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Background  The significant mortality associated with breast cancer (BCa) suggests a need to improve current research strategies to identify new genes that predispose women to breast cancer. Differential allele-specific expression (DASE) has been shown to contribute to phenotypic variables in humans and recently to the pathogenesis of cancer. We previously reported that nonsense-mediated mRNA decay (NMD) could lead to DASE of  BRCA1/2,  which is associated with elevated susceptibility to breast cancer. In addition to truncation mutations, multiple genetic and epigenetic factors can contribute to DASE, and we propose that DASE is a functional index for  cis -acting regulatory variants and pathogenic mutations, and that global analysis of DASE in breast cancer precursor tissues can be used to identify novel causative alleles for breast cancer susceptibility.    Results  To test our hypothesis, we employed the Illumina® Omni1-Quad BeadChip in paired genomic DNA (gDNA) and double-stranded cDNA (ds-cDNA) samples prepared from eight BCa patient-derived normal mammary epithelial lines (HMEC). We filtered original array data according to heterozygous genotype calls and calculated DASE values using the Log ratio of cDNA allele intensity, which was normalized to the corresponding gDNA. We developed two statistical methods, SNP- and gene-based approaches, which allowed us to identify a list of 60 candidate DASE loci (DASE ≥ 2.00,  P  ≤ 0.01, FDR ≤ 0.05) by both methods. Ingenuity Pathway Analysis of DASE loci revealed one major breast cancer-relevant interaction network, which includes two known cancer causative genes,  ZNF331  (DASE = 2.31,  P  = 0.0018, FDR = 0.040) and  USP6  (DASE = 4.80,  P  = 0.0013, FDR = 0.013), and a breast cancer causative gene,  DMBT1  (DASE=2.03,  P  = 0.0017, FDR = 0.014). Sequence analysis of a 5′ RACE product of  DMBT1  demonstrated that  rs2981745,  a putative breast cancer risk locus, appears to be one of the causal variants leading to DASE in  DMBT1 .    Conclusions  Our study demonstrated for the first time that global DASE analysis is a powerful new approach to identify breast cancer risk allele(s).

Identifying breast cancer risk loci by global differential allele-specific expression (DASE) analysis in mammary epithelial transcriptome