title: Kernel machine SNP-set testing under multiple candidate kernels.
abstract: Joint testing for the cumulative effect of multiple single-nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large-scale genetic association studies. The kernel machine (KM)-testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori because this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest P-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power vs. using the best candidate kernel.
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7420, USA. mwu@bios.unc.edu
authors.forename: Michael C
authors.initials: MC
authors.lastname: Wu
authors.forename: Arnab
authors.initials: A
authors.lastname: Maity
authors.forename: Seunggeun
authors.initials: S
authors.lastname: Lee
authors.forename: Elizabeth M
authors.initials: EM
authors.lastname: Simmons
authors.forename: Quaker E
authors.initials: QE
authors.lastname: Harmon
authors.forename: Xinyi
authors.initials: X
authors.lastname: Lin
authors.forename: Stephanie M
authors.initials: SM
authors.lastname: Engel
authors.forename: Jeffrey J
authors.initials: JJ
authors.lastname: Molldrem
authors.forename: Paul M
authors.initials: PM
authors.lastname: Armistead
mesh_terms: D003198:Computer Simulation; D005260:Female; D056726:Genetic Association Studies; D020022:Genetic Predisposition to Disease; D006801:Humans; D007231:Infant, Newborn; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D011247:Pregnancy; D047928:Premature Birth / Q000235:genetics*; D012984:Software