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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2005-07

AUTHORS

Eric E Schadt, John Lamb, Xia Yang, Jun Zhu, Steve Edwards, Debraj GuhaThakurta, Solveig K Sieberts, Stephanie Monks, Marc Reitman, Chunsheng Zhang, Pek Yee Lum, Amy Leonardson, Rolf Thieringer, Joseph M Metzger, Liming Yang, John Castle, Haoyuan Zhu, Shera F Kash, Thomas A Drake, Alan Sachs, Aldons J Lusis

ABSTRACT

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. More... »

PAGES

710-717

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/ng1589

    DOI

    http://dx.doi.org/10.1038/ng1589

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1048690521

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/15965475


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