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Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits

机译：数量性状遗传图谱研究中的边缘上位性试验检测上位性

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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.
机译：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。已经开发出许多统计方法来建模和识别遗传变异之间的上位相互作用。但是，由于相互作用的组合搜索空间很大，因此大多数上位制图方法面临巨大的计算挑战，并且由于多次测试校正而常常遭受较低的统计能力。在这里，我们提出了一种新颖的替代方法，用于映射上位性：不是直接识别单个的成对或更高阶的相互作用，而是关注具有非零边际上位效应的变异体的映射-给定变异体和所有变异体之间的组合的成对相互作用效应其他变体。通过测试边际上位效应，我们可以识别参与上位性的候选变异，而无需确定变异与之相互作用的确切伴侣，从而有可能减轻与标准上位作图程序相关的大量统计和计算负担。我们的方法基于方差分量模型，并依赖于最新开发的方差分量估计方法来进行有效的参数推断和p值计算。我们将我们的方法称为“总体电子静态测试”或MAPIT。通过仿真，我们展示了如何使用MAPIT来估计和测试边际上位效应，在零位下生成校准的测试统计数据，以及促进成对上位相互作用的检测。我们通过分析来自GEUVADIS联盟的400多个个体的基因表达数据，进一步说明了MAPIT在QTL定位研究中的优势。

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