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Orchestrating multi-state QTL analysis with bioconductor.

Christina B Del Azodi^1,2, Amelia M Dunstone^1,2, Davis J McCarthy^3,4

¹St. Vincent's Institute of Medical Research, Fitzroy, VIC, Australia.

BMC Bioinformatics

|June 3, 2026

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This summary is machine-generated.

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New R packages, QTLExperiment and multistateQTL, simplify multi-state Quantitative Trait Locus (QTL) mapping analysis. These tools aid in understanding gene regulation across states and discovering novel QTLs.

Area of Science:

Genetics and Bioinformatics
Computational Biology
Systems Biology

Background:

Quantitative Trait Locus (QTL) mapping studies increasingly incorporate diverse cellular and environmental states.
Analyzing high-dimensional, multi-state QTL data presents significant challenges for biological interpretation.

Purpose of the Study:

To develop user-friendly R packages for efficient multi-state QTL data analysis.
To provide tools for storing, manipulating, and analyzing complex QTL datasets across different states.

Main Methods:

Introduction of the QTLExperiment package for robust storage and management of QTL summary statistics and metadata.
Development of the multistateQTL package offering methods for statistical analysis, QTL association classification, and data visualization.

Keywords:

Quantitative Trait Locus Gene Expression Visualization Data Infrastructure Bioconductor R Open source

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Implementation of simulation tools within multistateQTL for generating multi-state QTL summary statistics.

Main Results:

QTLExperiment provides a consistent, user-friendly, and well-documented container for multi-state QTL data.
MultistateQTL facilitates comprehensive analysis, including quantification of QTL sharing and identification of state-specific associations.
Both packages are open-source and available on Bioconductor, promoting accessibility.

Conclusions:

The QTLExperiment and multistateQTL packages offer an intuitive workflow for downstream analysis of multi-state QTL data.
These tools empower researchers to investigate gene regulation differences across states and identify unique QTLs.
Multi-state QTL analysis, supported by these open-source tools, can uncover disease-relevant QTLs masked in traditional studies.