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Abstract:
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The traditional statistical analysis of genome-wide association studies (GWAS) attempts to assess the association between a single nucleotide polymorphism (SNP) and a disease phenotype. Recently, kernel machine-based tests for association between a SNP set (e.g. SNPs in a gene) and the observed phenotype have been proposed to improve the power of capturing the association. We apply smoothing spline with a generalized F-test on testing the genetic effects with familial data, including main and interaction effects, and compare with two existing R packages, famSKAT and KMFAM, for family-based sequence kernel association testing. We use simulation studies to evaluate the performance by type I error rate and power of the test from difference sample sizes. The results show that the type I error rate is controlled and the power is higher than competing methods in detecting association from correlated individuals. The key advantage is that it allows for an unspecified function in the linear mixed model. We hope this will facilitate data analysis to identify novel genes that are associated with diseases.
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