A complete tool set for molecular QTL discovery and analysis
Olivier DelaneauiD, Halit OngeniD, Andrew Anand BrowniD, Alexandre Fort, Nikolaos Panousis, Emmanouil Dermitzakis
Abstract
Population scale studies combining genetic information with molecular phenotypes (e.g. gene expression) become a standard to dissect the effects of genetic variants onto organismal. This kind of datasets requires powerful, fast and versatile methods able to discover molecular Quantitative Trait Loci (molQTL). Here we propose such a solution, QTLtools, a modular framework that contains multiple methods to prepare the data, to discover proximal and distal molQTLs and to finally integrate them with GWAS variants and functional annotations of the genome. We demonstrate its utility by performing a complete expression QTL study in a few and easy-to-perform steps. QTLtools is open source and available at https://gtltools.github.io/gtltools/
02 /Specification & Classification
Click any tag to browse matching papers- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Qtlizer: comprehensive QTL annotation of GWAS results
Matthias Munz, Inken Wohlers, … Jeanette Erdmann·Scientific ReportsPeer-reviewed·Nov 24, 2020
- Integrating multi-omics and multi-context QTL data with GWAS reveals the genetic architecture of complex traits and improves the discovery of risk genes
Sheng Qian, Kaixuan Luo, … Xin He·medRxiv·Jan 30, 2026
- Effective QTL Discovery Incorporating Genomic Annotations
Xiaoquan Wen·bioRxiv·Nov 16, 2015
- Fast and efficient QTL mapper for thousands of molecular phenotypes
Halit Ongen, Alfonso Buil, … Olivier Delaneau·bioRxiv·Aug 7, 2015
- A Bayesian Framework for Multiple Trait Colocalization from Summary Association Statistics
Claudia Giambartolomei, Jimmy Zhenli Liu, … Panos Roussos·BioinformaticsPeer-reviewed·Mar 8, 2018
- ProGeM: A framework for the prioritisation of candidate causal genes at molecular quantitative trait loci
David Stacey, Eric B. Fauman, … Dirk S. Paul·Nucleic Acids ResearchPeer-reviewed·Sep 11, 2018