An integrative genomics approach to infer causal associations between gene expression and disease


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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Figure 1: Using QTL data to infer relationships between RNA levels and complex traits.
Figure 2: Strong gametic phase disequilibrium between genes with significant cis-acting eQTLs simulates independence events.
Figure 3: Use of conditional correlations support Hsd11b1 as causal for OFPM at the chromosome 1 OFPM QTL.
Figure 4: Three genes in the OFPM causality list achieve validation in genetically modified mice.

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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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Corresponding author

Correspondence to Eric E Schadt.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Testing the power of the LCMS procedure to identify relationships among complex traits. (PDF 26 kb)

Supplementary Fig. 2

Disruption of C3ar1. (PDF 34 kb)

Supplementary Fig. 3

Diagram outlining the multi-step procedure defined in the main text to identify causal genes for obesity in mice. (PDF 14 kb)

Supplementary Fig. 4

Disruption of Tgfbr2. (PDF 30 kb)

Supplementary Fig. 5

Expression of the human ZFP90 transgene and the murine Zfp90 gene in 6 mouse tissues. (PDF 52 kb)

Supplementary Table 1

Liver gene expression traits significantly correlated with omental fat pad mass in the BXD cross. (PDF 24 kb)

Supplementary Table 2

Genes with at least 2 eQTL overlapping OFPM QTL tested for causal associations to OFPM. (PDF 8 kb)

Supplementary Methods (PDF 63 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).

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