Whole Genomic Characterization of Sleep Apnea Traits and Comorbid Disorders

项目摘要(摘要)。睡眠呼吸暂停(SA)是一种常见的疾病，与死亡率增加有关 以及多种不利的健康后果。然而，SA遗传结构的主要方面在很大程度上仍然 未知。了解影响生理相关SA表型的基因可以提供重要的 为治疗干预措施的发展铺平道路。降低成本和提高基因分辨率 继续提高遗传分析的吸引力，以帮助发现 复杂的疾病，如SA。我在SA的基因分析中发挥了核心作用 世界上最大的个体基因组数据库与客观睡眠表型的协调。这 导致了几个基因组水平上与SA特征的显著关联的发现，其中包括 具有生物吸引力的路径。进一步的进展需要对更大的更高分辨率的数据集进行分析 基因数据，使稀有和功能变异的详细特征以及战略后续行动成为可能 阐明机械途径的生理学研究。这是一个前所未有的机会来进行这项工作 通过与电子健康记录(EHR)相关的基因数据，在超过150,000人中进行了研究 个人和NHLBI精密医学全基因组(TOPMed)联盟的建立。 最大的遗传分辨率将通过对1,000个成员的全基因组测序来提供 克利夫兰家庭研究，以及在队列中进行多导睡眠监测的另外3000名无关个体。至 发现SA的因果遗传变异，我将a)使用基于EHR的病例对照设计进行GWAS；b) 使用这些关联和我们最近确定的88个其他关联来询问SA的因果变量 使用来自表型良好的队列的TOPMed全基因组测序数据；c)应用多变量分析 利用具有心脏代谢特征的SA的共同遗传结构来增加统计能力和 阐明共同的生物学。高度可遗传的生理相关SA特征将被用来发现其他 使用临床呼吸暂停低通气指数(AHI)测量不明显的相关基因座。进阶 整合了关联分析的统计数据，来自富含稀有变异的家系的连锁结果， 调控区域和蛋白质突变严重程度将最大限度地发挥常见和罕见变异的作用 关联精细映射。美国国立卫生研究院对TOPMed和 精准医学倡议展示了遗传学领域充满希望和可持续的未来 流行病学和对受过适当培训的调查人员的需求。建议对金黄色葡萄球菌进行基因组分析 应对美国国立卫生研究院睡眠障碍研究计划的多个目标。拟议的工作和 发展出的技能将使我成为一名独立的研究人员，能够进一步确定基因的作用 睡眠呼吸暂停的病因和共病的变异。

Project Summary (Abstract). Sleep apnea (SA) is a common disorder, associated with increased mortality and multiple adverse health outcomes. Yet major aspects of the genetic architecture of SA remain largely unknown. Knowledge of genes that influence physiologically relevant SA phenotypes can provide an important avenue for the development of therapeutic interventions. Decreasing costs and increasing genetic resolution continue to raise the attractiveness of genetic analyses to aid in discovery of the molecular mechanisms of complex disorders such as SA. I have played a central role in the genetic analysis of SA with the assembly and harmonization of the largest genomic dataset of individuals with objective sleep phenotyping in the world. This has led to the discovery of several genome-level significant associations with SA traits, which include loci in biologically compelling pathways. Further advances require analysis of larger datasets with higher resolution genetic data, permitting detailed characterization of rare and functional variants, as well as strategic follow-up physiological studies to elucidate mechanistic pathways. An unprecedented opportunity to conduct this research has emerged through genetic data linked to electronic health records (EHR) in over 150,000 individuals and the establishment of the NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium. Maximal genetic resolution will be provided through whole-genome sequencing of 1,000 members of the Cleveland Family Study, and >3,000 additional unrelated individuals in cohorts with polysomnography. To discover causal genetic variants for SA, I will a) perform a GWAS using an EHR-based case-control design; b) use these associations and 88 other associations we recently identified to interrogate causal variants for SA using TOPMed whole-genome sequencing data from well-phenotyped cohorts; c) apply multivariate analyses that exploit the shared genetic architecture for SA with cardiometabolic traits to increase statistical power and elucidate shared biology. Highly heritable, physiologically relevant SA traits will be used to discover additional associated loci that are not evident using the clinical Apnea Hypopnea Index (AHI) measure. Advanced statistics that integrate association analyses, linkage results from families enriched with rare variants, regulatory regions, and protein mutation severity will maximize power for common and rare variant association fine-mapping. The prominent NIH commitment to large-scale studies such as TOPMed and the Precision Medicine Initiative demonstrates a promising and sustained future for the field of genetic epidemiology and a need for suitably trained investigators. The proposed genomic analysis of SA is further responsive by addressing multiple goals of the NIH Sleep Disorders Research Plan. The proposed work and developed skills will position me as an independent researcher capable of further identifying the role of genetic variants in the etiology and co-morbidity of sleep apnea.