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Abstract：
Expression quantitative trait locus (eQTL) analysis is a useful tool to identify genetic loci that are associated with gene expression levels. Large collaborative efforts such as the Genotype-Tissue Expression (GTEx) project provide valuable resources for eQTL analysis in different tissues. Most existing methods, however, either focus on one tissue at a time, or analyze multiple tissues to identify eQTLs jointly present in multiple tissues. There is a lack of powerful methods to identify eQTLs in a target tissue while effectively borrowing strength from auxiliary tissues. In this paper, we propose a novel statistical framework to improve the eQTL detection efficacy in the tissue of interest with auxiliary information from other tissues. This framework can enhance the power of the hypothesis test for eQTL effects by incorporating shared and specific effects from multiple tissues into the test statistics. We also devise data-driven and distributed computing approaches for efficient implementation of eQTL detection when the number of tissues is large. Numerical studies in simulation demonstrate the efficacy of the proposed method, and the real data analysis of the GTEx example provides novel insights into eQTL findings in different tissues.
摘要：
表达数量性状基因座（eQTL）分析是鉴定与基因表达水平相关的遗传基因座的有用工具。诸如基因型-组织表达(GTEx)项目之类的大型协作努力为不同组织中的eQTL分析提供了宝贵的资源。大多数现有的方法，然而,要么一次集中在一个组织上，或分析多个组织以鉴定联合存在于多个组织中的eQTL。缺乏有效的方法来识别靶组织中的eQTL,同时有效地借用辅助组织的强度。在本文中,我们提出了一种新的统计框架，利用来自其他组织的辅助信息来提高感兴趣组织中的eQTL检测效率。该框架可以通过将来自多个组织的共享和特定效应纳入测试统计来增强对eQTL效应的假设检验的能力。我们还设计了数据驱动和分布式计算方法，以在组织数量大时有效实现eQTL检测。模拟中的数值研究证明了所提出方法的有效性，GTEx实例的真实数据分析提供了对不同组织中eQTL发现的新见解。
