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
基本信息
- 批准号:10724185
- 负责人:
- 金额:$ 10.98万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:Academic skillsAffectAll of Us Research ProgramAwardBackBenignBiochemicalBiological AssayCalibrationCardiovascular DiseasesCardiovascular systemCaringCase SeriesChildhoodClassificationClinicalClinical DataCohort StudiesComplexComputer ModelsCongenital DisordersDNA Sequence AlterationDataData AnalysesData SetDatabasesDevelopmentDiagnosisDiagnosticDiagnostic testsDiseaseElectronic Health RecordEnsureFunding OpportunitiesFutureGene FrequencyGenesGeneticGenetic DiseasesGenetic TranscriptionGenetic VariationGenotypeGoalsGrantHereditary hemorrhagic telangiectasiaHumanHuman GeneticsIndividualInvestigationKnowledgeLaboratoriesLearningLinkLiteratureMarfan SyndromeMeasurementMeasuresMedicalMedical GeneticsMedical HistoryMendelian disorderMentorsMethodsModelingMolecularMutationOnset of illnessOutcomePathogenicityPatientsPersonsPhenotypePhysiciansPhysiologicalPopulationPrognosisPropertyReproducibilityResearchResearch ProposalsScience of geneticsScientistSeaSeveritiesSeverity of illnessSpecific qualifier valueSymptomsSyndromeTechnical ExpertiseTechnologyTestingTrainingTranscriptUncertaintyValidationVariantWritingbiobankbiomedical data sciencecareer developmentclinical diagnosticsclinical effectclinical practicedesigndisease heterogeneitydisorder riskgenetic testinggenetic variantgenome sequencinggenomic datahealth care qualityimprovedin silicoineffective therapiesinsightloss of functionmodel buildingmultiple datasetspredictive modelingprogramsprotein structurerare variantresearch clinical testingtooltraitvariant of unknown significancewasting
项目摘要
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
虽然个别罕见,孟德尔疾病是共同的。近1%的人有
可以追溯到单个基因的医学状况。对于患有以下疾病的患者尤其如此:
心血管疾病基因检测现在普遍用于临床实践。因此,
很明显,我们对基因突变如何导致孟德尔疾病的理解并不完整。
一些携带有害突变的患者表现出非常严重的症状,而另一些患者几乎完全
不受影响即使是有严重心血管症状的儿童期发病的疾病,如马凡氏症,
综合征了解疾病严重程度的范围对诊断、预后
和管理过去,队列研究和病例系列被用来收集这类信息,
但这些产生了对疾病异质性的不完整和有偏见的看法。因此,新的研究方法
迫切需要孟德尔疾病的遗传和表型多样性。
与电子健康记录(EHR)相关的人口规模生物库可以提供一个更少偏见的观点,
基因型与表型的关系受试者被纳入这些数据集中,无论其医疗状况如何。
历史EHR数据还提供了每个受试者的详细表型信息。最后,这些生物银行现在
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
因此,我们假设,人口规模的生物库可以提供新的见解基因型表型
先天性心血管综合征(CCS)的基础关系。这一假设将在两个
具体目标。在目标1中,我们将使用生物库开发可重复总结CCS的定量评分-
相关的表型严重性。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相关罕见变异的表型效应的计算模型
上下文一旦得到验证,这些模型应减少临床实践中的诊断不确定性。
我是一名临床遗传学家和医学科学家,致力于提高医疗质量
提供给孟德尔疾病患者。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,
和进步。我的K38研究计划完全符合这些目标。此外,它还将提供
有价值的职业发展。将获得EHR数据分析和统计遗传学方面的新技术技能,
学术能力也是一样比如写研究报告在获奖期间,我将受到以下领域领导人的指导:
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
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生科学家发展的重要里程碑。
项目成果
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