BioinformaticsGenomicsPeer-reviewed journal article
TreeQTL: hierarchical error control for eQTL findings
Christine Peterson, Marina BogomoloviD, Yoav BenjaminiiD, Chiara SabattiiD
Abstract
SummaryCommonly used multiplicity adjustments fail to control the error rate for reported findings in many expression quantitative trait loci (eQTL) studies. TreeQTL implements a stage-wise multiple testing procedure which allows control of appropriate error rates defined relative to a hierarchical grouping of the eQTL hypotheses.\n\nAvailability and ImplementationThe R package TreeQTL is available for download at http://bioinformatics.org/treeqtl.\n\nContactsabatti@stanford.edu
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
- Power, false discovery rate and Winner’s Curse in eQTL studies
Qin Qin Huang, Scott C. Ritchie, … Michael Inouye·Nucleic Acids ResearchPeer-reviewed·Aug 17, 2018
- Flexible and efficient count-distribution and mixed-model methods for eQTL mapping with quasar
Jeffrey M. Pullin, Jarny Choi, … Chris Wallace·medRxiv·Jul 17, 2025
- A novel quantile regression approach for eQTL discovery
Xiaoyu Song, Gen Li, … Ying Wei·bioRxiv·Aug 17, 2016
- TreeMap: A Structured Approach to Fine Mapping of eQTL Variants
Li Liu, Pramod Bharadwaj Chandrashekar, … Greg C. Gibson·BioinformaticsPeer-reviewed·Oct 21, 2020
- eQTpLot: a user-friendly R package for the visualization and colocalization of eQTL and GWAS signals
Theodore G. Drivas, Anastasia Lucas, Marylyn DeRiggi Ritchie·BioData MiningPeer-reviewed·Jul 17, 2021
- ClipperQTL: ultrafast and powerful eGene identification method
Heather J. Zhou, Xinzhou Ge, Jingyi Jessica Li·Genome biologyPeer-reviewed·Jul 16, 2025