An empirical Bayes approach for multiple tissue eQTL analysis. (25th September 2017)

• Record Type: Journal Article
• Authors: Li, Gen; Shabalin, Andrey A; Rusyn, Ivan; Wright, Fred A; Nobel, Andrew B
• Is Part Of: Biostatistics. Volume 19:Number 3(2018)
• Page Start: 391
• Page End: 406
• Publication Date: 2017-09-25
• Subjects: GTEx -- Hierarchical Bayesian model -- Local false discovery rate -- MT-eQTL -- Tissue specificity; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxx048
• Languages: English
• ISSNs: 1465-4644

Abstract

SUMMARY: 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.