Abstract
One of the justifiable criticisms of human genetic studies is the underrepresentation of participants from diverse populations. Lack of inclusion must be addressed at-scale to identify causal disease factors and understand the genetic causes of health disparities. We present genome-wide associations for 2068 traits from 635,969 participants in the Department of Veterans Affairs Million Veteran Program, a longitudinal study of diverse United States Veterans. Systematic analysis revealed 13,672 genomic risk loci; 1608 were only significant after including non-European populations. Fine-mapping identified causal variants at 6318 signals across 613 traits. One-third (n = 2069) were identified in participants from non-European populations. This reveals a broadly similar genetic architecture across populations, highlights genetic insights gained from underrepresented groups, and presents an extensive atlas of genetic associations.
| Original language | English |
|---|---|
| Article number | eadj1182 |
| Journal | Science |
| Volume | 385 |
| Issue number | 6706 |
| DOIs | |
| State | Published - Jul 19 2024 |
Funding
We thank the Million Veteran Program, Office of Research and Development, and Veterans Health Administration for supporting this work. A complete acknowledgment of contributions to MVP is provided in the supplementary text (9). We would like to sincerely thank T. Zacharia for providing access to the supercomputers at the Oak Ridge National Laboratory Leadership Computing Facility, and D. Kusenov, the previous DOE Headquarters lead for the VA-DOE partnership, for his invaluable guidance and support. Their contributions have been instrumental in the successful completion of this study. We want to thank NCBI’s dbGAP team, particularly M. Feolo, N. Gupta, Z. Wang, and A. Sturcke for all their hard work enabling the public release of this large data resource; G. Wang and Y. Zou for their help deriving the formula for residual associations used to tune the parameters for fine-mapping; We thank former staff members and volunteers, who have contributed to MVP. Most of all, we thank MVP participants for their service and their continued contributions to our nation through participation in this study. This publication does not represent the views of the Department of Veteran Affairs or the US Government. Funding: The work was supported by the Million Veteran Program award #MVP000. This research used resources from the Knowledge Discovery Infrastructure at the Oak Ridge National Laboratory, supported by the Office of Science of the US Department of Energy under contract DE-AC05-00OR22725 and the Department of Veterans Affairs Office of Information Technology Inter-Agency Agreement with the Department of Energy under IAA VA118-16-M-1062. Other support by the National Institute of General Medical Sciences includes grant R01GM138597 (to A.V.); National Institute Health grant T32 AA028259 (to J.D.D.); National Library of Medicine grant 5R01LM010685 (to R.J.C.); National Human Genome Research Institute grant K99HG012222 (to W.Z.); National Institute of Arthritis and Musculoskeletal and Skin Diseases grant P30AR072577 (to K.P.L.); National Institute of Diabetes and Digestive and Kidney Diseases grant DK126194 (to B.F.V.); National Institute of Health grants NIR01AG067025, K08MH122911 (to G.V.); National Institute of Health grants BX004189, R01AG065582, R01AG067025 (to P.R.); Office of Research and Development, Veterans Health Administration award I01CX001849-01 (to J.G.); Office of Research and Development, Veterans Health Administration awards BX004821, CX001737, BX005831 (to Y.S.V.); Veterans Health Administration awards IK2-CX001780 (to S.M.D.); Veterans Health Administration award BX003364 (to S.K.I.); Cleveland Institute for Computational Biology, NIH Core grants P30 EY025585, P30 EY011373 (to S.K.I.); Clinical and Translational Science Collaborative of Cleveland UL1TR002548 from National Center for Advancing Translational Sciences (to S.K.I.).