Genetics of Extreme Phenotypes of OSA and Associated Upper Airway Anatomy

OSA 极端表型的遗传学及相关上呼吸道解剖学

• 批准号: 10555809
• 负责人: Richard J. Schwab
• 金额: $ 56.74万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10555809
• 关键词: Address; Affect; Anatomy; Biological; Biological Models; Body Weight decreased; Caring; Cells; Clinic; Clinical; Complement; Complex; Computerized Medical Record; DNA Sequence Alteration; Data; Development; Disease; Early Diagnosis; Early identification; Early treatment; Etiology; Face; Family; Fatty acid glycerol esters; Frequencies; Genes; Genetic; Genetic Risk; Genetic Variation; Genetic study; Head; Head and neck structure; Heritability; Heterogeneity; Human; Image; Individual; Knowledge; Length; Link; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Mandible; Medicine; Methods; Obesity; Obstructive Sleep Apnea; Oral; Outcome; Pathogenesis; Pathway interactions; Patients; Penetrance analysis; Pennsylvania; Phenotype; Process; Risk; Risk Factors; Role; Shapes; Signal Transduction; Skeleton; Sleep; Sleep Apnea Syndromes; Structure; Susceptibility Gene; Techniques; Thinness; Tongue; Translations; Universities; Variant; Woman; Work; X-Ray Computed Tomography; biobank; causal variant; clinical application; clinical care; cohort; craniofacial; design; exome sequencing; genetic analysis; genetic association; genetic predictors; genetic variant; genome wide association study; genomic locus; improved; in silico; innovation; insight; machine learning algorithm; men; middle age; novel; phenome; polygenic risk score; poor health outcome; population based; precision medicine; programs; rare variant; risk variant; screening; soft tissue; tool; trait

ABSTRACT The overall objective of Project 01 is to improve our ability to identify genetic factors relevant to OSA by studying upper airway anatomy and OSA extreme phenotypes. There are known anatomic risk factors (reduced craniofacial skeleton and enlarged soft tissue structures, including tongue fat) for OSA that have been shown to be heritable. We postulate that certain OSA genetic risk variants operate via changes in anatomy, leading to heterogeneous and extreme OSA. A foundational aspect of this hypothesis is that studying genetic associations with intermediate anatomical phenotypes known to cause OSA will facilitate identification of genetic factors in the presence of heterogeneous etiologies. To address the overarching hypothesis, we have three Specific Aims. In Aim 1 we apply automatic, large-scale, high-throughput and advanced machine-learning techniques to clinically-obtained MR (magnetic resonance) and CT (computed tomography) images of the head and neck to quantify upper airway anatomical risk factors for OSA in patients with linked biobank data. Using these phenotypes, we will then perform genome-wide association studies (GWAS) to identify variants related to anatomy which, in turn, are expected to influence risk for OSA. These data will be used to generate enhanced polygenic risk scores for OSA that incorporate genetic predictors of anatomic risk factors (e.g., tongue fat, mandibular length). In Aim 2 we will perform in silico analyses of genetic loci to identify core biological mechanisms and prioritize likely causal variants and genes underlying association signals. These analyses will provide insights into the significance of GWAS loci and be directly complemented by downstream analyses in cell-based and model systems being performed in Project 04. Finally, in Aim 3 we will use an extreme phenotype design to identify novel anatomical associations and rare genetic variants in genes prioritized in Aims 1-2, which will be utilized to further enhance polygenic risk scores and understanding of disease mechanisms. This proposal has a very strong investigative team, with expertise in both OSA anatomy and genetic analysis, and uses innovative strategies including deep anatomic phenotyping, novel machine learning algorithms, and cutting-edge analysis approaches for identifying and interrogating OSA-susceptibility loci. Findings from this proposal will result in a greater understanding of the impact of genetics and upper airway anatomy on OSA heterogeneity, the downstream clinical impact of these genomic alterations, and their biological underpinnings. This deep dive into the genetic underpinnings of quantitative anatomic intermediate traits for OSA will significantly move the field forward by connecting genetic variation to biological mechanisms, enhance the development of polygenic risk scores that have wide-ranging applications from early detection and treatment to screening and case identification of OSA in the electronic medical record. In combination with other Projects in this Program, results are expected to facilitate translation of GWAS to more personalized and precise clinical care.

抽象的 项目01的总体目标是提高我们确定与OSA相关的遗传因素的能力 研究上呼吸道解剖结构和OSA极端表型。有已知的解剖风险因素（减少 颅面骨骼和放大的软组织结构，包括舌头脂肪），已显示为OSA 遗传。我们假设某些OSA遗传风险变体通过解剖学的变化起作用，导致 异质和极端OSA。该假设的一个基本方面是研究遗传关联 随着已知导致OSA的中间解剖表型，将有助于鉴定遗传因素 异质病因的存在。为了解决总体假设，我们有三个具体的目标。 在AIM 1中，我们将自动，大规模，高通量和高级机器学习技术应用于 临床上有用的MR（磁共振）和CT（计算机断层扫描）的头部和颈部图像 量化具有连接生物库数据的患者OSA的上呼吸道解剖风险因素。使用这些 表型，然后我们将进行全基因组关联研究（GWAS），以确定与 反过来，解剖结构预计会影响OSA的风险。这些数据将用于生成增强 OSA的多基因风险评分结合了解剖风险因素的遗传预测因子（例如，舌头脂肪， 下颌长度）。在AIM 2中，我们将在遗传基因座的硅分析中进行识别核心生物学 机制和优先级可能的因果变异和基因信号。这些分析将 提供有关GWAS基因座意义的见解，并通过下游分析直接补充 在项目04中执行基于单元格和模型系统。最后，在AIM 3中，我们将使用极端表型 设计在AIMS 1-2中优先确定基因中的新型解剖结合和稀有遗传变异的设计， 将用于进一步增强多基因风险评分和对疾病机制的理解。这个建议 具有非常强大的调查团队，在OSA解剖学和遗传分析方面具有专业知识，并使用 创新的策略，包括深层解剖表型，新颖的机器学习算法和尖端 分析方法，用于识别和询问OSA敏感性基因座。该提议的发现将 导致对遗传学和上呼吸道解剖学对OSA异质性的影响有更深入的了解， 这些基因组改变及其生物基础的下游临床影响。深入研究 OSA定量解剖中间性状的遗传基础将显着移动该领域 通过将遗传变异与生物学机制联系起来，增强多基因风险的发展 从早期检测和治疗到筛查和案例的广泛应用的分数 在电子病历中识别OSA。结合该计划中的其他项目，结果 期望将GWAS转换为更个性化和精确的临床护理。