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.
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.
|Zhang, Yiwei; Pan, Wei (2015) Principal component regression and linear mixed model in association analysis of structured samples: competitors or complements? Genet Epidemiol 39:149-55|
|Wei, Peng; Tang, Hongwei; Li, Donghui (2014) Functional logistic regression approach to detecting gene by longitudinal environmental exposure interaction in a case-control study. Genet Epidemiol 38:638-51|
|Xu, Zhiyuan; Shen, Xiaotong; Pan, Wei et al. (2014) Longitudinal analysis is more powerful than cross-sectional analysis in detecting genetic association with neuroimaging phenotypes. PLoS One 9:e102312|
|Cao, Ying; Wei, Peng; Bailey, Matthew et al. (2014) A versatile omnibus test for detecting mean and variance heterogeneity. Genet Epidemiol 38:51-9|
|Tang, Hongwei; Wei, Peng; Duell, Eric J et al. (2014) Axonal guidance signaling pathway interacting with smoking in modifying the risk of pancreatic cancer: a gene- and pathway-based interaction analysis of GWAS data. Carcinogenesis 35:1039-45|
|Kim, Junghi; Wozniak, Jeffrey R; Mueller, Bryon A et al. (2014) Comparison of statistical tests for group differences in brain functional networks. Neuroimage 101:681-94|
|Zhang, Yiwei; Xu, Zhiyuan; Shen, Xiaotong et al. (2014) Testing for association with multiple traits in generalized estimation equations, with application to neuroimaging data. Neuroimage 96:309-25|
|Tang, Hongwei; Wei, Peng; Duell, Eric J et al. (2014) Genes-environment interactions in obesity- and diabetes-associated pancreatic cancer: a GWAS data analysis. Cancer Epidemiol Biomarkers Prev 23:98-106|
|Pan, Wei; Kim, Junghi; Zhang, Yiwei et al. (2014) A powerful and adaptive association test for rare variants. Genetics 197:1081-95|