Polygenic inherited predisposition to breast cancer.
ABSTRACT The known breast cancer predisposing genes account for only about 20% of inherited susceptibility. Epidemiological analyses suggest that much of the remaining 80% is explained by the combined effect of many individually weak genetic variants, rather than by further rare, highly penetrant mutations. In the near term, identification of variants may indicate new pathways or mechanisms in breast cancer development. The polygenic model implies a wide distribution of risk in the population. In the longer term, it may be possible to construct individual risk profiles to guide public health interventions. The search for genetic variants has so far proved difficult. A key unanswered question is the "genetic architecture" of predisposition-that is, strong or weak alleles, common or rare. We describe a genome-wide scan designed to provide a first-pass answer to this question.
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ABSTRACT: When treated with 17β-estradiol, female ACI rats (Rattus norvegicus) rapidly develop mammary cancers that share multiple phenotypes with luminal breast cancers. Seven distinct quantitative trait loci that harbor genetic determinants of susceptibility to 17β-estradiol-induced mammary cancer have been mapped in reciprocal intercrosses between susceptible ACI rats and resistant Brown Norway (BN) rats. A panel of unique congenic rat strains has now been generated and characterized to confirm the existence of these quantitative trait loci, designated Emca3 through Emca9, and to quantify their individual effects on susceptibility to 17β-estradiol-induced mammary cancer. Each congenic strain carries BN alleles spanning an individual Emca locus, introgressed onto the ACI genetic background. Data presented herein indicate that BN alleles at Emca3, Emca4, Emca5, Emca6 and Emca9 reduce susceptibility to 17β-estradiol-induced mammary cancer, whereas BN alleles at Emca7 increase susceptibility, thereby confirming the previous interval mapping data. All of these Emca loci are orthologous to regions of the human genome that have been demonstrated in genome wide association studies to harbor genetic variants that influence breast cancer risk. Moreover, four of the Emca loci are orthologous to loci in humans that have been associated with mammographic breast density, a biomarker of breast cancer risk. This study further establishes the relevance of the ACI and derived congenic rat models of 17β-estradiol-induced mammary cancer for defining the genetic bases of breast cancer susceptibility and elucidating the mechanisms through which 17β-estradiol contributes to breast cancer development.G3-Genes Genomes Genetics 05/2014; DOI:10.1534/g3.114.011163
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ABSTRACT: Breast cancer (BC) is a complex disease, and the incidence rates for BC increase with age. Both environmental factors and genetics have an impact on the risk of BC. Although the effects of environmental factors may vary with age, it has been assumed generally that the penetrance of single nucleotide polymorphisms (SNPs) is constant throughout life. In the current study, the results demonstrated that certain SNPs exhibit BC risk associations that vary considerably with age. SNPs in 12 steroid hormone pathway genes were investigated for associations with BC risk in white women who were enrolled in an age-matched, case-control (1:2 for cases and controls, respectively) study that consisted of a discovery set (n = 5000 women) and an independent validation set (n = 1583 women). Significant age-related trends were identified and confirmed for SNPs in 4 genes associated with BC risk. The cytosine/cytosine (C/C) genotype of cytochrome P450 XIB2 (CYP11B2) was associated with decreased risk at younger ages (ages 30-44 years) but an increased risk at older ages (ages 55-69 years). The homozygous cytosine-guanine (CG/CG) genotype of uridine phosphorylase glycosyltransferase 1A7 (UGT1A7) was associated with increased risk at younger ages but decreased risk at older ages. Associations in cytochrome P450 19 (CYP19) and progesterone receptor (PGR) were confined to middle age (ages 45-54 years). The identification of age-specific genetic associations may have profound implications for future etiologic studies of BC and for the use of SNP genotyping to accurately predict the risk of BC in women.Cancer 05/2007; 109(10):1940-8. DOI:10.1002/cncr.22634
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ABSTRACT: Germline mutations in the BRCA1 or BRCA2 tumour-suppressor genes are strong predictors of breast and/or ovarian cancer development. The contribution of these mutations to breast cancer risk within any specific population is a function of both their prevalence and their penetrance. Mutation prevalence varies among ethnic groups and may be influenced by founder mutations. Penetrance can be influenced by mutation-specific phenotypes and the potential modifying effects of the patient's own genetic and environmental background. Although estimates of both mutation prevalence and mutation penetrance rates are inconsistent and occasionally controversial, understanding them is crucial for providing accurate risk information to each patient.Nature Reviews Cancer 01/2008; 7(12):937-48. DOI:10.1038/nrc2054