Discovery of genes for sleep traits

描述（由申请人提供）：睡眠不足、睡眠质量差和夜班工作会增加心血管疾病、2型糖尿病、肥胖、情绪障碍和全因死亡的风险。睡眠障碍本身造成了巨大的公共卫生和经济负担。尽管睡眠是一种具有重要遗传作用的基本行为，但人类睡眠调节变异性的遗传基础以及与慢性疾病共享的生物学途径几乎完全未知。睡眠时间、时间和质量是可遗传的，这为识别潜在的基因和生物学途径提供了机会。然而，睡眠表型还取决于社会和环境因素以及疾病状况，需要大量数据集和仔细考虑协变量来检测遗传影响。我们假设，利用现有的大规模公开的基于人群的数据集和增强的混合关联和协变量建模的统计方法，对全基因组关联研究（GWAS）进行荟萃分析，将识别出对睡眠调节重要的新基因和生物学途径。为了验证这一假设，我们提出了以下具体目标：1)在公开可用的数据集中协调自我报告的睡眠时间、时间和质量表型；2)通过对欧洲和非洲裔美国人（AA）血统的受试者进行GWAS和荟萃分析，确定与遗传睡眠特征相关的遗传变异。通过对公开可用的GWAS队列的二次分析来识别睡眠表型基因是一种经济有效的方法，可以深入了解睡眠调节的生物学途径。这些知识对于开发新的睡眠障碍诊断和治疗方法，以及理解睡眠与相关慢性疾病之间的因果关系，从而对这些疾病进行有效干预是必要的。

DESCRIPTION (provided by applicant): Sleep deficiency, poor sleep and night shift work increase risk of cardiovascular disease, type 2 diabetes, obesity, mood disorders and all cause mortality. Sleep disorders themselves pose a large public health and economic burden. Although sleep is a fundamental behavior with a significant genetic contribution, the genetic basis of variability in sleep regulation in the human population and shared biological pathways with chronic disease is almost completely unknown. Sleep duration, timing and quality are heritable, providing opportunities to identify underlying genes and biological pathways. However, sleep phenotypes also depend on social and environmental factors and disease conditions, requiring large datasets and careful consideration of covariates to detect genetic effects. We hypothesize that meta-analysis of genome-wide association studies (GWAS) using existing large-scale publicly available population-based datasets and enhanced statistical methods for admixture association and covariate modeling will identify new genes and biological pathways important for sleep regulation. In order to test this hypothesis, we propose the following specific aims: 1) To harmonize self-reported sleep duration, timing and quality phenotypes across publicly available datasets, and 2) To identify genetic variants associated with heritable sleep traits by performing GWAS and meta-analyses in subjects of European and African American (AA) ancestry. Identifying genes for sleep phenotypes using secondary analysis of publicly available GWAS cohorts is a cost-effective and efficient way to gain insights into biological pathways underlying sleep regulation. This knowledge is necessary for development of novel diagnostics and therapeutics for sleep disorders and for understanding causal relationships between sleep and associated chronic diseases to enable effective interventions for these conditions.