Abstract
The recent development of whole genome association studies has lead to the robust identification of several loci involved in different common human diseases. Interestingly, some of the strongest signals of association observed in these studies arise from non-coding regions located in very large introns or far away from any annotated genes, raising the possibility that these regions are involved in the etiology of the disease through some unidentified regulatory mechanisms. These findings highlight the importance of better understanding the mechanisms leading to inter-individual differences in gene expression in humans. Most of the existing approaches developed to identify common regulatory polymorphisms are based on linkage/association mapping of gene expression to genotypes. However, these methods have some limitations, notably their cost and the requirement of extensive genotyping information from all the individuals studied which limits their applications to a specific cohort or tissue. Here we describe a robust and high-throughput method to directly measure differences in allelic expression for a large number of genes using the Illumina Allele-Specific Expression BeadArray platform and quantitative sequencing of RT-PCR products. We show that this approach allows reliable identification of differences in the relative expression of the two alleles larger than 1.5-fold (i.e., deviations of the allelic ratio larger than 60∶40) and offers several advantages over the mapping of total gene expression, particularly for studying humans or outbred populations. Our analysis of more than 80 individuals for 2,968 SNPs located in 1,380 genes confirms that differential allelic expression is a widespread phenomenon affecting the expression of 20% of human genes and shows that our method successfully captures expression differences resulting from both genetic and epigenetic cis-acting mechanisms.
Highlights
Understanding the genetic causes of phenotypic variation in humans still remains a major challenge for human genetics
We first assessed the extent of differential allelic expression at 1,432 exonic SNPs using 81 individual lymphoblastoid cell lines (LCLs) with the Illumina Allele-Specific Expression (ASE) technology (Figure 1 and Table S1 for the composition of the Illumina ASE Cancer Panel)
The extent of differential allelic expression at each SNP was obtained by comparing the relative amount of each allele in RNA to the ratio observed in DNA
Summary
Understanding the genetic causes of phenotypic variation in humans still remains a major challenge for human genetics. One of the approaches commonly used to identify regulatory polymorphisms is to look for statistical associations between variation in gene expression and individual genotypes [9,10]. This method offers the advantage of simultaneously analyzing thousands of genes using gene expression arrays and has yielded fascinating results in yeast [11,12] and mouse [13,14,15,16]. Its application in humans [17,18,19,20,21,22,23,24] suffers from relatively low statistical power due to potential inter-individual differences in a large number of causal variants involved in the regulation of a specific gene [25], their modest effects and the burden of the multiple testing correction necessary to take into
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