Genotype Value Decomposition: Simple Methods for the Computation of Kernel Statistics

Abstract

Recent advances in sequencing technologies enable genome-wide analyses for thousands of individuals. The sequential kernel association test (SKAT) is a widely used method to test for associations between a phenotype and a set of rare variants. As the sample size of human genetics studies increases, the computational time required to calculate a kernel is becoming more and more problematic. In this study, a new method to obtain kernel statistics without calculating a kernel matrix is proposed. A simple method for the computation of two kernel statistics, namely, a kernel statistic based on a genetic relationship matrix (GRM) and one based on an identity by state (IBS) matrix, are proposed. By using this method, calculation of the kernel statistics can be conducted using vector calculation without matrix calculation. The proposed method enables one to conduct SKAT for large samples of human genetics.