The objective of the proposed research is to develop multivariate statistical methods for joint analyses of functionally related biological information n association and interaction using existing GWA data. Specifically, we propose to develop methods for joint analyzing multiple SNPs in a SNP set (e.g. a gene), and methods for jointly analyzing multivariate secondary phenotypes with potential ignorable and non-ignorable missing data. We further propose to develop methods for genome-wide gene-gene interaction analysis, in which groups of between- gene SNP-SNP correlations will be analyzed together to detect interactions. In addition, we propose to integrate a priori knowledge in our genome-wide association or interaction analyses;and we group sets of predictors by a priori knowledge and design flexible regularized regression approaches to constrain the parameter estimation and achieve efficiency. The proposed research is motivated by opportunities and needs in GWA studies, and much of our proposed work can be cost-efficiently implemented with publicly accessible GWA data for different cancers to improve our understanding of the genetic basis and disease etiology of cancer.

Public Health Relevance

The objective of the proposed research is to develop multivariate statistical methods for joint analyses of functionally related biological information n association and in interaction studies using existing GWA data with integration of a priori knowledge. The proposed research is motivated by opportunities and needs in GWA studies, and much of our proposed work can be cost-efficiently implemented with publicly accessible GWA data for different cancers to improve our understanding of the genetic basis and disease etiology of cancer.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Small Research Grants (R03)
Project #
1R03CA174984-01
Application #
8486199
Study Section
Special Emphasis Panel (ZCA1-RPRB-0 (J1))
Program Officer
Chen, Huann-Sheng
Project Start
2013-03-07
Project End
2015-02-28
Budget Start
2013-03-07
Budget End
2014-02-28
Support Year
1
Fiscal Year
2013
Total Cost
$79,000
Indirect Cost
$29,000
Name
University of Chicago
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
005421136
City
Chicago
State
IL
Country
United States
Zip Code
60637
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