Genome‐wide association studies (GWAS) are used to investigate genetic variants contributing to complex traits. Despite discovering many loci, a large proportion of “missing” heritability remains unexplained. Gene–gene interactions may help explain some of this gap. Traditionally, gene–gene interactions have been evaluated using parametric statistical methods such as linear and logistic regression, with multifactor dimensionality reduction (MDR) used to address sparseness of data in high dimensions. We propose a method for the analysis of gene–gene interactions across independent single‐nucleotide polymorphisms (SNPs) in two genes. Typical methods for this problem use statistics based on an asymptotic chi‐squared mixture distribution, which is not easy to use. Here, we propose a Kullback–Leibler‐type statistic, which follows an asymptotic, positive, normal distribution under the null hypothesis of no relationship between SNPs in the two genes, and normally distributed under the alternative hypothesis. The performance of the proposed method is evaluated by simulation studies, which show promising results. The method is also used to analyze real data and identifies gene–gene interactions among RAB3A, MADD, and PTPRN on type 2 diabetes (T2D) status.

中文翻译：

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使用 Kullback-Leibler 散度测量基因-基因相互作用

全基因组关联研究 (GWAS) 用于研究导致复杂性状的遗传变异。尽管发现了许多基因座，但很大一部分“缺失”的遗传性仍然无法解释。基因-基因相互作用可能有助于解释这种差距。传统上，已经使用参数统计方法（例如线性回归和逻辑回归）评估基因 - 基因相互作用，并使用多因素降维 (MDR) 来解决高维数据的稀疏问题。我们提出了一种分析两个基因中独立单核苷酸多态性 (SNP) 之间的基因-基因相互作用的方法。此问题的典型方法使用基于渐近卡方混合分布的统计数据，这并不容易使用。在这里，我们提出了一个 Kullback-Leibler 类型的统计量，它遵循渐近、在两个基因中 SNP 之间没有关系的零假设下正态分布，在备择假设下正态分布。通过仿真研究评估了所提出方法的性能，结果显示出有希望的结果。该方法还用于分析真实数据并确定 RAB3A、MADD 和 PTPRN 之间的基因-基因相互作用对 2 型糖尿病 (T2D) 状态的影响。