A robust association test with multiple genetic variants and covariates
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Jen-Yu Lee
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Pao-Sheng Shen
Abstract
Due to the advancement of genome sequencing techniques, a great stride has been made in exome sequencing such that the association study between disease and genetic variants has become feasible. Some powerful and well-known association tests have been proposed to test the association between a group of genes and the disease of interest. However, some challenges still remain, in particular, many factors can affect the performance of testing power, e.g., the sample size, the number of causal and non-causal variants, and direction of the effect of causal variants. Recently, a powerful test, called T REM , is derived based on a random effects model. T REM has the advantages of being less sensitive to the inclusion of non-causal rare variants or low effect common variants or the presence of missing genotypes. However, the testing power of T REM can be low when a portion of causal variants has effects in opposite directions. To improve the drawback of T REM , we propose a novel test, called T ROB , which keeps the advantages of T REM and is more robust than T REM in terms of having adequate power in the case of variants with opposite directions of effect. Simulation results show that T ROB has a stable type I error rate and outperforms T REM when the proportion of risk variants decreases to a certain level and its advantage over T REM increases as the proportion decreases. Furthermore, T ROB outperforms several other competing tests in most scenarios. The proposed methodology is illustrated using the Shanghai Breast Cancer Study.
Funding source: Ministry of Science and Technology, Taiwan
Award Identifier / Grant number: MOST 108-2118-M-035 -002 -
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Author contribution: J.Y. Lee conceived and designed the experiments, analyzed the data, prepared figures and tables, authored the initial draft. P.S. Shen reviewed of the paper, approved the final draft. K.F. Cheng concived and designed the experiments, and approved the final draft.
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Research funding: This research was supported in part by the Ministry of Science and Technology of Taiwan under Grants (MOST 108-2118-M-035-002-).
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Conflict of interest statement: The authors declare that there is no conflict of interest.
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Artikel in diesem Heft
- Review Article
- Challenges for machine learning in RNA-protein interaction prediction
- Research Articles
- Distinct characteristics of correlation analysis at the single-cell and the population level
- pwrBRIDGE: a user-friendly web application for power and sample size estimation in batch-confounded microarray studies with dependent samples
- Use of SVM-based ensemble feature selection method for gene expression data analysis
- A robust association test with multiple genetic variants and covariates
- Estimation of the covariance structure from SNP allele frequencies
- GMEPS: a fast and efficient likelihood approach for genome-wide mediation analysis under extreme phenotype sequencing
- Sparse latent factor regression models for genome-wide and epigenome-wide association studies
