Bioinformatics · 2022 · 23 citations · 9 references
Life SciencesBioinformaticsGeneticsGenomic Data AnalysisGenomicsMedical GeneticsExpression Quantitative Trait Loci AnalysisGenome-wide Association StudiesGenotype-phenotype Association AnalysisQuantitative Trait LociQuantitative Trait Loci MappingQuantitative Trait Locus MappingCausal MarkersColocalization AnalysisExpression Quantitative Trait LociExpression Quantitative Trait Loci MappingExpression Quantitative Trait Locus AnalysisGenome-wide Association MappingGenome-wide Association Study DesignQuantitative Trait Loci (Qtl) Mapping
SUMMARY: Identifying genomic features responsible for genome-wide association study (GWAS) signals has proven to be a difficult challenge; many researchers have turned to colocalization analysis of GWAS signals with expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL) to connect GWAS signals to candidate causal genes. The ColocQuiaL pipeline provides a framework to perform these colocalization analyses at scale across the genome and returns summary files and locus visualization plots to allow for detailed review of the results. As an example, we used ColocQuiaL to perform colocalization between a recent type 2 diabetes GWAS and Genotype-Tissue Expression (GTEx) v8 single-tissue eQTL and sQTL data. AVAILABILITY AND IMPLEMENTATION: ColocQuiaL is primarily written in R and is freely available on GitHub: https://github.com/bvoightlab/ColocQuiaL.
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BEDTools: a flexible suite of utilities for comparing genomic features
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LocusZoom: regional visualization of genome-wide association scan results
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