients from 20 Fre...</td>\n",
+       "      <td>Found in a French patient with HPA and 2 unrel...</td>\n",
+       "      <td>Found in a French patient with HPA and 2 unrel...</td>\n",
+       "      <td>ClinGenPhenylketonuriaExpertPanelSpecification...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>26</td>\n",
+       "      <td>NM_000277.2(PAH):c.500A&gt;T (p.Asn167Ile)</td>\n",
+       "      <td>PAH</td>\n",
+       "      <td>phenylketonuria</td>\n",
+       "      <td>MONDO:0009861</td>\n",
+       "      <td>Likely Pathogenic</td>\n",
+       "      <td>Autosomal recessive inheritance</td>\n",
+       "      <td>Phenylketonuria</td>\n",
+       "      <td>2019-04-06</td>\n",
+       "      <td>PM3-Strong</td>\n",
+       "      <td>not_met</td>\n",
+       "      <td>PubMed:26666653</td>\n",
+       "      <td>Genotype: c. [500A &gt; T] (p.Asn167Ile); [1066-1...</td>\n",
+       "      <td>Patient 664  with genotype N167I/Rl58Q (Pathog...</td>\n",
+       "      <td>Patient 664  with genotype N167I/Rl58Q (Pathog...</td>\n",
+       "      <td>ClinGenPhenylketonuriaExpertPanelSpecification...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>771</th>\n",
+       "      <td>9523</td>\n",
+       "      <td>NM_000180.4(GUCY2D):c.1762C&gt;T (p.Arg588Trp)</td>\n",
+       "      <td>GUCY2D</td>\n",
+       "      <td>GUCY2D-related recessive retinopathy</td>\n",
+       "      <td>MONDO:0100453</td>\n",
+       "      <td>Pathogenic</td>\n",
+       "      <td>Autosomal recessive inheritance</td>\n",
+       "      <td>Leber Congenital Amaurosis/early onset Retinal...</td>\n",
+       "      <td>2025-01-30</td>\n",
+       "      <td>PS3-Supporting</td>\n",
+       "      <td>not_met</td>\n",
+       "      <td>PubMed:36274938</td>\n",
+       "      <td>The variant exhibited &lt;1.5% of wt activity whe...</td>\n",
+       "      <td>The variant exhibited &lt;1.5% of wt activity whe...</td>\n",
+       "      <td>The variant exhibited &lt;1.5% of wt activity whe...</td>\n",
+       "      <td>ClinGenLeberCongenitalAmaurosisearlyonsetRetin...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>772</th>\n",
+       "      <td>9547</td>\n",
+       "      <td>NM_000180.4(GUCY2D):c.3271C&gt;T (p.Arg1091Ter)</td>\n",
+       "      <td>GUCY2D</td>\n",
+       "      <td>GUCY2D-related recessive retinopathy</td>\n",
+       "      <td>MONDO:0100453</td>\n",
+       "      <td>Pathogenic</td>\n",
+       "      <td>Autosomal recessive inheritance</td>\n",
+       "      <td>Leber Congenital Amaurosis/early onset Retinal...</td>\n",
+       "      <td>2025-01-30</td>\n",
+       "      <td>BS3</td>\n",
+       "      <td>not_met</td>\n",
+       "      <td>PubMed:27881908</td>\n",
+       "      <td>AAV-packaged WT or mutant GUCY2D expression co...</td>\n",
+       "      <td>The variant showed successsful rescue of rod a...</td>\n",
+       "      <td>The variant showed successsful rescue of rod a...</td>\n",
+       "      <td>ClinGenLeberCongenitalAmaurosisearlyonsetRetin...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773</th>\n",
+       "      <td>9547</td>\n",
+       "      <td>NM_000180.4(GUCY2D):c.3271C&gt;T (p.Arg1091Ter)</td>\n",
+       "      <td>GUCY2D</td>\n",
+       "      <td>GUCY2D-related recessive retinopathy</td>\n",
+       "      <td>MONDO:0100453</td>\n",
+       "      <td>Pathogenic</td>\n",
+       "      <td>Autosomal recessive inheritance</td>\n",
+       "      <td>Leber Congenital Amaurosis/early onset Retinal...</td>\n",
+       "      <td>2025-01-30</td>\n",
+       "      <td>PS3</td>\n",
+       "      <td>not_met</td>\n",
+       "      <td>PubMed:27881908</td>\n",
+       "      <td>AAV-packaged WT or mutant GUCY2D expression co...</td>\n",
+       "      <td>Activity is reduced to 25% but is not less tha...</td>\n",
+       "      <td>Activity is reduced to 25% but is not less tha...</td>\n",
+       "      <td>ClinGenLeberCongenitalAmaurosisearlyonsetRetin...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>774</th>\n",
+       "      <td>9551</td>\n",
+       "      <td>NM_000180.4(GUCY2D):c.1724C&gt;T (p.Pro575Leu)</td>\n",
+       "      <td>GUCY2D</td>\n",
+       "      <td>GUCY2D-related recessive retinopathy</td>\n",
+       "      <td>MONDO:0100453</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Autosomal recessive inheritance</td>\n",
+       "      <td>Leber Congenital Amaurosis/early onset Retinal...</td>\n",
+       "      <td>2025-01-30</td>\n",
+       "      <td>BS3-Supporting</td>\n",
+       "      <td>not_met</td>\n",
+       "      <td>PubMed:24616660</td>\n",
+       "      <td>The variant exhibited ~75% enzymatic activity ...</td>\n",
+       "      <td>The variant exhibited ~75% enzymatic activity ...</td>\n",
+       "      <td>The variant exhibited ~75% enzymatic activity ...</td>\n",
+       "      <td>ClinGenLeberCongenitalAmaurosisearlyonsetRetin...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>775</th>\n",
+       "      <td>9559</td>\n",
+       "      <td>NM_000180.4(GUCY2D):c.1371C&gt;T (p.Cys457=)</td>\n",
+       "      <td>GUCY2D</td>\n",
+       "      <td>GUCY2D-related recessive retinopathy</td>\n",
+       "      <td>MONDO:0100453</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Autosomal recessive inheritance</td>\n",
+       "      <td>Leber Congenital Amaurosis/early onset Retinal...</td>\n",
+       "      <td>2025-01-30</td>\n",
+       "      <td>BA1</td>\n",
+       "      <td>met</td>\n",
+       "      <td>PubMed:18682808</td>\n",
+       "      <td>Seong, et al, 2008, tested 20 Korean patients ...</td>\n",
+       "      <td>This variant is present in gnomAD v.4.1.0 at a...</td>\n",
+       "      <td>This variant is present in gnomAD v.4.1.0 at a...</td>\n",
+       "      <td>ClinGenLeberCongenitalAmaurosisearlyonsetRetin...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>776 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     entry_index                                       variant hgnc_gene  \\\n",
+       "0              8       NM_000277.2(PAH):c.331C>T (p.Arg111Ter)       PAH   \n",
+       "1             23        NM_000277.2(PAH):c.158G>A (p.Arg53His)       PAH   \n",
+       "2             23        NM_000277.2(PAH):c.158G>A (p.Arg53His)       PAH   \n",
+       "3             26       NM_000277.2(PAH):c.500A>T (p.Asn167Ile)       PAH   \n",
+       "4             26       NM_000277.2(PAH):c.500A>T (p.Asn167Ile)       PAH   \n",
+       "..           ...                                           ...       ...   \n",
+       "771         9523   NM_000180.4(GUCY2D):c.1762C>T (p.Arg588Trp)    GUCY2D   \n",
+       "772         9547  NM_000180.4(GUCY2D):c.3271C>T (p.Arg1091Ter)    GUCY2D   \n",
+       "773         9547  NM_000180.4(GUCY2D):c.3271C>T (p.Arg1091Ter)    GUCY2D   \n",
+       "774         9551   NM_000180.4(GUCY2D):c.1724C>T (p.Pro575Leu)    GUCY2D   \n",
+       "775         9559     NM_000180.4(GUCY2D):c.1371C>T (p.Cys457=)    GUCY2D   \n",
+       "\n",
+       "                                  disease       mondo_id  \\\n",
+       "0                         phenylketonuria  MONDO:0009861   \n",
+       "1                         phenylketonuria  MONDO:0009861   \n",
+       "2                         phenylketonuria  MONDO:0009861   \n",
+       "3                         phenylketonuria  MONDO:0009861   \n",
+       "4                         phenylketonuria  MONDO:0009861   \n",
+       "..                                    ...            ...   \n",
+       "771  GUCY2D-related recessive retinopathy  MONDO:0100453   \n",
+       "772  GUCY2D-related recessive retinopathy  MONDO:0100453   \n",
+       "773  GUCY2D-related recessive retinopathy  MONDO:0100453   \n",
+       "774  GUCY2D-related recessive retinopathy  MONDO:0100453   \n",
+       "775  GUCY2D-related recessive retinopathy  MONDO:0100453   \n",
+       "\n",
+       "                  assertion                 mode_inheritance  \\\n",
+       "0                Pathogenic  Autosomal recessive inheritance   \n",
+       "1    Uncertain Significance  Autosomal recessive inheritance   \n",
+       "2    Uncertain Significance  Autosomal recessive inheritance   \n",
+       "3         Likely Pathogenic  Autosomal recessive inheritance   \n",
+       "4         Likely Pathogenic  Autosomal recessive inheritance   \n",
+       "..                      ...                              ...   \n",
+       "771              Pathogenic  Autosomal recessive inheritance   \n",
+       "772              Pathogenic  Autosomal recessive inheritance   \n",
+       "773              Pathogenic  Autosomal recessive inheritance   \n",
+       "774                  Benign  Autosomal recessive inheritance   \n",
+       "775                  Benign  Autosomal recessive inheritance   \n",
+       "\n",
+       "                                          expert_panel    pub_date  \\\n",
+       "0                                      Phenylketonuria  2019-04-09   \n",
+       "1                                      Phenylketonuria  2019-04-08   \n",
+       "2                                      Phenylketonuria  2019-04-08   \n",
+       "3                                      Phenylketonuria  2019-04-06   \n",
+       "4                                      Phenylketonuria  2019-04-06   \n",
+       "..                                                 ...         ...   \n",
+       "771  Leber Congenital Amaurosis/early onset Retinal...  2025-01-30   \n",
+       "772  Leber Congenital Amaurosis/early onset Retinal...  2025-01-30   \n",
+       "773  Leber Congenital Amaurosis/early onset Retinal...  2025-01-30   \n",
+       "774  Leber Congenital Amaurosis/early onset Retinal...  2025-01-30   \n",
+       "775  Leber Congenital Amaurosis/early onset Retinal...  2025-01-30   \n",
+       "\n",
+       "       evidence_code met_status             pmid  \\\n",
+       "0    PM3-Very Strong    not_met  PubMed:26322415   \n",
+       "1                PM3        met  PubMed:26322415   \n",
+       "2       PP4-Moderate    not_met  PubMed:26322415   \n",
+       "3       PP4-Moderate    not_met  PubMed:26666653   \n",
+       "4         PM3-Strong    not_met  PubMed:26666653   \n",
+       "..               ...        ...              ...   \n",
+       "771   PS3-Supporting    not_met  PubMed:36274938   \n",
+       "772              BS3    not_met  PubMed:27881908   \n",
+       "773              PS3    not_met  PubMed:27881908   \n",
+       "774   BS3-Supporting    not_met  PubMed:24616660   \n",
+       "775              BA1        met  PubMed:18682808   \n",
+       "\n",
+       "                                              comments  \\\n",
+       "0    Detected in 13 patients with: c.611A>G, p.E280...   \n",
+       "1    Patient genotype: c.[158G>A];[728G>A],p.[R53H]...   \n",
+       "2    c.158G>A p.R53H identified on 8 alleles. All p...   \n",
+       "3    364 hyperphenylalaninemic patients from 20 Fre...   \n",
+       "4    Genotype: c. [500A > T] (p.Asn167Ile); [1066-1...   \n",
+       "..                                                 ...   \n",
+       "771  The variant exhibited <1.5% of wt activity whe...   \n",
+       "772  AAV-packaged WT or mutant GUCY2D expression co...   \n",
+       "773  AAV-packaged WT or mutant GUCY2D expression co...   \n",
+       "774  The variant exhibited ~75% enzymatic activity ...   \n",
+       "775  Seong, et al, 2008, tested 20 Korean patients ...   \n",
+       "\n",
+       "                                               summary  \\\n",
+       "0    Detected with: p.R158Q (P, 11 submitters); p.R...   \n",
+       "1    Detected in trans with p.R243Q (P, 7 submitter...   \n",
+       "2    Detected in multiple patients with mild hyperp...   \n",
+       "3    Found in a French patient with HPA and 2 unrel...   \n",
+       "4    Patient 664  with genotype N167I/Rl58Q (Pathog...   \n",
+       "..                                                 ...   \n",
+       "771  The variant exhibited <1.5% of wt activity whe...   \n",
+       "772  The variant showed successsful rescue of rod a...   \n",
+       "773  Activity is reduced to 25% but is not less tha...   \n",
+       "774  The variant exhibited ~75% enzymatic activity ...   \n",
+       "775  This variant is present in gnomAD v.4.1.0 at a...   \n",
+       "\n",
+       "                                      summary_comments  \\\n",
+       "0    Detected with: p.R158Q (P, 11 submitters); p.R...   \n",
+       "1    Detected in trans with p.R243Q (P, 7 submitter...   \n",
+       "2    Detected in multiple patients with mild hyperp...   \n",
+       "3    Found in a French patient with HPA and 2 unrel...   \n",
+       "4    Patient 664  with genotype N167I/Rl58Q (Pathog...   \n",
+       "..                                                 ...   \n",
+       "771  The variant exhibited <1.5% of wt activity whe...   \n",
+       "772  The variant showed successsful rescue of rod a...   \n",
+       "773  Activity is reduced to 25% but is not less tha...   \n",
+       "774  The variant exhibited ~75% enzymatic activity ...   \n",
+       "775  This variant is present in gnomAD v.4.1.0 at a...   \n",
+       "\n",
+       "                                                  path  \n",
+       "0    ClinGenPhenylketonuriaExpertPanelSpecification...  \n",
+       "1    ClinGenPhenylketonuriaExpertPanelSpecification...  \n",
+       "2    ClinGenPhenylketonuriaExpertPanelSpecification...  \n",
+       "3    ClinGenPhenylketonuriaExpertPanelSpecification...  \n",
+       "4    ClinGenPhenylketonuriaExpertPanelSpecification...  \n",
+       "..                                                 ...  \n",
+       "771  ClinGenLeberCongenitalAmaurosisearlyonsetRetin...  \n",
+       "772  ClinGenLeberCongenitalAmaurosisearlyonsetRetin...  \n",
+       "773  ClinGenLeberCongenitalAmaurosisearlyonsetRetin...  \n",
+       "774  ClinGenLeberCongenitalAmaurosisearlyonsetRetin...  \n",
+       "775  ClinGenLeberCongenitalAmaurosisearlyonsetRetin...  \n",
+       "\n",
+       "[776 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "910cfb99-64b2-4a54-98ba-9de34c2dff07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# /Users/owenqueen/Desktop/stanford_research/SHERLOCK_HOME/clingen_benchmark/data/VCI/parsing_csr_criteria/version_csv_individual\n",
+    "\n",
+    "def get_criteria_per_row(row):\n",
+    "    base_path = row[\"path\"]\n",
+    "    criteria = pd.read_csv(os.path.join(DATA_PATH, \"VCI/parsing_csr_criteria/version_csv_individual\", base_path))\n",
+    "    criteria_trim = criteria.loc[criteria[\"Gene\"].apply(lambda x: x.split(\" \")[0]) == row[\"hgnc_gene\"],:]\n",
+    "    return criteria, criteria_trim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "255c35dc-dd04-40c7-b233-40a6637da7a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clist = []\n",
+    "ctrim_list = []\n",
+    "for i, row in df.iterrows():\n",
+    "    c, ctrim = get_criteria_per_row(row)\n",
+    "    clist.append(c)\n",
+    "    ctrim_list.append(ctrim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "5e3c0fde-55d7-4bd0-a9b1-b4bcc5702e75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "size = []\n",
+    "for c in ctrim_list:\n",
+    "    size.append(c.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "467a6806-3778-4a1d-a159-fb49b77f464b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 17,  18,  19,  62,  63,  64,  65,  67,  68,  69,  70,  71,  72,\n",
+       "         73,  74, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n",
+       "        131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n",
+       "        144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n",
+       "        157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 191, 192,\n",
+       "        193, 194, 195, 196, 197, 198, 199, 200, 201, 208, 209, 210, 211,\n",
+       "        212, 213, 216, 217, 218, 219, 220, 223, 224, 225, 226, 227, 228,\n",
+       "        229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 306, 307,\n",
+       "        308, 309, 310, 311, 313, 325, 326, 327, 332, 339, 340, 341, 342,\n",
+       "        351, 352, 353, 354, 362, 363, 364, 388, 390, 391, 392, 393, 394,\n",
+       "        406, 436, 447, 455, 456, 457, 458, 459, 460, 461, 462, 513, 514,\n",
+       "        515, 516, 517, 521, 522, 523, 524, 532, 533, 562, 563, 581, 582,\n",
+       "        583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595,\n",
+       "        596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608,\n",
+       "        609, 610, 611, 612, 613, 614, 615, 619, 620, 639, 651, 654, 655,\n",
+       "        656, 658, 659, 677, 678, 681, 682, 683, 711, 713, 736, 738, 758]),)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(np.array(size) == 0).nonzero()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "29fdb42d-ee60-49c7-ae8f-c570b9bd41d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ucount = []\n",
+    "for ind in (np.array(size) == 0).nonzero()[0]:\n",
+    "    ucount.append(clist[ind].Gene.unique().shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "2513b7b7-44ca-4e1f-89f8-abf4e85a7abb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "entry_index                                                       141\n",
+       "variant                     NM_004004.5(GJB2):c.35delG (p.Gly12Valfs)\n",
+       "hgnc_gene                                                        GJB2\n",
+       "disease                                 nonsyndromic genetic deafness\n",
+       "mondo_id                                                MONDO:0019497\n",
+       "assertion                                                  Pathogenic\n",
+       "mode_inheritance                      Autosomal recessive inheritance\n",
+       "expert_panel                                             Hearing Loss\n",
+       "pub_date                                                   2019-07-17\n",
+       "evidence_code                                                     PS4\n",
+       "met_status                                                        met\n",
+       "pmid                                                  PubMed:26969326\n",
+       "comments            3.2% (72/2238) of alleles reported in this stu...\n",
+       "summary             In 1 large study (Sloan-Heggen 2015) and in 1 ...\n",
+       "summary_comments    In 1 large study (Sloan-Heggen 2015) and in 1 ...\n",
+       "path                ClinGenHearingLossExpertPanelSpecificationstot...\n",
+       "Name: 17, dtype: object"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[17]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "b1e2c9a2-bc9c-452c-8930-62f6776059c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_row_code(row):\n",
+    "    code, strength = row[\"Code\"], row[\"Strength\"]\n",
+    "\n",
+    "    if strength.startswith(\"Original ACMG\"):\n",
+    "        return code\n",
+    "    else:\n",
+    "        return f\"{code}_{strength.replace(' ', '')}\"\n",
+    "\n",
+    "def cascade_descriptions(criteria_df):\n",
+    "\n",
+    "    modify_criteria_df = criteria_df.copy()\n",
+    "\n",
+    "    for code in modify_criteria_df[\"Code\"].unique():\n",
+    "        in_code = modify_criteria_df.loc[modify_criteria_df[\"Code\"] == code]\n",
+    "\n",
+    "        mask_in = in_code[\"Strength\"].apply(lambda x: x.startswith(\"Original ACMG\"))\n",
+    "        if not (mask_in.sum() == 1):\n",
+    "            print(in_code)\n",
+    "            raise ValueError\n",
+    "\n",
+    "        org_row = in_code[mask_in].iloc[0]\n",
+    "        org_desc = org_row[\"Description\"]\n",
+    "\n",
+    "        for i in range(in_code.shape[0]):\n",
+    "            strength = in_code[\"Strength\"].iloc[i]\n",
+    "            if not strength.startswith(\"Original ACMG\"):\n",
+    "                new_description = \"General code description: \" + org_desc + \"\\n\\n\" + \"Detailed code description: \" + in_code[\"Description\"].iloc[i]\n",
+    "            else:\n",
+    "                new_description = \"General code description: \" + org_desc\n",
+    "                \n",
+    "            modify_criteria_df.loc[\n",
+    "                (modify_criteria_df[\"Code\"] == code) & (modify_criteria_df[\"Strength\"] == strength),\n",
+    "                \"Description\"\n",
+    "            ] = new_description\n",
+    "\n",
+    "    return modify_criteria_df\n",
+    "    \n",
+    "\n",
+    "def get_criteria_per_row_FULL(row):\n",
+    "    base_path = row[\"path\"]\n",
+    "    criteria = pd.read_csv(os.path.join(DATA_PATH, \"VCI/parsing_csr_criteria/version_csv_individual\", base_path))\n",
+    "    if criteria[\"Gene\"].unique().shape[0] == 1:\n",
+    "        # Aggregate codes:\n",
+    "        criteria[\"aggregate_code\"] = [convert_row_code(row) for i, row in criteria.iterrows()]\n",
+    "        return cascade_descriptions(criteria)\n",
+    "    else:     \n",
+    "        print(\"HERE\")\n",
+    "        criteria_trim = criteria.loc[criteria[\"Gene\"].apply(lambda x: x.split(\" \")[0]) == row[\"hgnc_gene\"],:]\n",
+    "        criteria_trim[\"aggregate_code\"] = [convert_row_code(row) for i, row in criteria_trim.iterrows()]\n",
+    "        return cascade_descriptions(criteria_trim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "23ed83e5-3991-409a-9826-c325b161a521",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Base operation:\n",
+    "clist = []\n",
+    "for i, row in df.iterrows():\n",
+    "    c = get_criteria_per_row_FULL(row)\n",
+    "    clist.append(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "1313a1bc-05d1-49e4-9857-1980d8d3e5a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "size = []\n",
+    "for c in clist:\n",
+    "    size.append(c.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "ba8aafaa-cd48-457b-a934-3ba451092f1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HERE\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/3q/fmvp_t4528b2sghqyyn20hw80000gn/T/ipykernel_11715/3675522798.py:49: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  criteria_trim[\"aggregate_code\"] = [convert_row_code(row) for i, row in criteria_trim.iterrows()]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get unique criteria:\n",
+    "path_unique = df[\"path\"].unique().tolist()\n",
+    "all_codes = []\n",
+    "clist_unique = []\n",
+    "for p in path_unique:\n",
+    "    row = df[df[\"path\"] == p].iloc[0]\n",
+    "    c = get_criteria_per_row_FULL(row)\n",
+    "    clist_unique.append(c)\n",
+    "    all_codes.append(df[\"evidence_code\"].loc[df[\"path\"] == p].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "a4937bd0-b56a-40fd-8db2-4a78227da190",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gene</th>\n",
+       "      <th>Code</th>\n",
+       "      <th>Strength</th>\n",
+       "      <th>Description</th>\n",
+       "      <th>Modification Type</th>\n",
+       "      <th>aggregate_code</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>PVS1</td>\n",
+       "      <td>Original ACMG Summary</td>\n",
+       "      <td>General code description: Null variant (nonsen...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>PVS1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>PVS1</td>\n",
+       "      <td>Very Strong</td>\n",
+       "      <td>General code description: Null variant (nonsen...</td>\n",
+       "      <td>Disease-specific</td>\n",
+       "      <td>PVS1_VeryStrong</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>PVS1</td>\n",
+       "      <td>Strong</td>\n",
+       "      <td>General code description: Null variant (nonsen...</td>\n",
+       "      <td>Disease-specific</td>\n",
+       "      <td>PVS1_Strong</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>PS1</td>\n",
+       "      <td>Original ACMG Summary</td>\n",
+       "      <td>General code description: Same amino acid chan...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>PS1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>PS1</td>\n",
+       "      <td>Strong</td>\n",
+       "      <td>General code description: Same amino acid chan...</td>\n",
+       "      <td>Disease-specific</td>\n",
+       "      <td>PS1_Strong</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>BP5</td>\n",
+       "      <td>Supporting</td>\n",
+       "      <td>General code description: Variant found in a c...</td>\n",
+       "      <td>No change</td>\n",
+       "      <td>BP5_Supporting</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>BP6</td>\n",
+       "      <td>Original ACMG Summary</td>\n",
+       "      <td>General code description: Reputable source rec...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>BP6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>BP7</td>\n",
+       "      <td>Original ACMG Summary</td>\n",
+       "      <td>General code description: A synonymous variant...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>BP7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>BP7</td>\n",
+       "      <td>Strong</td>\n",
+       "      <td>General code description: A synonymous variant...</td>\n",
+       "      <td>Strength</td>\n",
+       "      <td>BP7_Strong</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>PAH (HGNC:8582)</td>\n",
+       "      <td>BP7</td>\n",
+       "      <td>Supporting</td>\n",
+       "      <td>General code description: A synonymous variant...</td>\n",
+       "      <td>Gene-specific,None</td>\n",
+       "      <td>BP7_Supporting</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>62 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Gene  Code               Strength  \\\n",
+       "0   PAH (HGNC:8582)  PVS1  Original ACMG Summary   \n",
+       "1   PAH (HGNC:8582)  PVS1            Very Strong   \n",
+       "2   PAH (HGNC:8582)  PVS1                 Strong   \n",
+       "3   PAH (HGNC:8582)   PS1  Original ACMG Summary   \n",
+       "4   PAH (HGNC:8582)   PS1                 Strong   \n",
+       "..              ...   ...                    ...   \n",
+       "57  PAH (HGNC:8582)   BP5             Supporting   \n",
+       "58  PAH (HGNC:8582)   BP6  Original ACMG Summary   \n",
+       "59  PAH (HGNC:8582)   BP7  Original ACMG Summary   \n",
+       "60  PAH (HGNC:8582)   BP7                 Strong   \n",
+       "61  PAH (HGNC:8582)   BP7             Supporting   \n",
+       "\n",
+       "                                          Description   Modification Type  \\\n",
+       "0   General code description: Null variant (nonsen...                 NaN   \n",
+       "1   General code description: Null variant (nonsen...    Disease-specific   \n",
+       "2   General code description: Null variant (nonsen...    Disease-specific   \n",
+       "3   General code description: Same amino acid chan...                 NaN   \n",
+       "4   General code description: Same amino acid chan...    Disease-specific   \n",
+       "..                                                ...                 ...   \n",
+       "57  General code description: Variant found in a c...           No change   \n",
+       "58  General code description: Reputable source rec...                 NaN   \n",
+       "59  General code description: A synonymous variant...                 NaN   \n",
+       "60  General code description: A synonymous variant...            Strength   \n",
+       "61  General code description: A synonymous variant...  Gene-specific,None   \n",
+       "\n",
+       "     aggregate_code  \n",
+       "0              PVS1  \n",
+       "1   PVS1_VeryStrong  \n",
+       "2       PVS1_Strong  \n",
+       "3               PS1  \n",
+       "4        PS1_Strong  \n",
+       "..              ...  \n",
+       "57   BP5_Supporting  \n",
+       "58              BP6  \n",
+       "59              BP7  \n",
+       "60       BP7_Strong  \n",
+       "61   BP7_Supporting  \n",
+       "\n",
+       "[62 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clist_unique[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "710da496-c9a2-4d4a-a375-4c2e637fed93",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['PM3-Very Strong', 'PM3', 'PP4-Moderate', 'PM3-Strong', 'PP4',\n",
+       "       'PP1', 'PM3-Supporting', 'PS3-Supporting'], dtype=object)"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Are the codes in the DF?\n",
+    "all_codes[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "a4bd0689-9375-45fb-b7f5-7ba4833870fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease.\\nNote: May be used as stronger evidence with increasing segregation data.'"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clist_unique[0].loc[clist_unique[0][\"Code\"] == \"PP1\"][\"Description\"].iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "8899563c-04ec-45b6-89a9-8a5e691cc4ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3 affected segregations + 0 unaffected segregations OR\\n\\n\\n2 affected segregations + 3 unaffected segregations'"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

VCI/parsing_csr_criteria/tests/posthoc_process_cvg.py

@@ -0,0 +1,39 @@
+import pandas as pd
+import re
+
+def extract_panel_name(title):
+    """
+    Extract the panel name from a ClinGen title.
+    For example:
+    "ClinGen PAH Expert Panel Specifications..." -> "PAH"
+    "ClinGen Monogenic Diabetes Expert Panel Specifications..." -> "Monogenic Diabetes"
+    """
+    # Pattern to match text between "ClinGen" and "Expert Panel"
+    pattern = r"ClinGen\s+(.*?)\s+Expert Panel"
+    match = re.search(pattern, title)
+    if match:
+        return match.group(1).strip()
+    return None
+
+def main():
+    # Read the CSV file
+    df = pd.read_csv("../cspec_version_guide.csv").iloc[:, 1:]
+    
+    # Apply the extraction function to the Title column
+    df['Title'] = df['Title'].apply(extract_panel_name)
+
+    # Unroll genes in lists:
+    df["Genes"] = df["Genes"].apply(lambda x: x.split(",") if isinstance(x, str) else x)
+
+    # Explode the Genes column to create separate rows for each gene
+    df = df.explode('Genes')
+    
+    # Clean up any whitespace in gene names
+    df['Genes'] = df['Genes'].str.strip()
+
+    df.to_csv("cspec_version_guide_processed.csv", index=False)
+
+
+
+if __name__ == "__main__":
+    main()

VCI/parsing_csr_criteria/tests/test_vcep_name_mapping.py

@@ -0,0 +1,55 @@
+import pandas as pd
+import re
+
+def extract_panel_name(title):
+    """
+    Extract the panel name from a ClinGen title.
+    For example:
+    "ClinGen PAH Expert Panel Specifications..." -> "PAH"
+    "ClinGen Monogenic Diabetes Expert Panel Specifications..." -> "Monogenic Diabetes"
+    """
+    # Pattern to match text between "ClinGen" and "Expert Panel"
+    pattern = r"ClinGen\s+(.*?)\s+Expert Panel"
+    match = re.search(pattern, title)
+    if match:
+        return match.group(1).strip()
+    return None
+
+map_vcep = {
+    'Mitochondrial Diseases': "MITO", 
+    'Severe Combined Immunodeficiency Disease ': 'Severe Combined Immunodeficiency Disease', 
+    'von Willebrand Disease ': 'von Willebrand Disease'
+}
+
+def main():
+    # Read the CSV file
+    df = pd.read_csv("../cspec_version_guide.csv")
+    
+    # Apply the extraction function to the Title column
+    df['Title'] = df['Title'].apply(extract_panel_name)
+
+    pmft = pd.read_csv("../../clingen_vci_pubmed_fulltext.csv")
+
+    #pmft["expert_panel"] = pmft["expert_panel"].map(map_vcep, na_action="ignore")
+
+    # See if they match:
+    counter, out_counter = 0, 0
+    out_vceps = []
+
+    pmft_unique = pmft["expert_panel"].unique()
+
+    for i, vcep_name in enumerate(pmft_unique):
+        #vcep_name = row["expert_panel"]
+
+        if vcep_name in map_vcep.keys():
+            vcep_name = map_vcep[vcep_name]
+        if (df['Title'] == vcep_name).sum() > 0:
+            counter += 1
+        else:
+            out_counter += 1
+            out_vceps.append(vcep_name)
+    print(f"Counter: {counter}")
+    print(f"Out counter: {out_counter}")
+    print(out_vceps)
+if __name__ == "__main__":
+    main()

VCI/parsing_csr_criteria/version_csv_individual/ClinGenACADVLExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesVersion1_version=1.0.0.csv

