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.

Large-Scale Identification of Common Trait and Disease Variants Affecting Gene Expression

“…Therefore, it does not prove causality, similarly to all current methods. [7][8][9][10][11][12] In fact, if gene functions are often pleiotropic, for which we found some evidence in the current study, the assumption of independence between the instrument variable and the outcome of Mendelian randomization approaches is more likely to be violated. 34 Still, the inferences made here can potentially guide phenotypic studies of gene functions in model systems.…”

10 Years of GWAS Discovery: Biology, Function, and Translation

“…The use of additional information, such as prior knowledge of the likely function of specific variants given their location and surrounding DNA motif(s), 139,140 could help to reduce the set of statistical candidates to a smaller number. This is already a fertile area of statistical and bioinformatic research 56,62,131,141,142 bringing together trait or disease GWAS results with those of tissue gene expression. More research on the resolution of fine-mapping is warranted, and this will be fueled by an expected increase in GWAS data on tissue-and cell-specific gene expression.…”

Genomic dissection of bipolar disorder and schizophrenia including 28 subphenotypes

“…We sought to identify additional disease specific loci by incorporating expression information with association results to perform fine-mapping and identify novel variants [27][28][29][30] . Here, we applied the summary-data-based…”