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 的总体目标是通过以下方式提高我们识别与 OSA 相关遗传因素的能力： 研究上呼吸道解剖学和 OSA 极端表型。有已知的解剖学危险因素（减少 颅面骨骼和扩大的软组织结构（包括舌头脂肪）对于 OSA 已被证明可以 可遗传。我们假设某些 OSA 遗传风险变异通过解剖结构的变化起作用，从而导致 异质且极端的 OSA。该假设的一个基本方面是研究遗传关联 具有已知导致 OSA 的中间解剖表型将有助于识别遗传因素 异质病因的存在。为了解决总体假设，我们有三个具体目标。 在目标 1 中，我们应用自动、大规模、高通量和先进的机器学习技术来 临床获得的头颈部 MR（磁共振）和 CT（计算机断层扫描）图像 通过链接的生物库数据量化患者 OSA 的上呼吸道解剖学危险因素。使用这些 表型，然后我们将进行全基因组关联研究（GWAS）来识别与 解剖结构预计会影响 OSA 的风险。这些数据将用于生成增强型 OSA 的多基因风险评分纳入了解剖学风险因素（例如舌头脂肪、 下颌长度）。在目标 2 中，我们将对基因位点进行计算机分析，以确定核心生物学 机制并优先考虑可能的因果变异和关联信号背后的基因。这些分析将 提供对 GWAS 位点重要性的见解，并通过下游分析直接补充 项目 04 中正在执行基于细胞的模型系统。最后，在目标 3 中，我们将使用极端表型 设计旨在识别目标 1-2 优先考虑的基因中的新解剖关联和罕见遗传变异，其中 将用于进一步提高多基因风险评分和对疾病机制的理解。这个提议 拥有一支非常强大的调查团队，拥有 OSA 解剖学和遗传分析方面的专业知识，并使用 创新策略，包括深度解剖表型、新颖的机器学习算法和尖端技术 用于识别和询问 OSA 易感位点的分析方法。该提案的调查结果将 可以更好地了解遗传学和上呼吸道解剖结构对 OSA 异质性的影响， 这些基因组改变的下游临床影响及其生物学基础。此次深入探讨 OSA 定量解剖中间性状的遗传基础将显着推动该领域的发展 通过将遗传变异与生物机制联系起来，促进多基因风险的发展 分数具有广泛的应用，从早期检测和治疗到筛查和案例 电子病历中 OSA 的识别。与本计划中的其他项目相结合，结果 预计将促进 GWAS 转化为更加个性化和精确的临床护理。