GenomeMUSter mouse genetic variation service enables multitrait, multipopulation data integration and analysis

Author:

Ball Robyn L.ORCID,Bogue Molly A.ORCID,Liang HongpingORCID,Srivastava AnujORCID,Ashbrook David G.ORCID,Lamoureux AnnaORCID,Gerring Matthew W.ORCID,Hatoum Alexander S.ORCID,Kim Matthew J.ORCID,He Hao,Emerson JakeORCID,Berger Alexander K.ORCID,Walton David O.ORCID,Sheppard Keith,El Kassaby BahaORCID,Castellanos FranciscoORCID,Kunde-Ramamoorthy GovindarajanORCID,Lu Lu,Bluis John,Desai Sejal,Sundberg Beth A.ORCID,Peltz GaryORCID,Fang Zhuoqing,Churchill Gary A.ORCID,Williams Robert W.ORCID,Agrawal ArpanaORCID,Bult Carol J.ORCID,Philip Vivek M.ORCID,Chesler Elissa J.ORCID

Abstract

Hundreds of inbred mouse strains and intercross populations have been used to characterize the function of genetic variants that contribute to disease. Thousands of disease-relevant traits have been characterized in mice and made publicly available. New strains and populations including consomics, the collaborative cross, expanded BXD, and inbred wild-derived strains add to existing complex disease mouse models, mapping populations, and sensitized backgrounds for engineered mutations. The genome sequences of inbred strains, along with dense genotypes from others, enable integrated analysis of trait–variant associations across populations, but these analyses are hampered by the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense variant resource by harmonizing multiple data sets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extendable to other model organisms. The result is a web- and programmatically accessible data service called GenomeMUSter, comprising single-nucleotide variants covering 657 strains at 106.8 million segregating sites. Interoperation with phenotype databases, analytic tools, and other resources enable a wealth of applications, including multitrait, multipopulation meta-analysis. We show this in cross-species comparisons of type 2 diabetes and substance use disorder meta-analyses, leveraging mouse data to characterize the likely role of human variant effects in disease. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease.

Funder

National Institutes of Health

Center for Systems Neurogenetics of Addiction

Jackson Laboratory, Cube Initiative Program Fund

Jackson Laboratory

UT Center for Integrative and Translational Genomics

UT-ORNL Governor's Chair

NIH/National Institute on Drug Abuse

NIH/Office of Research Infrastructure Programs

NIH/National Institute on Deafness and Other Communication Disorders

JAX Cancer Center

Publisher

Cold Spring Harbor Laboratory

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