ColocQuiaL: a QTL-GWAS colocalization pipeline

Area of Science:

• Genomics
• Statistical Genetics
• Bioinformatics

Background:

• Identifying causal genomic variants from genome-wide association study (GWAS) signals is a significant challenge in genetic research.
• Colocalization analysis, integrating GWAS data with expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL) data, is a key strategy to link GWAS signals to candidate causal genes.

Purpose of the Study:

• To introduce ColocQuiaL, a scalable computational pipeline designed for performing genome-wide colocalization analyses.
• To facilitate the identification of genomic features associated with GWAS signals by connecting them to eQTL and sQTL data.
• To provide summary files and visualization tools for detailed interpretation of colocalization results.

Main Methods:

• Development of the ColocQuiaL pipeline, implemented in R, to conduct large-scale colocalization analyses.
• Application of ColocQuiaL to integrate a type 2 diabetes GWAS dataset with Genotype-Tissue Expression (GTEx) v8 single-tissue eQTL and sQTL data.
• Generation of summary statistics and locus visualization plots for comprehensive result review.

Main Results:

• The ColocQuiaL pipeline successfully performed large-scale colocalization analyses across the genome.
• The example analysis demonstrated the pipeline's utility in connecting type 2 diabetes GWAS signals with eQTL and sQTL data from GTEx v8.
• The pipeline provides actionable summary files and visualizations for detailed examination of colocalization events.

Conclusions:

• ColocQuiaL offers a robust framework for conducting scalable colocalization analyses, essential for interpreting GWAS findings.
• The pipeline aids researchers in prioritizing candidate causal genes by integrating diverse genetic and functional genomics datasets.
• ColocQuiaL is freely available, promoting its adoption and advancement in the field of genetic association studies.