Purpose To describe the genetic and clinical features of nineteen patients from eleven unrelated Chinese pedigrees with OPA1-related autosomal dominant optic atrophy (ADOA) and define the phenotype-genotype correlations. Methods Detailed ophthalmic examinations were performed. Targeted next-generation sequencing (NGS) was conducted in the eleven probands using a custom designed panel PS400. Sanger sequencing and cosegregation were used to verify the identified variants. The pathogenicity of gene variants was evaluated according to American College of Medical Genetics and Genomics (ACMG) guidelines. Results Nineteen patients from the eleven unrelated Chinese ADOA pedigrees had impaired vision and optic disc pallor. Optical coherence tomography showed significant thinning of the retinal nerve fiber layer. The visual field showed varying degrees of central or paracentral scotoma. The onset of symptoms occurred between 3 and 24 years of age (median age 6 years). Eleven variants in OPA1 were identified in the cohort, and nine novel variants were identified. Among the novel variants, two splicing variants c.984 + 1_984 + 2delGT, c.1194 + 2 T > C, two stop-gain variants c.1937C > G, c.2830G > T, and one frameshift variant c.2787_2794del8, were determined to be pathogenic based on ACMG. A novel splicing variant c.1316-10 T > G was determined to be likely pathogenic. In addition, a novel missense c.1283A > C (p.N428T) and two novel splicing variants c.2496G > A and c.1065 + 5G > C were of uncertain significance. Conclusions Six novel pathogenic variants were identified. The findings will facilitate genetic counselling by expanding the pathogenic mutation spectrum of OPA1.

Next-generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification. In this study, we developed Pathogenicity Prediction Tool for missense variants (mvPPT), a highly sensitive and accurate missense variant classifier based on gradient boosting. mvPPT adopts high-confidence training sets with a wide spectrum of variant profiles, and extracts three categories of features, including scores from existing prediction tools, frequencies (allele frequencies, amino acid frequencies, and genotype frequencies), and genomic context. Compared with established predictors, mvPPT achieves superior performance in all test sets, regardless of data source. In addition, our study also provides guidance for training set and feature selection strategies, as well as reveals highly relevant features, which may further provide biological insights into variant pathogenicity. mvPPT is freely available at http://www.mvppt.club/.

Next generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification. In this study, we developed mvPPT (Pathogenicity Prediction Tool for missense variants), a highly sensitive and accurate missense variant classifier based on gradient boosting. MvPPT adopts high-confidence training sets with a wide spectrum of variant profiles, and extracts three categories of features, including scores from existing prediction tools, allele, amino acid and genotype frequencies, and genomic context. Compared with established predictors, mvPPT achieved superior performance in all test sets, regardless of data source. In addition, our study also provides guidance for training set and feature selection strategies, as well as reveals highly relevant features, which may further provide biological insights of variant pathogenicity.