An empirical Bayes approach for multiple tissue eQTL analysis. Academic Article uri icon


  • 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.

published proceedings

  • Biostatistics

altmetric score

  • 3.5

author list (cited authors)

  • Li, G., Shabalin, A. A., Rusyn, I., Wright, F. A., & Nobel, A. B.

citation count

  • 38

complete list of authors

  • Li, Gen||Shabalin, Andrey A||Rusyn, Ivan||Wright, Fred A||Nobel, Andrew B

publication date

  • July 2018