@@ -0,0 +1,255 @@
+Gene,Code,Strength,Description,Modification Type
+ACADVL (HGNC:92),PVS1,Original ACMG Summary,"Null variant (nonsense, frameshift, canonical +/−1 or 2 splice sites, initiation codon, single or multi-exon deletion) in a gene where loss of function (LOF) is a known mechanism of disease.
+Caveats:
+ • Beware of genes where LOF is not a known disease mechanism (e.g. GFAP, MYH7).
+ • Use caution interpreting LOF variants at the extreme 3’ end of a gene.
+ • Use caution with splice variants that are predicted to lead to exon skipping but leave the remainder of the protein intact.
+ • Use caution in the presence of multiple transcripts.",
+ACADVL (HGNC:92),PVS1,Very Strong,"Loss of function is a known mechanism for VLCAD Deficiency. The specifications below are based on published guidance for assigning strength of evidence for PVS1 (Abou Tayoun et al., 2018; PMID: 30192042). There are multiple transcripts for ACADVL. The major isoform, NM_000018.4, encodes a 655 amino acid precursor protein that contains a 40 amino acid N-terminal target sequence that is removed during uptake (Aoyama et al., 1995; PMID: 7668252). In a joint project between NCBI and EMBL-EBI (MANE), NM_000018.4 was designated as the most relevant transcript.
+Nonsense or Frameshift:
+
+
+
+
+Use caution when interpreting LOF variants at the 3’ end of the gene.
+
+
+All nonsense and frameshift variants will meet PVS1 unless the variant is predicted to be missed by nonsense-mediated decay (NMD).
+
+
+NMD is not predicted if the variant is in the last exon (exon 20) or in the last 50 nucleotides of the penultimate exon (exon 19).
+Canonical Splice Site (+1, +2, -1, -2):
+
+
+All donor/acceptor sites follow the GT/AG rule, except for the donor splice site of intron 8, which begins with GC. PVS1 should not be applied for variants in the splice donor site of intron 8 since the impact of GC donor splice sites is not well understood.
+
+
+For +1 or +2 GT donor splice site variants, the exon immediately 5’ of the variant is predicted to be skipped. For -1 or -2 AG acceptor splice site variants, the exon immediately 3’ of the variants is predicted to be skipped. For the predicted in frame/out of frame consequences for exon skipping in ACADVL (NM_000018.4), see Appendix 1.
+Deletions:
+
+
+For single or multi-exon deletions that result in an out-of-frame consequence, use PVS1 unless NMD is not predicted to occur. If NMD is not predicted to occur, use PVS1_Moderate.
+
+
+If a deletion results in an in-frame consequence, the deletion must encompass one or more exons in order to apply PVS1. Consult Appendix 1 and the PVS1 decision tree to assign a strength.
+Duplications:
+
+
+Single and multi-exon duplications have not been reported in ACADVL. Consult the PVS1 decision tree to assign the strength.
+Initiation codon:
+
+
+The next in-frame methionine is at position 6 (on transcript NM_000018). However, the first 40 amino acids comprise the leader sequence in the precursor peptide and are important for proper localization of the protein (Aoyama et al., 1995; PMID: 7668252). Therefore, initiator codon variants will meet PVS1_Strong.",Disease-specific
+ACADVL (HGNC:92),PS1,Original ACMG Summary,"Same amino acid change as a previously established pathogenic variant regardless of nucleotide change.
+Example: Val->Leu caused by either G>C or G>T in the same codon.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+ACADVL (HGNC:92),PS1,Strong,Same amino acid change as a previously established pathogenic variant regardless of nucleotide change.,No change
+ACADVL (HGNC:92),PS2,Original ACMG Summary,"De novo (both maternity and paternity confirmed) in a patient with the disease and no family history.
+Note: Confirmation of paternity only is insufficient. Egg donation, surrogate motherhood, errors in embryo transfer, etc. can contribute to non-maternity.",NA
+ACADVL (HGNC:92),PS3,Original ACMG Summary,"Well-established in vitro or in vivo functional studies supportive of a damaging effect on the gene or gene product.
+Note: Functional studies that have been validated and shown to be reproducible and robust in a clinical diagnostic laboratory setting are considered the most well-established.",
+ACADVL (HGNC:92),PS3,Strong,"Functional evidence from non-patient derived material with only a single variant best reflects the variant-level phenotype. Apply patient-derived evidence in PP4.
+
+
+Apply criteria at the level determined by validation parameters (see flowchart below). VLCAD assays available now do not meet the criteria that is being proposed now regarding the types of controls etc. but the published VLCAD assays are well established with many positive and negative controls being run.
+
+
+Hesse et al., 2018 (PMID 30194637) reviewed enzymatic testing in lymphocytes as a confirmatory tool in newborns identified by screening. Molecular testing was performed after in most patients.
+
+
+If an enzyme activity assay has >20% activity it cannot be weighted above PS3_supporting regardless of flowchart results.",Disease-specific
+ACADVL (HGNC:92),PS4,Original ACMG Summary,"The prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls.
+Note 1: Relative risk (RR) or odds ratio (OR), as obtained from case-control studies, is >5.0 and the confidence interval around the estimate of RR or OR does not include 1.0. See manuscript for detailed guidance.
+Note 2: In instances of very rare variants where case-control studies may not reach statistical significance, the prior observation of the variant in multiple unrelated patients with the same phenotype, and its absence in controls, may be used as moderate level of evidence.",NA
+ACADVL (HGNC:92),PM1,Original ACMG Summary,Located in a mutational hot spot and/or critical and well-established functional domain (e.g. active site of an enzyme) without benign variation.,
+ACADVL (HGNC:92),PM1,Moderate,"Examples of mutational hot spots:
+It has been cited that the CpG dinucleotides in the codon for arginine326 and arginine429 are mutational hot spots, since CrT (R326C and R429W) and GrA (R326H and R429Q) mutations of the CpG di- nucleotide are present in both codons. Article for reference is Clear Correlation of Genotype with Disease Phenotype in Very–Long-Chain Acyl-CoA Dehydrogenase Deficiency – by Andresen et al., 1999 (PMID 9973285).
+
+
+Please refer to “Structural Basis for Substrate Fatty Acyl Chain Specificity: Crystal Structure of Human Very-Long-Chain acyl-CoA Dehydrogenase” by McAndrew et al., 2008 (PMID 18227065) (note – numbering in this reference is for the mature peptide without the mitochondrial signal peptide, so amino acid position numbers are lower by 40 than numbering based on NM-000018.4) and “Compared effects of missense mutations in Very-Long-Chain Acyl-CoA Dehydrogenase deficiency: Combined analysis by structural, functional and pharmacological approaches” by Gobin-Limballe et al., 2010 (PMID 20060901) for identifying important structural regions for proper enzyme function such as binding sites and active sites of the enzyme. Based on this and information from UniProt the following regions are designated to be important functional domains:
+
+
+p.214-223, p.249-251, p.460-466, p.562: Nucleotide and substrate binding.
+
+
+p.481-516: Membrane binding.
+
+
+p.1-40: Mitochondrial signal peptide.
+
+
+Curators may seek approval from the expert panel for identifying new hot spots or critical regions as discovered in literature searches, in uniport, HGMD, etc. for inclusion.",Disease-specific
+ACADVL (HGNC:92),PM2,Original ACMG Summary,"Absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.
+Caveat: Population data for indels may be poorly called by next generation sequencing.",
+ACADVL (HGNC:92),PM2,Supporting,"Variants with a highest population minor allele frequency (MAF) <0.001 (0.1%) in any continental population with >2000 alleles in gnomAD will meet PM2_supporting.
+
+
+The 0.001 cutoff is based on a disease frequency of 1:100,000, genetic heterogeneity of 100%, penetrance of 75%, and maximum allelic contribution of 20%, based on the most common pathogenic ACADVL variant, c.848T>C (p.Val283Ala): gnomAD MAF: 0.002238 (289/129106; 1 homozygote; European Non-Finnish) and overall frequency: 0.001224 (346/282744; 2 homozygotes). This variant would not meet PM2_supporting, but it is assumed that the most common variants have already been identified so a conservative cutoff was chosen.
+
+
+See Appendix 2 for calculations.
+
+
+It is acceptable for an ACADVL variant to be present in controls because VLCAD deficiency is a recessive condition. It is also possible for homozygous ACADVL variants to be present in population databases due to later onset of the condition. If homozygous variants are present, the number should be noted and discussed with an expert.
+
+
+SVI guidance: use PM2 as Supporting (not Moderate) based on absence or rarity not being in the odds range expected for a moderate piece of evidence. This EP will track the impact of not using Moderate for PM2.",Disease-specific
+ACADVL (HGNC:92),PM3,Original ACMG Summary,"For recessive disorders, detected in trans with a pathogenic variant
+Note: This requires testing of parents (or offspring) to determine phase.",
+ACADVL (HGNC:92),PM3,Moderate,"Details of the cDNA change must be used to describe any variants used as evidence for PM3. If the variant is described only as an amino acid change, this is not sufficient. Probands must also meet PP4 criteria to be counted.
+
+
+If more than one case has the same genotype and the variants are not confirmed in trans, then only one case should be used for assigning points to avoid overcounting evidence if the variants are actually in cis and hence inherited together in multiple individuals or potentially counting the same case twice. If the variants are confirmed to be in trans, more than one individual with the same genotype can be counted as long as the reports do not represent the same case.
+
+
+Following SVI guidance for PM3, use the scoring system below to determine the strength of evidence for PM3.
+
+
+These variant interpretation guidelines should be used to determine the classification of the “other variant” in order to determine the appropriate number of points to assign.
+
+
+For a variant to be “confirmed in trans” in a compound heterozygous patient, parental testing, or another appropriate molecular method (such as cloning each allele separately followed by sequencing), must have been performed. Otherwise, the phase of the variants is unknown. Parental testing is not required for homozygous cases.",Disease-specific
+ACADVL (HGNC:92),PM4,Original ACMG Summary,Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants.,
+ACADVL (HGNC:92),PM4,Moderate,Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants.,No change
+ACADVL (HGNC:92),PM5,Original ACMG Summary,"Novel missense change at an amino acid residue where a different missense change determined to be pathogenic has been seen before.
+Example: Arg156His is pathogenic; now you observe Arg156Cys.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+ACADVL (HGNC:92),PM5,Moderate,"These variant interpretation guidelines should be used to determine the classification of the other missense change in order to determine whether this rule can be applied.
+
+
+Use PM5_Supporting if the other variant is Likely Pathogenic.",Disease-specific
+ACADVL (HGNC:92),PM6,Original ACMG Summary,"Assumed de novo, but without confirmation of paternity and maternity.",
+ACADVL (HGNC:92),PM6,Moderate,"Assumed de novo, but without confirmation of paternity and maternity.",No change
+ACADVL (HGNC:92),PP1,Original ACMG Summary,"Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease.
+Note: May be used as stronger evidence with increasing segregation data.",
+ACADVL (HGNC:92),PP1,Supporting,"Following SVI guidance, use the scoring system below to determine the strength of evidence for PP1.
+
+
+For segregation counting, do not count probands as a segregation.
+o Affected segregations = # affected individuals in the family with the variants - 1.
+
+
+Affected segregations are defined as affected family members (typically siblings) who harbor the variant in question and a second variant on the remaining allele.
+
+
+Unaffected segregations are defined as unaffected family members, typically siblings, who are at risk to inherit the two variants identified in the proband. These individuals should be either wild-type for both variants identified in the proband, or a heterozygous carrier for a single variant.
+
+
+Unaffected, carrier parents DO NOT count as unaffected segregations.
+
+
+There may be scenarios where individuals other than siblings could be counted as segregations, such as in families where one parent is affected with the autosomal recessive disorder, in large families with multiple branches, or in consanguineous families.",Disease-specific
+ACADVL (HGNC:92),PP2,Original ACMG Summary,Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease.,NA
+ACADVL (HGNC:92),PP3,Original ACMG Summary,"Multiple lines of computational evidence support a deleterious effect on the gene or gene product (conservation, evolutionary, splicing impact, etc.).
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. PP3 can be used only once in any evaluation of a variant.",
+ACADVL (HGNC:92),PP3,Supporting,"Missense changes with a REVEL score >0.75 will meet PP3.
+
+
+For in-frame deletions and insertions, use PROVEAN and Mutation Taster. Results must be consistent to count.
+
+
+For non-canonical splice site variants, use Splice AI, MaxEntScn and NNSplice. Based on data from Jaganathan et al., 2019 (PMID: 30661751), Houdayer et al., 2012 (PMID: 22505045) and Tang et al., 2016 (PMID: 27313609), PP3 can be applied if there is, a SpliceAI “high score” (Δ Score ≥ 0.5 “confidently predicted splice variants”) (exclude any results with Δ Score ≤ 0.2 from consideration of pathogenicity, <0.2 are not “predicted to alter splicing”), >15% reduction using MaxEntScn and >5% reduction using NNSplice. Two out of the three predictors must be consistent to count.
+
+
+For SpliceAI’s cryptic splice-site rules, the creation of a new splice-site with Δ Score ≥ 0.5 may be enough to produce a large proportion of aberrant transcripts.
+
+
+If a new splice site is predicted to be generated, this rule can be applied if the newly generated splice site is significantly stronger than the wild type site (Δ Score ≥ 0.5 using SpliceAI; >15% difference using MaxEntScn).
+
+
+Do not apply this rule for canonical splice site changes meeting PVS1.",Disease-specific
+ACADVL (HGNC:92),PP4,Original ACMG Summary,Patient’s phenotype or family history is highly specific for a disease with a single genetic etiology.,
+ACADVL (HGNC:92),PP4,Moderate,"Abnormal tests that are consistent with VLCAD deficiency include deficient VLCAD enzyme activity in patient cells (leukocytes, fibroblasts, liver, heart, or skeletal muscle, or amniocytes), abnormal C14:1 acylcarnitine values from newborn screening (NBS), and abnormal acylcarnitine values from follow-up plasma analysis.
+
+
+To award a given tier of evidence, at least one proband harboring the variant must meet at least one of the minimum requirements below. If multiple analyses are completed, only one result needs to be within the allowed ranges to meet this rule as values can fluctuate due to age or diet: 
+
+
+PP4_moderate (Must meet at least one of the below criteria):
+
+
+VLCAD enzyme activity (β-Oxidation Flux) ≤20% of normal.
+
+
+NBS C14:1 Levels ≥ 1.0 μM AND:
+
+
+VLCAD enzyme activity (β-Oxidation Flux) 21-27% of normal OR Assertion of reduced VLCAD activity without specific levels OR Follow-Up Plasma Acylcarnitine analysis “consistent with VLCADD” without specific levels.",Disease-specific
+ACADVL (HGNC:92),PP4,Supporting,"Abnormal tests that are consistent with VLCAD deficiency include deficient VLCAD enzyme activity in patient cells (leukocytes, fibroblasts, liver, heart, or skeletal muscle, or amniocytes), abnormal C14:1 acylcarnitine values from newborn screening (NBS), and abnormal acylcarnitine values from follow-up plasma analysis.
+
+
+To award a given tier of evidence, at least one proband harboring the variant must meet at least one of the minimum requirements below. If multiple analyses are completed, only one result needs to be within the allowed ranges to meet this rule as values can fluctuate due to age or diet: 
+
+
+PP4_supporting (Must meet at least one of the below criteria):
+
+
+VLCAD enzyme activity (β-Oxidation Flux) 21-27% of normal.
+
+
+Assertion of reduced VLCAD activity without specific levels.
+
+
+NBS C14:1 Levels from >0.8 μM.
+
+
+Assertion of abnormal NBS “consistent with VLCADD” without specific levels AND: Follow-Up Plasma Acylcarnitine analysis “consistent with VLCADD” without specific levels.",Disease-specific
+ACADVL (HGNC:92),PP5,Original ACMG Summary,"Reputable source recently reports variant as pathogenic, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+ACADVL (HGNC:92),BA1,Original ACMG Summary,"Allele frequency is above 5% in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.",
+ACADVL (HGNC:92),BA1,Stand Alone,"Variants with a highest population minor allele frequency (MAF) ≥0.007 (0.7%) in any continental population with >2000 alleles in gnomAD will meet BA1.
+
+
+See Appendix 2 for calculations.",Disease-specific
+ACADVL (HGNC:92),BS1,Original ACMG Summary,Allele frequency is greater than expected for disorder.,
+ACADVL (HGNC:92),BS1,Strong,"Variants with a highest population minor allele frequency (MAF) ≥0.0035 (0.35%) in any continental population with >2000 alleles in gnomAD will meet BS1.
+
+
+See Appendix 2 for calculations.",Disease-specific
+ACADVL (HGNC:92),BS2,Original ACMG Summary,"Observed in a healthy adult individual for a recessive (homozygous), dominant (heterozygous), or X-linked (hemizygous) disorder, with full penetrance expected at an early age.",NA
+ACADVL (HGNC:92),BS3,Original ACMG Summary,Well-established in vitro or in vivo functional studies show no damaging effect on protein function or splicing.,
+ACADVL (HGNC:92),BS3,Strong,"In vitro expression:
+
+
+
+
+
+
+50% enzyme activity
+Splicing assays:
+
+
+
+
+
+
+For non-canonical splicing variants, use BS3 if there is evidence demonstrating normal splicing with no evidence of abnormal splicing (RT-PCR and/or RNA sequencing).
+
+
+These studies can be performed using patient-derived cells or heterologous cultured cells.
+
+
+Any variants meeting the requirement for in vitro expression or splicing assays can meet BS3, but if the variant meets the description for both, BS3 should only be counted once.",Disease-specific
+ACADVL (HGNC:92),BS4,Original ACMG Summary,"Lack of segregation in affected members of a family.
+Caveat: The presence of phenocopies for common phenotypes (i.e. cancer, epilepsy) can mimic lack of segregation among affected individuals. Also, families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent lack of segregation.",
+ACADVL (HGNC:92),BS4,Strong,Lack of segregation in affected members of a family.,No change
+ACADVL (HGNC:92),BP1,Original ACMG Summary,Missense variant in a gene for which primarily truncating variants are known to cause disease.,NA
+ACADVL (HGNC:92),BP2,Original ACMG Summary,Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder or observed in cis with a pathogenic variant in any inheritance pattern.,
+ACADVL (HGNC:92),BP2,Supporting,Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder; or observed in cis with a pathogenic variant in any inheritance pattern.,No change
+ACADVL (HGNC:92),BP3,Original ACMG Summary,In frame-deletions/insertions in a repetitive region without a known function.,NA
+ACADVL (HGNC:92),BP4,Original ACMG Summary,"Multiple lines of computational evidence suggest no impact on gene or gene product (conservation, evolutionary, splicing impact, etc)
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm cannot be counted as an independent criterion. BP4 can be used only once in any evaluation of a variant.",
+ACADVL (HGNC:92),BP4,Supporting,"Missense changes with a REVEL score <0.5 will meet BP4.
+
+
+For in-frame deletions and insertions, use PROVEAN and Mutation Taster. Results must be consistent to count.
+For non-canonical splice site variants, use Splice AI, MaxEntScn and NNSplice. Based on data from Jaganathan et al., 2019 (PMID: 30661751), Houdayer et al., 2012 (PMID: 22505045) and Tang et al., 2016 (PMID: 27313609), PP3 can be applied if there is a with Δ Score ≤ 0.2 , <10% reduction using MaxEntScn and <2% reduction using NNSplice. Two out of the three predictors must be consistent to count.
+
+
+Do not apply this rule if there is evidence for creation of a cryptic splice site.
+
+
+Can be used with BP7 code.",Disease-specific
+ACADVL (HGNC:92),BP5,Original ACMG Summary,Variant found in a case with an alternate molecular basis for disease.,NA
+ACADVL (HGNC:92),BP6,Original ACMG Summary,"Reputable source recently reports variant as benign, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+ACADVL (HGNC:92),BP7,Original ACMG Summary,A synonymous variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site AND the nucleotide is not highly conserved.,
+ACADVL (HGNC:92),BP7,Supporting,Can be used with BP4 code.,No change

VCI/parsing_csr_criteria/version_csv_individual/ClinGenBrainMalformationsExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesVersion1.1.0_version=1.1.0.csv

@@ -0,0 +1,104 @@
+Gene,Code,Strength,Description,Modification Type
+AKT3 (HGNC:393),PVS1,Original ACMG Summary,"Null variant (nonsense, frameshift, canonical +/−1 or 2 splice sites, initiation codon, single or multi-exon deletion) in a gene where loss of function (LOF) is a known mechanism of disease.
+Caveats:
+ • Beware of genes where LOF is not a known disease mechanism (e.g. GFAP, MYH7).
+ • Use caution interpreting LOF variants at the extreme 3’ end of a gene.
+ • Use caution with splice variants that are predicted to lead to exon skipping but leave the remainder of the protein intact.
+ • Use caution in the presence of multiple transcripts.",NA
+AKT3 (HGNC:393),PS1,Original ACMG Summary,"Same amino acid change as a previously established pathogenic variant regardless of nucleotide change.
+Example: Val->Leu caused by either G>C or G>T in the same codon.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+AKT3 (HGNC:393),PS1,Strong,No change.,None
+AKT3 (HGNC:393),PS2,Original ACMG Summary,"De novo (both maternity and paternity confirmed) in a patient with the disease and no family history.
+Note: Confirmation of paternity only is insufficient. Egg donation, surrogate motherhood, errors in embryo transfer, etc. can contribute to non-maternity.",
+AKT3 (HGNC:393),PS2,Strong,"Award the PS2_Strong point if Criteria 1 AND Criteria 2 are fulfilled.  
+
+
+  Criteria 1. The variant is present at a detectable allele fraction but is absent from parental samples with confirmed maternity and paternity.
+
+
+  Criteria 2. The variant is present at a detectable allele fraction in an affected tissue sample but is absent from or detected at a lower allelic fraction in another tissue (e.g. if present in 5% of brain tissue but absent from the blood or skin this point can be awarded).
+
+
+For the sake of implementation, these criteria are intended to apply to high-confidence somatic mutations identified by the reporting CLIA laboratory. The expert panel recognizes that in practice there may be significant heterogeneity in the technical methods and thresholds used to identify such variants as 'high confidence', and flags the need to establish consensus statistical frameworks (e.g. Phred-scaled genotype qualities) or experimental approaches (e.g., confirmation of somatic variants by sequencing on orthogonal platforms) by which quality thresholds can be consistently applied.","Disease-specific,Strength"
+AKT3 (HGNC:393),PS2,Moderate,"Award the PS2_Moderate point if Criteria 1 is fulfilled, OR if parents are not available but Criteria 2 is fulfilled.","Disease-specific,Strength"
+AKT3 (HGNC:393),PS3,Original ACMG Summary,"Well-established in vitro or in vivo functional studies supportive of a damaging effect on the gene or gene product.
+Note: Functional studies that have been validated and shown to be reproducible and robust in a clinical diagnostic laboratory setting are considered the most well-established.",
+AKT3 (HGNC:393),PS3,Strong,"Follow recommendations set forth by the SVI in conjunction with specifications added by the BMVCEP for quality metrics and minimum validation controls required. (Supplemental Document 1) Animal models are considered in a different manner.
+
+
+Award PS4_Strong if the animal model generated with the variant of interest expressed in neural progenitors shows a complementary brain phenotype.
+
+
+Award PS3 if the functional assay meets the acceptability criteria delimited in (PMID: 31892348) with specifications added by the BMVCEP. Quality metrics and minimum validation controls required can be found in Supplementary Document 1.
+
+
+Animal models are considered in a different manner. Award PS4_Strong if the animal model generated with the variant of interest expressed in neural progenitors show a complementary brain phenotype.
+
+
+Caveat: Studies of cell lines derived from the affected patient as the only source of functional characterization are by themselves insufficient to provide strong evidence of pathogenicity. This is because cells derived from patient affected tissue are likely to exhibit the desired phenotype since the patient tissue exhibits the phenotype. It is therefore impossible to determine whether the variant of interest was solely responsible for that phenotype. Instead, functional readout of patient-derived cells are now included in PS4.",Disease-specific
+AKT3 (HGNC:393),PS3,Moderate,Follow recommendations set forth by the SVI in conjunction with specifications added by the BMVCEP for quality metrics and minimum validation controls required (PMID: 31892348). Animal models are considered in a different manner. Award PS4_Moderate if the animal model generated with the variant of interest expressed in non-neural tissues show an increased cancer burden.,Strength
+AKT3 (HGNC:393),PS3,Supporting,Follow recommendations set forth by the SVI in conjunction with specifications added by the BMVCEP for quality metrics and minimum validation controls required (PMID: 31892348).,Strength
+AKT3 (HGNC:393),PS4,Original ACMG Summary,"The prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls.
+Note 1: Relative risk (RR) or odds ratio (OR), as obtained from case-control studies, is >5.0 and the confidence interval around the estimate of RR or OR does not include 1.0. See manuscript for detailed guidance.
+Note 2: In instances of very rare variants where case-control studies may not reach statistical significance, the prior observation of the variant in multiple unrelated patients with the same phenotype, and its absence in controls, may be used as moderate level of evidence.",
+AKT3 (HGNC:393),PS4,Very Strong,"Points are assigned for phenotype according to (Table 2A). Phenotype criteria can only be used if the variant meets criteria for (PM2). Strength of evidence is determined by points according to (Table 2B). PS4_VeryStrong ≥ 16 points. For PS4, for cases reported in the literature, we recommend assigning each one to the SINGLE category below that is associated with the highest point value (Table 2A). The total score obtained for all reported cases with a particular variant will determine the strength of PS4 assigned according to the scale (Table 2B)
+*
+.
+
+
+PS4_VeryStrong ≥ 16 points.
+
+
+*
+Applicable if the variant is absent/rare from controls according to PM2 to ensure the variant is not simply present due to beinging common in the general population.","Disease-specific,Strength"
+AKT3 (HGNC:393),PS4,Strong,Points are assigned for phenotype according to (Table 2A). Phenotype criteria can only be used if the variant is absent from controls (PM2). Strength of evidence is determined by points according to (Table 2B). PS4_Strong = 3.5-15.75 points.,Disease-specific
+AKT3 (HGNC:393),PS4,Moderate,Points are assigned for phenotype according to (Table 2A). Phenotype criteria can only be used if the variant is absent from controls (PM2). Strength of evidence is determined by points according to (Table 2B). PS4_Moderate = 1.5-3.25 points.,Strength
+AKT3 (HGNC:393),PS4,Supporting,Points are assigned for phenotype according to (Table 2A). Phenotype criteria can only be used if the variant is absent from controls (PM2). Strength of evidence is determined by points according to (Table 2B). PS4_Supporting = 0.5 – 1.25 points.,Strength
+AKT3 (HGNC:393),PM1,Original ACMG Summary,Located in a mutational hot spot and/or critical and well-established functional domain (e.g. active site of an enzyme) without benign variation.,
+AKT3 (HGNC:393),PM1,Supporting,Residues affecting critical functional domains provided in Table 4 for each gene.,Strength
+AKT3 (HGNC:393),PM2,Original ACMG Summary,"Absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.
+Caveat: Population data for indels may be poorly called by next generation sequencing.",
+AKT3 (HGNC:393),PM2,Supporting,Absent/rare from controls in an ethnically-matched cohort population sample ( ≥1).,Disease-specific
+AKT3 (HGNC:393),PM3,Original ACMG Summary,"For recessive disorders, detected in trans with a pathogenic variant
+Note: This requires testing of parents (or offspring) to determine phase.",NA
+AKT3 (HGNC:393),PM4,Original ACMG Summary,Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants.,NA
+AKT3 (HGNC:393),PM5,Original ACMG Summary,"Novel missense change at an amino acid residue where a different missense change determined to be pathogenic has been seen before.
+Example: Arg156His is pathogenic; now you observe Arg156Cys.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+AKT3 (HGNC:393),PM5,Moderate,No change.,None
+AKT3 (HGNC:393),PM6,Original ACMG Summary,"Assumed de novo, but without confirmation of paternity and maternity.",NA
+AKT3 (HGNC:393),PP1,Original ACMG Summary,"Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease.
+Note: May be used as stronger evidence with increasing segregation data.",NA
+AKT3 (HGNC:393),PP2,Original ACMG Summary,Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease.,
+AKT3 (HGNC:393),PP2,Supporting,"Missense constraint computed in ExAC/gnomAD was utilized. Award PP2 if the z-score > 3.09. (applicable to MTOR, PIK3CA and AKT3 but not PIK3R2).",Disease-specific
+AKT3 (HGNC:393),PP3,Original ACMG Summary,"Multiple lines of computational evidence support a deleterious effect on the gene or gene product (conservation, evolutionary, splicing impact, etc.).
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. PP3 can be used only once in any evaluation of a variant.",NA
+AKT3 (HGNC:393),PP4,Original ACMG Summary,Patient’s phenotype or family history is highly specific for a disease with a single genetic etiology.,NA
+AKT3 (HGNC:393),PP5,Original ACMG Summary,"Reputable source recently reports variant as pathogenic, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+AKT3 (HGNC:393),BA1,Original ACMG Summary,"Allele frequency is above 5% in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.",
+AKT3 (HGNC:393),BA1,Stand Alone,"Allele frequency (>0.0926%). An allele frequency (>0.0926%) was approved.
+ Note: this was adjusted from ACMG Guidelines due to maintaining the 5x threshold for benign (consistent with previously established guidelines)",Disease-specific
+AKT3 (HGNC:393),BS1,Original ACMG Summary,Allele frequency is greater than expected for disorder.,
+AKT3 (HGNC:393),BS1,Strong,Allele frequency (>0.0185%). An allele frequency (>0.0185%) was approved. (Supplemental Table 3).,Disease-specific
+AKT3 (HGNC:393),BS2,Original ACMG Summary,"Observed in a healthy adult individual for a recessive (homozygous), dominant (heterozygous), or X-linked (hemizygous) disorder, with full penetrance expected at an early age.",
+AKT3 (HGNC:393),BS2,Strong,"Award BS2 if ≥3 homozygotes present in gnomAD or ≥3 heterozygous in well phenotyped family members.
+Clinical laboratories are encouraged to accumulate more than 2 (≥3) instances of well phenotyped family members before applying this strong criterion. To be considered for this point, the variant should be either germline (most common), or somatic in a relevant tissue. Homozygous occurrences in gnomAD or ExAC can also be counted for this point (≥3).",Disease-specific
+AKT3 (HGNC:393),BS3,Original ACMG Summary,Well-established in vitro or in vivo functional studies show no damaging effect on protein function or splicing.,
+AKT3 (HGNC:393),BS3,Strong,Follow recommendations set forth by the SVI in conjunction with specifications added by the Brain Malformation Group for quality metrics and minimum validation controls required.,Disease-specific
+AKT3 (HGNC:393),BS3,Supporting,Follow recommendations set forth by the SVI in conjunction with specifications added by the Brain Malformation Group for quality metrics and minimum validation controls required.,Strength
+AKT3 (HGNC:393),BS4,Original ACMG Summary,"Lack of segregation in affected members of a family.
+Caveat: The presence of phenocopies for common phenotypes (i.e. cancer, epilepsy) can mimic lack of segregation among affected individuals. Also, families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent lack of segregation.",NA
+AKT3 (HGNC:393),BP1,Original ACMG Summary,Missense variant in a gene for which primarily truncating variants are known to cause disease.,NA
+AKT3 (HGNC:393),BP2,Original ACMG Summary,Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder or observed in cis with a pathogenic variant in any inheritance pattern.,
+AKT3 (HGNC:393),BP2,Supporting,Observed in cis or trans with a known pathogenic variant in the same gene.,None
+AKT3 (HGNC:393),BP3,Original ACMG Summary,In frame-deletions/insertions in a repetitive region without a known function.,NA
+AKT3 (HGNC:393),BP4,Original ACMG Summary,"Multiple lines of computational evidence suggest no impact on gene or gene product (conservation, evolutionary, splicing impact, etc)
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm cannot be counted as an independent criterion. BP4 can be used only once in any evaluation of a variant.",
+AKT3 (HGNC:393),BP4,Supporting,"Award BP4 for a synonymous, intronic positions (except canonical splice sites) or non-coding variants in the UTRs, if two out of three of the splicing prediction tools predicted no impact on splicing function.
+Not applicable for any variant type except for synonymous, intronic positions (except canonical splice sites) and non-coding variants in the UTRs,. This criterion can be applied when two of three splicing prediction tools predict no splicing change. The splicing prediction tools used are: varSEAK, spliceAI and MaxEntScan.",Disease-specific
+AKT3 (HGNC:393),BP5,Original ACMG Summary,Variant found in a case with an alternate molecular basis for disease.,
+AKT3 (HGNC:393),BP5,Supporting,No change.,None
+AKT3 (HGNC:393),BP6,Original ACMG Summary,"Reputable source recently reports variant as benign, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+AKT3 (HGNC:393),BP7,Original ACMG Summary,A synonymous variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site AND the nucleotide is not highly conserved.,
+AKT3 (HGNC:393),BP7,Supporting,"For synonymous, intronic positions (except canonical splice sites) and non-coding variants in the UTRs, if the nucleotide is non-conserved award this point (PhyloP score <0.1).",Disease-specific

VCI/parsing_csr_criteria/version_csv_individual/ClinGenBrainMalformationsExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesVersion1_version=1.0.0.csv

@@ -0,0 +1,81 @@
+Gene,Code,Strength,Description,Modification Type
+AKT3 (HGNC:393),PVS1,Original ACMG Summary,"Null variant (nonsense, frameshift, canonical +/−1 or 2 splice sites, initiation codon, single or multi-exon deletion) in a gene where loss of function (LOF) is a known mechanism of disease.
+Caveats:
+ • Beware of genes where LOF is not a known disease mechanism (e.g. GFAP, MYH7).
+ • Use caution interpreting LOF variants at the extreme 3’ end of a gene.
+ • Use caution with splice variants that are predicted to lead to exon skipping but leave the remainder of the protein intact.
+ • Use caution in the presence of multiple transcripts.",NA
+AKT3 (HGNC:393),PS1,Original ACMG Summary,"Same amino acid change as a previously established pathogenic variant regardless of nucleotide change.
+Example: Val->Leu caused by either G>C or G>T in the same codon.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+AKT3 (HGNC:393),PS1,Strong,No change.,None
+AKT3 (HGNC:393),PS2,Original ACMG Summary,"De novo (both maternity and paternity confirmed) in a patient with the disease and no family history.
+Note: Confirmation of paternity only is insufficient. Egg donation, surrogate motherhood, errors in embryo transfer, etc. can contribute to non-maternity.",
+AKT3 (HGNC:393),PS2,Strong,"Award the PS2_Strong point if Criteria 1 AND Criteria 2 are fulfilled.
+Criteria 1: if the variant is present at a detectable allelic fraction in a proband with the disease but is absent from parental samples with confirmed maternity and paternity.
+Criteria 2: can also be awarded if the variant is present at a detectable allele fraction in an affected tissue sample but is absent from or detected at a lower allelic fraction in another tissue (e.g. if present in 5% of brain tissue but absent from the blood or skin this point can be awarded).","Disease-specific,Strength"
+AKT3 (HGNC:393),PS2,Moderate,"Award the PS2_Moderate point if Criteria 1 is fulfilled, OR if parents are not available but Criteria 2 is fulfilled. 
+ Criteria 1: if the variant is present at a detectable allelic fraction in a proband with the disease but is absent from parental samples with confirmed maternity and paternity. 
+Criteria 2: can also be awarded if the variant is present at a detectable allele fraction in an affected tissue sample but is absent from or detected at a lower allelic fraction in another tissue (e.g. if present in 5% of brain tissue but absent from the blood or skin this point can be awarded).","Disease-specific,Strength"
+AKT3 (HGNC:393),PS3,Original ACMG Summary,"Well-established in vitro or in vivo functional studies supportive of a damaging effect on the gene or gene product.
+Note: Functional studies that have been validated and shown to be reproducible and robust in a clinical diagnostic laboratory setting are considered the most well-established.",
+AKT3 (HGNC:393),PS3,Strong,"Follow recommendations set forth by the SVI in conjunction with specifications added by the Brain Malformation Group for quality metrics and minimum validation controls required. 
+Animal models are considered in a different manner. Award PS4_Strong if the animal model generated with the variant of interest expressed in neural progenitors show a complementary brain phenotype.",Disease-specific
+AKT3 (HGNC:393),PS3,Moderate,"Follow recommendations set forth by the SVI in conjunction with specifications added by the Brain Malformation Group for quality metrics and minimum validation controls required. 
+Animal models are considered in a different manner. Award PS4_Moderate if the animal model generated with the variant of interest expressed in non-neural tissues show an increased cancer burden.",Strength
+AKT3 (HGNC:393),PS3,Supporting,Follow recommendations set forth by the SVI in conjunction with specifications added by the Brain Malformation Group for quality metrics and minimum validation controls required.,Strength
+AKT3 (HGNC:393),PS4,Original ACMG Summary,"The prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls.
+Note 1: Relative risk (RR) or odds ratio (OR), as obtained from case-control studies, is >5.0 and the confidence interval around the estimate of RR or OR does not include 1.0. See manuscript for detailed guidance.
+Note 2: In instances of very rare variants where case-control studies may not reach statistical significance, the prior observation of the variant in multiple unrelated patients with the same phenotype, and its absence in controls, may be used as moderate level of evidence.",
+AKT3 (HGNC:393),PS4,Very Strong,"Points are assigned for phenotype according to (Table 2A). Phenotype criteria can only be used if the variant is absent from controls (PM2). Strength of evidence is determined by points according to (Table 2B).
+PS4_VeryStrong = >16 points.","Disease-specific,Strength"
+AKT3 (HGNC:393),PS4,Strong,"Points are assigned for phenotype according to (Table 2A). Phenotype criteria can only be used if the variant is absent from controls (PM2). Strength of evidence is determined by points according to (Table 2B).
+PS4 = 3.5-15.99 points.",Disease-specific
+AKT3 (HGNC:393),PS4,Moderate,"Points are assigned for phenotype according to (Table 2A). Phenotype criteria can only be used if the variant is absent from controls (PM2). Strength of evidence is determined by points according to (Table 2B).
+PS4_Moderate = 1.5-3.49 points.",Strength
+AKT3 (HGNC:393),PS4,Supporting,"The prevalence of the variant in affected individuals is significantly increased compared with the prevalence in controls. Points are assigned for phenotype according to (Supplementary Table 3). Strength of evidence is determined by points according to (Supplementary Table 2).
+PS4_Supporting = 0.5 - 1.49 points.",Strength
+AKT3 (HGNC:393),PM1,Original ACMG Summary,Located in a mutational hot spot and/or critical and well-established functional domain (e.g. active site of an enzyme) without benign variation.,
+AKT3 (HGNC:393),PM1,Supporting,Residues affecting critical functional domains provided in Table 4 for each gene.,Strength
+AKT3 (HGNC:393),PM2,Original ACMG Summary,"Absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.
+Caveat: Population data for indels may be poorly called by next generation sequencing.",
+AKT3 (HGNC:393),PM2,Supporting,Absent/rare from controls in an ethnically-matched cohort population sample ( ≥1).,Disease-specific
+AKT3 (HGNC:393),PM3,Original ACMG Summary,"For recessive disorders, detected in trans with a pathogenic variant
+Note: This requires testing of parents (or offspring) to determine phase.",NA
+AKT3 (HGNC:393),PM4,Original ACMG Summary,Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants.,NA
+AKT3 (HGNC:393),PM5,Original ACMG Summary,"Novel missense change at an amino acid residue where a different missense change determined to be pathogenic has been seen before.
+Example: Arg156His is pathogenic; now you observe Arg156Cys.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+AKT3 (HGNC:393),PM5,Moderate,No change.,None
+AKT3 (HGNC:393),PM6,Original ACMG Summary,"Assumed de novo, but without confirmation of paternity and maternity.",NA
+AKT3 (HGNC:393),PP1,Original ACMG Summary,"Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease.
+Note: May be used as stronger evidence with increasing segregation data.",NA
+AKT3 (HGNC:393),PP2,Original ACMG Summary,Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease.,
+AKT3 (HGNC:393),PP2,Supporting,"Missense constraint computed in ExAC/gnomAD was utilized. Award PP2 if the z-score > 3.09. (applicable to MTOR, PIK3CA and AKT3 but not PIK3R2).",Disease-specific
+AKT3 (HGNC:393),PP3,Original ACMG Summary,"Multiple lines of computational evidence support a deleterious effect on the gene or gene product (conservation, evolutionary, splicing impact, etc.).
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. PP3 can be used only once in any evaluation of a variant.",NA
+AKT3 (HGNC:393),PP4,Original ACMG Summary,Patient’s phenotype or family history is highly specific for a disease with a single genetic etiology.,NA
+AKT3 (HGNC:393),PP5,Original ACMG Summary,"Reputable source recently reports variant as pathogenic, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+AKT3 (HGNC:393),BA1,Original ACMG Summary,"Allele frequency is above 5% in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.",
+AKT3 (HGNC:393),BA1,Stand Alone,Allele frequency (≥0.185%).,Disease-specific
+AKT3 (HGNC:393),BS1,Original ACMG Summary,Allele frequency is greater than expected for disorder.,
+AKT3 (HGNC:393),BS1,Strong,Allele frequency (≥0.037%).,Disease-specific
+AKT3 (HGNC:393),BS2,Original ACMG Summary,"Observed in a healthy adult individual for a recessive (homozygous), dominant (heterozygous), or X-linked (hemizygous) disorder, with full penetrance expected at an early age.",
+AKT3 (HGNC:393),BS2,Strong,Award BS2 if ≥3 homozygotes present in gnomAD or well phenotyped family members.,Disease-specific
+AKT3 (HGNC:393),BS3,Original ACMG Summary,Well-established in vitro or in vivo functional studies show no damaging effect on protein function or splicing.,
+AKT3 (HGNC:393),BS3,Strong,Follow recommendations set forth by the SVI in conjunction with specifications added by the Brain Malformation Group for quality metrics and minimum validation controls required.,Disease-specific
+AKT3 (HGNC:393),BS3,Supporting,Follow recommendations set forth by the SVI in conjunction with specifications added by the Brain Malformation Group for quality metrics and minimum validation controls required.,Strength
+AKT3 (HGNC:393),BS4,Original ACMG Summary,"Lack of segregation in affected members of a family.
+Caveat: The presence of phenocopies for common phenotypes (i.e. cancer, epilepsy) can mimic lack of segregation among affected individuals. Also, families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent lack of segregation.",NA
+AKT3 (HGNC:393),BP1,Original ACMG Summary,Missense variant in a gene for which primarily truncating variants are known to cause disease.,NA
+AKT3 (HGNC:393),BP2,Original ACMG Summary,Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder or observed in cis with a pathogenic variant in any inheritance pattern.,
+AKT3 (HGNC:393),BP2,Supporting,No change.,None
+AKT3 (HGNC:393),BP3,Original ACMG Summary,In frame-deletions/insertions in a repetitive region without a known function.,NA
+AKT3 (HGNC:393),BP4,Original ACMG Summary,"Multiple lines of computational evidence suggest no impact on gene or gene product (conservation, evolutionary, splicing impact, etc)
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm cannot be counted as an independent criterion. BP4 can be used only once in any evaluation of a variant.",
+AKT3 (HGNC:393),BP4,Supporting,"Award BP4 for a synonymous, intronic positions (except canonical splice sites) or non-coding variants in the UTRs, if two out of three of the splicing prediction tools predicted no impact on splicing function.",Disease-specific
+AKT3 (HGNC:393),BP5,Original ACMG Summary,Variant found in a case with an alternate molecular basis for disease.,
+AKT3 (HGNC:393),BP5,Supporting,No change.,None
+AKT3 (HGNC:393),BP6,Original ACMG Summary,"Reputable source recently reports variant as benign, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+AKT3 (HGNC:393),BP7,Original ACMG Summary,A synonymous variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site AND the nucleotide is not highly conserved.,
+AKT3 (HGNC:393),BP7,Supporting,"For synonymous, intronic positions (except canonical splice sites) and non-coding variants in the UTRs, if the nucleotide is non-conserved award this point (PhyloP score <0.1).",Disease-specific

VCI/parsing_csr_criteria/version_csv_individual/ClinGenCDH1ExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesVersion2_version=2.0.0.csv

