Motivation: At present, a number of correlation analysis methods between SNPs and ROIs have been devised to explore the pathogenic mechanism of Alzheimer's disease. However, some of the deficiencies inherent in these methods, including lack of statistical efficacy and biological meaning. This study aims at addressing issues: insufficient correlation by previous methods (relative high regression error) and the lack of biological meaning in association analysis.

Results: In this paper, a novel three-stage SNPs and ROIs correlation analysis framework is proposed. Firstly, clustering algorithm is applied to remove the potential linkage unbalanced structure of two SNPs. Then, the group sparse model is used to introduce prior information such as gene structure and linkage unbalanced structure to select feature SNPs. After the above steps, each SNP has a weight vector corresponding to each ROI, and the importance of SNPs can be judged according to the weights in the feature vector, and then the feature SNPs can be selected. Finally, for the selected feature SNPS, a support vector machine regression model is used to implement the prediction of the ROIs phenotype values. The experimental results under multiple performance measures show that the proposed method has better accuracy than other methods.

动机：目前，已经设计出许多SNP与ROI之间的相关性分析方法以探索阿尔茨海默氏病的致病机理。但是，这些方法固有的一些缺陷，包括缺乏统计功效和生物学意义。本研究旨在解决以下问题：以前的方法之间的相关性不足（相对较高的回归误差）以及关联分析中缺乏生物学意义。

结果：本文提出了一种新颖的三阶段单核苷酸多态性和投资回报率相关性分析框架。首先，采用聚类算法去除两个单核苷酸多态性潜在的连锁不平衡结构。然后，使用群体稀疏模型引入先验信息，例如基因结构和连锁不平衡结构，以选择特征SNP。经过上述步骤，每个SNP具有对应于每个ROI的权重向量，可以根据特征向量中的权重来判断SNP的重要性，然后可以选择特征SNP。最后，对于所选特征SNPS，支持向量机回归模型用于实现ROI表型值的预测。在多种性能指标下的实验结果表明，该方法具有比其他方法更好的精度。