Sleep is an essential state of decreased activity and alertness but molecular factors regulating sleep duration remain unknown. Through genome-wide association analysis in 446,118 adults of European ancestry from the UK Biobank, we identify 78 loci for self-reported habitual sleep duration (p < 5 × 10−8; 43 loci at p < 6 × 10−9). Replication is observed for PAX8, VRK2, and FBXL12/UBL5/PIN1 loci in the CHARGE study (n = 47,180; p < 6.3 × 10−4), and 55 signals show sign-concordant effects. The 78 loci further associate with accelerometer-derived sleep duration, daytime inactivity, sleep efficiency and number of sleep bouts in secondary analysis (n = 85,499). Loci are enriched for pathways including striatum and subpallium development, mechanosensory response, dopamine binding, synaptic neurotransmission and plasticity, among others. Genetic correlation indicates shared links with anthropometric, cognitive, metabolic, and psychiatric traits and two-sample Mendelian randomization highlights a bidirectional causal link with schizophrenia. This work provides insights into the genetic basis for inter-individual variation in sleep duration implicating multiple biological pathways.
Excessive daytime sleepiness (EDS) affects 10–20% of the population and is associated with substantial functional deficits. Here, we identify 42 loci for self-reported daytime sleepiness in GWAS of 452,071 individuals from the UK Biobank, with enrichment for genes expressed in brain tissues and in neuronal transmission pathways. We confirm the aggregate effect of a genetic risk score of 42 SNPs on daytime sleepiness in independent Scandinavian cohorts and on other sleep disorders (restless legs syndrome, insomnia) and sleep traits (duration, chronotype, accelerometer-derived sleep efficiency and daytime naps or inactivity). However, individual daytime sleepiness signals vary in their associations with objective short vs long sleep, and with markers of sleep continuity. The 42 sleepiness variants primarily cluster into two predominant composite biological subtypes - sleep propensity and sleep fragmentation. Shared genetic links are also seen with obesity, coronary heart disease, psychiatric diseases, cognitive traits and reproductive ageing.
Daytime napping is a common, heritable behavior, but its genetic basis and causal relationship with cardiometabolic health remain unclear. Here, we perform a genome-wide association study of self-reported daytime napping in the UK Biobank (n = 452,633) and identify 123 loci of which 61 replicate in the 23andMe research cohort (n = 541,333). Findings include missense variants in established drug targets for sleep disorders (HCRTR1,HCRTR2), genes with roles in arousal (TRPC6,PNOC), and genes suggesting an obesity-hypersomnolence pathway (PNOC, PATJ). Association signals are concordant with accelerometer-measured daytime inactivity duration and 33 loci colocalize with loci for other sleep phenotypes. Cluster analysis identifies three distinct clusters of nap-promoting mechanisms with heterogeneous associations with cardiometabolic outcomes. Mendelian randomization shows potential causal links between more frequent daytime napping and higher blood pressure and waist circumference.
Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such asLPLandPCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.
1 May 2021 in American Journal of Respiratory and Critical Care Medicine
Rationale: Symptoms and morbidities associated with obstructive sleep apnea (OSA) vary across individuals and are not predicted by the apnea–hypopnea index (AHI). Respiratory event duration is a heritable trait associated with mortality that may further characterize OSA. Objectives: We evaluated how hypopnea and apnea durations in non-REM (NREM) sleep vary across demographic groups and quantified their associations with physiological traits (loop gain, arousal threshold, circulatory delay, and pharyngeal collapsibility). Methods: Data were analyzed from 1,546 participants from the Multi-Ethnic Study of Atherosclerosis with an AHI ≥5. Physiological traits were derived using a validated model fit to the polysomnographic airflow signal. Multiple linear regression models were used to evaluate associations of event duration with demographic and physiological factors. Measurements and Main Results: Participants had a mean age ± SD of 68.9 ± 9.2 years, mean NREM hypopnea duration of 21.73 ± 5.60, and mean NREM apnea duration of 23.87 ± 7.44 seconds. In adjusted analyses, shorter events were associated with younger age, female sex, higher body mass index (P < 0.01, all), and Black race (P < 0.05). Longer events were associated with Asian race (P < 0.01). Shorter event durations were associated with lower circulatory delay (2.53 ± 0.13 s, P < 0.01), lower arousal threshold (1.39 ± 0.15 s, P < 0.01), reduced collapsibility (−0.71 ± 0.16 s, P < 0.01), and higher loop gain (−0.27 ± 0.11 s, P < 0.05) per SD change. Adjustment for physiological traits attenuated age, sex, and obesity associations and eliminated racial differences in event duration. Conclusions: Average event duration varies across population groups and provides information on ventilatory features and airway collapsibility not captured by the AHI.