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SNP Set Association Analysis for Familial Data
Author(s) -
Schifano Elizabeth D.,
Epstein Michael P.,
Bielak Lawrence F.,
Jhun Min A.,
Kardia Sharon L. R.,
Peyser Patricia A.,
Lin Xihong
Publication year - 2012
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.21676
Subject(s) - snp , genome wide association study , genetic association , single nucleotide polymorphism , epistasis , covariate , association test , genetics , tag snp , biology , kernel (algebra) , computational biology , genotype , statistics , mathematics , gene , combinatorics
Genome‐wide association studies (GWAS) are a popular approach for identifying common genetic variants and epistatic effects associated with a disease phenotype. The traditional statistical analysis of such GWAS attempts to assess the association between each individual single‐nucleotide polymorphism (SNP) and the observed phenotype. Recently, kernel machine‐based tests for association between a SNP set (e.g., SNPs in a gene) and the disease phenotype have been proposed as a useful alternative to the traditional individual‐SNP approach, and allow for flexible modeling of the potentially complicated joint SNP effects in a SNP set while adjusting for covariates. We extend the kernel machine framework to accommodate related subjects from multiple independent families, and provide a score‐based variance component test for assessing the association of a given SNP set with a continuous phenotype, while adjusting for additional covariates and accounting for within‐family correlation. We illustrate the proposed method using simulation studies and an application to genetic data from the Genetic Epidemiology Network of Arteriopathy (GENOA) study.