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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
9R01GM113250-11A1
Application #
8635676
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Brazhnik, Paul
Project Start
2002-01-01
Project End
2018-08-31
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
11
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Minnesota Twin Cities
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
City
Minneapolis
State
MN
Country
United States
Zip Code
55455
Deng, Yangqing; Pan, Wei (2017) Testing Genetic Pleiotropy with GWAS Summary Statistics for Marginal and Conditional Analyses. Genetics 207:1285-1299
Xu, Zhiyuan; Wu, Chong; Pan, Wei et al. (2017) Imaging-wide association study: Integrating imaging endophenotypes in GWAS. Neuroimage 159:159-169
Kwak, Il-Youp; Pan, Wei (2017) Gene- and pathway-based association tests for multiple traits with GWAS summary statistics. Bioinformatics 33:64-71
Deng, Yangqing; Pan, Wei (2017) Conditional analysis of multiple quantitative traits based on marginal GWAS summary statistics. Genet Epidemiol 41:427-436
Gao, Chen; Kim, Junghi; Pan, Wei (2017) ADAPTIVE TESTING OF SNP-BRAIN FUNCTIONAL CONNECTIVITY ASSOCIATION VIA A MODULAR NETWORK ANALYSIS. Pac Symp Biocomput 22:58-69
Xu, Zhiyuan; Xu, Gongjun; Pan, Wei et al. (2017) Adaptive testing for association between two random vectors in moderate to high dimensions. Genet Epidemiol 41:599-609
Xu, Zhiyuan; Wu, Chong; Wei, Peng et al. (2017) A Powerful Framework for Integrating eQTL and GWAS Summary Data. Genetics 207:893-902
Kim, Junghi; Pan, Wei; Alzheimer's Disease Neuroimaging Initiative (2017) Adaptive testing for multiple traits in a proportional odds model with applications to detect SNP-brain network associations. Genet Epidemiol 41:259-277
Xu, Gongjun; Lin, Lifeng; Wei, Peng et al. (2016) An adaptive two-sample test for high-dimensional means. Biometrika 103:609-624
Liu, Binghui; Shen, Xiaotong; Pan, Wei (2016) Nonlinear Joint Latent Variable Models and Integrative Tumor Subtype Discovery. Stat Anal Data Min 9:106-116

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