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PLoS One. 2019 Oct 3;14(10):e0223273. doi: 10.1371/journal.pone.0223273. eCollection 2019.

eQTL mapping of rare variant associations using RNA-seq data: An evaluation of approaches.

Author information

1
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA, United States of America.
2
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America.
3
Department of Biostatistics and Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States of America.
4
Center for Genes, Environment, and Health, National Jewish Health, Denver, CO, United States of America.

Abstract

Expression quantitative trait loci (eQTL) provide insight on transcription regulation and illuminate the molecular basis of phenotypic outcomes. High-throughput RNA sequencing (RNA-seq) is becoming a popular technique to measure gene expression abundance. Traditional eQTL mapping methods for microarray expression data often assume the expression data follow a normal distribution. As a result, for RNA-seq data, total read count measurements can be normalized by normal quantile transformation in order to fit the data using a linear regression. Other approaches model the total read counts using a negative binomial regression. While these methods work well for common variants (minor allele frequencies > 5% or 1%), an extension of existing methodology is needed to accommodate a collection of rare variants in RNA-seq data. Here, we examine 2 approaches that are direct applications of existing methodology and apply these approaches to RNAseq studies: 1) collapsing the rare variants in the region and using either negative binomial regression or Poisson regression and 2) using the normalized read counts with the Sequence Kernel Association Test (SKAT), the burden test for SKAT (SKAT-Burden), or an optimal combination of these two tests (SKAT-O). We evaluated these approaches via simulation studies under numerous scenarios and applied these approaches to the 1,000 Genomes Project.

PMID:
31581212
DOI:
10.1371/journal.pone.0223273
Free PMC Article

Conflict of interest statement

The authors have declared that no competing interests exist.

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