@@ -0,0 +1,108 @@
+Gene,Code,Strength,Description,Modification Type
+CDH1 (HGNC:1748),PVS1,Original ACMG Summary,"Null variant (nonsense, frameshift, canonical +/−1 or 2 splice sites, initiation codon, single or multi-exon deletion) in a gene where loss of function (LOF) is a known mechanism of disease.
+Caveats:
+ • Beware of genes where LOF is not a known disease mechanism (e.g. GFAP, MYH7)
+ • Use caution interpreting LOF variants at the extreme 3’ end of a gene
+ • Use caution with splice variants that are predicted to lead to exon skipping but leave the remainder of the protein intact
+ • Use caution in the presence of multiple transcripts",
+CDH1 (HGNC:1748),PVS1,Very Strong,"Per ClinGen SVI guidelines with the exception of canonical splice sites
+
+
+
+
+Apply to initiation codon variant",
+CDH1 (HGNC:1748),PVS1,Strong,"Per ClinGen SVI guidelines
+Other CDH1 caveats:
+
+
+
+
+Use the strong strength of evidence for canonical splice sites
+
+
+CDH1 Exonic deletions or tandem duplications of in-frame exons
+
+
+Truncations in NMD-resistant zone located upstream the most 3’ well-characterized pathogenic variant c.2506G>T (p.Glu836*). Use PVS1_moderate if premature stop is downstream of this variant",
+CDH1 (HGNC:1748),PVS1,Moderate,"Per ClinGen SVI guidelines
+ Other CDH1 caveats:
+
+
+
+
+G to non-G variants disrupting the last nucleotide of an exon
+
+
+Canonical splice sites located in exons demonstrated experimentally to result in in-frame partial skipping/insertion (e.g., Exon 3 donor site)",
+CDH1 (HGNC:1748),PVS1,Supporting,Per ClinGen SVI guidelines,
+CDH1 (HGNC:1748),PS1,Original ACMG Summary,"Same amino acid change as a previously established pathogenic variant regardless of nucleotide change.
+Example: Val->Leu caused by either G>C or G>T in the same codon
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level",
+CDH1 (HGNC:1748),PS1,Strong,Per original ACMG/AMP guidelines,
+CDH1 (HGNC:1748),PS2,Original ACMG Summary,"De novo (both maternity and paternity confirmed) in a patient with the disease and no family history
+Note: Confirmation of paternity only is insufficient. Egg donation, surrogate motherhood, errors in embryo transfer, etc. can contribute to non-maternity",
+CDH1 (HGNC:1748),PS2,Very Strong,≥Two patients with DGC &/or LBC w/ parental confirmation,
+CDH1 (HGNC:1748),PS2,Strong,One patient with DGC &/or LBC w/ parental confirmation,
+CDH1 (HGNC:1748),PS3,Original ACMG Summary,"Well-established in vitro or in vivo functional studies supportive of a damaging effect on the gene or gene product
+Note: Functional studies that have been validated and shown to be reproducible and robust in a clinical diagnostic laboratory setting are considered the most well-established",
+CDH1 (HGNC:1748),PS3,Strong,RNA assay demonstrating abnormal out-of-frame transcripts,
+CDH1 (HGNC:1748),PS3,Supporting,RNA assay demonstrating abnormal in-frame transcripts,
+CDH1 (HGNC:1748),PS4,Original ACMG Summary,"The prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls
+Note 1: Relative risk (RR) or odds ratio (OR), as obtained from case-control studies, is >5.0 and the confidence interval around the estimate of RR or OR does not include 1.0. See manuscript for detailed guidance.
+Note 2: In instances of very rare variants where case-control studies may not reach statistical significance, the prior observation of the variant in multiple unrelated patients with the same phenotype, and its absence in controls, may be used as moderate level of evidence.",
+CDH1 (HGNC:1748),PS4,Very Strong,Sixteen families meet HDGC criteria,
+CDH1 (HGNC:1748),PS4,Strong,Four families meet HDGC criteria,
+CDH1 (HGNC:1748),PS4,Moderate,Two families meet HDGC criteria,
+CDH1 (HGNC:1748),PS4,Supporting,One family meets HDGC criteria,
+CDH1 (HGNC:1748),PM1,Original ACMG Summary,Located in a mutational hot spot and/or critical and well-established functional domain (e.g. active site of an enzyme) without benign variation,NA
+CDH1 (HGNC:1748),PM2,Original ACMG Summary,"Absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium
+Caveat: Population data for indels may be poorly called by next generation sequencing",
+CDH1 (HGNC:1748),PM2,Moderate,"Less than one out of 100,000 alleles in gnomAD cohort; if present in >=2 individuals, must be present in less than one out of 50,000 alleles within a sub-population",
+CDH1 (HGNC:1748),PM3,Original ACMG Summary,"For recessive disorders, detected in trans with a pathogenic variant
+Note: This requires testing of parents (or offspring) to determine phase",NA
+CDH1 (HGNC:1748),PM4,Original ACMG Summary,Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants.,
+CDH1 (HGNC:1748),PM4,Moderate,Per original ACMG/AMP guidelines,
+CDH1 (HGNC:1748),PM5,Original ACMG Summary,"Novel missense change at an amino acid residue where a different missense change determined to be pathogenic has been seen before
+Example: Arg156His is pathogenic; now you observe Arg156Cys
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level",NA
+CDH1 (HGNC:1748),PM6,Original ACMG Summary,"Assumed de novo, but without confirmation of paternity and maternity",
+CDH1 (HGNC:1748),PM6,Very Strong,≥Four patients with DGC &/or LBC w/o parental confirmation,
+CDH1 (HGNC:1748),PM6,Strong,≥Two patients with DGC &/or LBC w/o parental confirmation,
+CDH1 (HGNC:1748),PM6,Moderate,One patient with DGC &/or LBC w/o parental confirmation,
+CDH1 (HGNC:1748),PP1,Original ACMG Summary,"Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease
+Note: May be used as stronger evidence with increasing segregation data",
+CDH1 (HGNC:1748),PP1,Strong,≥Seven meioses across ≥2 families,
+CDH1 (HGNC:1748),PP1,Moderate,Five-six meioses across ≥1 families,
+CDH1 (HGNC:1748),PP1,Supporting,Three-four meioses across ≥1 families,
+CDH1 (HGNC:1748),PP2,Original ACMG Summary,Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease,NA
+CDH1 (HGNC:1748),PP3,Original ACMG Summary,"Multiple lines of computational evidence support a deleterious effect on the gene or gene product (conservation, evolutionary, splicing impact, etc)
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. PP3 can be used only once in any evaluation of a variant.",
+CDH1 (HGNC:1748),PP3,Moderate,Variants affecting the same splice site as a well-characterized variant with similar or worse in silico/ RNA predictions,
+CDH1 (HGNC:1748),PP3,Supporting,"At least three in silico splicing predictors in agreement (.Human Splicing Finder (HSF), Maximum Entropy (MaxEnt), Berkeley Drosophilia Genome Project (BDGP), or ESEfinder)",
+CDH1 (HGNC:1748),PP4,Original ACMG Summary,Patient’s phenotype or family history is highly specific for a disease with a single genetic etiology,NA
+CDH1 (HGNC:1748),PP5,Original ACMG Summary,"Reputable source recently reports variant as pathogenic, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+CDH1 (HGNC:1748),BA1,Original ACMG Summary,"Allele frequency is above 5% in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium",
+CDH1 (HGNC:1748),BA1,Stand Alone,MAF cutoff of 0.2%,
+CDH1 (HGNC:1748),BS1,Original ACMG Summary,Allele frequency is greater than expected for disorder.,
+CDH1 (HGNC:1748),BS1,Stand Alone,MAF cutoff of 0.1%,
+CDH1 (HGNC:1748),BS2,Original ACMG Summary,"Observed in a healthy adult individual for a recessive (homozygous), dominant (heterozygous), or X-linked (hemizygous) disorder with full penetrance expected at an early age",
+CDH1 (HGNC:1748),BS2,Strong,"Variant seen in ≥10 individuals w/o DCG, SRC tumors, or LBC & whose families do not suggest HDGC",
+CDH1 (HGNC:1748),BS2,Supporting,"Variant seen in ≥3 individuals w/o DCG, SRC tumors, or LBC & whose families do not suggest HDGC",
+CDH1 (HGNC:1748),BS3,Original ACMG Summary,Well-established in vitro or in vivo functional studies show no damaging effect on protein function or splicing.,
+CDH1 (HGNC:1748),BS3,Strong,Functional RNA studies demonstrating no impact on transcript composition,
+CDH1 (HGNC:1748),BS4,Original ACMG Summary,"Lack of segregation in affected members of a family
+Caveat: The presence of phenocopies for common phenotypes (i.e. cancer, epilepsy) can mimic lack of segregation among affected individuals. Also, families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent lack of segregation",
+CDH1 (HGNC:1748),BS4,Strong,Per original ACMG/AMP guidelines,
+CDH1 (HGNC:1748),BP1,Original ACMG Summary,Missense variant in a gene for which primarily truncating variants are known to cause disease,NA
+CDH1 (HGNC:1748),BP2,Original ACMG Summary,Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder or observed in cis with a pathogenic variant in any inheritance pattern.,
+CDH1 (HGNC:1748),BP2,Strong,"Variant observed in trans w/known pathogenic variant (phase confirmed) OR observed in the homozygous state in individual w/o personal &/or family history of DGC, LBC, or SRC tumors",
+CDH1 (HGNC:1748),BP2,Supporting,Variant is observed in cis (or phase is unknown) w/ a pathogenic variant,
+CDH1 (HGNC:1748),BP3,Original ACMG Summary,In frame-deletions/insertions in a repetitive region without a known function.,NA
+CDH1 (HGNC:1748),BP4,Original ACMG Summary,"Multiple lines of computational evidence suggest no impact on gene or gene product (conservation, evolutionary, splicing impact, etc)
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm cannot be counted as an independent criterion. BP4 can be used only once in any evaluation of a variant.",
+CDH1 (HGNC:1748),BP4,Supporting,"Splicing predictions only. At least three in silico splicing predictors in agreement (Human Splicing Finder (HSF), Maximum Entropy (MaxEnt), Berkeley Drosophilia Genome Project (BDGP), or ESEfinder)",
+CDH1 (HGNC:1748),BP5,Original ACMG Summary,Variant found in a case with an alternate molecular basis for disease.,
+CDH1 (HGNC:1748),BP5,Supporting,Per original ACMG/AMP guidelines,
+CDH1 (HGNC:1748),BP6,Original ACMG Summary,"Reputable source recently reports variant as benign, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+CDH1 (HGNC:1748),BP7,Original ACMG Summary,A synonymous variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site AND the nucleotide is not highly conserved.,
+CDH1 (HGNC:1748),BP7,Supporting,Synonymous variants where nucleotide is not highly conserved; variant is the reference nucleotide in one primate and/or >3 mammal species,

VCI/parsing_csr_criteria/version_csv_individual/ClinGenCDH1ExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesVersion3.1_version=3.1.0.csv

@@ -0,0 +1,101 @@
+Gene,Code,Strength,Description,Modification Type
+CDH1 (HGNC:1748),PVS1,Original ACMG Summary,"Null variant (nonsense, frameshift, canonical +/−1 or 2 splice sites, initiation codon, single or multi-exon deletion) in a gene where loss of function (LOF) is a known mechanism of disease.
+Caveats:
+ • Beware of genes where LOF is not a known disease mechanism (e.g. GFAP, MYH7).
+ • Use caution interpreting LOF variants at the extreme 3’ end of a gene.
+ • Use caution with splice variants that are predicted to lead to exon skipping but leave the remainder of the protein intact.
+ • Use caution in the presence of multiple transcripts.",
+CDH1 (HGNC:1748),PVS1,Very Strong,Per modified CDH1 PVS1 decision tree.,
+CDH1 (HGNC:1748),PVS1,Strong,"Per modified CDH1 PVS1 decision tree.
+Other CDH1 caveats:
+
+
+
+
+Use PVS1_Strong as the default strength of evidence for canonical splice site variants and follow the site-specific recommendations in the splicing table. 
+
+
+CDH1 Exonic deletions or tandem duplications of in-frame exons (exon 4,5,8,9,12,13,15).",
+CDH1 (HGNC:1748),PVS1,Moderate,"Per modified CDH1 PVS1 decision tree.
+Other CDH1 caveats:
+
+
+
+
+G to non-G variants disrupting the last nucleotide of an exon.
+
+
+Canonical splice sites predicted or demonstrated experimentally to result in in-frame partial skipping/insertion (e.g., Exon 3 donor site).",
+CDH1 (HGNC:1748),PS1,Original ACMG Summary,"Same amino acid change as a previously established pathogenic variant regardless of nucleotide change.
+Example: Val->Leu caused by either G>C or G>T in the same codon.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",NA
+CDH1 (HGNC:1748),PS2,Original ACMG Summary,"De novo (both maternity and paternity confirmed) in a patient with the disease and no family history.
+Note: Confirmation of paternity only is insufficient. Egg donation, surrogate motherhood, errors in embryo transfer, etc. can contribute to non-maternity.",
+CDH1 (HGNC:1748),PS2,Very Strong,≥Two patients meet the HDGC individual phenotype criteria w/ parental confirmation.,
+CDH1 (HGNC:1748),PS2,Strong,One patient meets the HDGC individual phenotype criteria w/ parental confirmation.,
+CDH1 (HGNC:1748),PS3,Original ACMG Summary,"Well-established in vitro or in vivo functional studies supportive of a damaging effect on the gene or gene product.
+Note: Functional studies that have been validated and shown to be reproducible and robust in a clinical diagnostic laboratory setting are considered the most well-established.",
+CDH1 (HGNC:1748),PS3,Strong,RNA assay demonstrating abnormal out-of-frame transcripts.,
+CDH1 (HGNC:1748),PS3,Moderate,RNA assay demonstrating abnormal in-frame transcript.,
+CDH1 (HGNC:1748),PS4,Original ACMG Summary,"The prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls.
+Note 1: Relative risk (RR) or odds ratio (OR), as obtained from case-control studies, is >5.0 and the confidence interval around the estimate of RR or OR does not include 1.0. See manuscript for detailed guidance.
+Note 2: In instances of very rare variants where case-control studies may not reach statistical significance, the prior observation of the variant in multiple unrelated patients with the same phenotype, and its absence in controls, may be used as moderate level of evidence.",
+CDH1 (HGNC:1748),PS4,Very Strong,≥Sixteen families meet HDGC criteria.,
+CDH1 (HGNC:1748),PS4,Strong,Four - Fifteen families meet HDGC criteria.,
+CDH1 (HGNC:1748),PS4,Moderate,Two or three families meet HDGC criteria.,
+CDH1 (HGNC:1748),PS4,Supporting,One family meets HDGC criteria.,
+CDH1 (HGNC:1748),PM1,Original ACMG Summary,Located in a mutational hot spot and/or critical and well-established functional domain (e.g. active site of an enzyme) without benign variation.,NA
+CDH1 (HGNC:1748),PM2,Original ACMG Summary,"Absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.
+Caveat: Population data for indels may be poorly called by next generation sequencing.",
+CDH1 (HGNC:1748),PM2,Supporting,"≤ One out of 100,000 alleles in gnomAD cohort; if present in ≥2 individuals within a subpopulation, must be present in ≤ One out of 50,000 alleles.",
+CDH1 (HGNC:1748),PM3,Original ACMG Summary,"For recessive disorders, detected in trans with a pathogenic variant
+Note: This requires testing of parents (or offspring) to determine phase.",NA
+CDH1 (HGNC:1748),PM4,Original ACMG Summary,Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants.,
+CDH1 (HGNC:1748),PM4,Moderate,"Only apply to stop-loss variants
+Variant example: CDH1 c.2647T>C (p.Ter883Glnext*29).",
+CDH1 (HGNC:1748),PM5,Original ACMG Summary,"Novel missense change at an amino acid residue where a different missense change determined to be pathogenic has been seen before.
+Example: Arg156His is pathogenic; now you observe Arg156Cys.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+CDH1 (HGNC:1748),PM5,Supporting,PM5_supporting is applicable to nonsense and frameshift variants that are predicted/proved to undergo NMD or located upstream of the last known pathogenic truncating variant. Site-specific recommendations for the application of PM5_Supporting for canonical splicing variants.,
+CDH1 (HGNC:1748),PM6,Original ACMG Summary,"Assumed de novo, but without confirmation of paternity and maternity.",
+CDH1 (HGNC:1748),PM6,Very Strong,Four patients meet the HDGC individual phenotype criteria w/o parental confirmation.,
+CDH1 (HGNC:1748),PM6,Strong,≥Two patients meet the HDGC individual phenotype criteria w/o parental confirmation.,
+CDH1 (HGNC:1748),PM6,Moderate,One patient meets the HDGC individual phenotype criteria w/o parental confirmation,
+CDH1 (HGNC:1748),PP1,Original ACMG Summary,"Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease.
+Note: May be used as stronger evidence with increasing segregation data.",
+CDH1 (HGNC:1748),PP1,Strong,≥Seven informative meioses across ≥2 families.,
+CDH1 (HGNC:1748),PP1,Moderate,Five-six informative meioses across ≥1 family.,
+CDH1 (HGNC:1748),PP1,Supporting,Three-four informative meioses across ≥1 family.,
+CDH1 (HGNC:1748),PP2,Original ACMG Summary,Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease.,NA
+CDH1 (HGNC:1748),PP3,Original ACMG Summary,"Multiple lines of computational evidence support a deleterious effect on the gene or gene product (conservation, evolutionary, splicing impact, etc.).
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. PP3 can be used only once in any evaluation of a variant.",
+CDH1 (HGNC:1748),PP3,Moderate,Variants affecting the same splice site as a well-characterized variant with similar or worse in silico/ RNA predictions.,
+CDH1 (HGNC:1748),PP3,Supporting,"At least three in silico splicing predictors in agreement (SpliceAI, MaxEntScan, SSF, GeneSplicer, HSF, TraP, varSEAK).",
+CDH1 (HGNC:1748),PP4,Original ACMG Summary,Patient’s phenotype or family history is highly specific for a disease with a single genetic etiology.,NA
+CDH1 (HGNC:1748),PP5,Original ACMG Summary,"Reputable source recently reports variant as pathogenic, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+CDH1 (HGNC:1748),BA1,Original ACMG Summary,"Allele frequency is above 5% in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.",
+CDH1 (HGNC:1748),BA1,Stand Alone,MAF cutoff of 0.2%.,
+CDH1 (HGNC:1748),BS1,Original ACMG Summary,Allele frequency is greater than expected for disorder.,
+CDH1 (HGNC:1748),BS1,Strong,MAF cutoff of 0.1%.,
+CDH1 (HGNC:1748),BS2,Original ACMG Summary,"Observed in a healthy adult individual for a recessive (homozygous), dominant (heterozygous), or X-linked (hemizygous) disorder, with full penetrance expected at an early age.",
+CDH1 (HGNC:1748),BS2,Strong,"Variant seen in ≥10 individuals w/o GC, DGC, gSRC tumors, or LBC & whose families do not suggest HDGC.",
+CDH1 (HGNC:1748),BS2,Supporting,"Variant seen in ≥3 individuals w/o GC, DGC, SRC tumors, or LBC & whose families do not suggest HDGC.",
+CDH1 (HGNC:1748),BS3,Original ACMG Summary,Well-established in vitro or in vivo functional studies show no damaging effect on protein function or splicing.,
+CDH1 (HGNC:1748),BS3,Strong,Functional RNA studies demonstrating no impact on transcript composition.,
+CDH1 (HGNC:1748),BS4,Original ACMG Summary,"Lack of segregation in affected members of a family.
+Caveat: The presence of phenocopies for common phenotypes (i.e. cancer, epilepsy) can mimic lack of segregation among affected individuals. Also, families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent lack of segregation.",
+CDH1 (HGNC:1748),BS4,Strong,Per original ACMG/AMP guidelines.,
+CDH1 (HGNC:1748),BP1,Original ACMG Summary,Missense variant in a gene for which primarily truncating variants are known to cause disease.,NA
+CDH1 (HGNC:1748),BP2,Original ACMG Summary,Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder or observed in cis with a pathogenic variant in any inheritance pattern.,
+CDH1 (HGNC:1748),BP2,Strong,"Variant observed in trans w/known pathogenic variant (phase confirmed) OR observed in the homozygous state in individual w/o personal &/or family history of DGC, LBC, or SRC tumors.",
+CDH1 (HGNC:1748),BP2,Supporting,"Variant is observed in cis (or phase is unknown) w/ a pathogenic variant
+OR observed in the homozygous state in gnomAD.",
+CDH1 (HGNC:1748),BP3,Original ACMG Summary,In frame-deletions/insertions in a repetitive region without a known function.,NA
+CDH1 (HGNC:1748),BP4,Original ACMG Summary,"Multiple lines of computational evidence suggest no impact on gene or gene product (conservation, evolutionary, splicing impact, etc)
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm cannot be counted as an independent criterion. BP4 can be used only once in any evaluation of a variant.",
+CDH1 (HGNC:1748),BP4,Supporting,"Splicing predictions only. At least three in silico splicing predictors in agreement (SpliceAI, MaxEntScan, SSF, GeneSplicer, HSF, TraP, varSEAK).",
+CDH1 (HGNC:1748),BP5,Original ACMG Summary,Variant found in a case with an alternate molecular basis for disease.,
+CDH1 (HGNC:1748),BP5,Supporting,Per original ACMG/AMP guidelines.,
+CDH1 (HGNC:1748),BP6,Original ACMG Summary,"Reputable source recently reports variant as benign, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+CDH1 (HGNC:1748),BP7,Original ACMG Summary,A synonymous variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site AND the nucleotide is not highly conserved.,
+CDH1 (HGNC:1748),BP7,Supporting,Synonymous and intronic variants at or beyond +7 to -21 locations.,

VCI/parsing_csr_criteria/version_csv_individual/ClinGenCDH1ExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesVersion3_version=3.0.0.csv

@@ -0,0 +1,101 @@
+Gene,Code,Strength,Description,Modification Type
+CDH1 (HGNC:1748),PVS1,Original ACMG Summary,"Null variant (nonsense, frameshift, canonical +/−1 or 2 splice sites, initiation codon, single or multi-exon deletion) in a gene where loss of function (LOF) is a known mechanism of disease.
+Caveats:
+ • Beware of genes where LOF is not a known disease mechanism (e.g. GFAP, MYH7).
+ • Use caution interpreting LOF variants at the extreme 3’ end of a gene.
+ • Use caution with splice variants that are predicted to lead to exon skipping but leave the remainder of the protein intact.
+ • Use caution in the presence of multiple transcripts.",
+CDH1 (HGNC:1748),PVS1,Very Strong,Per modified CDH1 PVS1 decision tree.,
+CDH1 (HGNC:1748),PVS1,Strong,"Per modified CDH1 PVS1 decision tree.
+Other CDH1 caveats:
+
+
+
+
+Use PVS1_Strong as the default strength of evidence for canonical splice site variants and follow the site-specific recommendations in the splicing table. 
+
+
+CDH1 Exonic deletions or tandem duplications of in-frame exons (exon 4,5,8,9,12,13,15).",
+CDH1 (HGNC:1748),PVS1,Moderate,"Per modified CDH1 PVS1 decision tree.
+Other CDH1 caveats:
+
+
+
+
+G to non-G variants disrupting the last nucleotide of an exon.
+
+
+Canonical splice sites predicted or demonstrated experimentally to result in in-frame partial skipping/insertion (e.g., Exon 3 donor site).",
+CDH1 (HGNC:1748),PS1,Original ACMG Summary,"Same amino acid change as a previously established pathogenic variant regardless of nucleotide change.
+Example: Val->Leu caused by either G>C or G>T in the same codon.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",NA
+CDH1 (HGNC:1748),PS2,Original ACMG Summary,"De novo (both maternity and paternity confirmed) in a patient with the disease and no family history.
+Note: Confirmation of paternity only is insufficient. Egg donation, surrogate motherhood, errors in embryo transfer, etc. can contribute to non-maternity.",
+CDH1 (HGNC:1748),PS2,Very Strong,≥Two patients meet the HDGC individual phenotype criteria w/ parental confirmation.,
+CDH1 (HGNC:1748),PS2,Strong,One patient meets the HDGC individual phenotype criteria w/ parental confirmation.,
+CDH1 (HGNC:1748),PS3,Original ACMG Summary,"Well-established in vitro or in vivo functional studies supportive of a damaging effect on the gene or gene product.
+Note: Functional studies that have been validated and shown to be reproducible and robust in a clinical diagnostic laboratory setting are considered the most well-established.",
+CDH1 (HGNC:1748),PS3,Strong,RNA assay demonstrating abnormal out-of-frame transcripts.,
+CDH1 (HGNC:1748),PS3,Moderate,RNA assay demonstrating abnormal in-frame transcript.,
+CDH1 (HGNC:1748),PS4,Original ACMG Summary,"The prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls.
+Note 1: Relative risk (RR) or odds ratio (OR), as obtained from case-control studies, is >5.0 and the confidence interval around the estimate of RR or OR does not include 1.0. See manuscript for detailed guidance.
+Note 2: In instances of very rare variants where case-control studies may not reach statistical significance, the prior observation of the variant in multiple unrelated patients with the same phenotype, and its absence in controls, may be used as moderate level of evidence.",
+CDH1 (HGNC:1748),PS4,Very Strong,≥Sixteen families meet HDGC criteria.,
+CDH1 (HGNC:1748),PS4,Strong,Four - Fifteen families meet HDGC criteria.,
+CDH1 (HGNC:1748),PS4,Moderate,Two or three families meet HDGC criteria.,
+CDH1 (HGNC:1748),PS4,Supporting,One family meets HDGC criteria.,
+CDH1 (HGNC:1748),PM1,Original ACMG Summary,Located in a mutational hot spot and/or critical and well-established functional domain (e.g. active site of an enzyme) without benign variation.,NA
+CDH1 (HGNC:1748),PM2,Original ACMG Summary,"Absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.
+Caveat: Population data for indels may be poorly called by next generation sequencing.",
+CDH1 (HGNC:1748),PM2,Supporting,"≤ One out of 100,000 alleles in gnomAD cohort; if present in ≥2 individuals within a subpopulation, must be present in ≤ One out of 50,000 alleles.",
+CDH1 (HGNC:1748),PM3,Original ACMG Summary,"For recessive disorders, detected in trans with a pathogenic variant
+Note: This requires testing of parents (or offspring) to determine phase.",NA
+CDH1 (HGNC:1748),PM4,Original ACMG Summary,Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants.,
+CDH1 (HGNC:1748),PM4,Moderate,"Only apply to stop-loss variants
+Variant example: CDH1 c.2647T>C (p.Ter883Glnext*29).",
+CDH1 (HGNC:1748),PM5,Original ACMG Summary,"Novel missense change at an amino acid residue where a different missense change determined to be pathogenic has been seen before.
+Example: Arg156His is pathogenic; now you observe Arg156Cys.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+CDH1 (HGNC:1748),PM5,Supporting,PM5_supporting is applicable to nonsense and frameshift variants that are predicted/proved to undergo NMD. Site-specific recommendations for the application of PM5_Supporting for canonical splicing variants are provided in the splicing table.,
+CDH1 (HGNC:1748),PM6,Original ACMG Summary,"Assumed de novo, but without confirmation of paternity and maternity.",
+CDH1 (HGNC:1748),PM6,Very Strong,Four patients meet the HDGC individual phenotype criteria w/o parental confirmation.,
+CDH1 (HGNC:1748),PM6,Strong,≥Two patients meet the HDGC individual phenotype criteria w/o parental confirmation.,
+CDH1 (HGNC:1748),PM6,Moderate,One patient meets the HDGC individual phenotype criteria w/o parental confirmation,
+CDH1 (HGNC:1748),PP1,Original ACMG Summary,"Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease.
+Note: May be used as stronger evidence with increasing segregation data.",
+CDH1 (HGNC:1748),PP1,Strong,≥Seven informative meioses across ≥2 families.,
+CDH1 (HGNC:1748),PP1,Moderate,Five-six informative meioses across ≥1 family.,
+CDH1 (HGNC:1748),PP1,Supporting,Three-four informative meioses across ≥1 family.,
+CDH1 (HGNC:1748),PP2,Original ACMG Summary,Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease.,NA
+CDH1 (HGNC:1748),PP3,Original ACMG Summary,"Multiple lines of computational evidence support a deleterious effect on the gene or gene product (conservation, evolutionary, splicing impact, etc.).
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. PP3 can be used only once in any evaluation of a variant.",
+CDH1 (HGNC:1748),PP3,Moderate,Variants affecting the same splice site as a well-characterized variant with similar or worse in silico/ RNA predictions.,
+CDH1 (HGNC:1748),PP3,Supporting,"At least three in silico splicing predictors in agreement (SpliceAI, MaxEntScan, SSF, GeneSplicer, HSF, TraP, varSEAK).",
+CDH1 (HGNC:1748),PP4,Original ACMG Summary,Patient’s phenotype or family history is highly specific for a disease with a single genetic etiology.,NA
+CDH1 (HGNC:1748),PP5,Original ACMG Summary,"Reputable source recently reports variant as pathogenic, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+CDH1 (HGNC:1748),BA1,Original ACMG Summary,"Allele frequency is above 5% in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.",
+CDH1 (HGNC:1748),BA1,Stand Alone,MAF cutoff of 0.2%.,
+CDH1 (HGNC:1748),BS1,Original ACMG Summary,Allele frequency is greater than expected for disorder.,
+CDH1 (HGNC:1748),BS1,Strong,MAF cutoff of 0.1%.,
+CDH1 (HGNC:1748),BS2,Original ACMG Summary,"Observed in a healthy adult individual for a recessive (homozygous), dominant (heterozygous), or X-linked (hemizygous) disorder, with full penetrance expected at an early age.",
+CDH1 (HGNC:1748),BS2,Strong,"Variant seen in ≥10 individuals w/o GC, DGC, gSRC tumors, or LBC & whose families do not suggest HDGC.",
+CDH1 (HGNC:1748),BS2,Supporting,"Variant seen in ≥3 individuals w/o GC, DGC, SRC tumors, or LBC & whose families do not suggest HDGC.",
+CDH1 (HGNC:1748),BS3,Original ACMG Summary,Well-established in vitro or in vivo functional studies show no damaging effect on protein function or splicing.,
+CDH1 (HGNC:1748),BS3,Strong,Functional RNA studies demonstrating no impact on transcript composition.,
+CDH1 (HGNC:1748),BS4,Original ACMG Summary,"Lack of segregation in affected members of a family.
+Caveat: The presence of phenocopies for common phenotypes (i.e. cancer, epilepsy) can mimic lack of segregation among affected individuals. Also, families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent lack of segregation.",
+CDH1 (HGNC:1748),BS4,Strong,Per original ACMG/AMP guidelines.,
+CDH1 (HGNC:1748),BP1,Original ACMG Summary,Missense variant in a gene for which primarily truncating variants are known to cause disease.,NA
+CDH1 (HGNC:1748),BP2,Original ACMG Summary,Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder or observed in cis with a pathogenic variant in any inheritance pattern.,
+CDH1 (HGNC:1748),BP2,Strong,"Variant observed in trans w/known pathogenic variant (phase confirmed) OR observed in the homozygous state in individual w/o personal &/or family history of DGC, LBC, or SRC tumors.",
+CDH1 (HGNC:1748),BP2,Supporting,"Variant is observed in cis (or phase is unknown) w/ a pathogenic variant
+OR observed in the homozygous state in gnomAD.",
+CDH1 (HGNC:1748),BP3,Original ACMG Summary,In frame-deletions/insertions in a repetitive region without a known function.,NA
+CDH1 (HGNC:1748),BP4,Original ACMG Summary,"Multiple lines of computational evidence suggest no impact on gene or gene product (conservation, evolutionary, splicing impact, etc)
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm cannot be counted as an independent criterion. BP4 can be used only once in any evaluation of a variant.",
+CDH1 (HGNC:1748),BP4,Supporting,"Splicing predictions only. At least three in silico splicing predictors in agreement (SpliceAI, MaxEntScan, SSF, GeneSplicer, HSF, TraP, varSEAK).",
+CDH1 (HGNC:1748),BP5,Original ACMG Summary,Variant found in a case with an alternate molecular basis for disease.,
+CDH1 (HGNC:1748),BP5,Supporting,Per original ACMG/AMP guidelines.,
+CDH1 (HGNC:1748),BP6,Original ACMG Summary,"Reputable source recently reports variant as benign, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+CDH1 (HGNC:1748),BP7,Original ACMG Summary,A synonymous variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site AND the nucleotide is not highly conserved.,
+CDH1 (HGNC:1748),BP7,Supporting,Synonymous and intronic variants at or beyond +7 to -21 locations.,

VCI/parsing_csr_criteria/version_csv_individual/ClinGenCDH1ExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforCDH1Version1.0.0_version=1.0.0.csv

@@ -0,0 +1,101 @@
+Gene,Code,Strength,Description,Modification Type
+CDH1 (HGNC:1748),PVS1,Original ACMG Summary,"Null variant (nonsense, frameshift, canonical +/−1 or 2 splice sites, initiation codon, single or multi-exon deletion) in a gene where loss of function (LOF) is a known mechanism of disease.
+Caveats:
+ • Beware of genes where LOF is not a known disease mechanism (e.g. GFAP, MYH7).
+ • Use caution interpreting LOF variants at the extreme 3’ end of a gene.
+ • Use caution with splice variants that are predicted to lead to exon skipping but leave the remainder of the protein intact.
+ • Use caution in the presence of multiple transcripts.",
+CDH1 (HGNC:1748),PVS1,Very Strong,Per ClinGen SVI guidelines with the exception of canonical splice sites.,
+CDH1 (HGNC:1748),PVS1,Strong,"Per modified CDH1 PVS1 decision tree. Other CDH1 caveats:
+
+
+
+
+Use the strong strength of evidence for canonical splice sites.
+
+
+CDH1 Exonic deletions or tandem duplications of in-frame exons.
+
+
+Truncations in NMD-resistant zone located upstream the most 3’ well-characterized pathogenic variant c.2506G>T (p.Glu836*). Use PVS1_moderate if premature stop is downstream of this variant.",
+CDH1 (HGNC:1748),PVS1,Moderate,"Per modified CDH1 PVS1 decision tree. Other CDH1 caveats:
+
+
+
+
+G to non-G variants disrupting the last nucleotide of an exon.
+
+
+Canonical splice sites located in exons demonstrated experimentally to result in in-frame partial skipping/insertion (e.g., Exon 3 donor site).",
+CDH1 (HGNC:1748),PVS1,Supporting,Per ClinGen SVI guidelines,
+CDH1 (HGNC:1748),PS1,Original ACMG Summary,"Same amino acid change as a previously established pathogenic variant regardless of nucleotide change.
+Example: Val->Leu caused by either G>C or G>T in the same codon.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+CDH1 (HGNC:1748),PS1,Strong,Per original ACMG/AMP guidelines.,
+CDH1 (HGNC:1748),PS2,Original ACMG Summary,"De novo (both maternity and paternity confirmed) in a patient with the disease and no family history.
+Note: Confirmation of paternity only is insufficient. Egg donation, surrogate motherhood, errors in embryo transfer, etc. can contribute to non-maternity.",
+CDH1 (HGNC:1748),PS2,Very Strong,≥Two patients with DGC &/or LBC w/ parental confirmation.,
+CDH1 (HGNC:1748),PS2,Strong,One patient with DGC &/or LBC w/ parental confirmation.,
+CDH1 (HGNC:1748),PS3,Original ACMG Summary,"Well-established in vitro or in vivo functional studies supportive of a damaging effect on the gene or gene product.
+Note: Functional studies that have been validated and shown to be reproducible and robust in a clinical diagnostic laboratory setting are considered the most well-established.",
+CDH1 (HGNC:1748),PS3,Strong,RNA assay demonstrating abnormal out-of-frame transcripts.,
+CDH1 (HGNC:1748),PS3,Supporting,RNA assay demonstrating abnormal in-frame transcripts.,
+CDH1 (HGNC:1748),PS4,Original ACMG Summary,"The prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls.
+Note 1: Relative risk (RR) or odds ratio (OR), as obtained from case-control studies, is >5.0 and the confidence interval around the estimate of RR or OR does not include 1.0. See manuscript for detailed guidance.
+Note 2: In instances of very rare variants where case-control studies may not reach statistical significance, the prior observation of the variant in multiple unrelated patients with the same phenotype, and its absence in controls, may be used as moderate level of evidence.",
+CDH1 (HGNC:1748),PS4,Very Strong,Sixteen families meet HDGC criteria.,
+CDH1 (HGNC:1748),PS4,Strong,Four families meet HDGC criteria.,
+CDH1 (HGNC:1748),PS4,Moderate,Two families meet HDGC criteria.,
+CDH1 (HGNC:1748),PS4,Supporting,One family meets HDGC criteria.,
+CDH1 (HGNC:1748),PM1,Original ACMG Summary,Located in a mutational hot spot and/or critical and well-established functional domain (e.g. active site of an enzyme) without benign variation.,NA
+CDH1 (HGNC:1748),PM2,Original ACMG Summary,"Absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.
+Caveat: Population data for indels may be poorly called by next generation sequencing.",
+CDH1 (HGNC:1748),PM2,Moderate,"<One out of 100,000 alleles in gnomAD cohort; if present in >2 individuals, must be present in <One out of 50,000 alleles within a sub-population.",
+CDH1 (HGNC:1748),PM3,Original ACMG Summary,"For recessive disorders, detected in trans with a pathogenic variant
+Note: This requires testing of parents (or offspring) to determine phase.",NA
+CDH1 (HGNC:1748),PM4,Original ACMG Summary,Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants.,
+CDH1 (HGNC:1748),PM4,Moderate,Per original ACMG/AMP guidelines.,
+CDH1 (HGNC:1748),PM5,Original ACMG Summary,"Novel missense change at an amino acid residue where a different missense change determined to be pathogenic has been seen before.
+Example: Arg156His is pathogenic; now you observe Arg156Cys.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",NA
+CDH1 (HGNC:1748),PM6,Original ACMG Summary,"Assumed de novo, but without confirmation of paternity and maternity.",
+CDH1 (HGNC:1748),PM6,Very Strong,≥Four patients with DGC &/or LBC w/o parental confirmation.,
+CDH1 (HGNC:1748),PM6,Strong,≥Two patients with DGC &/or LBC w/o parental confirmation.,
+CDH1 (HGNC:1748),PM6,Moderate,One patient with DGC &/or LBC w/o parental confirmation.,
+CDH1 (HGNC:1748),PP1,Original ACMG Summary,"Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease.
+Note: May be used as stronger evidence with increasing segregation data.",
+CDH1 (HGNC:1748),PP1,Strong,≥Seven meioses across ≥2 families.,
+CDH1 (HGNC:1748),PP1,Moderate,Five-six informative meioses across ≥1 family.,
+CDH1 (HGNC:1748),PP1,Supporting,Three-four informative meioses across ≥1 family.,
+CDH1 (HGNC:1748),PP2,Original ACMG Summary,Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease.,NA
+CDH1 (HGNC:1748),PP3,Original ACMG Summary,"Multiple lines of computational evidence support a deleterious effect on the gene or gene product (conservation, evolutionary, splicing impact, etc.).
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. PP3 can be used only once in any evaluation of a variant.",
+CDH1 (HGNC:1748),PP3,Moderate,Variants affecting the same splice site as a well-characterized variant with similar or worse in silico/ RNA predictions.,
+CDH1 (HGNC:1748),PP3,Supporting,"At least three in silico splicing predictors in agreement (.Human Splicing Finder (HSF), Maximum Entropy (MaxEnt), Berkeley Drosophilia Genome Project (BDGP), or ESEfinder).",
+CDH1 (HGNC:1748),PP4,Original ACMG Summary,Patient’s phenotype or family history is highly specific for a disease with a single genetic etiology.,NA
+CDH1 (HGNC:1748),PP5,Original ACMG Summary,"Reputable source recently reports variant as pathogenic, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+CDH1 (HGNC:1748),BA1,Original ACMG Summary,"Allele frequency is above 5% in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.",
+CDH1 (HGNC:1748),BA1,Stand Alone,MAF cutoff of 0.2%.,
+CDH1 (HGNC:1748),BS1,Original ACMG Summary,Allele frequency is greater than expected for disorder.,
+CDH1 (HGNC:1748),BS1,Strong,MAF cutoff of 0.1%.,
+CDH1 (HGNC:1748),BS2,Original ACMG Summary,"Observed in a healthy adult individual for a recessive (homozygous), dominant (heterozygous), or X-linked (hemizygous) disorder, with full penetrance expected at an early age.",
+CDH1 (HGNC:1748),BS2,Strong,"Variant seen in ≥10 individuals w/o DCG, SRC tumors, or LBC & whose families do not suggest HDGC.",
+CDH1 (HGNC:1748),BS2,Supporting,"Variant seen in ≥3 individuals w/o DCG, SRC tumors, or LBC & whose families do not suggest HDGC.",
+CDH1 (HGNC:1748),BS3,Original ACMG Summary,Well-established in vitro or in vivo functional studies show no damaging effect on protein function or splicing.,
+CDH1 (HGNC:1748),BS3,Strong,Functional RNA studies demonstrating no impact on transcript composition.,
+CDH1 (HGNC:1748),BS4,Original ACMG Summary,"Lack of segregation in affected members of a family.
+Caveat: The presence of phenocopies for common phenotypes (i.e. cancer, epilepsy) can mimic lack of segregation among affected individuals. Also, families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent lack of segregation.",
+CDH1 (HGNC:1748),BS4,Strong,Per original ACMG/AMP guidelines.,
+CDH1 (HGNC:1748),BP1,Original ACMG Summary,Missense variant in a gene for which primarily truncating variants are known to cause disease.,NA
+CDH1 (HGNC:1748),BP2,Original ACMG Summary,Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder or observed in cis with a pathogenic variant in any inheritance pattern.,
+CDH1 (HGNC:1748),BP2,Strong,"Variant observed in trans w/known pathogenic variant (phase confirmed) OR observed in the homozygous state in individual w/o personal &/or family history of DGC, LBC, or SRC tumors.",
+CDH1 (HGNC:1748),BP2,Supporting,Variant is observed in cis (or phase is unknown) w/ a pathogenic variant.,
+CDH1 (HGNC:1748),BP3,Original ACMG Summary,In frame-deletions/insertions in a repetitive region without a known function.,NA
+CDH1 (HGNC:1748),BP4,Original ACMG Summary,"Multiple lines of computational evidence suggest no impact on gene or gene product (conservation, evolutionary, splicing impact, etc)
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm cannot be counted as an independent criterion. BP4 can be used only once in any evaluation of a variant.",
+CDH1 (HGNC:1748),BP4,Supporting,"Splicing predictions only. At least three in silico splicing predictors in agreement (Human Splicing Finder (HSF), Maximum Entropy (MaxEnt), Berkeley Drosophilia Genome Project (BDGP), or ESEfinder).",
+CDH1 (HGNC:1748),BP5,Original ACMG Summary,Variant found in a case with an alternate molecular basis for disease.,
+CDH1 (HGNC:1748),BP5,Supporting,Per original ACMG/AMP guidelines.,
+CDH1 (HGNC:1748),BP6,Original ACMG Summary,"Reputable source recently reports variant as benign, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+CDH1 (HGNC:1748),BP7,Original ACMG Summary,A synonymous variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site AND the nucleotide is not highly conserved.,
+CDH1 (HGNC:1748),BP7,Supporting,Synonymous variants where nucleotide is not highly conserved; variant is the reference nucleotide in one primate and/or >3 mammal species.,

