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The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource

Elliot Sollis, Abayomi Mosaku, Ala Abid and 27 more

Nucleic Acids Research · 2022 · CC BY

doi:10.1093/nar/gkac1010

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.

References 23

  1. 1.
  2. 2.
    Ensembl 2022 · 2021
    No notice
  3. 3.

    Ramos E.M., Hoffman D., Junkins H.A., Maglott D., Phan L., Sherry S.T., Feolo M., Hindorff L.A. Phenotype-Genotype Integrator (PheGenI): synthesizing genome-wide association study (GWAS) data with existing genomic resources. Eur. J. Hum. Genet. EJHG. 2014; 22:144–147.

    Not in our library10.1038/ejhg.2013.96

  4. 4.
  5. 5.
  6. 6.
  7. 7.

    Beck T., Shorter T., Brookes A.J. GWAS Central: a comprehensive resource for the discovery and comparison of genotype and phenotype data from genomewide association studies. Nucleic Acids Res.2020; 48:D933–D940.

    Not in our library10.1093/nar/gkz895

  8. 8.
  9. 9.
  10. 10.

    McMahon A., Lewis E., Buniello A., Cerezo M., Hall P., Sollis E., Parkinson H., Hindorff L.A., Harris L.W., MacArthur J.A.L. Sequencing-based genome-wide association studies reporting standards. Cell Genomics. 2021; 1:100005.

    Not in our library10.1016/j.xgen.2021.100005

  11. 11.

    Auwerx C., Lepamets M., Sadler M.C., Patxot M., Stojanov M., Baud D., Mägi R., Team EstonianBiobankResearch, Porcu E., Reymond A. et al. The individual and global impact of copy-number variants on complex human traits. Am. J. Hum. Genet.2022; 109:647–668.

    Not in our library10.1016/j.ajhg.2022.02.010

  12. 12.

    Fitzgerald T., Birney E. CNest: a novel copy number association discovery method uncovers 862 new associations from 200,629 whole-exome sequence datasets in the UK Biobank. Cell Genomics. 2022; 2:100167.

    Not in our library10.1016/j.xgen.2022.100167

  13. 13.

    Foster I. Globus Online: accelerating and democratizing science through cloudbased services. IEEE Internet Comput.2011; 15:70–73.

    Not in our library

  14. 14.

    Allen B., Bresnahan J., Childers L., Foster I., Kandaswamy G., Kettimuthu R., Kordas J., Link M., Martin S., Pickett K. et al. Software as a service for data scientists. Commun. ACM. 2012; 55:81–88.

    Not in our library

  15. 15.
  16. 16.

    Malone J., Holloway E., Adamusiak T., Kapushesky M., Zheng J., Kolesnikov N., Zhukova A., Brazma A., Parkinson H. Modeling sample variables with an Experimental Factor Ontology. Bioinforma. Oxf. Engl.2010; 26:1112–1118.

    Not in our library10.1093/bioinformatics/btq099

  17. 17.

    Lambert S.A., Gil L., Jupp S., Ritchie S.C., Xu Y., Buniello A., McMahon A., Abraham G., Chapman M., Parkinson H. et al. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nat. Genet.2021; 53:420–425.

    Not in our library10.1038/s41588-021-00783-5

  18. 18.

    Cacheiro P., Muñoz-Fuentes V., Murray S.A., Dickinson M.E., Bucan M., Nutter L.M.J., Peterson K.A., Haselimashhadi H., Flenniken A.M., Morgan H. et al. Human and mouse essentiality screens as a resource for disease gene discovery. Nat. Commun.2020; 11:655.

    Not in our library10.1038/s41467-020-14284-2

  19. 19.

    MacArthur J.A.L., Buniello A., Harris L.W., Hayhurst J., McMahon A., Sollis E., Cerezo M., Hall P., Lewis E., Whetzel P.L. et al. Workshop proceedings: GWAS summary statistics standards and sharing. Cell Genomics. 2021; 1:100004.

    Not in our library10.1016/j.xgen.2021.100004

  20. 20.

    Popejoy A.B., Fullerton S.M. Genomics is failing on diversity. Nature. 2016; 538:161–164.

    Not in our library10.1038/538161a

  21. 21.

    Martin A.R., Kanai M., Kamatani Y., Okada Y., Neale B.M., Daly M.J. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet.2019; 51:584–591.

    Not in our library10.1038/s41588-019-0379-x

  22. 22.
  23. 23.

    Mills M.C., Rahal C. The GWAS Diversity Monitor tracks diversity by disease in real time. Nat. Genet.2020; 52:242–243.

    Not in our library10.1038/s41588-020-0580-y

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