School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA.
Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Bioinformatics. 2022 Sep 15;38(18):4409-4411. doi: 10.1093/bioinformatics/btac512.
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.
ColocQuiaL is primarily written in R and is freely available on GitHub: https://github.com/bvoightlab/ColocQuiaL.
确定全基因组关联研究 (GWAS) 信号所负责的基因组特征一直是一个具有挑战性的难题；许多研究人员已经转向 GWAS 信号与表达数量性状基因座 (eQTL) 和剪接数量性状基因座 (sQTL) 的共定位分析，以将 GWAS 信号与候选因果基因联系起来。ColocQuiaL 管道提供了一个在整个基因组范围内进行这些共定位分析的框架，并返回摘要文件和基因座可视化图，以允许对结果进行详细审查。例如，我们使用 ColocQuiaL 对最近的 2 型糖尿病 GWAS 与 Genotype-Tissue Expression (GTEx) v8 单组织 eQTL 和 sQTL 数据进行了共定位分析。
ColocQuiaL 主要用 R 编写，并可在 GitHub 上免费获得：https://github.com/bvoightlab/ColocQuiaL。
