LOG-LINEAR MODEL-BASED MULTIFACTOR DIMENSIONALITY

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，识别和表征影响常见和复杂疾病风险的易感基因仍然是一个统计和计算挑战。这在一定程度上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，Ritchie等人提出了多因素降维(MDR)方法。检测基因与基因的相互作用或基因与环境的相互作用。MDR方法通过将高维遗传因素压缩成一个维度来识别与常见和复杂的多因素疾病相关的多态组合。也就是说，MDR方法根据病例和对照数量比率的比较，将多基因座基因型组合分为高风险组和低风险组。然而，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。MDR方法不能将空单元格归类为高风险或低风险，并将其保留为未确定。结果：本文提出了一种基于对数线性模型的多因素降维方法来提高稀疏或空单元格的降维效果。LM MDR方法从简约的对数线性模型估计空单元的频率，以便将它们分配到高风险组和低风险组。此外，当饱和对数线性模型被拟合时，LM MDR将MDR作为特例包括在内。仿真研究表明，与MDR方法相比，LM MDR方法具有更高的功率和更小的误码率。以散发性阿尔茨海默病为例，将LM MDR方法与MDR方法进行了比较。

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.