Age-related macular degeneration (AMD) is a leading cause of irreversible blindness worldwide. Successful genome-wide association studies (GWAS) of AMD have identified many disease-susceptibility genes. Through great efforts from international GWAS consortium and large-scale collaborative projects, massive datasets including high-quality GWAS data and well-characterized clinical phenotypes are now available in public repositories such as dbGaP and UK Biobank. Clinically, color fundus images have been extensively used by ophthalmologists to diagnose AMD and its severity level. The combination of wealthy GWAS data and fundus image data provides an unprecedented opportunity for researchers to test new hypotheses that are beyond the objectives of original projects. Among them, predictive models for AMD development and its progression based on both GWAS and fundus image data have not been explored. Most existing prediction models only focus on classic statistical approaches, often regression models with a limited number of predictors (e.g., SNPs). Moreover, most predictions only give static risks rather than dynamic risk trajectories over time, of which the latter is more informative for a progressive disease like AMD. Recent advances of machine learning techniques, particularly deep learning, have been proven to significantly improve prediction accuracy by incorporating multiple layers of hidden non-linear effects when large-scale training datasets with well-defined phenotypes are available. Despite its success in many areas, deep learning has not been fully explored in AMD and other eye diseases. Motivated by multiple large-scale studies of AMD development or progression, where GWAS and/or longitudinal fundus image data have been collected, we propose novel deep learning methods for predicting AMD status and its progression, and to identify subgroups with significant different risk profiles. Specially, in Aim 1, we will construct a novel local convolutional neural network to predict disease occurrence (AMD or not) and severity (e.g., mild AMD, intermediate AMD, late AMD) based on (1a): a large cohort of 35,000+ individuals with GWAS data and (1b): a smaller cohort of 4,000+ individuals with both GWAS and fundus image data.
In Aim 2, we will develop a novel deep neural network survival model for predicting individual disease progression trajectory (e.g., time to late-AMD). In both aims, we will use the local linear approximation technique to identify important predictors that contribute to individual risk profile prediction and to identify subgroups with different risk profiles.
In Aim 3, we will validate and calibrate our methods using independent cohorts and implement proposed methods into user-friendly software and easy-to-access web interface. With the very recent FDA approval for Beovu, a novel injection treatment for wet AMD (one type of late AMD) by inhibiting VEGF and thus suppressing the growth of abnormal blood vessels, it makes our study more significant, as it will provide most cutting-edge and comprehensive prediction models for AMD which have great potential to facilitate early diagnosis and tailored treatment and clinical management of the disease.
The objective of this proposal is to develop new analytic methods and software tools to facilitate novel prediction of AMD development and its progression. The successful completion of the project will generate the first comprehensive set of deep-learning-based prediction models and web-based interfaces, which jointly analyzes large-scale GWAS and fundus image data and has the great potential to enhance the early diagnosis and current clinical management of AMD. The analytic approach can be applied to other eye diseases where large-scale genetics and/or image data are collected.
