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ClipperQTL: ultrafast and powerful eGene identification method
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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Cold Spring Harbor Laboratory
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491229/ https://www.ncbi.nlm.nih.gov/pubmed/37693523 http://dx.doi.org/10.1101/2023.08.28.555191 |
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author | Zhou, Heather J. Ge, Xinzhou Li, Jingyi Jessica |
author_facet | Zhou, Heather J. Ge, Xinzhou Li, Jingyi Jessica |
author_sort | Zhou, Heather J. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10491229 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-104912292023-09-09 ClipperQTL: ultrafast and powerful eGene identification method Zhou, Heather J. Ge, Xinzhou Li, Jingyi Jessica bioRxiv Article 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. Cold Spring Harbor Laboratory 2023-08-29 /pmc/articles/PMC10491229/ /pubmed/37693523 http://dx.doi.org/10.1101/2023.08.28.555191 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Zhou, Heather J. Ge, Xinzhou Li, Jingyi Jessica ClipperQTL: ultrafast and powerful eGene identification method |
title | ClipperQTL: ultrafast and powerful eGene identification method |
title_full | ClipperQTL: ultrafast and powerful eGene identification method |
title_fullStr | ClipperQTL: ultrafast and powerful eGene identification method |
title_full_unstemmed | ClipperQTL: ultrafast and powerful eGene identification method |
title_short | ClipperQTL: ultrafast and powerful eGene identification method |
title_sort | clipperqtl: ultrafast and powerful egene identification method |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491229/ https://www.ncbi.nlm.nih.gov/pubmed/37693523 http://dx.doi.org/10.1101/2023.08.28.555191 |
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