Journal
NATURE GENETICS
Volume 37, Issue 7, Pages 710-717Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/ng1589
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Funding
- NHLBI NIH HHS [P01 HL028481-21A10010, P01 HL028481-21A1, P01 HL030568-220011, P01 HL028481, R01 HL094322, P01 HL030568, P01 HL030568-220009] Funding Source: Medline
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A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA- variation and gene- expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene- perturbation experiments using gene- expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.
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