An empirical Bayes approach for multiple tissue eQTL analysis
An empirical Bayes approach for multiple tissue eQTL analysis
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DOI：
10.1093/biostatistics/kxx048
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发表时间：
2018-07-01
期刊：
影响因子：
2.1
通讯作者：
Nobel, Andrew B.
中科院分区：
文献类型：
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作者：
Li, Gen;Shabalin, Andrey A.;Nobel, Andrew B.
表达数量性状基因座（eQTL）分析鉴定与基因表达相关的遗传标记。大多数最新的eQTL研究考虑遗传变异和单个组织中表达之间的联系。多组织分析有可能改善单一组织中的发现，并阐明组织间差异的基因型基础。在这篇文章中，我们开发了一个层次贝叶斯模型（MT-eQTL）多组织eQTL分析。MT-eQTL明确地捕获eQTL存在或不存在时的变异模式，以及组织间效应大小的异质性。我们设计了一个有效的期望最大化（EM）算法模型拟合。关于eQTL检测和eQTL跨组织配置的推断分别来自于局部错误发现率的自适应阈值和最大后验估计。我们还提供了自适应程序的理论依据。我们通过对GTEx计划的9个组织数据集进行广泛分析，研究了MT-eQTL模型。
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.