Although recently published genome-wide association studies (GWASs) have localized many disease-associated genetic variants, they only account for tiny proportions of heritable phenotypic variations, suggesting that only a small fraction of causal loci have been identified, due to genetic heterogeneity (i.e. multiple genetic variants associated with a complex trait), confirmed small to modest effect sizes of common genetic variants and limited statistical power of current analysis methods. On the other hand, GWAS data also offer an exciting opportunity for personalized medicine, aiming to assign the most suitable treatment or intervention to an individual based on his/her clinical and genetic information. However, there is still quite a distance in translating GWAS data to practice of personalized medicine, largely due to the paucity of powerful analysis methods. This research is devoted to several emerging topics in personalized medicine with high-dimensional genetic and clinical data. Building on the advances in penalized regression and classification made during the previous funding period, we propose developing innovative and powerful statistical methods for GWAS data to discover novel gene pathways and utilize them in personalized medicine. In particular, we aim to discover de novo gene pathways containing SNPs with individually weak, but collectively strong, effects on complex disease and traits. We combine the available Lung Health Study (LHS) clinical data and GWAS data from two different sources, applying the developed statistical methods to explore how genetic variants and baseline clinical variables possibly modify the effects of smoking interventions, and how to determine an optimal individualized intervention rule for any given subject.

Public Health Relevance

This proposed research is expected not only to advance statistical methodology and theory for analysis of large-scale genomic data and for personalized treatment selection and identification, but also to contribute valuable statistical and computational tools for such analyses with possible applications in the practice of personalized medicine and public health.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Papanicolaou, George
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University of Minnesota Twin Cities
Biostatistics & Other Math Sci
Schools of Public Health
United States
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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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