Abstract
Genetic variation modulates gene expression transcriptionally or post-transcriptionally, and can profoundly alter an individual’s phenotype. Measuring allelic differential expression at heterozygous loci within an individual, a phenomenon called allele-specific expression (ASE), can assist in identifying such factors. Massively parallel DNA and RNA sequencing and advances in bioinformatic methodologies provide an outstanding opportunity to measure ASE genome-wide. In this study, matched DNA and RNA sequencing, genotyping arrays and computationally phased haplotypes were integrated to comprehensively and conservatively quantify ASE in a single human brain and liver tissue sample. We describe a methodological evaluation and assessment of common bioinformatic steps for ASE quantification, and recommend a robust approach to accurately measure SNP, gene and isoform ASE through the use of personalized haplotype genome alignment, strict alignment quality control and intragenic SNP aggregation. Our results indicate that accurate ASE quantification requires careful bioinformatic analyses and is adversely affected by sample specific alignment confounders and random sampling even at moderate sequence depths. We identified multiple known and several novel ASE genes in liver, including WDR72, DSP and UBD, as well as genes that contained ASE SNPs with imbalance direction discordant with haplotype phase, explainable by annotated transcript structure, suggesting isoform derived ASE. The methods evaluated in this study will be of use to researchers performing highly conservative quantification of ASE, and the genes and isoforms identified as ASE of interest to researchers studying those loci.
Highlights
A major goal of medical genomics is linking genetics with complex traits and diseases, and one mechanism by which genetic variability contributes to phenotype is through modulation of PLOS ONE | DOI:10.1371/journal.pone.0126911 May 12, 2015allele-specific expression (ASE) in Human Brain and Liver gene expression [1,2,3]
To robustly interrogate the biases associated with ASE identification, we have generated highresolution, high-quality data consisting of; (i) two technical replicates of total RNA sequencing (RNA-seq) from two human tissue samples with an average of 96.96 million uniquely mapped reads per sample; (ii) whole genome sequencing (WGS) from the same samples with an average coverage of 24.44, and (iii) matching genotyping arrays using the Illumina Omni Quad 1 million SNP chips
We called SNPs from WGS data for each individual, phased haplotypes using a subset of these SNPs independently verified by array genotyping
Summary
A major goal of medical genomics is linking genetics with complex traits and diseases, and one mechanism by which genetic variability contributes to phenotype is through modulation of PLOS ONE | DOI:10.1371/journal.pone.0126911 May 12, 2015ASE in Human Brain and Liver gene expression [1,2,3]. Small changes in gene expression can lead to profound phenotypic change, and may predispose an individual to disease [4,5,6,7,8]. Genetic variations can modulate transcription rate via epigenetic changes [9,10,11,12], through modification of transcription factor binding affinity [13], or post-transcriptionally by changing splicing patterns [14,15,16,17]. An effective method to assist in the identification of cis acting genetic factors is to study allelic imbalanced expression at heterozygous SNPs within an individual, a phenomenon commonly referred to as allele-specific expression (ASE). ASE is a complex phenomenon caused by a variety of mechanisms including imprinting, X-chromosome inactivation and the abovementioned transcriptional and post-transcriptional acting factors (for reviews, see references [18,19,20])
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