Diversity and Scale: Genetic Architecture of 2,068 Traits in the VA Million Veteran Program

Abstract

Genome-wide association studies (GWAS) have underrepresented individuals from non-European populations, impeding progress in characterizing the genetic architecture and consequences of health and disease traits. To address this, we present a population-stratified phenome-wide GWAS followed by a multi-population meta-analysis for 2,068 traits derived from electronic health records of 635,969 participants in the Million Veteran Program (MVP), a longitudinal cohort study of diverse U.S. Veterans genetically similar to the respective African (121,177), Admixed American (59,048), East Asian (6,702), and European (449,042) superpopulations defined by the 1000 Genomes Project. We identified 38,270 independent variants associating with one or more traits at experiment-wide (P < 4.6x10-11) significance; fine-mapping 6,318 signals identified from 613 traits to single-variant resolution. Among these, a third (2,069) of the associations were found only among participants genetically similar to non-European reference populations, demonstrating the importance of expanding diversity in genetic studies. Our work provides a comprehensive atlas of phenome-wide genetic associations for future studies dissecting the architecture of complex traits in diverse populations.

Gene Discovery and Biological Insights into Anxiety Disorders from a Multi-Ancestry Genome-wide Association Study of >1.2 Million Participants

“…To investigate the shared genetic mechanisms between ANX and human traits and diseases, we performed a phenome-wide genetic correlation analysis leveraging 11,175 phenotypes available from UKB, MVP, and FinnGen [27][28][29] . After Bonferroni correction (p<4.…”

“…After excluding the MHC region (chr6:26,000,000-34,000,000), we applied mixture models available from MixeR 25 to estimate the number of influential variants ANX shares with other psychiatric disorders. We then applied LAVA 26 To assess ANX pleiotropic mechanisms across the human phenotypic spectrum, we performed an LDSC genetic correlation analysis 78 for 11,175 phenotypes available from UKB (details available at https://pan.ukbb.broadinstitute.org/), FinnGen (Release 9, details available at https://finngen.gitbook.io/documentation/v/r9/), and MVP 29 . Bonferroni correction accounting for the number of tests performed was applied to define statistically significant genetic correlations (p<4.…”

CXCL12drives natural variation in coronary artery anatomy across diverse populations

“…Population membership to all genotyped participants was assigned using a reference dataset of unrelated individuals from the 1000 Genomes Project (1KGP). This assignment was performed centrally as part of a core MVP project and made available as a core resource for all MVP investigators 33 . Specifically, the smartpca module in the EIGENSOFT package 48 was used to project the principal components (PCs) loadings from the 1KGP to genotyped MVP participants.…”

“…We applied LD-score regression (LDSC) 32 using available GWAS summary statistics for CAD from a recently completed phenome-wide GWAS across 2,068 traits in 449,042 MVP EUR participants 33 , and separately, a recently completed CardiogramplusC4D consortium meta-analysis of summary statistics for CAD in largely EUR populations that did not include MVP 56 to evaluate pairwise genome-wide genetic correlations (rg) between coronary dominance and other traits. We used pre-calculated European LD scores and restricted the analysis to SNPs found in HapMap Phase 3 57 removing the human leukocyte antigen (HLA) region due to its unusual LD structure and genetic architecture.…”

Efficient storage and regression computation for population-scale genome sequencing studies

“…The era of big data in human genetics has ushered new opportunities for understanding human health and disease and generating effective therapeutic hypotheses [1][2][3] . Large-scale biobanks, aggregating genetic data from diverse populations, have become a cornerstone for this new wave of research [4][5][6][7][8] . These repositories offer new insights into the genetic underpinnings of diseases, facilitating discoveries.…”