Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program
Open accessPublished version of a preprint
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
220 (84%) of the 261 lead variants had at least one pleiotropic association across 2,068 traits.
“A total of 220 (84%) lead variants had at least one pleiotropic association (Supplementary Table 13), among which 208 were multi-ancestry lead variants, 8 were European genetic ancestry lead variants and 3 were male sex-stratified lead variants.”
From Genomic and transcriptomic analyses of aortic stenosis enhance therapeutic target discovery and disease prediction (2026) · cites this paper as [20]
The Results section, “Cross-trait genetic architecture identifies pleiotropic genes” (page unavailable), reports pleiotropy for genes, not the claimed proportion of lead variants. Searches for 220, 261, 84%, and the ancestry breakdown found no passage in our copy establishing that 220 of 261 lead variants had a pleiotropic association. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247 · see citing works
The Structure of Haplotype Blocks in the Human Genome
Gabriel, S. B., Schaffner, S. F., Nguyen, H., Moore, J. M., Roy, J., Blumenstiel, B., Higgins, J., DeFelice, M., Lochner, A., Faggart, M., et al. (2002) · Science · doi:10.1126/science.1069424 · Cited by 5,863 · see citing works
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559 · see citing works
METAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205 · see citing works
Clinical use of current polygenic risk scores may exacerbate health disparities
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019) · Nature Genetics · doi:10.1038/s41588-019-0379-x · Cited by 2,831 · see citing works
SLC24A5, a putative cation exchanger, affects pigmentation in zebrafish and humans
Lamason, R. L., Mohideen, M.-A. P. K., Mest, J. R., Wong, A. C., Norton, H. L., Aros, M. C., Jurynec, M. J., Mao, X., Humphreville, V. R., Humbert, J. E., et al. (2005) · Science · doi:10.1126/science.1116238 · Cited by 1,043 · see citing works
Chen, Z., Chen, J., Collins, R., Guo, Y., Peto, R., Wu, F., Li, L., & on behalf of the China Kadoorie Biobank (CKB) collaborative group (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr120 · Cited by 1,034 · see citing works
Peterson, R. E., Kuchenbaecker, K., Walters, R. K., Chen, C.-Y., Popejoy, A. B., Periyasamy, S., Lam, M., Iyegbe, C., Strawbridge, R. J., Brick, L., et al. (2019) · Cell · doi:10.1016/j.cell.2019.08.051 · Cited by 797 · see citing works
Chen, M.-H., Raffield, L. M., Mousas, A., Sakaue, S., Huffman, J. E., Moscati, A., Trivedi, B., Jiang, T., Akbari, P., Vuckovic, D., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.045 · Cited by 661 · see citing works
Fine-mapping from summary data with the “Sum of Single Effects” model
Zou, Y., Carbonetto, P., Wang, G., & Stephens, M. (2022) · PLOS Genetics · doi:10.1371/journal.pgen.1010299 · Cited by 429 · cited 2× · see citing works
“…of putative causal genetic variants that could be qualitatively and quantitatively compared across population groups, we performed within-population group fine-mapping using the Sum of Single Effects model implemented in SuSiE ( 18 , 19 ) followed by multipopulation credible set integration.”
