Imperial College London
Alternate

DrLeonardoBottolo

Faculty of Medicine, National Heart & Lung Institute

Visiting Researcher
 
 
 
//

Contact

 

l.bottolo

 
 
//

Location

 

542Huxley BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Lewin:2015:bioinformatics/btv568,
author = {Lewin, A and Saadi, H and Peters, JE and Moreno-Moral, A and Lee, JC and Smith, KGC and Petretto, E and Bottolo, L and Richardson, S},
doi = {bioinformatics/btv568},
journal = {Bioinformatics},
pages = {523--532},
title = {MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues},
url = {http://dx.doi.org/10.1093/bioinformatics/btv568},
volume = {32},
year = {2015}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Lewin,A
AU  - Saadi,H
AU  - Peters,JE
AU  - Moreno-Moral,A
AU  - Lee,JC
AU  - Smith,KGC
AU  - Petretto,E
AU  - Bottolo,L
AU  - Richardson,S
DO  - bioinformatics/btv568
EP  - 532
PY  - 2015///
SN  - 1367-4803
SP  - 523
TI  - MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues
T2  - Bioinformatics
UR  - http://dx.doi.org/10.1093/bioinformatics/btv568
UR  - http://hdl.handle.net/10044/1/41065
VL  - 32
ER  -
Download