Abstract
SummaryWe present EPISPOT, a fully joint framework which exploits large panels of epigenetic annotations as variant-level information to enhance molecular quantitative trait locus (QTL) mapping. Thanks to a purpose-built Bayesian inferential algorithm, EPISPOT accommodates functional information for both cis and trans actions, including QTL hotspot effects. It effectively couples simultaneous QTL analysis of thousands of genetic variants and molecular traits with hypothesis-free selection of biologically interpretable annotations which directly contribute to the QTL effects. This unified, epigenome-aided learning boosts statistical power and sheds light on the regulatory basis of the uncovered hits; EPISPOT therefore marks an essential step toward improving the challenging detection and functional interpretation of trans-acting genetic variants and hotspots. We illustrate the advantages of EPISPOT in simulations emulating real-data conditions and in a monocyte expression QTL study, which confirms known hotspots and finds other signals, as well as plausible mechanisms of action. In particular, by highlighting the role of monocyte DNase-I sensitivity sites from >150 epigenetic annotations, we clarify the mediation effects and cell-type specificity of major hotspots close to the lysozyme gene. Our approach forgoes the daunting and underpowered task of one-annotation-at-a-time enrichment analyses for prioritizing cis and trans QTL hits and is tailored to any transcriptomic, proteomic, or metabolomic QTL problem. By enabling principled epigenome-driven QTL mapping transcriptome-wide, EPISPOT helps progress toward a better functional understanding of genetic regulation.
Highlights
Molecular datasets and annotation databases are growing in size and in diversity
Data generation and simulation set-up The series of simulation studies presented have the dual purpose of (1) illustrating the effectiveness of EPISPOT in learning from the epigenome when the epigenetic annotations at hand are sufficiently informative, and (2) evaluating the method in weakly informative scenarios or scenarios where the module partition supplied to MEPISPOT is misspecified
We simulate data so as to best emulate molecular quantitative trait locus (QTL) regulation and the role of the epigenome in triggering this regulation; the general data-generation procedure is detailed in the supplemental material and methods and we further tailor it to each simulation experiment in their dedicated sections
Summary
Genetic data are routinely collected along with gene, protein, or metabolite level measurements and analyzed in molecular quantitative trait locus (QTL) studies, with the aim of unravelling the regulatory mechanisms underlying common diseases. These studies present additional complexities compared to classical genome-wide association studies (GWASs). They entail a very different statistical paradigm: while GWASs consider a single or a few related clinical traits, molecular QTL studies typically involve hundreds or thousands of molecular traits, regressed on hundreds of thousands of genetic variants. Pleiotropic or hotspot genetic variants may exert weak trans effects on many molecular traits
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