Investigating the Genotype-Phenotype Relationships that Underlie Congenital Disorders with Cardiovascular Symptoms through Population-scale Analyses

通过人群规模分析研究具有心血管症状的先天性疾病背后的基因型-表型关系

• 批准号: 10724185
• 负责人: David Randall Blair
• 金额: $ 10.98万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10724185
• 关键词: Academic skills; Affect; All of Us Research Program; Award; Back; Benign; Biochemical; Biological Assay; Calibration; Cardiovascular Diseases; Cardiovascular system; Caring; Case Series; Childhood; Classification; Clinical; Clinical Data; Cohort Studies; Complex; Computer Models; Congenital Disorders; DNA Sequence Alteration; Data; Data Analyses; Data Set; Databases; Development; Diagnosis; Diagnostic; Diagnostic tests; Disease; Electronic Health Record; Ensure; Funding Opportunities; Future; Gene Frequency; Genes; Genetic; Genetic Diseases; Genetic Transcription; Genetic Variation; Genotype; Goals; Grant; Hereditary hemorrhagic telangiectasia; Human; Human Genetics; Individual; Investigation; Knowledge; Laboratories; Learning; Link; Literature; Marfan Syndrome; Measurement; Measures; Medical; Medical Genetics; Medical History; Mendelian disorder; Mentors; Methods; Modeling; Molecular; Mutation; Onset of illness; Outcome; Pathogenicity; Patients; Persons; Phenotype; Physicians; Physiological; Population; Prognosis; Property; Reproducibility; Research; Research Proposals; Science of genetics; Scientist; Sea; Severities; Severity of illness; Specific qualifier value; Symptoms; Syndrome; Technical Expertise; Technology; Testing; Training; Transcript; Uncertainty; Validation; Variant; Writing; biobank; biomedical data science; career development; clinical diagnostics; clinical effect; clinical practice; design; disease heterogeneity; disorder risk; genetic testing; genetic variant; genome sequencing; genomic data; health care quality; improved; in silico; ineffective therapies; insight; loss of function; model building; multiple datasets; predictive modeling; programs; protein structure; rare variant; research clinical testing; tool; trait; variant of unknown significance; wasting

PROJECT SUMMARY/ABSTRACT Although individually rare, Mendelian diseases are collectively common. Nearly 1% of people have a medical condition that can be traced back to a single gene. This is particularly true for patients with cardiovascular disease. Genetic testing is now commonly employed in clinical practice. As a result, it has become clear that we have an incomplete understanding of how genetic mutations cause Mendelian disease. Some patients with deleterious mutations display very severe symptoms while others are almost entirely unaffected. This is true even for childhood-onset disorders with severe cardiovascular symptoms, like Marfan Syndrome. Understanding the range of disease severity has important implications for diagnosis, prognosis, and management. In the past, cohort studies and case series have been used to gather this type of information, but these yield incomplete and biased views of disease heterogeneity. Therefore, new methods for studying Mendelian disease genetic and phenotypic diversity are urgently needed. Population-scale biobanks linked to electronic health records (EHRs) can provide a less biased view of genotype-to-phenotype relationships. Subjects are included in these datasets regardless of their medical history. EHR data also provides detailed phenotypic information on each subject. Finally, these biobanks now include hundreds of thousands of individuals, capturing rare genetic variation on an unprecedented scale. As a result, we hypothesize that population-scale biobanks can provide new insight into the genotype-to-phenotype relationships that underlie congenital cardiovascular syndromes (CCSs). This hypothesis will be tested in two specific aims. In Aim 1, we will use biobanks to develop quantitative scores that reproducibly summarize CCS- related phenotypic severity. These traits have multiple applications. In Aim 2, we will use them to build computational models that predict the phenotypic effects of CCS-related rare variants directly from sequence context. Once validated, these models should reduce diagnostic uncertainty in clinical practice. I am a clinical geneticist and physician-scientist devoted to improving the quality of healthcare provided to Mendelian disease patients. Long term, I plan to develop an independent research program that uses complex clinical and genetic datasets to improve our understanding of Mendelian disease risk, variability, and progression. My K38 research proposal is entirely consistent with these goals. In addition, it will provide valuable career development. New technical skills in EHR data analysis and statistical genetics will be acquired, as will academic skills like grant writing. During the award, I will be mentored by leaders in the fields of biomedical data science and human genetics, including Dr. Atul Butte and Dr. Neil Risch. Finally, the K38 award will serve as springboard for future funding opportunities and research independence. Therefore, the K38 StARRTS award will serve as a critical milestone in my development as a physician scientist.

项目概要/摘要 孟德尔疾病虽然个别罕见，但总体上很常见。近 1% 的人拥有 可以追溯到单个基因的医疗状况。对于患有以下疾病的患者尤其如此 心血管疾病。基因检测目前广泛应用于临床实践。结果，它有 很明显，我们对基因突变如何导致孟德尔病的了解还不完全。 一些携带有害突变的患者表现出非常严重的症状，而另一些患者则几乎完全没有症状。 不受影响。即使对于具有严重心血管症状的儿童期发病的疾病（例如马凡氏症）也是如此 综合症。了解疾病严重程度的范围对于诊断、预后、 和管理。过去，队列研究和案例系列已被用来收集此类信息， 但这些产生了对疾病异质性的不完整和有偏见的观点。因此，新的研究方法 迫切需要孟德尔疾病遗传和表型多样性。 与电子健康记录 (EHR) 相关的人口规模生物库可以提供较少偏见的观点 基因型与表型的关系。无论受试者的医疗状况如何，都包含在这些数据集中 历史。 EHR 数据还提供每个受试者的详细表型信息。最后，这些生物库现在 包括数十万个体，以前所未有的规模捕捉罕见的遗传变异。作为一个 结果，我们假设人口规模的生物库可以提供对基因型到表型的新见解 先天性心血管综合征（CCS）背后的关系。这个假设将分两部分进行检验 具体目标。在目标 1 中，我们将使用生物样本库来开发定量评分，以可重复地总结 CCS- 相关的表型严重程度。这些特征有多种应用。在目标 2 中，我们将使用它们来构建 直接从序列预测 CCS 相关罕见变异表型效应的计算模型 语境。一旦经过验证，这些模型将减少临床实践中诊断的不确定性。 我是一名临床遗传学家和医师科学家，致力于提高医疗保健质量 提供给孟德尔病患者。从长远来看，我计划开发一个独立的研究项目 使用复杂的临床和遗传数据集来提高我们对孟德尔疾病风险、变异性的理解， 和进展。我的 K38 研究计划与这些目标完全一致。此外，还将提供 有价值的职业发展。将获得电子病历数据分析和统计遗传学方面的新技术技能， 学术技能（如资助写作）也是如此。在颁奖期间，我将受到以下领域的领导者的指导 生物医学数据科学和人类遗传学，包括 Atul Butte 博士和 Neil Risch 博士。最后是K38 该奖项将作为未来资助机会和研究独立性的跳板。因此， K38 StARRTS 奖将成为我作为一名医师科学家发展过程中的一个重要里程碑。