Joint mouse–human phenome-wide association to test gene function and disease risk

Xusheng Wang, Ashutosh K. Pandey, Megan K. Mulligan, Evan G. Williams, Khyobeni Mozhui, Zhengsheng Li, Virginija Jovaisaite, L. Darryl Quarles, Zhousheng Xiao, Jinsong Huang, John A. Capra, Zugen Chen, William L. Taylor, Lisa Bastarache, Xinnan Niu, Katherine S. Pollard, Daniel C. Ciobanu, Alexander O. Reznik, Artem V. Tishkov, Igor B. Zhulin, Junmin Peng, Stanley F. Nelson, Joshua C. Denny, Johan Auwerx, Lu Lu and Robert W. Williams ()
Additional contact information
Xusheng Wang: Genomics and Informatics, University of Tennessee Health Science Center
Ashutosh K. Pandey: Genomics and Informatics, University of Tennessee Health Science Center
Megan K. Mulligan: Genomics and Informatics, University of Tennessee Health Science Center
Evan G. Williams: Laboratory of Integrative and Systems Physiology, School of Life Sciences, École Polytechnique Fédérale de Lausanne
Khyobeni Mozhui: Genomics and Informatics, University of Tennessee Health Science Center
Zhengsheng Li: Genomics and Informatics, University of Tennessee Health Science Center
Virginija Jovaisaite: Laboratory of Integrative and Systems Physiology, School of Life Sciences, École Polytechnique Fédérale de Lausanne
L. Darryl Quarles: University of Tennessee Health Science Center
Zhousheng Xiao: University of Tennessee Health Science Center
Jinsong Huang: Genomics and Informatics, University of Tennessee Health Science Center
John A. Capra: Vanderbilt University School of Medicine
Zugen Chen: University of California
William L. Taylor: Molecular Resource Center, University of Tennessee Health Science Center
Lisa Bastarache: Vanderbilt University School of Medicine
Xinnan Niu: Vanderbilt University School of Medicine
Katherine S. Pollard: Gladstone Institutes
Daniel C. Ciobanu: Genomics and Informatics, University of Tennessee Health Science Center
Alexander O. Reznik: Joint Institute for Computational Sciences, University of Tennessee—Oak Ridge National Laboratory
Artem V. Tishkov: Joint Institute for Computational Sciences, University of Tennessee—Oak Ridge National Laboratory
Igor B. Zhulin: Joint Institute for Computational Sciences, University of Tennessee—Oak Ridge National Laboratory
Junmin Peng: St Jude Proteomics Facility, St Jude Children's Research Hospital
Stanley F. Nelson: University of California
Joshua C. Denny: Vanderbilt University School of Medicine
Johan Auwerx: Laboratory of Integrative and Systems Physiology, School of Life Sciences, École Polytechnique Fédérale de Lausanne
Lu Lu: Genomics and Informatics, University of Tennessee Health Science Center
Robert W. Williams: Genomics and Informatics, University of Tennessee Health Science Center

Nature Communications, 2016, vol. 7, issue 1, 1-13

Abstract: Abstract Phenome-wide association is a novel reverse genetic strategy to analyze genome-to-phenome relations in human clinical cohorts. Here we test this approach using a large murine population segregating for ∼5 million sequence variants, and we compare our results to those extracted from a matched analysis of gene variants in a large human cohort. For the mouse cohort, we amassed a deep and broad open-access phenome consisting of ∼4,500 metabolic, physiological, pharmacological and behavioural traits, and more than 90 independent expression quantitative trait locus (QTL), transcriptome, proteome, metagenome and metabolome data sets—by far the largest coherent phenome for any experimental cohort ( www.genenetwork.org ). We tested downstream effects of subsets of variants and discovered several novel associations, including a missense mutation in fumarate hydratase that controls variation in the mitochondrial unfolded protein response in both mouse and Caenorhabditis elegans, and missense mutations in Col6a5 that underlies variation in bone mineral density in both mouse and human.

Date: 2016
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DOI: 10.1038/ncomms10464

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