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

Authors

Xusheng Wang, University of Tennessee Health Science Center
Ashutosh K. Ciobanu, University of Tennessee Health Science Center
Megan K. Mulligan, University of Tennessee Health Science Center
Evan G. Williams, School of Life Sciences
Khyobeni Mozhui, University of Tennessee Health Science Center
Zhengsheng Li, University of Tennessee Health Science Center
Virginija Jovaisaite, School of Life Sciences
L. Darryl Quarles, University of Tennessee Health Science Center
Zhousheng Xiao, University of Tennessee Health Science Center
Jinsong Huang, University of Tennessee Health Science Center
John A. Capra, Vanderbilt University School of Medicine
Zugen Chen, University of California, Los Angeles
William L. Taylor, University of Tennessee Health Science CenterFollow
Lisa Bastarache, Vanderbilt University School of Medicine
Xinnan Niu, Vanderbilt University School of Medicine
Katherine S. Pollard, University of California, San Francisco
Daniel C. Ciobanu, University of Nebraska-LincolnFollow
Alexander O. Reznik, University of Tennessee—Oak Ridge National Laboratory
Artem V. Tishkov, University of Tennessee—Oak Ridge National Laboratory
Igor B. Zhulin, University of Tennessee—Oak Ridge National Laboratory
Junmin Peng, St Jude Children’s Research Hospital
Stanley F. Nelson, University of California, Los Angeles
Joshua C. Denny, Vanderbilt University School of MedicineFollow
Johan Auwerx, School of Life Sciences
Lu Lu, University of Tennessee Health Science CenterFollow
Robert W. Williams, University of Tennessee Health Science CenterFollow

Date of this Version

2016

Citation

Wang, X. et al. Joint mouse-human phenome-wide association to test gene function and disease risk. Nat. Commun. 7:10464 doi: 10.1038/ncomms10464 (2016).

Comments

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Phenome-wide association is a novel reverse genetic strategy to analyze genome-tophenome 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.