Title: SNP set association analysis for familial data.
Authors: Schifano, Elizabeth D; Epstein, Michael P; Bielak, Lawrence F; Jhun, Min A; Kardia, Sharon L R; Peyser, Patricia A; Lin, Xihong
Published In Genet Epidemiol, (2012 Dec)
Abstract: 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.
PubMed ID: 22968922
MeSH Terms: Acid Ceramidase/genetics; Algorithms; Artificial Intelligence; Chromosomes, Human, Pair 10/genetics; Family*; Genes/genetics; Genetic Association Studies*; Genome-Wide Association Study; Humans; Models, Genetic*; Phenotype; Polymorphism, Single Nucleotide/genetics*