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

基本信息

批准号：
10191922
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
项目类别：
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/10191922
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 Structure 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

项目摘要

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.

项目概要/摘要该提案描述了一个为期 5 年的培训计划，旨在发展学术生涯，重点是提高通过基因组和表型风险的结合来预测青光眼风险。我将使用监督式、半用于定义新颖的结构和纵向图像的监督和无监督机器学习方法基于 POAG 的内表型与疾病亚型和进展相一致。这些内表型将可用于发现新的疾病相关基因组位点。通过包含纵向数据，我们的目标是确定进行性疾病的遗传标记。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了 POAG 的几个候选全基因组多基因风险评分 (PRS)。每个候选 PRS（无论是否添加人口统计和图像特征）都将对其实用性进行测试预测青光眼风险是独立的 NEIGHBORHOOD 和 LIFE 队列。我们假设基于 PRS 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全基因组关联研究的 PRS 进行比较。拟议的研究有具有深入了解疾病发病机制并提高基因检测预测能力的潜力我完全有能力进行这项研究并接受这里提出的培训。我有很强的定量科学背景，拥有工程学位、统计培训和既定记录大型数据库研究。此外，我还提出了一份详细的职业发展计划，使我能够 1）学习基于机器学习的自动化方法的基础知识、应用和局限性眼底图像分析，2) 了解计算生物学和统计方法来处理大数据基因组学数据集。我的培训计划包括 HSPH 定量方法 MPH 重点课程在计算生物学和统计学习中。此外，我得到了多学科团队的支持忠诚的导师致力于我的学术成长并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 博士和 Segre 博士以及统计和机器领域的领导者合作学习，Elze 博士和 Kalpathy-Cramer 博士。我将可以充分利用 MEE、合作伙伴的丰富资源医疗保健和哈佛系统对这项工作和我的职业发展有帮助。这里概述的研究将提高我们对青光眼发病机制的了解，并奠定基础开发用于青光眼筛查和治疗的多模式精准医学方法的基础诊断。这项研究是前沿的，为我作为独立的 NIH 资助的职业生涯做好了准备研究人员的目标是在多方面使用纵向多模式临床、成像、测试和多组学数据少数族裔青光眼患者 1) 了解视力丧失的途径，2) 开发精准医疗方法在出现症状前识别功能性视力丧失和进展高风险的患者，并且 3) 进行这些技术融入临床现实，以减少不必要的失明负担。