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Abstract:
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Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). We introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci displaying allelic heterogeneity: containing multiple LD-independent eQTLs. MRLocus makes use of a colocalization step applied to each eQTL, followed by an MR analysis step across eQTLs. We demonstrate the operating characteristics of our method on simulated data using an existing simulation framework. We then apply our method to previously reported loci using publicly available eQTL data, and GWAS for coronary artery disease and lipid levels. Finally, we apply our method to progenitor and neuronal cell line caQTL and eQTL datasets, along with neuropsychiatric GWAS.
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