title: [Application of gene-based logistic kernel-machine regression model on studies related to the genome-wide association].
abstract: To explore the gene-based logistic kernel-machine regression model and its application in genome-wide association study(GWAS). Using the simulated genome-wide single-nucleotide polymorphism(SNPs)genotypes data, we proposed a practical statistical analysis strategy-named 'the logistic kernel-machine regression model', based on the gene levels to assess the association between genetic variations and complex diseases. The results from simulation showed that the P value of genes in related diseases was the smallest among all the genes. The results of simulation indicated that not only it could borrow information from different SNPs that were grouped in genes and reducing the degree of freedom through hypothesis testing, but could also incorporate the covariate effects and the complex SNPs interactions. The gene-based logistic kernel-machine regression model seemed to have certain statistical power for testing the association between genetic genes and diseases in GWAS.
authors.affiliation: Department of Epidemiology and Biostatistics,School of Public Health, Nanjing Medical University, Nanjing 211166, China.
authors.forename: Hong-mei
authors.initials: HM
authors.lastname: Wo
authors.forename: Hong-gang
authors.initials: HG
authors.lastname: Yi
authors.forename: Hong-xing
authors.initials: HX
authors.lastname: Pan
authors.forename: Shao-wen
authors.initials: SW
authors.lastname: Tang
authors.forename: Yang
authors.initials: Y
authors.lastname: Zhao
authors.forename: Feng
authors.initials: F
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D016015:Logistic Models; D008957:Models, Genetic; D012984:Software