title: On testing an unspecified function through a linear mixed effects model with multiple variance components.
abstract: We examine a generalized F-test of a nonparametric function through penalized splines and a linear mixed effects model representation. With a mixed effects model representation of penalized splines, we imbed the test of an unspecified function into a test of some fixed effects and a variance component in a linear mixed effects model with nuisance variance components under the null. The procedure can be used to test a nonparametric function or varying-coefficient with clustered data, compare two spline functions, test the significance of an unspecified function in an additive model with multiple components, and test a row or a column effect in a two-way analysis of variance model. Through a spectral decomposition of the residual sum of squares, we provide a fast algorithm for computing the null distribution of the test, which significantly improves the computational efficiency over bootstrap. The spectral representation reveals a connection between the likelihood ratio test (LRT) in a multiple variance components model and a single component model. We examine our methods through simulations, where we show that the power of the generalized F-test may be higher than the LRT, depending on the hypothesis of interest and the true model under the alternative. We apply these methods to compute the genome-wide critical value and p-value of a genetic association test in a genome-wide association study (GWAS), where the usual bootstrap is computationally intensive (up to 10(8) simulations) and asymptotic approximation may be unreliable and conservative.
authors.affiliation: Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032, USA. yuanjia.wang@columbia.edu
authors.forename: Yuanjia
authors.initials: Y
authors.lastname: Wang
authors.forename: Huaihou
authors.initials: H
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D002318:Cardiovascular Diseases / Q000453:epidemiology* / Q000235:genetics*; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D056726:Genetic Association Studies / Q000379:methods; D020022:Genetic Predisposition to Disease / Q000453:epidemiology / Q000235:genetics; D006801:Humans; D015994:Incidence; D016014:Linear Models; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D018570:Risk Assessment / Q000379:methods