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MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues

Title: MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues
Author(s): Lewin, A
Saadi, H
Peters, JE
Moreno-Moral, A
Lee, JC
Smith, KGC
Petretto, E
Bottolo, L
Richardson, S
Item Type: Journal Article
Abstract: Motivation: Analysing the joint association between a large set of responses and predictors is a fundamental statistical task in integrative genomics, exemplified by numerous expression Quantitative Trait Loci (eQTL) studies. Of particular interest are the so-called ‘hotspots’, important genetic variants that regulate the expression of many genes. Recently, attention has focussed on whether eQTLs are common to several tissues, cell-types or, more generally, conditions or whether they are specific to a particular condition. Results: We have implemented MT-HESS, a Bayesian hierarchical model that analyses the association between a large set of predictors, e.g. SNPs, and many responses, e.g. gene expression, in multiple tissues, cells or conditions. Our Bayesian sparse regression algorithm goes beyond ‘one-at-a-time’ association tests between SNPs and responses and uses a fully multivariate model search across all linear combinations of SNPs, coupled with a model of the correlation between condition/tissue-specific responses. In addition, we use a hierarchical structure to leverage shared information across different genes, thus improving the detection of hotspots. We show the increase of power resulting from our new approach in an extensive simulation study. Our analysis of two case studies highlights new hotspots that would remain undetected by standard approaches and shows how greater prediction power can be achieved when several tissues are jointly considered.
Publication Date: 26-Oct-2015
Date of Acceptance: 3-Sep-2015
URI: http://hdl.handle.net/10044/1/41065
DOI: http://dx.doi.org/10.1093/bioinformatics/btv568
ISSN: 1367-4803
Publisher: Oxford University Press
Start Page: 523
End Page: 532
Journal / Book Title: Bioinformatics
Volume: 32
Issue: 4
Copyright Statement: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Sponsor/Funder: Medical Research Council (MRC)
Funder's Grant Number: G1002319
Keywords: Science & Technology
Life Sciences & Biomedicine
Technology
Physical Sciences
Biochemical Research Methods
Biotechnology & Applied Microbiology
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Statistics & Probability
Biochemistry & Molecular Biology
Computer Science
Mathematics
QUANTITATIVE TRAIT LOCI
VARIABLE SELECTION
GENE-EXPRESSION
GENOME
IDENTIFICATION
ALLELES
NETWORK
DISEASE
RISK
SETS
Bioinformatics
01 Mathematical Sciences
06 Biological Sciences
08 Information And Computing Sciences
Publication Status: Published
Appears in Collections:Clinical Sciences
Molecular Sciences
Faculty of Medicine
Epidemiology, Public Health and Primary Care



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