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

@article{schadt05, 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.}, author = {Schadt, Eric E. and Lamb, John and Yang, Xia and Zhu, Jun and Edwards, Steve and Guhathakurta, Debraj and Sieberts, Solveig K. and Monks, Stephanie and Reitman, Marc and Zhang, Chunsheng and Lum, Pek Yee Y. and Leonardson, Amy and Thieringer, Rolf and Metzger, Joseph M. and Yang, Liming and Castle, John and Zhu, Haoyuan and Kash, Shera F. and Drake, Thomas A. and Sachs, Alan and Lusis, Aldons J.}, citeulike-article-id = {241639}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/ng1589}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/ng1589}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/15965475}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=15965475}, day = {19}, doi = {10.1038/ng1589}, issn = {1061-4036}, journal = {Nature genetics}, keywords = {expression, gene, gengen, transcriptomic}, month = {July}, number = {7}, pages = {710--717}, posted-at = {2009-02-02 16:40:05}, priority = {2}, publisher = {Nature Publishing Group}, title = {An integrative genomics approach to infer causal associations between gene expression and disease.}, url = {http://dx.doi.org/10.1038/ng1589}, volume = {37}, year = {2005} }

TY - JOUR ID - schadt05 L3 - citeulike-article-id:241639 N2 - 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. IS - 7 JF - Nature genetics SN - 1061-4036 EP - 717 TI - An integrative genomics approach to infer causal associations between gene expression and disease. PB - Nature Publishing Group VL - 37 SP - 710 KW - expression KW - gene KW - gengen KW - transcriptomic AU - Schadt, Eric AU - Lamb, John AU - Yang, Xia AU - Zhu, Jun AU - Edwards, Steve AU - Guhathakurta, Debraj AU - Sieberts, Solveig AU - Monks, Stephanie AU - Reitman, Marc AU - Zhang, Chunsheng AU - Lum, Pek Yee AU - Leonardson, Amy AU - Thieringer, Rolf AU - Metzger, Joseph AU - Yang, Liming AU - Castle, John AU - Zhu, Haoyuan AU - Kash, Shera AU - Drake, Thomas AU - Sachs, Alan AU - Lusis, Aldons PY - 2005/07/19/ UR - http://dx.doi.org/10.1038/ng1589 ER -