Resolving Incomplete Penetrance in the Cardiomyopathies and Channelopathies

解决心肌病和通道病的不完全外显率

• 批准号: 8572102
• 负责人: Rahul Chandrakant Deo
• 金额: $ 235.5万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-30 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8572102
• 关键词: Affect; Biological Assay; Cardiomyopathies; Cataloging; Catalogs; Complex; Defibrillators; Disease; Environment; Failure; Family; Family member; Genes; Genetic Variation; Genetic screening method; Genome; Genomics; Hand; Heart failure; Human; Individual; Inherited; Knowledge; Lead; Mutation; Myocardium; Patient Care; Patients; Pattern; Penetrance; Population; Positioning Attribute; Preventive; Recommendation; Risk; Severity of illness; System; Technology; United States; Variant; disease-causing mutation; genetic variant; implantation; sudden cardiac death

DESCRIPTION (provided by applicant): Cardiomyopathies (CMPs) and channelopathies (CLPs) are debilitating inherited diseases of the heart muscle and conduction system, which collectively affect well over one million patients in the United States, and can lead to heart failure and sudden cardiac death. Although some CMP/CLPs arise sporadically, many show a strong pattern of familial inheritance. Genetic testing applies knowledge of CMP/CLP genes towards the care of patients and their family members. In any given patient, if one knows the actual mutation responsible for their condition, one can very easily determine if other family members carry it, thereby facilitating careful surveillance and potential preventive therapies. Remarkably, despite knowing in many cases exactly which mutation causes the diseases in a given family, we can often do very little when it comes to predicting what is likely to befall a specific individual. This phenomenon, where not all individuals who inherit a mutation actually develop a disease is known as incomplete penetrance. It affects not only CMPs and CLPs, but nearly all inherited disease. Incomplete penetrance has been ascribed to the complex interplay between genes and environment, so that many modifying influences can influence the severity of the disease. Although this represents a conceptually satisfying explanation, it does little to help assess risk in patients. This failure of accurate prognostication, even in such highly heritable diseases, has real practical consequences, such as when it comes to deciding on such therapies as implantation of a defibrillator or recommendation, which themselves carry significant risk. In this proposal, I describe a stepwise approach to beginning to resolve incomplete penetrance in CMPs/CLPs. I first identify which genetic variants in the human population are likely to modify severity of disease in the CMPs and CLPs. This step requires harnessing remarkable recent advances in massively parallel genomic technology, where tens of thousands of variants can be interrogated at once for their effect on regulating gene activity. With this catalogue of variants in hand, one can build assays to assess the status of patients at each of these positions in the genome, as well as determine the identify of the underlying causal mutation(s). The final step is too look at actual CMP/CLP patients, and determine whether knowledge of modifying genetic variation can help predict who is likely to develop severe disease, and who will have a milder course.

描述（由申请人提供）：心肌病（CMP）和通道病（CLP）是令人衰弱的心脏肌肉和传导系统的遗传疾病，这些疾病在美国总体影响超过一百万的患者，并可能导致心力衰竭和猝死。尽管某些CMP/CLP偶尔出现，但许多CMP/CLP表现出强烈的家族遗传模式。基因检测将CMP/CLP基因的知识应用于患者及其家人的护理。在任何给定的患者中，如果知道对其状况负责的实际突变，则可以很容易地确定其他家庭成员是否携带它，从而促进仔细的监视和潜在的预防疗法。值得注意的是，尽管在许多情况下确切地知道哪些突变会导致给定家庭中的疾病，但在预测可能落入特定人的情况下，我们通常几乎无法做到。这种现象并非所有继承突变的人实际发展出疾病的人都被称为不完整的渗透率。它不仅影响CMP和CLP，而且影响几乎所有遗传性疾病。不完整的渗透率已归因于基因和环境之间的复杂相互作用，因此许多修改的影响会影响疾病的严重程度。尽管这是一个概念上令人满意的解释，但它几乎没有帮助评估患者的风险。准确预后的失败，即使在这种高度遗传的疾病中，也会产生实际的后果，例如在决定植入除颤器或建议等疗法时，这些疗法本身会带来很大的风险。在此提案中，我描述了一种逐步开始解决CMP/CLP中不完整的渗透率的方法。我首先确定人口中哪些遗传变异可能会改变CMP和CLP中疾病的严重程度。此步骤需要利用巨大平行基因组技术的近期进展，其中可以立即对成千上万的变体进行质疑，以便它们对调节基因活性的影响。借助这种变体目录，可以建立测定法以评估基因组中每个位置的患者状态，并确定基本因果突变的识别（S）。最后一步也是考虑实际的CMP/CLP患者，并确定修改遗传变异的知识是否可以帮助预测谁可能发展出严重的疾病，并且谁会有更温和的病程。

项目成果

Learning About Machine Learning: The Promise and Pitfalls of Big Data and the Electronic Health Record.

• DOI: 10.1161/circoutcomes.116.003308
• 发表时间: 2016-11
• 期刊: Circulation. Cardiovascular quality and outcomes
• 作者: Deo RC;Nallamothu BK
• 通讯作者: Nallamothu BK

Precision Phenotyping of Dilated Cardiomyopathy Using Multidimensional Data.

• DOI: 10.1016/j.jacc.2022.03.375
• 发表时间: 2022-06-07
• 期刊: JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY
• 影响因子: 24
• 作者: Tayal, Upasana;Verdonschot, Job A. J.;Hazebroek, Mark R.;Howard, James;Gregson, John;Newsome, Simon;Gulati, Ankur;Pua, Chee Jian;Halliday, Brian P.;Lota, Amrit S.;Buchan, Rachel J.;Whiffin, Nicola;Kanapeckaite, Lina;Baruah, Resham;Jarman, Julian W. E.;O'Regan, Declan P.;Barton, Paul J. R.;Ware, James S.;Pennell, Dudley J.;Adriaans, Bouke P.;Bekkers, Sebastiaan C. A. M.;Donovan, Jackie;Frenneaux, Michael;Cooper, Leslie T.;Januzzi, James L., Jr.;Cleland, John G. F.;Cook, Stuart A.;Deo, Rahul C.;Heymans, Stephane R. B.;Prasad, Sanjay K.
• 通讯作者: Prasad, Sanjay K.