LocusFocus: A web-based colocalization tool for the annotation and functional follow-up of GWAS

Abstract

Genome-wide association studies (GWAS) have primarily identified trait-associated loci in the non-coding genome. Colocalization analyses of SNP-level associations from GWAS with expression quantitative trait loci (eQTL) evidence enable the generation of hypotheses about responsible mechanism, genes and tissues of origin to guide functional characterization. Here, we present a web-based colocalization browsing and testing tool named LocusFocus (https://locusfocus.research.sickkids.ca). LocusFocus formally tests colocalization using our established Simple Sum method to identify the most relevant genes and tissues for a particular GWAS locus in the presence of high linkage disequilibrium and/or allelic heterogeneity. Full documentation and source code for LocusFocus are publicly available.

A flexible summary statistics-based colocalization method with application to the mucin cystic fibrosis lung disease modifier locus

“… 60 This will be addressed in future work. SS2 is implemented in a web-based colocalization tool, LocusFocus, 25 which enables integration of GWAS summary statistics with any secondary SNP-level dataset by using p values and LD for the region of interest. The eQTL summary statistics from GTEx are made available for selection within the web server to test colocalization with tissues and genes from GTEx.…”

“…The SS2 framework as delineated here does not integrate summary statistics from greater than two studies, although there would be value in colocalizing GWAS summary data with multiple molecular phenotypes and multiple GWAS traits simultaneously as proposed in Giambartolomei et al 60 This will be addressed in future work. SS2 is implemented in a web-based colocalization tool, LocusFocus, 25 which enables integration of GWAS summary statistics with any secondary SNP-level dataset by using p values and LD for the region of interest. The eQTL summary statistics from GTEx are made available for selection within the web server to test colocalization with tissues and genes from GTEx.…”

“…The SS2 has been implemented in the application LocusFocus. 25 For method comparison, we choose COLOC and CO-LOC2 from the class of Bayesian methods, and SMR and SMR-multi from the class of frequentist methods. Among the three Bayesian approaches that consider the issue of the composite null hypothesis (COLOC, GWAS-PW, and COLOC2), COLOC has been shown to have better performance than GWAS-PW when a single causal variant is shared between the two studies, 15 while GWAS-PW can have better performance when eQTL and GWAS causal variants are distinct.…”

A flexible summary-based colocalization method with application to the mucin Cystic Fibrosis lung disease modifier locus

“…Similarly, the SS2 could assess colocalization using any SNP-level data including DNA methylation (meQTLs), protein QTLs (pQTLs) or metabolites (metQTLs). SS2 is implemented in a web-based colocalization tool, LocusFocus 21 which enables integration of GWAS summary statistics with any secondary SNP-level dataset by using p-values and LD for the region of interest. The eQTL summary statistics from GTEx are made available for selection within the web server to test colocalization with tissues and genes from GTEx.…”

“…The SS2 has been implemented in the application LocusFocus. 21 For method comparison, we choose COLOC and COLOC2 from the class of Bayesian methods, and SMR and SMR-multi from the class of frequentist methods. Among the three Bayesian approaches that consider the issue of the composite null hypothesis (COLOC, GWAS-PW and COLOC2), COLOC has been shown to have better performance than GWAS-PW when a single causal variant is shared between the two studies, 15 while GWAS-PW can have better performance when the two causal variants are distinct.…”