Susceptibility to common diseases such as breast cancer is complex. Minimally the etiology of susceptibility is centered on a large number of interacting genetic elements which individually and collectively interact with environmental components. In order to assign risks to individuals, in contrast to populations, it will be necessary to refine an individual's inherent risk alleles and their interaction with each other and the individual's environment. To accomplish the goals of individual risk estimation and its mitigation, disease-specific integrated genetic systems networks are needed. Work will focus on breast cancer genome-wide association studies (GWAS) SNPs in ~10% of the human genome that is homologous to the highly defined mammary susceptibility QTLs in the rat. High throughput gene expression measurements from at least 50 reduction mammoplasty human mammary epithelial cells (HMEC) samples from healthy women together with full genome SNP genotypes will be obtained. Expression quantitative trait loci (eQTL) will be identified and integrated with GWAS results for breast cancer risk. The integrated data sets will be used for three important purposes. First, they will be used to assign function to a group of tag SNP alleles from breast cancer GWAS. The second will be to establish network systems models that suggest potential causal relationships among SNPs and downstream phenotypes. The third application of these integrated data sets will be to prioritize suspected but not yet validated tag SNP risk alleles for further validation studies using Wisconsin breast cancer case-control DNA samples (n = ~7,000). Next, investigating the effects of environmental factors on gene expression in HMEC will further develop and functionally explore the groups/networks of transcripts identified above. Primary cultures of HMEC will be exposed to xenobiotics chosen using prior knowledge. The expression levels of genes of interest will be evaluated asking if such agents (toxic and preventive) can modulate the expression of important groups of transcripts associated with GWAS SNPs and if exposure significantly alters network structure. GWAS SNPs that are associated with gene expression changes caused by specific xenobiotics will be used to determine if stratification by these SNPs modifies relative risk for that environmental agent in the Wisconsin case-control population. Finally, in vivo validation studies using the congenic rat mammary carcinogenesis models initially used to focus human studies will be conducted.

Public Health Relevance

The goal of this project is to develop an integrated approach combining global genetic information together with environmental exposure to form a network model that begins to describe the etiology of breast cancer. Such a model, when complete, could allow us to move from the estimation of population risk for breast cancer to individual risk. This model will also provide functional information underlying genetic/environmental risk that could lead to strategies for risk reduction to this disease.

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Research Project (R01)
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Special Emphasis Panel (ZCA1-GRB-I (O3))
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Mcallister, Kimberly A
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University of Wisconsin Madison
Internal Medicine/Medicine
Schools of Medicine
United States
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Burnside, Elizabeth S; Liu, Jie; Wu, Yirong et al. (2016) Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy. Acad Radiol 23:62-9
Fan, Jun; Wu, Yirong; Yuan, Ming et al. (2016) Structure-Leveraged Methods in Breast Cancer Risk Prediction. J Mach Learn Res 17:
Wu, Yirong; Abbey, Craig K; Liu, Jie et al. (2016) Discriminatory power of common genetic variants in personalized breast cancer diagnosis. Proc SPIE Int Soc Opt Eng 9787:
Wu, Yirong; Liu, Jie; Del Rio, Alejandro Munoz et al. (2015) Developing a clinical utility framework to evaluate prediction models in radiogenomics. Proc SPIE Int Soc Opt Eng 9416:
Liu, Jie; Wu, Yirong; Ong, Irene et al. (2015) Leveraging Interaction between Genetic Variants and Mammographic Findings for Personalized Breast Cancer Diagnosis. AMIA Jt Summits Transl Sci Proc 2015:107-11
Wu, Yirong; Abbey, Craig K; Chen, Xianqiao et al. (2015) Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation. J Med Imaging (Bellingham) 2:041005
Liu, Jie; Page, David; Peissig, Peggy et al. (2014) New genetic variants improve personalized breast cancer diagnosis. AMIA Jt Summits Transl Sci Proc 2014:83-9
Wu, Yirong; Liu, Jie; Page, David et al. (2014) Comparing the value of mammographic features and genetic variants in breast cancer risk prediction. AMIA Annu Symp Proc 2014:1228-37
Liu, Jie; Page, David; Nassif, Houssam et al. (2013) Genetic variants improve breast cancer risk prediction on mammograms. AMIA Annu Symp Proc 2013:876-85
Leng, Ning; Dawson, John A; Thomson, James A et al. (2013) EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics 29:1035-43

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