Figure 1: Overview of eQTeL model.

(a) Input and output of eQTeL. eQTeL takes genotype and gene expression across samples, epigenetic and interaction features for each SNP and LD block as input. It outputs regulatory SNPs and their target genes, their effect sizes and regulatory-interaction potentials, as well as estimated feature importance of each epigenetic and interaction feature. (b) eQTeL is composed of two coupled regression models (i) a Bayesian variable selection with informative priors models expression as a linear combination of SNPs. Given the regulatory and interaction priors, this hierarchical model first identifies LD blocks and then combinations of SNPs that explains expression variance and that also have high regulatory and interaction potentials. (ii) A Bayesian logistic regression specifies the regulatory and interaction potential as linear model of epigenetic and interaction features in semi-supervised manner. The logistic regression passes the regulatory and interaction potentials to the variable selection model, while the variable selection model passes expression-regulators to the logistic regression model.