The genetics of sleep patterns and their relationship to obesity and Type 2 diabetes

睡眠模式的遗传学及其与肥胖和 2 型糖尿病的关系

• 批准号: MR/P012167/1
• 负责人: Michael Weedon
• 金额: $ 41.38万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FP012167%2F1
• 关键词: genetics; sleep; patterns; their; relationship

Too much, too little or poor quality sleep is associated with several human diseases, in particular metabolic disorders such as obesity and Type 2 diabetes. For example, individuals who sleep < 6 hours per night have a 75% increased risk of obesity. Another aspect of sleep patterns is our individual circadian rhythm - the 24 hour cycle of changes in hormones, body temperature and most body systems which regulate our feelings of wakefulness and sleepiness. Disrupting our circadian rhythms is strongly associated with disease. For example, shift-workers have a >40% increased risk of heart disease. While these associations are strong and robust, the nature of these associations means that we can't say whether sleep patterns are causing disease, whether the diseases affect sleep patterns or if something else associated with both (for example, socioeconomic status) explains the association. One way of addressing the causal direction is to use genetics - because an individual's genetics doesn't change over their lifetime we can use genetic variants as causal "anchors". A now widely-used technique called Mendelian Randomisation uses genetic variants associated with a trait of interest (e.g. chronotype) to allow us to test whether it causes an increased risk of disease (e.g. obesity) or vice versa. Identifying genetic variants associated with normal variation in sleep patterns will also provide new insights in the biology of sleep and circadian rhythms and provide new targets for therapeutics to treat sleep disorders.In this proposal we first aim to identify genetic variants associated with sleep patterns. We will do this by initially testing >20,000,000 genetic variants in 480,000 individuals from the UK Biobank study against traits such as sleep duration, sleep efficiency and measures of circadian rhythms. We will determine these sleep variables for each individual using both self-reported and activity-monitor based estimates of sleep. Using accelerometer derived estimates of sleep will be important because there may be reporting inaccuracies from self-reported measures. In UK Biobank self-reported measures of sleep duration, sleep efficiency and chronotype will be available in all 500,000 individuals and we will be able to validate the associations in a subset of 100,000 individuals with activity monitor data. We will replicate the associations identified from UK Biobank using data from >200,000 including international studies such as 23andMe, the CHARGE and chronogen consortia. This replication stage is important to ensure the genetic associations are robust.We will use the associated genetic variants signals in two ways. First, we will provide biological insights at each of the individual variants using a range of in silico approaches to gain insights into individual genes important in sleep and circadian rhythms. We will look for connections across association signals to highlight pathways, biological systems and tissues that are important in these phenotypes. Second, we will perform Mendelian Randomisation analyses to test the causal direction of the epidemiological association between sleep patterns and metabolic disease. We will use robustly associated variants and test whether they are associated with BMI, Type 2 diabetes and heart disease from independent very large-scale genome-wide association results. We will also perform the reverse analyses and test whether robustly associated BMI and Type 2 diabetes variants are associated with sleep patterns. We will use the latest Mendelian Randomisation techniques such as Egger's regression to overcome potential biases. This work will lead to important new insights into the biology of sleep and circadian rhythms and help determine the causal nature of the association between metabolic disease and disrupted sleep.

过多，太少或质量差的睡眠与几种人类疾病有关，特别是肥胖和2型糖尿病等代谢疾病。例如，每晚睡觉<6个小时的人的肥胖风险增加了75％。睡眠方式的另一个方面是我们的单个昼夜节律 - 激素变化，体温和大多数身体系统的24小时周期，这些周期调节了我们的清醒和嗜睡感。破坏我们的昼夜节律与疾病密切相关。例如，转班工人的心脏病风险增加> 40％。尽管这些关联是牢固而坚固的，但这些关联的性质意味着我们不能说睡眠模式是否引起疾病，这些疾病是影响睡眠模式还是与两者相关的其他事物（例如，社会经济地位）解释了这种关联。解决因果方向的一种方法是使用遗传学 - 因为个人的遗传学在他们的生命中不会改变，所以我们可以将遗传变体用作因果“锚”。现在广泛使用的称为Mendelian随机化的技术使用了与感兴趣特征（例如计时型）相关的遗传变异，以使我们能够测试它是否导致疾病风险增加（例如肥胖），反之亦然。识别与睡眠模式正常变异相关的遗传变异还将提供有关睡眠和昼夜节律生物学生物学的新见解，并为治疗睡眠障碍的治疗剂提供新的靶标。在此提案中，我们首先旨在识别与睡眠模式相关的遗传变异。我们将通过最初对来自英国生物银行研究的480,000个人进行针对睡眠时间，睡眠效率和昼夜节律措施等性状的480,000个人的遗传变异进行测试。我们将使用基于自我报告的睡眠估计值来确定每个人的睡眠变量。使用加速度计得出的睡眠估计值将很重要，因为可能会出现自我报告的措施的不准确性。在英国，在所有500,000个个人中，都将提供自我报告的睡眠持续时间量度，睡眠效率和时间型，我们将能够验证100,000个具有活动监视器数据的100,000个个人的关联。我们将使用> 200,000的数据（包括23andMe，Chuse和Chronogen Consortia等国际研究）复制从英国生物库中确定的关联。这个复制阶段对于确保遗传关联是鲁棒的很重要。我们将通过两种方式使用相关的遗传变异信号。首先，我们将使用一系列的硅方法在每个单个变体上提供生物学见解，以获得对睡眠和昼夜节律重要的单个基因的见解。我们将寻找跨关联信号的连接，以突出在这些表型中很重要的途径，生物系统和组织。其次，我们将进行孟德尔随机分析，以测试睡眠模式与代谢疾病之间流行病学关联的因果方向。我们将使用牢固相关的变体，并测试它们是否与BMI，2型糖尿病和心脏病相关。我们还将执行反向分析，并测试与睡眠模式相关的牢固相关的BMI和2型糖尿病。我们将使用最新的孟德尔随机化技术，例如Egger的回归来克服潜在的偏见。这项工作将导致对睡眠和昼夜节律生物学生物学的重要新见解，并有助于确定代谢疾病与睡眠破坏之间关联的因果性质。

项目成果

Is disrupted sleep a risk factor for Alzheimer's disease? Evidence from a two-sample Mendelian randomization analysis.

• DOI: 10.1093/ije/dyaa183
• 发表时间: 2021-07-09
• 期刊: International journal of epidemiology
• 影响因子: 7.7
• 作者: Anderson EL;Richmond RC;Jones SE;Hemani G;Wade KH;Dashti HS;Lane JM;Wang H;Saxena R;Brumpton B;Korologou-Linden R;Nielsen JB;Åsvold BO;Abecasis G;Coulthard E;Kyle SD;Beaumont RN;Tyrrell J;Frayling TM;Munafò MR;Wood AR;Ben-Shlomo Y;Howe LD;Lawlor DA;Weedon MN;Davey Smith G
• 通讯作者: Davey Smith G

Genetic determinants of daytime napping and effects on cardiometabolic health.

• DOI: 10.1038/s41467-020-20585-3
• 发表时间: 2021-02-10
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Dashti HS;Daghlas I;Lane JM;Huang Y;Udler MS;Wang H;Ollila HM;Jones SE;Kim J;Wood AR;23andMe Research Team;Weedon MN;Aslibekyan S;Garaulet M;Saxena R
• 通讯作者: Saxena R