Association of mitochondrial DNA variation with high myopia in a Han Chinese population.

High myopia (HM), which is characterized by oxidative stress, is one of the leading causes of visual impairment and blindness across the world. Family and population genetic studies have uncovered nuclear-genome variants in proteins functioned in the mitochondria. However, whether mitochondrial DNA mutations are involved in HM remains unexplored. Here, we performed the first large-scale whole-mitochondrial genome study in 9613 HM cases and 9606 control subjects of Han Chinese ancestry for identifying HM-associated mitochondrial variants. The single-variant association analysis identified nine novel genetic variants associated with HM reaching the entire mitochondrial wide significance level, including rs370378529 in ND2 with an odds ratio (OR) of 5.25. Interestingly, eight out of nine variants were predominantly located in related sub-haplogroups, i.e. m.5261G > A in B4b1c, m.12280A > G in G2a4, m.7912G > A in D4a3b, m.94G > A in D4e1, m.14857 T > C in D4e3, m.14280A > G in D5a2, m.16272A > G in G2a4, m.8718A > G in M71 and F1a3, indicating that the sub-haplogroup background can increase the susceptible risk for high myopia. The polygenic risk score analysis of the target and validation cohorts indicated a high accuracy for predicting HM with mtDNA variants (AUC = 0.641). Cumulatively, our findings highlight the critical roles of mitochondrial variants in untangling the genetic etiology of HM.

DeepGraFT: A novel semantic segmentation auxiliary ROI-based deep learning framework for effective fundus tessellation classification.

Fundus tessellation (FT) is a prevalent clinical feature associated with myopia and has implications in the development of myopic maculopathy, which causes irreversible visual impairment. Accurate classification of FT in color fundus photo can help predict the disease progression and prognosis. However, the lack of precise detection and classification tools has created an unmet medical need, underscoring the importance of exploring the clinical utility of FT. Thus, to address this gap, we introduce an automatic FT grading system (called DeepGraFT) using classification-and-segmentation co-decision models by deep learning. ConvNeXt, utilizing transfer learning from pretrained ImageNet weights, was employed for the classification algorithm, aligning with a region of interest based on the ETDRS grading system to boost performance. A segmentation model was developed to detect FT exits, complementing the classification for improved grading accuracy. The training set of DeepGraFT was from our in-house cohort (MAGIC), and the validation sets consisted of the rest part of in-house cohort and an independent public cohort (UK Biobank). DeepGraFT demonstrated a high performance in the training stage and achieved an impressive accuracy in validation phase (in-house cohort: 86.85 %; public cohort: 81.50 %). Furthermore, our findings demonstrated that DeepGraFT surpasses machine learning-based classification models in FT classification, achieving a 5.57 % increase in accuracy. Ablation analysis revealed that the introduced modules significantly enhanced classification effectiveness and elevated accuracy from 79.85 % to 86.85 %. Further analysis using the results provided by DeepGraFT unveiled a significant negative association between FT and spherical equivalent (SE) in the UK Biobank cohort. In conclusion, DeepGraFT accentuates potential benefits of the deep learning model in automating the grading of FT and allows for potential utility as a clinical-decision support tool for predicting progression of pathological myopia.