Deep learning to enable the genetic analysis of aorta

深度学习可实现主动脉的遗传分析

• 批准号: 10807379
• 负责人: James Pirruccello
• 金额: $ 16.26万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10807379
• 关键词: Advisory Committees; Aneurysm; Aorta; Aortic Aneurysm; Aortic Diseases; Architecture; Award; Cardiology; Cardiovascular Diseases; Cardiovascular system; Cementation; Cholesterol; Clinical; Complication; Computer Vision Systems; Computer software; Custom; Data; Dedications; Developed Countries; Development; Diameter; Dilatation - action; Disease Outcome; Dissection; Educational process of instructing; Evaluation; FBN1; Faculty; Fellowship; General Hospitals; Genes; Genetic; Genetic Risk; Genetic Variation; Goals; Human Genetics; Image; Individual; Knowledge; Learning; Life; Link; Machine Learning; Magnetic Resonance Imaging; Manuals; Manuscripts; Marfan Syndrome; Massachusetts; Measurement; Measures; Mentors; Mentorship; Modeling; Molecular; Morbidity - disease rate; Nature; Operative Surgical Procedures; Participant; Pathologic; Peer Review; Persons; Phenotype; Play; Positioning Attribute; Preventive therapy; Property; Publishing; Research; Research Personnel; Research Training; Risk; Risk Factors; Role; Software Engineering; Students; Sudden Death; Testing; Thoracic aorta; Training; Variant; Work; abdominal aorta; ascending aorta; biobank; career; causal variant; clinical risk; clinical training; clinically relevant; cohort; college; computer science; deep learning; deep learning model; exome sequencing; experience; genetic analysis; genetic risk factor; genetic variant; genome wide association study; genome-wide; genomic locus; high risk; human data; insight; interest; medical schools; new therapeutic target; rare variant; screening guidelines; skills; therapeutic target; trait; university student

Project Summary / Abstract Aortic disease is an important contributor to cardiovascular morbidity and sudden death. Key discoveries, including identification of the causal gene for Marfan’s syndrome (FBN1), have advanced our knowledge of syndromic aneurysm and dissection, but to date there remains insufficient information on sporadic thoracic aortic disease. For example, despite growing knowledge of the importance of aortic disease, there is no guideline for screening for ascending aortic disease, and no therapy to treat its underlying molecular mechanisms. While there is likely some overlap between thoracic and abdominal aortic disease, they are embryologically distinct and likely have different genetic and clinical risk factors. In Dr. Pirruccello’s preliminary work, he developed an automated deep learning model to quantify the diameter of the thoracic aorta using cardiovascular magnetic resonance imaging (MRI). He applied the model in the UK Biobank and conducted a genome-wide association study for the diameter of ascending and descending thoracic aorta in nearly 40,000 participants. These results cemented the feasibility of the approach of (1) training deep learning models to extract biologically relevant information from imaging, and (2) conducting genetic analyses on these deep learning model-based phenotypes. This now paves the way for a more comprehensive analysis of additional aortic traits, and downstream evaluation of genetic risk factors for both thoracic and abdominal aortic disease. First, Dr. Pirruccello proposes to develop models for additional aortic traits including thoracic aortic strain and distensibility, and abdominal aortic diameter. Second, after developing additional models to extract those features, Dr. Pirruccello proposes to conduct genetic analyses on these traits in the UK Biobank, elucidating the common and rare genetic variation that leads to variability in the aorta’s size and distensibility at several levels. Third, he proposes to produce polygenic scores, permitting modeling of the clinical and genetic risk for abnormalities in aortic size and distensibility that may predispose to aortic aneurysm and dissection. This work will take place in the Division of Cardiology at the Massachusetts General Hospital, and at the Broad Institute of MIT and Harvard. Dr. Pirruccello will perform this research under the mentorship of Dr. Patrick Ellinor, the Director of the Cardiovascular Disease Initiative at the Broad Institute, and Dr. Mark Lindsay, an expert in genetic aortic disease at the Massachusetts General Hospital Thoracic Aortic Center. Dr. Pirruccello’s goal is to become a computational cardiovascular geneticist with expertise in machine learning. He is dedicated to becoming an independent investigator and to use the research performed for the K08 as a springboard for an R01.

项目总结/摘要 主动脉疾病是心血管疾病发病率和猝死的重要因素。关键发现， 包括马凡氏综合征（FBN 1）的致病基因的鉴定，提高了我们对 综合征性动脉瘤和夹层，但迄今为止，关于散发性胸主动脉的信息仍然不足 疾病例如，尽管对主动脉疾病的重要性的认识越来越多，但没有指导方针， 筛查升主动脉疾病，并且没有治疗其潜在分子机制的疗法。虽然 胸主动脉和腹主动脉疾病之间可能有一些重叠，它们在胚胎学上不同， 有不同的遗传和临床风险因素。 在Pappuccello博士的初步工作中，他开发了一个自动化的深度学习模型来量化直径 使用心血管磁共振成像（MRI）的胸主动脉。他在英国应用了这一模式 生物库并对升胸廓和降胸廓直径进行了全基因组关联研究 近4万名参与者的主动脉。这些结果巩固了（1）深度训练方法的可行性 学习模型以从成像中提取生物学相关信息，以及（2）进行遗传分析 这些基于深度学习模型的表型。这为更全面的分析铺平了道路 其他主动脉性状，以及胸部和腹部的遗传风险因素的下游评估 主动脉疾病 首先，Pandaluccello博士建议开发其他主动脉特征的模型，包括胸主动脉应变和 扩张性和腹主动脉直径。其次，在开发了额外的模型来提取这些 特征，Pappuccello博士建议在英国生物库中对这些特征进行遗传分析，阐明 常见和罕见的遗传变异，导致主动脉的大小和扩张性在几个层面的变化。 第三，他建议产生多基因评分，允许模拟临床和遗传风险， 主动脉大小和扩张性异常，可能易患主动脉瘤和夹层。 这项工作将在马萨诸塞州总医院心脏科和布罗德医院进行。 麻省理工学院和哈佛研究所。Pappuccello博士将在帕特里克Ellinor博士的指导下进行这项研究， 布罗德研究所心血管疾病倡议主任马克林赛博士， 遗传性主动脉疾病在马萨诸塞州总医院胸主动脉中心。 博士Pappuccello的目标是成为一名拥有机器学习专业知识的计算心血管遗传学家。 他致力于成为一名独立的调查员，并将为K 08进行的研究作为一项 R 01的跳板