EXTENSIONS OF THE MDR METHOD

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。 目前，MDR分析中有四种方法可用于处理缺失数据。第一种方法只使用没有缺失数据的完整观测，这可能导致严重的数据丢失。第二种方法是将缺失值视为额外的基因型类别，但结果的解释可能不清楚，结论可能会产生误导。此外，当病例组和对照组之间的缺失率不平衡时，它的表现很差。第三种方法是一种简单的插补方法，将缺失的基因型插补为最常见的基因型，这也可能产生偏倚结果。第四种方法，可用，使用给定位点的所有可用数据来增加功效。在任何真实的数据分析中，当存在缺失数据时，不清楚应该使用哪种MDR方法。我们考虑了一种新的EM Impute方法来更适当地处理缺失数据。通过仿真研究，我们比较了所提出的EM输入方法与当前方法的性能。我们的研究结果表明，可用和EM输入方法在功率和精度方面优于其他三种当前方法。

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. Currently, there are four approaches available in MDR analysis to handle missing data. The first approach uses only complete observations that have no missing data, which can cause a severe loss of data. The second approach is to treat missing values as an additional genotype category, but interpretation of the results may then be not clear and the conclusions may be misleading. Furthermore, it performs poorly when the missing rates are unbalanced between the case and control groups. The third approach is a simple imputation method that imputes missing genotypes as the most frequent genotype, which may also produce biased results. The fourth approach, Available, uses all data available for the given loci to increase power. In any real data analysis, it is not clear which MDR approach one should use when there are missing data. We consider a new EM Impute approach to handle missing data more appropriately. Through simulation studies, we compared the performance of the proposed EM Impute approach with the current approaches. Our results showed that Available and EM Impute approaches perform better than the three other current approaches in terms of power and precision.