A Novel Three-Stage Framework for Association Analysis Between SNPs and Brain Regions - Zhou, Juan; Qiu, Yangping; Chen, Shuo; Liu, Liyue; Liao, Huifa; Chen, Hongli; Lv, Shanguo; Li, Xiong (Frontiers in Genetics, 2020)
Motivation: At present, some methods of correlation analysis between SNP and ROI have been widely used to explore Alzheimer's disease. However, most of these methods are based on monomorphic image data, which usually only reflect part of the information related to brain abnormalities from a certain side, and lack statistical efficacy. This study aims at addressing issues: insufficient correlation by previous methods (the quantitative indication of characteristic SNP to brain region is difficult to predict) and the lack of biological meaning in association analysis. Results: In this paper, a novel three-stage SNP and ROI 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 implementation of this stage, each SNP has a weight vector corresponding to each ROI, and the importance of SNP can be judged according to the weight in the feature vector, and then the feature SNP can be selected. Finally, a support vector regression model is used to analyze the degree of correlation between feature SNP and ROI. From the perspective of multiple performance measures, this method shows better accuracy than other methods.