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


METHODS
C[Formula: see text] source code and documentation including compilation instructions are available under GNU licence at http://www.mrc-bsu.cam.ac.uk/software/.
Department of Mathematics, Brunel University London.
Alex
A
Lewin
Department of Epidemiology and Biostatistics, Imperial College London, London.
Habib
H
Saadi
Cambridge Institute for Medical Research, University of Cambridge, Cambridge, MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge.
James E
JE
Peters
MRC Clinical Sciences Centre, Imperial College London, London, UK.
Aida
A
Moreno-Moral
Cambridge Institute for Medical Research, University of Cambridge, Cambridge.
James C
JC
Lee
Cambridge Institute for Medical Research, University of Cambridge, Cambridge.
Kenneth G C
KG
Smith
MRC Clinical Sciences Centre, Imperial College London, London, UK, Duke-NUS Graduate Medical School, Singapore, Singapore.
Enrico
E
Petretto
Department of Mathematics, Imperial College London, London, UK and Department of Medical Genetics, University of Cambridge.
Leonardo
L
Bottolo
MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge.
Sylvia
S
Richardson
D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D023281:Genomics / Q000379:methods; D006801:Humans; D007249:Inflammation / Q000235:genetics*; D015212:Inflammatory Bowel Diseases / Q000235:genetics*; D008962:Models, Theoretical; D009928:Organ Specificity; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D011381:Programming Languages; D040641:Quantitative Trait Loci / Q000235:genetics*; D051381:Rats; D012984:Software; D014018:Tissue Distribution