The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource

Abstract

The NHGRI-EBI GWAS Catalog (www.ebi.ac.uk/gwas) is a FAIR knowledgebase providing detailed, structured, standardised and interoperable genome-wide association study (GWAS) data to >200 000 users per year from academic research, healthcare and industry. The Catalog contains variant-trait associations and supporting metadata for >45 000 published GWAS across >5000 human traits, and >40 000 full P-value summary statistics datasets. Content is curated from publications or acquired via author submission of prepublication summary statistics through a new submission portal and validation tool. GWAS data volume has vastly increased in recent years. We have updated our software to meet this scaling challenge and to enable rapid release of submitted summary statistics. The scope of the repository has expanded to include additional data types of high interest to the community, including sequencing-based GWAS, gene-based analyses and copy number variation analyses. Community outreach has increased the number of shared datasets from under-represented traits, e.g. cancer, and we continue to contribute to awareness of the lack of population diversity in GWAS. Interoperability of the Catalog has been enhanced through links to other resources including the Polygenic Score Catalog and the International Mouse Phenotyping Consortium, refinements to GWAS trait annotation, and the development of a standard format for GWAS data.

eXclusionarY: Ten years later, where are the sex chromosomes in GWAS?

“…To address whether lack of sex chromosomal GWAS results were due to lack of appropriate (or any) analysis of the sex chromosomes, we calculated the proportion of GWAS summary statistics that included sex chromosome results, regardless if there were significant findings. There were 19,935 GWAS summary statistics published in 2021 and posted at the NHGRI-EBI GWAS catalog 5,6 (Resources). These GWAS submissions came from 136 publications, of which most provided 1-2 sets of summary statistics, but four provided > 1,000 sets (Supplementary Data S1).…”

“…We further examined the reporting standard in the original publications of the X chromosomal signals documented in the NHGRI-EBI GWAS catalog 5,6 (downloaded on 2020-03-08 and initially reported in 72 ) using the genome-wide significance level of p-value < 5 x 10 -8 . Out of the 3,869 studies available at that time (male-only studies excluded), 195 reported a total of 253 genome-wide significant loci on the X chromosome.…”

“…The paucity of research on the sex chromosomes includes both the X and Y chromosomes. For the Y chromosome 7 , as of November 29, 2022, only nine out of 447,939 associations reported in NHGRI-EBI GWAS Catalog 5,6 belong to the Y chromosome. Coverage is scarce on GWAS arrays for the male-only Y chromosome, in part due to repetitive sequences that makes variant calling difficult.…”

The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences

“…There are ontologies that focus on or include quantitative and measurable trait terms, such as VT, EFO and the Clinical Measurement Ontology (CMO) 72,73 . EFO curators, for example, maintain a branch in the ontology for "measurement" terms that are used in annotation by the GWAS Catalog 13 . "Measurement" terms, such as "urinary sodium measurement" (EFO:0021522), have a broad applicability in annotation.…”

“…The identification of the phenotypically and clinically relevant subset of the new DNA variants in both human and veterinary medicine, as well the characterisation of the mechanisms of how these variations exert their phenotypic effects, pose serious challenges that cannot be met successfully without advancements in data integration and computational tools 11 . A standardised and computationally amenable representation of traits is critical for many biomedical and agricultural use cases involving DNA variants, from genome-wide association studies (GWAS) to Quantitative Trait Loci (QTL) mappings [12][13][14][15][16] . Currently, the lack of consistent computational modelling and annotation of traits from various data sources restricts their interoperability and hinders not only genetic mechanisms of discovery for medicine, but also agriculture, biodiversity, and evolutionary biology.…”