Large-language models facilitate discovery of the molecular signatures regulating sleep and activity.

Sleep, locomotor and social activities are essential animal behaviors, but their reciprocal relationships and underlying mechanisms remain poorly understood. Here, we elicit information from a cutting-edge large-language model (LLM), generative pre-trained transformer (GPT) 3.5, which interprets 10.2-13.8% of Drosophila genes known to regulate the 3 behaviors. We develop an instrument for simultaneous video tracking of multiple moving objects, and conduct a genome-wide screen. We have identified 758 fly genes that regulate sleep and activities, including mre11 which regulates sleep only in the presence of conspecifics, and NELF-B which regulates sleep regardless of whether conspecifics are present. Based on LLM-reasoning, an educated signal web is modeled for understanding of potential relationships between its components, presenting comprehensive molecular signatures that control sleep, locomotor and social activities. This LLM-aided strategy may also be helpful for addressing other complex scientific questions.

Association of genetically determined chronotype with circulating testosterone: a Mendelian randomization study.

Low testosterone levels in men have been linked to decreased physical and mental function, as well as a reduced quality of life. Previous prospective observational studies have suggested an association between testosterone and sleep traits, but the causality of this relationship remains unclear. We aimed to explore the potential causal link between genetically determined sleep traits and testosterone levels in men using Mendelian randomization (MR) analysis from the UK Biobank dataset. Our exposures were genetic variants associated with sleep traits (chronotype and sleep duration), whereas our outcomes were traits of sex steroid hormones (total testosterone, TT; bioavailable testosterone, BAT; and sex hormone-binding globulin, SHBG). We employed inverse variance weighted (IVW) and weighted median (WM) methods to assess the causal associations. The IVW method offers a robust estimate of causality, whereas the WM method provides reliable results even when some genetic variants are invalid instruments. Our main analysis involving sex steroid hormones and chronotype identified 155 chronotype-related variants. The primary findings from the analysis, which used chronotype as the exposure and sex steroid hormones as the outcomes, showed that a genetically predicted chronotype score was significantly associated with an increased levels of TT (association coefficient ß, 0.08; 95% confidence interval [CI], 0.02-0.14; P = 0.008) and BAT (ß, 0.08; 95% CI, 0.02-0.14; P = 0.007), whereas there was no significant association with SHBG (ß, 0.01; 95% CI, -0.02-0.03; P = 0.64). Meanwhile, MR analysis of sex steroid hormones and sleep duration was performed, and 69 variants associated with sleep duration were extracted. There were no significant association between sleep duration and sex steroid hormones (TT, P = 0.91; BAT, P = 0.82; and SHBG, P = 0.95). Our data support a causal association between chronotype and circulating testosterone levels in men. These findings underscore a potential causal relationship between chronotype and testosterone levels in men, suggesting that lifestyle adjustments are crucial for men's health. Recognizing factors that influence testosterone is essential. One limitation of this study is the use of one-sample MR, which can introduce potential bias due to non-independence of genetic associations for exposure and outcome. In conclusion, our findings indicate that a morning preference is correlated with circulating testosterone levels, emphasizing the potential impact of lifestyle habits on testosterone levels in men.

Mice harboring the T316N variant in the GABAAR γ2 subunit exhibit sleep-related hypermotor epilepsy phenotypes and hypersynchronization in the thalamocortical pathway.

OBJECTIVE: Sleep-related hypermotor epilepsy (SHE) is a focal epilepsy syndrome characterized by seizures that predominantly occur during sleep. The pathogenesis of these seizures remains unclear. We previously detected rare variants in GABRG2, which encodes the γ2 subunit of γ-aminobutyric acid type A receptor (GABAAR), in patients with SHE and demonstrated that these variants impaired GABAAR function in vitro. However, the mechanisms by which GABRG2 variants contribute to seizure attacks during sleep remain unclear. METHODS: In this study, we designed a knock-in (KI) mouse expressing the mouse Gabrg2 T316N variant, corresponding to human GABRG2 T317N variant, using CRISPR/Cas9. Continuous video-electroencephalogram monitoring and in vivo multichannel electrophysiological recordings were performed to explore seizure susceptibility to pentylenetetrazol (PTZ), alterations in the sleep-wake cycle, spontaneous seizure patterns, and synchronized activity in the motor thalamic nuclei (MoTN) and secondary motor cortex (M2). Circadian variations in the expression of total, membrane-bound, and synaptic GABAAR subunits were also investigated. RESULTS: No obvious changes in gross morphology were detected in Gabrg2T316N/+ mice compared to their wild-type (Gabrg2+/+) littermates. Gabrg2T316N/+ mice share key phenotypes with patients, including sleep fragmentation and spontaneous seizures during sleep. Gabrg2T316N/+ mice showed increased susceptibility to PTZ-induced seizures and higher mortality after seizures. Synchronization of the local field potentials between the MoTN and M2 was abnormally enhanced in Gabrg2T316N/+ mice during light phase, when sleep dominates, accompanied by increased local activities in the MoTN and M2. Interestingly, in Gabrg2+/+ mice, GABAAR γ2 subunits showed a circadian increase on the neuronal membrane and synaptosomes in the transition from dark phase to light phase, which was absent in Gabrg2T316N/+ mice. CONCLUSION: We generated a new SHE mouse model and provided in vivo evidence that rare variants of GABRG2 contribute to seizure attacks during sleep in SHE.