Reich, D., Nalls, M. A., Kao, W. H. L., Akylbekova, E. L., Tandon, A., Patterson, N., Mullikin, J., Hsueh, W.-C., Cheng, C.-Y., Coresh, J., et al. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000360 · Cited by 423 · see citing works
Park, J.-H., Gail, M. H., Weinberg, C. R., Carroll, R. J., Chung, C. C., Wang, Z., Chanock, S. J., Fraumeni, J. F., & Chatterjee, N. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1114759108 · Cited by 280 · see citing works
Self-citationDiversity and inclusion for the All of Us research program: A scoping review
Mapes, B. M., Foster, C. S., Kusnoor, S. V., Epelbaum, M. I., AuYoung, M., Jenkins, G., Lopez-Class, M., Richardson-Heron, D., Elmi, A., Surkan, K., et al. (2020) · PLOS ONE · doi:10.1371/journal.pone.0234962 · Cited by 273 · see citing works
Flagged first, then by citations
Research works printed but not linked: 43
- [1]Mills MC, Rahal C, The GWAS Diversity Monitor tracks diversity by disease in real time. Nat. Genet. 52, 242– 243 (2020). doi: 10.1038/s41588-020-0580-y; pmid: 32139905 32139905 · DOI
- [4]Nagai A, BioBank Japan Cooperative Hospital Group, Overview of the BioBank Japan Project: Study design and profile. J. Epidemiol. 27 (3S), S2– S8 (2017). doi: 10.1016/j.je.2016.12.005; pmid: 28189464 28189464 PMC5350590 · DOI · Paper page
- [6]Denny JC, The “All of Us” Research Program. N. Engl. J. Med. 381, 668– 676 (2019). doi: 10.1056/NEJMsr1809937; pmid: 9403360 31412182 PMC8291101 · DOI · Paper page
- [7]Gaziano JM, Million Veteran Program: A mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214– 223 (2016). doi: 10.1016/j.jclinepi.2015.09.016; pmid: 26441289 26441289 · DOI · Paper page
- [10]Zhou W, Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335– 1341 (2018). doi: 10.1038/s41588-018-0184-y; pmid: 30104761 30104761 PMC6119127 · DOI
- [11]Sollis E, The NHGRI-EBI GWAS Catalog: Knowledgebase and deposition resource. Nucleic Acids Res. 51, D977– D985 (2023). doi: 10.1093/nar/gkac1010; pmid: 36350656 36350656 PMC9825413 · DOI · Paper page
- [12]Ghoussaini M, Open Targets Genetics: Systematic identification of trait-associated genes using large-scale genetics and functional genomics. Nucleic Acids Res. 49, D1311– D1320 (2021). doi: 10.1093/nar/gkaa840; pmid: 33045747 33045747 PMC7778936 · DOI
- [13]Brown BC, Ye CJ, Price AL, Zaitlen N, Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, Transethnic Genetic-Correlation Estimates from Summary Statistics. Am. J. Hum. Genet. 99, 76– 88 (2016). doi: 10.1016/j.ajhg.2016.05.001; pmid: 27321947 27321947 PMC5005434 · DOI · Paper page
- [14]Bulik-Sullivan B, ReproGen Consortium; Psychiatric Genomics Consortium; Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236– 1241 (2015). doi: 10.1038/ng.3406; pmid: 26414676 26414676 PMC4797329 · DOI · Paper page
- [16]Bentley AR, APOL1 G1 genotype modifies the association between HDLC and kidney function in African Americans. BMC Genomics 16, 421 (2015). doi: 10.1186/s12864-015-1645-7; pmid: 26025194 26025194 PMC4448293 · DOI
- [17]Joshi TP, Epidemiology of alopecia areata in the Hispanic/Latinx community: A cross-sectional analysis of the All of Us database. J. Am. Acad. Dermatol. 89, e61– e62 (2023). doi: 10.1016/j.jaad.2023.02.054; pmid: 36921806 36921806 · DOI
- [18]Wang G, Sarkar A, Carbonetto P, Stephens M, A simple new approach to variable selection in regression, with application to genetic fine mapping. J. R. Stat. Soc. Series B Stat. Methodol. 82, 1273– 1300 (2020). doi: 10.1111/rssb.12388; pmid: 37220626 37220626 PMC10201948 · DOI · Paper page
- [20]Kanai M, Insights from complex trait fine-mapping across diverse populations. medRxiv 2021.09.03.21262975 [Preprint] (2021); doi: 10.1101/2021.09.03.21262975 · DOI
