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Sequence Kernel Association Analysis of Rare Variant Set Based on the Marginal Regression Model for Binary Traits
Author(s) -
Wu Baolin,
Pankow James S.,
Guan Weihua
Publication year - 2015
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21913
Subject(s) - association test , kernel (algebra) , univariate , type i and type ii errors , sequence (biology) , computational biology , regression , biology , likelihood ratio test , set (abstract data type) , genetic association , logistic regression , statistics , genetics , computer science , gene , mathematics , multivariate statistics , genotype , combinatorics , single nucleotide polymorphism , programming language
Recent sequencing efforts have focused on exploring the influence of rare variants on the complex diseases. Gene level based tests by aggregating information across rare variants within a gene have become attractive to enrich the rare variant association signal. Among them, the sequence kernel association test (SKAT) has proved to be a very powerful method for jointly testing multiple rare variants within a gene. In this article, we explore an alternative SKAT. We propose to use the univariate likelihood ratio statistics from the marginal model for individual variants as input into the kernel association test. We show how to compute its significance P ‐value efficiently based on the asymptotic chi‐square mixture distribution. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to associations between rare exonic variants and type 2 diabetes (T2D) in the Atherosclerosis Risk in Communities (ARIC) study. We identified an exome‐wide significant rare variant set in the gene ZZZ3 worthy of further investigations.