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Elucidation of Genetic Effects on Sleep and Circadian Traits

阐明遗传对睡眠和昼夜节律特征的影响

基本信息

批准号：
10216326
负责人：
Philip Richard Gehrman
金额：
$ 76.76万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2023-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10216326
关键词：
ATAC-seq Accelerometer Accidents Address Animal Model Anxiety Behavior Behavioral Biological Biological Models CRISPR/Cas technology Cardiovascular system Chromatin Circadian Dysregulation Circadian Rhythm Disorder Circadian Rhythms Clinical Computational Biology Computing Methodologies Data Data Discovery Data Set Desire for food Development Diagnostic Drosophila genus Enhancers Epidemiology Epigenetic Process Etiology Family Genes Genetic Genome Genomic Segment Genomics Genotype Goals Heritability Human Human Genome In Vitro Individual Insulin Resistance Italy Knowledge Light Link Location Maintenance Measures Mental Depression Mental Health Mining Molecular Morbidity - disease rate Motor Vehicles Nuclear Pore Complex Obesity Pathology Pathway interactions Patient Care Patient Self-Report Pharmacology Phenotype Play Preventive treatment Public Domains Public Health Risk Role Science Signal Transduction Sleep Sleep disturbances Sleeplessness Symptoms System Systems Biology Time Translating Untranslated RNA Variant Workplace biobank causal variant chromosome conformation capture circadian cohort fly gene discovery genetic information genetic variant genome wide association study genomic variation improved novel novel therapeutics physical conditioning risk variant sleep regulation stem cells tool trait

项目摘要

PROJECT SUMMARY Sleep and circadian rhythms play critical roles in the maintenance of physical and mental health, with disturbances in these domains associated with major public health consequences. Despite the abundance of epidemiological evidence linking sleep and circadian traits to diverse pathologies, little is known about the genetic factors that underlie these traits. Sleep and circadian traits, such as insomnia and chronotype, are highly heritable and aggregate in families, suggesting a substantial proportion of risk is due to genetics. Progress has been made in identifying risk variants for these traits using genome-wide association studies (GWAS). However, GWAS results principally reside in non-coding regions of the genome and rarely pinpoint the precise location of the actual effector genes. Mounting evidence is indicating that simply attributing GWAS signals to the genes encoded in the closest genomic regions is not always accurate. As such, GWAS alone is primarily beneficial to signal discovery, not functional gene discovery. The difficulty in elucidating the actual effector genes for human GWAS signals is a significant barrier to identifying novel molecular regulators of sleep. Despite wide-ranging tools utilizing model systems and in vitro approaches to conduct such functional analyses, as well as computational methods to implicate causal variants, these approaches are underutilized in the sleep and circadian domain. This proposal seeks to fill this critical knowledge gap by employing a team science approach, bringing together expertise in the genomics of sleep and circadian rhythms that spans humans, model systems, and computational biology, while simultaneously leveraging large existing datasets for genetic discovery. In Aim 1, we will leverage existing biobank data for discovery of risk variants for insomnia and chronotype. This will be followed, in Aim 2, by examination via a battery of spatial genomic analyses of these loci using IPSC-derived neuro progenitor cells to identify causal variants. Finally, we will determine which genes have functional relevance using Drosophila as a model system. Our global hypothesis is that genomic variation is strongly associated with the behavioral manifestation of insomnia and chronotype and that our cutting-edge molecular approaches will elucidate the causal variants and the corresponding effector genes at these loci. We aim to translate genetic information into meaningful benefits for patient care by uncovering the correct functional context of GWAS identified genomic variants involved in these traits and understanding how they operate in this context.

项目总结睡眠和昼夜节律在维持身心健康方面发挥着关键作用，这些领域的骚乱与重大公共卫生后果有关。尽管有丰富的流行病学证据将睡眠和昼夜节律特征与不同的病理联系起来，对此我们知之甚少这些特征背后的遗传因素。睡眠和昼夜节律特征，如失眠和时型，高度遗传性和聚集性在家族中，这表明相当大比例的风险是由遗传引起的。利用全基因组关联研究在识别这些特征的风险变异方面取得了进展 (GWAS)。然而，Gwas的结果主要存在于基因组的非编码区，很少精确定位实际效应基因的精确位置。越来越多的证据表明，简单地将全球气候变化在最接近的基因组区域编码的基因的信号并不总是准确的。因此，仅GWAS一家就是主要有益于信号发现，而不是功能基因发现。澄清实际情况的困难在于人类GWAS信号的效应基因是识别新的分子调控因子的重要障碍睡吧。尽管利用模型系统和体外方法的广泛工具来进行这种功能分析以及推断因果变量的计算方法，这些方法在以下方面没有得到充分利用睡眠和昼夜节律领域。这项提议试图通过雇佣一个团队来填补这一关键的知识缺口科学方法，汇集了睡眠和昼夜节律基因组学方面的专业知识人类、模型系统和计算生物学，同时利用现有的大型数据集用于基因发现。在目标1中，我们将利用现有的生物库数据来发现失眠的风险变异和时型。在目标2中，这之后将通过一系列空间基因组分析进行检查这些基因座使用IPSC来源的神经前体细胞来识别因果变异。最后，我们将确定哪一个以果蝇为模型系统，基因具有功能相关性。我们的全球假设是基因组变异与失眠和时型的行为表现密切相关，我们的尖端的分子方法将阐明因果变异和相应的效应基因这些轨迹。我们的目标是通过揭示基因信息为患者护理带来的有意义的好处 GWAS的正确功能上下文确定了与这些特征有关的基因组变异，并理解了他们在这种背景下运作。