Paper Info
Title
A generalized association test based on U statistics.
Abstract
Motivation: 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 phenotypes. 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. Results: 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's disease neuroimaging initiative data, identifying three genes, APOE, APOC1 and TOMM40, associated with imaging phenotype.
Year
DOI
Venue
2017
10.1093/bioinformatics/btx103
BIOINFORMATICS
Field
DocType
Volume
Kernel (linear algebra),Data mining,Multivariate statistics,Source code,Computer science,Robustness (computer science),Genetic association,Whole genome sequencing,Statistics,Univariate,Generalized estimating equation
Journal
33
Issue
ISSN
Citations 
13
1367-4803
0
PageRank 
References 
Authors
0.34
1
2
Name
Order
Citations
PageRank
Changshuai Wei100.68
Qing Lu211.70