title: Gene- or region-based association study via kernel principal component analysis.
abstract: BACKGROUND
  In genetic association study, especially in GWAS, gene- or region-based methods have been more popular to detect the association between multiple SNPs and diseases (or traits). Kernel principal component analysis combined with logistic regression test (KPCA-LRT) has been successfully used in classifying gene expression data. Nevertheless, the purpose of association study is to detect the correlation between genetic variations and disease rather than to classify the sample, and the genomic data is categorical rather than numerical. Recently, although the kernel-based logistic regression model in association study has been proposed by projecting the nonlinear original SNPs data into a linear feature space, it is still impacted by multicolinearity between the projections, which may lead to loss of power. We, therefore, proposed a KPCA-LRT model to avoid the multicolinearity.
  
  
  RESULTS
  Simulation results showed that KPCA-LRT was always more powerful than principal component analysis combined with logistic regression test (PCA-LRT) at different sample sizes, different significant levels and different relative risks, especially at the genewide level (1E-5) and lower relative risks (RR = 1.2, 1.3). Application to the four gene regions of rheumatoid arthritis (RA) data from Genetic Analysis Workshop16 (GAW16) indicated that KPCA-LRT had better performance than single-locus test and PCA-LRT.
  
  
  CONCLUSIONS
  KPCA-LRT is a valid and powerful gene- or region-based method for the analysis of GWAS data set, especially under lower relative risks and lower significant levels.
authors.affiliation: Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan 250012, China.
authors.forename: Qingsong
authors.initials: Q
authors.lastname: Gao
authors.forename: Yungang
authors.initials: Y
authors.lastname: He
authors.forename: Zhongshang
authors.initials: Z
authors.lastname: Yuan
authors.forename: Jinghua
authors.initials: J
authors.lastname: Zhao
authors.forename: Bingbing
authors.initials: B
authors.lastname: Zhang
authors.forename: Fuzhong
authors.initials: F
authors.lastname: Xue
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D056726:Genetic Association Studies; D020022:Genetic Predisposition to Disease; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis