Measuring genetic variation in the multi-ethnic Million Veteran Program (MVP)

Hunter-Zinck, H.; Shi,, Y.; Li, M.; Gorman, B. R.; Ji, S.-G.; Sun, N.; Webster, T.; Liem, A.; Hsieh, P.; Devineni, P.; Karnam, P.; Radhakrishnan, L.; Schmidt, J.; Assimes, T. L.; Huang, J.; Pan, C.; Humphries, D.; Brophy, M.; Moser, J.; Muralidhar, S.; Huang, G. D.; Przygodzki, R.; Concato, J.; Gaziano, J. M.; Gelernter, J.; ODonnell, C. J.; Hauser, E. R.; Zhao, H.-y.; OLeary, T. J.; Tsao, P. S.; Pyarajan, S.

doi:10.1101/2020.01.06.896613

Home/Archive/bioRxiv

bioRxiv · 10.1101/2020.01.06.896613

Measuring genetic variation in the multi-ethnic Million Veteran Program (MVP)

View original source ↗Export metadata ↓

Abstract

The Million Veteran Program (MVP), initiated by the Department of Veterans Affairs (VA), aims to collect consented biosamples from at least one million Veterans. Presently, blood samples have been collected from over 800,000 enrolled participants. The size and diversity of the MVP cohort, as well as the availability of extensive VA electronic health records make it a promising resource for precision medicine. MVP is conducting array-based genotyping to provide genome-wide scan of the entire cohort, in parallel with whole genome sequencing, methylation, and other omics assays. Here, we present the design and performance of MVP 1.0 custom Axiom(R) array, which was designed and developed as a single assay to be used across the multi-ethnic MVP cohort. A unified genetic quality control analysis was developed and conducted on an initial tranche of 485,856 individuals leading to a high-quality dataset of 459,777 unique individuals. 668,418 genetic markers passed quality control and showed high quality genotypes not only on common variants but also on rare variants. We confirmed the substantial ancestral diversity of MVP with nearly 30% non-European individuals, surpassing other large biobanks. We also demonstrated the quality of the MVP dataset by replicating established genetic associations with height in European Americans and African Americans ancestries. This current data set has been made available to approved MVP researchers for genome-wide association studies and other downstream analyses. Further data releases will be available for analysis as recruitment at the VA continues and the cohort expands both in size and diversity.

Explore related subjects

genetics Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Hunter-Zinck, H., Shi,, Y., Li, M., Gorman, B. R., Ji, S.-G., Sun, N., Webster, T., Liem, A., Hsieh, P., Devineni, P., Karnam, P., Radhakrishnan, L., Schmidt, J., Assimes, T. L., Huang, J., Pan, C., Humphries, D., Brophy, M., Moser, J., Muralidhar, S., Huang, G. D., Przygodzki, R., Concato, J., Gaziano, J. M., Gelernter, J., ODonnell, C. J., Hauser, E. R., Zhao, H.-y., OLeary, T. J., Tsao, P. S., Pyarajan, S.. 2020-01-07. Measuring genetic variation in the multi-ethnic Million Veteran Program (MVP). https://doi.org/10.1101/2020.01.06.896613

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

SARDINE unlocks EWAS summary statistics for large-scale epigenetic biomarker analysis

Epigenetic clocks are increasingly used to study biological aging, yet their interpretation and utility remain uncertain. Meanwhile, the growing wealth of epigenome-wide association study (EWAS) summary statistics remains largely untapped for evaluating these biomarkers. Here, we introduce SARDINE, a statistical framework that estimates epigenetic clock-phenotype associations using EWAS summary statistics, clock weights, and reference methylation covariance, without individual-level data. SARDINE accurately reproduced individual-level association estimates across 38 epigenetic biomarkers and four phenotypes in 15,677 individuals. Applying SARDINE to diverse EWAS datasets revealed substantial heterogeneity in biomarker associations with aging, diseases, and lifestyle factors. By transforming CpG-level EWAS results into interpretable biomarker-level associations, SARDINE unlocks a largely underutilized data resource and establishes a scalable paradigm for systematic biomarker evaluation and discovery in aging research.

genetics↗

bioRxiv2026

Deep transcriptomic analysis of molecular mediators of complex traits in heterogeneous stock (HS) rats

Rats are a powerful model organism for genome-wide association studies (GWAS) of physiological and behavioral traits. Heterogeneous Stock (HS) rats, which were created by intercrossing 8 inbred rat strains, are heavily used for this purpose because of their genetic diversity and accumulated recombinations due to over 100 generations of outbreeding, which improves mapping precision. However, functional genomics resources for post-GWAS interpretation of genomic findings have been limited. We processed 3,160 RNA-seq samples, 1,932 of which were previously unpublished, from 13 tissues as part of RatGTEx. We used Pantry to quantify six modalities of transcriptome regulation (expression, isoform ratio, intron excision ratio, alternative transcription start site (TSS), alternative poly(A), and RNA stability) in each sample, which allowed us to identify 144,876 independent quantitative trait loci (QTL) for 22,599 genes. We performed a transcriptome-wide association study (TWAS) with 19,796 unique genes across tissues and transcriptional modalities and 308 traits collected from 14 studies using FUSION, which identified 7,473 significant gene-trait associations. This work presents a significant expansion of the RatGTEx resource in terms of samples, modes of transcriptome regulation, and genetic analyses. Our findings provide molecular evidence for the involvement of specific genes in mediating GWAS signals through a range of transcriptome regulatory mechanisms. These gene-to-phenotype relationships can be easily applied to genetic analyses in humans and other species. Further experiments can use this evidence to confirm these molecular mediators and target them for the treatment of behavioral and physiological traits and disorders. To facilitate use of these results, we have updated results on RatGTEx.org and provided a new interactive TWAS web portal (twas.ratgtex.org).

genetics↗

bioRxiv2026

Genomic and Epigenomic Relationships Capture Different Patterns of Genotype-by-Environment Interaction in Grapevine Clones

Genotype-by-environment interaction (GEI) challenges selection in clonally propagated perennial crops by reducing the consistency of clone performance across years. In perennial crops, both genetic and epigenetic variation are likely to contribute to complex trait variation among clones. However, the extent to which each molecular source contribute to GEI remains poorly understood. Here, we used UMR-seq, an assay that recovers SNPs and unmethylated-region (UMR; DNA methylation) profiles from a single library, to generate paired genomic and epigenomic profiles for 206 Riesling (Vitis vinifera L.) clones phenotyped for yield and juice quality traits across nine growing seasons. We then partitioned the contribution of each omic layer to clonal variation and GEI, and examined the genomic distribution of their effects. In models without explicit GEI, environmental and residual effects were the largest source of phenotypic variance and the clonal main effect was modest. Genomic relationships accounted for most of the clonal variance in four of five traits, and adding an epigenomic relationship matrix (ERM) to the genomic relationship matrix (GRM) had little effect. In contrast, when GEI effects were modelled using factor-analytic covariance structures, the epigenomic layer captured a substantial proportion of GEI variance and exceeded the genomic contribution in four of five traits. The two omic layers differed not only in the amount of GEI captured, but also in the pattern of clone re-ranking across years. When back-solved effects were aggregated into genomic windows, the largest genomic and epigenomic effects tended to fall in different regions, with overlap no greater than expected by chance. Together, these results show that genomic and epigenomic relationships capture complementary components of year-to-year variation in clone performance, with the epigenomic contribution expressed primarily through GEI. These findings provide a basis for investigating the molecular drivers of GEI and for testing whether genomic and epigenomic information can inform selection for stable clonal performance across variable environments.

genetics↗