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Open Access Highly Accessed Research article

Whole transcriptome RNA-Seq allelic expression in human brain

Ryan M Smith1*, Amy Webb2, Audrey C Papp1, Leslie C Newman1, Samuel K Handelman1, Adam Suhy1, Roshan Mascarenhas1, John Oberdick13 and Wolfgang Sadee14

Author Affiliations

1 Department of Pharmacology, Program in Pharmacogenomics; College of Medicine, The Ohio State University Wexner Medical Center, 5184A Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA

2 Department of Biomedical Informatics, Program in Pharmacogenomics; College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA

3 Department of Neuroscience; College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA

4 Departments of Pharmacology, Psychiatry, Human Genetics/Internal Medicine, College of Medicine, College of Pharmacy, and Environmental Health Sciences, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA

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BMC Genomics 2013, 14:571  doi:10.1186/1471-2164-14-571

Published: 22 August 2013

Abstract

Background

Measuring allelic RNA expression ratios is a powerful approach for detecting cis-acting regulatory variants, RNA editing, loss of heterozygosity in cancer, copy number variation, and allele-specific epigenetic gene silencing. Whole transcriptome RNA sequencing (RNA-Seq) has emerged as a genome-wide tool for identifying allelic expression imbalance (AEI), but numerous factors bias allelic RNA ratio measurements. Here, we compare RNA-Seq allelic ratios measured in nine different human brain regions with a highly sensitive and accurate SNaPshot measure of allelic RNA ratios, identifying factors affecting reliable allelic ratio measurement. Accounting for these factors, we subsequently surveyed the variability of RNA editing across brain regions and across individuals.

Results

We find that RNA-Seq allelic ratios from standard alignment methods correlate poorly with SNaPshot, but applying alternative alignment strategies and correcting for observed biases significantly improves correlations. Deploying these methods on a transcriptome-wide basis in nine brain regions from a single individual, we identified genes with AEI across all regions (SLC1A3, NHP2L1) and many others with region-specific AEI. In dorsolateral prefrontal cortex (DLPFC) tissues from 14 individuals, we found evidence for frequent regulatory variants affecting RNA expression in tens to hundreds of genes, depending on stringency for assigning AEI. Further, we find that the extent and variability of RNA editing is similar across brain regions and across individuals.

Conclusions

These results identify critical factors affecting allelic ratios measured by RNA-Seq and provide a foundation for using this technology to screen allelic RNA expression on a transcriptome-wide basis. Using this technology as a screening tool reveals tens to hundreds of genes harboring frequent functional variants affecting RNA expression in the human brain. With respect to RNA editing, the similarities within and between individuals leads us to conclude that this post-transcriptional process is under heavy regulatory influence to maintain an optimal degree of editing for normal biological function.

Keywords:
RNA-Seq; Whole transcriptome; Allele expression; mRNA expression; Functional genetics; Regulatory polymorphism; eQTL; Read alignment; Next generation sequencing; Bioinformatics