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

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.

This study will provide new insights into sleep and circadian rhythm biology and help determine the causal direction of the association between metabolic disease and disrupted sleep. There are well-known associations between sleep patterns and metabolic disease. The observational nature of the associations in human studies means that convincing evidence of causality in humans is lacking. In this study, we will identify genetic variants associated with sleep patterns and use these in Mendelian Randomisation analyses to test the causal role of sleep on BMI and Type 2 diabetes. Identifying genetic variants associated with normal variation in sleep will also provide new insights in the biology of sleep patterns and provide new therapeutics targets for sleep disorders.

We will perform GWAS on >20,000,000 genetic variants in 500,000 individuals from the UK Biobank on a range of sleep traits. We will use data from self-report on all individuals and in a subset of 100,000 individuals we will derive activity-monitor based estimates of sleep traits. We will perform GWAS on both the self-report and objective estimates and integrate and cross-validate the results. We will replicate the associations in >200,000 individuals from a range of studies including 23andMe, the CHARGE and Chronogen consortia and the Rotterdam and CoLaus studies.

We use the Mendelian Randomisation techniques of instrumental variables analysis and Eggers regression on robustly associated variants to test whether any sleep patterns are causally associated with obesity and Type 2 diabetes. To avoid overfitting biases we will test the variants against the phenotypes in independent large-scale genome-wide consortia datasets such as the GIANT consortium. We will also use robustly associated signals and perform in silico fine-mapping analyses to identify likely causal variants at each loci and we will perform pathway-based analyses to look for biological connections between associated loci.

By identifying new biological insights into normal variation in sleep, new therapeutic targets for sleep disorders and clarifying the causal nature of the association between sleep patterns and metabolic disease this project will have an:

Impact on the pharmaceutical industry: Insomnia affects or has affected up to 40% of the UK population and sleeping pills are amongst the most widely prescribed medicines. Our project will provide new targets for treating sleep disorders. The ability of genetics to identify new biological targets for therapeutics is shown by the examples of new drugs being developed against PCSK9 for lowering LDL levels based on genetic variant associations. Recent work by GSK (Nature Genetics, 2015) showed that results from genome-wide association studies provide value by prioritizing therapeutic targets.

Impact on public health and policy makers: Obesity and Type 2 diabetes are at epidemic levels and are a significant burden on the NHS. By providing evidence for a causal link between sleep and metabolic disease we could have a significant impact on policy makers and public health efforts could be directed to improving sleep quality as one way of reducing the burden of these diseases.

Impact of the UK economy: Insufficient sleep and disrupted circadian rhythms from, for example, jet lag and shift work are increasing problems. Lack of sleep can significantly affects workers productivity and are major problems throughout the developed world. We are sleeping less and being less productive. Poor productivity damages the economy and has been highlighted recently in the news because British workers are less productive than those from France for example. We do not know if the association between less sleep and poorer productivity is causal, but our studies will help understand if it is or not. By increasing our understanding of the biology of sleep and by identifying novel therapeutic targets, our work may in the long term have a significant impact on sleep quality and increase the productivity of the UK workforce.

Impact on UK science and informatics capacity: The University of Exeter has invested heavily in STEM subjects. One example of this is the new £27 million Research, Innovation and Knowledge centre that has recently been built at the University of Exeter. This brings together individuals, including the Exeter co-applicants, with expertise in genetics, genomics, epigenetics and diabetes. The research program that we propose here will represent a big step towards increasing the goal of the expansion of genomics and systems medicine research at these universities. It will also help forge links between these institutions in these cutting-edge areas of research. Through teaching on undergraduate and MSc courses, this project will provide exposure to our research and skills to talented students in these areas and serve as a platform for recruitment of the future generation of biological scientists.