Block-based association tests for rare variants using Kullback-Leibler divergence

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.