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Nature and nurture: the complex genetics of myopia and refractive error.

ABSTRACT: The refractive errors, myopia and hyperopia, are optical defects of the visual system that can cause blurred vision. Uncorrected refractive errors are the most common causes of visual impairment worldwide. It is estimated that 2.5 billion people will be affected by myopia alone within the next decade. Experimental, epidemiological and clinical research has shown that refractive development is influenced by both environmental and genetic factors. Animal models have showed that eye growth and refractive maturation during infancy are tightly regulated by visually guided mechanisms. Observational data in human populations provide compelling evidence that environmental influences and individual behavioral factors play crucial roles in myopia susceptibility. Nevertheless, the majority of the variance of refractive error within populations is thought to be because of hereditary factors. Genetic linkage studies have mapped two dozen loci, while association studies have implicated more than 25 different genes in refractive variation. Many of these genes are involved in common biological pathways known to mediate extracellular matrix (ECM) composition and regulate connective tissue remodeling. Other associated genomic regions suggest novel mechanisms in the etiology of human myopia, such as mitochondrial-mediated cell death or photoreceptor-mediated visual signal transmission. Taken together, observational and experimental studies have revealed the complex nature of human refractive variation, which likely involves variants in several genes and functional pathways. Multiway interactions between genes and/or environmental factors may also be important in determining individual risks of myopia, and may help explain the complex pattern of refractive error in human populations.

SUBMITTER: Wojciechowski R

PROVIDER: S-EPMC3058260 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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Nature and nurture: the complex genetics of myopia and refractive error.

Wojciechowski R R

Clinical genetics 20101213 4

The refractive errors, myopia and hyperopia, are optical defects of the visual system that can cause blurred vision. Uncorrected refractive errors are the most common causes of visual impairment worldwide. It is estimated that 2.5 billion people will be affected by myopia alone within the next decade. Experimental, epidemiological and clinical research has shown that refractive development is influenced by both environmental and genetic factors. Animal models have showed that eye growth and refr ...[more]

PMID: 21155761

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Project description:SummaryUltrawide field fundus images could be applied in deep learning models to predict the refractive error of myopic patients. The predicted error was related to the older age and greater spherical power.PurposeTo explore the possibility of predicting the refractive error of myopic patients by applying deep learning models trained with ultrawide field (UWF) images.MethodsUWF fundus images were collected from left eyes of 987 myopia patients of Eye and ENT Hospital, Fudan University between November 2015 and January 2019. The fundus images were all captured with Optomap Daytona, a 200° UWF imaging device. Three deep learning models (ResNet-50, Inception-v3, Inception-ResNet-v2) were trained with the UWF images for predicting refractive error. 133 UWF fundus images were also collected after January 2021 as an the external validation data set. The predicted refractive error was compared with the "true value" measured by subjective refraction. Mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient (R 2) value were calculated in the test set. The Spearman rank correlation test was applied for univariate analysis and multivariate linear regression analysis on variables affecting MAE. The weighted heat map was generated by averaging the predicted weight of each pixel.ResultsResNet-50, Inception-v3 and Inception-ResNet-v2 models were trained with the UWF images for refractive error prediction with R 2 of 0.9562, 0.9555, 0.9563 and MAE of 1.72(95%CI: 1.62-1.82), 1.75(95%CI: 1.65-1.86) and 1.76(95%CI: 1.66-1.86), respectively. 29.95%, 31.47% and 29.44% of the test set were within the predictive error of 0.75D in the three models. 64.97%, 64.97%, and 64.47% was within 2.00D predictive error. The predicted MAE was related to older age (P < 0.01) and greater spherical power(P < 0.01). The optic papilla and macular region had significant predictive power in the weighted heat map.ConclusionsIt was feasible to predict refractive error in myopic patients with deep learning models trained by UWF images with the accuracy to be improved.

Project description:BackgroundImplantable collamer lens (ICL) has been widely accepted for its excellent visual outcomes for myopia correction. It is a new challenge in phakic IOL power calculation, especially for those with low and moderate myopia. This study aimed to establish a novel stacking machine learning (ML) model for predicting postoperative refraction errors and calculating EVO-ICL lens power.MethodsWe enrolled 2767 eyes of 1678 patients (age: 27.5 ± 6.33 years, 18-54 years) who underwent non-toric (NT)-ICL or toric-ICL (TICL) implantation during 2014 to 2021. The postoperative spherical equivalent (SE) and sphere were predicted using stacking ML models [support vector regression (SVR), LASSO, random forest, and XGBoost] and training based on ocular dimensional parameters from NT-ICL and TICL cases, respectively. The accuracy of the stacking ML models was compared with that of the modified vergence formula (MVF) based on the mean absolute error (MAE), median absolute error (MedAE), and percentages of eyes within ± 0.25, ± 0.50, and ± 0.75 diopters (D) and Bland-Altman analyses. In addition, the recommended spheric lens power was calculated with 0.25 D intervals and targeting emmetropia.ResultsAfter NT-ICL implantation, the random forest model demonstrated the lowest MAE (0.339 D) for predicting SE. Contrarily, the SVR model showed the lowest MAE (0.386 D) for predicting the sphere. After TICL implantation, the XGBoost model showed the lowest MAE for predicting both SE (0.325 D) and sphere (0.308 D). Compared with MVF, ML models had numerically lower values of standard deviation, MAE, and MedAE and comparable percentages of eyes within ± 0.25 D, ± 0.50 D, and ± 0.75 D prediction errors. The difference between MVF and ML models was larger in eyes with low-to-moderate myopia (preoperative SE > - 6.00 D). Our final optimal stacking ML models showed strong agreement between the predictive values of MVF by Bland-Altman plots.ConclusionWith various ocular dimensional parameters, ML models demonstrate comparable accuracy than existing MVF models and potential advantages in low-to-moderate myopia, and thus provide a novel nomogram for postoperative refractive error prediction and lens power calculation.

Dataset Information

Nature and nurture: the complex genetics of myopia and refractive error.

Publications

Nature and nurture: the complex genetics of myopia and refractive error.

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