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ezQTL: Web Platform for Interactive Visualization and Colocalization of Quantitative Trait Loci (QTL) and GWAS

Description

Expression quantitative trait locus (eQTLs) and other molecular QTL studies have been valuable resources in identifying candidate causal genes from GWAS loci through statistical colocalization methods. While QTL colocalization is becoming a standard analysis in post-GWAS investigation, an easy web tool for users to perform formal colocalization analyses with either user-provided or public GWAS and eQTL datasets has been lacking. ezQTL is a web-based bioinformatic application to interactively visualize and analyze genetic association data such as GWAS and molecular QTLs under different linkage disequilibrium (LD) patterns (1000 Genomes, UK Biobank, or user-provided). ezQTL facilitates mapping disease susceptibility regions and assists researchers in characterizing and prioritizing functional genes and variants based on the genotype-phenotype associations.

Details

The ezQTL web application includes seven modules: Locus QC, Locus LD, Locus Alignment, Locus Colocalization, Locus Table, Locus Quantification, and Locus Download. ezQTL performs variant level QC before colocalization analysis for all user-supplied and pre-provided datasets and also generates reports and plots for future investigation. ezQTL hosts a large number of datasets (QTL, GWAS, and LD matrix data, including those from different genome builds) for each data type and allows users to perform colocalization analysis using mixed data sources and two state-of-the-art methodologies (eCAVIAR and HyPrColoc). Currently, 594 datasets are collectively listed on the searchable Public Data Source tab. In addition, serial visualizations have been implemented in ezQTL, including interactive dual-LocusZoom plot, LD matrix visualization, QTL association plot. For the same dataset, ezQTL also allows users to perform analysis for multiple loci by inputting different locus information. In addition, ezQTL allows users to submit the job to ezQTL server and an email with a link to access all the results will return to users.

Website

ezQTL web application

Manual Documentation page on ezQTL website

Support

E-mail: NCIezQTLWebAdmin@mail.nih.gov

Reference

  • Tongwu Zhang, Alyssa Klein, Jian Sang, Jiyeon Choi, Kevin M. Brown, ezQTL: A Web Platform for Interactive Visualization and Colocalization of Quantitative Trait Loci (QTL) and GWAS. Genomics, Proteomics and Bioinformatics. 2022. (Manuscript Under Review)
  • Posted:

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