EXTENSIONS OF THE MDR METHOD

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 在遗传关联研究中，识别和表征增加常见复杂多因素疾病易感性的基因是一项具有挑战性的任务。Ritchie等人提出并实现了多因素降维(MDR)方法。(2001)确定与特定疾病相关的多位点基因型别和离散环境因素的组合。然而，最初的MDR方法基于病例和对照数量比率的简单比较，以一种特别的方式将多基因座基因组合分为高风险组和低风险组。当基因组合中病例和对照的数量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。我们发展了一种基于优势比的多因素降维(ORMDR)方法，该方法使用优势比作为一种新的疾病风险量化指标，不仅提供了优势比作为风险的定量衡量，而且还提供了多基因组合从最高风险到最低风险组的排序。此外，该方法为每个多位点组合的优势比提供了一个可信区间，这在判断其作为风险因素的重要性时非常有用。当高阶相互作用模型考虑多维因素时，列联表中可能存在许多稀疏或空单元格。目前，MDR分析中有四种方法可用于处理丢失的数据。第一种方法只使用没有丢失数据的完整观测，这可能会导致严重的数据丢失。第二种方法是将缺失值作为一个额外的基因类别来处理，但对结果的解释可能不清楚，结论可能具有误导性。此外，当病例组和对照组之间的缺失率不平衡时，它的性能很差。第三种方法是一种简单的归类方法，它将缺失的基因类型归结为最常见的基因，这也可能产生有偏见的结果。第四种方法是可用的，它使用给定基因座的所有可用数据来增加能量。在任何实际数据分析中，当存在缺失数据时，都不清楚应该使用哪种MDR方法。我们考虑一种新的EM归因法来更恰当地处理丢失的数据。通过仿真研究，比较了本文提出的EM归并方法与现有方法的性能。我们的结果表明，可用的方法和EM归因法在能量和精度方面比其他三种当前的方法表现得更好。

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.