Abstract
Allelic imbalance (AI) is a phenomenon where the two alleles of a given gene are expressed at different levels in a given cell, either because of epigenetic inactivation of one of the two alleles, or because of genetic variation in regulatory regions. Recently, Bing et al. have described the use of genotyping arrays to assay AI at a high resolution (∼750,000 SNPs across the autosomes). In this paper, we investigate computational approaches to analyze this data and identify genomic regions with AI in an unbiased and robust statistical manner. We propose two families of approaches: (i) a statistical approach based on z-score computations, and (ii) a family of machine learning approaches based on Hidden Markov Models. Each method is evaluated using previously published experimental data sets as well as with permutation testing. When applied to whole genome data from 53 HapMap samples, our approaches reveal that allelic imbalance is widespread (most expressed genes show evidence of AI in at least one of our 53 samples) and that most AI regions in a given individual are also found in at least a few other individuals. While many AI regions identified in the genome correspond to known protein-coding transcripts, others overlap with recently discovered long non-coding RNAs. We also observe that genomic regions with AI not only include complete transcripts with consistent differential expression levels, but also more complex patterns of allelic expression such as alternative promoters and alternative 3′ end. The approaches developed not only shed light on the incidence and mechanisms of allelic expression, but will also help towards mapping the genetic causes of allelic expression and identify cases where this variation may be linked to diseases.
Highlights
IntroductionThe vast majority of microarray-based or RNA sequencing-based gene expression studies do not distinguish between the two copies and measure the sum of the expression of the two alleles
In a diploid cell, each gene is present in two copies
New genotyping technologies enable the measurement of expression of both copies of a particular gene, at loci that are heterozygous within a particular individual
Summary
The vast majority of microarray-based or RNA sequencing-based gene expression studies do not distinguish between the two copies and measure the sum of the expression of the two alleles. This hides the fact that the two alleles are not necessarily expressed at equal levels, a phenomenon called allelic imbalance (AI) [1]. The complete shut down of one allele results in monoallelic expression (ME). Less drastic is random monoallelic expression, whereby a randomly selected copy of a gene or chromosomal region is silenced by epigenetic mechanisms (e.g. methylation). While monoallelic expression completely silences one of the two alleles, less drastic allelic expression differences can result from a heterozygous Aa regulatory site. A key problem with this type of approach is that environmental differences across individuals can affect gene expression, making the mapping problem very challenging
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