title: Block-based association tests for rare variants using Kullback-Leibler divergence.
abstract: Although genome-wide association studies have successfully detected numerous associations between common variants and complex diseases, these variants typically can only explain a small part of the heritable component of a disease. With the advent of next-generation sequencing, attention has turned to rare variants. Recently, a variety of approaches for detecting associations of rare variants have been proposed, including the Kullback-Leibler divergence-based tests (KLTs) for detecting genotypic differences between cases and controls. However, few of these approaches consider linkage disequilibrium (LD) structure among rare variants and common variants. In this study, we propose two block-based association tests for testing the effects of rare variants on a disease. The main idea for this approach comes from the hypothesis that a region of interest may consist of two or more LD blocks such that single-nucleotide variants (SNVs) within each block are correlated, whereas SNVs in different blocks are independent or weakly correlated. Under this hypothesis, we propose two tests that are generalizations of the KLTs by taking the block structure into account. A simulation study under various scenarios shows that the proposed methods have well-controlled type I error rates and outperform some leading methods in the literature. Moreover, application to the Dallas Heart Study data demonstrates the feasibility and performance of the two proposed methods in a realistic setting.
authors.affiliation: Department of Applied Mathematics, Nanjing Forestry University, Nanjing, China.|School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China.
authors.forename: Degang
authors.initials: D
authors.lastname: Zhu
authors.affiliation: Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China.
authors.forename: Yue-Qing
authors.initials: YQ
authors.lastname: Hu
authors.affiliation: Department of Statistics, The Ohio State University, Columbus, OH, USA.
authors.forename: Shili
authors.initials: S
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D066264:Datasets as Topic; D056726:Genetic Association Studies / Q000379:methods*; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D055106:Genome-Wide Association Study / Q000379:methods; D006801:Humans; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results