期刊文献+

应用多因子降维法MDR分析基因-基因的交互作用 被引量:6

The application of multifactor dimensionality reduction for detecting gene-gene interactions
下载PDF
导出
摘要 目的介绍在遗传流行病学病例对照研究中,应用多因子降维法(MDR)分析基因-基因交互作用。方法简述MDR的基本步骤、原理及其优缺点,并结合研究实例说明在病例对照研究中进行MDR分析。结果相对于传统的统计学方法 ,MDR是一种无参数、无遗传模式的分析交互作用的方法 ,理论和实例研究均表明其分析交互作用具有较好的效能,目前已成功应用于散发性乳腺癌、心房颤动和原发性高血压等疾病的研究。结论 MDR能够应用于病例对照研究进行基因-基因交互作用的分析,且具有较传统的统计学分析方法无法比拟的优势。 Objective To introduce the application of Multifactor Dimensionality Reduction (MDR) method for detecting gene-gene interactions in genetic case-control studies.Methods A brief overview on basic steps involved in the implementation,theoretical details,as well as the use and features of the MDR method were discussed based on a practical research case.Results Advantages of MDR were compared to the conventional statistical approaches,showing that MDR method was a novel,nonparametric,genetic model-free approach that was developed specifically for detecting gene-gene interactions.Theoretical and empirical studies suggested that MDR was having reasonable power for detecting gene-gene interactions.Applications of MDR method had found the evidence of gene-gene interactions in several diseases such as sporadic breast cance,atrial fibrillation and essential hypertension.Conclusion MDR method could be used for detecting gene-gene interactions in genetic case-control studies as having great advantages versus the conventional statistical approaches.
出处 《中国老年保健医学》 2012年第6期31-34,共4页 Chinese Journal of Geriatric Care
基金 国家自然科学基金项目(编号:30972709 81061120527)资助
关键词 病例对照研究 多因子降维法 基因 - 基因交互作用 Case-control study Multifactor dimensionality reduction Gene-gene interactions
  • 相关文献

参考文献10

  • 1Ritchie MD, Hahn LW, Roodi N, et al. Muhifactnr-dimensionality re- duction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer [ J ]. Am J Hum Genet, 2001,69: 138 - 147.
  • 2Nelson MR, Kardia SL, Ferrell RE, et al. A combinatorial partitioning method to identify multi locus genotypic partions that predict quantita- tive trait variation [ J ]. Genome Res ,2001,11:458 -470.
  • 3HAHN, L, RITCHIE, M. AND MOORE, J. Muitifactor dimensionality ~duction software for detecting gene-gene and gene-environment inter- action [ J]. Bi@fformatics,2003 ,19 :376 - 382.
  • 4Moore, J. H. and William, S. M. New strategies for identifying gene-gene interactions in hypertension [ J ]. Ann. Med,2002,34 : 88 - 95.
  • 5Moore, J. H. et al. Symbolic discriminate analysis of microarray data in autoimmune disease [ J]. Genet. Epidemi01, 2002,23:57 - 69.
  • 6MEE YOUNG PARK. Penalized logistic regression for detecting gene interactions [ J ]. Biostatistics ,2008,9 ( 1 ) :30 - 50.
  • 7Moore JH. Computational analysis of gene-gene interactions using multi- factor dimensionality reduction [ J ]. Expert Rev Mol Deign, 2004, 4 : 795 - 803.
  • 8Coffey CS, Hebert PR, Ritchie MD, et al. An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene interactions on risk of myocardial infarction : the importance of model validation [ J ]. BMC Bioinformatics,2004,5:49.
  • 9Ritchie MD. Hahn LW. Moore JH. Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of geno- typing error, missing data, photocopy, and genetic heterogeneity [ J ]. Genet Epidemiology ,2003,24 : 150 - 157.
  • 10唐迅,李娜,胡永华.应用多因子降维法分析基因-基因交互作用[J].中华流行病学杂志,2006,27(5):437-441. 被引量:30

二级参考文献12

  • 1Moore JH.The ubiquitous nature of epistasis in determining susceptibility to common human diseases.Hum Hered,2003,56:73-82.
  • 2Moore JH,Ritchie MD.The challenges of whole-genome approaches to common diseases.JAMA,2004,291:1642-1643.
  • 3Ritchie MD,Hahn LW,Roodi N,et al.Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer.Am J Hum Genet,2001,69:138-147.
  • 4Hahn LW,Ritchie MD,Moore JH.Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions.Bioinformatics,2003,19:376-382.
  • 5Nelson MR,Kardia SL,Ferrell RE,et al.A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation.Genome Res,2001,11:458-470.
  • 6Coffey CS,Hebert PR,Ritchie MD,et al.An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene interactions on risk of myocardial infarction:the importance of model validation.BMC Bioinformatics,2004,5:49.
  • 7Moore JH.Computational analysis of gene-gene interactions using multifactor dimensionality reduction.Expert Rev Mol Diagn,2004,4:795-803.
  • 8Robnik-Sikonja M,Kononenko I.Theoretical and empirical analysis of Relief F and RRelief F.Mach Lear J,2003,53:23-69.
  • 9Tsai CT,Lai LP,Lin JL,et al.Renin-angiotensin system gene polymorphisms and atrial fibrillation.Circulation,2004,109:1640-1646.
  • 10Hahn LW,Moore JH.Ideal discrimination of discrete clinical endpoints using multilocus genotypes.In Silico Biol,2004,4:183-194.

共引文献29

同被引文献57

引证文献6

二级引证文献12

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部