To explore the gene-based principal component logistic regression model and its application in genome-wide association study.Using the simulated genome-wide single nucleotide polymorphism (SNPs) genotypes data,we proposed a practical statistical analysis strategy-'the principal component logistic regression model',based on the gene levels to assess the association between genetic variations and complex diseases.The simulation results showed that the P value of genes in related diseases was the smallest among the results from all the genes.The results of simulation indicated that not only it could reduce the degree of freedom through hypothesis testing but could also better understand the correlations between SNPs.The gene-based principal component logistic regression model seemed to have certain statistical power for testing the association between genetic genes and diseases in the genome-wide association studies.

[Introduction] To explore the gene-based logistic kemel-machine regression model and its application in genome-wide association study (GWAS).Using the simulated genome-wide singlenucleotide polymorphism (SNPs) genotypes data,we proposed a practical statistical analysis strategynamed ‘ 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.