Bootstrap Restricted Likelihood Ratio Test for the Detection of Rare Variants

Title:Bootstrap Restricted Likelihood Ratio Test for the Detection of Rare Variants

Volume: 16 Issue: 3

Author(s): Ping Zeng and Ting Wang

Affiliation:

Keywords: Rare variants association study, Variance component, Likelihood ratio test, Linear mixed model, Bootstrap test, Distribution approximation.

Abstract: In this paper the detection of rare variants association with continuous phenotypes of interest is investigated via the likelihood-ratio based variance component test under the framework of linear mixed models. The hypothesis testing is challenging and nonstandard, since under the null the variance component is located on the boundary of its parameter space. In this situation the usual asymptotic chisquare distribution of the likelihood ratio statistic does not necessarily hold. To circumvent the derivation of the null distribution we resort to the bootstrap method due to its generic applicability and being easy to implement. Both parametric and nonparametric bootstrap likelihood ratio tests are studied. Numerical studies are implemented to evaluate the performance of the proposed bootstrap likelihood ratio test and compare to some existing methods for the identification of rare variants. To reduce the computational time of the bootstrap likelihood ratio test we propose an effective approximation mixture for the bootstrap null distribution. The GAW17 data is used to illustrate the proposed test.

Cite this article as:

Zeng Ping and Wang Ting, Bootstrap Restricted Likelihood Ratio Test for the Detection of Rare Variants, Current Genomics 2015; 16 (3) . https://dx.doi.org/10.2174/1389202916666150304234203