Deep-learning-based prediction of AMD and its progression with GWAS and fundus image data

老年性黄斑变性(AMD)是世界范围内导致不可逆性失明的主要原因。成功 AMD的全基因组关联研究已经确定了许多疾病易感基因。穿过 国际GWAS财团和大型合作项目、海量数据集的巨大努力 包括高质量的GWAS数据和特征良好的临床表型现已公开提供 DBGaP和UK Biobank等存储库。在临床上，彩色眼底图像已被广泛应用于 眼科医生诊断AMD及其严重程度。丰富的地理信息系统数据和眼底的结合 图像数据为研究人员提供了一个前所未有的机会来测试超越 原始项目的目标。其中，基于AMD发展的预测模型及其进展 无论是在GWAS上还是在眼底图像上，都没有探索过数据。大多数现有的预测模型只关注于 经典的统计方法，通常是带有有限数量预测因子的回归模型(例如，SNP)。 此外，大多数预测只给出静态风险，而不是随时间推移的动态风险轨迹，其中 后者对于像AMD这样的进展性疾病更具信息量。机器学习技术的最新进展， 特别是深度学习，已被证明通过结合 当具有明确定义的表型的大规模训练数据集被 可用。尽管深度学习在许多领域取得了成功，但在AMD和其他眼科领域，深度学习还没有得到充分的探索 疾病。受多项关于AMD发展或进展的大规模研究的推动，其中Gwas和/或 已收集到的眼底图像数据，我们提出了新的深度学习方法来预测 AMD的状况及其进展，并确定具有显著不同风险特征的亚组。特别是，在AIM 1，我们将构建一种新的局部卷积神经网络来预测疾病的发生(AMD或非AMD)和 严重程度(例如，轻度AMD、中度AMD、晚期AMD)基于(1a)：35,000名患有 GWAS数据和(1b)：拥有GWAS和眼底图像数据的4,000人的较小队列。在目标2中， 我们将开发一种新的深度神经网络生存模型来预测个体疾病的进展 轨迹(例如，迟到时间-AMD)。在这两个目标中，我们将使用局部线性逼近技术来识别 有助于个人风险概况预测和识别具有不同风险的子组的重要预测因素 配置文件。在目标3中，我们将使用独立的队列来验证和校准我们的方法，并实施 方法转化为用户友好的软件和易于访问的Web界面。最近FDA批准了 Beovu，一种通过抑制血管内皮生长因子从而抑制湿性AMD(一种晚期AMD)的新型注射疗法 异常血管的生长，使我们的研究更有意义，因为它将提供最前沿的 和AMD的综合预测模型，具有促进早期诊断和治疗的巨大潜力 为疾病量身定做的治疗和临床管理。

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.