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Sherlock: detecting gene-disease associations by matching patterns of expression QTL and GWAS.

Abstract：

:Genetic mapping of complex diseases to date depends on variations inside or close to the genes that perturb their activities. A strong body of evidence suggests that changes in gene expression play a key role in complex diseases and that numerous loci perturb gene expression in trans. The information in trans variants, however, has largely been ignored in the current analysis paradigm. Here we present a statistical framework for genetic mapping by utilizing collective information in both cis and trans variants. We reason that for a disease-associated gene, any genetic variation that perturbs its expression is also likely to influence the disease risk. Thus, the expression quantitative trait loci (eQTL) of the gene, which constitute a unique "genetic signature," should overlap significantly with the set of loci associated with the disease. We translate this idea into a computational algorithm (named Sherlock) to search for gene-disease associations from GWASs, taking advantage of independent eQTL data. Application of this strategy to Crohn disease and type 2 diabetes predicts a number of genes with possible disease roles, including several predictions supported by solid experimental evidence. Importantly, predicted genes are often implicated by multiple trans eQTL with moderate associations. These genes are far from any GWAS association signals and thus cannot be identified from the GWAS alone. Our approach allows analysis of association data from a new perspective and is applicable to any complex phenotype. It is readily generalizable to molecular traits other than gene expression, such as metabolites, noncoding RNAs, and epigenetic modifications.

journal_name

Am J Hum Genet

journal_title

American journal of human genetics

authors

He X,Fuller CK,Song Y,Meng Q,Zhang B,Yang X,Li H

doi

10.1016/j.ajhg.2013.03.022

subject

Has Abstract

pub_date

2013-05-02 00:00:00

pages

667-80

issue

5

eissn

0002-9297

issn

1537-6605

pii

S0002-9297(13)00159-6

journal_volume

92

pub_type

杂志文章

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