Title: Generalized Similarity U: A Non-parametric Test of Association Based on Similarity,
Abstract: Second generation sequencing technologies are being increasingly used for
genetic association studies, where the main research interest is to identify
sets of genetic variants that contribute to various phenotype. The phenotype
can be univariate disease status, multivariate responses and even
high-dimensional outcomes. Considering the genotype and phenotype as two
complex objects, this also poses a general statistical problem of testing
association between complex objects. We here proposed a similarity-based test,
generalized similarity U (GSU), that can test the association between complex
objects. We first studied the theoretical properties of the test in a general
setting and then focused on the application of the test to sequencing
association studies. Based on theoretical analysis, we proposed to use
Laplacian kernel based similarity for GSU to boost power and enhance
robustness. Through simulation, we found that GSU did have advantages over
existing methods in terms of power and robustness. We further performed a whole
genome sequencing (WGS) scan for Alzherimer Disease Neuroimaging Initiative
(ADNI) data, identifying three genes, APOE, APOC1 and TOMM40, associated with
imaging phenotype. We developed a C++ package for analysis of whole genome
sequencing data using GSU. The source codes can be downloaded at
this https URL.