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Cardiovascular Disorders


Identification of Genes and Networks Driving Cardiovascular and Metabolic Phenotypes in a Mouse F2 Intercross
Published: Tuesday, December 14, 2010
Author: Jonathan M. J. Derry et al.

by Jonathan M. J. Derry, Hua Zhong, Cliona Molony, Doug MacNeil, Debraj Guhathakurta, Bin Zhang, John Mudgett, Kersten Small, Lahcen El Fertak, Alain Guimond, Mohammed Selloum, Wenqing Zhao, Marie France Champy, Laurent Monassier, Tom Vogt, Doris Cully, Andrew Kasarskis, Eric E. Schadt

To identify the genes and pathways that underlie cardiovascular and metabolic phenotypes we performed an integrated analysis of a mouse C57BL/6J x A/J F2 (B6AF2) cross by relating genome-wide gene expression data from adipose, kidney, and liver tissues to physiological endpoints measured in the population. We have identified a large number of trait QTLs including loci driving variation in cardiac function on chromosomes 2 and 6 and a hotspot for adiposity, energy metabolism, and glucose traits on chromosome 8. Integration of adipose gene expression data identified a core set of genes that drive the chromosome 8 adiposity QTL. This chromosome 8 trans eQTL signature contains genes associated with mitochondrial function and oxidative phosphorylation and maps to a subnetwork with conserved function in humans that was previously implicated in human obesity. In addition, human eSNPs corresponding to orthologous genes from the signature show enrichment for association to type II diabetes in the DIAGRAM cohort, supporting the idea that the chromosome 8 locus perturbs a molecular network that in humans senses variations in DNA and in turn affects metabolic disease risk. We functionally validate predictions from this approach by demonstrating metabolic phenotypes in knockout mice for three genes from the trans eQTL signature, Akr1b8, Emr1, and Rgs2. In addition we show that the transcriptional signatures for knockout of two of these genes, Akr1b8 and Rgs2, map to the F2 network modules associated with the chromosome 8 trans eQTL signature and that these modules are in turn very significantly correlated with adiposity in the F2 population. Overall this study demonstrates how integrating gene expression data with QTL analysis in a network-based framework can aid in the elucidation of the molecular drivers of disease that can be translated from mice to humans.
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