GenomeMUSter mouse genetic variation service enables multitrait, multipopulation data integration and analysis
- Robyn L. Ball1,
- Molly A. Bogue1,
- Hongping Liang1,
- Anuj Srivastava2,
- David G. Ashbrook3,
- Anna Lamoureux1,
- Matthew W. Gerring1,
- Alexander S. Hatoum4,5,
- Matthew J. Kim6,
- Hao He1,
- Jake Emerson1,
- Alexander K. Berger1,
- David O. Walton1,
- Keith Sheppard1,
- Baha El Kassaby1,
- Francisco Castellanos2,
- Govindarajan Kunde-Ramamoorthy1,
- Lu Lu3,
- John Bluis1,
- Sejal Desai1,
- Beth A. Sundberg1,
- Gary Peltz7,
- Zhuoqing Fang7,
- Gary A. Churchill1,
- Robert W. Williams3,
- Arpana Agrawal8,
- Carol J. Bult1,
- Vivek M. Philip1 and
- Elissa J. Chesler1
- 1The Jackson Laboratory, Bar Harbor, Maine 04609, USA;
- 2The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06032, USA;
- 3University of Tennessee Health Science Center, Memphis, Tennessee 38163, USA;
- 4Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri 63130, USA;
- 5Artificial Intelligence and the Internet of Things Institute, Washington University School of Medicine, St. Louis, Missouri 63110, USA;
- 6University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada;
- 7Department of Anesthesia, Pain and Perioperative Medicine, Stanford University School of Medicine, Stanford, California 94305, USA;
- 8Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110, USA
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.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.278157.123.
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Freely available online through the Genome Research Open Access option.
- Received August 8, 2023.
- Accepted January 10, 2024.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
