Prioritization of risk genes for Alzheimer’s disease: an analysis framework using spatial and temporal gene expression data in the human brain based on support vector machine

Frontiers in Genetics

Genetics

Xixian Fang¹  Ying Yang¹  Congying Yang¹  Shiyu Wang¹  Tianxiao Zhang²  Xiang Wen³ 

[1] Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, China;Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, China;National Anti-Drug Laboratory Shaanxi Regional Center, Xi’an, China;Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Beijing, China;

关键词: Alzheimer’s disease;    risk gene prioritization;    gene expression patterns;    machine learning;    genome-wide association analyses;   

DOI  :  10.3389/fgene.2023.1190863

 received in 2023-05-02, accepted in 2023-09-26,  发布年份 2023

来源: Frontiers

【 摘 要 】

Background: Alzheimer’s disease (AD) is a complex disorder, and its risk is influenced by multiple genetic and environmental factors. In this study, an AD risk gene prediction framework based on spatial and temporal features of gene expression data (STGE) was proposed.Methods: We proposed an AD risk gene prediction framework based on spatial and temporal features of gene expression data. The gene expression data of providers of different tissues and ages were used as model features. Human genes were classified as AD risk or non-risk sets based on information extracted from relevant databases. Support vector machine (SVM) models were constructed to capture the expression patterns of genes believed to contribute to the risk of AD.Results: The recursive feature elimination (RFE) method was utilized for feature selection. Data for 64 tissue-age features were obtained before feature selection, and this number was reduced to 19 after RFE was performed. The SVM models were built and evaluated using 19 selected and full features. The area under curve (AUC) values for the SVM model based on 19 selected features (0.740 [0.690–0.790]) and full feature sets (0.730 [0.678–0.769]) were very similar. Fifteen genes predicted to be risk genes for AD with a probability greater than 90% were obtained.Conclusion: The newly proposed framework performed comparably to previous prediction methods based on protein-protein interaction (PPI) network properties. A list of 15 candidate genes for AD risk was also generated to provide data support for further studies on the genetic etiology of AD.

【 授权许可】

Unknown   
Copyright © 2023 Wang, Fang, Wen, Yang, Yang and Zhang.