Genetic Determinants and Progression of Mitral Valve Prolapse

二尖瓣脱垂的遗传决定因素和进展

基本信息

项目摘要

DESCRIPTION (provided by applicant): The K23 Award will support rigorous training for the development of a career as an independent translational investigator with a focus on the genetics of mitral valve prolapse (MVP) and its progression. The principle investigator has completed a fellowship in cardiology and advanced imaging at Beth Israel Deaconess Medical Center (BIMDC) and a fellowship in echocardiography at Massachusetts General Hospital (MGH). The candidate has gained expertise in linkage analysis studies and phenotypic-genotypic correlations. As a staff cardiologist at BIDMC, she is now seeking new skills in genetic epidemiology and genome-wide association studies (GWAS). She will have access to a state-of-the-art genotyping facility (FHS), and echocardiography laboratories (BIDMC and FHS). Her multidisciplinary mentoring team includes experts in GWAS (Dr Ramachandran), and valvular disease/echocardiography (Drs Manning and Levine). A Master of Public Health in Clinical Effectiveness (Harvard School of Public Health) will provide the coursework necessary for her academic success. Although MVP is the most common cause of isolated mitral regurgitation (MR) requiring surgical repair, little is known about the genetic determinants of MVP and its progression. To date, 3 loci for autosomal dominant MVP have been described on chromosomes 11, 16 and 13, but gene finding has proved elusive. In addition, while the prevalence of MVP in the community is 2-5%, the prevalence of familial MVP is unknown. In the chromosome 13 family, previously non-diagnostic morphologies often represent early stages of expression in gene carriers and share two salient features with fully diagnostic MVP: an anteriorly displaced coaptation point and posterior leaflet asymmetry. This proposal tests the hypotheses that early stages of MVP exist and can progress in the FHS community, and that MVP heritability is high. In addition, several MVP loci can be identified through a multicenter GWAS. To test these hypotheses, the candidate will: 1) Measure mitral leaflet coaptation height, displacement, thickness, and degree of MR by echocardiography in the FHS Offspring Cohort and in a hospital-based sample (BIDMC) at baseline and at available follow-up of 11-17 (FHS) and 5 years (BIDMC). 2) Evaluate the presence of MVP in Gen 3 individuals whose parents in the Offspring Cohort have MVP. High MVP heritability would lead to specific gene-finding strategies such as whole-exome analysis and clarify if routine screening of family members in the community is warranted. 3) Search for MVP loci using GWAS both in FHS and in other US and European cohorts (> 2500 MVP subjects) (Leducq MITRAL Network). These aims represent crucial steps towards future gene-finding strategies and understanding of the variability of progression and clinical outcomes among MVP patients in the community, with the potential of developing therapeutic targets to prevent progression at an early stage.
描述(由申请人提供):K23奖将支持严格的培训,以发展为独立翻译调查员的职业,重点是二尖瓣脱垂(MVP)及其进步。这位主要研究人员已在贝丝以色列执事医疗中心(BIMDC)完成了心脏病学和高级成像研究金,并在马萨诸塞州综合医院(MGH)完成了超声心动图研究金。候选人在连锁分析研究和表型基因型相关性方面获得了专业知识。作为BIDMC的员工心脏病专家,她现在正在寻求遗传流行病学和全基因组协会研究(GWAS)的新技能。她将可以使用最先进的基因分型设施(FHS)和超声心动图实验室(BIDMC和FHS)。她的多学科指导团队包括GWAS(Ramachandran博士)和瓣膜疾病/超声心动图(Manning博士和莱文)的专家。公共卫生硕士临床有效性(哈佛大学公共卫生学院)将为她的学业成功提供必要的课程。尽管MVP是需要手术修复的孤立二尖瓣反流(MR)的最常见原因,但对MVP的遗传决定因素及其进展知之甚少。迄今为止,已经在11、16和13染色体上描述了常染色体显性MVP的3个基因座,但吉恩发现被证明难以捉摸。此外,尽管社区中MVP的患病率为2-5%,但家族性MVP的患病率尚不清楚。在第13染色体家族中,以前非诊断的形态通常代表基因载体表达的早期阶段,并具有两个完全诊断的MVP的显着特征:前移位的腔内点和后叶不对称。该提案检验了MVP早期存在并可以在FHS社区中进展的假设,并且MVP的遗传力很高。此外,可以通过多中心GWAS识别几个MVP基因座。为了检验这些假设,候选人将:1)测量二尖瓣的叶片配合高度,位移,厚度,厚度和MR的MR通过超声心动图在FHS后代组中以及基于医院的样本(BIDMC)(BIDMC)的基线和可用的随访和11-17-17(FHS)和5年(BIDMC)(BIDMC)(BIDMC)。 2)评估在后代队列中的父母具有MVP的Gen 3个个体中MVP的存在。高MVP的遗传力将导致特定的基因调查策略,例如全异位分析,并阐明社区中家庭成员的常规筛查是否有必要。 3)在FHS和其他美国和欧洲队列中使用GWAS(> 2500 MVP受试者)(LEDUCQ二尖瓣网络)搜索MVP基因座。这些目标代表着朝着未来基因调查策略的关键步骤,并了解社区中MVP患者的进展和临床结果的变异性,并具有开发治疗靶标,以防止早期进展。

