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yQTL Pipeline: A structured computational workflow for large scale quantitative trait loci discovery and downstream

Mengze Li1,2, Zeyuan Song3, Anastasia Gurinovich3,4

  • 1Bioinformatics Program, Faculty of Computing & Data Sciences, Boston University, Boston, Massachusetts, United States of America.

Plos One
|June 4, 2024
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Summary

The yQTL Pipeline automates quantitative trait loci (QTL) discovery for large-scale genetic analysis. This tool accelerates analysis and enhances reproducibility, identifying 14,983 metabolite-QTL associations in a human serum metabolomics study.

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Area of Science:

  • Genetics and Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Quantitative trait loci (QTL) analysis links DNA variation to phenotypic traits.
  • Manual QTL discovery is complex, labor-intensive, and prone to errors.
  • Automating QTL analysis is crucial for large-scale genetic studies.

Purpose of the Study:

  • To develop an automated and reproducible pipeline for quantitative trait loci (QTL) discovery.
  • To facilitate large-scale genetic association analyses.
  • To provide user-friendly tools for data visualization and interpretation.

Main Methods:

  • The yQTL Pipeline utilizes Nextflow for parallelized analysis, ensuring reproducibility.
  • It supports linear mixed-effect models and linear models for genome-wide association tests, accommodating covariates and genetic relationships.
  • An integrated R Shiny App provides interactive visualization of QTL results.

Main Results:

  • The pipeline successfully analyzed 9.1 million SNPs and 1,052 metabolites from the New England Centenarians Study.
  • Analysis identified 14,983 metabolite-QTLs (mQTLs) associated with 312 metabolites at a p-value cutoff of 5e-8.
  • Parallelization reduced analysis time from approximately 90 minutes to 26 minutes, and revealed shared mQTLs across metabolites.

Conclusions:

  • The yQTL Pipeline effectively automates and accelerates large-scale QTL discovery.
  • The tool enhances reproducibility and provides valuable insights through integrated visualization.
  • It is a valuable resource for genetic association studies, particularly in metabolomics.