Year: 2025
Type: article
Abstract: A central task in expression quantitative trait locus (eQTL) analysis is to identify cis-eGenes (henceforth “eGenes”), i.e., genes whose expression levels are regulated by at least one local genetic variant. Among the existing eGene identification methods, FastQTL is considered the gold standard but is computationally expensive as it requires thousands of permutations for each gene. Alternative methods such as eigenMT and TreeQTL have lower power than FastQTL. In this work, we propose ClipperQTL, which reduces the number of permutations needed from thousands to 20 for data sets with large sample sizes ( > 450) by using the contrastive strategy developed in Clipper; for data sets with smaller sample sizes, it uses the same permutation-based approach as FastQTL. We show that ClipperQTL performs as well as FastQTL and runs about 500 times faster if the contrastive strategy is used and 50 times faster if the conventional permutation-based approach is used. The R package ClipperQTL is available at https://github.com/heatherjzhou/ClipperQTL .
Source: Genome biology
Authors: Heather J. Zhou, Xinzhou Ge, Jingyi Jessica Li
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,490
Funders: National Science Foundation, Alfred P. Sloan Foundation, W. M. Keck Foundation, National Institutes of Health, Johnson and Johnson, National Heart, Lung, and Blood Institute, National Institute of General Medical Sciences, David Geffen School of Medicine, University of California, Los Angeles
Awards: G1740316563, G1765693861, Collaborative Research: Development of Classification Theory and Methods for Objective Asymmetry, Sample Size Limitation, Labeling Ambiguity, and Feature Importance, G2108729386, CAREER: Advancing the Bioinformatic Infrastructure and Methodology for Single-cell RNA Sequencing, G6140085306, NRT-HDR: Modeling and Understanding Human Behavior: Harnessing Data from Genes to Social Networks, G9000025607, Robust Identification and accurate quantification of RNA transcripts on a system wide scale
OpenAlex ID: W4412467073