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Am J Hum Genet. 2016 Dec 1;99(6):1245-1260. doi: 10.1016/j.ajhg.2016.10.003. Epub 2016 Nov 17.

Colocalization of GWAS and eQTL Signals Detects Target Genes.

Author information

1
Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA.
2
Oxford Centre for Diabetes, Endocrinology, & Metabolism, University of Oxford, Oxford OX3 7LJ, UK; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.
3
Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
4
Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095, USA; Semel Center for Informatics and Personalized Genomics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
5
Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
6
Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
7
Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: eeskin@cs.ucla.edu.

Abstract

The vast majority of genome-wide association study (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual's disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue could play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWASs and expression quantitative trail locus (eQTL) studies is challenging because of the uncertainty induced by linkage disequilibrium and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present eCAVIAR, a probabilistic method that has several key advantages over existing methods. First, our method can account for more than one causal variant in any given locus. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Using publicly available eQTL data on 45 different tissues, we demonstrate that eCAVIAR can prioritize likely relevant tissues and target genes for a set of glucose- and insulin-related trait loci.

PMID:
27866706
PMCID:
PMC5142122
DOI:
10.1016/j.ajhg.2016.10.003
[Indexed for MEDLINE]
Free PMC Article

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