Accurate eQTL prioritization with an ensemble-based framework

Abstract

We present a novel ensemble-based computational framework, EnsembleExpr, that achieved the best performance in the Fourth Critical Assessment of Genome Interpretation (CAGI4) “eQTL-causal SNPs” challenge for identifying eQTLs and prioritizing their gene expression effects. Expression quantitative trait loci (eQTLs) are genome sequence variants that result in gene expression changes and thus are prime suspects in the search for contributions to the causality of complex traits. When EnsembleExpr is trained on data from massively parallel reporter assays (MPRA) it accurately predicts reporter expression levels from unseen regulatory sequences and identifies sequence variants that exhibit significant changes in reporter expression. Compared with other state-of-the-art methods, EnsembleExpr achieved competitive performance when applied on eQTL datasets determined by other protocols. We envision EnsembleExpr to be a resource to help interpret non-coding regulatory variants and prioritize disease-associated mutations for downstream validation.

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