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 #
5R01HL105397-02
Application #
8238373
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
2012-01-01
Budget End
2012-12-31
Support Year
2
Fiscal Year
2012
Total Cost
$358,616
Indirect Cost
$108,616
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
Gao, Chen; Kim, Junghi; Pan, Wei (2016) ADAPTIVE TESTING OF SNP-BRAIN FUNCTIONAL CONNECTIVITY ASSOCIATION VIA A MODULAR NETWORK ANALYSIS. Pac Symp Biocomput 22:58-69
Liu, Binghui; Shen, Xiaotong; Pan, Wei (2016) Integrative and regularized principal component analysis of multiple sources of data. Stat Med 35:2235-50
Xu, Zhiyuan; Pan, Wei (2016) Binomial Mixture Model Based Association Testing to Account for Genetic Heterogeneity for GWAS. Genet Epidemiol 40:202-9
Kim, Junghi; Zhang, Yiwei; Pan, Wei et al. (2016) Powerful and Adaptive Testing for Multi-trait and Multi-SNP Associations with GWAS and Sequencing Data. Genetics 203:715-31
Wei, Peng; Cao, Ying; Zhang, Yiwei et al. (2016) On Robust Association Testing for Quantitative Traits and Rare Variants. G3 (Bethesda) 6:3941-3950
Wu, Chong; Chen, Jun; Kim, Junghi et al. (2016) An adaptive association test for microbiome data. Genome Med 8:56
Kim, Junghi; Wozniak, Jeffrey R; Mueller, Bryon A et al. (2015) Testing group differences in brain functional connectivity: using correlations or partial correlations? Brain Connect 5:214-31
Kim, Junghi; Pan, Wei; Alzheimer's Disease Neuroimaging Initiative (2015) A cautionary note on using secondary phenotypes in neuroimaging genetic studies. Neuroimage 121:136-45
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
Pan, Wei; Kwak, Il-Youp; Wei, Peng (2015) A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants. Am J Hum Genet 97:86-98

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