This project aims to develop statistical and computational methods for Identifying and haracterizing SNPs (genes) and environmental factors in the involvement of disease occurrence and progression using state-of-art data that are recently generated by high throughput technologies.
The first aim proposes to develop and study likelihood based regression strategies to construct flexible combinations of environmental factors that modify genetic effects in association studies that include binary, continuous and time-to-event data. Boosting and regularized regression strategies are used in structured and unstructured interaction models.
The second aim proposes the development of improved test statistics for assessing the association of rare variants with disease risk in sequencing studies by adaptively selecting variants variants to use, and by incorporating genome-wide association studies on additional subjects who are not sequenced to increase power.
A third aim i s concerned with methods development for estimating age-specific absolute risk of genetic variants and environmental factors from case-control studies, which includes a flexible and efficient estimation for time-varying attributable risk function critical in obtaining unbiased estimators of absolute risk and semi- and non-parametric estimation of composite incidence rate from the family history data on cases and controls.
A final aim will develop methods to test and estimate the causal effect of an exposure on a clinical outcome, using genetic variants as instruments. The methods exploit the Mendelian randomization of genetic variants and the dose correspondence of genetic effects on the exposure and the the outcome. The project draws on the strength of the studies in which the five investigators are directly involved and addresses the needs and barriers that these studies face. The methods and tools developed in this project will have a broad application to large-scale genetic epidemiologic studies.

Public Health Relevance

This project proposes to develop statistical methods to explain genetic variation contributed to common diseases;to predict or intervene the disease process using the intermdiate causal pathways;and to help devise targeted public health prevention and intervention strategies by identifying and evaluating environmental factors that modify genetic effects.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program Projects (P01)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1-GRB-S)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Fred Hutchinson Cancer Research Center
United States
Zip Code
Prentice, Ross L; Zhao, Shanshan (2016) Nonparametric estimation of the multivariate survivor function: the multivariate Kaplan-Meier estimator. Lifetime Data Anal :
Dai, James Y; Zhang, Xinyi Cindy; Wang, Ching-Yun et al. (2016) Augmented case-only designs for randomized clinical trials with failure time endpoints. Biometrics 72:30-8
Prentice, R L (2016) Higher Dimensional Clayton-Oakes Models for Multivariate Failure Time Data. Biometrika 103:231-236
Wang, Zhu; Ma, Shuangge; Zappitelli, Michael et al. (2016) Penalized count data regression with application to hospital stay after pediatric cardiac surgery. Stat Methods Med Res 25:2685-2703
Koopmeiners, Joseph S; Feng, Ziding (2016) Group sequential testing of the predictive accuracy of a continuous biomarker with unknown prevalence. Stat Med 35:1267-80
Petralia, Francesca; Song, Won-Min; Tu, Zhidong et al. (2016) New Method for Joint Network Analysis Reveals Common and Different Coexpression Patterns among Genes and Proteins in Breast Cancer. J Proteome Res 15:743-54
Bryan, Matthew; Heagerty, Patrick J (2016) Multivariate analysis of longitudinal rates of change. Stat Med 35:5117-5134
Cheng, Yichen; Dai, James Y; Kooperberg, Charles (2016) Group association test using a hidden Markov model. Biostatistics 17:221-34
Dai, James Y; Tapsoba, Jean de Dieu; Buas, Matthew F et al. (2016) Constrained Score Statistics Identify Genetic Variants Interacting with Multiple Risk Factors in Barrett's Esophagus. Am J Hum Genet 99:352-65
Maziarz, Marlena; Heagerty, Patrick; Cai, Tianxi et al. (2016) On longitudinal prediction with time-to-event outcome: Comparison of modeling options. Biometrics :

Showing the most recent 10 out of 279 publications