Liam F Spurr1,2,3, Nawaf Alomran3, Pavlos Bousounis3, Dacian Reece-Stremtan4, N M Prashant3, Hongyu Liu3, Piotr Słowiński5, Muzi Li3, Qianqian Zhang6,7, Justin Sein3, Gabriel Asher3, Keith A Crandall8, Krasimira Tsaneva-Atanasova5,9, Anelia Horvath3,6,10. 1. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. 2. Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. 3. Biochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center. 4. Computer Applications Support Services, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA. 5. Department of Mathematics & Living Systems Institute, University of Exeter, Exeter EX4 4QD, UK. 6. Department of Biochemistry and Molecular Medicine. 7. Department of Biostatistics and Bioinformatics, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA. 8. Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA. 9. EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter EX4 4QJ, UK. 10. Department of Pharmacology and Physiology, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA.
Abstract
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. AVAILABILITY AND IMPLEMENTATION: A toolkit to perform ReQTL analyses is available at https://github.com/HorvathLab/ReQTL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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. AVAILABILITY AND IMPLEMENTATION: A toolkit to perform ReQTL analyses is available at https://github.com/HorvathLab/ReQTL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Prashant N M; Hongyu Liu; Christian Dillard; Helen Ibeawuchi; Turkey Alsaeedy; Hang Chan; Anelia Dafinova Horvath Journal: Genes (Basel) Date: 2021-09-30 Impact factor: 4.096