VCI/parsing_csr_criteria/version_csv_individual/ClinGenCardiomyopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesVersion1_version=1.0.0.csv

@@ -0,0 +1,72 @@
+Gene,Code,Strength,Description,Modification Type
+MYH7 (HGNC:7577),PVS1,Original ACMG Summary,"Null variant (nonsense, frameshift, canonical +/−1 or 2 splice sites, initiation codon, single or multi-exon deletion) in a gene where loss of function (LOF) is a known mechanism of disease.
+Caveats:
+ • Beware of genes where LOF is not a known disease mechanism (e.g. GFAP, MYH7).
+ • Use caution interpreting LOF variants at the extreme 3’ end of a gene.
+ • Use caution with splice variants that are predicted to lead to exon skipping but leave the remainder of the protein intact.
+ • Use caution in the presence of multiple transcripts.",
+MYH7 (HGNC:7577),PVS1,Moderate,Null Variant in gene with evidence supporting LOF as disease mechanism,Modified rule strength
+MYH7 (HGNC:7577),PS1,Original ACMG Summary,"Same amino acid change as a previously established pathogenic variant regardless of nucleotide change.
+Example: Val->Leu caused by either G>C or G>T in the same codon.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+MYH7 (HGNC:7577),PS1,Strong,Different nucleotide change (same amino acid) as a previously established pathogenic variant,No change
+MYH7 (HGNC:7577),PS2,Original ACMG Summary,"De novo (both maternity and paternity confirmed) in a patient with the disease and no family history.
+Note: Confirmation of paternity only is insufficient. Egg donation, surrogate motherhood, errors in embryo transfer, etc. can contribute to non-maternity.",
+MYH7 (HGNC:7577),PS2,Strong,De novo (paternity confirmed) in a patient with disease and no family history,"Disease-specific,Gene-specific"
+MYH7 (HGNC:7577),PS3,Original ACMG Summary,"Well-established in vitro or in vivo functional studies supportive of a damaging effect on the gene or gene product.
+Note: Functional studies that have been validated and shown to be reproducible and robust in a clinical diagnostic laboratory setting are considered the most well-established.",
+MYH7 (HGNC:7577),PS3,Strong,Functional studies of mammalian knock-in models supportive of a damaging effect on the gene or gene product,"Disease-specific,Gene-specific"
+MYH7 (HGNC:7577),PS4,Original ACMG Summary,"The prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls.
+Note 1: Relative risk (RR) or odds ratio (OR), as obtained from case-control studies, is >5.0 and the confidence interval around the estimate of RR or OR does not include 1.0. See manuscript for detailed guidance.
+Note 2: In instances of very rare variants where case-control studies may not reach statistical significance, the prior observation of the variant in multiple unrelated patients with the same phenotype, and its absence in controls, may be used as moderate level of evidence.",
+MYH7 (HGNC:7577),PS4,Strong,Prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls -OR- Variant identified in ≥15 probands with consistent phenotypes,"Disease-specific,Gene-specific"
+MYH7 (HGNC:7577),PS4,Moderate,Variant identified in >=6 probands with consistent phenotypes,Modified rule strength
+MYH7 (HGNC:7577),PS4,Supporting,Variant identified in >=2 probands with consistent phenotypes,Modified rule strength
+MYH7 (HGNC:7577),PM1,Original ACMG Summary,Located in a mutational hot spot and/or critical and well-established functional domain (e.g. active site of an enzyme) without benign variation.,
+MYH7 (HGNC:7577),PM1,Moderate,Hotspot/est. functional domain (amino acids 181-937) without benign variation,"Disease-specific,Gene-specific"
+MYH7 (HGNC:7577),PM2,Original ACMG Summary,"Absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.
+Caveat: Population data for indels may be poorly called by next generation sequencing.",
+MYH7 (HGNC:7577),PM2,Moderate,Absent/extremely rare (<0.004%) from large population studies.,"Disease-specific,Gene-specific"
+MYH7 (HGNC:7577),PM3,Original ACMG Summary,"For recessive disorders, detected in trans with a pathogenic variant
+Note: This requires testing of parents (or offspring) to determine phase.",NA
+MYH7 (HGNC:7577),PM4,Original ACMG Summary,Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants.,
+MYH7 (HGNC:7577),PM4,Moderate,Protein length changes due to in-frame deletions/insertions of any size in a non-repeat region or stop-loss variants,"Disease-specific,Gene-specific"
+MYH7 (HGNC:7577),PM5,Original ACMG Summary,"Novel missense change at an amino acid residue where a different missense change determined to be pathogenic has been seen before.
+Example: Arg156His is pathogenic; now you observe Arg156Cys.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+MYH7 (HGNC:7577),PM5,Moderate,Missense change at an amino acid residue where a different missense change previously established as pathogenic,No change
+MYH7 (HGNC:7577),PM6,Original ACMG Summary,"Assumed de novo, but without confirmation of paternity and maternity.",
+MYH7 (HGNC:7577),PM6,Moderate,Confirmed de novo without confirmation of paternity,"Disease-specific,Gene-specific"
+MYH7 (HGNC:7577),PP1,Original ACMG Summary,"Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease.
+Note: May be used as stronger evidence with increasing segregation data.",
+MYH7 (HGNC:7577),PP1,Strong,Variant segregates with >= 7 meioses,Modified rule strength
+MYH7 (HGNC:7577),PP1,Moderate,Variant segragates in >=5 meioses,Modified rule strength
+MYH7 (HGNC:7577),PP1,Supporting,Variant segragates in >=3 meioses,"Disease-specific,Gene-specific"
+MYH7 (HGNC:7577),PP2,Original ACMG Summary,Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease.,NA
+MYH7 (HGNC:7577),PP3,Original ACMG Summary,"Multiple lines of computational evidence support a deleterious effect on the gene or gene product (conservation, evolutionary, splicing impact, etc.).
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. PP3 can be used only once in any evaluation of a variant.",
+MYH7 (HGNC:7577),PP3,Supporting,Multiple lines of computational evidence support a deleterious effect on the gene or gene product,No change
+MYH7 (HGNC:7577),PP4,Original ACMG Summary,Patient’s phenotype or family history is highly specific for a disease with a single genetic etiology.,NA
+MYH7 (HGNC:7577),PP5,Original ACMG Summary,"Reputable source recently reports variant as pathogenic, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+MYH7 (HGNC:7577),BA1,Original ACMG Summary,"Allele frequency is above 5% in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.",
+MYH7 (HGNC:7577),BA1,Stand Alone,Allele frequency is >= 0.1% based on the filtering allele frequency (FAF) in ExAC,"Disease-specific,Gene-specific"
+MYH7 (HGNC:7577),BS1,Original ACMG Summary,Allele frequency is greater than expected for disorder.,
+MYH7 (HGNC:7577),BS1,Strong,Allele frequency is >=0.02% based on the filtering allele frequency (FAF) in ExAC provided there is no conflicting information,"Disease-specific,Gene-specific"
+MYH7 (HGNC:7577),BS2,Original ACMG Summary,"Observed in a healthy adult individual for a recessive (homozygous), dominant (heterozygous), or X-linked (hemizygous) disorder, with full penetrance expected at an early age.",NA
+MYH7 (HGNC:7577),BS3,Original ACMG Summary,Well-established in vitro or in vivo functional studies show no damaging effect on protein function or splicing.,
+MYH7 (HGNC:7577),BS3,Strong,Functional studies of mammalian knock-in models supportive of no damaging effect on protein function or splicing,No change
+MYH7 (HGNC:7577),BS4,Original ACMG Summary,"Lack of segregation in affected members of a family.
+Caveat: The presence of phenocopies for common phenotypes (i.e. cancer, epilepsy) can mimic lack of segregation among affected individuals. Also, families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent lack of segregation.",
+MYH7 (HGNC:7577),BS4,Strong,Non-segregation in affected members of a family,"Disease-specific,Gene-specific"
+MYH7 (HGNC:7577),BP1,Original ACMG Summary,Missense variant in a gene for which primarily truncating variants are known to cause disease.,NA
+MYH7 (HGNC:7577),BP2,Original ACMG Summary,Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder or observed in cis with a pathogenic variant in any inheritance pattern.,
+MYH7 (HGNC:7577),BP2,Supporting,Observed as comp het (in trans) or double het in genes with overlapping function (e.g. sarcomere genes) without increased disease severity -OR- Observed in cis with a pathogenic variant in any inheritance pattern,"Disease-specific,Gene-specific"
+MYH7 (HGNC:7577),BP3,Original ACMG Summary,In frame-deletions/insertions in a repetitive region without a known function.,NA
+MYH7 (HGNC:7577),BP4,Original ACMG Summary,"Multiple lines of computational evidence suggest no impact on gene or gene product (conservation, evolutionary, splicing impact, etc)
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm cannot be counted as an independent criterion. BP4 can be used only once in any evaluation of a variant.",
+MYH7 (HGNC:7577),BP4,Supporting,Multiple lines of computational evidence suggest no impact on gene or gene product,No change
+MYH7 (HGNC:7577),BP5,Original ACMG Summary,Variant found in a case with an alternate molecular basis for disease.,
+MYH7 (HGNC:7577),BP5,Supporting,Variant found in a case with an alternate molecular basis for disease,"Disease-specific,Gene-specific"
+MYH7 (HGNC:7577),BP6,Original ACMG Summary,"Reputable source recently reports variant as benign, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+MYH7 (HGNC:7577),BP7,Original ACMG Summary,A synonymous variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site AND the nucleotide is not highly conserved.,
+MYH7 (HGNC:7577),BP7,Supporting,A silent variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site -AND- the nucleotide is not highly conserved,No change

VCI/parsing_csr_criteria/version_csv_individual/ClinGenCardiomyopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforACTC1Version1.0.0_version=1.0.0.csv

@@ -0,0 +1,871 @@
+Gene,Code,Strength,Description,Modification Type
+ACTC1 (HGNC:143),PVS1,Original ACMG Summary,"Null variant (nonsense, frameshift, canonical +/−1 or 2 splice sites, initiation codon, single or multi-exon deletion) in a gene where loss of function (LOF) is a known mechanism of disease.
+Caveats:
+ • Beware of genes where LOF is not a known disease mechanism (e.g. GFAP, MYH7).
+ • Use caution interpreting LOF variants at the extreme 3’ end of a gene.
+ • Use caution with splice variants that are predicted to lead to exon skipping but leave the remainder of the protein intact.
+ • Use caution in the presence of multiple transcripts.",NA
+ACTC1 (HGNC:143),PS1,Original ACMG Summary,"Same amino acid change as a previously established pathogenic variant regardless of nucleotide change.
+Example: Val->Leu caused by either G>C or G>T in the same codon.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+ACTC1 (HGNC:143),PS1,Strong,"No cardiomyopathy specifications. Apply as outlined by Richards 
+et al
+. 2015
+1
+.
+
+
+Example of when rule should NOT be applied. NM_000256.3(
+MYBPC3
+): c.2308G>A (p.Asp770Asn) has an established impact on splicing leading to nonsense mediated decay (NMD) and should not be used to provide evidence for other variants observed to result in the same amino acid change.",No change
+ACTC1 (HGNC:143),PS2,Original ACMG Summary,"De novo (both maternity and paternity confirmed) in a patient with the disease and no family history.
+Note: Confirmation of paternity only is insufficient. Egg donation, surrogate motherhood, errors in embryo transfer, etc. can contribute to non-maternity.",
+ACTC1 (HGNC:143),PS2,Strong,"Refer to SVI guidance on number/combination of cases required based on phenotype specificity
+2
+.
+
+
+For most cardiomyopathies, it is recommended to default to 
+Phenotype consistency: “Phenotype consistent with gene but not highly specific”
+. Clinical judgment is required for shifting to a higher or lower category. 
+
+
+For use as a STRONG or VERY STRONG criterion, ideally parents have been thoroughly clinically evaluated without evidence of cardiomyopathy (ideally using a combination of ECG and echocardiogram or cardiac MRI for maximum sensitivity).
+
+
+A family history consistent with 
+de novo
+ inheritance should not have any clinical signs or symptoms suggestive of cardiomyopathy in a 1
+st
+ or 2
+nd
+ degree relative, for example: 
+
+
+
+
+Sudden death under 60 years of age
+
+
+Heart transplant
+
+
+Implantable cardiac defibrillator (ICD) under 60 years of age
+
+
+Features of cardiomyopathy (e.g., systolic dysfunction, hypertrophy, left ventricular enlargement in an individual without risk factors).
+
+
+Other related/overlapping cardiomyopathies
+
+
+
+
+Examples of non-suspicious family history may include non-specific clinical features (e.g., palpitations, syncope, borderline/inconclusive echocardiogram findings, heart attack if age appropriate and suspected to result from coronary artery disease), but every attempt should be made to clarify features. 
+
+
+Generally, this criterion is only applicable in the ABSENCE of any other possible disease-causing variants.  If other pathogenic or likely pathogenic variants are present, consider decreasing points assigned or overall weight.",Disease-specific
+ACTC1 (HGNC:143),PS3,Original ACMG Summary,"Well-established in vitro or in vivo functional studies supportive of a damaging effect on the gene or gene product.
+Note: Functional studies that have been validated and shown to be reproducible and robust in a clinical diagnostic laboratory setting are considered the most well-established.",
+ACTC1 (HGNC:143),PS3,Strong,"In vitro splicing assays (e.g., RNA studies)
+
+
+In vitro
+ splicing assays may be considered as 
+STRONG
+ evidence, providing the following criteria are met.
+
+
+
+
+Prior knowledge of predominant transcripts in cardiac tissue
+
+
+
+
+Analysis undertaken using RNA extracted from cardiac tissue from the individual with the variant
+
+
+Analysis undertaken using RNA extracted from whole blood providing the relevant transcripts (isoforms) are expressed in blood and are at sufficient levels to assess splice disruption.
+
+
+Assay shows a clear, reproducible and convincing effect on splicing (i.e. a distinct splice product, present at a level comparable to the splice product from the wild-type allele), which is not observed in controls
+
+
+
+
+Confirmation of abnormal splice product by Sanger sequencing
+
+
+
+
+NOTE:
+ Mini-gene assay in non-patient derived cell lines are NOT considered to provide STRONG evidence.
+
+
+NOTE:
+  Whether to activate this rule needs to be reconciled with the variant spectrum and disease mechanism for the gene at hand (i.e., consider whether the effect is likely to lead to LOF or an in-frame alteration and whether this type of effect is expected to be disease causing) (Abou Tayoun 
+et al.
+ 2018
+3
+).",Disease-specific
+ACTC1 (HGNC:143),PS3,Moderate,"In vivo models (e.g., variant knock-in animal models)
+
+
+Mammalian variant-specific knock-in animal models that produce a phenotype consistent with the clinical phenotype in humans (e.g., structural and/or functional cardiac abnormalities, premature death, arrhythmia) may be considered as 
+MODERATE
+ evidence
+
+
+NOTE:
+ The following assays/models do NOT meet criteria
+
+
+
+
+Assays that are known to be associated with non-specific cardiac phenotypes (e.g., morpholino-induced pericardial edema in zebrafish)
+
+
+In vivo evidence that is not variant specific, such as whole gene alterations (i.e., cDNA or whole gene transgenic mice and whole or partial gene knock-out mice)",Disease-specific
+ACTC1 (HGNC:143),PS3,Supporting,"In vitro
+ 
+assays (e.g., biochemical assays of myofilament function, motility assays, human iPSC-CM)
+
+
+While some 
+in vitro
+ assays may provide evidence that a variant in a cardiomyopathy gene has an effect on protein and/or myofilament function, at present, there are no validated “gold-standard” assays that are considered to reliably predict the clinical phenotype.
+
+
+As such, in the cardiomyopathy genes listed in these guidelines, data from individual 
+in vitro
+ studies are unlikely to meet the criteria required to assign this rule at more than SUPPORTING level.",Disease-specific
+ACTC1 (HGNC:143),PS4,Original ACMG Summary,"The prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls.
+Note 1: Relative risk (RR) or odds ratio (OR), as obtained from case-control studies, is >5.0 and the confidence interval around the estimate of RR or OR does not include 1.0. See manuscript for detailed guidance.
+Note 2: In instances of very rare variants where case-control studies may not reach statistical significance, the prior observation of the variant in multiple unrelated patients with the same phenotype, and its absence in controls, may be used as moderate level of evidence.",
+ACTC1 (HGNC:143),PS4,Strong,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+5
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+STRONG
+ evidence requires the lower bound of the 95% confidence interval (CI) around the odds ratio (OR) estimate to be 
+≥20
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+6
+; Oechslin 
+et al.
+ 2017
+7
+; Hershberger 
+et al.
+ 2017
+8
+; Ross 
+et al.
+ 2020
+9
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+ACTC1 (HGNC:143),PS4,Moderate,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+5
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+MODERATE
+ evidence requires the lower bound of the 95% CI around the OR to be 
+≥10
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+6
+; Oechslin 
+et al.
+ 2017
+7
+; Hershberger 
+et al.
+ 2017
+8
+; Ross 
+et al.
+ 2020
+9
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+ACTC1 (HGNC:143),PS4,Supporting,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+5
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+SUPPORTING
+ evidence requires the lower bound of the 95% CI around the OR to be 
+≥5
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+6
+; Oechslin 
+et al.
+ 2017
+7
+; Hershberger 
+et al.
+ 2017
+8
+; Ross 
+et al.
+ 2020
+9
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+ACTC1 (HGNC:143),PM1,Original ACMG Summary,Located in a mutational hot spot and/or critical and well-established functional domain (e.g. active site of an enzyme) without benign variation.,NA
+ACTC1 (HGNC:143),PM2,Original ACMG Summary,"Absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.
+Caveat: Population data for indels may be poorly called by next generation sequencing.",
+ACTC1 (HGNC:143),PM2,Supporting,"The values used to calculate the PM2 thresholds were derived from studies in Northern European populations that have been relatively well-characterized with regards to disease prevalence and variant spectrum. These thresholds can be applied to any population where disease prevalence is considered comparable (1/500 or lower), where the most frequent pathogenic variant accounts for no more than 2% of cases (e.g., has an allele frequency of ≤0.02 in cases based on the upper bound of 95% CI), and where the penetrance of a pathogenic variant is expected to be at least 50% (Kelly 
+et al.
+ 2018
+10
+).
+
+
+A threshold of 
+≤0.00004
+ in the subpopulation with the highest frequency when using the upper bound of the 95% CI activates this rule.
+
+
+
+
+Alternatively, this is equivalent to the variant NOT being observed more than once (≤1 allele) in gnomAD v.2.1.1 in one of the non-founder populations (e.g., absence required from the Other and Ashkenazi Jewish subpopulations).
+
+
+Applying a threshold of ≤0.00004 (upper bound of 95% CI of the allele frequency in gnomAD) is equivalent to the variant being seen in a single subpopulation and that subpopulation meets any of the following:
+
+
+Allele Count (AC) in Allele Number (AN)
+
+
+≤1 in ≥120,000
+
+
+≤2 in ≥160,000
+
+
+≤3 in ≥195,000
+
+
+≤4 in ≥230,000
+
+
+
+
+
+
+
+
+gnomAD is the preferred database for this calculation, but currently only displays the filtering allele frequency (FAF), which is equivalent to a lower bound estimate of the 95% CI, when the upper bound is what is needed.
+
+
+
+
+Confidence interval tools, such as 
+Confit-de-MAF
+, can be used to determine the upper bound of the 95% CI of the observed allele frequency.
+
+
+
+
+Due to current technical limitations of next generation sequencing technologies, minor allele frequencies for complex variants (e.g., large indels) may not be accurately represented in population databases.
+
+
+Caution should be used when a variant is only identified, or over-represented, in one of the smaller gnomAD populations, as the gnomAD allele frequencies may not accurately represent the true population frequency.
+
+
+Population databases may contain affected or pre-symptomatic individuals for diseases with reduced penetrance/variable onset.",Disease-specific
+ACTC1 (HGNC:143),PM3,Original ACMG Summary,"For recessive disorders, detected in trans with a pathogenic variant
+Note: This requires testing of parents (or offspring) to determine phase.",NA
+ACTC1 (HGNC:143),PM4,Original ACMG Summary,Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants.,
+ACTC1 (HGNC:143),PM4,Moderate,"Strength of rule should be carefully considered and may require downgrading to SUPPORTING based on the predicted impact of the variant, including the size of the deletion/insertion, its location, and conservation of the region. 
+
+
+For genes where PVS1 is not applicable (i.e., where there is no evidence that pLOF variants cause disease), consider using this rule at MODERATE or SUPPORTING strength for truncating variants that do NOT undergo nonsense mediated decay (NMD).",General recommendation
+ACTC1 (HGNC:143),PM5,Original ACMG Summary,"Novel missense change at an amino acid residue where a different missense change determined to be pathogenic has been seen before.
+Example: Arg156His is pathogenic; now you observe Arg156Cys.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+ACTC1 (HGNC:143),PM5,Moderate,"This criterion can be used at MODERATE if a different missense variant at the same codon has been classified as 
+pathogenic
+ using these modified guidelines without application of PM5.
+
+
+The impact of the amino acid change being evaluated needs to be compared to the impact of the amino acid change that is established as pathogenic (e.g., a change of Ala to His is less severe than Ala to Cys change). Consider reducing the strength of this rule to SUPPORTING if the predicted impact is not expected to be equivalent or more severe.
+
+
+PM5 should not be combined with PM1.  If both are applicable at MODERATE weight, use of PM5 is most appropriate since it is variant specific.",General recommendation
+ACTC1 (HGNC:143),PM5,Supporting,"This criterion can be considered at SUPPORTING if a different missense variant at the same codon has been classified as 
+likely pathogenic
+ using these modified guidelines without application of PM5.
+
+
+The impact of the amino acid change being evaluated needs to be compared to the impact of the amino acid change that is established as likely pathogenic (e.g., a change of Ala to His is less severe than Ala to Cys change). Consider reducing the strength of this rule to NOT APPLICABLE if the predicted impact is not expected to be equivalent or more severe.
+
+
+PM5 should not be combined with PM1.  The one with the higher strength should be applied, but if both are applicable at SUPPORTING weight, use of PM5 is most appropriate since it is variant specific.",General recommendation
+ACTC1 (HGNC:143),PM6,Original ACMG Summary,"Assumed de novo, but without confirmation of paternity and maternity.",
+ACTC1 (HGNC:143),PM6,Moderate,"Refer to SVI guidance on number/combination of cases required based on phenotype specificity
+2
+.
+
+
+For most cardiomyopathies, it is recommended to default to “phenotype consistent with gene but not highly specific”. Clinical judgment is required for shifting to a higher or lower phenotypic consistency. 
+
+
+See PS2 for additional considerations.",Disease-specific
+ACTC1 (HGNC:143),PP1,Original ACMG Summary,"Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease.
+Note: May be used as stronger evidence with increasing segregation data.",
+ACTC1 (HGNC:143),PP1,Strong,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥7
+ 
+segregations
+ (LOD score of 2.1) for 
+STRONG
+.
+
+
+Although rare for inherited cardiomyopathies, when the phenotype/presentation of a variant within and across families is highly specific (e.g., early-onset severe RCM in all affected individuals), the following thresholds as proposed by Jarvik and Browning (2016)
+11
+ can be considered: 
+
+
+
+
+STRONG evidence requires ≥5 segregations (LOD score of 1.5)
+
+
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1.
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+ACTC1 (HGNC:143),PP1,Moderate,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥5
+ 
+segregations
+ (LOD score of 1.5) for 
+MODERATE
+.
+
+
+Although rare for inherited cardiomyopathies, when the phenotype/presentation of a variant within and across families is highly specific (e.g., early-onset severe RCM in all affected individuals), the following thresholds as proposed by Jarvik and Browning (2016)
+11
+ can be considered: 
+
+
+
+
+MODERATE evidence requires ≥4 segregations (LOD score of 1.2)
+
+
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1 (see below).
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+ACTC1 (HGNC:143),PP1,Supporting,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥3
+ 
+segregations
+ (LOD score of 0.9) for 
+SUPPORTING
+. The thresholds as proposed by Jarvik and Browning (2016)
+11
+ are the same at ≥3 segregations (LOD score of 0.9) for supporting.
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1 (see below).
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+ACTC1 (HGNC:143),PP2,Original ACMG Summary,Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease.,NA
+ACTC1 (HGNC:143),PP3,Original ACMG Summary,"Multiple lines of computational evidence support a deleterious effect on the gene or gene product (conservation, evolutionary, splicing impact, etc.).
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. PP3 can be used only once in any evaluation of a variant.",
+ACTC1 (HGNC:143),PP3,Supporting,"As many 
+in silico
+ algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. Meta-predictors, such as REVEL, are preferred over multiple individual predictors.
+
+
+Use of REVEL (Ioannidis 
+et al.
+ 2016
+12
+) is recommended at thresholds of 
+≥0.70 for PP3
+.
+
+
+Clinical judgment is needed if any individual algorithms or conservation data are contradictory to REVEL data.
+
+
+Positive predictive value for benign/no impact predictions is generally higher than for pathogenic/impact predictions.
+
+
+SpliceAI
+13
+ is recommended for evaluation of predicted splice impacts.",Disease-specific
+ACTC1 (HGNC:143),PP4,Original ACMG Summary,Patient’s phenotype or family history is highly specific for a disease with a single genetic etiology.,NA
+ACTC1 (HGNC:143),PP5,Original ACMG Summary,"Reputable source recently reports variant as pathogenic, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+ACTC1 (HGNC:143),BA1,Original ACMG Summary,"Allele frequency is above 5% in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.",
+ACTC1 (HGNC:143),BA1,Stand Alone,"Allele frequency is 
+≥0.001
+ based on the 
+filtering allele frequency (FAF)
+ in 
+gnomAD
+ in the subpopulation with the highest frequency (popmax).
+
+
+The values used to calculate the BA1 threshold were derived from studies in Northern European populations that have been relatively well-characterized with regards to disease prevalence and variant spectrum. These thresholds can be applied to any population where disease prevalence is considered comparable (1/300 or lower).
+
+
+The threshold is applicable when assessing variants in the context of autosomal dominant cardiomyopathy. 
+
+
+gnomAD is the preferred database for this calculation. If a subpopulation specific FAF other than the popmax is needed, this value can be calculated using the AlleleFrequencyApp on the 
+CardioDB website
+.
+
+
+
+
+Using the Inverse AF tab, enter in the population size and the number of alleles identified and it will calculate the FAF.  
+
+
+Set confidence to 0.95 (95%).
+
+
+If the FAF is ≥0.001, this rule can be applied.
+
+
+
+
+The FAF by platform (e.g., exome vs. genome; v.2.1.1 vs. v.3.1.1) should be considered, the larger population is most likely to have the most accurate representation of “true” population allele frequency.
+
+
+Caution is needed when considering any population cohorts that are smaller than the smallest subpopulations within gnomAD v.2.1.1 (e.g., ~5000 individuals or ~10,000 alleles). Despite this conservative nature of this threshold and approach, in smaller cohorts, the observed allele frequency may less accurately reflect the true allele frequency. Traditionally, once a variant is classified as Benign, it is rarely re-evaluated and so the highest confidence is needed to establish that classification on an allele frequency alone.",Disease-specific
+ACTC1 (HGNC:143),BS1,Original ACMG Summary,Allele frequency is greater than expected for disorder.,
+ACTC1 (HGNC:143),BS1,Strong,"Allele frequency is 
+≥0.0001 for
+ 
+ACTC1
+ based on the 
+filtering allele frequency (FAF)
+ in 
+gnomAD
+ in the subpopulation with the highest frequency (popmax).
+
+
+Criterion BS1 may only be used as standalone evidence to classify a variant as Likely Benign in the absence of conflicting data. See SVI guidance (Tavtigian 
+et al.
+ 2018
+14
+; Tavtigian 
+et al.
+ 2020
+15
+). 
+
+
+See BA1 for additional specifications that also apply to BS1.","Disease-specific,Gene-specific"
+ACTC1 (HGNC:143),BS2,Original ACMG Summary,"Observed in a healthy adult individual for a recessive (homozygous), dominant (heterozygous), or X-linked (hemizygous) disorder, with full penetrance expected at an early age.",NA
+ACTC1 (HGNC:143),BS3,Original ACMG Summary,Well-established in vitro or in vivo functional studies show no damaging effect on protein function or splicing.,
+ACTC1 (HGNC:143),BS3,Strong,See PS3 specifications.,Disease-specific
+ACTC1 (HGNC:143),BS3,Moderate,See PS3 specifications.,Disease-specific
+ACTC1 (HGNC:143),BS3,Supporting,See PS3 specifications.,Disease-specific
+ACTC1 (HGNC:143),BS4,Original ACMG Summary,"Lack of segregation in affected members of a family.
+Caveat: The presence of phenocopies for common phenotypes (i.e. cancer, epilepsy) can mimic lack of segregation among affected individuals. Also, families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent lack of segregation.",
+ACTC1 (HGNC:143),BS4,Strong,"Any non-segregations should be carefully evaluated to rule out a phenocopy or the presence of a second disease-causing variant before considering it as conflicting or benign evidence. 
+
+
+
+
+The presence of “phenocopies” (e.g., athlete’s heart, hypertensive heart disease, ischemic cardiomyopathy, alcoholic cardiomyopathy, diabetic cardiomyopathy) can mimic non-segregation (i.e., lack of segregation) among affected individuals. 
+
+
+Families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent ‘non-segregation’.
+
+
+
+
+Because of these possibilities, 
+multiple (≥2) non-segregations
+ that are highly unlikely to be phenocopies or due to alternate variants (e.g., those without a possible alternate cause) 
+are required to apply this rule
+.  A higher number of non-segregations is necessary for instances where alternative causes are possible (e.g., non-segregation in a sibling with childhood onset cardiomyopathy versus a grandparent with hypertension and HCM).
+
+
+Careful consideration of the above points is required when using this data as conflicting evidence, especially when overall evidence supports likely pathogenic or pathogenic.",Disease-specific
+ACTC1 (HGNC:143),BP1,Original ACMG Summary,Missense variant in a gene for which primarily truncating variants are known to cause disease.,NA
+ACTC1 (HGNC:143),BP2,Original ACMG Summary,Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder or observed in cis with a pathogenic variant in any inheritance pattern.,
+ACTC1 (HGNC:143),BP2,Supporting,"Other variants must be pathogenic as defined by these specifications.
+
+
+Testing of parents or other informative relatives is often required to determine 
+cis
+/
+trans
+ status.
+
+
+If a variant is seen in 
+trans
+ (or as double heterozygous) with another pathogenic variant in ≥2 cases and the phenotype is not more severe than when either of the two variants are seen in isolation, this rule may be applied (i.e., high confidence this variant is NOT contributing to disease).
+
+
+
+
+<1% of cases of HCM have >1 pathogenic or likely pathogenic variant (0.6%; Alfares 
+et al.
+ 2015
+16
+).
+
+
+
+
+This rule cannot be applied when the variant has only been observed in 
+cis
+ with a pathogenic variant as its significance in isolation is unknown in this scenario. 
+
+
+Caution is needed if using this criterion as a primary piece of evidence for classifying a variant as likely benign/benign (i.e., only 2 SUPPORTING criteria are sufficient for a likely benign classification).",Disease-specific
+ACTC1 (HGNC:143),BP3,Original ACMG Summary,In frame-deletions/insertions in a repetitive region without a known function.,NA
+ACTC1 (HGNC:143),BP4,Original ACMG Summary,"Multiple lines of computational evidence suggest no impact on gene or gene product (conservation, evolutionary, splicing impact, etc)
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm cannot be counted as an independent criterion. BP4 can be used only once in any evaluation of a variant.",
+ACTC1 (HGNC:143),BP4,Supporting,"As many 
+in silico
+ algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. Meta-predictors, such as REVEL, are preferred over multiple individual predictors.
+
+
+Use of REVEL (Ioannidis et al. 2016
+12
+) is recommended at thresholds of 
+≤0.40 for BP4
+.
+
+
+Clinical judgment is needed if any individual algorithms or conservation data are contradictory to REVEL data.
+
+
+Positive predictive value for benign/no impact predictions is generally higher than for pathogenic/impact predictions.
+
+
+SpliceAI
+13
+ is recommended for evaluation of predicted splice impacts.",Disease-specific
+ACTC1 (HGNC:143),BP5,Original ACMG Summary,Variant found in a case with an alternate molecular basis for disease.,NA
+ACTC1 (HGNC:143),BP6,Original ACMG Summary,"Reputable source recently reports variant as benign, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+ACTC1 (HGNC:143),BP7,Original ACMG Summary,A synonymous variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site AND the nucleotide is not highly conserved.,
+ACTC1 (HGNC:143),BP7,Supporting,"Also applicable to 
+intronic variants outside the splice consensus sequence (-4 and +7 outward)
+ for which splicing prediction algorithms predict no impact to the splice consensus sequence NOR the creation of a new splice site AND the nucleotide is not highly conserved.
+
+
+Rule can be combined with BP4 to make a variant likely benign per Richards 
+et al.
+ 2015
+1
+.",General recommendation

