Candidate SNP analyses integrated with mRNA expression and hormone levels reveal influence on mammographic density and breast cancer risk

Background: Mammographic density (MD) is a well-known risk factor for breast cancer. Genetic factors may account for as much as 30-60% of variation in MD, but the specific genes responsible for MD remain largely unknown. In the current study, we use a candidate gene approach to identify genes with a putative effect on MD. Genotypic profiles of single nucleotide polymorphisms (SNPs) within these genes were obtained and tested for association with MD. In addition expression profiles and hormone data were used to further investigate these associations. Methods: We have analyzed 257 SNPs in 165 genes in two sample materials (n=454) using a discovery and verification approach in order to identify SNP markers of MD. Hormone and, when available, gene expression levels obtained from biopsies taken from breasts with varying density were also included in the analyses in order to investigate the functional role of the identified genetic factors in MD. Results: We identified 28 SNPs associated with MD in both datasets, ten of which have a p-value ≤ 0.05. Of these ten, seven are associated in cis (p≤ 0.05) with mRNA expression levels measured from breast biopsies of which four are directly involved in the signalling, metabolism and regulation of estradiol. Conclusion: SNPs residing in genes belonging to the estradiol-signaling pathway were found associated with MD in two cohorts of Norwegian postmenopausal women. Coupled with gene expression, these results aid in the understanding of the molecular signature in mammographically dense breasts.