应用BP人工神经网络探讨PPAR-γ和RXR-α基因多态性与汉族人群2型糖尿病遗传易感性的关系  

Application study of BPANN on genetic variants in peroxisome proliferators activated receptor-γ (PPAR-γ) and retinoid X receptor-α (RXR-α) gene and type 2 diabetes risk in a Chinese Han population

作　　者：陆莹[1] 杜文聪[1] 李倩[2] 叶新华[3] 俞晓芳[4] 马建华[3] 成金罗[3] 高燕勤[4] 杜娟[1] 石慧[1] 曹园园[1] 周玲[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,江苏南京210029 [2]南京医科大学附属南京市第一医院内分泌科,江苏南京210006 [3]南京医科大学附属常州第二人民医院内分泌科,江苏常州213003 [4]南京医科大学第三附属医院内分泌科,江苏仪征211400

出　　处：《南京医科大学学报（自然科学版）》2011年第1期1-7,共7页Journal of Nanjing Medical University(Natural Sciences)

基　　金：国家自然科学基金(30771858);江苏省自然科学基金(BK2007229)资助

摘　　要：目的:探讨BP人工神经网络(BPANN)在研究过氧化物酶体增殖物激活受体γ(PPAR-γ)和视黄醛α受体(RXR-α)基因单核苷酸多态性(SNP)位点与中国南方地区汉族人群2型糖尿病(T2DM)易感性关系中的应用特点。方法:采用BPANN分析方法,对591例2型糖尿病患者和724例正常对照者的基因多态性位点的分型结果、血清脂联素水平以及其他所有可能的影响因素按照平均影响值(MIV)的绝对值大小排序,并与Logistic回归模型的分析结果相比较,用多因子降维法(MDR)分析基因间的交互作用。结果:BPANN多因素分析中,2型糖尿病危险因子的顺位为血清脂联素浓度、高血压史、腰围、rs4240711、rs3132291、rs3856806、2型糖尿病家族史、饮酒、高脂血症史、吸烟、年龄、BMI指数、rs1045570、性别、rs2920502、rs6537944、rs4842194、rs17827276、rs1801282;而多因素Logistic回归分析中只有8个变量入选最终模型,因子顺位为高血压史、T2DM家族史、腰围、饮酒、吸烟、rs4240711、rs4842194、血清脂联素浓度;多因子降维法(MDR)分析结果显示模型X1X2X3(rs3856806,rs3132291,rs4240711)为最佳模型(交叉验证一致性10/10,P=0.0107)。结论:PPAR-γ和RXR-α基因多态性改变的交互作用对于中国南方汉族T2DM遗传易感性可能具有一定的作用。BPANN用于筛选T2DM等复杂多病因疾病的影响因素,可能提供更切合实际情况的模型。Objective： To explore the applied characteristics of back-propagation artificial neural network（BPANN） on studying the genetic variants in PPAR-γ and RXR-α gene and type 2 diabetes risk in a Chinese Han population.Methods： With BPANN as fitting model based upon data gathered from type 2 diabetes patients（n=591） and normal controls（n=724）,the mean impact value（MIV） for each input variables and sequencing the factors according to their absolute MIVs were calculated.The results from BPANN were compared with multiple logistic regression analysis,and multifactor dimensionality reduction（MDR） method was used to consid-er the joint effects of PPAR-γ and RXR-α gene.Results：By BPANN analysis,the risk factors of diabetes mellitus were serum adiponectin level,hypertension,waist,rs4240711,rs3132291,rs3856806,diabetes mellitus family history,no alcohol drinking hy-perlipoproteinmia,smoking,age,body mass index,rs1045570,gender,rs2920502,rs6537944,rs4842194,rs17827276 and rs1801282.However,only 8 factors were statistically significant in multiple logistic regression analy-sis,arrayed according to the important valne： hypertension,diabetes mellitus family history,waist,no alcohol drinkiy,smoking,rs4240711,rs4842194 and serum adiponectin level.Model X1 X2 X3（rs3856806,rs3132291,rs4240711） was the best model（CV Consistency=10 / 10,P=0.0107） with MDR method.Conclusion： These re-sults suggested that the interactions of PPAR-γ and RXR-α gene might have important role in the susceptibility of T2DM.Neural net-work could be used to analyze the risk factors of diseases and more complicated relationships（main effects and interactions） betweeninputs and outputs,better than using the traditional methods.

关 键 词：BP神经网络 2型糖尿病 PPAR-γ基因 RXR-α基因 脂联素 

分 类 号：Q349.17[生物学—遗传学]