ORIGINAL RESEARCH article
Front. Neurol.
Sec. Neurogenetics
This article is part of the Research TopicGenotype-Phenotype Correlations, Genetic Mechanisms of Phenotypic Heterogeneity, Optimized Diagnosis and Targeted Therapies in Epilepsy and Neurodevelopmental DisordersView all articles
Epilepsy-Associated CHD2 Missense Variants and Optimization Strategies for Genetic Diagnosis: A Comparative Analysis of Algorithms
Provisionally accepted
- 1Ganzhou People's Hospital, Ganzhou, China
- 2Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
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Abstract: Background: The CHD2 gene is one of the most common causative genes of developmental and epileptic encephalopathy (DEE). With the employment of variant screening in patients with DEE, many CHD2 variants have been discovered. With the advent of high-throughput sequencing, identifying CHD2 variants has increased, necessitating evaluation of the gene-specific performance of widely used tools, as genome-wide benchmarks may mask such heterogeneity. Methods: The dataset of pathogenic and control CHD2 missense variants was curated from ClinVar, HGMD, and PubMed databases. Tools included SIFT, SIFT4G, Polyphen2_HDIV, Polyphen2_HVAR, MutationAssessor, PROVEAN, MetaSVM, MetaLR, MetaRNN, M-CAP, MutPred2, PrimateAI, DEOGEN2, BayesDel_addAF, BayesDel_noAF, ClinPred, LIST-S2, ESM1b, AlphaMissense, and fathmm-XF_coding. The in silico tools were evaluated based on accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), Matthews correlation coefficient (MCC), F-score, and area under the ROC curve (AUC). Result: A total of 27 missense variants which were classified as pathogenic or likely pathogenic were used as a positive set, and 57 missense variants were used as a negative set. The top tools in accuracy are MutPred2, ESM1b, AlphaMissense, and PROVEAN. In terms of the MCC and F1 score, the higher degree was observed in MutPred2 and AlphaMissense (MCC score >0.8). ClinPred, AlphaMissense, and BayesDel_addAF had a higher AUC score (AUC>0.99). SIFT, SIFT4G, Polyphen2_HDIV, Polyphen2_HVAR, ClinPred, and AlphaMissense scores exhibited a distinct bimodal distribution. While scores from other predictors showed a wider distribution range. Conclusion: Our study highlights the significant variation in the performance of different in silico tools for predicting CHD2 missense variant pathogenicity. Given its overall performance, MutPred2 and AlphaMissense may be the preferred choice for clinical application in CHD2-associated DEE, providing possible reference in optimizing genetic diagnosis and classification of CHD2 missense variants.
Keywords: missense variant, In silico tools, CHD2, MutPred2, AlphaMissense, Developmental and epileptic encephalopathy, Optimizing genetic diagnosis
Received: 21 Oct 2025; Accepted: 07 Nov 2025.
Copyright: © 2025 Gu, Wang, Fu, Lai, Chen, Liu and Guan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xianghong Liu, lxh7176@126.com
Bao-Zhu Guan, bzguan@qq.com
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