基因－基因(环境)交互作用分析方法的比较
PDF下载 (936)袁 芳 1,刘盼盼 1,徐 进 1,费丽娟 1,郝玲妹 2,邱旭君 2,张莉娜 1*.基因－基因(环境)交互作用分析方法的比较[J].宁波大学学报(理工版),2012,25(04):115-119.DOI:
YUAN Fang 1,LIU Pan-pan 1,XU Jin 1,FEI Li-juan 1,HAO Ling-mei 2,QIU Xu-jun 2,ZHANG Li-na 1*.Analytical Methods Comparison for Gene-gene (Environment) Interactions[J].Journal of Ningbo University(Natural Science & Engineering Edition),2012,25(04):115-119.DOI:
| Title: | Analytical Methods Comparison for Gene-gene (Environment) Interactions |
| 作者: | 袁 芳 1, 刘盼盼 1, 徐 进 1, 费丽娟 1, 郝玲妹 2, 邱旭君 2, 张莉娜 1* |
| Author(s): | YUAN Fang 1, LIU Pan-pan 1, XU Jin 1, FEI Li-juan 1, HAO Ling-mei 2, QIU Xu-jun 2, ZHANG Li-na 1* |
| 关键词: | 叉生分析; Logistic回归; 多因子降维法; 交互作用 |
| Keywords: | crossover analysis; Logistic regression; multifactor dimensionality reduction; interaction |
| 分类号: | R181; R195.1 |
| 文献标识码: | A |
| 摘要: | 对复杂疾病病因研究中基因－基因(环境)交互作用的3种分析方法进行了比较, 剖析了它们的适用条件和优缺点. 结果表明: 叉生分析简单易行, 但只适用于分析单个遗传和单个环境因素的交互作用; Logistic回归易解释交互作用的流行病学意义且能很好地分析主效应, 但在分析高阶交互作用时存在局限性; 多因子降维法无需指定特定的遗传模式且对高维数据敏感, 但无法估计主效应. 鉴于这3种方法在分析交互作用时各有其优点, 三者联合应用于交互作用分析效果更佳. |
| Abstract: | Three methods of analyzing the gene-gene (environment) interactions are compared in the etiology research of complex diseases to analyze their applicability conditions and the advantages and disadvantages. It shows that crossover analysis is simple and easy to apply, but only applicable for analyzing interactions of single genetic factor and single environment factor. Logistic regression is straight-forward in explaining the epidemiological significance of interaction and performs well in analyzing the main effects, but still has limitations in analyzing higher order interactions. Multifactor dimensionality reduction renders a model-free method and is sensitive to high dimensional data, but it is short of accuracy in estimating the main effects. Considering the advantages identified with each of these three methods in analyzing interaction, the author makes some efforts in this paper to integrate these methods aiming at improving effectiveness interaction analysis. |
| 参考文献 /References: | [1] Thornton-Wells T A, Moore J H, Haines J L. Genetics, statistics and human disease: Analytical retooling for complexity[J]. Trends Genet, 2004, 20:640-647. [2] Moore J H. The ubiquitous nature of epistasis in determining susceptibility to common human diseases[J]. Hum Hered, 2003, 56:73-82. [3] Sing C F, Stengard J H, Kardia S L. Dynamic relationships between the genome and exposures to environments as causes of common human diseases[J]. World Rev Nutr Diet, 2004, 93:77-91. [4] Chen L, Yu G, Langefeld C D, et al. Comparative analysis of methods for detecting interacting loci[J]. BMC Genomics, 2011, 12:344. [5] Garcia Magarinos M, Lopez-de-Ullibarri I, Cao R, et al. Evaluating the ability of tree-based methods and logistic regression for the detection of SNP-SNP interaction[J]. Annals of Human Genetics, 2009, 73:360-369. [6] Ottman R. Gene-environment interaction: Definitions and study designs[J]. Prev Med, 1996, 25:764-770. [7] Botto L D, Khoury M J. Commentary: Facing the challenge of gene-environment interaction: The two-by- four table and beyond[J]. Am J Epidemiol, 2001, 153: 1016-1020. [8] Rothman K J, Greenland S, Lash T L. Modern epidemiology[M]. 3nd. Philadelphia: Lippincott Williams & Wilkins Publishers, 2008:71-85. [9] Khoury M J, Flanders W D. Nontraditional epidemiologic approaches in the analysis of gene-environment interaction: Case-control studies with no controls[J]. Am J Epidemiol, 1996, 144:207-213. [10] Knol M J, Van Der Tweel I, Grobbee D E, et al. Estimating interaction on an additive scale between continuous determinants in a logistic regression model[J]. International Journal of Epidemiology, 2007, 36:1111. [11] Hosmer D W, Lemeshow S. Applied logistic regression [M]. 2nd. Hoboken: John Wiley & Sons Publishers, 2000:260-263. [12] Park M Y, Hastie T. Penalized logistic regression for detecting gene interactions[J]. Biostatistics, 2008, 9:30. [13] Ritchie M D, Hahn L W, Roodi N, et al. Multifactor- dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer[J]. Am J Hum Genet, 2001, 69: 138-147. [14] Yang C, Wan X, He Z, et al. The choice of null distributions for detecting gene-gene interactions in genome-wide association studies[J]. BMC Bioinformatics, 2011, 12(Suppl):S26. |
| 备注/Memo: | 收稿日期: 2012?05?15. 基金项目: 浙江省教育厅重点科研项目（Z201017918）; 宁波市自然科学基金（2011A610037）. 第一作者: 袁 芳（1986－）, 女, 湖北黄冈人, 在读硕士研究生, 主要研究方向: 慢性病流行病学. E-mail: yuanfang40@163.com *通讯作者: 张莉娜（1963－）, 女, 甘肃兰州人, 副教授, 主要研究方向: 慢性病流行病学. E-mail: zhanglina@nbu.edu.cn 宁波大学学报（理工版）网址：http://journallg.nbu.edu.cn/ |
