Differential allelic expression in normal breast tissue

Most loci contributing to breast cancer susceptibility identified by the recent genome-wide association studies (GWAS) are predicted to have a regulatory function. Additionally, the early phases of the GWAS studies have generated long lists of possible candidates that need to be prioritised for follow-up studies. Integration of allelic expression mapping and disease association data should enable the identification of those GWAS hits with higher cis-regulatory potential. Our aims are (1) to assess how well functional data and disease risk are correlated and (2) to help prioritise and select the strongest candidates from the GWAS scan for further follow-up and functional analysis. We are performing genome-wide differential allelic expression (DAE) analysis to identify loci with cis-regulatory potential in sixty-four normal breast tissue samples. Using the Illumina Exon510S-Duo BeadChips, we have identified 34K informative SNPs of which approximately 8K (23.5%) displayed DAE. Two SNPs showed monoallelic expression suggesting possible new imprinted loci. We are mapping the cis-regulatory loci by fitting a linear regression model with permutations to DAE ratios vs genotype at SNPs within ±250kb of the RefSeq gene corresponding to the DAE SNP. We will combine our data with the UK GWAS1 and GWAS2 studies for breast cancer susceptibility, to generate a list of genes that show regulatory variation for further evaluation as candidates. This is the first genome-wide differential allelic expression study in normal breast tissue, and one of the first in a primary tissue. We predict that this will be a powerful approach to validate/identify susceptibility loci and to unravel some of the biology underlying breast cancer susceptibility. Allelic gene expression of normal breast sample from healthy controls.