Integration of genetic, gene expression and environmental data to inform biological basis of mammographic density

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ABSTRACT 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.