Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits
Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits
复制标题
DOI：
10.1371/journal.pgen.1006869
复制
发表时间：
2017-07-01
期刊：
影响因子：
4.5
通讯作者：
Zhou, Xiang
中科院分区：
文献类型：
--
作者：
Crawford, Lorin;Zeng, Ping;Zhou, Xiang
上位性通常被定义为多个基因之间的相互作用，是表型变异的重要遗传成分。许多统计方法已经发展到模型和确定遗传变异之间的上位相互作用。然而，由于相互作用的组合搜索空间很大，大多数上位性映射方法面临着巨大的计算挑战，并且由于多次检验校正，往往存在统计能力低的问题。在这里，我们提出了一种新的、可替代的上位性映射策略：我们不是直接识别个体的成对或高阶相互作用，而是专注于映射具有非零边际上位性效应的变体——给定变体与所有其他变体之间的组合成对相互作用效应。通过测试边际上位性效应，我们可以识别与上位性相关的候选变异，而无需识别与变异相互作用的确切伙伴，从而潜在地减轻了与标准上位性映射程序相关的大部分统计和计算负担。我们的方法基于方差分量模型，并依赖于最近发展的方差分量估计方法来进行有效的参数推断和p值计算。我们把我们的方法称为“边际上位性测试”，简称MAPIT。通过模拟，我们展示了MAPIT如何用于估计和测试边际上位性效应，在null下产生校准的测试统计量，并促进成对上位性相互作用的检测。我们通过分析来自GEUVADIS联盟的400多个个体的基因表达数据，在QTL定位研究中进一步说明了MAPIT的好处。
Epistasis, commonly defined as the interaction between multiple genes, is an important genetic component underlying phenotypic variation. Many statistical methods have been developed to model and identify epistatic interactions between genetic variants. However, because of the large combinatorial search space of interactions, most epistasis mapping methods face enormous computational challenges and often suffer from low statistical power due to multiple test correction. Here, we present a novel, alternative strategy for mapping epistasis: instead of directly identifying individual pairwise or higher-order interactions, we focus on mapping variants that have non-zero marginal epistatic effects-the combined pairwise interaction effects between a given variant and all other variants. By testing marginal epistatic effects, we can identify candidate variants that are involved in epistasis without the need to identify the exact partners with which the variants interact, thus potentially alleviating much of the statistical and computational burden associated with standard epistatic mapping procedures. Our method is based on a variance component model, and relies on a recently developed variance component estimation method for efficient parameter inference and p-value computation. We refer to our method as the "MArginal ePIstasis Test", or MAPIT. With simulations, we show how MAPIT can be used to estimate and test marginal epistatic effects, produce calibrated test statistics under the null, and facilitate the detection of pairwise epistatic interactions. We further illustrate the benefits of MAPIT in a QTL mapping study by analyzing the gene expression data of over 400 individuals from the GEUVADIS consortium.