Validation of Candidate Causal Genes for Abdominal Obesity Which Affect Shared Metabolic Pathways and Networks

• Published in: Nature genetics Vol. 41; no. 4; pp. 415 - 423
• Main Authors: Yang, Xia, Deignan, Joshua L., Qi, Hongxiu, Zhu, Jun, Qian, Su, Zhong, Judy, Torosyan, Gevork, Majid, Sana, Falkard, Brie, Kleinhanz, Robert R., Karlsson, Jenny, Castellani, Lawrence W., Mumick, Sheena, Wang, Kai, Xie, Tao, Coon, Michael, Zhang, Chunsheng, Estrada-Smith, Daria, Farber, Charles R., Wang, Susanna S., Van Nas, Atila, Ghazalpour, Anatole, Zhang, Bin, MacNeil, Douglas J., Lamb, John R., Dipple, Katrina M., Reitman, Marc L., Mehrabian, Margarete, Lum, Pek Y., Schadt, Eric E., Lusis, Aldons J., Drake, Thomas A.
• Format: Journal Article
• Language: English
• Published: 08.03.2009
• ISSN: 1061-4036; 1546-1718
• DOI: 10.1038/ng.325

A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7 , Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes and identification of involved pathways and networks.