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
Deng, Yangqing; Pan, Wei (2018) Significance Testing for Allelic Heterogeneity. Genetics 210:25-32
Wu, Chong; Pan, Wei (2018) Integrating eQTL data with GWAS summary statistics in pathway-based analysis with application to schizophrenia. Genet Epidemiol 42:303-316
Wu, Chong; Park, Jun Young; Guan, Weihua et al. (2018) An adaptive gene-based test for methylation data. BMC Proc 12:60
Park, Jun Young; Wu, Chong; Basu, Saonli et al. (2018) Adaptive SNP-Set Association Testing in Generalized Linear Mixed Models with Application to Family Studies. Behav Genet 48:55-66
Park, Jun Young; Wu, Chong; Pan, Wei (2018) An adaptive gene-level association test for pedigree data. BMC Genet 19:68
Wu, Chong; Pan, Wei (2018) Integration of Enhancer-Promoter Interactions with GWAS Summary Results Identifies Novel Schizophrenia-Associated Genes and Pathways. Genetics 209:699-709
Deng, Yangqing; Pan, Wei (2018) Improved Use of Small Reference Panels for Conditional and Joint Analysis with GWAS Summary Statistics. Genetics 209:401-408
Xu, Zhiyuan; Wu, Chong; Pan, Wei et al. (2017) Imaging-wide association study: Integrating imaging endophenotypes in GWAS. Neuroimage 159:159-169
Xu, Zhiyuan; Wu, Chong; Wei, Peng et al. (2017) A Powerful Framework for Integrating eQTL and GWAS Summary Data. Genetics 207:893-902
Liu, Binghui; Wu, Chong; Shen, Xiaotong et al. (2017) A NOVEL AND EFFICIENT ALGORITHM FOR DE NOVO DISCOVERY OF MUTATED DRIVER PATHWAYS IN CANCER. Ann Appl Stat 11:1481-1512

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