powerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis
Author(s) -
Xianjun Dong,
Xiaoqi Li,
Tzuu-Wang Chang,
Clemens R. Scherzer,
Scott T. Weiss,
Weiliang Qiu
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab385
Subject(s) - r package , sample (material) , sample size determination , software package , computer science , expression quantitative trait loci , power (physics) , statistical power , statistics , software , computational biology , mathematics , biology , chemistry , physics , computational science , chromatography , genetics , operating system , single nucleotide polymorphism , genotype , gene , quantum mechanics
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.
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