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The genetic basis of sleep is still poorly understood. Despite the moderate to high heritability of sleep-related phenotypes, known genetic variants explain only a small proportion of the phenotypical variance. However, most previous studies were based solely upon self-report measures. The present study aimed to conduct the first genome-wide association (GWA) of actigraphic sleep phenotypes. The analyses included 956 middle- to older-aged subjects (40-79 years) from the LIFE Adult Study. The SenseWear Pro 3 Armband was used to collect 11 actigraphic parameters of night- and daytime sleep and three parameters of rest (lying down). The parameters comprised measures of sleep timing, quantity and quality. A total of 7 141 204 single nucleotide polymorphisms (SNPs) were analysed after imputation and quality control. We identified several variants below the significance threshold of P ≤ 5× 10 (not corrected for analysis of multiple traits). The most significant was a hit near UFL1 associated with sleep efficiency on weekdays (P = 1.39 × 10 ). Further SNPs were close to significance, including an association between sleep latency and a variant in CSNK2A1 (P = 8.20 × 10 ), a gene known to be involved in the regulation of circadian rhythm. In summary, our GWAS identified novel candidate genes with biological plausibility being promising candidates for replication and further follow-up studies.

Background: Different levels of brain arousal can be delineated not only during sleep but also during wakefulness. Electroencephalography (EEG) is the gold standard to assess different levels of brain arousal. A novel EEG-and electrooculography (EOG)-based tool, the Vigilance Algorithm Leipzig (VIGALL 2.0), allows determining the level of EEG-vigilance (indicating brain arousal). Considering the frequency patterns and LORETA-based cortical distribution of electroencephalic activity, VIGALL 2.0 automatically attributes one out of seven vigilance stages to each EEG segment (1-sec EEG segments by default), ranging from high alertness (stage 0), to relaxed wakefulness (stage A1 to A3), to drowsiness (stage B1 to B2/3) up to sleep onset (stage C). Building on the time series of these seven vigilance stages across 20 min, two parameterizations of the temporal dynamic (brain arousal regulation) are calculated: the lability score and the slope index. Methods: 27 healthy participants (age = 22.93 ± 3.44 years, 18 females) underwent two sessions (7 days apart) of a twenty-minute eyes-closed resting EEG paradigm. Results:The test-retest reliability coefficients for the EEG-vigilance stages were between rho = .53 and .86 (all p < .01). For the temporal dynamic of the stages across 20 min, the test-retest reliability coefficients were rho = .70 (lability score, p < .001) and .71 (slope index, p < .001).Conclusions: This study demonstrated some trait aspects of brain arousal regulation by confirming the stability of temporal dynamic of EEG-vigilance stages as assessed with VIGALL 2.0. Considering the "first day in lab" effect identified in the present study, more adaptation to the lab surrounding and a stricter control of other state factors should be taken into account, which might improve reliability. Additionally, in a clinical context, a broader range of brain arousal regulation patterns might be found, possibly leading to higher test-retest reliability than was found in this homogenous healthy sample. This would be desirable, as parameters of brain arousal regulation are promising diagnostic and prognostic biomarkers for diseases with arousal disturbances, such as affective disorders, ADHD and fatigue.