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

Author:

Dong Xianjun123ORCID,Li Xiaoqi1ORCID,Chang Tzuu-Wang4,Scherzer Clemens R23,Weiss Scott T5,Qiu Weiliang6

Affiliation:

1. Genomics and Bioinformatics Hub, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA

2. Center for Advanced Parkinson Research and Precision Neurology Program, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA

3. Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA

4. Molecular Pathological Epidemiology Program, Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA

5. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA

6. Non-Clinical Efficacy & Safety, Biostatistics & Programming Department, Sanofi, Framingham, MA 01701, USA

Abstract

Abstract 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.

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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