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SNP Set Association Analysis for Familial Data Running Title: SNP Set Analysis for Familial Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Schifano, Elizabeth D. Epstein, Michael P. Bielak, Lawrence F. Jhun, Min A. Kardia, Sharon L. R. Peyser, Patricia A. Lin, Xihong |
| Copyright Year | 2012 |
| 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://content.sph.harvard.edu/xlin/pub/Schifano_GE_main.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
