A nonparametric method to test for associations between rare variants and multiple traits. (2016)

• Record Type: Journal Article
• Authors: ZHOU, YING; CHENG, YANGYANG; ZHU, WENSHENG; ZHOU, QIAN
• Is Part Of: Genetical research. Volume 98(2016)
• Publication Date: 2016
• Subjects: Genetics -- Periodicals; Heredity -- Periodicals; 576.5
• DOI: 10.1017/S0016672315000269
• Languages: English
• ISSNs: 0016-6723

Abstract

Summary: More and more rare genetic variants are being detected in the human genome, and it is believed that besides common variants, some rare variants also explain part of the phenotypic variance for human diseases. Due to the importance of rare variants, many statistical methods have been proposed to test for associations between rare variants and human traits. However, in existing studies, most methods only test for associations between multiple loci and one trait; therefore, the joint information of multiple traits has not been considered simultaneously and sufficiently. In this article, we present a study of testing for associations between rare variants and multiple traits, where trait value can be binary, ordinal, quantitative and/or any mixture of them. Based on the method of generalized Kendall's τ, a nonparametric method called NM-RV is proposed. A new kernel function for U-statistic, which could incorporate the information of each rare variant itself, is also presented and is expected to enhance the power of rare variant analysis. We further consider the asymptotic distribution of the proposed association test statistic. Our simulation work suggests that the proposed method is more powerful and robust than existing methods in testing for associations between rare variants and multiple traits, especially for multivariate ordinal traits.