ReQTL: Identifying correlations between expressed SNVs and gene expression using RNA-sequencing data

Authors

Liam F. Spurr, Dana-Farber Cancer Institute
Nawaf Alomran, McCormick Genomics and Proteomics Center
Pavlos Bousounis, McCormick Genomics and Proteomics Center
Dacian Reece-Stremtan, The George Washington University
N. M. Prashant, McCormick Genomics and Proteomics Center
Hongyu Liu, McCormick Genomics and Proteomics Center
Piotr Słowiński, University of Exeter
Muzi Li, McCormick Genomics and Proteomics Center
Qianqian Zhang, The George Washington University
Justin Sein, McCormick Genomics and Proteomics Center
Gabriel Asher, McCormick Genomics and Proteomics Center
Keith A. Crandall, Milken Institute School of Public Health
Krasimira Tsaneva-Atanasova, University of Exeter
Anelia Horvath, McCormick Genomics and Proteomics Center

Document Type

Journal Article

Publication Date

3-1-2020

Journal

Bioinformatics

Volume

36

Issue

5

DOI

10.1093/bioinformatics/btz750

Abstract

© 2019 The Author(s). Published by Oxford University Press. Motivation: By testing for associations between DNA genotypes and gene expression levels, expression quantitative trait locus (eQTL) analyses have been instrumental in understanding how thousands of single nucleotide variants (SNVs) may affect gene expression. As compared to DNA genotypes, RNA genetic variation represents a phenotypic trait that reflects the actual allele content of the studied system. RNA genetic variation at expressed SNV loci can be estimated using the proportion of alleles bearing the variant nucleotide (variant allele fraction, VAFRNA). VAFRNA is a continuous measure which allows for precise allele quantitation in loci where the RNA alleles do not scale with the genotype count. We describe a method to correlate VAFRNA with gene expression and assess its ability to identify genetically regulated expression solely from RNA-sequencing (RNA-seq) datasets. Results: We introduce ReQTL, an eQTL modification which substitutes the DNA allele count for the variant allele fraction at expressed SNV loci in the transcriptome (VAFRNA). We exemplify the method on sets of RNA-seq data from human tissues obtained though the Genotype-Tissue Expression (GTEx) project and demonstrate that ReQTL analyses are computationally feasible and can identify a subset of expressed eQTL loci.

APA Citation

Spurr, L., Alomran, N., Bousounis, P., Reece-Stremtan, D., Prashant, N., Liu, H., Słowiński, P., Li, M., Zhang, Q., Sein, J., Asher, G., Crandall, K., Tsaneva-Atanasova, K., & Horvath, A. (2020). ReQTL: Identifying correlations between expressed SNVs and gene expression using RNA-sequencing data. Bioinformatics, 36 (5). http://dx.doi.org/10.1093/bioinformatics/btz750