VCI/parsing_csr_criteria/version_csv_individual/ClinGenCardiomyopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforMYBPC3Version1.0.0_version=1.0.0.csv

@@ -0,0 +1,952 @@
+Gene,Code,Strength,Description,Modification Type
+MYBPC3 (HGNC:7551),PVS1,Original ACMG Summary,"Null variant (nonsense, frameshift, canonical +/−1 or 2 splice sites, initiation codon, single or multi-exon deletion) in a gene where loss of function (LOF) is a known mechanism of disease.
+Caveats:
+ • Beware of genes where LOF is not a known disease mechanism (e.g. GFAP, MYH7).
+ • Use caution interpreting LOF variants at the extreme 3’ end of a gene.
+ • Use caution with splice variants that are predicted to lead to exon skipping but leave the remainder of the protein intact.
+ • Use caution in the presence of multiple transcripts.",
+MYBPC3 (HGNC:7551),PVS1,Very Strong,"Currently only applicable to 
+MYBPC3
+ where LOF is an established disease mechanism.
+
+
+Refer to SVI guidance for the interpretation of this criterion (Abou Tayoun 
+et al.
+ 2018
+1
+).
+
+
+SpliceAI
+2
+ is recommended for evaluation of predicted splice impacts.
+
+
+Factors to consider when assessing the consequences of putative LOF variants in the 
+MYBPC3
+ gene:
+
+
+
+
+Codon p.1254 is located 50 nucleotides upstream of the most 3' exon-exon junction (exon 33:34) in 
+MYBPC3.
+ As such, nonsense variants introducing a premature termination codon after this point may escape nonsense mediated decay (NMD) and consequently not result in protein haploinsufficiency (Nagy & Maquat 1998
+3
+).
+
+
+When assessing variants predicted to affect splicing of micro-exons (exons 10, 11 and 14), be aware that 
+in silico
+ splice site predictions may be less reliable in this setting and the consequences of variants affecting splice sites at these exons less predictable (Frank-Hansen 
+et al.
+ 2008
+4
+).
+
+
+When assessing variants affecting splice sites of in-frame exons (exons 2-4, 8-11, 14, 20, 22, 24-27), be aware that although most of these exons encode domains that have been shown to play critical roles in protein function, and/or harbor functionally important residues (Carrier 
+et al
+. 2015
+5
+), in general, the consequences of in-frame deletions are less predictable.
+
+
+
+
+For canonical splice site variants where other canonical splice variants have been reported, application of the PS1 rule may be considered if the other variant affecting the same slice site is 1) predicted to have a similar or more deleterious effect and 2) has been classified as pathogenic according to these modified guidelines without use of PS1 for other splice variants.
+
+
+For genes where haploinsufficiency is NOT an established mechanism, see PM4 for truncating variants that do NOT undergo NMD.",Gene-specific
+MYBPC3 (HGNC:7551),PS1,Original ACMG Summary,"Same amino acid change as a previously established pathogenic variant regardless of nucleotide change.
+Example: Val->Leu caused by either G>C or G>T in the same codon.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+MYBPC3 (HGNC:7551),PS1,Strong,"No cardiomyopathy specifications. Apply as outlined by Richards 
+et al
+. 2015
+6
+.
+
+
+Example of when rule should NOT be applied. NM_000256.3(
+MYBPC3
+): c.2308G>A (p.Asp770Asn) has an established impact on splicing leading to nonsense mediated decay (NMD) and should not be used to provide evidence for other variants observed to result in the same amino acid change.",No change
+MYBPC3 (HGNC:7551),PS2,Original ACMG Summary,"De novo (both maternity and paternity confirmed) in a patient with the disease and no family history.
+Note: Confirmation of paternity only is insufficient. Egg donation, surrogate motherhood, errors in embryo transfer, etc. can contribute to non-maternity.",
+MYBPC3 (HGNC:7551),PS2,Strong,"Refer to SVI guidance on number/combination of cases required based on phenotype specificity
+7
+.
+
+
+For most cardiomyopathies, it is recommended to default to 
+Phenotype consistency: “Phenotype consistent with gene but not highly specific”
+. Clinical judgment is required for shifting to a higher or lower category. 
+
+
+For use as a STRONG or VERY STRONG criterion, ideally parents have been thoroughly clinically evaluated without evidence of cardiomyopathy (ideally using a combination of ECG and echocardiogram or cardiac MRI for maximum sensitivity).
+
+
+A family history consistent with 
+de novo
+ inheritance should not have any clinical signs or symptoms suggestive of cardiomyopathy in a 1
+st
+ or 2
+nd
+ degree relative, for example: 
+
+
+
+
+Sudden death under 60 years of age
+
+
+Heart transplant
+
+
+Implantable cardiac defibrillator (ICD) under 60 years of age
+
+
+Features of cardiomyopathy (e.g., systolic dysfunction, hypertrophy, left ventricular enlargement in an individual without risk factors).
+
+
+Other related/overlapping cardiomyopathies
+
+
+
+
+Examples of non-suspicious family history may include non-specific clinical features (e.g., palpitations, syncope, borderline/inconclusive echocardiogram findings, heart attack if age appropriate and suspected to result from coronary artery disease), but every attempt should be made to clarify features. 
+
+
+Generally, this criterion is only applicable in the ABSENCE of any other possible disease-causing variants.  If other pathogenic or likely pathogenic variants are present, consider decreasing points assigned or overall weight.",Disease-specific
+MYBPC3 (HGNC:7551),PS3,Original ACMG Summary,"Well-established in vitro or in vivo functional studies supportive of a damaging effect on the gene or gene product.
+Note: Functional studies that have been validated and shown to be reproducible and robust in a clinical diagnostic laboratory setting are considered the most well-established.",
+MYBPC3 (HGNC:7551),PS3,Strong,"In vitro splicing assays (e.g., RNA studies)
+
+
+In vitro
+ splicing assays may be considered as 
+STRONG
+ evidence, providing the following criteria are met.
+
+
+
+
+Prior knowledge of predominant transcripts in cardiac tissue
+
+
+
+
+Analysis undertaken using RNA extracted from cardiac tissue from the individual with the variant
+
+
+Analysis undertaken using RNA extracted from whole blood providing the relevant transcripts (isoforms) are expressed in blood and are at sufficient levels to assess splice disruption.
+
+
+Assay shows a clear, reproducible and convincing effect on splicing (i.e. a distinct splice product, present at a level comparable to the splice product from the wild-type allele), which is not observed in controls
+
+
+
+
+Confirmation of abnormal splice product by Sanger sequencing
+
+
+
+
+NOTE:
+ Mini-gene assay in non-patient derived cell lines are NOT considered to provide STRONG evidence.
+
+
+NOTE:
+  Whether to activate this rule needs to be reconciled with the variant spectrum and disease mechanism for the gene at hand (i.e., consider whether the effect is likely to lead to LOF or an in-frame alteration and whether this type of effect is expected to be disease causing) (Abou Tayoun 
+et al.
+ 2018
+1
+).",Disease-specific
+MYBPC3 (HGNC:7551),PS3,Moderate,"In vivo models (e.g., variant knock-in animal models)
+
+
+Mammalian variant-specific knock-in animal models that produce a phenotype consistent with the clinical phenotype in humans (e.g., structural and/or functional cardiac abnormalities, premature death, arrhythmia) may be considered as 
+MODERATE
+ evidence
+
+
+NOTE:
+ The following assays/models do NOT meet criteria
+
+
+
+
+Assays that are known to be associated with non-specific cardiac phenotypes (e.g., morpholino-induced pericardial edema in zebrafish)
+
+
+In vivo evidence that is not variant specific, such as whole gene alterations (i.e., cDNA or whole gene transgenic mice and whole or partial gene knock-out mice)",Disease-specific
+MYBPC3 (HGNC:7551),PS3,Supporting,"In vitro
+ 
+assays (e.g., biochemical assays of myofilament function, motility assays, human iPSC-CM)
+
+
+While some 
+in vitro
+ assays may provide evidence that a variant in a cardiomyopathy gene has an effect on protein and/or myofilament function, at present, there are no validated “gold-standard” assays that are considered to reliably predict the clinical phenotype.
+
+
+As such, in the cardiomyopathy genes listed in these guidelines, data from individual 
+in vitro
+ studies are unlikely to meet the criteria required to assign this rule at more than SUPPORTING level.",Disease-specific
+MYBPC3 (HGNC:7551),PS4,Original ACMG Summary,"The prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls.
+Note 1: Relative risk (RR) or odds ratio (OR), as obtained from case-control studies, is >5.0 and the confidence interval around the estimate of RR or OR does not include 1.0. See manuscript for detailed guidance.
+Note 2: In instances of very rare variants where case-control studies may not reach statistical significance, the prior observation of the variant in multiple unrelated patients with the same phenotype, and its absence in controls, may be used as moderate level of evidence.",
+MYBPC3 (HGNC:7551),PS4,Strong,"{Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+9
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+STRONG
+ evidence requires the lower bound of the 95% confidence interval (CI) around the odds ratio (OR) estimate to be 
+≥20
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+10
+; Oechslin 
+et al.
+ 2017
+11
+; Hershberger 
+et al.
+ 2017
+12
+; Ross 
+et al.
+ 2020
+13
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+MYBPC3 (HGNC:7551),PS4,Moderate,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+9
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+MODERATE
+ evidence requires the lower bound of the 95% CI around the OR to be 
+≥10
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+10
+; Oechslin 
+et al.
+ 2017
+11
+; Hershberger 
+et al.
+ 2017
+12
+; Ross 
+et al.
+ 2020
+13
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+MYBPC3 (HGNC:7551),PS4,Supporting,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+9
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+SUPPORTING
+ evidence requires the lower bound of the 95% CI around the OR to be 
+≥5
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+10
+; Oechslin 
+et al.
+ 2017
+11
+; Hershberger 
+et al.
+ 2017
+12
+; Ross 
+et al.
+ 2020
+13
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+MYBPC3 (HGNC:7551),PM1,Original ACMG Summary,Located in a mutational hot spot and/or critical and well-established functional domain (e.g. active site of an enzyme) without benign variation.,
+MYBPC3 (HGNC:7551),PM1,Moderate,"Applicable to missense variants in 
+MYBPC3
+ in the specific regions listed below (Walsh 
+et al.
+ 2019
+14
+). 
+
+
+
+
+Transcripts ENST00000545968 and NM_000256.3
+
+
+Codons 485-502 and 1248-1266
+
+
+
+
+Data from HCM case cohorts was used to derive these cluster regions. Therefore, this rule should NOT be applied when additional evidence for the variant supports that the variant causes a phenotype other than HCM (e.g., variant seen in multiple DCM cases).
+
+
+Enrichment was not observed for DCM in any genes.
+
+
+Rule should NOT be combined with PM5 because presence of pathogenic variants in the same codon/region were used to determine clustering and would be double-counting evidence.","Disease-specific,Gene-specific"
+MYBPC3 (HGNC:7551),PM2,Original ACMG Summary,"Absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.
+Caveat: Population data for indels may be poorly called by next generation sequencing.",
+MYBPC3 (HGNC:7551),PM2,Supporting,"The values used to calculate the PM2 thresholds were derived from studies in Northern European populations that have been relatively well-characterized with regards to disease prevalence and variant spectrum. These thresholds can be applied to any population where disease prevalence is considered comparable (1/500 or lower), where the most frequent pathogenic variant accounts for no more than 2% of cases (e.g., has an allele frequency of ≤0.02 in cases based on the upper bound of 95% CI), and where the penetrance of a pathogenic variant is expected to be at least 50% (Kelly 
+et al.
+ 2018
+15
+).
+
+
+A threshold of 
+≤0.00004
+ in the subpopulation with the highest frequency when using the upper bound of the 95% CI activates this rule.
+
+
+
+
+Alternatively, this is equivalent to the variant NOT being observed more than once (≤1 allele) in gnomAD v.2.1.1 in one of the non-founder populations (e.g., absence required from the Other and Ashkenazi Jewish subpopulations).
+
+
+Applying a threshold of ≤0.00004 (upper bound of 95% CI of the allele frequency in gnomAD) is equivalent to the variant being seen in a single subpopulation and that subpopulation meets any of the following:
+
+
+Allele Count (AC) in Allele Number (AN)
+
+
+≤1 in ≥120,000
+
+
+≤2 in ≥160,000
+
+
+≤3 in ≥195,000
+
+
+≤4 in ≥230,000
+
+
+
+
+
+
+
+
+gnomAD is the preferred database for this calculation, but currently only displays the filtering allele frequency (FAF), which is equivalent to a lower bound estimate of the 95% CI, when the upper bound is what is needed.
+
+
+
+
+Confidence interval tools, such as 
+Confit-de-MAF
+, can be used to determine the upper bound of the 95% CI of the observed allele frequency.
+
+
+
+
+Due to current technical limitations of next generation sequencing technologies, minor allele frequencies for complex variants (e.g., large indels) may not be accurately represented in population databases.
+
+
+Caution should be used when a variant is only identified, or over-represented, in one of the smaller gnomAD populations, as the gnomAD allele frequencies may not accurately represent the true population frequency.
+
+
+Population databases may contain affected or pre-symptomatic individuals for diseases with reduced penetrance/variable onset.",Disease-specific
+MYBPC3 (HGNC:7551),PM3,Original ACMG Summary,"For recessive disorders, detected in trans with a pathogenic variant
+Note: This requires testing of parents (or offspring) to determine phase.",NA
+MYBPC3 (HGNC:7551),PM4,Original ACMG Summary,Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants.,
+MYBPC3 (HGNC:7551),PM4,Moderate,"Strength of rule should be carefully considered and may require downgrading to SUPPORTING based on the predicted impact of the variant, including the size of the deletion/insertion, its location, and conservation of the region. 
+
+
+For genes where PVS1 is not applicable (i.e., where there is no evidence that pLOF variants cause disease), consider using this rule at MODERATE or SUPPORTING strength for truncating variants that do NOT undergo nonsense mediated decay (NMD).",General recommendation
+MYBPC3 (HGNC:7551),PM5,Original ACMG Summary,"Novel missense change at an amino acid residue where a different missense change determined to be pathogenic has been seen before.
+Example: Arg156His is pathogenic; now you observe Arg156Cys.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+MYBPC3 (HGNC:7551),PM5,Moderate,"This criterion can be used at MODERATE if a different missense variant at the same codon has been classified as 
+pathogenic
+ using these modified guidelines without application of PM5.
+
+
+The impact of the amino acid change being evaluated needs to be compared to the impact of the amino acid change that is established as pathogenic (e.g., a change of Ala to His is less severe than Ala to Cys change). Consider reducing the strength of this rule to SUPPORTING if the predicted impact is not expected to be equivalent or more severe.
+
+
+PM5 should not be combined with PM1.  If both are applicable at MODERATE weight, use of PM5 is most appropriate since it is variant specific.",General recommendation
+MYBPC3 (HGNC:7551),PM5,Supporting,"This criterion can be considered at SUPPORTING if a different missense variant at the same codon has been classified as 
+likely pathogenic
+ using these modified guidelines without application of PM5.
+
+
+The impact of the amino acid change being evaluated needs to be compared to the impact of the amino acid change that is established as likely pathogenic (e.g., a change of Ala to His is less severe than Ala to Cys change). Consider reducing the strength of this rule to NOT APPLICABLE if the predicted impact is not expected to be equivalent or more severe.
+
+
+PM5 should not be combined with PM1.  The one with the higher strength should be applied, but if both are applicable at SUPPORTING weight, use of PM5 is most appropriate since it is variant specific.",General recommendation
+MYBPC3 (HGNC:7551),PM6,Original ACMG Summary,"Assumed de novo, but without confirmation of paternity and maternity.",
+MYBPC3 (HGNC:7551),PM6,Moderate,"Refer to SVI guidance on number/combination of cases required based on phenotype specificity
+7
+.
+
+
+For most cardiomyopathies, it is recommended to default to “phenotype consistent with gene but not highly specific”. Clinical judgment is required for shifting to a higher or lower phenotypic consistency. 
+
+
+See PS2 for additional considerations.",Disease-specific
+MYBPC3 (HGNC:7551),PP1,Original ACMG Summary,"Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease.
+Note: May be used as stronger evidence with increasing segregation data.",
+MYBPC3 (HGNC:7551),PP1,Strong,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥7
+ 
+segregations
+ (LOD score of 2.1) for 
+STRONG
+.
+
+
+Although rare for inherited cardiomyopathies, when the phenotype/presentation of a variant within and across families is highly specific (e.g., early-onset severe RCM in all affected individuals), the following thresholds as proposed by Jarvik and Browning (2016)
+16
+ can be considered: 
+
+
+
+
+STRONG evidence requires ≥5 segregations (LOD score of 1.5)
+
+
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1.
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+MYBPC3 (HGNC:7551),PP1,Moderate,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥5
+ 
+segregations
+ (LOD score of 1.5) for 
+MODERATE
+.
+
+
+Although rare for inherited cardiomyopathies, when the phenotype/presentation of a variant within and across families is highly specific (e.g., early-onset severe RCM in all affected individuals), the following thresholds as proposed by Jarvik and Browning (2016)
+16
+ can be considered: 
+
+
+
+
+MODERATE evidence requires ≥4 segregations (LOD score of 1.2)
+
+
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1 (see below).
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+MYBPC3 (HGNC:7551),PP1,Supporting,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥3
+ 
+segregations
+ (LOD score of 0.9) for 
+SUPPORTING
+. The thresholds as proposed by Jarvik and Browning (2016)
+16
+ are the same at ≥3 segregations (LOD score of 0.9) for supporting.
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1 (see below).
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+MYBPC3 (HGNC:7551),PP2,Original ACMG Summary,Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease.,NA
+MYBPC3 (HGNC:7551),PP3,Original ACMG Summary,"Multiple lines of computational evidence support a deleterious effect on the gene or gene product (conservation, evolutionary, splicing impact, etc.).
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. PP3 can be used only once in any evaluation of a variant.",
+MYBPC3 (HGNC:7551),PP3,Supporting,"As many 
+in silico
+ algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. Meta-predictors, such as REVEL, are preferred over multiple individual predictors.
+
+
+Use of REVEL (Ioannidis 
+et al.
+ 2016
+17
+) is recommended at thresholds of 
+≥0.70 for PP3
+.
+
+
+Clinical judgment is needed if any individual algorithms or conservation data are contradictory to REVEL data.
+
+
+Positive predictive value for benign/no impact predictions is generally higher than for pathogenic/impact predictions.
+
+
+SpliceAI
+2
+ is recommended for evaluation of predicted splice impacts.",Disease-specific
+MYBPC3 (HGNC:7551),PP4,Original ACMG Summary,Patient’s phenotype or family history is highly specific for a disease with a single genetic etiology.,NA
+MYBPC3 (HGNC:7551),PP5,Original ACMG Summary,"Reputable source recently reports variant as pathogenic, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+MYBPC3 (HGNC:7551),BA1,Original ACMG Summary,"Allele frequency is above 5% in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.",
+MYBPC3 (HGNC:7551),BA1,Stand Alone,"Allele frequency is 
+≥0.001
+ based on the 
+filtering allele frequency (FAF)
+ in 
+gnomAD
+ in the subpopulation with the highest frequency (popmax).
+
+
+The values used to calculate the BA1 threshold were derived from studies in Northern European populations that have been relatively well-characterized with regards to disease prevalence and variant spectrum. These thresholds can be applied to any population where disease prevalence is considered comparable (1/300 or lower).
+
+
+The threshold is applicable when assessing variants in the context of autosomal dominant cardiomyopathy. 
+
+
+gnomAD is the preferred database for this calculation. If a subpopulation specific FAF other than the popmax is needed, this value can be calculated using the AlleleFrequencyApp on the 
+CardioDB website
+.
+
+
+
+
+Using the Inverse AF tab, enter in the population size and the number of alleles identified and it will calculate the FAF.  
+
+
+Set confidence to 0.95 (95%).
+
+
+If the FAF is ≥0.001, this rule can be applied.
+
+
+
+
+The FAF by platform (e.g., exome vs. genome; v.2.1.1 vs. v.3.1.1) should be considered, the larger population is most likely to have the most accurate representation of “true” population allele frequency.
+
+
+Caution is needed when considering any population cohorts that are smaller than the smallest subpopulations within gnomAD v.2.1.1 (e.g., ~5000 individuals or ~10,000 alleles). Despite this conservative nature of this threshold and approach, in smaller cohorts, the observed allele frequency may less accurately reflect the true allele frequency. Traditionally, once a variant is classified as Benign, it is rarely re-evaluated and so the highest confidence is needed to establish that classification on an allele frequency alone.",Disease-specific
+MYBPC3 (HGNC:7551),BS1,Original ACMG Summary,Allele frequency is greater than expected for disorder.,
+MYBPC3 (HGNC:7551),BS1,Strong,"Allele frequency is 
+≥0.0002 for
+ 
+MYBPC3
+ based on the 
+filtering allele frequency (FAF)
+ in 
+gnomAD
+ in the subpopulation with the highest frequency (popmax).
+
+
+Criterion BS1 may only be used as standalone evidence to classify a variant as Likely Benign in the absence of conflicting data. See SVI guidance (Tavtigian 
+et al.
+ 2018
+18
+; Tavtigian 
+et al.
+ 2020
+19
+). 
+
+
+See BA1 for additional specifications that also apply to BS1.","Disease-specific,Gene-specific"
+MYBPC3 (HGNC:7551),BS2,Original ACMG Summary,"Observed in a healthy adult individual for a recessive (homozygous), dominant (heterozygous), or X-linked (hemizygous) disorder, with full penetrance expected at an early age.",NA
+MYBPC3 (HGNC:7551),BS3,Original ACMG Summary,Well-established in vitro or in vivo functional studies show no damaging effect on protein function or splicing.,
+MYBPC3 (HGNC:7551),BS3,Strong,See PS3 specifications.,Disease-specific
+MYBPC3 (HGNC:7551),BS3,Moderate,See PS3 specifications.,Disease-specific
+MYBPC3 (HGNC:7551),BS3,Supporting,See PS3 specifications.,Disease-specific
+MYBPC3 (HGNC:7551),BS4,Original ACMG Summary,"Lack of segregation in affected members of a family.
+Caveat: The presence of phenocopies for common phenotypes (i.e. cancer, epilepsy) can mimic lack of segregation among affected individuals. Also, families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent lack of segregation.",
+MYBPC3 (HGNC:7551),BS4,Strong,"Any non-segregations should be carefully evaluated to rule out a phenocopy or the presence of a second disease-causing variant before considering it as conflicting or benign evidence. 
+
+
+
+
+The presence of “phenocopies” (e.g., athlete’s heart, hypertensive heart disease, ischemic cardiomyopathy, alcoholic cardiomyopathy, diabetic cardiomyopathy) can mimic non-segregation (i.e., lack of segregation) among affected individuals. 
+
+
+Families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent ‘non-segregation’.
+
+
+
+
+Because of these possibilities, 
+multiple (≥2) non-segregations
+ that are highly unlikely to be phenocopies or due to alternate variants (e.g., those without a possible alternate cause) 
+are required to apply this rule
+.  A higher number of non-segregations is necessary for instances where alternative causes are possible (e.g., non-segregation in a sibling with childhood onset cardiomyopathy versus a grandparent with hypertension and HCM).
+
+
+Careful consideration of the above points is required when using this data as conflicting evidence, especially when overall evidence supports likely pathogenic or pathogenic.",Disease-specific
+MYBPC3 (HGNC:7551),BP1,Original ACMG Summary,Missense variant in a gene for which primarily truncating variants are known to cause disease.,NA
+MYBPC3 (HGNC:7551),BP2,Original ACMG Summary,Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder or observed in cis with a pathogenic variant in any inheritance pattern.,
+MYBPC3 (HGNC:7551),BP2,Supporting,"Other variants must be pathogenic as defined by these specifications.
+
+
+Testing of parents or other informative relatives is often required to determine 
+cis
+/
+trans
+ status.
+
+
+If a variant is seen in 
+trans
+ (or as double heterozygous) with another pathogenic variant in ≥2 cases and the phenotype is not more severe than when either of the two variants are seen in isolation, this rule may be applied (i.e., high confidence this variant is NOT contributing to disease).
+
+
+
+
+<1% of cases of HCM have >1 pathogenic or likely pathogenic variant (0.6%; Alfares 
+et al.
+ 2015
+20
+).
+
+
+
+
+This rule cannot be applied when the variant has only been observed in 
+cis
+ with a pathogenic variant as its significance in isolation is unknown in this scenario. 
+
+
+Caution is needed if using this criterion as a primary piece of evidence for classifying a variant as likely benign/benign (i.e., only 2 SUPPORTING criteria are sufficient for a likely benign classification).",Disease-specific
+MYBPC3 (HGNC:7551),BP3,Original ACMG Summary,In frame-deletions/insertions in a repetitive region without a known function.,NA
+MYBPC3 (HGNC:7551),BP4,Original ACMG Summary,"Multiple lines of computational evidence suggest no impact on gene or gene product (conservation, evolutionary, splicing impact, etc)
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm cannot be counted as an independent criterion. BP4 can be used only once in any evaluation of a variant.",
+MYBPC3 (HGNC:7551),BP4,Supporting,"As many 
+in silico
+ algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. Meta-predictors, such as REVEL, are preferred over multiple individual predictors.
+
+
+Use of REVEL (Ioannidis 
+et al.
+ 2016
+17
+) is recommended at thresholds of 
+≤0.40 for BP4
+.
+
+
+Clinical judgment is needed if any individual algorithms or conservation data are contradictory to REVEL data.
+
+
+Positive predictive value for benign/no impact predictions is generally higher than for pathogenic/impact predictions.
+
+
+SpliceAI
+2
+ is recommended for evaluation of predicted splice impacts.",Disease-specific
+MYBPC3 (HGNC:7551),BP5,Original ACMG Summary,Variant found in a case with an alternate molecular basis for disease.,NA
+MYBPC3 (HGNC:7551),BP6,Original ACMG Summary,"Reputable source recently reports variant as benign, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+MYBPC3 (HGNC:7551),BP7,Original ACMG Summary,A synonymous variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site AND the nucleotide is not highly conserved.,
+MYBPC3 (HGNC:7551),BP7,Supporting,"Also applicable to 
+intronic variants outside the splice consensus sequence (-4 and +7 outward)
+ for which splicing prediction algorithms predict no impact to the splice consensus sequence NOR the creation of a new splice site AND the nucleotide is not highly conserved.
+
+
+Rule can be combined with BP4 to make a variant likely benign per Richards 
+et al.
+ 2015
+6
+.",General recommendation

VCI/parsing_csr_criteria/version_csv_individual/ClinGenCardiomyopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforMYH7Version2.0.0_version=2.0.0.csv

