Author
Listed:
- Xusheng Wang
(Genomics and Informatics, University of Tennessee Health Science Center
St Jude Proteomics Facility, St Jude Children's Research Hospital)
- 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
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
University of California)
- Daniel C. Ciobanu
(Genomics and Informatics, University of Tennessee Health Science Center
University of Nebraska)
- 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
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)
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.
Suggested Citation
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 &, 2016.
"Joint mouse–human phenome-wide association to test gene function and disease risk,"
Nature Communications, Nature, vol. 7(1), pages 1-13, April.
Handle:
RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms10464
DOI: 10.1038/ncomms10464
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