Sieberts Solveig K, Schadt Eric E
Rosetta Inpharmatics, LLC, 401 Terry Avenue N., Seattle, Washington 98109, USA.
Mamm Genome. 2007 Jul;18(6-7):389-401. doi: 10.1007/s00335-007-9040-6. Epub 2007 Jul 26.
Testing hundreds of thousands of DNA markers in human, mouse, and other species for association to complex traits like disease is now a reality. However, information on how variations in DNA impact complex physiologic processes flows through transcriptional and other molecular networks. In other words, DNA variations impact complex diseases through the perturbations they cause to transcriptional and other biological networks, and these molecular phenotypes are intermediate to clinically defined disease. Because it is also now possible to monitor transcript levels in a comprehensive fashion, integrating DNA variation, transcription, and phenotypic data has the potential to enhance identification of the associations between DNA variation and diseases like obesity and diabetes, as well as characterize those parts of the molecular networks that drive these diseases. Toward that end, we review methods for integrating expression quantitative trait loci (eQTLs), gene expression, and clinical data to infer causal relationships among gene expression traits and between expression and clinical traits. We further describe methods to integrate these data in a more comprehensive manner by constructing coexpression gene networks that leverage pairwise gene interaction data to represent more general relationships. To infer gene networks that capture causal information, we describe a Bayesian algorithm that further integrates eQTLs, expression, and clinical phenotype data to reconstruct whole-gene networks capable of representing causal relationships among genes and traits in the network. These emerging network approaches, aimed at processing high-dimensional biological data by integrating data from multiple sources, represent some of the first steps in statistical genetics to identify multiple genetic perturbations that alter the states of molecular networks and that in turn push systems into disease states. Evolving statistical procedures that operate on networks will be critical to extracting information related to complex phenotypes like disease, as research goes beyond a single-gene focus. The early successes achieved with the methods described herein suggest that these more integrative genomics approaches to dissecting disease traits will significantly enhance the identification of key drivers of disease beyond what could be achieved by genetic association studies alone.
目前，在人类、小鼠和其他物种中检测数十万种DNA标记与疾病等复杂性状的关联已成为现实。然而，关于DNA变异如何影响复杂生理过程的信息是通过转录和其他分子网络传递的。换句话说，DNA变异通过对转录和其他生物网络造成的扰动来影响复杂疾病，而这些分子表型是临床定义疾病的中间环节。由于现在也能够以全面的方式监测转录水平，整合DNA变异、转录和表型数据有可能加强对DNA变异与肥胖和糖尿病等疾病之间关联的识别，并描绘出驱动这些疾病的分子网络的那些部分。为此，我们回顾了整合表达定量性状位点（eQTL）、基因表达和临床数据以推断基因表达性状之间以及表达与临床性状之间因果关系的方法。我们进一步描述了通过构建共表达基因网络以更全面方式整合这些数据的方法，该网络利用成对基因相互作用数据来表示更普遍的关系。为了推断捕获因果信息的基因网络，我们描述了一种贝叶斯算法，该算法进一步整合eQTL、表达和临床表型数据，以重建能够表示网络中基因和性状之间因果关系的全基因网络。这些新兴的网络方法旨在通过整合来自多个来源的数据来处理高维生物数据，代表了统计遗传学中识别多个改变分子网络状态进而将系统推向疾病状态的基因扰动的一些初步步骤。随着研究超越单基因关注，对网络进行操作的不断发展的统计程序对于提取与疾病等复杂表型相关的信息至关重要。本文所述方法取得的早期成功表明，这些用于剖析疾病性状的更综合的基因组学方法将显著增强对疾病关键驱动因素的识别，这是仅通过基因关联研究无法实现的。
