A genome-wide linkage study of mammographic density, a risk factor for breast cancer

Program in Genetics & Genome Biology, The Hospital for Sick Children, 101 College Street, East Tower, Toronto, ON M5G 1L7, Canada.
Breast cancer research: BCR (Impact Factor: 5.49). 12/2011; 13(6):R132. DOI: 10.1186/bcr3078
Source: PubMed


Mammographic breast density is a highly heritable (h2 > 0.6) and strong risk factor for breast cancer. We conducted a genome-wide linkage study to identify loci influencing mammographic breast density (MD).
Epidemiological data were assembled on 1,415 families from the Australia, Northern California and Ontario sites of the Breast Cancer Family Registry, and additional families recruited in Australia and Ontario. Families consisted of sister pairs with age-matched mammograms and data on factors known to influence MD. Single nucleotide polymorphism (SNP) genotyping was performed on 3,952 individuals using the Illumina Infinium 6K linkage panel.
Using a variance components method, genome-wide linkage analysis was performed using quantitative traits obtained by adjusting MD measurements for known covariates. Our primary trait was formed by fitting a linear model to the square root of the percentage of the breast area that was dense (PMD), adjusting for age at mammogram, number of live births, menopausal status, weight, height, weight squared, and menopausal hormone therapy. The maximum logarithm of odds (LOD) score from the genome-wide scan was on chromosome 7p14.1-p13 (LOD = 2.69; 63.5 cM) for covariate-adjusted PMD, with a 1-LOD interval spanning 8.6 cM. A similar signal was seen for the covariate adjusted area of the breast that was dense (DA) phenotype. Simulations showed that the complete sample had adequate power to detect LOD scores of 3 or 3.5 for a locus accounting for 20% of phenotypic variance. A modest peak initially seen on chromosome 7q32.3-q34 increased in strength when only the 513 families with at least two sisters below 50 years of age were included in the analysis (LOD 3.2; 140.7 cM, 1-LOD interval spanning 9.6 cM). In a subgroup analysis, we also found a LOD score of 3.3 for DA phenotype on chromosome 12.11.22-q13.11 (60.8 cM, 1-LOD interval spanning 9.3 cM), overlapping a region identified in a previous study.
The suggestive peaks and the larger linkage signal seen in the subset of pedigrees with younger participants highlight regions of interest for further study to identify genes that determine MD, with the goal of understanding mammographic density and its involvement in susceptibility to breast cancer.

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    • "Interestingly, the peak LOD regions of Emca3, Emca4, Emca6 and the distal segment of Emca7 are orthologous to regions of the human genome that have been associated with mammographic breast density, a biomarker of breast cancer risk (Woolcott et al. 2009; Odefrey et al. 2010; Greenwood et al. 2011; Vachon et al. 2012; Fernandez-Navarro et al. 2013). Mammographic density is determined by the relative amounts of radiodense epithelium and stroma relative to the amount of radiolucent adipose tissue. "
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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; 4(8). DOI:10.1534/g3.114.011163 · 3.20 Impact Factor
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    • "A case–control study demonstrated a 45% reduction in MPD in 14% of 387 women treated with AI therapy for an average of 10 months, which was not statistically different from matched controls (Vachon et al, 2013). Population-based studies have demonstrated that genetic effects can affect MPD (Boyd et al, 2002; Douglas et al, 2008; Ursin et al, 2009; Greenwood et al, 2011; Varghese et al, 2012). In twins, heritable factors account for about two-thirds of the variation in MPD (Boyd et al, 2002). "
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    ABSTRACT: Background: Change in breast density may predict outcome of women receiving adjuvant hormone therapy for breast cancer. We performed a prospective clinical trial to evaluate the impact of inherited variants in genes involved in oestrogen metabolism and signalling on change in mammographic percent density (MPD) with aromatase inhibitor (AI) therapy. Methods: Postmenopausal women with breast cancer who were initiating adjuvant AI therapy were enrolled onto a multicentre, randomised clinical trial of exemestane vs letrozole, designed to identify associations between AI-induced change in MPD and single-nucleotide polymorphisms in candidate genes. Subjects underwent unilateral craniocaudal mammography before and following 24 months of treatment. Results: Of the 503 enrolled subjects, 259 had both paired mammograms at baseline and following 24 months of treatment and evaluable DNA. We observed a statistically significant decrease in mean MPD from 17.1 to 15.1% (P<0.001), more pronounced in women with baseline MPD ⩾20%. No AI-specific difference in change in MPD was identified. No significant associations between change in MPD and inherited genetic variants were observed. Conclusion: Subjects with higher baseline MPD had a greater average decrease in MPD with AI therapy. There does not appear to be a substantial effect of inherited variants in biologically selected candidate genes.
    British Journal of Cancer 10/2013; 109(9). DOI:10.1038/bjc.2013.587 · 4.84 Impact Factor
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    • "All participants in addition completed a food frequency questionnaire, and provided a blood sample from which DNA was extracted. Informed consent was obtained from all participants for a larger study [16], which included the current analysis. "
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    ABSTRACT: Introduction Percent mammographic density (PMD) is a strong and highly heritable risk factor for breast cancer. Studies of the role of PMD in familial breast cancer may require controls, such as the sisters of cases, selected from the same 'risk set' as the cases. The use of sister controls would allow control for factors that have been shown to influence risk of breast cancer such as race/ethnicity, socioeconomic status and a family history of breast cancer, but may introduce 'overmatching' and attenuate case-control differences in PMD. Methods To examine the potential effects of using sister controls rather than unrelated controls in a case-control study, we examined PMD in triplets, each comprised of a case with invasive breast cancer, an unaffected full sister control, and an unaffected unrelated control. Both controls were matched to cases on age at mammogram. Total breast area and dense area in the mammogram were measured in the unaffected breast of cases and a randomly selected breast in controls, and the non-dense area and PMD calculated from these measurements. Results The mean difference in PMD between cases and controls, and the standard deviation (SD) of the difference, were slightly less for sister controls (4.2% (SD = 20.0)) than for unrelated controls (4.9% (SD = 25.7)). We found statistically significant correlations in PMD between cases (n = 228) and sister controls (n = 228) (r = 0.39 (95% CI: 0.28, 0.50; P <0.0001)), but not between cases and unrelated controls (n = 228) (r = 0.04 (95% CI: -0.09, 0.17; P = 0.51)). After adjusting for other risk factors, square root transformed PMD was associated with an increased risk of breast cancer when comparing cases to sister controls (adjusted odds ratio (inter-quintile odds ratio (IQOR) = 2.19, 95% CI = 1.20, 4.00) or to unrelated controls (adjusted IQOR = 2.62, 95% CI = 1.62, 4.25). Conclusions The use of sister controls in case-control studies of PMD resulted in a modest attenuation of case-control differences and risk estimates, but showed a statistically significant association with risk and allowed control for race/ethnicity, socioeconomic status and family history.
    Breast cancer research: BCR 05/2013; 15(3):R43. DOI:10.1186/bcr3430 · 5.49 Impact Factor
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