LOG-LINEAR MODEL-BASED MULTIFACTOR DIMENSIONALITY

基于对数线性模型的多因子维度

• 批准号: 7723462
• 负责人: SE-JIN LEE
• 金额: $ 0.91万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7723462
• 关键词: Address; Alzheimer' s Disease; Cells; Complex; Computer Retrieval of Information on Scientific Projects Database; Dimensions; Disease; Environmental Risk Factor; Frequencies; Funding; Genes; Genetic; Genetic Polymorphism; Genotype; Grant; Institution; Left; Log-Linear Models; Methods; Modeling; Numbers; Rate; Research; Research Personnel; Resources; Risk; Source; Susceptibility Gene; United States National Institutes of Health; base; case control; gene environment interaction; gene interaction; genetic association; genetic variant; improved; simulation

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The identification and characterization of susceptibility genes that influence the risk of common and complex diseases remains a statistical and computational challenge in genetic association studies. This is partly because the effect of any single genetic variant for a common and complex disease may be dependent on other genetic variants (gene-gene interaction) and environmental factors (gene-environment interaction). To address this problem, the multifactor dimensionality reduction (MDR) method has been proposed by Ritchie et al. to detect gene-gene interactions or gene-environment interactions. The MDR method identifies polymorphism combinations associated with the common and complex multifactorial diseases by collapsing high-dimensional genetic factors into a single dimension. That is, the MDR method classifies the combination of multilocus genotypes into high-risk and low-risk groups based on a comparison of the ratios of the numbers of cases and controls. When a high-order interaction model is considered with multi-dimensional factors, however, there may be many sparse or empty cells in the contingency tables. The MDR method cannot classify an empty cell as high risk or low risk and leaves it as undetermined. RESULTS: In this article, we propose the log-linear model-based multifactor dimensionality reduction (LM MDR) method to improve the MDR in classifying sparse or empty cells. The LM MDR method estimates frequencies for empty cells from a parsimonious log-linear model so that they can be assigned to high-and low-risk groups. In addition, LM MDR includes MDR as a special case when the saturated log-linear model is fitted. Simulation studies show that the LM MDR method has greater power and smaller error rates than the MDR method. The LM MDR method is also compared with the MDR method using as an example sporadic Alzheimer's disease.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 影响常见和复杂疾病风险的易感基因的识别和表征仍然是遗传关联研究中的统计和计算挑战。这部分是因为任何单一遗传变异对常见和复杂疾病的影响可能取决于其他遗传变异（基因-基因相互作用）和环境因素（基因-环境相互作用）。为了解决这个问题，里奇等人提出了多因素降维（MDR）方法来检测基因-基因相互作用或基因-环境相互作用。MDR方法通过将高维遗传因素折叠成一维来识别与常见和复杂的多因素疾病相关的多态性组合。也就是说，MDR方法基于病例数和对照数的比率的比较，将多位点基因型的组合分为高风险组和低风险组。然而，当考虑具有多维因子的高阶相互作用模型时，列联表中可能存在许多稀疏或空单元。MDR方法不能将空单元格分类为高风险或低风险，并将其保留为未确定。研究结果：在这篇文章中，我们提出了基于对数线性模型的多因素降维（LM MDR）方法，以改善MDR在分类稀疏或空细胞。LM MDR方法从一个简约的对数线性模型中估计空细胞的频率，以便将它们分配到高风险组和低风险组。此外，LM MDR包括MDR作为饱和对数线性模型拟合时的特殊情况。仿真研究表明，LM MDR方法比MDR方法具有更大的功率和更小的错误率。LM MDR方法也比较MDR方法作为一个例子，散发性阿尔茨海默氏病。