Colocalization of GWAS and eQTL Signals Detects Target Genes

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.

Genome‐wide association studies of multiple sclerosis

“…This is likely a limitation in power, and larger sample sizes may help the resolution of these approaches. Alternative fine‐mapping strategies53, 54 have not yet been applied to MS data, but in other instances have performed well and are likely to prove useful in MS locus dissection.…”

Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization

“…The advantages of the model-based colocalization analysis methods over the empirical methodologies (e.g., Nica et al [6]) have been fully demonstrated through both rigorous theoretical arguments [8, 13] and carefully constructed simulation studies [12]. In this paper, we show that both coloc and eCAVIAR can be viewed as special cases of the proposed approach.…”

“…Our proposed method is most similar to the probabilistic model-based approaches coloc [8] and eCAVIAR [12], which represent the state-of-the-art in the current literature. The advantages of the model-based colocalization analysis methods over the empirical methodologies (e.g., Nica et al [6]) have been fully demonstrated through both rigorous theoretical arguments [8, 13] and carefully constructed simulation studies [12].…”

eQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLs

“…Relying on publicly available eQTL summary statistics has several limitations. First, many downstream use cases such as fine-mapping [10,11] and colocalisation [12,13] require full summary statistics from the region of interest, but some studies have only released either eQTL lead variants or variants below a certain p-value threshold. Second, studies often test a different subset of variants in the cis region of each gene, meaning that variants tested in one study might be missing from another study.…”