{"id":1562,"date":"2021-01-25T16:55:34","date_gmt":"2021-01-25T21:55:34","guid":{"rendered":"https:\/\/labs.icahn.mssm.edu\/minervalab\/?page_id=1562"},"modified":"2026-10-29T14:12:08","modified_gmt":"2026-10-29T18:12:08","slug":"gwas-summary-statistics","status":"publish","type":"page","link":"https:\/\/labs.icahn.mssm.edu\/minervalab\/resources\/data-ark\/gwas-summary-statistics\/","title":{"rendered":"GWAS Summary Statistics"},"content":{"rendered":"

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Scientific Computing and Data<\/a> \/ Research Data Services<\/a>\u00a0\/\u00a0Data Ark: Data Commons<\/a> \/ GWAS Summary Statistics<\/p>\n

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GWAS Summary Statistics<\/span><\/strong><\/h1>\n

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Overview<\/h2>\n

Genome-wide association studies (GWAS) provide a powerful tool for identifying genetic loci associated with phenotypes of interest. The sharing of GWAS summary statistics has enabled a range of secondary research applications that do not require access to the individual level data such as gene prioritization, fine-mapping, pathway enrichment analyses, causal inference of exposures, risk prediction, genetic correlation and heritability estimation.<\/p>\n

Several thousand GWAS summary statistics are available in the Data Ark, obtained from the IEU Open GWAS Project<\/a> including:<\/p>\n

* ebi-a (n = 288): GWAS satisfying minimum requirements imported from the EBI database of complete GWAS summary data<\/p>\n

* ieu-a (n = 440): GWAS generated by many different consortia that have been manually collected and curated, initially developed for MR-Base<\/p>\n

* ieu-b (n = 37): GWAS generated by many different consortia that have been manually collected and curated, initially developed for MR-Base (round 2)<\/p>\n

* ukb-b (n = 2514): IEU analysis of UK Biobank phenotypes<\/p>\n

These GWAS are stored on the Data Ark in the GWAS-VCF format, which provides a consistent and robust approach to storing genetic variants, annotations and metadata enabling interoperability and reusability consistent with the FAIR principles [1]. Crucially, this ensures that all the provided GWAS are harmonized so that eg. the ALT allele corresponds to the effect allele and that all the files utilize a consistent labeling scheme.<\/p>\n

More details on the GWAS-VCF format (illustrated above) and the available Open-source tools for working with GWAS-VCFs can be found in the corresponding publication. Lyon, M. et al. (2021). The variant call format provides efficient and robust storage of GWAS summary statistics.<\/em> Genome Biol 22, 32<\/a>, and are included in the Data Ark.<\/p>\n

Now available in the Data Ark:\u00a0<\/strong><\/p>\n