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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This work was supported in part by grants from the US National Institutes of Health (A.J.L.).
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Testing the power of the LCMS procedure to identify relationships among complex traits. (PDF 26 kb)
Disruption of C3ar1. (PDF 34 kb)
Diagram outlining the multi-step procedure defined in the main text to identify causal genes for obesity in mice. (PDF 14 kb)
Disruption of Tgfbr2. (PDF 30 kb)
Expression of the human ZFP90 transgene and the murine Zfp90 gene in 6 mouse tissues. (PDF 52 kb)
Liver gene expression traits significantly correlated with omental fat pad mass in the BXD cross. (PDF 24 kb)
Genes with at least 2 eQTL overlapping OFPM QTL tested for causal associations to OFPM. (PDF 8 kb)
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Schadt, E., Lamb, J., Yang, X. et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet 37, 710–717 (2005). https://doi.org/10.1038/ng1589
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