@@ -0,0 +1,900 @@
+Gene,Code,Strength,Description,Modification Type
+MYH7 (HGNC:7577),PVS1,Original ACMG Summary,"Null variant (nonsense, frameshift, canonical +/−1 or 2 splice sites, initiation codon, single or multi-exon deletion) in a gene where loss of function (LOF) is a known mechanism of disease.
+Caveats:
+ • Beware of genes where LOF is not a known disease mechanism (e.g. GFAP, MYH7).
+ • Use caution interpreting LOF variants at the extreme 3’ end of a gene.
+ • Use caution with splice variants that are predicted to lead to exon skipping but leave the remainder of the protein intact.
+ • Use caution in the presence of multiple transcripts.",NA
+MYH7 (HGNC:7577),PS1,Original ACMG Summary,"Same amino acid change as a previously established pathogenic variant regardless of nucleotide change.
+Example: Val->Leu caused by either G>C or G>T in the same codon.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+MYH7 (HGNC:7577),PS1,Strong,"No cardiomyopathy specifications. Apply as outlined by Richards 
+et al
+. 2015
+1
+.
+
+
+Example of when rule should NOT be applied. NM_000256.3(
+MYBPC3
+): c.2308G>A (p.Asp770Asn) has an established impact on splicing leading to nonsense mediated decay (NMD) and should not be used to provide evidence for other variants observed to result in the same amino acid change.",No change
+MYH7 (HGNC:7577),PS2,Original ACMG Summary,"De novo (both maternity and paternity confirmed) in a patient with the disease and no family history.
+Note: Confirmation of paternity only is insufficient. Egg donation, surrogate motherhood, errors in embryo transfer, etc. can contribute to non-maternity.",
+MYH7 (HGNC:7577),PS2,Strong,"Refer to SVI guidance on number/combination of cases required based on phenotype specificity
+2
+).
+
+
+For most cardiomyopathies, it is recommended to default to 
+Phenotype consistency: “Phenotype consistent with gene but not highly specific”
+. Clinical judgment is required for shifting to a higher or lower category. 
+
+
+For use as a STRONG or VERY STRONG criterion, ideally parents have been thoroughly clinically evaluated without evidence of cardiomyopathy (ideally using a combination of ECG and echocardiogram or cardiac MRI for maximum sensitivity).
+
+
+A family history consistent with 
+de novo
+ inheritance should not have any clinical signs or symptoms suggestive of cardiomyopathy in a 1
+st
+ or 2
+nd
+ degree relative, for example: 
+
+
+
+
+Sudden death under 60 years of age
+
+
+Heart transplant
+
+
+Implantable cardiac defibrillator (ICD) under 60 years of age
+
+
+Features of cardiomyopathy (e.g., systolic dysfunction, hypertrophy, left ventricular enlargement in an individual without risk factors).
+
+
+Other related/overlapping cardiomyopathies
+
+
+
+
+Examples of non-suspicious family history may include non-specific clinical features (e.g., palpitations, syncope, borderline/inconclusive echocardiogram findings, heart attack if age appropriate and suspected to result from coronary artery disease), but every attempt should be made to clarify features. 
+
+
+Generally, this criterion is only applicable in the ABSENCE of any other possible disease-causing variants.  If other pathogenic or likely pathogenic variants are present, consider decreasing points assigned or overall weight.",Disease-specific
+MYH7 (HGNC:7577),PS3,Original ACMG Summary,"Well-established in vitro or in vivo functional studies supportive of a damaging effect on the gene or gene product.
+Note: Functional studies that have been validated and shown to be reproducible and robust in a clinical diagnostic laboratory setting are considered the most well-established.",
+MYH7 (HGNC:7577),PS3,Strong,"In vitro splicing assays (e.g., RNA studies)
+
+
+In vitro
+ splicing assays may be considered as 
+STRONG
+ evidence, providing the following criteria are met.
+
+
+
+
+Prior knowledge of predominant transcripts in cardiac tissue
+
+
+
+
+Analysis undertaken using RNA extracted from cardiac tissue from the individual with the variant
+
+
+Analysis undertaken using RNA extracted from whole blood providing the relevant transcripts (isoforms) are expressed in blood and are at sufficient levels to assess splice disruption.
+
+
+Assay shows a clear, reproducible and convincing effect on splicing (i.e. a distinct splice product, present at a level comparable to the splice product from the wild-type allele), which is not observed in controls
+
+
+
+
+Confirmation of abnormal splice product by Sanger sequencing
+
+
+
+
+NOTE:
+ Mini-gene assay in non-patient derived cell lines are NOT considered to provide STRONG evidence.
+
+
+NOTE:
+  Whether to activate this rule needs to be reconciled with the variant spectrum and disease mechanism for the gene at hand (i.e., consider whether the effect is likely to lead to LOF or an in-frame alteration and whether this type of effect is expected to be disease causing) (Abou Tayoun 
+et al.
+ 2018
+3
+).",Disease-specific
+MYH7 (HGNC:7577),PS3,Moderate,"In vivo models (e.g., variant knock-in animal models)
+
+
+Mammalian variant-specific knock-in animal models that produce a phenotype consistent with the clinical phenotype in humans (e.g., structural and/or functional cardiac abnormalities, premature death, arrhythmia) may be considered as 
+MODERATE
+ evidence
+
+
+NOTE:
+ The following assays/models do NOT meet criteria
+
+
+
+
+Assays that are known to be associated with non-specific cardiac phenotypes (e.g., morpholino-induced pericardial edema in zebrafish)
+
+
+In vivo evidence that is not variant specific, such as whole gene alterations (i.e., cDNA or whole gene transgenic mice and whole or partial gene knock-out mice)",Disease-specific
+MYH7 (HGNC:7577),PS3,Supporting,"In vitro
+ 
+assays (e.g., biochemical assays of myofilament function, motility assays, human iPSC-CM)
+
+
+While some 
+in vitro
+ assays may provide evidence that a variant in a cardiomyopathy gene has an effect on protein and/or myofilament function, at present, there are no validated “gold-standard” assays that are considered to reliably predict the clinical phenotype.
+
+
+As such, in the cardiomyopathy genes listed in these guidelines, data from individual 
+in vitro
+ studies are unlikely to meet the criteria required to assign this rule at more than SUPPORTING level.",Disease-specific
+MYH7 (HGNC:7577),PS4,Original ACMG Summary,"The prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls.
+Note 1: Relative risk (RR) or odds ratio (OR), as obtained from case-control studies, is >5.0 and the confidence interval around the estimate of RR or OR does not include 1.0. See manuscript for detailed guidance.
+Note 2: In instances of very rare variants where case-control studies may not reach statistical significance, the prior observation of the variant in multiple unrelated patients with the same phenotype, and its absence in controls, may be used as moderate level of evidence.",
+MYH7 (HGNC:7577),PS4,Strong,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+8
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+STRONG
+ evidence requires the lower bound of the 95% confidence interval (CI) around the odds ratio (OR) estimate to be 
+≥20
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+9
+; Oechslin 
+et al.
+ 2017
+6
+; Hershberger 
+et al.
+ 2017
+5
+; Ross 
+et al.
+ 2020
+7
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+MYH7 (HGNC:7577),PS4,Moderate,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+8
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+MODERATE
+ evidence requires the lower bound of the 95% CI around the OR to be 
+≥10
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+9
+; Oechslin 
+et al.
+ 2017
+6
+; Hershberger 
+et al.
+ 2017
+5
+; Ross 
+et al.
+ 2020
+7
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+MYH7 (HGNC:7577),PS4,Supporting,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+8
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+SUPPORTING
+ evidence requires the lower bound of the 95% CI around the OR to be 
+≥5
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+9
+; Oechslin 
+et al.
+ 2017
+6
+; Hershberger 
+et al.
+ 2017
+5
+; Ross 
+et al.
+ 2020
+7
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+MYH7 (HGNC:7577),PM1,Original ACMG Summary,Located in a mutational hot spot and/or critical and well-established functional domain (e.g. active site of an enzyme) without benign variation.,
+MYH7 (HGNC:7577),PM1,Moderate,"Applicable to missense variants in 
+MYH7
+ in the specific regions listed below (Walsh 
+et al.
+ 2019
+10
+). 
+
+
+
+
+Transcripts ENST00000355349 & NM_000257.4
+
+
+Codons 167-931*
+
+
+
+
+Data from HCM case cohorts was used to derive these cluster regions. Therefore, this rule should NOT be applied when additional evidence for the variant supports that the variant causes a phenotype other than HCM (e.g., variant seen in multiple DCM cases).
+
+
+Enrichment was not observed for DCM in any genes.
+
+
+Rule should NOT be combined with PM5 because presence of pathogenic variants in the same codon/region were used to determine clustering and would be double-counting evidence.
+
+
+* This region is updated from v1.0 (Kelly et al. 2018
+11
+).","Disease-specific,Gene-specific"
+MYH7 (HGNC:7577),PM2,Original ACMG Summary,"Absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.
+Caveat: Population data for indels may be poorly called by next generation sequencing.",
+MYH7 (HGNC:7577),PM2,Supporting,"The values used to calculate the PM2 thresholds were derived from studies in Northern European populations that have been relatively well-characterized with regards to disease prevalence and variant spectrum. These thresholds can be applied to any population where disease prevalence is considered comparable (1/500 or lower), where the most frequent pathogenic variant accounts for no more than 2% of cases (e.g., has an allele frequency of ≤0.02 in cases based on the upper bound of 95% CI), and where the penetrance of a pathogenic variant is expected to be at least 50% (Kelly 
+et al.
+ 2018
+11
+).
+
+
+A threshold of 
+≤0.00004
+ in the subpopulation with the highest frequency when using the upper bound of the 95% CI activates this rule.
+
+
+
+
+Alternatively, this is equivalent to the variant NOT being observed more than once (≤1 allele) in gnomAD v.2.1.1 in one of the non-founder populations (e.g., absence required from the Other and Ashkenazi Jewish subpopulations).
+
+
+Applying a threshold of ≤0.00004 (upper bound of 95% CI of the allele frequency in gnomAD) is equivalent to the variant being seen in a single subpopulation and that subpopulation meets any of the following:
+
+
+Allele Count (AC) in Allele Number (AN)
+
+
+≤1 in ≥120,000
+
+
+≤2 in ≥160,000
+
+
+≤3 in ≥195,000
+
+
+≤4 in ≥230,000
+
+
+
+
+
+
+
+
+gnomAD is the preferred database for this calculation, but currently only displays the filtering allele frequency (FAF), which is equivalent to a lower bound estimate of the 95% CI, when the upper bound is what is needed.
+
+
+
+
+Confidence interval tools, such as 
+Confit-de-MAF
+, can be used to determine the upper bound of the 95% CI of the observed allele frequency.
+
+
+
+
+Due to current technical limitations of next generation sequencing technologies, minor allele frequencies for complex variants (e.g., large indels) may not be accurately represented in population databases.
+
+
+Caution should be used when a variant is only identified, or over-represented, in one of the smaller gnomAD populations, as the gnomAD allele frequencies may not accurately represent the true population frequency.
+
+
+Population databases may contain affected or pre-symptomatic individuals for diseases with reduced penetrance/variable onset.",Disease-specific
+MYH7 (HGNC:7577),PM3,Original ACMG Summary,"For recessive disorders, detected in trans with a pathogenic variant
+Note: This requires testing of parents (or offspring) to determine phase.",NA
+MYH7 (HGNC:7577),PM4,Original ACMG Summary,Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants.,
+MYH7 (HGNC:7577),PM4,Moderate,"Strength of rule should be carefully considered and may require downgrading to SUPPORTING based on the predicted impact of the variant, including the size of the deletion/insertion, its location, and conservation of the region. 
+
+
+For genes where PVS1 is not applicable (i.e., where there is no evidence that pLOF variants cause disease), consider using this rule at MODERATE or SUPPORTING strength for truncating variants that do NOT undergo nonsense mediated decay (NMD).",Disease-specific
+MYH7 (HGNC:7577),PM5,Original ACMG Summary,"Novel missense change at an amino acid residue where a different missense change determined to be pathogenic has been seen before.
+Example: Arg156His is pathogenic; now you observe Arg156Cys.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+MYH7 (HGNC:7577),PM5,Moderate,"This criterion can be used at MODERATE if a different missense variant at the same codon has been classified as 
+pathogenic
+ using these modified guidelines without application of PM5.
+
+
+The impact of the amino acid change being evaluated needs to be compared to the impact of the amino acid change that is established as pathogenic (e.g., a change of Ala to His is less severe than Ala to Cys change). Consider reducing the strength of this rule to SUPPORTING if the predicted impact is not expected to be equivalent or more severe.
+
+
+PM5 should not be combined with PM1.  If both are applicable at MODERATE weight, use of PM5 is most appropriate since it is variant specific.",General recommendation
+MYH7 (HGNC:7577),PM5,Supporting,"This criterion can be considered at SUPPORTING if a different missense variant at the same codon has been classified as 
+likely pathogenic
+ using these modified guidelines without application of PM5.
+
+
+The impact of the amino acid change being evaluated needs to be compared to the impact of the amino acid change that is established as likely pathogenic (e.g., a change of Ala to His is less severe than Ala to Cys change). Consider reducing the strength of this rule to NOT APPLICABLE if the predicted impact is not expected to be equivalent or more severe.
+
+
+PM5 should not be combined with PM1.  The one with the higher strength should be applied, but if both are applicable at SUPPORTING weight, use of PM5 is most appropriate since it is variant specific.",General recommendation
+MYH7 (HGNC:7577),PM6,Original ACMG Summary,"Assumed de novo, but without confirmation of paternity and maternity.",
+MYH7 (HGNC:7577),PM6,Moderate,"Refer to SVI guidance on number/combination of cases required based on phenotype specificity
+2
+.
+
+
+For most cardiomyopathies, it is recommended to default to “phenotype consistent with gene but not highly specific”. Clinical judgment is required for shifting to a higher or lower phenotypic consistency. 
+
+
+See PS2 for additional considerations.",Disease-specific
+MYH7 (HGNC:7577),PP1,Original ACMG Summary,"Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease.
+Note: May be used as stronger evidence with increasing segregation data.",
+MYH7 (HGNC:7577),PP1,Strong,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥7 segregations
+ (LOD score of 2.1) for 
+STRONG
+.
+
+
+Although rare for inherited cardiomyopathies, when the phenotype/presentation of a variant within and across families is highly specific (e.g., early-onset severe RCM in all affected individuals), the following thresholds as proposed by Jarvik and Browning (2016)
+12
+ can be considered: 
+
+
+
+
+STRONG evidence requires ≥5 segregations (LOD score of 1.5)
+
+
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1.
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+MYH7 (HGNC:7577),PP1,Moderate,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥5
+ 
+segregations
+ (LOD score of 1.5) for 
+MODERATE
+.
+
+
+Although rare for inherited cardiomyopathies, when the phenotype/presentation of a variant within and across families is highly specific (e.g., early-onset severe RCM in all affected individuals), the following thresholds as proposed by Jarvik and Browning (2016)
+12
+ can be considered: 
+
+
+
+
+MODERATE evidence requires ≥4 segregations (LOD score of 1.2)
+
+
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1 (see below).
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+MYH7 (HGNC:7577),PP1,Supporting,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥3
+ 
+segregations
+ (LOD score of 0.9) for 
+SUPPORTING
+. The thresholds as proposed by Jarvik and Browning (2016)
+12
+ are the same at ≥3 segregations (LOD score of 0.9) for supporting.
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1 (see below).
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+MYH7 (HGNC:7577),PP2,Original ACMG Summary,Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease.,NA
+MYH7 (HGNC:7577),PP3,Original ACMG Summary,"Multiple lines of computational evidence support a deleterious effect on the gene or gene product (conservation, evolutionary, splicing impact, etc.).
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. PP3 can be used only once in any evaluation of a variant.",
+MYH7 (HGNC:7577),PP3,Supporting,"As many 
+in silico
+ algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. Meta-predictors, such as REVEL, are preferred over multiple individual predictors.
+
+
+Use of REVEL (Ioannidis 
+et al.
+ 2016
+14
+) is recommended at thresholds of 
+≥0.70 for PP3
+.
+
+
+Clinical judgment is needed if any individual algorithms or conservation data are contradictory to REVEL data.
+
+
+Positive predictive value for benign/no impact predictions is generally higher than for pathogenic/impact predictions.
+
+
+SpliceAI
+13
+ is recommended for evaluation of predicted splice impacts.",Disease-specific
+MYH7 (HGNC:7577),PP4,Original ACMG Summary,Patient’s phenotype or family history is highly specific for a disease with a single genetic etiology.,NA
+MYH7 (HGNC:7577),PP5,Original ACMG Summary,"Reputable source recently reports variant as pathogenic, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+MYH7 (HGNC:7577),BA1,Original ACMG Summary,"Allele frequency is above 5% in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.",
+MYH7 (HGNC:7577),BA1,Stand Alone,"Allele frequency is 
+≥0.001
+ based on the 
+filtering allele frequency (FAF)
+ in 
+gnomAD
+ in the subpopulation with the highest frequency (popmax).
+
+
+The values used to calculate the BA1 threshold were derived from studies in Northern European populations that have been relatively well-characterized with regards to disease prevalence and variant spectrum. These thresholds can be applied to any population where disease prevalence is considered comparable (1/300 or lower).
+
+
+The threshold is applicable when assessing variants in the context of autosomal dominant cardiomyopathy. 
+
+
+gnomAD is the preferred database for this calculation. If a subpopulation specific FAF other than the popmax is needed, this value can be calculated using the AlleleFrequencyApp on the 
+CardioDB website
+.
+
+
+
+
+Using the Inverse AF tab, enter in the population size and the number of alleles identified and it will calculate the FAF.  
+
+
+Set confidence to 0.95 (95%).
+
+
+If the FAF is ≥0.001, this rule can be applied.
+
+
+
+
+The FAF by platform (e.g., exome vs. genome; v.2.1.1 vs. v.3.1.1) should be considered, the larger population is most likely to have the most accurate representation of “true” population allele frequency.
+
+
+Caution is needed when considering any population cohorts that are smaller than the smallest subpopulations within gnomAD v.2.1.1 (e.g., ~5000 individuals or ~10,000 alleles). Despite this conservative nature of this threshold and approach, in smaller cohorts, the observed allele frequency may less accurately reflect the true allele frequency. Traditionally, once a variant is classified as Benign, it is rarely re-evaluated and so the highest confidence is needed to establish that classification on an allele frequency alone.",Disease-specific
+MYH7 (HGNC:7577),BS1,Original ACMG Summary,Allele frequency is greater than expected for disorder.,
+MYH7 (HGNC:7577),BS1,Strong,"Allele frequency is 
+≥0.0001 for
+ 
+MYH7
+ based on the 
+filtering allele frequency (FAF)
+ in 
+gnomAD
+ in the subpopulation with the highest frequency (popmax).
+
+
+Criterion BS1 may only be used as standalone evidence to classify a variant as Likely Benign in the absence of conflicting data. See SVI guidance (Tavtigian 
+et al.
+ 2018
+16
+; Tavtigian 
+et al.
+ 2020
+15
+). 
+
+
+See BA1 for additional specifications that also apply to BS1.","Disease-specific,Gene-specific"
+MYH7 (HGNC:7577),BS2,Original ACMG Summary,"Observed in a healthy adult individual for a recessive (homozygous), dominant (heterozygous), or X-linked (hemizygous) disorder, with full penetrance expected at an early age.",NA
+MYH7 (HGNC:7577),BS3,Original ACMG Summary,Well-established in vitro or in vivo functional studies show no damaging effect on protein function or splicing.,
+MYH7 (HGNC:7577),BS3,Strong,See PS3 specifications.,No change
+MYH7 (HGNC:7577),BS3,Moderate,See PS3 specifications.,Disease-specific
+MYH7 (HGNC:7577),BS3,Supporting,See PS3 specifications.,Disease-specific
+MYH7 (HGNC:7577),BS4,Original ACMG Summary,"Lack of segregation in affected members of a family.
+Caveat: The presence of phenocopies for common phenotypes (i.e. cancer, epilepsy) can mimic lack of segregation among affected individuals. Also, families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent lack of segregation.",
+MYH7 (HGNC:7577),BS4,Strong,"Any non-segregations should be carefully evaluated to rule out a phenocopy or the presence of a second disease-causing variant before considering it as conflicting or benign evidence. 
+
+
+
+
+The presence of “phenocopies” (e.g., athlete’s heart, hypertensive heart disease, ischemic cardiomyopathy, alcoholic cardiomyopathy, diabetic cardiomyopathy) can mimic non-segregation (i.e., lack of segregation) among affected individuals. 
+
+
+Families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent ‘non-segregation’.
+
+
+
+
+Because of these possibilities, 
+multiple (≥2) non-segregations
+ that are highly unlikely to be phenocopies or due to alternate variants (e.g., those without a possible alternate cause) 
+are required to apply this rule
+.  A higher number of non-segregations is necessary for instances where alternative causes are possible (e.g., non-segregation in a sibling with childhood onset cardiomyopathy versus a grandparent with hypertension and HCM).
+
+
+Careful consideration of the above points is required when using this data as conflicting evidence, especially when overall evidence supports likely pathogenic or pathogenic.",Disease-specific
+MYH7 (HGNC:7577),BP1,Original ACMG Summary,Missense variant in a gene for which primarily truncating variants are known to cause disease.,NA
+MYH7 (HGNC:7577),BP2,Original ACMG Summary,Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder or observed in cis with a pathogenic variant in any inheritance pattern.,
+MYH7 (HGNC:7577),BP2,Supporting,"Other variants must be pathogenic as defined by these specifications.
+
+
+Testing of parents or other informative relatives is often required to determine 
+cis
+/
+trans
+ status.
+
+
+If a variant is seen in 
+trans
+ (or as double heterozygous) with another pathogenic variant in ≥2 cases and the phenotype is not more severe than when either of the two variants are seen in isolation, this rule may be applied (i.e., high confidence this variant is NOT contributing to disease).
+
+
+
+
+<1% of cases of HCM have >1 pathogenic or likely pathogenic variant (0.6%; Alfares 
+et al.
+ 2015
+17
+).
+
+
+
+
+This rule cannot be applied when the variant has only been observed in 
+cis
+ with a pathogenic variant as its significance in isolation is unknown in this scenario. 
+
+
+Caution is needed if using this criterion as a primary piece of evidence for classifying a variant as likely benign/benign (i.e., only 2 SUPPORTING criteria are sufficient for a likely benign classification).","Disease-specific,Gene-specific"
+MYH7 (HGNC:7577),BP3,Original ACMG Summary,In frame-deletions/insertions in a repetitive region without a known function.,NA
+MYH7 (HGNC:7577),BP4,Original ACMG Summary,"Multiple lines of computational evidence suggest no impact on gene or gene product (conservation, evolutionary, splicing impact, etc)
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm cannot be counted as an independent criterion. BP4 can be used only once in any evaluation of a variant.",
+MYH7 (HGNC:7577),BP4,Supporting,"As many 
+in silico
+ algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. Meta-predictors, such as REVEL, are preferred over multiple individual predictors.
+
+
+Use of REVEL (Ioannidis et al. 2016
+14
+) is recommended at thresholds of 
+≤0.40 for BP4
+.
+
+
+Clinical judgment is needed if any individual algorithms or conservation data are contradictory to REVEL data.
+
+
+Positive predictive value for benign/no impact predictions is generally higher than for pathogenic/impact predictions.
+
+
+SpliceAI
+13
+ is recommended for evaluation of predicted splice impacts.",No change
+MYH7 (HGNC:7577),BP5,Original ACMG Summary,Variant found in a case with an alternate molecular basis for disease.,NA
+MYH7 (HGNC:7577),BP6,Original ACMG Summary,"Reputable source recently reports variant as benign, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+MYH7 (HGNC:7577),BP7,Original ACMG Summary,A synonymous variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site AND the nucleotide is not highly conserved.,
+MYH7 (HGNC:7577),BP7,Supporting,"Also applicable to 
+intronic variants outside the splice consensus sequence (-4 and +7 outward)
+ for which splicing prediction algorithms predict no impact to the splice consensus sequence NOR the creation of a new splice site AND the nucleotide is not highly conserved.
+
+
+Rule can be combined with BP4 to make a variant likely benign per Richards 
+et al.
+ 2015
+1
+.",General recommendation

VCI/parsing_csr_criteria/version_csv_individual/ClinGenCardiomyopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforMYL2Version1.0.0_version=1.0.0.csv

@@ -0,0 +1,882 @@
+Gene,Code,Strength,Description,Modification Type
+MYL2 (HGNC:7583),PVS1,Original ACMG Summary,"Null variant (nonsense, frameshift, canonical +/−1 or 2 splice sites, initiation codon, single or multi-exon deletion) in a gene where loss of function (LOF) is a known mechanism of disease.
+Caveats:
+ • Beware of genes where LOF is not a known disease mechanism (e.g. GFAP, MYH7).
+ • Use caution interpreting LOF variants at the extreme 3’ end of a gene.
+ • Use caution with splice variants that are predicted to lead to exon skipping but leave the remainder of the protein intact.
+ • Use caution in the presence of multiple transcripts.",NA
+MYL2 (HGNC:7583),PS1,Original ACMG Summary,"Same amino acid change as a previously established pathogenic variant regardless of nucleotide change.
+Example: Val->Leu caused by either G>C or G>T in the same codon.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+MYL2 (HGNC:7583),PS1,Strong,"No cardiomyopathy specifications. Apply as outlined by Richards 
+et al
+. 2015
+1
+.
+
+
+Example of when rule should NOT be applied. NM_000256.3(
+MYBPC3
+): c.2308G>A (p.Asp770Asn) has an established impact on splicing leading to nonsense mediated decay (NMD) and should not be used to provide evidence for other variants observed to result in the same amino acid change.",No change
+MYL2 (HGNC:7583),PS2,Original ACMG Summary,"De novo (both maternity and paternity confirmed) in a patient with the disease and no family history.
+Note: Confirmation of paternity only is insufficient. Egg donation, surrogate motherhood, errors in embryo transfer, etc. can contribute to non-maternity.",
+MYL2 (HGNC:7583),PS2,Strong,"Refer to SVI guidance on number/combination of cases required based on phenotype specificity
+2
+.
+
+
+For most cardiomyopathies, it is recommended to default to 
+Phenotype consistency: “Phenotype consistent with gene but not highly specific”
+. Clinical judgment is required for shifting to a higher or lower category. 
+
+
+For use as a STRONG or VERY STRONG criterion, ideally parents have been thoroughly clinically evaluated without evidence of cardiomyopathy (ideally using a combination of ECG and echocardiogram or cardiac MRI for maximum sensitivity).
+
+
+A family history consistent with 
+de novo
+ inheritance should not have any clinical signs or symptoms suggestive of cardiomyopathy in a 1
+st
+ or 2
+nd
+ degree relative, for example: 
+
+
+
+
+Sudden death under 60 years of age
+
+
+Heart transplant
+
+
+Implantable cardiac defibrillator (ICD) under 60 years of age
+
+
+Features of cardiomyopathy (e.g., systolic dysfunction, hypertrophy, left ventricular enlargement in an individual without risk factors).
+
+
+Other related/overlapping cardiomyopathies
+
+
+
+
+Examples of non-suspicious family history may include non-specific clinical features (e.g., palpitations, syncope, borderline/inconclusive echocardiogram findings, heart attack if age appropriate and suspected to result from coronary artery disease), but every attempt should be made to clarify features. 
+
+
+Generally, this criterion is only applicable in the ABSENCE of any other possible disease-causing variants.  If other pathogenic or likely pathogenic variants are present, consider decreasing points assigned or overall weight.",Disease-specific
+MYL2 (HGNC:7583),PS3,Original ACMG Summary,"Well-established in vitro or in vivo functional studies supportive of a damaging effect on the gene or gene product.
+Note: Functional studies that have been validated and shown to be reproducible and robust in a clinical diagnostic laboratory setting are considered the most well-established.",
+MYL2 (HGNC:7583),PS3,Strong,"In vitro splicing assays (e.g., RNA studies)
+
+
+In vitro
+ splicing assays may be considered as 
+STRONG
+ evidence, providing the following criteria are met.
+
+
+
+
+Prior knowledge of predominant transcripts in cardiac tissue
+
+
+
+
+Analysis undertaken using RNA extracted from cardiac tissue from the individual with the variant
+
+
+Analysis undertaken using RNA extracted from whole blood providing the relevant transcripts (isoforms) are expressed in blood and are at sufficient levels to assess splice disruption.
+
+
+Assay shows a clear, reproducible and convincing effect on splicing (i.e. a distinct splice product, present at a level comparable to the splice product from the wild-type allele), which is not observed in controls
+
+
+
+
+Confirmation of abnormal splice product by Sanger sequencing
+
+
+
+
+NOTE:
+ Mini-gene assay in non-patient derived cell lines are NOT considered to provide STRONG evidence.
+
+
+NOTE:
+  Whether to activate this rule needs to be reconciled with the variant spectrum and disease mechanism for the gene at hand (i.e., consider whether the effect is likely to lead to LOF or an in-frame alteration and whether this type of effect is expected to be disease causing) (Abou Tayoun 
+et al.
+ 2018
+3
+).",Disease-specific
+MYL2 (HGNC:7583),PS3,Moderate,"In vivo models (e.g., variant knock-in animal models)
+
+
+Mammalian variant-specific knock-in animal models that produce a phenotype consistent with the clinical phenotype in humans (e.g., structural and/or functional cardiac abnormalities, premature death, arrhythmia) may be considered as 
+MODERATE
+ evidence
+
+
+NOTE:
+ The following assays/models do NOT meet criteria
+
+
+
+
+Assays that are known to be associated with non-specific cardiac phenotypes (e.g., morpholino-induced pericardial edema in zebrafish)
+
+
+In vivo evidence that is not variant specific, such as whole gene alterations (i.e., cDNA or whole gene transgenic mice and whole or partial gene knock-out mice)",Disease-specific
+MYL2 (HGNC:7583),PS3,Supporting,"In vitro
+ 
+assays (e.g., biochemical assays of myofilament function, motility assays, human iPSC-CM)
+
+
+While some 
+in vitro
+ assays may provide evidence that a variant in a cardiomyopathy gene has an effect on protein and/or myofilament function, at present, there are no validated “gold-standard” assays that are considered to reliably predict the clinical phenotype.
+
+
+As such, in the cardiomyopathy genes listed in these guidelines, data from individual 
+in vitro
+ studies are unlikely to meet the criteria required to assign this rule at more than SUPPORTING level.",Disease-specific
+MYL2 (HGNC:7583),PS4,Original ACMG Summary,"The prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls.
+Note 1: Relative risk (RR) or odds ratio (OR), as obtained from case-control studies, is >5.0 and the confidence interval around the estimate of RR or OR does not include 1.0. See manuscript for detailed guidance.
+Note 2: In instances of very rare variants where case-control studies may not reach statistical significance, the prior observation of the variant in multiple unrelated patients with the same phenotype, and its absence in controls, may be used as moderate level of evidence.",
+MYL2 (HGNC:7583),PS4,Strong,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+5
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+STRONG
+ evidence requires the lower bound of the 95% confidence interval (CI) around the odds ratio (OR) estimate to be 
+≥20
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+6
+; Oechslin 
+et al.
+ 2017
+7
+; Hershberger 
+et al.
+ 2017
+8
+; Ross 
+et al.
+ 2020
+9
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+MYL2 (HGNC:7583),PS4,Moderate,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+5
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+MODERATE
+ evidence requires the lower bound of the 95% CI around the OR to be 
+≥10
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+6
+; Oechslin 
+et al.
+ 2017
+7
+; Hershberger 
+et al.
+ 2017
+8
+; Ross 
+et al.
+ 2020
+9
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+MYL2 (HGNC:7583),PS4,Supporting,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+5
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+SUPPORTING
+ evidence requires the lower bound of the 95% CI around the OR to be 
+≥5
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+6
+; Oechslin 
+et al.
+ 2017
+7
+; Hershberger 
+et al.
+ 2017
+8
+; Ross 
+et al.
+ 2020
+9
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+MYL2 (HGNC:7583),PM1,Original ACMG Summary,Located in a mutational hot spot and/or critical and well-established functional domain (e.g. active site of an enzyme) without benign variation.,NA
+MYL2 (HGNC:7583),PM2,Original ACMG Summary,"Absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.
+Caveat: Population data for indels may be poorly called by next generation sequencing.",
+MYL2 (HGNC:7583),PM2,Supporting,"The values used to calculate the PM2 thresholds were derived from studies in Northern European populations that have been relatively well-characterized with regards to disease prevalence and variant spectrum. These thresholds can be applied to any population where disease prevalence is considered comparable (1/500 or lower), where the most frequent pathogenic variant accounts for no more than 2% of cases (e.g., has an allele frequency of ≤0.02 in cases based on the upper bound of 95% CI), and where the penetrance of a pathogenic variant is expected to be at least 50% (Kelly 
+et al.
+ 2018
+10
+).
+
+
+A threshold of 
+≤0.00004
+ in the subpopulation with the highest frequency when using the upper bound of the 95% CI activates this rule.
+
+
+
+
+Alternatively, this is equivalent to the variant NOT being observed more than once (≤1 allele) in gnomAD v.2.1.1 in one of the non-founder populations (e.g., absence required from the Other and Ashkenazi Jewish subpopulations).
+
+
+Applying a threshold of ≤0.00004 (upper bound of 95% CI of the allele frequency in gnomAD) is equivalent to the variant being seen in a single subpopulation and that subpopulation meets any of the following:
+
+
+Allele Count (AC) in Allele Number (AN)
+
+
+≤1 in ≥120,000
+
+
+≤2 in ≥160,000
+
+
+≤3 in ≥195,000
+
+
+≤4 in ≥230,000
+
+
+
+
+
+
+
+
+gnomAD is the preferred database for this calculation, but currently only displays the filtering allele frequency (FAF), which is equivalent to a lower bound estimate of the 95% CI, when the upper bound is what is needed.
+
+
+
+
+Confidence interval tools, such as 
+Confit-de-MAF
+, can be used to determine the upper bound of the 95% CI of the observed allele frequency.
+
+
+
+
+Due to current technical limitations of next generation sequencing technologies, minor allele frequencies for complex variants (e.g., large indels) may not be accurately represented in population databases.
+
+
+Caution should be used when a variant is only identified, or over-represented, in one of the smaller gnomAD populations, as the gnomAD allele frequencies may not accurately represent the true population frequency.
+
+
+Population databases may contain affected or pre-symptomatic individuals for diseases with reduced penetrance/variable onset.",Disease-specific
+MYL2 (HGNC:7583),PM3,Original ACMG Summary,"For recessive disorders, detected in trans with a pathogenic variant
+Note: This requires testing of parents (or offspring) to determine phase.",NA
+MYL2 (HGNC:7583),PM4,Original ACMG Summary,Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants.,
+MYL2 (HGNC:7583),PM4,Moderate,"Strength of rule should be carefully considered and may require downgrading to SUPPORTING based on the predicted impact of the variant, including the size of the deletion/insertion, its location, and conservation of the region. 
+
+
+For genes where PVS1 is not applicable (i.e., where there is no evidence that pLOF variants cause disease), consider using this rule at MODERATE or SUPPORTING strength for truncating variants that do NOT undergo nonsense mediated decay (NMD).",General recommendation
+MYL2 (HGNC:7583),PM5,Original ACMG Summary,"Novel missense change at an amino acid residue where a different missense change determined to be pathogenic has been seen before.
+Example: Arg156His is pathogenic; now you observe Arg156Cys.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+MYL2 (HGNC:7583),PM5,Moderate,"This criterion can be used at MODERATE if a different missense variant at the same codon has been classified as 
+pathogenic
+ using these modified guidelines without application of PM5.
+
+
+The impact of the amino acid change being evaluated needs to be compared to the impact of the amino acid change that is established as pathogenic (e.g., a change of Ala to His is less severe than Ala to Cys change). Consider reducing the strength of this rule to SUPPORTING if the predicted impact is not expected to be equivalent or more severe.
+
+
+PM5 should not be combined with PM1.  If both are applicable at MODERATE weight, use of PM5 is most appropriate since it is variant specific.",General recommendation
+MYL2 (HGNC:7583),PM5,Supporting,"This criterion can be considered at SUPPORTING if a different missense variant at the same codon has been classified as 
+likely pathogenic
+ using these modified guidelines without application of PM5.
+
+
+The impact of the amino acid change being evaluated needs to be compared to the impact of the amino acid change that is established as likely pathogenic (e.g., a change of Ala to His is less severe than Ala to Cys change). Consider reducing the strength of this rule to NOT APPLICABLE if the predicted impact is not expected to be equivalent or more severe.
+
+
+PM5 should not be combined with PM1.  The one with the higher strength should be applied, but if both are applicable at SUPPORTING weight, use of PM5 is most appropriate since it is variant specific.",General recommendation
+MYL2 (HGNC:7583),PM6,Original ACMG Summary,"Assumed de novo, but without confirmation of paternity and maternity.",
+MYL2 (HGNC:7583),PM6,Moderate,"Refer to SVI guidance on number/combination of cases required based on phenotype specificity
+2
+.
+
+
+For most cardiomyopathies, it is recommended to default to “phenotype consistent with gene but not highly specific”. Clinical judgment is required for shifting to a higher or lower phenotypic consistency. 
+
+
+See PS2 for additional considerations.",Disease-specific
+MYL2 (HGNC:7583),PP1,Original ACMG Summary,"Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease.
+Note: May be used as stronger evidence with increasing segregation data.",
+MYL2 (HGNC:7583),PP1,Strong,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥7
+ 
+segregations
+ (LOD score of 2.1) for 
+STRONG
+.
+
+
+Although rare for inherited cardiomyopathies, when the phenotype/presentation of a variant within and across families is highly specific (e.g., early-onset severe RCM in all affected individuals), the following thresholds as proposed by Jarvik and Browning (2016)
+11
+ can be considered: 
+
+
+
+
+STRONG evidence requires ≥5 segregations (LOD score of 1.5)
+
+
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1.
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+MYL2 (HGNC:7583),PP1,Moderate,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥5
+ 
+segregations
+ (LOD score of 1.5) for 
+MODERATE
+.
+
+
+Although rare for inherited cardiomyopathies, when the phenotype/presentation of a variant within and across families is highly specific (e.g., early-onset severe RCM in all affected individuals), the following thresholds as proposed by Jarvik and Browning (2016)
+11
+ can be considered: 
+
+
+
+
+MODERATE evidence requires ≥4 segregations (LOD score of 1.2)
+
+
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1 (see below).
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+MYL2 (HGNC:7583),PP1,Supporting,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥3
+ 
+segregations
+ (LOD score of 0.9) for 
+SUPPORTING
+. The thresholds as proposed by Jarvik and Browning (2016)
+11
+ are the same at ≥3 segregations (LOD score of 0.9) for supporting.
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1 (see below).
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+MYL2 (HGNC:7583),PP2,Original ACMG Summary,Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease.,NA
+MYL2 (HGNC:7583),PP3,Original ACMG Summary,"Multiple lines of computational evidence support a deleterious effect on the gene or gene product (conservation, evolutionary, splicing impact, etc.).
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. PP3 can be used only once in any evaluation of a variant.",
+MYL2 (HGNC:7583),PP3,Supporting,"As many 
+in silico
+ algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. Meta-predictors, such as REVEL, are preferred over multiple individual predictors.
+
+
+Use of REVEL (Ioannidis 
+et al.
+ 2016
+12
+) is recommended at thresholds of 
+≥0.70 for PP3
+.
+
+
+Clinical judgment is needed if any individual algorithms or conservation data are contradictory to REVEL data.
+
+
+Positive predictive value for benign/no impact predictions is generally higher than for pathogenic/impact predictions.
+
+
+SpliceAI
+13
+ is recommended for evaluation of predicted splice impacts.",Disease-specific
+MYL2 (HGNC:7583),PP4,Original ACMG Summary,Patient’s phenotype or family history is highly specific for a disease with a single genetic etiology.,NA
+MYL2 (HGNC:7583),PP5,Original ACMG Summary,"Reputable source recently reports variant as pathogenic, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+MYL2 (HGNC:7583),BA1,Original ACMG Summary,"Allele frequency is above 5% in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.",
+MYL2 (HGNC:7583),BA1,Stand Alone,"Allele frequency is 
+≥0.001
+ based on the 
+filtering allele frequency (FAF)
+ in 
+gnomAD
+ in the subpopulation with the highest frequency (popmax).
+
+
+The values used to calculate the BA1 threshold were derived from studies in Northern European populations that have been relatively well-characterized with regards to disease prevalence and variant spectrum. These thresholds can be applied to any population where disease prevalence is considered comparable (1/300 or lower).
+
+
+The threshold is applicable when assessing variants in the context of autosomal dominant cardiomyopathy. 
+
+
+
+
+Caution should be applied when assessing variants in the 
+MYL2
+ and 
+MYL3
+ genes, as homozygous or compound heterozygous variants have been reported to cause a recessive HCM and heterozygous individuals show no sign of disease.
+
+
+
+
+gnomAD is the preferred database for this calculation. If a subpopulation specific FAF other than the popmax is needed, this value can be calculated using the AlleleFrequencyApp on the 
+CardioDB website
+.
+
+
+
+
+Using the Inverse AF tab, enter in the population size and the number of alleles identified and it will calculate the FAF.  
+
+
+Set confidence to 0.95 (95%).
+
+
+If the FAF is ≥0.001, this rule can be applied.
+
+
+
+
+The FAF by platform (e.g., exome vs. genome; v.2.1.1 vs. v.3.1.1) should be considered, the larger population is most likely to have the most accurate representation of “true” population allele frequency.
+
+
+Caution is needed when considering any population cohorts that are smaller than the smallest subpopulations within gnomAD v.2.1.1 (e.g., ~5000 individuals or ~10,000 alleles). Despite this conservative nature of this threshold and approach, in smaller cohorts, the observed allele frequency may less accurately reflect the true allele frequency. Traditionally, once a variant is classified as Benign, it is rarely re-evaluated and so the highest confidence is needed to establish that classification on an allele frequency alone.",Disease-specific
+MYL2 (HGNC:7583),BS1,Original ACMG Summary,Allele frequency is greater than expected for disorder.,
+MYL2 (HGNC:7583),BS1,Strong,"Allele frequency is 
+≥0.0001 for
+ 
+MYL2
+ based on the 
+filtering allele frequency (FAF)
+ in 
+gnomAD
+ in the subpopulation with the highest frequency (popmax).
+
+
+Criterion BS1 may only be used as standalone evidence to classify a variant as Likely Benign in the absence of conflicting data. See SVI guidance (Tavtigian 
+et al.
+ 2018
+14
+; Tavtigian 
+et al.
+ 2020
+15
+). 
+
+
+See BA1 for additional specifications that also apply to BS1.","Disease-specific,Gene-specific"
+MYL2 (HGNC:7583),BS2,Original ACMG Summary,"Observed in a healthy adult individual for a recessive (homozygous), dominant (heterozygous), or X-linked (hemizygous) disorder, with full penetrance expected at an early age.",NA
+MYL2 (HGNC:7583),BS3,Original ACMG Summary,Well-established in vitro or in vivo functional studies show no damaging effect on protein function or splicing.,
+MYL2 (HGNC:7583),BS3,Strong,See PS3 specifications.,Disease-specific
+MYL2 (HGNC:7583),BS3,Moderate,See PS3 specifications.,Disease-specific
+MYL2 (HGNC:7583),BS3,Supporting,See PS3 specifications.,Disease-specific
+MYL2 (HGNC:7583),BS4,Original ACMG Summary,"Lack of segregation in affected members of a family.
+Caveat: The presence of phenocopies for common phenotypes (i.e. cancer, epilepsy) can mimic lack of segregation among affected individuals. Also, families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent lack of segregation.",
+MYL2 (HGNC:7583),BS4,Strong,"Any non-segregations should be carefully evaluated to rule out a phenocopy or the presence of a second disease-causing variant before considering it as conflicting or benign evidence. 
+
+
+
+
+The presence of “phenocopies” (e.g., athlete’s heart, hypertensive heart disease, ischemic cardiomyopathy, alcoholic cardiomyopathy, diabetic cardiomyopathy) can mimic non-segregation (i.e., lack of segregation) among affected individuals. 
+
+
+Families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent ‘non-segregation’.
+
+
+
+
+Because of these possibilities, 
+multiple (≥2) non-segregations
+ that are highly unlikely to be phenocopies or due to alternate variants (e.g., those without a possible alternate cause) 
+are required to apply this rule
+.  A higher number of non-segregations is necessary for instances where alternative causes are possible (e.g., non-segregation in a sibling with childhood onset cardiomyopathy versus a grandparent with hypertension and HCM).
+
+
+Careful consideration of the above points is required when using this data as conflicting evidence, especially when overall evidence supports likely pathogenic or pathogenic.",Disease-specific
+MYL2 (HGNC:7583),BP1,Original ACMG Summary,Missense variant in a gene for which primarily truncating variants are known to cause disease.,NA
+MYL2 (HGNC:7583),BP2,Original ACMG Summary,Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder or observed in cis with a pathogenic variant in any inheritance pattern.,
+MYL2 (HGNC:7583),BP2,Supporting,"Other variants must be pathogenic as defined by these specifications.
+
+
+Testing of parents or other informative relatives is often required to determine 
+cis
+/
+trans
+ status.
+
+
+If a variant is seen in 
+trans
+ (or as double heterozygous) with another pathogenic variant in ≥2 cases and the phenotype is not more severe than when either of the two variants are seen in isolation, this rule may be applied (i.e., high confidence this variant is NOT contributing to disease).
+
+
+
+
+<1% of cases of HCM have >1 pathogenic or likely pathogenic variant (0.6%; Alfares 
+et al.
+ 2015
+16
+).
+
+
+
+
+This rule cannot be applied when the variant has only been observed in 
+cis
+ with a pathogenic variant as its significance in isolation is unknown in this scenario. 
+
+
+Caution is needed if using this criterion as a primary piece of evidence for classifying a variant as likely benign/benign (i.e., only 2 SUPPORTING criteria are sufficient for a likely benign classification).",Disease-specific
+MYL2 (HGNC:7583),BP3,Original ACMG Summary,In frame-deletions/insertions in a repetitive region without a known function.,NA
+MYL2 (HGNC:7583),BP4,Original ACMG Summary,"Multiple lines of computational evidence suggest no impact on gene or gene product (conservation, evolutionary, splicing impact, etc)
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm cannot be counted as an independent criterion. BP4 can be used only once in any evaluation of a variant.",
+MYL2 (HGNC:7583),BP4,Supporting,"As many 
+in silico
+ algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. Meta-predictors, such as REVEL, are preferred over multiple individual predictors.
+
+
+Use of REVEL (Ioannidis et al. 2016
+12
+) is recommended at thresholds of 
+≤0.40 for BP4
+.
+
+
+Clinical judgment is needed if any individual algorithms or conservation data are contradictory to REVEL data.
+
+
+Positive predictive value for benign/no impact predictions is generally higher than for pathogenic/impact predictions.
+
+
+SpliceAI
+13
+ is recommended for evaluation of predicted splice impacts.",Disease-specific
+MYL2 (HGNC:7583),BP5,Original ACMG Summary,Variant found in a case with an alternate molecular basis for disease.,NA
+MYL2 (HGNC:7583),BP6,Original ACMG Summary,"Reputable source recently reports variant as benign, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+MYL2 (HGNC:7583),BP7,Original ACMG Summary,A synonymous variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site AND the nucleotide is not highly conserved.,
+MYL2 (HGNC:7583),BP7,Supporting,"Also applicable to 
+intronic variants outside the splice consensus sequence (-4 and +7 outward)
+ for which splicing prediction algorithms predict no impact to the splice consensus sequence NOR the creation of a new splice site AND the nucleotide is not highly conserved.
+
+
+Rule can be combined with BP4 to make a variant likely benign per Richards 
+et al.
+ 2015
+1
+.",General recommendation

VCI/parsing_csr_criteria/version_csv_individual/ClinGenCardiomyopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforMYL3Version1.0.0_version=1.0.0.csv

