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: 10283972
• 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 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10283972
• 关键词: Abdomen; Advisory Committees; Aneurysm; Aorta; Aortic Aneurysm; Aortic Diseases; Architecture; Award; Biological; 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; 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; 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 主动脉疾病是导致心血管疾病和猝死的重要因素。重大发现， 包括马凡氏综合征(FBN1)致病基因的鉴定，促进了我们对 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，尽管人们对主动脉疾病的重要性有了越来越多的了解，但对于 对升主动脉疾病进行筛查，没有治疗方法来治疗其潜在的分子机制。在那里的时候 可能是胸主动脉和腹主动脉疾病之间的一些重叠，它们在胚胎上是不同的，很可能 有不同的遗传和临床风险因素。 在皮鲁切洛博士的初步工作中，他开发了一个自动深度学习模型来量化直径 使用心血管磁共振成像(MRI)对胸主动脉进行检查。他在英国应用了这个模型 Biobank，并对上、下胸径进行了全基因组关联研究 近40,000名参与者的大动脉。这些结果证实了(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 附加的主动脉特征，以及胸腹部遗传危险因素的下游评估 大动脉疾病。 首先，Pirruccello博士建议开发其他主动脉特征的模型，包括胸主动脉张力和 膨胀性和腹主动脉直径。其次，在开发了额外的模型来提取这些 Pirruccello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大小和扩张性在几个水平上存在差异。 第三，他建议产生多基因评分，允许对临床和遗传风险进行建模 主动脉大小和扩张性异常，可能导致主动脉瘤和夹层。 这项工作将在马萨诸塞州总医院的心脏科和布罗德医院进行 麻省理工学院和哈佛大学。皮鲁切洛博士将在帕特里克·埃利诺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·林赛博士是 马萨诸塞州总医院胸主动脉中心的遗传性主动脉疾病。 皮鲁切洛博士的目标是成为一名拥有机器学习专业知识的心血管遗传学家。 他致力于成为一名独立的调查员，并将为K08所做的研究作为 R01的跳板。