项目成果

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Francesca N Delling其他文献

Cardiac magnetic resonance evidence of diffuse myocardial fibrosis in patients with mitral valve prolapse
  • DOI:
    10.1186/1532-429x-17-s1-p337
  • 发表时间:
    2015-02-03
  • 期刊:
  • 影响因子:
  • 作者:
    An H Bui;Sébastien Roujol;Murilo Foppa;Kraig V Kissinger;Beth Goddu;Thomas H Hauser;Peter J Zimetbaum;Warren J Manning;Reza Nezafat;Francesca N Delling
  • 通讯作者:
    Francesca N Delling
Papillary muscle native T<sub>1</sub> time is associated with severity of functional mitral regurgitation in patients with non-ischemic dilated cardiomyopathy
  • DOI:
    10.1186/1532-429x-18-s1-p244
  • 发表时间:
    2016-01-27
  • 期刊:
  • 影响因子:
  • 作者:
    Shingo Kato;Sébastien Roujol;Shadi Akhtari;Francesca N Delling;Jihye Jang;Tamer Basha;Sophie Berg;Kraig V Kissinger;Beth Goddu;Warren J Manning;Reza Nezafat
  • 通讯作者:
    Reza Nezafat

Francesca N Delling的其他文献

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{{ truncateString('Francesca N Delling', 18)}}的其他基金

Prospective sudden cardiac death risk stratification using CMR and echocardiography machine learning in mitral valve prolapse
使用 CMR 和超声心动图机器学习对二尖瓣脱垂进行前瞻性心脏性猝死风险分层
  • 批准号:
    10171903
  • 财政年份:
    2020
  • 资助金额:
    $ 12.71万
  • 项目类别:
Genetics of arrhythmic mitral valve prolapse: large pedigree collection within the UCSF MVP registry
心律失常二尖瓣脱垂的遗传学:UCSF MVP 登记处的大量谱系收集
  • 批准号:
    10850759
  • 财政年份:
    2020
  • 资助金额:
    $ 12.71万
  • 项目类别:
Prospective sudden cardiac death risk stratification using CMR and echocardiography machine learning in mitral valve prolapse
使用 CMR 和超声心动图机器学习对二尖瓣脱垂进行前瞻性心脏性猝死风险分层
  • 批准号:
    10600113
  • 财政年份:
    2020
  • 资助金额:
    $ 12.71万
  • 项目类别:
Prospective sudden cardiac death risk stratification using CMR and echocardiography machine learning in mitral valve prolapse
使用 CMR 和超声心动图机器学习对二尖瓣脱垂进行前瞻性心脏性猝死风险分层
  • 批准号:
    10390482
  • 财政年份:
    2020
  • 资助金额:
    $ 12.71万
  • 项目类别:
Prospective sudden cardiac death risk stratification using CMR and echocardiography machine learning in mitral valve prolapse
使用 CMR 和超声心动图机器学习对二尖瓣脱垂进行前瞻性心脏性猝死风险分层
  • 批准号:
    10034460
  • 财政年份:
    2020
  • 资助金额:
    $ 12.71万
  • 项目类别:

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