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.

睡眠过多、过少或质量差与多种人类疾病有关，特别是肥胖和 2 型糖尿病等代谢紊乱。例如，每晚睡眠时间< 6 小时的人患肥胖症的风险增加 75%。睡眠模式的另一个方面是我们个人的昼夜节律——激素、体温和大多数身体系统的 24 小时变化周期，这些系统调节我们的清醒和睡意。扰乱我们的昼夜节律与疾病密切相关。例如，轮班工人患心脏病的风险增加 40% 以上。虽然这些关联性很强，但这些关联的本质意味着我们不能说睡眠模式是否会导致疾病，这些疾病是否会影响睡眠模式，或者与两者相关的其他因素（例如社会经济地位）是否可以解释这种关联。解决因果方向的一种方法是使用遗传学——因为个体的遗传学在其一生中不会改变，我们可以使用遗传变异作为因果“锚”。现在广泛使用的一种称为孟德尔随机化的技术使用与感兴趣的特征（例如时间型）相关的遗传变异，使我们能够测试它是否会导致疾病风险增加（例如肥胖），反之亦然。识别与睡眠模式正常变化相关的遗传变异也将为睡眠和昼夜节律的生物学提供新的见解，并为治疗睡眠障碍的疗法提供新的目标。在本提案中，我们首先旨在识别与睡眠模式相关的遗传变异。为此，我们将首先测试英国生物银行研究中 480,000 名个体的超过 20,000,000 个基因变异，以评估睡眠持续时间、睡眠效率和昼夜节律测量等特征。我们将使用自我报告和基于活动监测的睡眠估计来确定每个人的这些睡眠变量。使用加速度计得出的睡眠估计非常重要，因为自我报告的测量结果可能会出现报告不准确的情况。在英国生物银行，所有 500,000 名个人将获得自我报告的睡眠持续时间、睡眠效率和睡眠时间类型测量结果，我们将能够通过活动监测数据验证 100,000 名个人的子集中的关联。我们将使用超过 200,000 个数据（包括 23andMe、CHARGE 和 chronogen 联盟等国际研究）的数据来复制从英国生物银行确定的关联。该复制阶段对于确保遗传关联的稳健性非常重要。我们将以两种方式使用相关的遗传变异信号。首先，我们将使用一系列计算机方法对每个个体变体提供生物学见解，以深入了解对睡眠和昼夜节律重要的个体基因。我们将寻找关联信号之间的联系，以突出这些表型中重要的途径、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。我们将使用稳健相关的变异，并通过独立的超大规模全基因组关联结果来测试它们是否与 BMI、2 型糖尿病和心脏病相关。我们还将进行反向分析并测试 BMI 和 2 型糖尿病变异是否与睡眠模式密切相关。我们将使用最新的孟德尔随机化技术（例如艾格回归）来克服潜在的偏差。这项工作将对睡眠和昼夜节律的生物学产生重要的新见解，并有助于确定代谢疾病与睡眠中断之间关联的因果关系。

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