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powerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis

SUMMARY: Genome-wide association studies (GWAS) have revealed thousands of genetic loci for common diseases. One of the main challenges in the post-GWAS era is to understand the causality of the genetic variants. Expression quantitative trait locus (eQTL) analysis is an effective way to address this...

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Detalles Bibliográficos
Autores principales: Dong, Xianjun, Li, Xiaoqi, Chang, Tzuu-Wang, Scherzer, Clemens R, Weiss, Scott T, Qiu, Weiliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9492284/
https://www.ncbi.nlm.nih.gov/pubmed/34009297
http://dx.doi.org/10.1093/bioinformatics/btab385
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author Dong, Xianjun
Li, Xiaoqi
Chang, Tzuu-Wang
Scherzer, Clemens R
Weiss, Scott T
Qiu, Weiliang
author_facet Dong, Xianjun
Li, Xiaoqi
Chang, Tzuu-Wang
Scherzer, Clemens R
Weiss, Scott T
Qiu, Weiliang
author_sort Dong, Xianjun
collection PubMed
description SUMMARY: Genome-wide association studies (GWAS) have revealed thousands of genetic loci for common diseases. One of the main challenges in the post-GWAS era is to understand the causality of the genetic variants. Expression quantitative trait locus (eQTL) analysis is an effective way to address this question by examining the relationship between gene expression and genetic variation in a sufficiently powered cohort. However, it is frequently a challenge to determine the sample size at which a variant with a specific allele frequency will be detected to associate with gene expression with sufficient power. This is a particularly difficult task for single-cell RNAseq studies. Therefore, a user-friendly tool to estimate statistical power for eQTL analyses in both bulk tissue and single-cell data is needed. Here, we presented an R package called powerEQTL with flexible functions to estimate power, minimal sample size or detectable minor allele frequency for both bulk tissue and single-cell eQTL analysis. A user-friendly, program-free web application is also provided, allowing users to calculate and visualize the parameters interactively. AVAILABILITY AND IMPLEMENTATION: The powerEQTL R package source code and online tutorial are freely available at CRAN: https://cran.r-project.org/web/packages/powerEQTL/. The R shiny application is publicly hosted at https://bwhbioinfo.shinyapps.io/powerEQTL/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-94922842022-09-22 powerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis Dong, Xianjun Li, Xiaoqi Chang, Tzuu-Wang Scherzer, Clemens R Weiss, Scott T Qiu, Weiliang Bioinformatics Applications Notes SUMMARY: Genome-wide association studies (GWAS) have revealed thousands of genetic loci for common diseases. One of the main challenges in the post-GWAS era is to understand the causality of the genetic variants. Expression quantitative trait locus (eQTL) analysis is an effective way to address this question by examining the relationship between gene expression and genetic variation in a sufficiently powered cohort. However, it is frequently a challenge to determine the sample size at which a variant with a specific allele frequency will be detected to associate with gene expression with sufficient power. This is a particularly difficult task for single-cell RNAseq studies. Therefore, a user-friendly tool to estimate statistical power for eQTL analyses in both bulk tissue and single-cell data is needed. Here, we presented an R package called powerEQTL with flexible functions to estimate power, minimal sample size or detectable minor allele frequency for both bulk tissue and single-cell eQTL analysis. A user-friendly, program-free web application is also provided, allowing users to calculate and visualize the parameters interactively. AVAILABILITY AND IMPLEMENTATION: The powerEQTL R package source code and online tutorial are freely available at CRAN: https://cran.r-project.org/web/packages/powerEQTL/. The R shiny application is publicly hosted at https://bwhbioinfo.shinyapps.io/powerEQTL/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-05-19 /pmc/articles/PMC9492284/ /pubmed/34009297 http://dx.doi.org/10.1093/bioinformatics/btab385 Text en © The Author(s) 2021. Published by Oxford University Press. 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Dong, Xianjun
Li, Xiaoqi
Chang, Tzuu-Wang
Scherzer, Clemens R
Weiss, Scott T
Qiu, Weiliang
powerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis
title powerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis
title_full powerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis
title_fullStr powerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis
title_full_unstemmed powerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis
title_short powerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis
title_sort powereqtl: an r package and shiny application for sample size and power calculation of bulk tissue and single-cell eqtl analysis
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9492284/
https://www.ncbi.nlm.nih.gov/pubmed/34009297
http://dx.doi.org/10.1093/bioinformatics/btab385
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