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.
© The Author(s) 2021. Published by Oxford University Press.