An emerging and challenging research ?eld in genetics is to detect associations between com- plex traits and rare variants (RVs) with next-generation sequencing and Exome Chip data. Due to ex- tremely low minor allele frequencies (MAFs) of RVs, many existing tests for common variants (CVs), such as the univariate test on each individual variant, most popular in genome-wide association studies (GWAS), may no longer be suitable. To boost power and facilitate biological interpretation, we propose combining information across multiple sources of data, which may or may not be of the same type. For the former, it leads to highly adaptive meta analysis suitable and powerful for com- bining multi-ethnic cohorts; for the latter, we integrate DNA genotype and sequencing data with gene networks, gene expression data and metabolomic data for association analysis of RVs. A common theme of the proposed methods is to explicitly account for genetic and phenotypic heterogeneity. For example, to account for genetic heterogeneity, we propose an adaptive network-based association test to aggregate information across multiple causal genes clustered in a network for a single cohort; for multiple cohorts, especially multi-ethnic ones, our proposed meta-analysis test is highly adaptive to heterogeneous and varying association patterns across cohorts (e.g. only few cohorts contain causal RVs) and among RVs. The developed methods will be applied to detect associations of RV- cardiovascular traits with the sequencing and other omic data from the ARIC study. We will develop and distribute software implementing the proposed methods. The proposed research is in line with the NHLBI's continuing interest in whole genome/exome sequencing and integrative omics analysis as evidenced by its TOPMed Program and NIH's other Precision Medicine initiatives.

Public Health Relevance

This proposed research is expected to not only advance data analysis and integration strategies for DNA sequencing and other omic data, but also contribute valuable computational tools with free software packages to the elucidation of genetic components of complex human diseases and traits.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL116720-06
Application #
9744804
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Luo, James
Project Start
2013-09-01
Project End
2021-07-31
Budget Start
2019-08-01
Budget End
2020-07-31
Support Year
6
Fiscal Year
2019
Total Cost
Indirect Cost
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
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
Deng, Yangqing; Pan, Wei (2018) Significance Testing for Allelic Heterogeneity. Genetics 210:25-32
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
McAllister, Kimberly; Mechanic, Leah E; Amos, Christopher et al. (2017) Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. Am J Epidemiol 186:753-761
Deng, Yangqing; Pan, Wei (2017) Testing Genetic Pleiotropy with GWAS Summary Statistics for Marginal and Conditional Analyses. Genetics 207:1285-1299

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