EXTENSIONS OF THE MDR METHOD

MDR 方法的扩展

• 批准号: 7723447
• 负责人: Taeshin Park
• 金额: $ 0.45万
• 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/7723447
• 关键词: Alzheimer' s Disease; Base Ratios; Cells; Complex; Computer Retrieval of Information on Scientific Projects Database; Confidence Intervals; Data; Disease; Environmental Risk Factor; Frequencies; Funding; Genes; Genotype; Grant; Institution; Left; Log-Linear Models; Measures; Methods; Modeling; Numbers; Odds Ratio; Predisposition; Rate; Research; Research Personnel; Resources; Risk; Risk Factors; Source; United States National Institutes of Health; base; case control; disorder risk; genetic association; 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 genes that increase the susceptibility to common complex multifactorial diseases is a challenging task in genetic association studies. The multifactor dimensionality reduction (MDR) method has been proposed and implemented by Ritchie et al. (2001) to identify the combinations of multilocus genotypes and discrete environmental factors that are associated with a particular disease. However, the original MDR method classifies the combination of multilocus genotypes into high-risk and low-risk groups in an ad hoc manner based on a simple comparison of the ratios of the number of case and controls. This method is prone to false positive and negative errors when the ratio of the number of cases and controls in a combination of genotypes is similar to that in the entire data, or when both the number of cases and controls is small. We developed an odds ratio based multifactor dimensionality reduction(OR MDR) method that uses the odds ratio as a new quantitative measure of disease risk, providing not only the odds ratio as a quantitative measure of risk, but also the ordering of the multilocus combinations from the highest risk to lowest risk groups. Furthermore, this method provides a confidence interval for the odds ratio for each multilocus combination, which is extremely informative in judging its importance as a risk factor. When a high-order interaction model is considered with multi-dimensional factors, 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. We further 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。 识别和表征增加常见复杂多因素疾病易感性的基因是遗传关联研究中的一项具有挑战性的任务。 里奇等人（2001）提出并实施了多因素降维（MDR）方法，以识别与特定疾病相关的多位点基因型和离散环境因素的组合。 然而，原始MDR方法基于病例和对照数量的比率的简单比较，以特设方式将多位点基因型的组合分类为高风险组和低风险组。当基因型组合中病例和对照的数量比例与整个数据中的比例相似时，或者当病例和对照的数量都很小时，这种方法容易出现假阳性和阴性错误。 我们开发了一种基于比值比的多因素降维（OR MDR）方法，该方法使用比值比作为疾病风险的新的定量度量，不仅提供了比值比作为风险的定量度量，而且还提供了从最高风险组到最低风险组的多位点组合的排序。 此外，该方法为每个多位点组合的比值比提供了置信区间，这在判断其作为危险因素的重要性方面是非常有用的。当考虑具有多维因子的高阶交互作用模型时，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。我们进一步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。