Abstract
This paper presents two simple rare variant (RV) burden tests based on the likelihood ratio test (LRT) and score statistics. LRT is one of the commonly used tests in practical data analysis, and we show here that there is no reason to ignore it in testing RV associations. With the Bartlett correction, we have numerically shown that the LRT-based test can have a reliable distribution. Our simulation study indicates that if the non-null variants are as common as the null variants, then the LRT and score statistics have comparable performance to the C-alpha test, and if the former is rarer than the null variants, then they outperform the C-alpha test.
Acknowledgments
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2013R1A1A1061332).
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©2015 by De Gruyter
Artikel in diesem Heft
- Frontmatter
- Research Articles
- A model selection criterion for model-based clustering of annotated gene expression data
- Sample size reassessment for a two-stage design controlling the false discovery rate
- A robust distribution-free test for genetic association studies of quantitative traits
- A parametric approach to kinship hypothesis testing using identity-by-descent parameters
- Likelihood ratio and score burden tests for detecting disease-associated rare variants
- On an extended interpretation of linkage disequilibrium in genetic case-control association studies
Artikel in diesem Heft
- Frontmatter
- Research Articles
- A model selection criterion for model-based clustering of annotated gene expression data
- Sample size reassessment for a two-stage design controlling the false discovery rate
- A robust distribution-free test for genetic association studies of quantitative traits
- A parametric approach to kinship hypothesis testing using identity-by-descent parameters
- Likelihood ratio and score burden tests for detecting disease-associated rare variants
- On an extended interpretation of linkage disequilibrium in genetic case-control association studies