2025-01-16 アルゴンヌ国立研究所 (ANL)
<関連情報>
- https://www.anl.gov/article/argonne-team-delivers-a-100x-speedup-of-genetic-data-analysis-from-the-million-veteran-program
- https://www.science.org/doi/10.1126/science.adj1182
- https://medibio.tiisys.com/122617/
多様性と規模: VA 百万退役軍人プログラムにおける 2068 の特性の遺伝的構造 Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program
Anurag Verma, Jennifer E. Huffman, Alex Rodriguez, Mitchell Conery, […], and Katherine P. Liao
Science Published:19 Jul 2024
DOI:https://doi.org/10.1126/science.adj1182
Editor’s summary
The number and size of human genomics datasets have been increasing but not uniformly, and most of the genetic data available to researchers are still derived from individuals of European descent. This shortcoming limits both the biological insights that can be gleaned from these data and their clinical applications to non-European patients, who may not match up well with the traditional study participants. To address this problem, the Million Veterans Program recruited hundreds of thousands of US veterans of various ethnic backgrounds for study. Verma et al. present this resource, as well as a few discoveries of genetic connections to disease that emerged from their diverse dataset (see the Perspective by Williamson and Fatumo). —Yevgeniya Nusinovich
Structured Abstract
INTRODUCTION
Findings from genome-wide association studies (GWASs) have provided foundational knowledge of the genetic basis of disease, facilitating precision approaches for prevention and treatment. Current GWAS results are limited by underrepresentation of individuals from diverse populations, leading to concerns with generalizability regarding our knowledge of the relationships between genes, traits, and disease. The Department of Veterans Affairs (VA) Million Veteran Program (MVP), one of the largest US-based biobanks, addresses this need; 29% of MVP comprises individuals genetically similar to African (AFR), Admixed American (AMR), and East Asian (EAS) reference populations. With over 635,000 participants and more than 44.3M genotyped variants linked with detailed phenotypic data from the electronic health record (EHR), the MVP has the scale and richness of data to fill in the gaps in our knowledge of genotype-phenotype associations across diverse populations.
RATIONALE
Leveraging dense MVP data, we conducted GWASs across 2068 traits in four population groups based on genetic similarity to AFR, AMR, EAS, and European (EUR) reference populations. We employed statistical fine-mapping to highlight putative causal variants. This effort allowed us to characterize the genetic architecture of complex traits within diverse populations and compare genetic predisposition between population groups. We also quantified the benefits of including individuals from non-EUR population groups in the study for variant discovery and fine-mapping precision. Fine-mapping provided a foundation for nominating putative effector genes at associated loci mapping the landscape of gene-trait associations across populations to highlight both pleiotropic and heterogeneous associations.
RESULTS
Among 635,969 participants, we identified 26,049 variant-trait associations across 1270 traits, with 3477 being significant only when individuals from non-EUR populations were included. Fine-mapping revealed 57,601 independent signals across 936 traits, with 15,045 of these signals mapped with high confidence to a single variant. Predominantly resulting from interpopulation allele frequency differences, 2069 high-confidence signals and 549 gene nominations were unique to non-EUR groups. Notably, a signal mapped to rs76024540 implicated SLC22A18/SLC22A18AS as effector genes for keloid scarring, a condition vastly more prevalent in the AFR than the EUR population. Apart from the APOE locus’s association with dementia, we observed few instances of effect size heterogeneity across populations for fine-mapped variants.
CONCLUSION
This study underscores the enhanced power of GWASs with increased participant diversity, achieving greater variant discovery and fine-mapping precision than possible in the EUR population alone. Our findings reveal more similarities than differences in genetic architectures across populations, with most differences attributable to allele frequency variations between populations.
Comprehensive phenome-wide genetic analysis across multiple populations.
Meta-analysis of 4045 GWASs comprising 2068 traits from four population groups identified 26,049 locus-trait associations, including 9989 previously unreported. Multi-population fine-mapping prioritized high confidence signals, highlighting shared associations and elucidated pleiotropic genes driving multiple variant-trait associations.
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.
