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lighteternal
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psychgnn-psychiatric-graph

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Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
train: int64
val: int64
test: int64
num_genes: int64
disorder_edge_mode: string
num_variants: int64
window_bp: int64
num_snp_gene_edges: int64
num_disorders: int64
num_snp_disorder_edges: int64
gws_threshold: double
num_disorder_disorder_edges: int64
seed: int64
disorders: list<item: string>
  child 0, item: string
to
{'num_variants': Value('int64'), 'num_genes': Value('int64'), 'num_disorders': Value('int64'), 'num_snp_disorder_edges': Value('int64'), 'num_snp_gene_edges': Value('int64'), 'num_disorder_disorder_edges': Value('int64'), 'gws_threshold': Value('float64'), 'window_bp': Value('int64'), 'seed': Value('int64'), 'disorders': List(Value('string')), 'disorder_edge_mode': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 289, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 124, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              train: int64
              val: int64
              test: int64
              num_genes: int64
              disorder_edge_mode: string
              num_variants: int64
              window_bp: int64
              num_snp_gene_edges: int64
              num_disorders: int64
              num_snp_disorder_edges: int64
              gws_threshold: double
              num_disorder_disorder_edges: int64
              seed: int64
              disorders: list<item: string>
                child 0, item: string
              to
              {'num_variants': Value('int64'), 'num_genes': Value('int64'), 'num_disorders': Value('int64'), 'num_snp_disorder_edges': Value('int64'), 'num_snp_gene_edges': Value('int64'), 'num_disorder_disorder_edges': Value('int64'), 'gws_threshold': Value('float64'), 'window_bp': Value('int64'), 'seed': Value('int64'), 'disorders': List(Value('string')), 'disorder_edge_mode': Value('string')}
              because column names don't match

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PsychGNN Graph Artifact

Overview

This repository contains the graph data structure used by the PsychGNN models and related research artifacts.

The graph links psychiatric genetic variants to nearby genes and psychiatric disorder groups. It is a structured research object for model training and variant-level analysis, not a patient-level dataset or clinical knowledge graph.

What is inside

Files currently published:

graph_artifact.pt: serialized graph tensors used by training and the Space
metadata.json: graph-level counts and configuration summary
split_counts.json: train / validation / test positive-edge split counts
gene_coordinates_grch37.parquet: published gene coordinate table used during graph construction

Graph scope

This release is a GWS-filtered SNP/gene/disorder graph.

Published graph metadata:

SNP nodes: 18,979
Gene nodes: 1,205
Disorder nodes: 11
SNP→Disorder positive edges: 22,687
SNP→Gene edges: 65,634
Disorder→Disorder edges: 0
GWS threshold: p < 5e-8
SNP→Gene window: ±100 kb

Disorder nodes in this graph:

ADHD
Anxiety
Autism
Bipolar disorder
Borderline personality disorder (bpd)
Eating disorders
Major depressive disorder
OCD
PTSD
Schizophrenia
Substance use

What this graph is and is not

This graph includes:

GWS psychiatric SNP nodes
positional SNP→Gene links
disorder nodes

This graph does not include:

a full pathway node layer
gene→gene regulatory edges
disorder→disorder edges in this release

How it was derived

High-level construction procedure:

Start from the harmonized psychiatric GWAS summary-statistics dataset:
- lighteternal/pgc-psychiatric-gwas-harmonized
Keep variants reaching genome-wide significance in at least one disorder.
Build SNP nodes and disorder nodes.
Map SNPs to nearby protein-coding genes using a positional window.
Serialize the resulting tensors for model training and Space inference.

Intended use

This artifact is intended for:

reproducing the current PsychGNN training run
powering the public Space
graph-level research analysis of psychiatric variant-disorder structure

It is not intended for:

clinical interpretation
independent causal claims
general human-genome annotation outside the published graph scope

Important caveats

only GWS variants are included, so this is a filtered graph rather than a full summary-statistics graph
disorder-disorder edges are a correlation proxy, not full LDSC estimates
biological coverage is intentionally narrower than the long-term PRD
downstream claims must match the published graph scope, not the aspirational architecture

Relationship to the rest of the project

This graph artifact is the shared input for the public PsychGNN model repositories.

built from: lighteternal/pgc-psychiatric-gwas-harmonized
consumed by:
- lighteternal/psychgnn-masked-edge-imputation-model
- lighteternal/psychgnn-all-tested-pairs-model

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Total file size:

3.17 MB