Glaucoma Risk Prediction Using Machine Learning Integration of Image-Based Phenotypes and Genetic Associations

使用基于图像的表型和遗传关联的机器学习集成进行青光眼风险预测

• 批准号: 10430101
• 负责人: Nazlee Zebardast
• 金额: $ 26.31万
• 依托单位: MASSACHUSETTS EYE AND EAR INFIRMARY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10430101
• 关键词: Address; Attention; Blindness; Clinical; Computational Biology; DNA; Data; Data Set; Databases; Demographic Accounting; Detection; Development; Development Plans; Diagnosis; Diagnostic tests; Disease; Disease Progression; Early treatment; Engineering; Etiology; Eye; Foundations; Funding; Fundus; Genetic; Genetic Markers; Genetic Predisposition to Disease; Genetic Risk; Genomics; Genotype; Glaucoma; Goals; Growth; Healthcare; Heritability; Image; Image Analysis; Individual; Learning; Left; Linear Regressions; Logistic Regressions; Machine Learning; Mentors; Meta-Analysis; Methods; Multiomic Data; Optic Nerve; Optical Coherence Tomography; Pathogenesis; Pathway interactions; Patients; Pattern; Phenotype; Physiologic Intraocular Pressure; Positioning Attribute; Primary Open Angle Glaucoma; Progressive Disease; ROC Curve; Records; Research; Research Personnel; Resources; Retina; Risk; Scanning; Science; Scientist; Series; Severity of illness; Supervision; System; Techniques; Technology; Testing; Thick; Training; Training Programs; United States National Institutes of Health; Variant; Visual Fields; Work; base; biobank; career; career development; case control; cohort; demographics; disorder risk; disorder subtype; endophenotype; functional loss; fundus imaging; genetic association; genetic risk factor; genetic testing; genetic variant; genome wide association study; genome-wide; genomic data; genomic locus; high intraocular pressure; high risk; imaging genetics; improved; insight; interest; learning strategy; machine learning method; macula; multi-ethnic; multidisciplinary; multimodality; nerve damage; novel; optic nerve disorder; polygenic risk score; precision medicine; predictive modeling; predictive test; risk prediction; risk variant; screening; serial imaging; statistical and machine learning; statistical learning; unsupervised learning

PROJECT SUMMARY/ ABSTRACT This proposal describes a 5-year training program to develop an academic career focused on improving glaucoma risk prediction through a combination of genomic and phenotypic risk. I will use supervised, semi- supervised and unsupervised machine learning methods to define novel structural and longitudinal image based endophenotypes for POAG aligned with disease subtype and progression. These endophenotypes will be used to discover new disease associated genomic loci. By including longitudinal data, we aim to identify genetic markers for progressive disease. We will use known POAG risk variants and novel genetic variants identified in these analyses to create several candidate genome wide polygenic risk scores (PRS) for POAG. Each candidate PRS with and without addition of demographic and image features will be tested for its utility to predict glaucoma risk is independent NEIGHBORHOOD and LIFE cohorts. We hypothesize that a PRS based on genetic variants associated with our endophenotypes will have improved POAG case predictive power compared to PRS based on cross-sectional genome wide association studies. The proposed studies have the potential to provide insight into disease pathogenesis and improve predictive power of genetic testing I am well positioned to conduct this research and undertake the training proposed here. I have a strong quantitative science background with a degree in engineering, statistical training and established track records of large database research. Additionally, I have proposed a detailed career development plan that will allow me to 1) learn the fundamentals, applications and limitations of machine learning based approaches for automated fundus image analysis and 2) understand computational biology and statistical approaches to handle large genomics datasets. My training plan includes an MPH in quantitative methods at the HSPH with concentration in computational biology and statistical learning. Additionally, I am supported by a multidisciplinary team of committed mentors dedicated to my academic growth and progression into an independent clinician scientist. I will work with glaucoma genetics experts, Drs Wiggs and Segre, and leaders in statistical and machine learning, Drs Elze and Kalpathy-Cramer. I will have full access to the extensive resources at MEE, Partners Healthcare and the Harvard system for this work and my career development. The research outlined here will improve our understanding of glaucoma pathogenesis and lay the foundation for development of multimodal precision medicine approaches for glaucoma screening and diagnosis. This research is cutting edge and prepares me well for my career as an independent NIH funded investigator with the aim to use longitudinal multi-modal clinical, imaging, testing and multi-omics data in multi- ethnic glaucoma patients to 1) understand pathways of vision loss, 2) develop precision medicine approaches to pre-symptomatically identify patients at high risk of functional vision loss and progression and 3) make these technologies a clinical reality in order to reduce the burden of unnecessary blindness.

项目摘要/摘要 这份提案描述了一项为期5年的培训计划，旨在发展专注于改进的学术生涯 通过基因组和表型风险的组合预测青光眼风险。我会用有监督的、半监督的 定义新的结构和纵向图像的监督和非监督机器学习方法 POAG的内表型与疾病亚型和进展相一致。这些内表型将 用于发现与疾病相关的新的基因组位点。通过包括纵向数据，我们的目标是确定 进展性疾病的遗传标记。我们将使用已知的POAG风险变异和新的遗传变异 在这些分析中确定，以创建POAG的几个候选全基因组多基因风险评分(PR)。 每个具有和不具有人口统计和图像特征的候选PR将被测试其实用性 预测青光眼风险的是独立的邻里和生活队列。我们假设一个基于PR的 与我们的内表型相关的遗传变异将提高POAG病例的预测能力 与基于横断面基因组宽关联研究的PR相比。拟议的研究具有 有可能提供对疾病发病机制的洞察并提高基因检测的预测能力 我很有能力进行这项研究，并接受这里提出的培训。我有一个很强的 具有量化科学背景，具有工程、统计培训学位，并建立了良好的记录 大型数据库研究。此外，我还提出了一个详细的职业发展计划，这将使我 1)了解基于机器学习的自动化方法的基本原理、应用和局限性 眼底图像分析和2)了解计算生物学和统计学方法来处理 基因组数据集。我的培训计划包括在HSPH集中精力进行定量方法的公共卫生硕士学位 在计算生物学和统计学习方面。此外，我还得到了一个多学科团队的支持 全心全意的导师致力于我的学术成长和发展，成为一名独立的临床科学家。我 将与青光眼遗传学专家Wiggs和Segre博士以及统计和机器领域的领导者合作 学习，埃尔兹和卡尔帕西-克雷默博士。我将完全访问MEE、合作伙伴的广泛资源 医疗保健和哈佛系统为这项工作和我的职业发展。 本文概述的研究将提高我们对青光眼发病机制的理解，并为 开发青光眼筛查和治疗的多模式精确医学方法的基础 诊断。这项研究是前沿的，为我的职业生涯做好了准备，成为一名独立的NIH资助的 研究人员，旨在使用纵向多模式临床，成像，测试和多组学数据在多 民族性青光眼患者1)了解视力丧失的途径，2)发展精准医学方法 在症状前识别功能性视力丧失和进展的高风险患者，以及3)使这些 技术使临床成为现实，以减少不必要的失明负担。