Title: An empirical Bayes approach for multiple tissue eQTL analysis.

Authors: Li, Gen; Shabalin, Andrey A; Rusyn, Ivan; Wright, Fred A; Nobel, Andrew B

Published In Biostatistics, (2018 07 01)

Abstract: 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.

PubMed ID: 29029013

MeSH Terms: Bayes Theorem; Biostatistics/methods*; Gene Expression*; Genomics/methods*; Genotyping Techniques/methods*; Humans; Models, Statistical*; Quantitative Trait Loci*