Maternal Childhood Adversity and Infant Epigenetic Aging: Moderation by Restless Sleep During Pregnancy.

Maternal exposure to childhood adversity is associated with detrimental health outcomes throughout the lifespan and may have implications for offspring. Evidence links maternal adverse childhood experiences (ACEs) to detrimental birth outcomes, yet the impact on the infant's epigenome is unclear. Moreover, maternal sleep habits during pregnancy may influence this association. Here, we explore whether restless sleep during pregnancy moderates the association between exposure to maternal childhood adversity and infant epigenetic age acceleration in 332 mother-infant dyads (56% female; 39% Black; 25% Hispanic). During the 2nd trimester, mothers self-reported childhood adversity and past-week restless sleep; DNA methylation from umbilical vein endothelial cells was used to estimate five epigenetic clocks. Multivariable linear regression was used to test study hypotheses. Despite no evidence of main effects, there was evidence of an interaction between maternal ACEs and restless sleep in predicting infant epigenetic age acceleration using the EPIC Gestational Age clock. Only infants whose mothers reported exposure to both ACEs and restless sleep demonstrated accelerated epigenetic aging. Results provide preliminary evidence that maternal childhood adversity and sleep may influence the infant epigenome.

No bidirectional relationship between sleep phenotypes and risk of proliferative diabetic retinopathy: a two-sample Mendelian randomization study.

This study aimed to investigate the probable existence of a causal relationship between sleep phenotypes and proliferative diabetic retinopathy (PDR). Single nucleotide polymorphisms associated with sleep phenotypes were selected as instrumental variables at the genome-wide significance threshold (P < 5 × 10-8). Inverse-variance weighted was applied as the primary Mendelian randomization (MR) analysis method, and MR Egger regression, weighted median, simple mode, and weighted mode methods were used as complementary analysis methods to estimate the causal association between sleep phenotypes and PDR. Results indicated that genetically predicted sleep phenotypes had no causal effects on PDR risk after Bonferroni correction (P = 0.05/10) [Chronotype: P = 0.143; Daytime napping: P = 0.691; Daytime sleepiness: P = 0.473; Insomnia: P = 0.181; Long sleep duration: P = 0.671; Morning person:P = 0.113; Short sleep duration: P = 0.517; Obstructive sleep apnea: P = 0.091; Sleep duration: P = 0.216; and snoring: P = 0.014]. Meanwhile, there are no reverse causality for genetically predicted PDR on sleep phenotypes [Chronotype: P = 0.100; Daytime napping: P = 0.146; Daytime sleepiness: P = 0.469; Insomnia: P = 0.571; Long sleep duration: P = 0.779; Morning person: P = 0.040; Short sleep duration: P = 0.875; Obstructive sleep apnea: P = 0.628; Sleep duration: P = 0.896; and snoring: P = 0.047]. This study's findings did not support the causal effect of between sleep phenotypes and PDR. Whereas, longitudinal studies can further verify results validation.

Gene‒environment interaction effect of hypothalamic‒pituitary‒adrenal axis gene polymorphisms and job stress on the risk of sleep disturbances.

Background: Studies have shown that chronic exposure to job stress may increase the risk of sleep disturbances and that hypothalamic‒pituitary‒adrenal (HPA) axis gene polymorphisms may play an important role in the psychopathologic mechanisms of sleep disturbances. However, the interactions among job stress, gene polymorphisms and sleep disturbances have not been examined from the perspective of the HPA axis. This study aimed to know whether job stress is a risk factor for sleep disturbances and to further explore the effect of the HPA axis gene × job stress interaction on sleep disturbances among railway workers. Methods: In this cross-sectional study, 671 participants (363 males and 308 females) from the China Railway Fuzhou Branch were included. Sleep disturbances were evaluated with the Pittsburgh Sleep Quality Index (PSQI), and job stress was measured with the Effort-Reward Imbalance scale (ERI). Generalized multivariate dimensionality reduction (GMDR) models were used to assess gene‒environment interactions. Results: We found a significant positive correlation between job stress and sleep disturbances (P < 0.01). The FKBP5 rs1360780-T and rs4713916-A alleles and the CRHR1 rs110402-G allele were associated with increased sleep disturbance risk, with adjusted ORs (95% CIs) of 1.75 [1.38-2.22], 1.68 [1.30-2.18] and 1.43 [1.09-1.87], respectively. However, the FKBP5 rs9470080-T allele was a protective factor against sleep disturbances, with an OR (95% CI) of 0.65 [0.51-0.83]. GMDR analysis indicated that under job stress, individuals with the FKBP5 rs1368780-CT, rs4713916-GG, and rs9470080-CT genotypes and the CRHR1 rs110402-AA genotype had the greatest risk of sleep disturbances. Conclusions: Individuals carrying risk alleles who experience job stress may be at increased risk of sleep disturbances. These findings may provide new insights into stress-related sleep disturbances in occupational populations.

