SNP Set Association Analysis for Familial Data

• Type of Material: Article
• Authors: Elizabeth D. Schifano, Harvard University; Michael Epstein, Emory University; Lawrence F. Bielak, University of Michigan; Min A. Jhun, University of Michigan; Sharon L. R. Kardia, University of Michigan; Patricia A. Peyser, University of Michigan; Xihong Lin, Harvard University
• Language: English
• Date: 2012-12-01
• Publisher: Wiley: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2012 Wiley Periodicals, Inc.
• Final Published Version (URL): http://dx.doi.org/10.1002/gepi.21676
• Title of Journal or Parent Work: Genetic Epidemiology
• ISSN: 0741-0395
• Volume: 36
• Issue: 8
• Start Page: 797
• End Page: 810
• Grant/Funding Information: This work was supported by the National Institutes of Health [T32 ES007142 and T32 ES016645 to EDS, R01 HG003618 to MPE, R01 HL87660 to SLRK, R37 CA076404 and P01 CA134294 to XL].
• Keywords: family association studies; MIXED MODELS; Science & Technology; LINKAGE; LOGISTIC-REGRESSION; variance component test; linear mixed model; GENETIC PATHWAY; QUANTITATIVE TRAITS; MULTIPLE-TESTING CORRECTION; SCORE TESTS; multilocus test; within-family correlation; GENOME-WIDE ASSOCIATION; Life Sciences & Biomedicine; TRAIT SIMILARITY REGRESSION; DISTANCE-BASED REGRESSION; Mathematical & Computational Biology; score statistics; Genetics & Heredity; kernel machine
• Research Categories: Biology, Genetics; Biology, Biostatistics; Health Sciences, Epidemiology

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.