Summary Humans show large inter-individual differences in organising their behaviour within the 24-h day-this is most obvious in their preferred timing of sleep and wakefulness. Sleep and wake times show a near-Gaussian distribution in a given population, with extreme early types waking up when extreme late types fall asleep. This distribution is predominantly based on differences in an individuals' circadian clock. The relationship between the circadian system and different ''chronotypes'' is formally and genetically well established in experimental studies in organisms ranging from unicells to mammals. To investigate the epidemiology of the human circadian clock, we developed a simple questionnaire (Munich ChronoType Questionnaire, MCTQ) to assess chronotype. So far, more than 55,000 people have completed the MCTQ, which has been validated with respect to the Horne-Østberg morningness-eveningness questionnaire (MEQ), objective measures of activity and rest (sleep-logs and actimetry), and physiological parameters. As a result of this large survey, we established an algorithm which optimises chronotype assessment by incorporating the information on timing of sleep and wakefulness for both work and free days. The timing and duration of sleep are generally independent. However, when the two are analysed separately for work and free days, sleep duration strongly depends on chronotype. In addition, chronotype is both age-and sex-dependent.

Obesity has reached crisis proportions in industrialized societies. Many factors converge to yield increased body mass index (BMI). Among these is sleep duration. The circadian clock controls sleep timing through the process of entrainment. Chronotype describes individual differences in sleep timing, and it is determined by genetic background, age, sex, and environment (e.g., light exposure). Social jetlag quantifies the discrepancy that often arises between circadian and social clocks, which results in chronic sleep loss. The circadian clock also regulates energy homeostasis, and its disruption-as with social jetlag-may contribute to weight-related pathologies. Here, we report the results from a large-scale epidemiological study, showing that, beyond sleep duration, social jetlag is associated with increased BMI. Our results demonstrate that living "against the clock" may be a factor contributing to the epidemic of obesity. This is of key importance in pending discussions on the implementation of Daylight Saving Time and on work or school times, which all contribute to the amount of social jetlag accrued by an individual. Our data suggest that improving the correspondence between biological and social clocks will contribute to the management of obesity.

Being a morning person is a behavioural indicator of a person’s underlying circadian rhythm. Using genome-wide data from 697,828 UK Biobank and 23andMe participants we increase the number of genetic loci associated with being a morning person from 24 to 351. Using data from 85,760 individuals with activity-monitor derived measures of sleep timing we find that the chronotype loci associate with sleep timing: the mean sleep timing of the 5% of individuals carrying the most morningness alleles is 25 min earlier than the 5% carrying the fewest. The loci are enriched for genes involved in circadian regulation, cAMP, glutamate and insulin signalling pathways, and those expressed in the retina, hindbrain, hypothalamus, and pituitary. Using Mendelian Randomisation, we show that being a morning person is causally associated with better mental health but does not affect BMI or risk of Type 2 diabetes. This study offers insights into circadian biology and its links to disease in humans.

Disrupted circadian rhythms and reduced sleep duration are associated with several human diseases, particularly obesity and type 2 diabetes, but until recently, little was known about the genetic factors influencing these heritable traits. We performed genome-wide association studies of self-reported chronotype (morning/evening person) and self-reported sleep duration in 128,266 white British individuals from the UK Biobank study. Sixteen variants were associated with chronotype (P<5x10-8), including variants near the known circadian rhythm genes RGS16 (1.21 odds of morningness, 95% CI [1.15, 1.27], P = 3x10-12) and PER2 (1.09 odds of morningness, 95% CI [1.06, 1.12], P = 4x10-10). The PER2 signal has previously been associated with iris function. We sought replication using self-reported data from 89,283 23andMe participants; thirteen of the chronotype signals remained associated at P<5x10-8 on meta-analysis and eleven of these reached P<0.05 in the same direction in the 23andMe study. We also replicated 9 additional variants identified when the 23andMe study was used as a discovery GWAS of chronotype (all P<0.05 and meta-analysis P<5x10-8). For sleep duration, we replicated one known signal in PAX8 (2.6 minutes per allele, 95% CI [1.9, 3.2], P = 5.7x10-16) and identified and replicated two novel associations at VRK2 (2.0 minutes per allele, 95% CI [1.3, 2.7], P = 1.2x10-9; and 1.6 minutes per allele, 95% CI [1.1, 2.2], P = 7.6x10-9). Although we found genetic correlation between chronotype and BMI (rG = 0.056, P = 0.05); undersleeping and BMI (rG = 0.147, P = 1x10-5) and oversleeping and BMI (rG = 0.097, P = 0.04), Mendelian Randomisation analyses, with limited power, provided no consistent evidence of causal associations between BMI or type 2 diabetes and chronotype or sleep duration. Our study brings the total number of loci associated with chronotype to 22 and with sleep duration to three, and provides new insights into the biology of sleep and circadian rhythms in humans.

