Genetic association studies aim to map disease genes through comparisons of frequencies of genetic variants among affected and unaffected individuals. Due to usually weak associations between genetic variants and disease, it is critical to apply powerful statistical tests to maximize the chance to locate disease loci. We propose developing novel and powerful multi-locus methods based on penalized regression to detect genetic association for population- or family-based studies with un- phased genotype data. Specifically, statistical methods and theory will be developed and evaluated for statistical inference and prediction based on penalized regression with novel nonconvex penalties for linear models, generalized linear models and generalized estimating equations. We will apply the developed methods to large cohorts in the Candidate gene Association Resource (CARe) for multi- locus analysis to discover atrial fibrillation (AF)-associated variants, possibly by considering gene by gene and gene by environment interactions. Known and newly discovered genetic and other risk factors will be used to predict AF.

Public Health Relevance

This proposed research is expected not only to contribute valuable analysis tools to the elucidation of genetic components of complex human diseases and traits, but also to advance statistical methodology and theory for high-dimensional data,

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL105397-04
Application #
8597451
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Papanicolaou, George
Project Start
2011-03-10
Project End
2014-12-31
Budget Start
2014-01-01
Budget End
2014-12-31
Support Year
4
Fiscal Year
2014
Total Cost
$321,719
Indirect Cost
$96,719
Name
University of Minnesota Twin Cities
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
555917996
City
Minneapolis
State
MN
Country
United States
Zip Code
55455
Zhang, Yiwei; Pan, Wei (2015) Principal component regression and linear mixed model in association analysis of structured samples: competitors or complements? Genet Epidemiol 39:149-55
Kim, Junghi; Wozniak, Jeffrey R; Mueller, Bryon A et al. (2014) Comparison of statistical tests for group differences in brain functional networks. Neuroimage 101:681-94
Zhang, Yiwei; Xu, Zhiyuan; Shen, Xiaotong et al. (2014) Testing for association with multiple traits in generalized estimation equations, with application to neuroimaging data. Neuroimage 96:309-25
Pan, Wei; Kim, Junghi; Zhang, Yiwei et al. (2014) A powerful and adaptive association test for rare variants. Genetics 197:1081-95
Xu, Zhiyuan; Shen, Xiaotong; Pan, Wei et al. (2014) Longitudinal analysis is more powerful than cross-sectional analysis in detecting genetic association with neuroimaging phenotypes. PLoS One 9:e102312
Zhang, Yiwei; Guan, Weihua; Pan, Wei (2013) Adjustment for population stratification via principal components in association analysis of rare variants. Genet Epidemiol 37:99-109
Zhang, Yiwei; Shen, Xiaotong; Pan, Wei (2013) Adjusting for population stratification in a fine scale with principal components and sequencing data. Genet Epidemiol 37:787-801
Pan, Wei; Shen, Xiaotong; Liu, Binghui (2013) Cluster Analysis: Unsupervised Learning via Supervised Learning with a Non-convex Penalty. J Mach Learn Res 14:1865
Zhu, Yunzhang; Shen, Xiaotong; Pan, Wei (2013) Simultaneous grouping pursuit and feature selection over an undirected graph. J Am Stat Assoc 108:713-725
Kim, Sunkyung; Pan, Wei; Shen, Xiaotong (2013) Network-based penalized regression with application to genomic data. Biometrics 69:582-93

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