Estrogen exposure during postmenopausal, reproductive, early childhood and even during the intra-uterine period may play roles in the development of breast cancer in women. The goal of this proposal is to examine whether polymorphic variants of genes that control the biosynthesis, cellular binding and metabolism of endogenous estrogen (CYP17, CYP19, ER, CYP1A1, CYP1B1, and COMT) are associated with breast cancer. All prior studies examining this hypothesis used a classical case-control design which is susceptible to population stratification bias and produced inconsistent results. We propose to use a family-based design which is free from such biases to examine this hypothesis among 3,0671 eligible nuclear families (with 5,916 family members) participating in the six international CFRBCS centers. All subjects will be genotyped for the proposed estrogen-gene variants using high-throughput laboratory methods (i.e., TaqMan and molecular beacon methods) which have been validated and are up-and running in our laboratories. Associations between estrogen-related gene variants using high-throughput laboratory methods (i.e., TaqMan and molecular beacon methods) which have been validated and are up and running in our laboratories. Associations between estrogen-related gene variants and breast cancer will be assessed by new analytic methods for family-based genetic association studies which we have developed. By virtue of the family based design, the proposed study will be able to assess, in an unbiased fashion, the impact of a woman's own as well as her mother's estrogen gene status on the development of her breast cancer. Findings of the study will be relevant for breast cancer prevention among women who may be inherently susceptible to the effect of estrogen-the key element in breast cancer etiology.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
2U01CA069398-08
Application #
6481733
Study Section
Special Emphasis Panel (ZCA1)
Project Start
1995-09-30
Project End
2006-11-30
Budget Start
Budget End
Support Year
8
Fiscal Year
2001
Total Cost
Indirect Cost
Name
Columbia University (N.Y.)
Department
Type
DUNS #
167204994
City
New York
State
NY
Country
United States
Zip Code
10032
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Dite, Gillian S; MacInnis, Robert J; Bickerstaffe, Adrian et al. (2016) Breast Cancer Risk Prediction Using Clinical Models and 77 Independent Risk-Associated SNPs for Women Aged Under 50 Years: Australian Breast Cancer Family Registry. Cancer Epidemiol Biomarkers Prev 25:359-65
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Dite, Gillian S; Mahmoodi, Maryam; Bickerstaffe, Adrian et al. (2013) Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model. Breast Cancer Res Treat 139:887-96
Gracia-Aznarez, Francisco Javier; Fernandez, Victoria; Pita, Guillermo et al. (2013) Whole exome sequencing suggests much of non-BRCA1/BRCA2 familial breast cancer is due to moderate and low penetrance susceptibility alleles. PLoS One 8:e55681

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