Whole transcriptome RNA-Seq allelic expression in human brain

Ryan M Smith,Amy Webb,Adam Suhy,Samuel K Handelman,Roshan Mascarenhas,Wolfgang Sadee,Leslie C Newman,Audrey C Papp,John Oberdick

doi:10.1186/1471-2164-14-571

Ryan M Smith, Amy Webb + Show 7 more

Open Access

https://doi.org/10.1186/1471-2164-14-571

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Abstract

BackgroundMeasuring 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.ResultsWe 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.ConclusionsThese 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.

Highlights

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
Because allelic expression imbalance (AEI) is an accurate and sensitive phenotype proximal to the functional genetic variant, this approach facilitates the detection of cis-acting regulatory polymorphisms affecting any mechanism that measurably changes RNA expression, even when those polymorphisms reside at a distance from the affected gene or in regions of high linkage disequilibrium [11]
Given the known alignment biases in allelic RNA expression ratios in RNA sequencing (RNA-Seq) [14,26,27,32,33], we compared allelic ratios at heterozygous exonic single-nucleotide polymorphisms (SNP) following three different alignment methods: 1) alignment to the standard NCBI Build 37/hg19 reference genome, after which reference allele counts were directly compared to variant allele counts, 2) a targeted allele-switching method requiring construction of a new “reference” genome whereby the wild-type nucleotide at 187 SNP locations in 58 genes in NCBI Build 37/hg19 were replaced with the variant nucleotide (Additional file 1: Table S1), Table 1 Tissue characteristics and mapping statistics for 9 brain regions

Summary

Introduction

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. 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. Because AEI is an accurate and sensitive phenotype proximal to the functional genetic variant, this approach facilitates the detection of cis-acting regulatory polymorphisms affecting any mechanism that measurably changes RNA expression, even when those polymorphisms reside at a distance from the affected gene or in regions of high linkage disequilibrium [11]. Directly measuring allelic-specific RNA expression in brain tumors revealed a dramatic increase in monoallelic expression of multiple oncogenes, the extent of which correlated with tumor progression and prognosis [19]

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