地理权重回归在人类群体空间遗传结构中的应用  

Application of the geographically weighted regression model in spatial genetic structure of the human population

作　　者：李骁[1] 薛付忠[1] 

机构地区：[1]山东大学公共卫生学院流行病与卫生统计学研究所,济南250012

出　　处：《山东大学学报（医学版）》2011年第2期119-124,共6页Journal of Shandong University:Health Sciences

基　　金：国家自然科学基金资助项目(30170527)

摘　　要：目的探讨地理权重回归(GWR)模型在分析人类群体空间遗传结构影响因素中的应用。方法利用全球血管紧张素转化酶(ACE)基因数据资料和气候监测数据,在因子分析和Kriging空间插值的基础上分别构建ACE基因D等位基因基因频率与气候因子之间的多元线性回归模型(全局模型)和GWR模型(局部模型),探讨基因频率与气候因子间的空间关系。结果经证实性因子分析共提取了2个气候因子。全局模型显示,2个气候潜因子均与基因频率有关(P<0.01);GWR局部模型显示R2及参数估计值具有明显的空间变异性,其拟合效果优于全局模型。结论 GWR模型能够准确刻画人类群体空间遗传结构与气候因子间的空间变化性,较全局模型具有明显的优越性。Objective：To explore the application of the geographically weighted regression（GWR） model in analyzing influencing factors of human population genetic structure.Methods：Using global ACE gene data and climate surveillance data,based on latent variable analysis and spatial statistical methods,synthesis factors from the climate variables were extracted by confirmatory factor analysis.Then spatial distribution of the ACE gene D allele frequency and the climate synthesis factors were estimated with the Kriging interpolation method.Finally the multiple linear regression model（global model） and GWR model（local model） were constructed to explore the relationship between the D allele frequency and the climate synthesis factors,respectively.Results：Two latent synthesis factors were extracted by confirmatory factor analysis.The multiple linear regression model showed that the two synthesis factors were both statistically related with D allele frequency（P0.01）.The local R2 and parameter estimation of each spatial unit of the GWR model displayed significant spatial variability,and its fitting effect was more desirable compared with the global model.Conclusion：The GWR model is more accurate in describing the spatial varying relationship between human population genetic structure and climate factors,and it is obviously superior to the global model.

关 键 词：空间遗传学 地理权重回归 空间自相关性 空间异质性 

分 类 号：R195.1[医药卫生—卫生统计学]