Genetics of Extreme Phenotypes of OSA and Associated Upper Airway Anatomy

摘要 项目01的总体目标是通过以下方式提高我们识别与OSA相关的遗传因素的能力 研究上呼吸道解剖学和阻塞性睡眠呼吸暂停综合征的极端表型存在已知的解剖学风险因素（降低 颅面骨骼和扩大的软组织结构，包括舌脂肪），已被证明， 可以遗传我们假设，某些OSA遗传风险变异通过解剖结构的变化起作用， 异构和极端OSA。这一假设的一个基本方面是，研究遗传关联 已知引起OSA的中间解剖表型将有助于识别 异质性病因的存在。为了解决总体假设，我们有三个具体目标。 在目标1中，我们应用自动化、大规模、高通量和先进的机器学习技术， 临床获得的头部和颈部的MR（磁共振）和CT（计算机断层扫描）图像， 用相关的生物库数据量化OSA患者的上气道解剖学风险因素。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 这反过来又会影响OSA的风险。这些数据将用于生成增强的 OSA的多基因风险评分结合了解剖学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，我们将对遗传基因座进行计算机分析，以确定核心生物学特性， 机制，并优先考虑可能的因果变异和相关信号的基因。这些分析将 提供对GWAS基因座的意义的见解，并直接通过下游分析进行补充， 在项目04中执行的基于细胞的系统和模型系统。最后，在目标3中，我们将使用极端表型 旨在确定目标1-2中优先考虑的基因中的新的解剖学关联和罕见的遗传变异， 将用于进一步提高多基因风险评分和疾病机制的理解。这项建议 拥有一支非常强大的调查团队，拥有OSA解剖和遗传分析方面的专业知识， 创新策略，包括深层解剖表型分析、新型机器学习算法和尖端 用于鉴定和询问OSA易感性基因座的分析方法。该提案的结果将 导致更好地理解遗传学和上气道解剖学对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。这种深入到 OSA的数量解剖学中间性状的遗传基础将显著地推动这一领域的发展 通过将遗传变异与生物学机制联系起来， 评分具有广泛的应用，从早期发现和治疗到筛查和病例 在电子病历中识别OSA。结合本计划中的其他项目， 预计将促进GWAS向更个性化和更精确的临床护理的转化。

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.