Wang Xin, Epstein Michael P, Tzeng Jung-Ying
Bioinformatics Research Center, North Carolina State University, Raleigh, N.C., USA.
Hum Hered. 2014;78(1):17-26. doi: 10.1159/000360161. Epub 2014 Jun 21.
Gene-gene interactions (G×G) are important to study because of their extensiveness in biological systems and their potential in explaining missing heritability of complex traits. In this work, we propose a new similarity-based test to assess G×G at the gene level, which permits the study of epistasis at biologically functional units with amplified interaction signals.
Under the framework of gene-trait similarity regression (SimReg), we propose a gene-based test for detecting G×G. SimReg uses a regression model to correlate trait similarity with genotypic similarity across a gene. Unlike existing gene-level methods based on leading principal components (PCs), SimReg summarizes all information on genotypic variation within a gene and can be used to assess the joint/interactive effects of two genes as well as the effect of one gene conditional on another.
Using simulations and a real data application to the Warfarin study, we show that the SimReg G×G tests have satisfactory power and robustness under different genetic architecture when compared to existing gene-based interaction tests such as PC analysis or partial least squares. A genome-wide association study with approx. 20,000 genes may be completed on a parallel computing system in 2 weeks.
基因-基因相互作用（G×G）因其在生物系统中的广泛性以及在解释复杂性状缺失遗传力方面的潜力而成为重要的研究对象。在本研究中，我们提出了一种基于新的相似性检验方法，用于在基因水平评估G×G，该方法允许在具有增强相互作用信号的生物功能单元上研究上位性。
在基因-性状相似性回归（SimReg）框架下，我们提出了一种基于基因的检测G×G的方法。SimReg使用回归模型将性状相似性与基因内的基因型相似性相关联。与现有的基于主成分（PC）的基因水平方法不同，SimReg总结了基因内基因型变异的所有信息，可用于评估两个基因的联合/相互作用效应以及一个基因在另一个基因条件下的效应。
通过模拟和对华法林研究的实际数据应用，我们表明，与现有的基于基因的相互作用检验（如PC分析或偏最小二乘法）相比，SimReg G×G检验在不同遗传结构下具有令人满意的功效和稳健性。在并行计算系统上，一项涉及约20,000个基因的全基因组关联研究可能在2周内完成。
