Genetics of Extreme Phenotypes of OSA and Associated Upper Airway Anatomy

摘要 项目01的总体目标是提高我们识别与阻塞性睡眠呼吸暂停综合征相关的遗传因素的能力 研究上呼吸道解剖和OSA极端表型。存在已知的解剖风险因素(减少 头面部骨骼和增大的软组织结构，包括舌头脂肪)，已被证明 可遗传的。我们推测，某些阻塞性睡眠呼吸暂停综合征的遗传风险变异通过解剖结构的改变而起作用，导致 异质性和极端阻塞性睡眠呼吸暂停综合征。这一假说的一个基本方面是研究遗传关联 已知的引起OSA的中间解剖表型将有助于识别遗传因素 异质性病因的存在。为了解决这一首要假设，我们有三个具体目标。 在目标1中，我们将自动化、大规模、高通量和先进的机器学习技术应用于 临床获得的头颈部MR(磁共振)和CT(计算机断层扫描)图像 用关联的生物库数据量化OSA患者的上呼吸道解剖危险因素。使用这些 表型，然后我们将进行全基因组关联研究(GWAS)，以确定与 解剖结构，预计将影响阻塞性睡眠呼吸暂停综合征的风险。这些数据将用于生成增强的 OSA的多基因风险分数，包括解剖风险因素的遗传预测因子(例如，舌头脂肪， 下颌长度)。在目标2中，我们将进行遗传基因座的电子分析，以确定核心生物学 机制和优先顺序可能的因果变异和潜在的关联信号的基因。这些分析将 提供对Gwas基因座的重要性的见解，并直接得到以下下游分析的补充 项目04中正在执行基于单元的系统和模型系统。最后，在目标3中，我们将使用一个极端的表型 设计以识别AIMS 1-2中优先考虑的基因中的新的解剖关联和罕见的遗传变异，这 将被用来进一步提高多基因风险评分和对疾病机制的理解。这项建议 拥有一支非常强大的调查团队，拥有OSA解剖学和基因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 识别和询问OSA易感基因座的分析方法。这项提案的结果将 结果更好地理解了遗传和上呼吸道解剖对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。这一次深入研究 OSA数量解剖中间性状的遗传基础将显著推动这一领域的发展 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 评分具有从早期发现和治疗到筛查和病例的广泛应用 OSA在电子病历中的标识。与本计划中的其他项目相结合，结果 预计将有助于将GWA转化为更个性化和更精确的临床护理。

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.