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
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
Pan, Wei; Chen, Yue-Ming; Wei, Peng (2015) Testing for polygenic effects in genome-wide association studies. Genet Epidemiol 39:306-16
Kim, Junghi; Bai, Yun; Pan, Wei (2015) An Adaptive Association Test for Multiple Phenotypes with GWAS Summary Statistics. Genet Epidemiol 39:651-63
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

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