Gu C Charles, Yang Wei Will, Kraja Aldi T, de las Fuentes Lisa, Dávila-Román Victor G
Division of Biostatistics, Washington University School of Medicine, 660 South Euclid Avenue, Box 8067, St, Louis, Missouri 63110, USA.
BMC Proc. 2009 Dec 15;3 Suppl 7(Suppl 7):S86. doi: 10.1186/1753-6561-3-s7-s86.
Studies of complex diseases collect panels of disease-related traits, also known as secondary phenotypes or endophenotypes. They reflect intermediate responses to environment exposures, and as such, are likely to contain hidden information of gene-environment (G x E) interactions. The information can be extracted and used in genetic association studies via latent-components analysis. We present such a method that extracts G x E information in longitudinal data of endophenotypes, and apply the method to repeated measures of multiple phenotypes related to coronary heart disease in Genetic Analysis Workshop 16 Problem 2. The new method identified many genes, including SCNN1B (sodium channel nonvoltage-gated 1 beta) and PKP2 (plakophilin 2), with potential time-dependent G x E interactions; and several others including a novel cardiac-specific kinase gene (TNNI3K), with potential G x E interactions independent of time and marginal effects.
复杂疾病研究收集了一系列与疾病相关的性状，也称为次要表型或内表型。它们反映了对环境暴露的中间反应，因此可能包含基因-环境（G×E）相互作用的隐藏信息。这些信息可以通过潜在成分分析从遗传关联研究中提取并加以利用。我们提出了一种在纵向内表型数据中提取G×E信息的方法，并将该方法应用于遗传分析研讨会16问题2中与冠心病相关的多个表型的重复测量。新方法鉴定出了许多基因，包括SCNN1B（非电压门控钠通道1β）和PKP2（桥粒芯蛋白2），它们具有潜在的时间依赖性G×E相互作用；还有其他几个基因，包括一个新的心脏特异性激酶基因（TNNI3K），具有独立于时间和边际效应的潜在G×E相互作用。
