An empirical Bayes approach for multiple tissue eQTL analysis

作者：Li Gen^*; Shabalin Andrey A; Rusyn Ivan; Wright Fred A; Nobel Andrew B

来源：Biostatistics, 2018, 19(3): 391-406.

DOI：10.1093/biostatistics/kxx048

• GTEx
• Hierarchical Bayesian model
• Local false discovery rate
• MT-eQTL
• Tissue specificity

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.

• 出版日期2018-7