RT Journal Article SR Electronic T1 Colocalization of GWAS and eQTL Signals Detects Target Genes JF bioRxiv FD Cold Spring Harbor Laboratory SP 065037 DO 10.1101/065037 A1 Farhad Hormozdiari A1 Martijn van de Bunt A1 Ayellet V. Segrè A1 Xiao Li A1 Jong Wha J Joo A1 Michael Bilow A1 Jae Hoon Sul A1 Sriram Sankararaman A1 Bogdan Pasaniuc A1 Eleazar Eskin YR 2016 UL http://biorxiv.org/content/early/2016/08/29/065037.abstract AB The vast majority of genome-wide association studies (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 may 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 GWAS and eQTL studies is challenging due to the uncertainty induced by linkage disequilibrium (LD) 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 a new method, eCAVIAR, that is capable of accounting for LD while computing the quantity we refer to as the colocalization posterior probability (CLPP). The CLPP is the probability that the same variant is responsible for both the GWAS and eQTL signal. eCAVIAR has several key advantages. First, our method can account for more than one causal variant in any loci. 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. Utilizing publicly available eQTL data on 45 different tissues, we demonstrate that computing CLPP can prioritize likely relevant tissues and target genes for a set of Glucose and Insulin-related traits loci. eCAVIAR is available at http://genetics.cs.ucla.edu/caviar/