Breast cancer is a major source of morbidity and mortality, and remains the most common cancer occurring in U.S. women, with over 183,000 new cases for 2008 (Ries et al. 2008). This application addresses two aspects of breast cancer development: germline mutations, as in two tumor suppressor genes BRCA1/2, and somatic mutations characterized by copy number changes and allelic loss.
The first aim of this application is on the estimation of the age-dependent penetrance function of candidate genes like BRCA1/2 from population-based case-control studies without external disease incidence data. The estimates obtained from the data and the modeling framework will be used to estimate the mutational carrier probability and predict the breast cancer risk of a woman. A shared frailty model will be used to account for the widely observed substantial risk heterogeneity among families. The model will be extended to a general multivariate frailty model to allow for varying degrees of the correlation for different types of relations and multiple candidate gene-related phenotypes. Despite recent successes in identifying novel candidate loci for breast cancer, about three- quarter of breast cancer cases having no clustering in families. It is believed that cancer develops as a result of an accumulation of genetic aberrations at chromosomal locations that are critical in maintaining normal cell functions. The loss and gain of genetic information from specific chromosomal locations are considered an indication for the involvement of tumor suppressor or oncogenes in the tumorigenesis. Using genomic instability data, the second aim of this application is on identifying novel oncogenic networks involved in breast cancer tumor development. Graphical model-based methods will be developed by using novel sparse regression and multiple testing techniques. As the high throughput genotyping technologies become widely available, researchers are able to conduct large scale genome-wide association and genomic instability studies. This allows us to identify novel candidate loci that may have only moderate effect on breast cancer risk or discover oncogenic networks at such a detail that is not previously feasible. There is a great need to characterize these novel genes and provide inference for these networks. The methods proposed here are efforts towards this direction.
This project aims to characterize the breast cancer risk in relation to genes in the population, estimate the mutation carrier probabilities, and predict a healthy individual's risk for developing breast cancer given the mutational status in these genes. It also aims to gain knowledge on the role of genomic instabilities in breast tumor initiation and progression. The ultimate goal is to provide insight in devising effective individual-tailored prevention strategies for reducing breast cancer risk.
|Su, Yu-Ru; Wang, Jane-Ling (2016) SEMIPARAMETRIC EFFICIENT ESTIMATION FOR SHARED-FRAILTY MODELS WITH DOUBLY-CENSORED CLUSTERED DATA. Ann Stat 44:1298-1331|
|Wang, Xianlong; Qin, Li; Zhang, Hexin et al. (2015) A regularized multivariate regression approach for eQTL analysis. Stat Biosci 7:129-146|
|Jiao, Shuo; Peters, Ulrike; Berndt, Sonja et al. (2015) Powerful Set-Based Gene-Environment Interaction Testing Framework for Complex Diseases. Genet Epidemiol 39:609-18|
|Hsu, Li; Jeon, Jihyoun; Brenner, Hermann et al. (2015) A model to determine colorectal cancer risk using common genetic susceptibility loci. Gastroenterology 148:1330-9.e14|
|Thrift, Aaron P; Gong, Jian; Peters, Ulrike et al. (2015) Mendelian Randomization Study of Body Mass Index and Colorectal Cancer Risk. Cancer Epidemiol Biomarkers Prev 24:1024-31|
|Gorfine, Malka; Hsu, Li; Zucker, David M et al. (2014) Calibrated predictions for multivariate competing risks models. Lifetime Data Anal 20:234-51|
|Di, Chongzhi; Crainiceanu, Ciprian M; Jank, Wolfgang S (2014) Multilevel sparse functional principal component analysis. Stat 3:126-143|
|Saegusa, Takumi; Di, Chongzhi; Chen, Ying Qing (2014) Hypothesis testing for an extended cox model with time-varying coefficients. Biometrics 70:619-28|
|Liu, Dandan; Zheng, Yingye; Prentice, Ross L et al. (2014) Estimating Risk with Time-to-Event Data: An Application to the Women's Health Initiative. J Am Stat Assoc 109:514-524|
|Qin, Li-Xuan; Breeden, Linda; Self, Steven G (2014) Finding gene clusters for a replicated time course study. BMC Res Notes 7:60|
Showing the most recent 10 out of 62 publications