- [21]Verma A, Diversity and Scale: Genetic Architecture of 2,068 Traits in the VA Million Veteran Program Data S1, Dryad (2023); doi: 10.1101/2023.06.28.23291975 PMC12857194 39024449 · DOI
- [23]Schoech AP, Quantification of frequency-dependent genetic architectures in 25 UK Biobank traits reveals action of negative selection. Nat. Commun. 10, 790 (2019). doi: 10.1038/s41467-019-08424-6; pmid: 30770844 30770844 PMC6377669 · DOI · Paper page
- [24]Wang G, Speakman JR, Analysis of Positive Selection at Single Nucleotide Polymorphisms Associated with Body Mass Index Does Not Support the “Thrifty Gene” Hypothesis. Cell Metab. 24, 531– 541 (2016). doi: 10.1016/j.cmet.2016.08.014; pmid: 27667669 27667669 · DOI
- [25]Wilde S, Direct evidence for positive selection of skin, hair, and eye pigmentation in Europeans during the last 5,000 y. Proc. Natl. Acad. Sci. U.S.A. 111, 4832– 4837 (2014). doi: 10.1073/pnas.1316513111; pmid: 24616518 24616518 PMC3977302 · DOI · Paper page
- [27]Rajabli F, Alzheimer’s Disease Sequencing Project, Alzheimer’s Disease Genetic Consortium, A locus at 19q13.31 significantly reduces the ApoE ε4 risk for Alzheimer’s Disease in African Ancestry. PLOS Genet. 18, e1009977 (2022). doi: 10.1371/journal.pgen.1009977; pmid: 35788729 PMC9286282 35788729 · DOI
- [28]Wrigley R, Pleiotropic effect of the ABCG2 gene in gout: Involvement in serum urate levels and progression from hyperuricemia to gout. Arthritis Res. Ther. 22, 45 (2020). doi: 10.1186/s13075-020-2136-z; pmid: 32164793 32164793 PMC7069001 · DOI
- [29]Pilon MO, An association study of ABCG2 rs2231142 on the concentrations of allopurinol and its metabolites. Clin. Transl. Sci. 15, 2024– 2034 (2022). doi: 10.1111/cts.13318; pmid: 35689378 35689378 PMC9372422 · DOI
- [30]Yu K-H, A comprehensive analysis of the association of common variants of ABCG2 with gout. Sci. Rep. 7, 9988 (2017). doi: 10.1038/s41598-017-10196-2; pmid: 28855613 28855613 PMC5577061 · DOI
- [31]Polfus LM., 23andMe Research Team; DIAMANTE Hispanic/Latino Consortium; MEta-analysis of type 2 DIabetes in African Americans Consortium, Genetic discovery and risk characterization in type 2 diabetes across diverse populations. HGG Adv. 2, 100029 (2021). doi: 10.1016/j.xhgg.2021.100029; pmid: 34604815 PMC8486151 34604815 · DOI
- [32]Chen J, Genome-wide association study of type 2 diabetes in Africa. Diabetologia 62, 1204– 1211 (2019). doi: 10.1007/s00125-019-4880-7; pmid: 31049640 31049640 PMC6560001 · DOI
- [33]Fulco CP, Activity-by-contact model of enhancer-promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51, 1664– 1669 (2019). doi: 10.1038/s41588-019-0538-0; pmid: 31784727 31784727 PMC6886585 · DOI · Paper page
- [34]Bhutia YD, SLC transporters as a novel class of tumour suppressors: Identity, function and molecular mechanisms. Biochem. J. 473, 1113– 1124 (2016). doi: 10.1042/BJ20150751; pmid: 27118869 27118869 PMC4930156 · DOI
- [35]Kim TW, Expression of SLC22A18 regulates oxaliplatin resistance by modulating the ERK pathway in colorectal cancer. Am. J. Cancer Res. 12, 1393– 1408 (2022). pmid: 35411243 PMC8984883
- [36]Kousathanas A, Whole-genome sequencing reveals host factors underlying critical COVID-19. Nature 607, 97– 103 (2022). doi: 10.1038/s41586-022-04576-6; pmid: 35255492 35255492 PMC9259496 · DOI
- [37]Darst BF, A Germline Variant at 8q24 Contributes to Familial Clustering of Prostate Cancer in Men of African Ancestry. Eur. Urol. 78, 316– 320 (2020). doi: 10.1016/j.eururo.2020.04.060; pmid: 32409115 32409115 PMC7805560 · DOI
