EPISPOT: An epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies.

Autor:	Ruffieux H; MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK. Electronic address: helene.ruffieux@mrc-bsu.cam.ac.uk., Fairfax BP; Department of Oncology, MRC Weatherall Institute for Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK., Nassiri I; Department of Oncology, MRC Weatherall Institute for Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK., Vigorito E; MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK., Wallace C; MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK; Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0AW, UK., Richardson S; MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK; The Alan Turing Institute, London NW1 2DB, UK., Bottolo L; MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK; The Alan Turing Institute, London NW1 2DB, UK; Department of Medical Genetics, University of Cambridge, Cambridge CB2 0QQ, UK.
Jazyk:	angličtina
Zdroj:	American journal of human genetics [Am J Hum Genet] 2021 Jun 03; Vol. 108 (6), pp. 983-1000. Date of Electronic Publication: 2021 May 01.
DOI:	10.1016/j.ajhg.2021.04.010
Abstrakt:	We 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. (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu