Discovery of genes for sleep traits

发现睡眠特征基因

基本信息

批准号：
8902257
负责人：
RICHA SAXENA
金额：
$ 12.85万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-08-01 至 2016-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8902257
关键词：
Admixture Adult Affect African American Age Factors Behavior Biological Biological Process Biology Blood Characteristics Chronic Chronic Disease Circadian Rhythms Comorbidity Data Data Set Development Diabetes Mellitus Diagnosis Disease Economic Burden Environmental Risk Factor Ethnic Origin European Genes Genetic Genetic Risk Goals Health Heart Heart Diseases Heritability Human Human Genetics Individual Institutes Knowledge Lead Life Link Lung Measurement Measures Meta-Analysis Metabolic Metabolic Diseases Modeling Molecular Mood Disorders Non-Insulin-Dependent Diabetes Mellitus Obesity Outcome Pathway interactions Patient Self-Report Phenotype Physiology Polysomnography Population Prevention Public Health Publishing Regulation Reporting Signal Transduction Sleep Sleep Disorders Sleep Disorders Therapy Sleep disturbances Statistical Methods Testing Time United States National Institutes of Health Variant base burden of illness cardiovascular disorder risk cohort cost effective effective intervention gene discovery genetic approach genetic association genetic variant genome wide association study genome-wide health economics improved insight mortality novel novel diagnostics novel therapeutics population based risk variant sex shift work sleep regulation social time use trait

项目摘要

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.

描述（由申请人提供）：睡眠不足，睡眠和夜班工作不足增加心血管疾病的风险，2型糖尿病，肥胖，情绪障碍以及所有导致死亡率。睡眠障碍本身造成了巨大的公共卫生和经济负担。尽管睡眠是具有重要遗传贡献的基本行为，但几乎完全未知，人口睡眠调节和与慢性疾病共享生物学途径的遗传基础几乎是完全未知的。睡眠持续时间，时机和质量是可遗传的，提供了识别潜在基因和生物学途径的机会。但是，睡眠表型还取决于社会和环境因素和疾病状况，需要大量数据集以及仔细考虑协变量来检测遗传效应。我们假设使用现有的大规模公开基于人群的数据集对全基因组关联研究（GWAS）进行荟萃分析，并增强了混合结合和协变量建模的统计方法，将确定对睡眠调节重要的新基因和生物学途径。为了检验这一假设，我们提出以下具体目的：1）在公开可用的数据集中协调自我报告的睡眠时间，时机和质量表型，以及2）确定通过执行GWAS和META来确定与可遗传睡眠特征相关的遗传变异的遗传变异 - 欧洲和非裔美国人（AA）血统的主题中的Analyses。使用公开可用的GWAS队列的二级分析来识别睡眠表型的基因是一种经济高效，有效的方法，可以洞悉对睡眠调节的生物学途径的见解。这些知识对于开发新的诊断和睡眠障碍疗法以及了解睡眠和相关慢性疾病之间的因果关系以便为这些疾病有效干预措施所必需。