- [38]Panagiotou OA, A genome-wide pleiotropy scan for prostate cancer risk. Eur. Urol. 67, 649– 657 (2015). doi: 10.1016/j.eururo.2014.09.020; pmid: 25277271 25277271 PMC4359641 · DOI
- [39]Chen F, Evidence of Novel Susceptibility Variants for Prostate Cancer and a Multiancestry Polygenic Risk Score Associated with Aggressive Disease in Men of African Ancestry. Eur. Urol. 84, 13– 23 (2023). doi: 10.1016/j.eururo.2023.01.022; pmid: 36872133 36872133 PMC10424812 · DOI
- [40]McCormick N, Racial and Sex Disparities in Gout Prevalence Among US Adults. JAMA Netw. Open 5, e2226804 (2022). doi: 10.1001/jamanetworkopen.2022.26804; pmid: 35969396 PMC9379746 · DOI
- [41]Weissbrod O, Functionally informed fine-mapping and polygenic localization of complex trait heritability. Nat. Genet. 52, 1355– 1363 (2020). doi: 10.1038/s41588-020-00735-5; pmid: 33199916 33199916 PMC7710571 · DOI · Paper page
- [42]Rao S, Yao Y, Bauer DE, Editing GWAS: Experimental approaches to dissect and exploit disease-associated genetic variation. Genome Med. 13, 41 (2021). doi: 10.1186/s13073-021-00857-3; pmid: 33691767 33691767 PMC7948363 · DOI
- [44]Reich DE, Linkage disequilibrium in the human genome. Nature 411, 199– 204 (2001). doi: 10.1038/35075590; pmid: 11346797 11346797 · DOI
- [45]Griswold AJ, Increased APOE ε4 expression is associated with the difference in Alzheimer’s disease risk from diverse ancestral backgrounds. Alzheimers Dement. 17, 1179– 1188 (2021). doi: 10.1002/alz.12287; pmid: 33522086 33522086 PMC8843031 · DOI · Paper page
- [46]Parra OD, Biobanking in Latinos: Current status, principles for conduct, and contribution of a new biobank, El Banco por Salud, designed to improve the health of Latino patients of Mexican ancestry with type 2 diabetes. BMJ Open Diabetes Res. Care 10, e002709 (2022). doi: 10.1136/bmjdrc-2021-002709; pmid: 35504695 PMC9066498 35504695 · DOI
- [47]Mulder N, H3Africa: Current perspectives. Pharm. Genomics Pers. Med. 11, 59– 66 (2018). doi: 10.2147/PGPM.S141546; pmid: 29692621 PMC5903476 29692621 · DOI
- [51]Chang CC, Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015). doi: 10.1186/s13742-015-0047-8; pmid: 25722852 25722852 PMC4342193 · DOI · Paper page
- [52]Watanabe K, Taskesen E, van Bochoven A, Posthuma D, Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017). doi: 10.1038/s41467-017-01261-5; pmid: 29184056 29184056 PMC5705698 · DOI · Paper page
- [53]Mägi R, Morris AP, GWAMA: Software for genome-wide association meta-analysis. BMC Bioinformatics 11, 288 (2010). doi: 10.1186/1471-2105-11-288; pmid: 20509871 20509871 PMC2893603 · DOI
- [54]McLaren W, The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016). doi: 10.1186/s13059-016-0974-4; pmid: 27268795 27268795 PMC4893825 · DOI · Paper page
- [55]Dong S, Zhao N, Spragins E, Kagda MS, Li M, Assis P, Jolanki O, Luo Y, Cherry JM, Boyle AP, Hitz BC, Annotating and prioritizing human non-coding variants with RegulomeDB. bioRxiv 2022.10.18.512627 [Preprint] (2022); doi: 10.1101/2022.10.18.512627 PMC10989417 37173523 · DOI
- [56]Kanehisa M, Goto S, KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27– 30 (2000). doi: 10.1093/nar/28.1.27; pmid: 10592173 10592173 PMC102409 · DOI · Paper page
Cited by
Sorted by citations
Small, A. M., Yang, T.-Y., Itoh, S., Thériault, S., Dufresne, L., Kurosawa, R., Komuro, I., Matsuda, K., Vy, H. M. T., Farber-Eger, E. H., et al. (2026) · Nature Genetics · doi:10.1038/s41588-025-02417-6 · Cited by 12 · see citing works
“We evaluated all 261 independent lead variants for pleiotropic associations across 2,068 traits, including 1,854 binary and 214 quantitative traits, using summary phenome-wide association study (PheWAS) data from the Million Veteran Program (MVP) 20 .”