@@ -0,0 +1,882 @@
+Gene,Code,Strength,Description,Modification Type
+MYL3 (HGNC:7584),PVS1,Original ACMG Summary,"Null variant (nonsense, frameshift, canonical +/−1 or 2 splice sites, initiation codon, single or multi-exon deletion) in a gene where loss of function (LOF) is a known mechanism of disease.
+Caveats:
+ • Beware of genes where LOF is not a known disease mechanism (e.g. GFAP, MYH7).
+ • Use caution interpreting LOF variants at the extreme 3’ end of a gene.
+ • Use caution with splice variants that are predicted to lead to exon skipping but leave the remainder of the protein intact.
+ • Use caution in the presence of multiple transcripts.",NA
+MYL3 (HGNC:7584),PS1,Original ACMG Summary,"Same amino acid change as a previously established pathogenic variant regardless of nucleotide change.
+Example: Val->Leu caused by either G>C or G>T in the same codon.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+MYL3 (HGNC:7584),PS1,Strong,"No cardiomyopathy specifications. Apply as outlined by Richards 
+et al
+. 2015
+1
+.
+
+
+Example of when rule should NOT be applied. NM_000256.3(
+MYBPC3
+): c.2308G>A (p.Asp770Asn) has an established impact on splicing leading to nonsense mediated decay (NMD) and should not be used to provide evidence for other variants observed to result in the same amino acid change.",No change
+MYL3 (HGNC:7584),PS2,Original ACMG Summary,"De novo (both maternity and paternity confirmed) in a patient with the disease and no family history.
+Note: Confirmation of paternity only is insufficient. Egg donation, surrogate motherhood, errors in embryo transfer, etc. can contribute to non-maternity.",
+MYL3 (HGNC:7584),PS2,Strong,"Refer to SVI guidance on number/combination of cases required based on phenotype specificity
+2
+.
+
+
+For most cardiomyopathies, it is recommended to default to 
+Phenotype consistency: “Phenotype consistent with gene but not highly specific”
+. Clinical judgment is required for shifting to a higher or lower category. 
+
+
+For use as a STRONG or VERY STRONG criterion, ideally parents have been thoroughly clinically evaluated without evidence of cardiomyopathy (ideally using a combination of ECG and echocardiogram or cardiac MRI for maximum sensitivity).
+
+
+A family history consistent with 
+de novo
+ inheritance should not have any clinical signs or symptoms suggestive of cardiomyopathy in a 1
+st
+ or 2
+nd
+ degree relative, for example: 
+
+
+
+
+Sudden death under 60 years of age
+
+
+Heart transplant
+
+
+Implantable cardiac defibrillator (ICD) under 60 years of age
+
+
+Features of cardiomyopathy (e.g., systolic dysfunction, hypertrophy, left ventricular enlargement in an individual without risk factors).
+
+
+Other related/overlapping cardiomyopathies
+
+
+
+
+Examples of non-suspicious family history may include non-specific clinical features (e.g., palpitations, syncope, borderline/inconclusive echocardiogram findings, heart attack if age appropriate and suspected to result from coronary artery disease), but every attempt should be made to clarify features. 
+
+
+Generally, this criterion is only applicable in the ABSENCE of any other possible disease-causing variants.  If other pathogenic or likely pathogenic variants are present, consider decreasing points assigned or overall weight.",Disease-specific
+MYL3 (HGNC:7584),PS3,Original ACMG Summary,"Well-established in vitro or in vivo functional studies supportive of a damaging effect on the gene or gene product.
+Note: Functional studies that have been validated and shown to be reproducible and robust in a clinical diagnostic laboratory setting are considered the most well-established.",
+MYL3 (HGNC:7584),PS3,Strong,"In vitro splicing assays (e.g., RNA studies)
+
+
+In vitro
+ splicing assays may be considered as 
+STRONG
+ evidence, providing the following criteria are met.
+
+
+
+
+Prior knowledge of predominant transcripts in cardiac tissue
+
+
+
+
+Analysis undertaken using RNA extracted from cardiac tissue from the individual with the variant
+
+
+Analysis undertaken using RNA extracted from whole blood providing the relevant transcripts (isoforms) are expressed in blood and are at sufficient levels to assess splice disruption.
+
+
+Assay shows a clear, reproducible and convincing effect on splicing (i.e. a distinct splice product, present at a level comparable to the splice product from the wild-type allele), which is not observed in controls
+
+
+
+
+Confirmation of abnormal splice product by Sanger sequencing
+
+
+
+
+NOTE:
+ Mini-gene assay in non-patient derived cell lines are NOT considered to provide STRONG evidence.
+
+
+NOTE:
+  Whether to activate this rule needs to be reconciled with the variant spectrum and disease mechanism for the gene at hand (i.e., consider whether the effect is likely to lead to LOF or an in-frame alteration and whether this type of effect is expected to be disease causing) (Abou Tayoun 
+et al.
+ 2018
+3
+).",Disease-specific
+MYL3 (HGNC:7584),PS3,Moderate,"In vivo models (e.g., variant knock-in animal models)
+
+
+Mammalian variant-specific knock-in animal models that produce a phenotype consistent with the clinical phenotype in humans (e.g., structural and/or functional cardiac abnormalities, premature death, arrhythmia) may be considered as 
+MODERATE
+ evidence
+
+
+NOTE:
+ The following assays/models do NOT meet criteria
+
+
+
+
+Assays that are known to be associated with non-specific cardiac phenotypes (e.g., morpholino-induced pericardial edema in zebrafish)
+
+
+In vivo evidence that is not variant specific, such as whole gene alterations (i.e., cDNA or whole gene transgenic mice and whole or partial gene knock-out mice)",Disease-specific
+MYL3 (HGNC:7584),PS3,Supporting,"In vitro
+ 
+assays (e.g., biochemical assays of myofilament function, motility assays, human iPSC-CM)
+
+
+While some 
+in vitro
+ assays may provide evidence that a variant in a cardiomyopathy gene has an effect on protein and/or myofilament function, at present, there are no validated “gold-standard” assays that are considered to reliably predict the clinical phenotype.
+
+
+As such, in the cardiomyopathy genes listed in these guidelines, data from individual 
+in vitro
+ studies are unlikely to meet the criteria required to assign this rule at more than SUPPORTING level.",Disease-specific
+MYL3 (HGNC:7584),PS4,Original ACMG Summary,"The prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls.
+Note 1: Relative risk (RR) or odds ratio (OR), as obtained from case-control studies, is >5.0 and the confidence interval around the estimate of RR or OR does not include 1.0. See manuscript for detailed guidance.
+Note 2: In instances of very rare variants where case-control studies may not reach statistical significance, the prior observation of the variant in multiple unrelated patients with the same phenotype, and its absence in controls, may be used as moderate level of evidence.",
+MYL3 (HGNC:7584),PS4,Strong,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+5
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+STRONG
+ evidence requires the lower bound of the 95% confidence interval (CI) around the odds ratio (OR) estimate to be 
+≥20
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+6
+; Oechslin 
+et al.
+ 2017
+7
+; Hershberger 
+et al.
+ 2017
+8
+; Ross 
+et al.
+ 2020
+9
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+MYL3 (HGNC:7584),PS4,Moderate,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+5
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+MODERATE
+ evidence requires the lower bound of the 95% CI around the OR to be 
+≥10
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+6
+; Oechslin 
+et al.
+ 2017
+7
+; Hershberger 
+et al.
+ 2017
+8
+; Ross 
+et al.
+ 2020
+9
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+MYL3 (HGNC:7584),PS4,Supporting,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+5
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+SUPPORTING
+ evidence requires the lower bound of the 95% CI around the OR to be 
+≥5
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+6
+; Oechslin 
+et al.
+ 2017
+7
+; Hershberger 
+et al.
+ 2017
+8
+; Ross 
+et al.
+ 2020
+9
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+MYL3 (HGNC:7584),PM1,Original ACMG Summary,Located in a mutational hot spot and/or critical and well-established functional domain (e.g. active site of an enzyme) without benign variation.,NA
+MYL3 (HGNC:7584),PM2,Original ACMG Summary,"Absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.
+Caveat: Population data for indels may be poorly called by next generation sequencing.",
+MYL3 (HGNC:7584),PM2,Supporting,"The values used to calculate the PM2 thresholds were derived from studies in Northern European populations that have been relatively well-characterized with regards to disease prevalence and variant spectrum. These thresholds can be applied to any population where disease prevalence is considered comparable (1/500 or lower), where the most frequent pathogenic variant accounts for no more than 2% of cases (e.g., has an allele frequency of ≤0.02 in cases based on the upper bound of 95% CI), and where the penetrance of a pathogenic variant is expected to be at least 50% (Kelly 
+et al.
+ 2018
+10
+).
+
+
+A threshold of 
+≤0.00004
+ in the subpopulation with the highest frequency when using the upper bound of the 95% CI activates this rule.
+
+
+
+
+Alternatively, this is equivalent to the variant NOT being observed more than once (≤1 allele) in gnomAD v.2.1.1 in one of the non-founder populations (e.g., absence required from the Other and Ashkenazi Jewish subpopulations).
+
+
+Applying a threshold of ≤0.00004 (upper bound of 95% CI of the allele frequency in gnomAD) is equivalent to the variant being seen in a single subpopulation and that subpopulation meets any of the following:
+
+
+Allele Count (AC) in Allele Number (AN)
+
+
+≤1 in ≥120,000
+
+
+≤2 in ≥160,000
+
+
+≤3 in ≥195,000
+
+
+≤4 in ≥230,000
+
+
+
+
+
+
+
+
+gnomAD is the preferred database for this calculation, but currently only displays the filtering allele frequency (FAF), which is equivalent to a lower bound estimate of the 95% CI, when the upper bound is what is needed.
+
+
+
+
+Confidence interval tools, such as 
+Confit-de-MAF
+, can be used to determine the upper bound of the 95% CI of the observed allele frequency.
+
+
+
+
+Due to current technical limitations of next generation sequencing technologies, minor allele frequencies for complex variants (e.g., large indels) may not be accurately represented in population databases.
+
+
+Caution should be used when a variant is only identified, or over-represented, in one of the smaller gnomAD populations, as the gnomAD allele frequencies may not accurately represent the true population frequency.
+
+
+Population databases may contain affected or pre-symptomatic individuals for diseases with reduced penetrance/variable onset.",Disease-specific
+MYL3 (HGNC:7584),PM3,Original ACMG Summary,"For recessive disorders, detected in trans with a pathogenic variant
+Note: This requires testing of parents (or offspring) to determine phase.",NA
+MYL3 (HGNC:7584),PM4,Original ACMG Summary,Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants.,
+MYL3 (HGNC:7584),PM4,Moderate,"Strength of rule should be carefully considered and may require downgrading to SUPPORTING based on the predicted impact of the variant, including the size of the deletion/insertion, its location, and conservation of the region. 
+
+
+For genes where PVS1 is not applicable (i.e., where there is no evidence that pLOF variants cause disease), consider using this rule at MODERATE or SUPPORTING strength for truncating variants that do NOT undergo nonsense mediated decay (NMD).",General recommendation
+MYL3 (HGNC:7584),PM5,Original ACMG Summary,"Novel missense change at an amino acid residue where a different missense change determined to be pathogenic has been seen before.
+Example: Arg156His is pathogenic; now you observe Arg156Cys.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+MYL3 (HGNC:7584),PM5,Moderate,"This criterion can be used at MODERATE if a different missense variant at the same codon has been classified as 
+pathogenic
+ using these modified guidelines without application of PM5.
+
+
+The impact of the amino acid change being evaluated needs to be compared to the impact of the amino acid change that is established as pathogenic (e.g., a change of Ala to His is less severe than Ala to Cys change). Consider reducing the strength of this rule to SUPPORTING if the predicted impact is not expected to be equivalent or more severe.
+
+
+PM5 should not be combined with PM1.  If both are applicable at MODERATE weight, use of PM5 is most appropriate since it is variant specific.",General recommendation
+MYL3 (HGNC:7584),PM5,Supporting,"This criterion can be considered at SUPPORTING if a different missense variant at the same codon has been classified as 
+likely pathogenic
+ using these modified guidelines without application of PM5.
+
+
+The impact of the amino acid change being evaluated needs to be compared to the impact of the amino acid change that is established as likely pathogenic (e.g., a change of Ala to His is less severe than Ala to Cys change). Consider reducing the strength of this rule to NOT APPLICABLE if the predicted impact is not expected to be equivalent or more severe.
+
+
+PM5 should not be combined with PM1.  The one with the higher strength should be applied, but if both are applicable at SUPPORTING weight, use of PM5 is most appropriate since it is variant specific.",General recommendation
+MYL3 (HGNC:7584),PM6,Original ACMG Summary,"Assumed de novo, but without confirmation of paternity and maternity.",
+MYL3 (HGNC:7584),PM6,Moderate,"Refer to SVI guidance on number/combination of cases required based on phenotype specificity
+2
+.
+
+
+For most cardiomyopathies, it is recommended to default to “phenotype consistent with gene but not highly specific”. Clinical judgment is required for shifting to a higher or lower phenotypic consistency. 
+
+
+See PS2 for additional considerations.",Disease-specific
+MYL3 (HGNC:7584),PP1,Original ACMG Summary,"Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease.
+Note: May be used as stronger evidence with increasing segregation data.",
+MYL3 (HGNC:7584),PP1,Strong,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥7
+ 
+segregations
+ (LOD score of 2.1) for 
+STRONG
+.
+
+
+Although rare for inherited cardiomyopathies, when the phenotype/presentation of a variant within and across families is highly specific (e.g., early-onset severe RCM in all affected individuals), the following thresholds as proposed by Jarvik and Browning (2016)
+11
+ can be considered: 
+
+
+
+
+STRONG evidence requires ≥5 segregations (LOD score of 1.5)
+
+
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1.
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+MYL3 (HGNC:7584),PP1,Moderate,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥5
+ 
+segregations
+ (LOD score of 1.5) for 
+MODERATE
+.
+
+
+Although rare for inherited cardiomyopathies, when the phenotype/presentation of a variant within and across families is highly specific (e.g., early-onset severe RCM in all affected individuals), the following thresholds as proposed by Jarvik and Browning (2016)
+11
+ can be considered: 
+
+
+
+
+MODERATE evidence requires ≥4 segregations (LOD score of 1.2)
+
+
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1 (see below).
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+MYL3 (HGNC:7584),PP1,Supporting,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥3
+ 
+segregations
+ (LOD score of 0.9) for 
+SUPPORTING
+. The thresholds as proposed by Jarvik and Browning (2016)
+11
+ are the same at ≥3 segregations (LOD score of 0.9) for supporting.
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1 (see below).
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+MYL3 (HGNC:7584),PP2,Original ACMG Summary,Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease.,NA
+MYL3 (HGNC:7584),PP3,Original ACMG Summary,"Multiple lines of computational evidence support a deleterious effect on the gene or gene product (conservation, evolutionary, splicing impact, etc.).
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. PP3 can be used only once in any evaluation of a variant.",
+MYL3 (HGNC:7584),PP3,Supporting,"As many 
+in silico
+ algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. Meta-predictors, such as REVEL, are preferred over multiple individual predictors.
+
+
+Use of REVEL (Ioannidis 
+et al.
+ 2016
+12
+) is recommended at thresholds of 
+≥0.70 for PP3
+.
+
+
+Clinical judgment is needed if any individual algorithms or conservation data are contradictory to REVEL data.
+
+
+Positive predictive value for benign/no impact predictions is generally higher than for pathogenic/impact predictions.
+
+
+SpliceAI
+13
+ is recommended for evaluation of predicted splice impacts.",Disease-specific
+MYL3 (HGNC:7584),PP4,Original ACMG Summary,Patient’s phenotype or family history is highly specific for a disease with a single genetic etiology.,NA
+MYL3 (HGNC:7584),PP5,Original ACMG Summary,"Reputable source recently reports variant as pathogenic, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+MYL3 (HGNC:7584),BA1,Original ACMG Summary,"Allele frequency is above 5% in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.",
+MYL3 (HGNC:7584),BA1,Stand Alone,"Allele frequency is 
+≥0.001
+ based on the 
+filtering allele frequency (FAF)
+ in 
+gnomAD
+ in the subpopulation with the highest frequency (popmax).
+
+
+The values used to calculate the BA1 threshold were derived from studies in Northern European populations that have been relatively well-characterized with regards to disease prevalence and variant spectrum. These thresholds can be applied to any population where disease prevalence is considered comparable (1/300 or lower).
+
+
+The threshold is applicable when assessing variants in the context of autosomal dominant cardiomyopathy. 
+
+
+
+
+Caution should be applied when assessing variants in the 
+MYL2
+ and 
+MYL3
+ genes, as homozygous or compound heterozygous variants have been reported to cause a recessive HCM and heterozygous individuals show no sign of disease.
+
+
+
+
+gnomAD is the preferred database for this calculation. If a subpopulation specific FAF other than the popmax is needed, this value can be calculated using the AlleleFrequencyApp on the 
+CardioDB website
+.
+
+
+
+
+Using the Inverse AF tab, enter in the population size and the number of alleles identified and it will calculate the FAF.  
+
+
+Set confidence to 0.95 (95%).
+
+
+If the FAF is ≥0.001, this rule can be applied.
+
+
+
+
+The FAF by platform (e.g., exome vs. genome; v.2.1.1 vs. v.3.1.1) should be considered, the larger population is most likely to have the most accurate representation of “true” population allele frequency.
+
+
+Caution is needed when considering any population cohorts that are smaller than the smallest subpopulations within gnomAD v.2.1.1 (e.g., ~5000 individuals or ~10,000 alleles). Despite this conservative nature of this threshold and approach, in smaller cohorts, the observed allele frequency may less accurately reflect the true allele frequency. Traditionally, once a variant is classified as Benign, it is rarely re-evaluated and so the highest confidence is needed to establish that classification on an allele frequency alone.",Disease-specific
+MYL3 (HGNC:7584),BS1,Original ACMG Summary,Allele frequency is greater than expected for disorder.,
+MYL3 (HGNC:7584),BS1,Strong,"Allele frequency is 
+≥0.0001 for
+ 
+MYL3
+ based on the 
+filtering allele frequency (FAF)
+ in 
+gnomAD
+ in the subpopulation with the highest frequency (popmax).
+
+
+Criterion BS1 may only be used as standalone evidence to classify a variant as Likely Benign in the absence of conflicting data. See SVI guidance (Tavtigian 
+et al.
+ 2018
+14
+; Tavtigian 
+et al.
+ 2020
+15
+). 
+
+
+See BA1 for additional specifications that also apply to BS1.","Disease-specific,Gene-specific"
+MYL3 (HGNC:7584),BS2,Original ACMG Summary,"Observed in a healthy adult individual for a recessive (homozygous), dominant (heterozygous), or X-linked (hemizygous) disorder, with full penetrance expected at an early age.",NA
+MYL3 (HGNC:7584),BS3,Original ACMG Summary,Well-established in vitro or in vivo functional studies show no damaging effect on protein function or splicing.,
+MYL3 (HGNC:7584),BS3,Strong,See PS3 specifications.,Disease-specific
+MYL3 (HGNC:7584),BS3,Moderate,See PS3 specifications.,Disease-specific
+MYL3 (HGNC:7584),BS3,Supporting,See PS3 specifications.,Disease-specific
+MYL3 (HGNC:7584),BS4,Original ACMG Summary,"Lack of segregation in affected members of a family.
+Caveat: The presence of phenocopies for common phenotypes (i.e. cancer, epilepsy) can mimic lack of segregation among affected individuals. Also, families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent lack of segregation.",
+MYL3 (HGNC:7584),BS4,Strong,"Any non-segregations should be carefully evaluated to rule out a phenocopy or the presence of a second disease-causing variant before considering it as conflicting or benign evidence. 
+
+
+
+
+The presence of “phenocopies” (e.g., athlete’s heart, hypertensive heart disease, ischemic cardiomyopathy, alcoholic cardiomyopathy, diabetic cardiomyopathy) can mimic non-segregation (i.e., lack of segregation) among affected individuals. 
+
+
+Families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent ‘non-segregation’.
+
+
+
+
+Because of these possibilities, 
+multiple (≥2) non-segregations
+ that are highly unlikely to be phenocopies or due to alternate variants (e.g., those without a possible alternate cause) 
+are required to apply this rule
+.  A higher number of non-segregations is necessary for instances where alternative causes are possible (e.g., non-segregation in a sibling with childhood onset cardiomyopathy versus a grandparent with hypertension and HCM).
+
+
+Careful consideration of the above points is required when using this data as conflicting evidence, especially when overall evidence supports likely pathogenic or pathogenic.",Disease-specific
+MYL3 (HGNC:7584),BP1,Original ACMG Summary,Missense variant in a gene for which primarily truncating variants are known to cause disease.,NA
+MYL3 (HGNC:7584),BP2,Original ACMG Summary,Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder or observed in cis with a pathogenic variant in any inheritance pattern.,
+MYL3 (HGNC:7584),BP2,Supporting,"Other variants must be pathogenic as defined by these specifications.
+
+
+Testing of parents or other informative relatives is often required to determine 
+cis
+/
+trans
+ status.
+
+
+If a variant is seen in 
+trans
+ (or as double heterozygous) with another pathogenic variant in ≥2 cases and the phenotype is not more severe than when either of the two variants are seen in isolation, this rule may be applied (i.e., high confidence this variant is NOT contributing to disease).
+
+
+
+
+<1% of cases of HCM have >1 pathogenic or likely pathogenic variant (0.6%; Alfares 
+et al.
+ 2015
+16
+).
+
+
+
+
+This rule cannot be applied when the variant has only been observed in 
+cis
+ with a pathogenic variant as its significance in isolation is unknown in this scenario. 
+
+
+Caution is needed if using this criterion as a primary piece of evidence for classifying a variant as likely benign/benign (i.e., only 2 SUPPORTING criteria are sufficient for a likely benign classification).",Disease-specific
+MYL3 (HGNC:7584),BP3,Original ACMG Summary,In frame-deletions/insertions in a repetitive region without a known function.,NA
+MYL3 (HGNC:7584),BP4,Original ACMG Summary,"Multiple lines of computational evidence suggest no impact on gene or gene product (conservation, evolutionary, splicing impact, etc)
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm cannot be counted as an independent criterion. BP4 can be used only once in any evaluation of a variant.",
+MYL3 (HGNC:7584),BP4,Supporting,"As many 
+in silico
+ algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. Meta-predictors, such as REVEL, are preferred over multiple individual predictors.
+
+
+Use of REVEL (Ioannidis et al. 2016
+12
+) is recommended at thresholds of 
+≤0.40 for BP4
+.
+
+
+Clinical judgment is needed if any individual algorithms or conservation data are contradictory to REVEL data.
+
+
+Positive predictive value for benign/no impact predictions is generally higher than for pathogenic/impact predictions.
+
+
+SpliceAI
+13
+ is recommended for evaluation of predicted splice impacts.",Disease-specific
+MYL3 (HGNC:7584),BP5,Original ACMG Summary,Variant found in a case with an alternate molecular basis for disease.,NA
+MYL3 (HGNC:7584),BP6,Original ACMG Summary,"Reputable source recently reports variant as benign, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+MYL3 (HGNC:7584),BP7,Original ACMG Summary,A synonymous variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site AND the nucleotide is not highly conserved.,
+MYL3 (HGNC:7584),BP7,Supporting,"Also applicable to 
+intronic variants outside the splice consensus sequence (-4 and +7 outward)
+ for which splicing prediction algorithms predict no impact to the splice consensus sequence NOR the creation of a new splice site AND the nucleotide is not highly conserved.
+
+
+Rule can be combined with BP4 to make a variant likely benign per Richards 
+et al.
+ 2015
+1
+.",General recommendation

VCI/parsing_csr_criteria/version_csv_individual/ClinGenCardiomyopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforTNNI3Version1.0.0_version=1.0.0.csv

@@ -0,0 +1,897 @@
+Gene,Code,Strength,Description,Modification Type
+TNNI3 (HGNC:11947),PVS1,Original ACMG Summary,"Null variant (nonsense, frameshift, canonical +/−1 or 2 splice sites, initiation codon, single or multi-exon deletion) in a gene where loss of function (LOF) is a known mechanism of disease.
+Caveats:
+ • Beware of genes where LOF is not a known disease mechanism (e.g. GFAP, MYH7).
+ • Use caution interpreting LOF variants at the extreme 3’ end of a gene.
+ • Use caution with splice variants that are predicted to lead to exon skipping but leave the remainder of the protein intact.
+ • Use caution in the presence of multiple transcripts.",NA
+TNNI3 (HGNC:11947),PS1,Original ACMG Summary,"Same amino acid change as a previously established pathogenic variant regardless of nucleotide change.
+Example: Val->Leu caused by either G>C or G>T in the same codon.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+TNNI3 (HGNC:11947),PS1,Strong,"No cardiomyopathy specifications. Apply as outlined by Richards 
+et al
+. 2015
+1
+.
+
+
+Example of when rule should NOT be applied. NM_000256.3(
+MYBPC3
+): c.2308G>A (p.Asp770Asn) has an established impact on splicing leading to nonsense mediated decay (NMD) and should not be used to provide evidence for other variants observed to result in the same amino acid change.",No change
+TNNI3 (HGNC:11947),PS2,Original ACMG Summary,"De novo (both maternity and paternity confirmed) in a patient with the disease and no family history.
+Note: Confirmation of paternity only is insufficient. Egg donation, surrogate motherhood, errors in embryo transfer, etc. can contribute to non-maternity.",
+TNNI3 (HGNC:11947),PS2,Strong,"Refer to SVI guidance on number/combination of cases required based on phenotype specificity
+2
+.
+
+
+For most cardiomyopathies, it is recommended to default to 
+Phenotype consistency: “Phenotype consistent with gene but not highly specific”
+. Clinical judgment is required for shifting to a higher or lower category. 
+
+
+For use as a STRONG or VERY STRONG criterion, ideally parents have been thoroughly clinically evaluated without evidence of cardiomyopathy (ideally using a combination of ECG and echocardiogram or cardiac MRI for maximum sensitivity).
+
+
+A family history consistent with 
+de novo
+ inheritance should not have any clinical signs or symptoms suggestive of cardiomyopathy in a 1
+st
+ or 2
+nd
+ degree relative, for example: 
+
+
+
+
+Sudden death under 60 years of age
+
+
+Heart transplant
+
+
+Implantable cardiac defibrillator (ICD) under 60 years of age
+
+
+Features of cardiomyopathy (e.g., systolic dysfunction, hypertrophy, left ventricular enlargement in an individual without risk factors).
+
+
+Other related/overlapping cardiomyopathies
+
+
+
+
+Examples of non-suspicious family history may include non-specific clinical features (e.g., palpitations, syncope, borderline/inconclusive echocardiogram findings, heart attack if age appropriate and suspected to result from coronary artery disease), but every attempt should be made to clarify features. 
+
+
+Generally, this criterion is only applicable in the ABSENCE of any other possible disease-causing variants.  If other pathogenic or likely pathogenic variants are present, consider decreasing points assigned or overall weight.",Disease-specific
+TNNI3 (HGNC:11947),PS3,Original ACMG Summary,"Well-established in vitro or in vivo functional studies supportive of a damaging effect on the gene or gene product.
+Note: Functional studies that have been validated and shown to be reproducible and robust in a clinical diagnostic laboratory setting are considered the most well-established.",
+TNNI3 (HGNC:11947),PS3,Strong,"In vitro splicing assays (e.g., RNA studies)
+
+
+In vitro
+ splicing assays may be considered as 
+STRONG
+ evidence, providing the following criteria are met.
+
+
+
+
+Prior knowledge of predominant transcripts in cardiac tissue
+
+
+
+
+Analysis undertaken using RNA extracted from cardiac tissue from the individual with the variant
+
+
+Analysis undertaken using RNA extracted from whole blood providing the relevant transcripts (isoforms) are expressed in blood and are at sufficient levels to assess splice disruption.
+
+
+Assay shows a clear, reproducible and convincing effect on splicing (i.e. a distinct splice product, present at a level comparable to the splice product from the wild-type allele), which is not observed in controls
+
+
+
+
+Confirmation of abnormal splice product by Sanger sequencing
+
+
+
+
+NOTE:
+ Mini-gene assay in non-patient derived cell lines are NOT considered to provide STRONG evidence.
+
+
+NOTE:
+  Whether to activate this rule needs to be reconciled with the variant spectrum and disease mechanism for the gene at hand (i.e., consider whether the effect is likely to lead to LOF or an in-frame alteration and whether this type of effect is expected to be disease causing) (Abou Tayoun 
+et al.
+ 2018
+3
+).",Disease-specific
+TNNI3 (HGNC:11947),PS3,Moderate,"In vivo models (e.g., variant knock-in animal models)
+
+
+Mammalian variant-specific knock-in animal models that produce a phenotype consistent with the clinical phenotype in humans (e.g., structural and/or functional cardiac abnormalities, premature death, arrhythmia) may be considered as 
+MODERATE
+ evidence
+
+
+NOTE:
+ The following assays/models do NOT meet criteria
+
+
+
+
+Assays that are known to be associated with non-specific cardiac phenotypes (e.g., morpholino-induced pericardial edema in zebrafish)
+
+
+In vivo evidence that is not variant specific, such as whole gene alterations (i.e., cDNA or whole gene transgenic mice and whole or partial gene knock-out mice)",Disease-specific
+TNNI3 (HGNC:11947),PS3,Supporting,"In vitro
+ 
+assays (e.g., biochemical assays of myofilament function, motility assays, human iPSC-CM)
+
+
+While some 
+in vitro
+ assays may provide evidence that a variant in a cardiomyopathy gene has an effect on protein and/or myofilament function, at present, there are no validated “gold-standard” assays that are considered to reliably predict the clinical phenotype.
+
+
+As such, in the cardiomyopathy genes listed in these guidelines, data from individual 
+in vitro
+ studies are unlikely to meet the criteria required to assign this rule at more than SUPPORTING level.",Disease-specific
+TNNI3 (HGNC:11947),PS4,Original ACMG Summary,"The prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls.
+Note 1: Relative risk (RR) or odds ratio (OR), as obtained from case-control studies, is >5.0 and the confidence interval around the estimate of RR or OR does not include 1.0. See manuscript for detailed guidance.
+Note 2: In instances of very rare variants where case-control studies may not reach statistical significance, the prior observation of the variant in multiple unrelated patients with the same phenotype, and its absence in controls, may be used as moderate level of evidence.",
+TNNI3 (HGNC:11947),PS4,Strong,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+5
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+STRONG
+ evidence requires the lower bound of the 95% confidence interval (CI) around the odds ratio (OR) estimate to be 
+≥20
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+6
+; Oechslin 
+et al.
+ 2017
+7
+; Hershberger 
+et al.
+ 2017
+8
+; Ross 
+et al.
+ 2020
+9
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+TNNI3 (HGNC:11947),PS4,Moderate,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+5
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+MODERATE
+ evidence requires the lower bound of the 95% CI around the OR to be 
+≥10
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+6
+; Oechslin 
+et al.
+ 2017
+7
+; Hershberger 
+et al.
+ 2017
+8
+; Ross 
+et al.
+ 2020
+9
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+TNNI3 (HGNC:11947),PS4,Supporting,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+5
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+SUPPORTING
+ evidence requires the lower bound of the 95% CI around the OR to be 
+≥5
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+6
+; Oechslin 
+et al.
+ 2017
+7
+; Hershberger 
+et al.
+ 2017
+8
+; Ross 
+et al.
+ 2020
+9
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+TNNI3 (HGNC:11947),PM1,Original ACMG Summary,Located in a mutational hot spot and/or critical and well-established functional domain (e.g. active site of an enzyme) without benign variation.,
+TNNI3 (HGNC:11947),PM1,Moderate,"Applicable to missense variants in 
+TNNI3
+ in the specific regions listed below (Walsh 
+et al.
+ 2019
+10
+). 
+
+
+
+
+Transcripts ENST00000344887 and NM_000363.5
+
+
+Codons 141-209
+
+
+
+
+Data from HCM case cohorts was used to derive these cluster regions. Therefore, this rule should NOT be applied when additional evidence for the variant supports that the variant causes a phenotype other than HCM (e.g., variant seen in multiple DCM cases).
+
+
+Enrichment was not observed for DCM in any genes.
+
+
+Rule should NOT be combined with PM5 because presence of pathogenic variants in the same codon/region were used to determine clustering and would be double-counting evidence.","Disease-specific,Gene-specific"
+TNNI3 (HGNC:11947),PM2,Original ACMG Summary,"Absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.
+Caveat: Population data for indels may be poorly called by next generation sequencing.",
+TNNI3 (HGNC:11947),PM2,Supporting,"The values used to calculate the PM2 thresholds were derived from studies in Northern European populations that have been relatively well-characterized with regards to disease prevalence and variant spectrum. These thresholds can be applied to any population where disease prevalence is considered comparable (1/500 or lower), where the most frequent pathogenic variant accounts for no more than 2% of cases (e.g., has an allele frequency of ≤0.02 in cases based on the upper bound of 95% CI), and where the penetrance of a pathogenic variant is expected to be at least 50% (Kelly 
+et al.
+ 2018
+11
+).
+
+
+A threshold of 
+≤0.00004
+ in the subpopulation with the highest frequency when using the upper bound of the 95% CI activates this rule.
+
+
+
+
+Alternatively, this is equivalent to the variant NOT being observed more than once (≤1 allele) in gnomAD v.2.1.1 in one of the non-founder populations (e.g., absence required from the Other and Ashkenazi Jewish subpopulations).
+
+
+Applying a threshold of ≤0.00004 (upper bound of 95% CI of the allele frequency in gnomAD) is equivalent to the variant being seen in a single subpopulation and that subpopulation meets any of the following:
+
+
+Allele Count (AC) in Allele Number (AN)
+
+
+≤1 in ≥120,000
+
+
+≤2 in ≥160,000
+
+
+≤3 in ≥195,000
+
+
+≤4 in ≥230,000
+
+
+
+
+
+
+
+
+gnomAD is the preferred database for this calculation, but currently only displays the filtering allele frequency (FAF), which is equivalent to a lower bound estimate of the 95% CI, when the upper bound is what is needed.
+
+
+
+
+Confidence interval tools, such as 
+Confit-de-MAF
+, can be used to determine the upper bound of the 95% CI of the observed allele frequency.
+
+
+
+
+Due to current technical limitations of next generation sequencing technologies, minor allele frequencies for complex variants (e.g., large indels) may not be accurately represented in population databases.
+
+
+Caution should be used when a variant is only identified, or over-represented, in one of the smaller gnomAD populations, as the gnomAD allele frequencies may not accurately represent the true population frequency.
+
+
+Population databases may contain affected or pre-symptomatic individuals for diseases with reduced penetrance/variable onset.",Disease-specific
+TNNI3 (HGNC:11947),PM3,Original ACMG Summary,"For recessive disorders, detected in trans with a pathogenic variant
+Note: This requires testing of parents (or offspring) to determine phase.",NA
+TNNI3 (HGNC:11947),PM4,Original ACMG Summary,Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants.,
+TNNI3 (HGNC:11947),PM4,Moderate,"Strength of rule should be carefully considered and may require downgrading to SUPPORTING based on the predicted impact of the variant, including the size of the deletion/insertion, its location, and conservation of the region. 
+
+
+For genes where PVS1 is not applicable (i.e., where there is no evidence that pLOF variants cause disease), consider using this rule at MODERATE or SUPPORTING strength for truncating variants that do NOT undergo nonsense mediated decay (NMD).",General recommendation
+TNNI3 (HGNC:11947),PM5,Original ACMG Summary,"Novel missense change at an amino acid residue where a different missense change determined to be pathogenic has been seen before.
+Example: Arg156His is pathogenic; now you observe Arg156Cys.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+TNNI3 (HGNC:11947),PM5,Moderate,"This criterion can be used at MODERATE if a different missense variant at the same codon has been classified as 
+pathogenic
+ using these modified guidelines without application of PM5.
+
+
+The impact of the amino acid change being evaluated needs to be compared to the impact of the amino acid change that is established as pathogenic (e.g., a change of Ala to His is less severe than Ala to Cys change). Consider reducing the strength of this rule to SUPPORTING if the predicted impact is not expected to be equivalent or more severe.
+
+
+PM5 should not be combined with PM1.  If both are applicable at MODERATE weight, use of PM5 is most appropriate since it is variant specific.",General recommendation
+TNNI3 (HGNC:11947),PM5,Supporting,"This criterion can be considered at SUPPORTING if a different missense variant at the same codon has been classified as 
+likely pathogenic
+ using these modified guidelines without application of PM5.
+
+
+The impact of the amino acid change being evaluated needs to be compared to the impact of the amino acid change that is established as likely pathogenic (e.g., a change of Ala to His is less severe than Ala to Cys change). Consider reducing the strength of this rule to NOT APPLICABLE if the predicted impact is not expected to be equivalent or more severe.
+
+
+PM5 should not be combined with PM1.  The one with the higher strength should be applied, but if both are applicable at SUPPORTING weight, use of PM5 is most appropriate since it is variant specific.",General recommendation
+TNNI3 (HGNC:11947),PM6,Original ACMG Summary,"Assumed de novo, but without confirmation of paternity and maternity.",
+TNNI3 (HGNC:11947),PM6,Moderate,"Refer to SVI guidance on number/combination of cases required based on phenotype specificity
+2
+.
+
+
+For most cardiomyopathies, it is recommended to default to “phenotype consistent with gene but not highly specific”. Clinical judgment is required for shifting to a higher or lower phenotypic consistency. 
+
+
+See PS2 for additional considerations.",Disease-specific
+TNNI3 (HGNC:11947),PP1,Original ACMG Summary,"Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease.
+Note: May be used as stronger evidence with increasing segregation data.",
+TNNI3 (HGNC:11947),PP1,Strong,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥7
+ 
+segregations
+ (LOD score of 2.1) for 
+STRONG
+.
+
+
+Although rare for inherited cardiomyopathies, when the phenotype/presentation of a variant within and across families is highly specific (e.g., early-onset severe RCM in all affected individuals), the following thresholds as proposed by Jarvik and Browning (2016)
+12
+ can be considered: 
+
+
+
+
+STRONG evidence requires ≥5 segregations (LOD score of 1.5)
+
+
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1.
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+TNNI3 (HGNC:11947),PP1,Moderate,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥5
+ 
+segregations
+ (LOD score of 1.5) for 
+MODERATE
+.
+
+
+Although rare for inherited cardiomyopathies, when the phenotype/presentation of a variant within and across families is highly specific (e.g., early-onset severe RCM in all affected individuals), the following thresholds as proposed by Jarvik and Browning (2016)
+12
+ can be considered: 
+
+
+
+
+MODERATE evidence requires ≥4 segregations (LOD score of 1.2)
+
+
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1 (see below).
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+TNNI3 (HGNC:11947),PP1,Supporting,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥3
+ 
+segregations
+ (LOD score of 0.9) for 
+SUPPORTING
+. The thresholds as proposed by Jarvik and Browning (2016)
+12
+ are the same at ≥3 segregations (LOD score of 0.9) for supporting.
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1 (see below).
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+TNNI3 (HGNC:11947),PP2,Original ACMG Summary,Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease.,NA
+TNNI3 (HGNC:11947),PP3,Original ACMG Summary,"Multiple lines of computational evidence support a deleterious effect on the gene or gene product (conservation, evolutionary, splicing impact, etc.).
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. PP3 can be used only once in any evaluation of a variant.",
+TNNI3 (HGNC:11947),PP3,Supporting,"As many 
+in silico
+ algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. Meta-predictors, such as REVEL, are preferred over multiple individual predictors.
+
+
+Use of REVEL (Ioannidis 
+et al.
+ 2016
+13
+) is recommended at thresholds of 
+≥0.70 for PP3
+.
+
+
+Clinical judgment is needed if any individual algorithms or conservation data are contradictory to REVEL data.
+
+
+Positive predictive value for benign/no impact predictions is generally higher than for pathogenic/impact predictions.
+
+
+SpliceAI
+14
+ is recommended for evaluation of predicted splice impacts.",Disease-specific
+TNNI3 (HGNC:11947),PP4,Original ACMG Summary,Patient’s phenotype or family history is highly specific for a disease with a single genetic etiology.,NA
+TNNI3 (HGNC:11947),PP5,Original ACMG Summary,"Reputable source recently reports variant as pathogenic, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+TNNI3 (HGNC:11947),BA1,Original ACMG Summary,"Allele frequency is above 5% in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.",
+TNNI3 (HGNC:11947),BA1,Stand Alone,"Allele frequency is 
+≥0.001
+ based on the 
+filtering allele frequency (FAF)
+ in 
+gnomAD
+ in the subpopulation with the highest frequency (popmax).
+
+
+The values used to calculate the BA1 threshold were derived from studies in Northern European populations that have been relatively well-characterized with regards to disease prevalence and variant spectrum. These thresholds can be applied to any population where disease prevalence is considered comparable (1/300 or lower).
+
+
+The threshold is applicable when assessing variants in the context of autosomal dominant cardiomyopathy. 
+
+
+gnomAD is the preferred database for this calculation. If a subpopulation specific FAF other than the popmax is needed, this value can be calculated using the AlleleFrequencyApp on the 
+CardioDB website
+.
+
+
+
+
+Using the Inverse AF tab, enter in the population size and the number of alleles identified and it will calculate the FAF.  
+
+
+Set confidence to 0.95 (95%).
+
+
+If the FAF is ≥0.001, this rule can be applied.
+
+
+
+
+The FAF by platform (e.g., exome vs. genome; v.2.1.1 vs. v.3.1.1) should be considered, the larger population is most likely to have the most accurate representation of “true” population allele frequency.
+
+
+Caution is needed when considering any population cohorts that are smaller than the smallest subpopulations within gnomAD v.2.1.1 (e.g., ~5000 individuals or ~10,000 alleles). Despite this conservative nature of this threshold and approach, in smaller cohorts, the observed allele frequency may less accurately reflect the true allele frequency. Traditionally, once a variant is classified as Benign, it is rarely re-evaluated and so the highest confidence is needed to establish that classification on an allele frequency alone.",Disease-specific
+TNNI3 (HGNC:11947),BS1,Original ACMG Summary,Allele frequency is greater than expected for disorder.,
+TNNI3 (HGNC:11947),BS1,Strong,"Allele frequency is 
+≥0.0001 for
+ 
+TNNI3
+ based on the 
+filtering allele frequency (FAF)
+ in 
+gnomAD
+ in the subpopulation with the highest frequency (popmax).
+
+
+Criterion BS1 may only be used as standalone evidence to classify a variant as Likely Benign in the absence of conflicting data. See SVI guidance (Tavtigian 
+et al.
+ 2018
+15
+; Tavtigian 
+et al.
+ 2020
+16
+). 
+
+
+See BA1 for additional specifications that also apply to BS1.","Disease-specific,Gene-specific"
+TNNI3 (HGNC:11947),BS2,Original ACMG Summary,"Observed in a healthy adult individual for a recessive (homozygous), dominant (heterozygous), or X-linked (hemizygous) disorder, with full penetrance expected at an early age.",NA
+TNNI3 (HGNC:11947),BS3,Original ACMG Summary,Well-established in vitro or in vivo functional studies show no damaging effect on protein function or splicing.,
+TNNI3 (HGNC:11947),BS3,Strong,See PS3 specifications.,Disease-specific
+TNNI3 (HGNC:11947),BS3,Moderate,See PS3 specifications.,Disease-specific
+TNNI3 (HGNC:11947),BS3,Supporting,See PS3 specifications.,Disease-specific
+TNNI3 (HGNC:11947),BS4,Original ACMG Summary,"Lack of segregation in affected members of a family.
+Caveat: The presence of phenocopies for common phenotypes (i.e. cancer, epilepsy) can mimic lack of segregation among affected individuals. Also, families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent lack of segregation.",
+TNNI3 (HGNC:11947),BS4,Strong,"Any non-segregations should be carefully evaluated to rule out a phenocopy or the presence of a second disease-causing variant before considering it as conflicting or benign evidence. 
+
+
+
+
+The presence of “phenocopies” (e.g., athlete’s heart, hypertensive heart disease, ischemic cardiomyopathy, alcoholic cardiomyopathy, diabetic cardiomyopathy) can mimic non-segregation (i.e., lack of segregation) among affected individuals. 
+
+
+Families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent ‘non-segregation’.
+
+
+
+
+Because of these possibilities, 
+multiple (≥2) non-segregations
+ that are highly unlikely to be phenocopies or due to alternate variants (e.g., those without a possible alternate cause) 
+are required to apply this rule
+.  A higher number of non-segregations is necessary for instances where alternative causes are possible (e.g., non-segregation in a sibling with childhood onset cardiomyopathy versus a grandparent with hypertension and HCM).
+
+
+Careful consideration of the above points is required when using this data as conflicting evidence, especially when overall evidence supports likely pathogenic or pathogenic.",Disease-specific
+TNNI3 (HGNC:11947),BP1,Original ACMG Summary,Missense variant in a gene for which primarily truncating variants are known to cause disease.,NA
+TNNI3 (HGNC:11947),BP2,Original ACMG Summary,Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder or observed in cis with a pathogenic variant in any inheritance pattern.,
+TNNI3 (HGNC:11947),BP2,Supporting,"Other variants must be pathogenic as defined by these specifications.
+
+
+Testing of parents or other informative relatives is often required to determine 
+cis
+/
+trans
+ status.
+
+
+If a variant is seen in 
+trans
+ (or as double heterozygous) with another pathogenic variant in ≥2 cases and the phenotype is not more severe than when either of the two variants are seen in isolation, this rule may be applied (i.e., high confidence this variant is NOT contributing to disease).
+
+
+
+
+<1% of cases of HCM have >1 pathogenic or likely pathogenic variant (0.6%; Alfares 
+et al.
+ 2015
+17
+).
+
+
+
+
+This rule cannot be applied when the variant has only been observed in 
+cis
+ with a pathogenic variant as its significance in isolation is unknown in this scenario. 
+
+
+Caution is needed if using this criterion as a primary piece of evidence for classifying a variant as likely benign/benign (i.e., only 2 SUPPORTING criteria are sufficient for a likely benign classification).",Disease-specific
+TNNI3 (HGNC:11947),BP3,Original ACMG Summary,In frame-deletions/insertions in a repetitive region without a known function.,NA
+TNNI3 (HGNC:11947),BP4,Original ACMG Summary,"Multiple lines of computational evidence suggest no impact on gene or gene product (conservation, evolutionary, splicing impact, etc)
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm cannot be counted as an independent criterion. BP4 can be used only once in any evaluation of a variant.",
+TNNI3 (HGNC:11947),BP4,Supporting,"As many 
+in silico
+ algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. Meta-predictors, such as REVEL, are preferred over multiple individual predictors.
+
+
+Use of REVEL (Ioannidis et al. 2016
+13
+) is recommended at thresholds of 
+≤0.40 for BP4
+.
+
+
+Clinical judgment is needed if any individual algorithms or conservation data are contradictory to REVEL data.
+
+
+Positive predictive value for benign/no impact predictions is generally higher than for pathogenic/impact predictions.
+
+
+SpliceAI
+14
+ is recommended for evaluation of predicted splice impacts.",Disease-specific
+TNNI3 (HGNC:11947),BP5,Original ACMG Summary,Variant found in a case with an alternate molecular basis for disease.,NA
+TNNI3 (HGNC:11947),BP6,Original ACMG Summary,"Reputable source recently reports variant as benign, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+TNNI3 (HGNC:11947),BP7,Original ACMG Summary,A synonymous variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site AND the nucleotide is not highly conserved.,
+TNNI3 (HGNC:11947),BP7,Supporting,"Also applicable to 
+intronic variants outside the splice consensus sequence (-4 and +7 outward)
+ for which splicing prediction algorithms predict no impact to the splice consensus sequence NOR the creation of a new splice site AND the nucleotide is not highly conserved.
+
+
+Rule can be combined with BP4 to make a variant likely benign per Richards 
+et al.
+ 2015
+1
+.",General recommendation

