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; 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.

项目摘要/摘要 孟德尔疾病虽然很少见，但总的来说是普遍的。近1％的人有一个 可以追溯到单个基因的疾病。对于患者 心血管疾病。基因检测现在通常用于临床实践。结果，它具有 清楚地表明，我们对遗传突变如何引起孟德尔病有不完全的了解。 一些有害突变的患者表现出非常严重的症状，而另一些则几乎完全是 不受影响。即使对于患有严重心血管症状的童年发作疾病，也是如此 综合征。了解疾病严重程度的范围对诊断，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， 但是，这些产生了对疾病异质性的不完整和偏见的看法。因此，研究的新方法 迫切需要孟德尔病遗传和表型多样性。 与电子健康记录相关的人口规模的生物库（EHR）可以提供较少的偏见的观点 基因型与表型关系。无论他们的医学如何 历史。 EHR数据还提供了有关每个主题的详细表型信息。最后，这些生物库现在 包括数十万个个体，以前所未有的量表捕获罕见的遗传变异。作为 结果，我们假设人口规模的生物库可以为基因型到表型提供新的见解 基于先天性心血管综合征（CCSS）的关系。该假设将在两个中进行检验 具体目标。在AIM 1中，我们将使用生物库来开发可重复总结CCS-的定量得分 相关的表型严重程度。这些特征有多个应用程序。在AIM 2中，我们将使用它们来构建 直接从序列直接预测CCS相关稀有变体的表型效应的计算模型 语境。一旦得到验证，这些模型应减少临床实践中的诊断不确定性。 我是一位致力于提高医疗保健质量的临床遗传学家和医师科学家 提供给门德尔病患者。长期，我计划制定一个独立的研究计划 使用复杂的临床和遗传数据集来提高我们对门德尔疾病风险，可变性， 和进展。我的K38研究建议与这些目标完全一致。此外，它将提供 宝贵的职业发展。将获得EHR数据分析和统计遗传学方面的新技术技能， 诸如授予写作之类的学术技巧也是如此。在奖励期间，我将受到领导者的指导 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，K38 奖项将成为未来资助机会和研究独立性的跳板。因此， K38 Starrts奖将成为我作为医师科学家发展的关键里程碑。