Whole Genomic Characterization of Sleep Apnea Traits and Comorbid Disorders

项目摘要（Abstract）睡眠呼吸暂停（SA）是一种常见的疾病，与死亡率增加有关 和多种不良健康后果。然而，SA遗传结构的主要方面仍然主要是 未知对影响生理相关SA表型的基因的了解可以提供重要的 开发治疗干预措施的途径。降低成本，提高遗传分辨率 继续提高遗传分析的吸引力，以帮助发现遗传病的分子机制。 复杂疾病如SA。我在SA的遗传分析中发挥了核心作用， 协调世界上最大的个体基因组数据集与客观睡眠表型。这 导致发现了几个基因组水平与SA性状的显著关联，其中包括 生物学上的强制途径进一步的发展需要分析更大的数据集和更高的分辨率 遗传数据，允许详细描述罕见和功能性变体，以及战略性后续行动 生理学研究，以阐明机制途径。这是一个前所未有的机会 研究已经通过与电子健康记录（EHR）相关联的遗传数据出现， NHLBI Trans-Omics for Precision Medicine（TOPMed）Consortium的成立。 将通过对1,000名成员的全基因组测序提供最大的遗传分辨率。 克利夫兰家庭研究，以及多导睡眠图队列中的> 3，000个额外的无关个体。到 发现SA的致病遗传变异，我将a）使用基于EHR的病例对照设计进行GWAS; B） 我们使用这些关联和我们最近发现的88个其他关联来询问SA的因果变异 使用来自表型良好的队列的TOPM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的共享遗传结构来增加统计功效， 阐明共同的生物学。高度遗传的，生理相关的SA性状将用于发现额外的 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。先进 整合关联分析的统计数据，来自富含罕见变异的家族的连锁结果， 调节区域和蛋白质突变严重程度将最大限度地提高常见和罕见变异的功效。 关联精细映射NIH对大规模研究（例如TOPMed和 精准医学计划展示了遗传学领域充满希望和可持续的未来 流行病学和需要经过适当培训的调查人员。建议的SA基因组分析进一步 通过解决NIH睡眠障碍研究计划的多个目标来响应。拟议工作并 发展的技能将使我成为一个独立的研究人员，能够进一步确定遗传的作用， 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.