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
Howard, Barbara V; Aragaki, Aaron K; Tinker, Lesley F et al. (2018) A Low-Fat Dietary Pattern and Diabetes: A Secondary Analysis From the Women's Health Initiative Dietary Modification Trial. Diabetes Care 41:680-687
Huang, Yijian; Wang, Ching-Yun (2018) Cox regression with dependent error in covariates. Biometrics 74:118-126
Su, Yu-Ru; Di, Chongzhi; Bien, Stephanie et al. (2018) A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics. Am J Hum Genet 102:904-919
Liu, Dandan; Cai, Tianxi; Lok, Anna et al. (2018) Nonparametric Maximum Likelihood Estimators of Time-Dependent Accuracy Measures for Survival Outcome Under Two-Stage Sampling Designs. J Am Stat Assoc 113:882-892
Yu, Hsiang; Cheng, Yu-Jen; Wang, Ching-Yun (2018) Methods for multivariate recurrent event data with measurement error and informative censoring. Biometrics 74:966-976
Monaco, John V; Gorfine, Malka; Hsu, Li (2018) General Semiparametric Shared Frailty Model: Estimation and Simulation with frailtySurv. J Stat Softw 86:
Dai, James Y; Wang, Xiaoyu; Buas, Matthew F et al. (2018) Whole-genome sequencing of esophageal adenocarcinoma in Chinese patients reveals distinct mutational signatures and genomic alterations. Commun Biol 1:174
Dai, James Y; Peters, Ulrike; Wang, Xiaoyu et al. (2018) Diagnostics for Pleiotropy in Mendelian Randomization Studies: Global and Individual Tests for Direct Effects. Am J Epidemiol 187:2672-2680
Dai, James Y; Liang, C Jason; LeBlanc, Michael et al. (2018) Case-only approach to identifying markers predicting treatment effects on the relative risk scale. Biometrics 74:753-763
Prentice, Ross L; Zhao, Shanshan (2018) Nonparametric estimation of the multivariate survivor function: the multivariate Kaplan-Meier estimator. Lifetime Data Anal 24:3-27

Showing the most recent 10 out of 319 publications