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) 和离子通道病 (CLP) 是使心肌和传导系统衰弱的遗传性疾病，在美国总共影响超过 100 万患者，并可能导致心力衰竭和突发心脏病死亡。尽管一些 CMP/CLP 是零星出现的，但许多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。对于任何特定患者，如果知道导致其病情的实际突变，就可以很容易地确定其他家庭成员是否携带该突变，从而促进仔细监测和潜在的预防性治疗。值得注意的是，尽管在许多情况下我们确切地知道哪种突变会导致特定家庭中的疾病，但在预测特定个体可能发生的情况时，我们往往无能为力。这种现象被称为不完全外显率，即并非所有遗传突变的个体实际上都会患上疾病。它不仅影响 CMP 和 CLP，而且影响几乎所有遗传性疾病。不完全外显率归因于基因和环境之间复杂的相互作用，因此许多修饰影响可以影响疾病的严重程度。尽管这在概念上是令人满意的解释，但它对评估患者的风险几乎没有帮助。即使在这种高度遗传的疾病中，准确预测的失败也会产生真正的实际后果，例如在决定植入除颤器或推荐等治疗方法时，这些方法本身就具有重大风险。在本提案中，我描述了一种逐步解决 CMP/CLP 不完全外显率问题的方法。我首先确定了人群中哪些基因变异可能会改变 CMP 和 CLP 疾病的严重程度。这一步骤需要利用大规模并行基因组技术的最新进展，可以同时询问数以万计的变异体对调节基因活性的影响。有了这份变异目录，人们就可以建立分析方法来评估患者在基因组中每个位置的状态，并确定潜在的因果突变的识别。最后一步是观察实际的 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为严重疾病，以及谁的病程较轻。

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.