Test for interaction between two unlinked loci
Test for interaction between two unlinked loci
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DOI：
10.1086/508571
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发表时间：
2006-11-01
影响因子：
9.8
通讯作者：
Xiong, Momiao
中科院分区：
文献类型：
--
作者：
Zhao, Jinying;Jin, Li;Xiong, Momiao
尽管人们越来越认识到在复杂疾病的遗传研究中测试基因-基因相互作用的重要性，但基因-基因相互作用的影响通常被定义为遗传加性效应的偏差，这本质上被视为遗传分析中的残差项，导致检测相互作用效应存在的能力较低。群体水平上基因-基因相互作用的定义在多大程度上反映了基因的生化或生理相互作用仍然是一个谜。在本文中，我们介绍了两个不连锁基因座（或基因）之间基因-基因相互作用的新定义和新测量方法。我们开发了研究双位点疾病模型下疾病群体连锁不平衡（LD）模式的一般理论。还研究了在疾病群体中使用 LD 测量作为两个不连锁基因座之间基因-基因相互作用测量的函数的特性。我们研究了两个基因座之间的相互作用如何在疾病群体中产生 LD，并表明两个基因座之间基因-基因相互作用的新定义的数学公式与两个基因座之间的 LD 相似。这一发现促使我们开发一种基于 LD 的统计方法来检测两个不连锁基因座之间的基因间相互作用。用于测试基因-基因相互作用的基于 LD 的统计数据的零分布和 I 型错误率通过广泛的模拟研究进行了验证。我们发现，在三个双位点疾病模型下，新的检验统计量比传统的逻辑回归更强大，并证明检验统计量的功效取决于基因与基因相互作用的度量。我们还研究了使用标记 SNP 来测试相互作用对检测两个未连锁基因座之间相互作用的能力的影响。最后，为了评估新方法的性能，我们将基于 LD 的统计应用于两个已发布的数据集。我们的结果表明，基于 LD 的统计量的 P 值小于其他方法（包括逻辑回归模型）获得的 P 值。
Despite the growing consensus on the importance of testing gene-gene interactions in genetic studies of complex diseases, the effect of gene-gene interactions has often been defined as a deviance from genetic additive effects, which is essentially treated as a residual term in genetic analysis and leads to low power in detecting the presence of interacting effects. To what extent the definition of gene-gene interaction at population level reflects the genes' biochemical or physiological interaction remains a mystery. In this article, we introduce a novel definition and a new measure of gene-gene interaction between two unlinked loci (or genes). We developed a general theory for studying linkage disequilibrium (LD) patterns in disease population under two-locus disease models. The properties of using the LD measure in a disease population as a function of the measure of gene-gene interaction between two unlinked loci were also investigated. We examined how interaction between two loci creates LD in a disease population and showed that the mathematical formulation of the new definition for gene-gene interaction between two loci was similar to that of the LD between two loci. This finding motived us to develop an LD-based statistic to detect gene-gene interaction between two unlinked loci. The null distribution and type I error rates of the LD-based statistic for testing gene-gene interaction were validated using extensive simulation studies. We found that the new test statistic was more powerful than the traditional logistic regression under three two-locus disease models and demonstrated that the power of the test statistic depends on the measure of gene-gene interaction. We also investigated the impact of using tagging SNPs for testing interaction on the power to detect interaction between two unlinked loci. Finally, to evaluate the performance of our new method, we applied the LD-based statistic to two published data sets. Our results showed that the P values of the LD-based statistic were smaller than those obtained by other approaches, including logistic regression models.