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README.md
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license: other
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license_name: embl-ebi
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license_link: https://www.ebi.ac.uk/about/terms-of-use/
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---
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license: other
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license_name: embl-ebi
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license_link: https://www.ebi.ac.uk/about/terms-of-use/
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---
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# GWAS Catalog Samples
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## Dataset Description
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This dataset contains sample-level metadata from the [NHGRI-EBI GWAS Catalog](https://ebi.ac.uk/gwas), describing the populations included in genome-wide association studies (GWAS).
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Each record represents a population sample used within a GWAS study, including information about sample size, ancestry category, and geographic origin. The dataset provides a structured representation of the participant populations contributing to published GWAS results.
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Unlike the [GWAS Catalog Studies dataset](https://huggingface.co/datasets/gwascatalog/studies), which focuses on publication-level metadata, and the [Associations dataset](https://huggingface.co/datasets/gwascatalog/associations), which captures SNP–trait associations, the Samples dataset describes the populations and cohorts contributing to GWAS analyses.
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This dataset is particularly valuable for understanding genetic ancestry representation in GWAS research, enabling analyses of population diversity, recruitment geography, and ancestry distribution across studies.
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Typical research uses include:
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* examining ancestry representation in GWAS cohorts
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* analysing geographic recruitment patterns in genomic studies
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* studying population diversity across GWAS datasets
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* assessing ancestry imbalance or underrepresentation in genetic studies
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* linking population metadata with study-level and association-level GWAS data
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The GWAS Catalog applies a standardised framework for representing ancestry data in genomics studies. For more details, please see:
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Morales, J., Welter, D., Bowler, E.H. et al. A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog. Genome Biol 19, 21 (2018). https://doi.org/10.1186/s13059-018-1396-2
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---
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## Dataset Summary
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* Task categories: genomics, biomedical metadata analysis
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* Data type: tabular
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* Primary domain: genome-wide association studies (GWAS)
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* Unit of observation: GWAS sample population
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* Source: curated literature database
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Typical uses include:
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* analysing ancestry composition of GWAS cohorts
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* evaluating population diversity in genomic research
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* linking ancestry metadata with GWAS study results
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* studying recruitment geography and sampling strategies in GWAS
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---
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# Dataset Structure
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Each row represents a specific sample population used in a GWAS study, typically corresponding to either the discovery stage or a replication stage cohort.
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A single study may contain multiple sample entries representing different cohorts, recruitment locations, or ancestry groups.
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## Columns
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| Column | Description |
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| ------------------------------- | --------------------------------------------------------------------------------------------------------------- |
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| STUDY ACCESSION | Accession identifier assigned to a GWAS study in the GWAS Catalog (e.g., GCST identifiers). |
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| PUBMEDID | PubMed identifier for the publication reporting the GWAS. |
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| FIRST AUTHOR | Last name and initials of the first author of the publication. |
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| DATE | Publication date (online/epub date if available). |
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| INITIAL SAMPLE DESCRIPTION | Description of the discovery-stage GWAS cohort, including sample size and ancestry information. |
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| REPLICATION SAMPLE DESCRIPTION | Description of replication cohort(s) used to validate associations reported in the study. |
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| STAGE | Stage of the GWAS to which the sample description applies (e.g., initial discovery stage or replication stage). |
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| NUMBER OF INDIVIDUALS | Number of individuals included in the sample population. |
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| BROAD ANCESTRAL CATEGORY | High-level ancestry classification assigned to place samples into broader genetic ancestry groups. |
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| COUNTRY OF ORIGIN | Country associated with the ancestral origin of the individuals in the sample. |
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| COUNTRY OF RECRUITMENT | Country where participants were recruited for the study. |
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| ADDITIONAL ANCESTRY DESCRIPTION | Additional descriptors providing more detailed ancestry information for the sample population. |
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---
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# Curation Process
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The GWAS Catalog is curated through a combination of automated and manual processes.
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### 1. Literature identification
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Publications describing genome-wide association studies are identified through:
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* literature searches
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* author submissions
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### 2. Manual curation
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Expert curators review publications and extract key information including:
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* variant identifiers (e.g., rsIDs)
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* associated traits or diseases
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* statistical significance metrics
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* effect sizes
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* sample descriptions
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* genomic location information
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### 3. Standardisation
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Extracted data are normalised using standardised vocabularies and identifiers where possible, including:
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* controlled trait terms from the [Experimental Factor Ontology (EFO)](https://www.ebi.ac.uk/efo/)
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* genomic coordinates
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* gene identifiers
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### 4. Annotation
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Variants are annotated with additional genomic information such as:
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* mapped genes
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* variant context (e.g., intronic, intergenic)
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* genomic distances to nearby genes
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### 5. Quality control
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Curated records undergo internal quality checks to ensure:
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* consistency across records
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* correct variant identifiers
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* valid genomic annotations
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For more information about the curation process, please see the [GWAS Catalog documentation](https://ebi.ac.uk/gwas/docs/methods).
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The Hugging Face dataset mirrors the [tabular ancestry records published by the GWAS Catalog on 2026-03-17](https://www.ebi.ac.uk/gwas/docs/file-downloads).
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---
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# Bias, Limitations, and Population Representation
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Genome-wide association studies have several limitations that affect analyses using this dataset.
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## Population Bias
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A large proportion of GWAS studies have historically been conducted with individuals genetically similar to European reference populations.
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Please note:
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* genetic associations may not generalise across populations
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* allele frequencies may differ substantially between ancestries
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* effect sizes may vary across populations
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Users should exercise caution when applying results derived from GWAS to diverse populations.
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## Publication Bias
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Because the catalog is derived from published studies, it may reflect:
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* overrepresentation of statistically significant findings
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* underrepresentation of null results
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* bias towards traits that are frequently studied
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## Study Heterogeneity
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GWAS studies vary substantially in:
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* sample size
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* cohort composition
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* phenotype definitions
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* genotyping platforms
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* statistical analysis methods
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These factors may influence comparability across studies.
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---
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# Credits
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This dataset is derived from the [NHGRI-EBI GWAS Catalog](https://ebi.ac.uk/gwas).
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We would like to thank:
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* authors who submit their data to the catalog, including full summary statistics
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* authors of the original GWAS publications included in the catalog
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* GWAS Catalog team members, past and present
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* research participants who contributed data to the underlying genetic studies
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---
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# Citation
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If you use this dataset in research, please cite the GWAS Catalog publication:
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Maria Cerezo et al.
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*The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity.*
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Nucleic Acids Research, 2025.
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```bibtex
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@article{cerezo2025nhgri,
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title={The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity},
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author={Cerezo, Maria and Sollis, Elliot and Ji, Yue and Lewis, Elizabeth and Abid, Ala and Bircan, Karatu{\u{g}} Ozan and Hall, Peggy and Hayhurst, James and John, Sajo and Mosaku, Abayomi and others},
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journal={Nucleic acids research},
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volume={53},
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number={D1},
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pages={D998--D1005},
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year={2025},
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publisher={Oxford University Press}
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}
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```
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---
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# Licence
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The NHGRI-EBI GWAS Catalog and all its contents are available under the [general Terms of Use for EMBL-EBI Services](https://ebi.ac.uk/about/terms-of-use). Summary statistics are made available under CC0 unless otherwise stated.
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Consumers of data hosted by the GWAS Catalog should review the licence terms of individual datasets where applicable to their specific use case.
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