Figure 3: eQTeL identify causal SNP accurately in semi-simulated data.

(a) Design of simulaton study: simulation study uses (i) 174800 SNPs from MAGNet Genotype (874 SNPs per gene) data for 313 samples, (ii) distribution of number of expression-regulators per gene from MAGNet data, (iii) distribution of explained expression variance estimated from MAGNet data, (iv) ENCODE epigenetic data for heart cell lines and (v) distribution of epigenetic data for regulators VISTA heart enhancers. Expression regulators per gene were chosen amongst regulators (1% of MAGNet SNPs). Using allele status of expression regulators in 313 samples expression of 200 genes was generated such that explained variance distribution matches MAGNets explained variance. Epigenetic data for regulators were generated using the epigenetic distribution estimated from VISTA heart enhancers. (b) Comparative performance assessment on simulated data. Methods include (i) Matrix-eQTL^11,25 (univariate-eQTL): univariate regression, (ii) LASSO⁴⁴: L1 regularizer multivariate regression, (iii) variable selection¹⁷: Bayesian variable selection, (iv) eqtnminer⁸: Bayesian variable selection with empirical-priors, (v) epigenetic-only: epigenetic feature weights derived from verified enhancers and used to prioritize SNPs, (vi) eQTeL: proposed method and (vii) known-epigenetic-priors-eQTeL: eQTeL with fixed epigenetic priors as in epigenetic-only. Number of SNPs each methods were controlled.