InfluentialSelf-citation
- Authors
- Verma, A., Huffman, J. E., Rodriguez, A., Conery, M., Liu, M., Ho, Y.-L., Kim, Y., Heise, D. A., Guare, L., Panickan, V. A., Garcon, H., Linares, F., Costa, L., Goethert, I., Tipton, R., Honerlaw, J., Davies, L., Whitbourne, S., Cohen, J., Posner, D. C., Sangar, R., Murray, M., Wang, X., Dochtermann, D. R., Devineni, P., Shi, Y., Nandi, T. N., Assimes, T. L., Brunette, C. A., Carroll, R. J., Clifford, R., Duvall, S., Gelernter, J., Hung, A., Iyengar, S. K., Joseph, J., Kember, R., Kranzler, H., Kripke, C. M., Levey, D., Luoh, S.-W., Merritt, V. C., Overstreet, C., Deak, J. D., Grant, S. F. A., Polimanti, R., Roussos, P., Shakt, G., Sun, Y. V., Tsao, N., Venkatesh, S., Voloudakis, G., Justice, A., Begoli, E., Ramoni, R., Tourassi, G., Pyarajan, S., Tsao, P., O'Donnell, C. J., Muralidhar, S., Moser, J., Casas, J. P., Bick, A. G., Zhou, W., Cai, T., Voight, B. F., Cho, K., Gaziano, J. M., Madduri, R. K., Damrauer, S., & Liao, K. P.
- Affiliations
- Philadelphia VA Medical Center (US)
- Harvard University (US)
- Argonne National Laboratory (US)
- University of Pennsylvania (US)
- Columbia University (US)
- VA Boston Healthcare System (US)
- Oak Ridge National Laboratory (US)
- Brigham and Women's Hospital (US)
- VA Palo Alto Health Care System (US)
- Vanderbilt University Medical Center (US)
- University of California San Diego (US)
- University of Utah (US)
- Yale University (US)
- Vanderbilt University (US)
- Case Western Reserve University (US)
- Brown University (US)
- Oregon Health & Science University (US)
- VA San Diego Healthcare System (US)
- Children's Hospital of Philadelphia (US)
- James J. Peters VA Medical Center (US)
- Emory University (US)
- United States Department of Veterans Affairs (US)
- Medicine, Cardiology, VA Boston Healthcare System, Boston, MA 02130, USA.
- Massachusetts General Hospital (US)
- Corresponding author
- Verma, Anurag
- Huffman, Jennifer E.
- Rodriguez, Alex
- Conery, Mitchell
- Liu, Molei
- Cai, Tianxi
- Voight, Benjamin F.
- Cho, Kelly
- Gaziano, J. Michael
- Madduri, Ravi K.
- Damrauer, Scott
- Liao, Katherine P.
- ORCID
- Verma, Anurag
- Huffman, Jennifer E.
- Rodriguez, Alex
- Conery, Mitchell
- Liu, Molei
- Ho, Yuk-Lam
- Kim, Youngdae
- Heise, David A.
- Guare, Lindsay
- Panickan, Vidul Ayakulangara
- Linares, Franciel
- Costa, Lauren
- Goethert, Ian
- Honerlaw, Jacqueline
- Davies, Laura
- Whitbourne, Stacey
- Cohen, Jeremy
- Posner, Daniel C.
- Wang, Xuan
- Dochtermann, Daniel R.
- Shi, Yunling
- Nandi, Tarak Nath
- Assimes, Themistocles L.
- Brunette, Charles A.
- Carroll, Robert J.
