Chronic sleep disturbances, associated with cardiometabolic diseases, psychiatric disorders and all-cause mortality1,2, affect 25 30% of adults worldwide3. Although environmental factors contribute substantially to self-reported habitual sleep duration and disruption, these traits are heritable4,5,6,7,8,9 and identification of the genes involved should improve understanding of sleep, mechanisms linking sleep to disease and development of new therapies. We report single- and multiple-trait genome-wide association analyses of self-reported sleep duration, insomnia symptoms and excessive daytime sleepiness in the UK Biobank (n = 112,586). We discover loci associated with insomnia symptoms (near MEIS1, TMEM132E, CYCL1 and TGFBI in females and WDR27 in males), excessive daytime sleepiness (near AR OPHN1) and a composite sleep trait (near PATJ (INADL) and HCRTR2) and replicate a locus associated with sleep duration (at PAX8). We also observe genetic correlation between longer sleep duration and schizophrenia risk (rg = 0.29, P = 1.90 10-13) and between increased levels of excessive daytime sleepiness and increased measures for adiposity traits (body mass index (BMI): rg = 0.20, P = 3.12 10-9; waist circumference: rg = 0.20, P = 2.12 10-7).
Lane JM et al . Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits. Nature Genetics volume 49, pages 274 281 (2017)
Sleep and chronotype and their causal links with cardiometabolic inflammatory diseases
The aim of this research is to better understand the associations and potential causal pathways linking sleep and/or chronotype with several common metabolic and inflammatory diseases that have major health, economic and societal impacts such as heart disease, diabetes, asthma and arthritis.
It is uncertain how sleep patterns and chronotype (morning/evening preference) are associated with disease and therefore: 1. We will study relationships with common diseases such as diabetes. 2. We will investigate how sleep duration and chronotype from questionnaires relate to movement sensor information. 3. We will observe the temporal relationships of disease onset and change in sleep/chronotype to see if one develops/changes after the other. 4. Finally, we will identify genes linked to sleep patterns and chronotype, and use this information to understand whether sleep patterns and chronotype have a role in causing or worsening common diseases.
The aim of this research is to better understand the associations and potential causal pathways linking sleep and/or chronotype with several common metabolic and inflammatory diseases that have major health, economic and societal impacts such as heart disease, diabetes, asthma and arthritis. The study also aims to discover genes associated with sleep patterns and chronotype. This new knowledge will be used to explore whether sleep patterns and chronotype have a role in causing these diseases. This research may guide the development of new treatments targeting sleep or the biological clock to prevent or treat cardiometabolic and chronic inflammatory diseases. We will relate the presence and severity of cardiometabolic and chronic inflammatory diseases to: 1. Sleep duration 2. Sleep disturbance (insomnia/waking) 3. Chronotype (morning/evening person) 4. Sleep apnoea symptoms (snoring/daytime sleepiness) 5. Shift work Each analysis will control/stratify for the other factors listed (1-5). Prospective studies will assess the temporal relationships between disorders of sleep/chronotype, shift work and common diseases. Genome-wide association studies will aim to identify genes linked to sleep duration, disturbance and chronotype and Mendelian randomisation studies will explore causal relationships between these factors and the development of common diseases. Whole cohort: a) cross-sectional studies; b) the prospective study assessing incident cardiometabolic and inflammatory diseases; c) genome-wide association studies; d) Mendelian randomisation studies. Individuals with accelerometer data: to relate to shift work, prevalent/incident disease, sleep/chronotype (questionnaire) and genotype (if adequately powered). 20,000 people assessed on two occasions over 4 years (including those with accelerometer data): assessing incident sleep disturbance and change in chronotype. GWAS and Mendelian Randomisation studies: Primary analyses will done when genotype data on 170K individuals is released and updated when data on the whole cohort is available and when imputation is complete.
1. Lane JM, Rutter M, Anderson S, Saxena R et al. Genome-wide association study reveals ten loci associated with chronotype in the UKBiobank. Nat Commun. 2016 Mar 9;7:10889. doi: 10.1038/ncomms10889. PubMed PMID: 26955885; PubMed Central PMCID: PMC4786869.
2. Lane JM et al. Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits.
3. Verrter et al. Night Shift Work, Genetic Risk and Type2 Diabetes in the UKBiobank. Diabetes Care 2018 Apr; 41(4): 762-769. doi:10.2337/dc17-1933
|Lead investigator:||Martin Rutter|
|Lead institution:||University of Manchester|