Genetic Epidemiology of Sleep Apnea and Comorbidities in Biobanks

摘要 睡眠呼吸暂停（SA）和失眠是两种最常见的睡眠障碍，两者都是单独造成的， 共同导致心肺、代谢和精神疾病的风险。尽管其发病率很高， SA和失眠症的治疗仍不理想。SA和失眠被认为是由不同的 亚型，这些亚型仍然定义不清，可能导致健康结果的不同风险。我们的目标是 使用机器学习对生物库进行精确的表型分析，以确定SA的遗传基础， 根据遗传学和合并症发现SA和失眠亚型，以减少 表型异质性，指导患者分层，并帮助发现更个性化的治疗。 我们的方法是将联合收割机医疗保健系统生物库数据与研究性多导睡眠图（PSG）相结合， 发现SA和失眠相关表型的遗传变异并表征其特征的统计能力 相关的临床结果和内在表型（生理机制）。我们将使用先进的自然 语言处理（NLP）方法，以大大提高SA和失眠表型的准确性。 我们预期的样本量将比之前的SA遗传研究大11倍，提供必要的信息。 基因发现的统计力量从我们的研究结果得出的多基因风险评分可用于量化 睡眠障碍风险，即使在那些没有睡眠表型的人中。机器学习方法可以识别 包含在医疗记录内的诊断聚类患者组的预测因子。精准深入- 表型PSG数据（例如低氧负荷）可以使用以下方法表征相关遗传基因座的内表型： 基因定位我们将得出先进的SA和失眠表型强大的人口统计学差异 跨生物库站点，对迄今为止验证的SA和失眠表型进行最大规模的遗传分析， 表征新的基因座，并研究与临床诊断数据的关联，以改善患者分类， 三个生物银行我们将探讨性别特异性协会和验证铅遗传协会在两个生物库。 我们的具体目标是：1）在不同的领域构建先进的SA和失眠表型算法 2）确定和描述SA和失眠症的遗传关联; 以及3）基于相关的共病来识别和表征不同的SA和失眠患者亚组 数据区.该项目的目标是改善心脏、肺、血液和睡眠障碍的治疗 通过潜在地解决疾病异质性，发现与睡眠障碍的新的遗传关联， 有助于澄清SA和失眠与心肺、代谢和精神疾病的重叠。

ABSTRACT Sleep apnea (SA) and insomnia are the two most common sleep disorders, and both contribute individually and jointly to the risk of cardiopulmonary, metabolic, and psychiatric diseases. Despite their high prevalence, treatments for SA and insomnia remain suboptimal. SA and insomnia are thought to be comprised of distinct subtypes, which remain poorly defined and may contribute to differing risks for health outcomes. Our goal is to use machine learning to apply precise phenotyping to biobanks to identify the genetic bases of SA and insomnia and discover SA and insomnia subtypes based on genetics and comorbidities in order to reduce phenotype heterogeneity, guide patient stratification and aid in the discovery of more personalized treatments. Our approach is to combine health care system biobank data with research polysomnography (PSG) to achieve statistical power to discover genetic variants for SA and insomnia-related phenotypes and characterize their associated clinical outcomes and endophenotypes (physiological mechanisms). We will use advanced natural language processing (NLP) methods to substantially improve the accuracy of SA and insomnia phenotyping. Our anticipated sample size will be >11-fold larger than prior genetic studies of SA, providing the necessary statistical power for genetic discovery. Polygenic risk scores derived from our results can be used to quantify sleep disorder risk, even among those without sleep phenotypes. Machine learning methods can identify predictors of diagnosis-clustered patient groups contained within the medical record. Precision deeply- phenotyped PSG data (eg hypoxic burden) can characterize endophenotypes at associated genetic loci using genetic localization. We will derive advanced SA and insomnia phenotypes robust to demographic differences across biobank sites, perform the largest genetic analysis of validated SA and insomnia phenotypes to date, characterize novel loci, and study associations with clinical diagnosis data to improve patient classification in three biobanks. We will explore sex-specific associations and validate lead genetic associations in two biobanks. Our specific aims are: 1) to construct advanced SA and insomnia phenotying algorithms across diverse demographic groups and sites; 2) to identify and characterize the genetic associations with SA and insomnia; and 3) to identify and characterize distinct SA and insomnia patient subgroups based on related comorbidity profiles. The proposed project has a goal of improving the treatment of heart, lung, blood, and sleep disorders by potentially resolving disease heterogeneity, discovering novel genetic associations with sleep disorders, and helping to clarify the overlap of SA and insomnia with cardiopulmonary, metabolic, and psychiatric disease.