Chen Guanjie, Yuan Ao, Zhou Jie, Bentley Amy R, Adeyemo Adebowale, Rotimi Charles N
Center for Research on Genomics and Global Health, NHGRI, NIH, Bethesda, Maryland, USA.
Bioinform Biol Insights. 2012;6:169-76. doi: 10.4137/BBI.S9867. Epub 2012 Jul 2.
Missing heritability is still a challenge for Genome Wide Association Studies (GWAS). Gene-gene interactions may partially explain this residual genetic influence and contribute broadly to complex disease. To analyze the gene-gene interactions in case-control studies of complex disease, we propose a simple, non-parametric method that utilizes the F-statistic. This approach consists of three steps. First, we examine the joint distribution of a pair of SNPs in cases and controls separately. Second, an F-test is used to evaluate the ratio of dependence in cases to that of controls. Finally, results are adjusted for multiple tests. This method was used to evaluate gene-gene interactions that are associated with risk of Type 2 Diabetes among African Americans in the Howard University Family Study. We identified 18 gene-gene interactions (P < 0.0001). Compared with the commonly-used logistical regression method, we demonstrate that the F-ratio test is an efficient approach to measuring gene-gene interactions, especially for studies with limited sample size.
对于全基因组关联研究（GWAS）而言，“缺失遗传力”仍是一项挑战。基因-基因相互作用可能部分解释了这种残余的遗传影响，并在复杂疾病中广泛发挥作用。为了在复杂疾病的病例对照研究中分析基因-基因相互作用，我们提出了一种利用F统计量的简单非参数方法。该方法包括三个步骤。首先，我们分别检查病例组和对照组中一对单核苷酸多态性（SNP）的联合分布。其次，使用F检验来评估病例组中依赖性与对照组中依赖性的比率。最后，对多次检验的结果进行校正。该方法用于评估霍华德大学家族研究中与非裔美国人2型糖尿病风险相关的基因-基因相互作用。我们识别出了18种基因-基因相互作用（P < 0.0001）。与常用的逻辑回归方法相比，我们证明F比率检验是测量基因-基因相互作用的有效方法，尤其适用于样本量有限的研究。
