Increased mammographic breast density is one of the strongest risk factors for breast cancer, is common in the population, and may account for a large proportion of cases of breast cancer. Importantly, up to two thirds of the variation in breast density appears to be genetically influenced. Recently, we found evidence for genetic linkage of breast density to a region of interest (ROI) on chromosome 5p (LOD=2.9) using 89 multigenerational families with 889 members. Additional fine mapping strengthened the evidence for linkage (LOD=4.2) and narrowed the size of the region, resulting in a putative locus responsible for up to 22% of the overall variance in breast density and 42% of the residual (covariate-adjusted) variance. An orthologous region on rat chromosome 2, known as Emca3, has recently been shown to influence estradiol susceptible mammary cancer in rats. In this application, we propose to follow-up this region (defined as the 2-LOD interval (~23.9 cM) surrounding the maximum LOD score from the fine-mapping linkage scan) to identify genes for breast density. Using two well-characterized study populations with blood samples and breast density estimates, our goal is to examine the association of genetic variation in the 78 genes residing in the ROI on chromosome 5p, with breast density and breast cancer.
In Specific Aim 1, we will assess the association of breast density with tagSNPs and functional SNPs identified from the 78 candidates in the ROI. We will examine them first within the family study (Aim 1a) in which we detected the linkage signal;following this, the top 20% (or 384) SNPs with the strongest statistical association will be examined in an independent sample of ~1000 women from a large prospective mammography cohort study (MMHS) (Aim 1b).
In Specific Aim 2, we will characterize all genetic variation in up to five genes displaying the strongest association with breast density identified in Aim 1b. Specifically, for each gene, we will resequence DNA from 60 women in the MMHS cohort who have extremes of breast density, identify additional SNPs and potential rare variants, and select tagSNPs using linkage disequilibrium (LD) analyses (Aim 2a). We will examine and confirm associations of these newly identified tagSNPs and those statistically significant SNPs from Aim 1b, with breast density in a second independent sample of ~1000 women from MMHS (Aim 2b). Because genes for breast density are ultimately important insofar as their influence on breast cancer, the Secondary Aim will examine whether the genes found to be associated with breast density also influence breast cancer risk using the case-cohort sample comprised of ~500 total (and 425-438 invasive) cases with DNA from the MMHS. At the conclusion of this work, we will have identified at least one gene with a strong genetic influence on breast density and examined the evidence for its role in breast cancer risk.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA128931-05
Application #
8212192
Study Section
Epidemiology of Cancer Study Section (EPIC)
Program Officer
Nelson, Stefanie A
Project Start
2008-04-14
Project End
2014-01-31
Budget Start
2012-02-01
Budget End
2014-01-31
Support Year
5
Fiscal Year
2012
Total Cost
$380,420
Indirect Cost
$128,653
Name
Mayo Clinic, Rochester
Department
Type
DUNS #
006471700
City
Rochester
State
MN
Country
United States
Zip Code
55905
Nielsen, Mads; Vachon, Celine M; Scott, Christopher G et al. (2014) Mammographic texture resemblance generalizes as an independent risk factor for breast cancer. Breast Cancer Res 16:R37
Lindström, Sara; Thompson, Deborah J; Paterson, Andrew D et al. (2014) Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk. Nat Commun 5:5303
Fowler, Erin E E; Vachon, Celine M; Scott, Christopher G et al. (2014) Automated Percentage of Breast Density Measurements for Full-field Digital Mammography Applications. Acad Radiol 21:958-70
Pettersson, Andreas; Graff, Rebecca E; Ursin, Giske et al. (2014) Mammographic density phenotypes and risk of breast cancer: a meta-analysis. J Natl Cancer Inst 106:
Reese, Sarah E; Archer, Kellie J; Therneau, Terry M et al. (2013) A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis. Bioinformatics 29:2877-83
Vachon, Celine M; Fowler, Erin Ee; Tiffenberg, Gail et al. (2013) Comparison of percent density from raw and processed full-field digital mammography data. Breast Cancer Res 15:R1
Bertrand, Kimberly A; Tamimi, Rulla M; Scott, Christopher G et al. (2013) Mammographic density and risk of breast cancer by age and tumor characteristics. Breast Cancer Res 15:R104
Vachon, Celine M; Li, Jingmei; Scott, Christopher G et al. (2012) No evidence for association of inherited variation in genes involved in mitosis and percent mammographic density. Breast Cancer Res 14:R7
Ghosh, Karthik; Brandt, Kathleen R; Reynolds, Carol et al. (2012) Tissue composition of mammographically dense and non-dense breast tissue. Breast Cancer Res Treat 131:267-75
Stevens, Kristen N; Lindstrom, Sara; Scott, Christopher G et al. (2012) Identification of a novel percent mammographic density locus at 12q24. Hum Mol Genet 21:3299-305

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