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

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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.