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eQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects
Using information from allele-specific gene expression (ASE) can improve the power to map gene expression quantitative trait loci (eQTLs). However, such practice has been limited, partly due to computational challenges and lack of clarification on the size of power gain or new findings besides impro...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947591/ https://www.ncbi.nlm.nih.gov/pubmed/35286297 http://dx.doi.org/10.1371/journal.pgen.1010076 |
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author | Zhabotynsky, Vasyl Huang, Licai Little, Paul Hu, Yi-Juan Pardo-Manuel de Villena, Fernando Zou, Fei Sun, Wei |
author_facet | Zhabotynsky, Vasyl Huang, Licai Little, Paul Hu, Yi-Juan Pardo-Manuel de Villena, Fernando Zou, Fei Sun, Wei |
author_sort | Zhabotynsky, Vasyl |
collection | PubMed |
description | Using information from allele-specific gene expression (ASE) can improve the power to map gene expression quantitative trait loci (eQTLs). However, such practice has been limited, partly due to computational challenges and lack of clarification on the size of power gain or new findings besides improved power. We have developed geoP, a computationally efficient method to estimate permutation p-values, which makes it computationally feasible to perform eQTL mapping with ASE counts for large cohorts. We have applied geoP to map eQTLs in 28 human tissues using the data from the Genotype-Tissue Expression (GTEx) project. We demonstrate that using ASE data not only substantially improve the power to detect eQTLs, but also allow us to quantify individual-specific genetic effects, which can be used to study the variation of eQTL effect sizes with respect to other covariates. We also compared two popular methods for eQTL mapping with ASE: TReCASE and RASQUAL. TReCASE is ten times or more faster than RASQUAL and it provides more robust type I error control. |
format | Online Article Text |
id | pubmed-8947591 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-89475912022-03-25 eQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects Zhabotynsky, Vasyl Huang, Licai Little, Paul Hu, Yi-Juan Pardo-Manuel de Villena, Fernando Zou, Fei Sun, Wei PLoS Genet Research Article Using information from allele-specific gene expression (ASE) can improve the power to map gene expression quantitative trait loci (eQTLs). However, such practice has been limited, partly due to computational challenges and lack of clarification on the size of power gain or new findings besides improved power. We have developed geoP, a computationally efficient method to estimate permutation p-values, which makes it computationally feasible to perform eQTL mapping with ASE counts for large cohorts. We have applied geoP to map eQTLs in 28 human tissues using the data from the Genotype-Tissue Expression (GTEx) project. We demonstrate that using ASE data not only substantially improve the power to detect eQTLs, but also allow us to quantify individual-specific genetic effects, which can be used to study the variation of eQTL effect sizes with respect to other covariates. We also compared two popular methods for eQTL mapping with ASE: TReCASE and RASQUAL. TReCASE is ten times or more faster than RASQUAL and it provides more robust type I error control. Public Library of Science 2022-03-14 /pmc/articles/PMC8947591/ /pubmed/35286297 http://dx.doi.org/10.1371/journal.pgen.1010076 Text en © 2022 Zhabotynsky et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zhabotynsky, Vasyl Huang, Licai Little, Paul Hu, Yi-Juan Pardo-Manuel de Villena, Fernando Zou, Fei Sun, Wei eQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects |
title | eQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects |
title_full | eQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects |
title_fullStr | eQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects |
title_full_unstemmed | eQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects |
title_short | eQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects |
title_sort | eqtl mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947591/ https://www.ncbi.nlm.nih.gov/pubmed/35286297 http://dx.doi.org/10.1371/journal.pgen.1010076 |
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