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xQTLbiolinks: a comprehensive and scalable tool for integrative analysis of molecular QTLs

  • Ruofan Ding
  • , Xudong Zou
  • , Yangmei Qin
  • , Lihai Gong
  • , Hui Chen
  • , Xuelian Ma
  • , Shouhong Guang
  • , Chen Yu
  • , Gao Wang
  • , Lei Li
  • Shenzhen Bay Laboratory
  • University of Science and Technology of China
  • Columbia University

Research output: Contribution to journal › Article › peer-review

17 Scopus citations

Abstract

Genome-wide association studies (GWAS) have identified thousands of disease-associated non-coding variants, posing urgent needs for functional interpretation. Molecular Quantitative Trait Loci (xQTLs) such as eQTLs serve as an essential intermediate link between these non-coding variants and disease phenotypes and have been widely used to discover disease-risk genes from many population-scale studies. However, mining and analyzing the xQTLs data presents several significant bioinformatics challenges, particularly when it comes to integration with GWAS data. Here, we developed xQTLbiolinks as the first comprehensive and scalable tool for bulk and single-cell xQTLs data retrieval, quality control and pre-processing from public repositories and our integrated resource. In addition, xQTLbiolinks provided a robust colocalization module through integration with GWAS summary statistics. The result generated by xQTLbiolinks can be flexibly visualized or stored in standard R objects that can easily be integrated with other R packages and custom pipelines. We applied xQTLbiolinks to cancer GWAS summary statistics as case studies and demonstrated its robust utility and reproducibility. xQTLbiolinks will profoundly accelerate the interpretation of disease-associated variants, thus promoting a better understanding of disease etiologies.

Original languageEnglish
Article numberbbad440
JournalBriefings in Bioinformatics
Volume25
Issue number1
DOIs
StatePublished - 1 Jan 2024
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • GWAS
  • bioinformatics
  • disease variants
  • eQTL
  • xQTL

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