Wang Tao, Peng Qidi, Liu Bo, Liu Xiaoli, Liu Yongzhuang, Peng Jiajie, Wang Yadong
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
Department of Neurology, Zhejiang Hospital, Hangzhou, China.
Front Genet. 2020 Jan 9;10:1309. doi: 10.3389/fgene.2019.01309. eCollection 2019.
Expression quantitative trait locus (eQTL) analyses are critical in understanding the complex functional regulatory natures of genetic variation and have been widely used in the interpretation of disease-associated variants identified by genome-wide association studies (GWAS). Emerging evidence has shown that -eQTL effects on remote gene expression could be mediated by local transcripts, which is known as the mediation effects. To discover the genome-wide eQTL mediation effects combing genomic and transcriptomic profiles, it is necessary to develop novel computational methods to rapidly scan large number of candidate associations while controlling for multiple testing appropriately. Here, we present eQTLMAPT, an R package aiming to perform eQTL mediation analysis with implementation of efficient permutation procedures in multiple testing correction. eQTLMAPT is advantageous in threefold. First, it accelerates mediation analysis by effectively pruning the permutation process through adaptive permutation scheme. Second, it can efficiently and accurately estimate the significance level of mediation effects by modeling the null distribution with generalized Pareto distribution (GPD) trained from a few permutation statistics. Third, eQTLMAPT provides flexible interfaces for users to combine various permutation schemes with different confounding adjustment methods. Experiments on real eQTL dataset demonstrate that eQTLMAPT provides higher resolution of estimated significance of mediation effects and is an order of magnitude faster than compared methods with similar accuracy.
表达数量性状基因座（eQTL）分析对于理解遗传变异的复杂功能调控本质至关重要，并且已广泛应用于解释全基因组关联研究（GWAS）中鉴定出的疾病相关变异。新出现的证据表明，eQTL对远程基因表达的影响可能由局部转录本介导，这被称为介导效应。为了结合基因组和转录组谱发现全基因组的eQTL介导效应，有必要开发新的计算方法，以便在适当控制多重检验的同时快速扫描大量候选关联。在这里，我们展示了eQTLMAPT，这是一个R包，旨在通过在多重检验校正中实施高效的置换程序来进行eQTL介导分析。eQTLMAPT具有三个优势。首先，它通过自适应置换方案有效地修剪置换过程，从而加速介导分析。其次，它可以通过用从少量置换统计数据训练的广义帕累托分布（GPD）对无效分布进行建模，高效准确地估计介导效应的显著性水平。第三，eQTLMAPT为用户提供了灵活的接口，以便将各种置换方案与不同的混杂调整方法相结合。对真实eQTL数据集的实验表明，eQTLMAPT提供了更高分辨率的介导效应估计显著性，并且比具有相似准确性的比较方法快一个数量级。