In public health, mood disorders are among the most important mental impairments. Patients with depressive episodes exhibit daily mood variations, abnormal patterns in sleep-wake behavior, and in the daily rhythms of several endocrine-metabolic parameters. Although the relationship between the sleep/circadian processes and mood disorders is poorly understood, clock-related therapies, such as light therapy, sleep deprivation, and rigid sleep schedules, have been shown to be effective treatments. Several studies investigated the relationship between circadian phenotype (chronotype) and depression. These focused mainly on urban populations and assessed diurnal preferences (Morningness-Eveningness score) rather than the actual timing of sleep and activity. Here, we used the Beck Depression Inventory (BDI) in an essentially rural population (N?=?4051), and investigated its relation to circadian phenotype (chronotype and social jetlag), assessed with the Munich Chronotype Questionnaire (MCTQ). In our study design, we (i) normalized both chronotype and BDI scores for age and sex (MSF(sas) and BDI(as), respectively); (ii) calculated individual social jetlag (misalignment of the biological and social time); and (iii) investigated the relationship between circadian phenotypes and BDI scores in a population homogeneous in respect to culture, socioeconomic factors, and daily light exposure. A 15.65% (N?=?634) of the participants showed mild to severe depressive BDI scores. Late chronotypes had a higher BDI(as) than intermediate and early types, which was independent of whether or not the participants were smokers. Both chronotype and BDI(as) correlated positively with social jetlag. BDI(as) was significantly higher in subjects with >2?h of social jetlag than in the rest of the population?again independent of smoking status. We also compared chronotype and social jetlag distributions between BDI categories (no symptoms, minimal symptoms, and mild to severe symptoms of depression) separately for men and women and for four age groups; specifically in the age group 31?40 yrs, subjects with mild to severe BDI scores were significantly later chronotypes and suffered from higher social jetlag. Our results indicate that misalignment of circadian and social time may be a risk factor for developing depression, especially in 31- to 40-yr-olds. These relationships should be further investigated in longitudinal studies to reveal if reduction of social jetlag should be part of prevention strategies. (Author correspondence: karla.allebrandt@med.uni-muenchen.de ).

It has come to our attention that one of the formulas in the Supplemental Experimental Procedures section ''Chronotype'' in the online Supplemental Information for this article contained a small but significant typo as originally published. The original formula read ''MSF sc = MSF 2 (SD f 2 SD w )/2''; the correct formula is ''MSF sc = MSF 2 (SD f 2 SD week )/2.'' That is, ''SD w '' stands for ''sleep duration on workdays''; the formula should refer to ''SD week ,'' representing the average ''sleep duration across the seven-day week.'' This error has now been corrected in the Supplemental Information online. The authors apologize for the error and any inconvenience that may have resulted.

Humans sleep approximately a third of their lifetime. The observation that individuals with either long or short sleep duration show associations with metabolic syndrome and psychiatric disorders suggests that the length of sleep is adaptive. Although sleep duration can be influenced by photoperiod (season) and phase of entrainment (chronotype), human familial sleep disorders indicate that there is a strong genetic modulation of sleep. Therefore, we conducted high-density genome-wide association studies for sleep duration in seven European populations (N=4251). We identified an intronic variant (rs11046205; P=3.99 × 10(-8)) in the ABCC9 gene that explains ≈5% of the variation in sleep duration. An influence of season and chronotype on sleep duration was solely observed in the replication sample (N=5949). Meta-analysis of the associations found in a subgroup of the replication sample, chosen for season of entry and chronotype, together with the discovery results showed genome-wide significance. RNA interference knockdown experiments of the conserved ABCC9 homologue in Drosophila neurons renders flies sleepless during the first 3 h of the night. ABCC9 encodes an ATP-sensitive potassium channel subunit (SUR2), serving as a sensor of intracellular energy metabolism.

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