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

Document Type

Article

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.