Given its strong association with breast cancer, mammographic density has been proposed as a surrogate
endpoint for breast cancer. We have previously conducted genome-wide association studies (GWAS) of
mammographic density phenotypes and identified multiple genetic loci that are shared between mammographic
density and breast cancer. Indeed, as a continuous, precise and highly heritable (~60%) outcome,
mammographic density has proven a powerful tool for identifying genetic risk factors for breast cancer.
We propose a suite of genetic association studies aiming to increase our understanding of genetic and
environmental predictors of mammographic density and thereby breast cancer. Specifically, we will expand our
previous work to three novel areas including (1) leveraging germline genetic and tissue-specific gene expression
data to identify novel loci associated with mammographic density, (2) the first genome-wide gene-environment
(GE) interaction studies of mammographic density and (3) the first Mendelian Randomization (MR) studies of
mammographic density. First, we will expand our knowledge of the genetic architecture of mammographic
density by conducting the largest GWAS and the first transcriptome-wide association study (TWAS) of
mammographic density in 33,000 women of European ancestry. To account for the cellular heterogeneity in
breast tissue, we will conduct cell type-specific TWAS. Second, we will identify genetic variants and genes
whose expression interact with established environmental risk factors to alter mammographic density by
conducting the first genome-wide SNP GE interaction and TWASxE studies in 25,000 women of European
ancestry. Third, we will conduct MR analysis for biomarkers proposed to influence mammographic density
including circulating hormones (SHBG, testosterone and estradiol) and CRP. We will leverage newly released
biomarker data from UK Biobank which has led to the identification of hundreds of genetic variants associated
with the biomarkers proposed here, allowing us to generate strong genetic instruments for MR analysis.
Our application is in response to PA-17-239: “Secondary Analysis and Integration of Existing Data to
Elucidate the Genetic Architecture of Cancer Risk and Related Outcomes”. We will capitalize on data from the
MODE consortium, which has assembled GWAS and mammographic density data on more than 33,000 women
of European ancestry and environmental risk factor data for a subset of 25,000 women. Throughout the proposed
work, we will build on our previous observation that mammographic density can serve as a powerful proxy for
breast cancer, and follow up our findings in BCAC, a large-scale collaboration with more than 120,000 breast
cancer cases. Completion of our aims will lead to identification of novel risk factors for mammographic density
and breast cancer, and shed light on mechanisms by which mammographic density increases breast cancer
risk. Identifying and characterizing genes associated with high breast density and breast cancer could
lead to prevention strategies that specifically target breast density reductions in the population.
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