Schadt Eric E, Lamb John, Yang Xia, Zhu Jun, Edwards Steve, Guhathakurta Debraj, Sieberts Solveig K, Monks Stephanie, Reitman Marc, Zhang Chunsheng, Lum Pek Yee, Leonardson Amy, Thieringer Rolf, Metzger Joseph M, Yang Liming, Castle John, Zhu Haoyuan, Kash Shera F, Drake Thomas A, Sachs Alan, Lusis Aldons J
Rosetta Inpharmatics, Seattle, Washington 98109, USA.
Nat Genet. 2005 Jul;37(7):710-7. doi: 10.1038/ng1589. Epub 2005 Jun 19.
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.
生物医学研究的一个关键目标是阐明复杂性状（如常见人类疾病）背后复杂的基因相互作用网络。在此，我们详细介绍一种多步骤程序，用于识别复杂性状的潜在关键驱动因素，该程序将DNA变异和基因表达数据与分离小鼠群体中的其他复杂性状数据整合在一起。通过系统地测试导致相对转录丰度变化的DNA变异是否在统计学上支持相对于所考虑的复杂性状的独立、因果或反应性功能，实现了将基因表达性状彼此之间以及相对于其他复杂性状进行排序。我们表明，这种方法可以在分离小鼠群体的背景下，使用基因表达数据预测对单基因扰动实验的转录反应。我们还通过鉴定并实验验证三个新基因参与肥胖易感性，证明了这种方法的实用性。
