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: 10613402
• 负责人: James Pirruccello
• 金额: $ 16.96万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10613402
• 关键词: Abdomen; Advisory Committees; Aneurysm; Aorta; Aortic Aneurysm; Aortic Diseases; Architecture; Award; Caliber; Cardiology; Cardiovascular Diseases; Cardiovascular system; Chest; Cholesterol; Clinical; Complication; Computer Vision Systems; Computer software; Custom; Data; Developed Countries; Development; Dilatation - action; Disease Outcome; Dissection; Evaluation; FBN1; Faculty; Fellowship; General Hospitals; Genes; Genetic; Genetic Risk; Genetic Variation; Goals; Human Genetics; Image; Individual; Institutes; 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; Syndrome; Testing; Thoracic aorta; Training; Variant; Work; abdominal aorta; base; biobank; career; causal variant; clinical risk; 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; imaging study; 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.

项目摘要 /摘要 主动脉疾病是心血管发病率和猝死的重要原因。关键发现， 包括鉴定马凡氏综合症的因果基因（FBN1），已经提高了我们对 综合征动脉瘤和解剖，但迄今为止，有关零星胸部主动脉的信息不足 疾病。例如，目的地对主动脉疾病重要性的了解不断提高，没有指导方向 筛查升高主动脉疾病，也没有治疗其潜在分子机制的疗法。那里 胸部主动脉疾病和腹主动脉疾病之间可能有些重叠，它们在胚胎学上是不同的，并且可能 具有不同的遗传和临床危险因素。 在Pirruccello博士的初步工作中，他开发了一种自动深度学习模型来量化直径 使用心血管磁共振成像（MRI）的胸主动脉的胸腔主动脉。他在英国应用了该模型 生物库并进行了全基因组关联研究，以实现上升和下降的胸腔直径 近40,000名参与者的主动脉。这些结果巩固了（1）训练深度的方法的可行性 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 在这些基于深度学习模型的表型上。现在，这为更全面的分析铺平了道路 其他主动脉性状，以及胸腔和腹部遗传危险因素的下游评估 主动脉疾病。 首先，Pirrruccello博士提出的提案，以开发用于其他主动脉特征在内的模型，包括胸腔主动脉菌株和 扩张性和腹主动脉直径。第二，在开发了其他模型以提取这些模型之后 Pirrruccello博士的特征提议对英国生物库的这些特征进行遗传分析，从而阐明了 常见且罕见的遗传变异会导致主动脉大小的变异性和多个级别的不满。 第三，他提出产生多基因评分，允许对临床和遗传风险进行建模 主动脉尺寸和不可信性的异常，可能易于主动脉瘤和解剖。 这项工作将在马萨诸塞州综合医院的心脏病学部门进行，并在广泛 麻省理工学院和哈佛研究所。 Pirrruccello博士将根据Patrick Ellinor博士的心态进行这项研究 Broad Institute的心血管疾病倡议主任Mark Lindsay博士 马萨诸塞州总医院胸主动脉中心的遗传主动脉疾病。 Pirruccello博士的目标是成为具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研究者，并使用针对K08进行的研究 R01的跳板。