title: Detecting the Common and Individual Effects of Rare Variants on Quantitative Traits by Using Extreme Phenotype Sampling.
abstract: Next-generation sequencing technology has made it possible to detect rare genetic variants associated with complex human traits. In recent literature, various methods specifically designed for rare variants are proposed. These tests can be broadly classified into burden and nonburden tests. In this paper, we take advantage of the burden and nonburden tests, and consider the common effect and the individual deviations from the common effect. To achieve robustness, we use two methods of combining p-values, Fisher's method and the minimum-p method. In rare variant association studies, to improve the power of the tests, we explore the advantage of the extreme phenotype sampling. At first, we dichotomize the continuous phenotypes before analysis, and the two extremes are treated as two different groups representing a dichotomous phenotype. We next compare the powers of several methods based on extreme phenotype sampling and random sampling. Extensive simulation studies show that our proposed methods by using extreme phenotype sampling are the most powerful or very close to the most powerful one in various settings of true models when the same sample size is used.
authors.affiliation: Department of Mathematics, School of Science, Harbin Institute of Technology, Harbin 150001, China. zhouyajing_0321@163.com.|School of Mathematical Sciences, Heilongjiang University, Harbin 150080, China. zhouyajing_0321@163.com.
authors.forename: Ya-Jing
authors.initials: YJ
authors.lastname: Zhou
authors.affiliation: Department of Mathematics, School of Science, Harbin Institute of Technology, Harbin 150001, China. mathwy@hit.edu.cn.
authors.forename: Yong
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Mathematics, School of Science, Harbin Institute of Technology, Harbin 150001, China. chenlili_02_06@163.com.|School of Mathematical Sciences, Heilongjiang University, Harbin 150080, China. chenlili_02_06@163.com.
authors.forename: Li-Li
authors.initials: LL
authors.lastname: Chen
keywords: association study; extreme sampling; random sampling; rare variants