DNA methylation signature of psychological resilience in young adults: Constructing a methylation risk score using a machine learning method

• Published in: Frontiers in genetics Vol. 13; p. 1046700
• Main Authors: Lu, Andrew Ke-Ming, Hsieh, Shulan, Yang, Cheng-Ta, Wang, Xin-Yu, Lin, Sheng-Hsiang
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 12.01.2023
• Subjects: DNA methylation; epigenetics; Genetics; methylation risk score; psychological resilience; psychology; DNA methylation; psychology; methylation risk score; epigenetics; psychological resilience
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2022.1046700

Resilience is a process associated with the ability to recover from stress and adversity. We aimed to explore the resilience-associated DNA methylation signatures and evaluate the abilities of methylation risk scores to discriminate low resilience (LR) individuals. The study recruited 78 young adults and used Connor-Davidson Resilience Scale (CD-RISC) to divide them into low and high resilience groups. We randomly allocated all participants of two groups to the discovery and validation sets. We used the blood DNA of the subjects to conduct a genome-wide methylation scan and identify the significant methylation differences of CpG Sites in the discovery set. Moreover, the classification accuracy of the DNA methylation probes was confirmed in the validation set by real-time quantitative methylation-specific polymerase chain reaction. In the genome-wide methylation profiling between LR and HR individuals, seventeen significantly differentially methylated probes were detected. In the validation set, nine DNA methylation signatures within gene coding regions were selected for verification. Finally, three methylation probes [cg18565204 (AARS), cg17682313 (FBXW7), and cg07167608 (LINC01107)] were included in the final model of the methylation risk score for LR versus HR. These methylation risk score models of low resilience demonstrated satisfactory discrimination by logistic regression and support vector machine, with an AUC of 0.81 and 0.93, accuracy of 72.3% and 87.1%, sensitivity of 75%, and 87.5%, and specificity of 70% and 80%. Our findings suggest that methylation signatures can be utilized to identify individuals with LR and establish risk score models that may contribute to the field of psychology.