てんかん関連のCHD2ミスセンスバリアントと遺伝子診断の最適化戦略：アルゴリズムの比較分析

The CHD2 gene is one of the most common causative genes of developmental and epileptic encephalopathy (DEE). 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.

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).

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 F 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.

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.