- Clifford, Royce
- Duvall, Scott
- Gelernter, Joel
- Hung, Adriana
- Iyengar, Sudha K.
- Joseph, Jacob
- Kember, Rachel
- Kranzler, Henry
- Kripke, Colleen M.
- Levey, Daniel
- Luoh, Shiuh-Wen
- Merritt, Victoria C.
- Overstreet, Cassie
- Deak, Joseph D.
- Grant, Struan F. A.
- Polimanti, Renato
- Roussos, Panos
- Shakt, Gabrielle
- Sun, Yan V.
- Tsao, Noah
- Venkatesh, Sanan
- Voloudakis, Georgios
- Justice, Amy
- Begoli, Edmon
- Ramoni, Rachel
- Tourassi, Georgia
- Pyarajan, Saiju
- Tsao, Philip
- O'Donnell, Christopher J.
- Muralidhar, Sumitra
- Moser, Jennifer
- Casas, Juan P.
- Bick, Alexander G.
- Zhou, Wei
- Cai, Tianxi
- Voight, Benjamin F.
- Cho, Kelly
- Gaziano, J. Michael
- Madduri, Ravi K.
- Damrauer, Scott
- Liao, Katherine P.
- Venue
- Science
- Published
- 2024-07-19
- Volume
- 385
- Issue
- 6706
- Pages
- eadj1182-eadj1182
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC12857194
- OpenAlex
- W4400770247
- Cited by
- 463
- References
- 14
- Citations by year
- 2026: 230 · 2025: 202 · 2024: 29 · 2023: 2
- FWCI
- 122.35
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by
- Locations
- Science (publishedVersion)
- medRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (acceptedVersion, cc-by)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- PubMed Central (submittedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy (AC05-00OR22725, DE--AC05--00OR22725, DE-AC05?, Contract No. DE-AC05-00OR22725, Contract DE-AC05-00OR22725, No. DE-AC05-00OR22725, 00OR22725)
- U.S. Department of Veterans Affairs (I01-CX001737, IK2-CX001780, DE-AC05-00OR22725, I01-BX004821, UL1TR002548, I01CX001849, BX004189, I01 BX004189, I01CX001849-01, I01 BX005831, MVP000, P30 EY025585, I01 BX003364)
- Battelle (DE-AC05- 00OR22725, DE-AC05)
- AstraZeneca
- GlaxoSmithKline
- Office of Research and Development (MVP#000)
- Alnylam Pharmaceuticals
- Celgene
- University of Utah
- American Society of Clinical Psychopharmacology
- Parexel
- UT-Battelle (DE-AC05, DEAC05-00OR22725, AC05-00OR22725)
- Novo Nordisk
- Astellas Pharma
- H. Lundbeck A/S
- National Institutes of Health (DE-AC05-00OR22725, UL1TR002548, EY011373, EY025585)
- Office of Science (DEAC05- 00OR22725, No. DE-AC05-00OR22725, AC05-00OR22725)
- Clinical and Translational Science Collaborative of Cleveland, School of Medicine, Case Western Reserve University (UL1TR002548)
- National Institute on Alcohol Abuse and Alcoholism (T32AA028259)
- National Institute on Aging (R01AG065582, R01AG067025)
- National Institute of Mental Health (K08MH122911)
- National Human Genome Research Institute (K99HG012222, R00HG012222)
- National Eye Institute (P30EY011373, P30EY025585)
- National Institute of General Medical Sciences (R01GM138597, DE-AC05-00OR22725)
- National Institute of Diabetes and Digestive and Kidney Diseases (DK126194, UM1DK126194)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (P30AR072577)
- U.S. National Library of Medicine (R01LM010685)
- National Center for Advancing Translational Sciences
- Janssen Pharmaceuticals
- Oak Ridge National Laboratory (AC05-00OR22725, Contract No. DE-AC05-00OR22725, DE-AC05-00OR22725)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Race, Genetics, and Society
- Keywords
- Million Veteran Program, genetic architecture, genome-wide association study, genetic susceptibility loci, fine mapping, non-European populations, underrepresented groups, health disparities, veterans, causal variants
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review