Colocalization of GWAS and eQTL Signals Detects Target Genes

By Farhad Hormozdiari, M. van de Bunt, A. Segrè, Xiao Li, J. W. Joo, Michael Bilow, J. Sul, Sriram Sankararaman, Bogdan Pasaniuc, Eleazar Eskin. bioRxiv, 2016

671 citations recorded by the source corpus.

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/

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a44ee4e44b8aa9e0257761dade1401c1ba18d7e3

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Citations in the corpus

A more accurate method for colocalisation analysis allowing for multiple causal variants (2021, background): “[13 ] Another focuses on a variant-level deﬁnition of colocalisation, estimating the probability that each variant in turn is causal for two traits, whilst allowing that other causal variants (shared or non-shared) may exist in the vicinity[14].”
An integrated framework for local genetic correlation analysis (2022, methodology): “7 and 8), although this was not the case for LAVA, which showed well-controlled type 1 error rates regardless of condition.”
Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses (2019, neutral): “…effectively tests whether the strongest signals for the two traits colocalise [10] which has been shown to be conservative [16]. e-CAVIAR [17] removes the assumption of a single causal variant per trait by integrating over the fine mapping posteriors for two traits, but requires signed…”
Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases (2020, background): “…to the causal gene(s) using protein-coding variants, genomic distance, enhancer–gene maps 11–16 or expression quantitative trait loci (eQTLs) 7,8 ; and second, similarity-based meth-ods that search for global patterns in associated genes and nominate those with similar functions, pathways…”
Measuring biological age using omics data (2022, methodology): “Modern human genetics methods, such as genetic colocalization 163 and Mendelian randomization 164 , can also be used to test causality in ageing clock models.”
Evidence for Weak Selective Constraint on Human Gene Expression (2018, background): “Across species, gene expression has been shown to evolve more slowly than expected under a neutral model (Chan et al. 2009; Brawand et al. 2011; Khan et al. 2013; Chen et al. 2018).”
A statistical framework for cross-tissue transcriptome-wide association analysis (2018, neutral): “12).”
Brain age prediction using deep learning uncovers associated sequence variants (2019, background): “However, identifying whether a variant is truly causal in both GWAS and eQTL is challenging because of the uncertainty caused by LD 79 .”