Application of gene-based logistic kernel-machine regression model on studies related to the genome-wide association

VernacularTitle:核函数logistic回归模型在全基因组关联研究中的应用
Author: Hong-Mei WO ¹ ; Hong-Gang YI ; Hong-Xing PAN ; Shao-Wen TANG ; Yang ZHAO ; Feng CHEN
Author Information

1. 211166,南京医科大学公共卫生学院流行病与卫生统计学系
Keywords: Kernel function; Logistic regression; Genome-wide association study
From: Chinese Journal of Epidemiology 2013;34(6):633-636
CountryChina
Language:Chinese
Abstract: [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.