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
Chen, Lin S; Prentice, Ross L; Wang, Pei (2014) A penalized EM algorithm incorporating missing data mechanism for Gaussian parameter estimation. Biometrics 70:312-22
Seguin, Rebecca; Buchner, David M; Liu, Jingmin et al. (2014) Sedentary behavior and mortality in older women: the Women's Health Initiative. Am J Prev Med 46:122-35
Di, Chongzhi; Crainiceanu, Ciprian M; Jank, Wolfgang S (2014) Multilevel sparse functional principal component analysis. Stat 3:126-143
Zheng, Cheng; Beresford, Shirley A; Van Horn, Linda et al. (2014) Simultaneous association of total energy consumption and activity-related energy expenditure with risks of cardiovascular disease, cancer, and diabetes among postmenopausal women. Am J Epidemiol 180:526-35
Dai, James Y; Li, Shuying S; Gilbert, Peter B (2014) Case-only method for cause-specific hazards models with application to assessing differential vaccine efficacy by viral and host genetics. Biostatistics 15:196-203
Zhao, Shanshan; Chlebowski, Rowan T; Anderson, Garnet L et al. (2014) Sex hormone associations with breast cancer risk and the mediation of randomized trial postmenopausal hormone therapy effects. Breast Cancer Res 16:R30
Sitlani, Colleen M; Heagerty, Patrick J (2014) Analyzing longitudinal data to characterize the accuracy of markers used to select treatment. Stat Med 33:2881-96
Beasley, Jeannette M; Gunter, Marc J; LaCroix, Andrea Z et al. (2014) Associations of serum insulin-like growth factor-I and insulin-like growth factor-binding protein 3 levels with biomarker-calibrated protein, dairy product and milk intake in the Women's Health Initiative. Br J Nutr 111:847-53
Logsdon, Benjamin A; Dai, James Y; Auer, Paul L et al. (2014) A variational Bayes discrete mixture test for rare variant association. Genet Epidemiol 38:21-30
Bryan, Matthew; Heagerty, Patrick J (2014) Direct regression models for longitudinal rates of change. Stat Med 33:2115-36

Showing the most recent 10 out of 178 publications