VCI/parsing_csr_criteria/version_csv_individual/ClinGenCardiomyopathyExpertPanelSpecificationstotheACMGAMPVariantInterpretationGuidelinesforTNNT2Version1.0.0_version=1.0.0.csv

@@ -0,0 +1,897 @@
+Gene,Code,Strength,Description,Modification Type
+TNNT2 (HGNC:11949),PVS1,Original ACMG Summary,"Null variant (nonsense, frameshift, canonical +/−1 or 2 splice sites, initiation codon, single or multi-exon deletion) in a gene where loss of function (LOF) is a known mechanism of disease.
+Caveats:
+ • Beware of genes where LOF is not a known disease mechanism (e.g. GFAP, MYH7).
+ • Use caution interpreting LOF variants at the extreme 3’ end of a gene.
+ • Use caution with splice variants that are predicted to lead to exon skipping but leave the remainder of the protein intact.
+ • Use caution in the presence of multiple transcripts.",NA
+TNNT2 (HGNC:11949),PS1,Original ACMG Summary,"Same amino acid change as a previously established pathogenic variant regardless of nucleotide change.
+Example: Val->Leu caused by either G>C or G>T in the same codon.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+TNNT2 (HGNC:11949),PS1,Strong,"No cardiomyopathy specifications. Apply as outlined by Richards 
+et al
+. 2015
+1
+.
+
+
+Example of when rule should NOT be applied. NM_000256.3(
+MYBPC3
+): c.2308G>A (p.Asp770Asn) has an established impact on splicing leading to nonsense mediated decay (NMD) and should not be used to provide evidence for other variants observed to result in the same amino acid change.",No change
+TNNT2 (HGNC:11949),PS2,Original ACMG Summary,"De novo (both maternity and paternity confirmed) in a patient with the disease and no family history.
+Note: Confirmation of paternity only is insufficient. Egg donation, surrogate motherhood, errors in embryo transfer, etc. can contribute to non-maternity.",
+TNNT2 (HGNC:11949),PS2,Strong,"Refer to SVI guidance on number/combination of cases required based on phenotype specificity
+2
+.
+
+
+For most cardiomyopathies, it is recommended to default to 
+Phenotype consistency: “Phenotype consistent with gene but not highly specific”
+. Clinical judgment is required for shifting to a higher or lower category. 
+
+
+For use as a STRONG or VERY STRONG criterion, ideally parents have been thoroughly clinically evaluated without evidence of cardiomyopathy (ideally using a combination of ECG and echocardiogram or cardiac MRI for maximum sensitivity).
+
+
+A family history consistent with 
+de novo
+ inheritance should not have any clinical signs or symptoms suggestive of cardiomyopathy in a 1
+st
+ or 2
+nd
+ degree relative, for example: 
+
+
+
+
+Sudden death under 60 years of age
+
+
+Heart transplant
+
+
+Implantable cardiac defibrillator (ICD) under 60 years of age
+
+
+Features of cardiomyopathy (e.g., systolic dysfunction, hypertrophy, left ventricular enlargement in an individual without risk factors).
+
+
+Other related/overlapping cardiomyopathies
+
+
+
+
+Examples of non-suspicious family history may include non-specific clinical features (e.g., palpitations, syncope, borderline/inconclusive echocardiogram findings, heart attack if age appropriate and suspected to result from coronary artery disease), but every attempt should be made to clarify features. 
+
+
+Generally, this criterion is only applicable in the ABSENCE of any other possible disease-causing variants.  If other pathogenic or likely pathogenic variants are present, consider decreasing points assigned or overall weight.",Disease-specific
+TNNT2 (HGNC:11949),PS3,Original ACMG Summary,"Well-established in vitro or in vivo functional studies supportive of a damaging effect on the gene or gene product.
+Note: Functional studies that have been validated and shown to be reproducible and robust in a clinical diagnostic laboratory setting are considered the most well-established.",
+TNNT2 (HGNC:11949),PS3,Strong,"In vitro splicing assays (e.g., RNA studies)
+
+
+In vitro
+ splicing assays may be considered as 
+STRONG
+ evidence, providing the following criteria are met.
+
+
+
+
+Prior knowledge of predominant transcripts in cardiac tissue
+
+
+
+
+Analysis undertaken using RNA extracted from cardiac tissue from the individual with the variant
+
+
+Analysis undertaken using RNA extracted from whole blood providing the relevant transcripts (isoforms) are expressed in blood and are at sufficient levels to assess splice disruption.
+
+
+Assay shows a clear, reproducible and convincing effect on splicing (i.e. a distinct splice product, present at a level comparable to the splice product from the wild-type allele), which is not observed in controls
+
+
+
+
+Confirmation of abnormal splice product by Sanger sequencing
+
+
+
+
+NOTE:
+ Mini-gene assay in non-patient derived cell lines are NOT considered to provide STRONG evidence.
+
+
+NOTE:
+  Whether to activate this rule needs to be reconciled with the variant spectrum and disease mechanism for the gene at hand (i.e., consider whether the effect is likely to lead to LOF or an in-frame alteration and whether this type of effect is expected to be disease causing) (Abou Tayoun 
+et al.
+ 2018
+3
+).",Disease-specific
+TNNT2 (HGNC:11949),PS3,Moderate,"In vivo models (e.g., variant knock-in animal models)
+
+
+Mammalian variant-specific knock-in animal models that produce a phenotype consistent with the clinical phenotype in humans (e.g., structural and/or functional cardiac abnormalities, premature death, arrhythmia) may be considered as 
+MODERATE
+ evidence
+
+
+NOTE:
+ The following assays/models do NOT meet criteria
+
+
+
+
+Assays that are known to be associated with non-specific cardiac phenotypes (e.g., morpholino-induced pericardial edema in zebrafish)
+
+
+In vivo evidence that is not variant specific, such as whole gene alterations (i.e., cDNA or whole gene transgenic mice and whole or partial gene knock-out mice)",Disease-specific
+TNNT2 (HGNC:11949),PS3,Supporting,"In vitro
+ 
+assays (e.g., biochemical assays of myofilament function, motility assays, human iPSC-CM)
+
+
+While some 
+in vitro
+ assays may provide evidence that a variant in a cardiomyopathy gene has an effect on protein and/or myofilament function, at present, there are no validated “gold-standard” assays that are considered to reliably predict the clinical phenotype.
+
+
+As such, in the cardiomyopathy genes listed in these guidelines, data from individual 
+in vitro
+ studies are unlikely to meet the criteria required to assign this rule at more than SUPPORTING level.",Disease-specific
+TNNT2 (HGNC:11949),PS4,Original ACMG Summary,"The prevalence of the variant in affected individuals is significantly increased compared to the prevalence in controls.
+Note 1: Relative risk (RR) or odds ratio (OR), as obtained from case-control studies, is >5.0 and the confidence interval around the estimate of RR or OR does not include 1.0. See manuscript for detailed guidance.
+Note 2: In instances of very rare variants where case-control studies may not reach statistical significance, the prior observation of the variant in multiple unrelated patients with the same phenotype, and its absence in controls, may be used as moderate level of evidence.",
+TNNT2 (HGNC:11949),PS4,Strong,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+5
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+STRONG
+ evidence requires the lower bound of the 95% confidence interval (CI) around the odds ratio (OR) estimate to be 
+≥20
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+6
+; Oechslin 
+et al.
+ 2017
+7
+; Hershberger 
+et al.
+ 2017
+8
+; Ross 
+et al.
+ 2020
+9
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+TNNT2 (HGNC:11949),PS4,Moderate,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+5
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+MODERATE
+ evidence requires the lower bound of the 95% CI around the OR to be 
+≥10
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+6
+; Oechslin 
+et al.
+ 2017
+7
+; Hershberger 
+et al.
+ 2017
+8
+; Ross 
+et al.
+ 2020
+9
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+TNNT2 (HGNC:11949),PS4,Supporting,"Currently few well-designed case-control studies have been performed for inherited cardiomyopathies.  Until such studies become available, comparative analyses can be undertaken using case data (e.g., internal and/or published cohorts) and control data from population-level cohorts (e.g., gnomAD). 
+
+
+Cohorts used in these analyses should meet the following criteria: 
+
+
+
+
+The cases have a clinical diagnosis of the specified disorder or related phenotype (e.g., all cases have HCM or another relevant phenotype*). 
+
+
+When assessing cases, it's important to consider how likely another potential cause of the phenotype has been excluded.  This includes considering the presence of other variants in relevant genes (particularly those likely to be contributing to phenotype) and the extent of testing performed (i.e., single gene sequencing, panel testing, whole exome/genome sequencing).
+
+
+
+
+
+
+The controls should not be derived from study populations that might be enriched for the specified disorder.
+
+
+The denominator of the cohorts must be available (e.g., variant detected in 5 out of 3,500 cases and 1 out of 60,000 controls).
+
+
+The cohorts do not include closely related individuals (i.e., family members are not included in the case counts).
+
+
+The cohorts do not overlap with other cohorts being used in the analysis (i.e., cases are not being counted more than once).
+
+
+The population diversity of the case and control cohorts are broadly similar.
+
+
+Consider the size of the case cohort — larger cohorts are likely to provide more accurate estimates of variant frequency; therefore, it may be preferable to use data from the largest available case series for case-control analyses (e.g., Walsh 
+et al.
+ 2017
+5
+, 
+DECIPHER
+).
+
+
+
+
+To account for limitations that arise when performing unmatched case-control analyses, the following stringent OR threshold is recommended:
+
+
+
+
+SUPPORTING
+ evidence requires the lower bound of the 95% CI around the OR to be 
+≥5
+
+
+
+
+A PS4 calculator is available at 
+www.cardiodb.org
+.
+
+
+If multiple cohorts are available, the final ORs and associated CIs need to be harmonized across all cohorts to determine the final level (e.g., if 2 large cohorts have an OR of ~6 and a third small cohort has an OR of 11, application at a SUPPORTING level should be considered).  
+
+
+*RELEVANT PHENOTYPES:
+
+
+
+
+Cases of HCM and RCM may be combined as they are considered part of the same disease spectrum. 
+
+
+For the eight genes covered by these guidelines, the combination of probands with other phenotypes should be reviewed by a clinical expert to determine if grouping is appropriate. 
+
+
+Additional considerations for LVNC and end-stage HCM: 
+
+
+Due to the current debate about whether isolated LVNC represents a true disease entity or variation of typical cardiac morphology (Anderson 
+et al.
+ 2017
+6
+; Oechslin 
+et al.
+ 2017
+7
+; Hershberger 
+et al.
+ 2017
+8
+; Ross 
+et al.
+ 2020
+9
+), individuals with isolated LVNC should NOT be added to proband or segregation counts (including individuals with isolated LVNC in a family with other cardiomyopathies).
+
+
+
+
+
+
+
+
+HCM and DCM have distinct mechanisms of disease and therefore pathogenetic variants are not anticipated to cause both primary phenotypes. While occurrence in both phenotypes may initially be considered as evidence against pathogenicity, end-stage HCM can present similarly to DCM. Careful consideration is needed before including DCM or related phenotypes in case or segregation data for primarily HCM variants.",Disease-specific
+TNNT2 (HGNC:11949),PM1,Original ACMG Summary,Located in a mutational hot spot and/or critical and well-established functional domain (e.g. active site of an enzyme) without benign variation.,
+TNNT2 (HGNC:11949),PM1,Supporting,"Applicable to missense variants in 
+TNNT2
+ in the specific regions listed below (Walsh 
+et al.
+ 2019
+10
+). 
+
+
+
+
+Transcripts ENST00000367318 with codons 79-179
+
+
+ENST00000656932.1 and NM_001276345.2 with codons 89-189
+
+
+
+
+Data from HCM case cohorts was used to derive these cluster regions. Therefore, this rule should NOT be applied when additional evidence for the variant supports that the variant causes a phenotype other than HCM (e.g., variant seen in multiple DCM cases).
+
+
+Enrichment was not observed for DCM in any genes.
+
+
+Rule should NOT be combined with PM5 because presence of pathogenic variants in the same codon/region were used to determine clustering and would be double-counting evidence.","Disease-specific,Gene-specific"
+TNNT2 (HGNC:11949),PM2,Original ACMG Summary,"Absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.
+Caveat: Population data for indels may be poorly called by next generation sequencing.",
+TNNT2 (HGNC:11949),PM2,Supporting,"The values used to calculate the PM2 thresholds were derived from studies in Northern European populations that have been relatively well-characterized with regards to disease prevalence and variant spectrum. These thresholds can be applied to any population where disease prevalence is considered comparable (1/500 or lower), where the most frequent pathogenic variant accounts for no more than 2% of cases (e.g., has an allele frequency of ≤0.02 in cases based on the upper bound of 95% CI), and where the penetrance of a pathogenic variant is expected to be at least 50% (Kelly 
+et al.
+ 2018
+11
+).
+
+
+A threshold of 
+≤0.00004
+ in the subpopulation with the highest frequency when using the upper bound of the 95% CI activates this rule.
+
+
+
+
+Alternatively, this is equivalent to the variant NOT being observed more than once (≤1 allele) in gnomAD v.2.1.1 in one of the non-founder populations (e.g., absence required from the Other and Ashkenazi Jewish subpopulations).
+
+
+Applying a threshold of ≤0.00004 (upper bound of 95% CI of the allele frequency in gnomAD) is equivalent to the variant being seen in a single subpopulation and that subpopulation meets any of the following:
+
+
+Allele Count (AC) in Allele Number (AN)
+
+
+≤1 in ≥120,000
+
+
+≤2 in ≥160,000
+
+
+≤3 in ≥195,000
+
+
+≤4 in ≥230,000
+
+
+
+
+
+
+
+
+gnomAD is the preferred database for this calculation, but currently only displays the filtering allele frequency (FAF), which is equivalent to a lower bound estimate of the 95% CI, when the upper bound is what is needed.
+
+
+
+
+Confidence interval tools, such as 
+Confit-de-MAF
+, can be used to determine the upper bound of the 95% CI of the observed allele frequency.
+
+
+
+
+Due to current technical limitations of next generation sequencing technologies, minor allele frequencies for complex variants (e.g., large indels) may not be accurately represented in population databases.
+
+
+Caution should be used when a variant is only identified, or over-represented, in one of the smaller gnomAD populations, as the gnomAD allele frequencies may not accurately represent the true population frequency.
+
+
+Population databases may contain affected or pre-symptomatic individuals for diseases with reduced penetrance/variable onset.",Disease-specific
+TNNT2 (HGNC:11949),PM3,Original ACMG Summary,"For recessive disorders, detected in trans with a pathogenic variant
+Note: This requires testing of parents (or offspring) to determine phase.",NA
+TNNT2 (HGNC:11949),PM4,Original ACMG Summary,Protein length changes due to in-frame deletions/insertions in a non-repeat region or stop-loss variants.,
+TNNT2 (HGNC:11949),PM4,Moderate,"Strength of rule should be carefully considered and may require downgrading to SUPPORTING based on the predicted impact of the variant, including the size of the deletion/insertion, its location, and conservation of the region. 
+
+
+For genes where PVS1 is not applicable (i.e., where there is no evidence that pLOF variants cause disease), consider using this rule at MODERATE or SUPPORTING strength for truncating variants that do NOT undergo nonsense mediated decay (NMD).",General recommendation
+TNNT2 (HGNC:11949),PM5,Original ACMG Summary,"Novel missense change at an amino acid residue where a different missense change determined to be pathogenic has been seen before.
+Example: Arg156His is pathogenic; now you observe Arg156Cys.
+Caveat: Beware of changes that impact splicing rather than at the amino acid/protein level.",
+TNNT2 (HGNC:11949),PM5,Moderate,"This criterion can be used at MODERATE if a different missense variant at the same codon has been classified as 
+pathogenic
+ using these modified guidelines without application of PM5.
+
+
+The impact of the amino acid change being evaluated needs to be compared to the impact of the amino acid change that is established as pathogenic (e.g., a change of Ala to His is less severe than Ala to Cys change). Consider reducing the strength of this rule to SUPPORTING if the predicted impact is not expected to be equivalent or more severe.
+
+
+PM5 should not be combined with PM1.  If both are applicable at MODERATE weight, use of PM5 is most appropriate since it is variant specific.",General recommendation
+TNNT2 (HGNC:11949),PM5,Supporting,"This criterion can be considered at SUPPORTING if a different missense variant at the same codon has been classified as 
+likely pathogenic
+ using these modified guidelines without application of PM5.
+
+
+The impact of the amino acid change being evaluated needs to be compared to the impact of the amino acid change that is established as likely pathogenic (e.g., a change of Ala to His is less severe than Ala to Cys change). Consider reducing the strength of this rule to NOT APPLICABLE if the predicted impact is not expected to be equivalent or more severe.
+
+
+PM5 should not be combined with PM1.  The one with the higher strength should be applied, but if both are applicable at SUPPORTING weight, use of PM5 is most appropriate since it is variant specific.",General recommendation
+TNNT2 (HGNC:11949),PM6,Original ACMG Summary,"Assumed de novo, but without confirmation of paternity and maternity.",
+TNNT2 (HGNC:11949),PM6,Moderate,"Refer to SVI guidance on number/combination of cases required based on phenotype specificity
+2
+.
+
+
+For most cardiomyopathies, it is recommended to default to “phenotype consistent with gene but not highly specific”. Clinical judgment is required for shifting to a higher or lower phenotypic consistency. 
+
+
+See PS2 for additional considerations.",Disease-specific
+TNNT2 (HGNC:11949),PP1,Original ACMG Summary,"Co-segregation with disease in multiple affected family members in a gene definitively known to cause the disease.
+Note: May be used as stronger evidence with increasing segregation data.",
+TNNT2 (HGNC:11949),PP1,Strong,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥7
+ 
+segregations
+ (LOD score of 2.1) for 
+STRONG
+.
+
+
+Although rare for inherited cardiomyopathies, when the phenotype/presentation of a variant within and across families is highly specific (e.g., early-onset severe RCM in all affected individuals), the following thresholds as proposed by Jarvik and Browning (2016)
+12
+ can be considered: 
+
+
+
+
+STRONG evidence requires ≥5 segregations (LOD score of 1.5)
+
+
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1.
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+TNNT2 (HGNC:11949),PP1,Moderate,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥5
+ 
+segregations
+ (LOD score of 1.5) for 
+MODERATE
+.
+
+
+Although rare for inherited cardiomyopathies, when the phenotype/presentation of a variant within and across families is highly specific (e.g., early-onset severe RCM in all affected individuals), the following thresholds as proposed by Jarvik and Browning (2016)
+12
+ can be considered: 
+
+
+
+
+MODERATE evidence requires ≥4 segregations (LOD score of 1.2)
+
+
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1 (see below).
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+TNNT2 (HGNC:11949),PP1,Supporting,"Due to the genotypic and phenotypic heterogeneity of inherited cardiomyopathies, segregation thresholds have been conservatively set at 
+≥3
+ 
+segregations
+ (LOD score of 0.9) for 
+SUPPORTING
+. The thresholds as proposed by Jarvik and Browning (2016)
+12
+ are the same at ≥3 segregations (LOD score of 0.9) for supporting.
+
+
+Only genotype positive/phenotype positive individuals are counted as segregations, which can include affected obligate carriers. Genotype positive/phenotype negative individuals are generally less informative for cardiomyopathy genes due to variable age at onset and reduced penetrance.
+
+
+Phenotypes should be clinically confirmed, whenever possible, and should not include individuals with a suspected diagnosis.  
+
+
+Important considerations include:
+
+
+
+
+Segregation of a variant within a single family or haplotype has the potential to represent linkage disequilibrium with another undetected variant.  If linkage disequilibrium is a concern, consider downgrading strength of segregation. 
+
+
+Use of segregation criteria should be carefully evaluated if variant frequency meets criteria for BS1 (see below).
+
+
+Caution is needed when counting segregations in presence of other possible disease-causing variants, as both variants may be contributing to the phenotype. 
+
+
+Caution is needed when distantly related (≥3
+rd
+ degree) affected individuals are connected by unknown or unaffected relatives (raises possibility of multiple causes of disease).",Disease-specific
+TNNT2 (HGNC:11949),PP2,Original ACMG Summary,Missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease.,NA
+TNNT2 (HGNC:11949),PP3,Original ACMG Summary,"Multiple lines of computational evidence support a deleterious effect on the gene or gene product (conservation, evolutionary, splicing impact, etc.).
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. PP3 can be used only once in any evaluation of a variant.",
+TNNT2 (HGNC:11949),PP3,Supporting,"As many 
+in silico
+ algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. Meta-predictors, such as REVEL, are preferred over multiple individual predictors.
+
+
+Use of REVEL (Ioannidis 
+et al.
+ 2016
+13
+) is recommended at thresholds of 
+≥0.70 for PP3
+.
+
+
+Clinical judgment is needed if any individual algorithms or conservation data are contradictory to REVEL data.
+
+
+Positive predictive value for benign/no impact predictions is generally higher than for pathogenic/impact predictions.
+
+
+SpliceAI
+14
+ is recommended for evaluation of predicted splice impacts.",Disease-specific
+TNNT2 (HGNC:11949),PP4,Original ACMG Summary,Patient’s phenotype or family history is highly specific for a disease with a single genetic etiology.,NA
+TNNT2 (HGNC:11949),PP5,Original ACMG Summary,"Reputable source recently reports variant as pathogenic, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+TNNT2 (HGNC:11949),BA1,Original ACMG Summary,"Allele frequency is above 5% in Exome Sequencing Project, 1000 Genomes or Exome Aggregation Consortium.",
+TNNT2 (HGNC:11949),BA1,Stand Alone,"Allele frequency is 
+≥0.001
+ based on the 
+filtering allele frequency (FAF)
+ in 
+gnomAD
+ in the subpopulation with the highest frequency (popmax).
+
+
+The values used to calculate the BA1 threshold were derived from studies in Northern European populations that have been relatively well-characterized with regards to disease prevalence and variant spectrum. These thresholds can be applied to any population where disease prevalence is considered comparable (1/300 or lower).
+
+
+The threshold is applicable when assessing variants in the context of autosomal dominant cardiomyopathy. 
+
+
+gnomAD is the preferred database for this calculation. If a subpopulation specific FAF other than the popmax is needed, this value can be calculated using the AlleleFrequencyApp on the 
+CardioDB website
+.
+
+
+
+
+Using the Inverse AF tab, enter in the population size and the number of alleles identified and it will calculate the FAF.  
+
+
+Set confidence to 0.95 (95%).
+
+
+If the FAF is ≥0.001, this rule can be applied.
+
+
+
+
+The FAF by platform (e.g., exome vs. genome; v.2.1.1 vs. v.3.1.1) should be considered, the larger population is most likely to have the most accurate representation of “true” population allele frequency.
+
+
+Caution is needed when considering any population cohorts that are smaller than the smallest subpopulations within gnomAD v.2.1.1 (e.g., ~5000 individuals or ~10,000 alleles). Despite this conservative nature of this threshold and approach, in smaller cohorts, the observed allele frequency may less accurately reflect the true allele frequency. Traditionally, once a variant is classified as Benign, it is rarely re-evaluated and so the highest confidence is needed to establish that classification on an allele frequency alone.",Disease-specific
+TNNT2 (HGNC:11949),BS1,Original ACMG Summary,Allele frequency is greater than expected for disorder.,
+TNNT2 (HGNC:11949),BS1,Strong,"Allele frequency is 
+≥0.0001 for
+ 
+TNNT2
+ based on the 
+filtering allele frequency (FAF)
+ in 
+gnomAD
+ in the subpopulation with the highest frequency (popmax).
+
+
+Criterion BS1 may only be used as standalone evidence to classify a variant as Likely Benign in the absence of conflicting data. See SVI guidance (Tavtigian 
+et al.
+ 2018
+15
+; Tavtigian 
+et al.
+ 2020
+16
+). 
+
+
+See BA1 for additional specifications that also apply to BS1.","Disease-specific,Gene-specific"
+TNNT2 (HGNC:11949),BS2,Original ACMG Summary,"Observed in a healthy adult individual for a recessive (homozygous), dominant (heterozygous), or X-linked (hemizygous) disorder, with full penetrance expected at an early age.",NA
+TNNT2 (HGNC:11949),BS3,Original ACMG Summary,Well-established in vitro or in vivo functional studies show no damaging effect on protein function or splicing.,
+TNNT2 (HGNC:11949),BS3,Strong,See PS3 specifications.,Disease-specific
+TNNT2 (HGNC:11949),BS3,Moderate,See PS3 specifications.,Disease-specific
+TNNT2 (HGNC:11949),BS3,Supporting,See PS3 specifications.,Disease-specific
+TNNT2 (HGNC:11949),BS4,Original ACMG Summary,"Lack of segregation in affected members of a family.
+Caveat: The presence of phenocopies for common phenotypes (i.e. cancer, epilepsy) can mimic lack of segregation among affected individuals. Also, families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent lack of segregation.",
+TNNT2 (HGNC:11949),BS4,Strong,"Any non-segregations should be carefully evaluated to rule out a phenocopy or the presence of a second disease-causing variant before considering it as conflicting or benign evidence. 
+
+
+
+
+The presence of “phenocopies” (e.g., athlete’s heart, hypertensive heart disease, ischemic cardiomyopathy, alcoholic cardiomyopathy, diabetic cardiomyopathy) can mimic non-segregation (i.e., lack of segregation) among affected individuals. 
+
+
+Families may have more than one pathogenic variant contributing to an autosomal dominant disorder, further confounding an apparent ‘non-segregation’.
+
+
+
+
+Because of these possibilities, 
+multiple (≥2) non-segregations
+ that are highly unlikely to be phenocopies or due to alternate variants (e.g., those without a possible alternate cause) 
+are required to apply this rule
+.  A higher number of non-segregations is necessary for instances where alternative causes are possible (e.g., non-segregation in a sibling with childhood onset cardiomyopathy versus a grandparent with hypertension and HCM).
+
+
+Careful consideration of the above points is required when using this data as conflicting evidence, especially when overall evidence supports likely pathogenic or pathogenic.",Disease-specific
+TNNT2 (HGNC:11949),BP1,Original ACMG Summary,Missense variant in a gene for which primarily truncating variants are known to cause disease.,NA
+TNNT2 (HGNC:11949),BP2,Original ACMG Summary,Observed in trans with a pathogenic variant for a fully penetrant dominant gene/disorder or observed in cis with a pathogenic variant in any inheritance pattern.,
+TNNT2 (HGNC:11949),BP2,Supporting,"Other variants must be pathogenic as defined by these specifications.
+
+
+Testing of parents or other informative relatives is often required to determine 
+cis
+/
+trans
+ status.
+
+
+If a variant is seen in 
+trans
+ (or as double heterozygous) with another pathogenic variant in ≥2 cases and the phenotype is not more severe than when either of the two variants are seen in isolation, this rule may be applied (i.e., high confidence this variant is NOT contributing to disease).
+
+
+
+
+<1% of cases of HCM have >1 pathogenic or likely pathogenic variant (0.6%; Alfares 
+et al.
+ 2015
+17
+).
+
+
+
+
+This rule cannot be applied when the variant has only been observed in 
+cis
+ with a pathogenic variant as its significance in isolation is unknown in this scenario. 
+
+
+Caution is needed if using this criterion as a primary piece of evidence for classifying a variant as likely benign/benign (i.e., only 2 SUPPORTING criteria are sufficient for a likely benign classification).",Disease-specific
+TNNT2 (HGNC:11949),BP3,Original ACMG Summary,In frame-deletions/insertions in a repetitive region without a known function.,NA
+TNNT2 (HGNC:11949),BP4,Original ACMG Summary,"Multiple lines of computational evidence suggest no impact on gene or gene product (conservation, evolutionary, splicing impact, etc)
+Caveat: As many in silico algorithms use the same or very similar input for their predictions, each algorithm cannot be counted as an independent criterion. BP4 can be used only once in any evaluation of a variant.",
+TNNT2 (HGNC:11949),BP4,Supporting,"As many 
+in silico
+ algorithms use the same or very similar input for their predictions, each algorithm should not be counted as an independent criterion. Meta-predictors, such as REVEL, are preferred over multiple individual predictors.
+
+
+Use of REVEL (Ioannidis et al. 2016
+13
+) is recommended at thresholds of 
+≤0.40 for BP4
+.
+
+
+Clinical judgment is needed if any individual algorithms or conservation data are contradictory to REVEL data.
+
+
+Positive predictive value for benign/no impact predictions is generally higher than for pathogenic/impact predictions.
+
+
+SpliceAI
+14
+ is recommended for evaluation of predicted splice impacts.",Disease-specific
+TNNT2 (HGNC:11949),BP5,Original ACMG Summary,Variant found in a case with an alternate molecular basis for disease.,NA
+TNNT2 (HGNC:11949),BP6,Original ACMG Summary,"Reputable source recently reports variant as benign, but the evidence is not available to the laboratory to perform an independent evaluation.",NA
+TNNT2 (HGNC:11949),BP7,Original ACMG Summary,A synonymous variant for which splicing prediction algorithms predict no impact to the splice consensus sequence nor the creation of a new splice site AND the nucleotide is not highly conserved.,
+TNNT2 (HGNC:11949),BP7,Supporting,"Also applicable to 
+intronic variants outside the splice consensus sequence (-4 and +7 outward)
+ for which splicing prediction algorithms predict no impact to the splice consensus sequence NOR the creation of a new splice site AND the nucleotide is not highly conserved.
+
+
+Rule can be combined with BP4 to make a variant likely benign per Richards 
+et al.
+ 2015
+1
+.",General recommendation