An emerging research area in genetics is to detect associations between complex traits and rare variants (RVs) with next-generation sequencing data. Due to extremely 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 statistical power, a common theme of existing association tests is to aggregate information across multiple RVs in a gene. With sequencing data, since the majority of RVs may not be causal, in which case most, if not all, existing association tests have severely deteriorating performance. We propose developing and evaluating an adaptive test that can maintain high power across various situations, including in the presence of opposite association directions and of many non-associated RVs. We will extend the proposed adaptive test to pathway analysis and multi-trait analysis. The developed methods will be applied to detect associations of RV-cardiovascular traits with the sequencing data from the CHARGE-S and ESP cohorts. We will develop and distribute software implementing the proposed methods.

Public Health Relevance

This proposed research is expected not only to advance data analysis methodology and practice for DNA sequencing data, but also to contribute valuable analysis tools to the elucidation of genetic components of complex human diseases and traits.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
Project #
Application #
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Applebaum-Bowden, Deborah
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Minnesota Twin Cities
Biostatistics & Other Math Sci
Schools of Public Health
United States
Zip Code
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
Ritchie, Marylyn D; Davis, Joe R; Aschard, Hugues et al. (2017) Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions. Am J Epidemiol 186:771-777
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, 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

Showing the most recent 10 out of 49 publications