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ColoQuium: A software package for performing colocalization between QTL and GWAS analyses and visual

Kumar, Nimay. BS. University of Pennsylvania; Bone, William. PhD. Recursion Pharmaceuticals; Chen, Brian. BS. University of Pennsylvania; Drivas, Theodore. MD, PhD. University of Pennsylvania; Lucas, Anastasia. BS. University of Pennsylvania; Veturi, Yogasudha, PhD. Penn State University; Ritchie, Marylyn. PhD. University of Pennsylvania Voight, Benjamin. PhD. University of Pennsylvania


Poster # 37


Statistical colocalization between genome wide association studies (GWAS) signals and eitherexpression quantitative trait loci (eQTL) or splicing quantitative trait loci (sQTL) is a popularmethod among researchers to connect GWAS signals to candidate causal genes. ColoQuiumintegrates three colocalization R packages into one streamlined pipeline for performingcolocalization and facilitating analysis through the detailed visualization of colocalizationresults. ColoQuium consists of the following: ColoGene performs colocalization between GWASsignificant SNPs and corresponding eQTLs for a given tissue on a gene by gene basis, and canaccommodate multiple causal variants per locus. This is best suited for analyses investigating aset of genes and tissues of interest (e.g. obtained from running transcriptome wide associationstudies) using corresponding gene expression and GWAS summary statistics. ColocQuiaL is aframework that performs colocalization between SNP based GWAS signals and eQTL/sQTL atscale and returns a summary of the colocalization results across the genome and locusvisualization plots. Given a gene trait pair, eQTpLot illustrates the colocalization and correlationbetween GWAS and eQTL P values, enrichment of eQTLs among trait significant variants, theLD landscape of the given locus, and the relationship between the direction of effect of eQTLsignals and the direction of effect of colocalizing GWAS peaks. ColoQuium lets researcherseasily perform SNP or gene based colocalization in one user friendly tool, providing a betterunderstanding of the interaction between gene expression and trait associations via tabulationand visualization of the results and GWAS summary statistics.

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