We propose developing, evaluating and comparing biologically motivated statistical methods in analyzing and interpreting heterogeneous and multiple types of genomic data. The overarching theme is that, to take account of genetic heterogeneity in complex diseases while maximizing the use of existing knowledge and data to boost statistical power for new discovery, we propose novel and powerful statistical methods that are adaptive and capable of integrating genotype data with gene pathway and functional annotations and other types of data, such as gene expression data. Specifically, we propose 1) developing powerful and flexible data-adaptive multilocus tests to detect genetic association with complex diseases, which can further integrate genotype and gene expres- sion data;2) extending the adaptive tests to gene pathway analysis;3) extending the adaptive tests to detect multiple trait-multilocus association;4) developing a novel and general framework for fi- nite mixture regressions to account for genetic heterogeneity. We will apply the proposed methods (and existing popular methods) to the ARIC data. We will implement the proposed methods in freely available software.

Public Health Relevance

This proposed research is expected not only to advance statistical methodology and theory for analysis of heterogeneous and multiple types of genomic data, but also to contribute valuable statistical and computational tools to the elucidation of genetic architectures of complex diseases and traits.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Brazhnik, Paul
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University of Minnesota Twin Cities
Biostatistics & Other Math Sci
Schools of Public Health
United States
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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
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
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
Kwak, Il-Youp; Pan, Wei (2017) Gene- and pathway-based association tests for multiple traits with GWAS summary statistics. Bioinformatics 33:64-71

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