基于基因水平主成分logistic回归模型在全基因组关联研究中的应用  

Gene-based principal component logistic regression model and its application on genome-wide association study

作　　者：易洪刚[1] 沃红梅[1] 赵杨[1] 张汝阳[1] 柏建岭[1] 魏永越[1] 陈峰[1] 

机构地区：[1]南京医科大学公共卫生学院流行病与卫生统计学系 ,210029

出　　处：《中华流行病学杂志》2012年第6期622-625,共4页Chinese Journal of Epidemiology

基　　金：基金项目：国家自然科学基金（81072389）;国家自然科学基金青年基金（30901232）;江苏省高校自然科学研究重大项目（10KJA330034）;高等学校博士学科点专项科研基金（20113234110002）;江苏高校优势学科建设工程项目

摘　　要：探讨基于基因水平的主成分logistic回归模型分析方法及其在全基因组关联研究中的应用。以全基因组关联研究基因型模拟数据为例，介绍基于主成分的logistic回归模型在基因水平检测遗传变异与复杂性疾病之间关联的分析策略。模拟结果表明致病位点所在基因假设检验的P值在所有基因检验结果中为最小。研究结果提示在全基因组关联研究中，采用基于基因水平的主成分logistic回归模型一方面能够降低检验的自由度，另一方面能够处理单核苷酸多态性之间相关性问题，在检测致病基因与疾病关联时具有一定的效能。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.

关 键 词：主成分分析 LOGISTIC回归模型 全基因组关联研究 关联 

分 类 号：R346[医药卫生—基础医学]