Li Gen, Shabalin Andrey A, Rusyn Ivan, Wright Fred A, Nobel Andrew B
Department of Biostatistics, Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY, 10032 USA
Center for Biomarker Research and Personalized Medicine, Virginia Commonwealth University, 1112 East Clay Street, Richmond, VA, 23298 USA.
Biostatistics. 2018 Jul 1;19(3):391-406. doi: 10.1093/biostatistics/kxx048.
Expression quantitative trait locus (eQTL) analyses identify genetic markers associated with the expression of a gene. Most up-to-date eQTL studies consider the connection between genetic variation and expression in a single tissue. Multi-tissue analyses have the potential to improve findings in a single tissue, and elucidate the genotypic basis of differences between tissues. In this article, we develop a hierarchical Bayesian model (MT-eQTL) for multi-tissue eQTL analysis. MT-eQTL explicitly captures patterns of variation in the presence or absence of eQTL, as well as the heterogeneity of effect sizes across tissues. We devise an efficient Expectation-Maximization (EM) algorithm for model fitting. Inferences concerning eQTL detection and the configuration of eQTL across tissues are derived from the adaptive thresholding of local false discovery rates, and maximum a posteriori estimation, respectively. We also provide theoretical justification of the adaptive procedure. We investigate the MT-eQTL model through an extensive analysis of a 9-tissue data set from the GTEx initiative.
表达数量性状基因座（eQTL）分析可识别与基因表达相关的遗传标记。大多数最新的eQTL研究考虑的是单一组织中遗传变异与表达之间的联系。多组织分析有可能改进单一组织中的研究结果，并阐明组织间差异的基因型基础。在本文中，我们开发了一种用于多组织eQTL分析的分层贝叶斯模型（MT-eQTL）。MT-eQTL明确捕捉eQTL存在或不存在时的变异模式，以及各组织间效应大小的异质性。我们设计了一种有效的期望最大化（EM）算法用于模型拟合。关于eQTL检测和跨组织eQTL配置的推断分别来自局部错误发现率的自适应阈值化和最大后验估计。我们还提供了自适应程序的理论依据。我们通过对来自GTEx计划的9组织数据集进行广泛分析，研究了MT-eQTL模型。
