文章摘要
周慧,丁一,武鸣,范伟,俞浩,周正元,顾淑君,张丽君,董晨,郭志荣.过氧化物酶体增殖物激活受体多态性及基因-基因交互作用对脉压的影响[J].中华流行病学杂志,2017,38(10):1404-1409
过氧化物酶体增殖物激活受体多态性及基因-基因交互作用对脉压的影响
Association and effects of gene-gene interactions between peroxisome proliferator-activated receptor and pulse pressure
收稿日期:2017-02-06  出版日期:2017-10-23
DOI:10.3760/cma.j.issn.0254-6450.2017.10.022
中文关键词: 脉压差;过氧化物酶体增殖物激活受体;广义多因子降维法;交互作用
英文关键词: Pulse pressure;Peroxisome proliferator-activated receptors;Generalized multifactor dimensionality reduction;Interaction
基金项目:国家自然科学基金(81502869);苏州市"科教兴卫"青年科技项目(KJXW2014060,KJXW2015063)
作者单位E-mail
周慧 215021 苏州市工业园区疾病防治中心  
丁一 215021 苏州市工业园区疾病防治中心  
武鸣 210009 南京, 江苏省疾病预防控制中心慢病所  
范伟 215123 苏州大学医学部公共卫生学院流行病与卫生统计教研室  
俞浩 210009 南京, 江苏省疾病预防控制中心慢病所  
周正元 215500 江苏省常熟市疾病预防控制中心慢病科  
顾淑君 215500 江苏省常熟市疾病预防控制中心慢病科  
张丽君 215123 苏州大学医学部公共卫生学院流行病与卫生统计教研室  
董晨 215123 苏州大学医学部公共卫生学院流行病与卫生统计教研室  
郭志荣 215123 苏州大学医学部公共卫生学院流行病与卫生统计教研室 guozhirong28@163.com 
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中文摘要:
      目的 探讨过氧化物酶体增殖物激活受体(PPAR)单核苷酸多态性(SNP)以及基因-基因交互作用对脉压差的影响。方法 基于江苏省代谢综合征和多代谢异常综合防治研究(PMMJS)队列,采用单纯随机抽样方法随机抽取其中研究对象820例,选取3个PPARα、2个PPARδ和5个PPARγ的SNP位点并进行检测,运用广义多因子降维法(GMDR)模型检测10个SNP的基因-基因交互作用与高血压的关联。结果 PPARγ的rs1805192的突变基因型(PA+AA)携带者与野生型(PP)相比,脉压差水平显著变化(1.341 mmHg,95% CI:0.431~2.252 mmHg)。GMDR模型分析显示,在脉压差≥ 30 mmHg的亚组中,PPARα基因的rs135539、PPARδ的rs2016520、PPARγ的rs10865710、rs1805192、rs709158、rs3856806组成的六阶模型平均检验准确度为0.577,交叉验证一致性为10/10,为最优模型。而在脉压差≤ 40 mmHg的亚组中,二阶交互作用模型与脉压差显著相关,平均检验准确度为0.628,交叉验证一致性为10/10。结论 PPARγ的rs1805192多态性与脉压差水平有关联,PPARα基因的rs135539、PPARδ的rs2016520和PPARγ的rs10865710、rs1805192、rs709158、rs3856806六个SNP间基因-基因交互作用与脉压差间具有显著性关联。
英文摘要:
      Objective To investigate the association between ten single nucleotide polymorphisms (SNPs) in the peroxisome proliferator-activated receptors and pulse pressure (PP) as well as the relationships between gene-gene interaction between PPARα/δ/γ genes and PP. Methods A total of 820 subjects, with 550 females and 270 males, were recruited from a cohort study of "Prevention of Metabolic Syndrome and Multi-metabolic Disorders in Jiangsu Province of China Study (PMMJS)". Ten SNPs of PPARα/δ/γ genes were selected. GMDR software (version 1.0.1) was used to evaluate the gene-gene interactions among PPARs SNPs associated with PP. Results The mean levels of PP in people with mutant genotype of rs1805192 in PPARγ genes (PA+AA) showed a significant increase by 1.341 mmHg (95% CI:0.431-2.252 mmHg) when compared to the persons with wild genotype (PP). In the subgroup of subjects with more than 30 mmHg levels of PP, a six-locus model comprised rs135539 of PPARα, rs2016520 of PPARδ, rs10865710, rs1805192, rs709158 and rs3856806 of PPARγ showed a highest level of prediction accuracy (0.577) and displayed a better cross-validation consistency (10/10). In the subgroup of subjects with less than 40 mmHg levels of PP, a two-locus model was statistically associated with PP with 0.628 of prediction accuracy and 10/10 of cross-validation consistency. Conclusion PPARγ rs1805192 was associated with the occurrence of PP. Gene-gene interactions among rs135539 of PPARα, rs2016520 of PPARδ, rs10865710, rs1805192, rs709158 and rs3856806 of PPARγ were all significantly related to PP.
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