No evidence linking sleep traits with white blood cell counts: Multivariable-adjusted and Mendelian randomization analyses.

BACKGROUND: Disturbances in habitual sleep have been associated with multiple age-associated diseases. However, the biological mechanisms underpinning these associations remain largely unclear. We assessed the possible involvement of the circulating immune system by determining the associations between sleep traits and white blood cell counts using multivariable-adjusted linear regression and Mendelian randomization. METHODS: Cross-sectional multivariable-adjusted linear regression analyses were done using participants within the normal range of total white blood cell counts (>4.5 × 109 and <11.0 × 109/µL) from UK Biobank. For the sleep traits, we examined (short and long) sleep duration, chronotype, insomnia symptoms and daytime dozing. Two-sample Mendelian randomization analyses were done using instruments for sleep traits derived from European-ancestry participants from UK Biobank (over 410,000 participants) and using SNP-outcome data derived from European-ancestry participants from the Blood Cell Consortium (N = 563,946) to which no data from UK Biobank contributed. RESULTS: Using data from 357,656 participants (mean [standard deviation] age: 56.5 [8.1] years, and 44.4% men), we did not find evidence that disturbances in any of the studied sleep traits were associated with differences in blood cell counts (total, lymphocytes, neutrophiles, eosinophiles and basophiles). Also, we did not find associations between disturbances in any of the studied sleep traits and white blood cell counts using Mendelian Randomization. CONCLUSION: Based on the results from two different methodologies, disturbances in habitual sleep are unlikely to cause changes in blood cell counts and thereby differences in blood cell counts are unlikely to be underlying the observed sleep-disease associations.

Shared genetic architecture and causal relationship between sleep behaviors and lifespan.

Poor sleep health is associated with a wide array of increased risk for cardiovascular, metabolic and mental health problems as well as all-cause mortality in observational studies, suggesting potential links between sleep health and lifespan. However, it has yet to be determined whether sleep health is genetically or/and causally associated with lifespan. In this study, we firstly studied the genome-wide genetic association between four sleep behaviors (short sleep duration, long sleep duration, insomnia, and sleep chronotype) and lifespan using GWAS summary statistics, and both sleep duration time and insomnia were negatively correlated with lifespan. Then, two-sample Mendelian randomization (MR) and multivariable MR analyses were applied to explore the causal effects between sleep behaviors and lifespan. We found that genetically predicted short sleep duration was causally and negatively associated with lifespan in univariable and multivariable MR analyses, and this effect was partially mediated by coronary artery disease (CAD), type 2 diabetes (T2D) and depression. In contrast, we found that insomnia had no causal effects on lifespan. Our results further confirmed the negative effects of short sleep duration on lifespan and suggested that extension of sleep may benefit the physical health of individuals with sleep loss. Further attention should be given to such public health issues.

Association of sleep traits with myopia in children and adolescents: A meta-analysis and Mendelian randomization study.

PURPOSE: The association between sleep and myopia in children and adolescents has been reported, yet it remains controversial and inconclusive. This study aimed to investigate the influence of different sleep traits on the risk of myopia using meta-analytical and Mendelian randomization (MR) techniques. METHODS: The literature search was performed in August 31, 2023 based on PubMed, Embase, Web of Science, and Cochrane library. The meta-analysis of observational studies reporting the relationship between sleep and myopia was conducted. MR analyses were carried out to assess the causal impact of genetic pre-disposition for sleep traits on myopia. RESULTS: The results of the meta-analysis indicated a significant association between the risk of myopia and both short sleep duration [odds ratio (OR) = 1.23, 95% confidence interval (CI) = 1.08-1.42, P = 0.003] and long sleep duration (OR = 0.75, 95% CI = 0.66-0.86, P < 0.001). MR analyses revealed no significant causal associations of genetically determined sleep traits with myopia, including chronotype, sleep duration, short sleep duration and long sleep duration (all P > 0.05). CONCLUSIONS: No evidence was found to support a causal relationship between sleep traits and myopia. While sleep may not independently predict the risk of myopia, the potential impact of sleep on the occurrence and development of myopia cannot be disregarded.

A patient-enriched MEIS1 coding variant causes a restless legs syndrome-like phenotype in mice.

Restless legs syndrome (RLS) is a neurological disorder characterized by uncomfortable or unpleasant sensations in the legs during rest periods. To relieve these sensations, patients move their legs, causing sleep disruption. While the pathogenesis of RLS has yet to be resolved, there is a strong genetic association with the MEIS1 gene. A missense variant in MEIS1 is enriched sevenfold in people with RLS compared to non-affected individuals. We generated a mouse line carrying this mutation (p.Arg272His/c.815G>A), referred to herein as Meis1R272H/R272H (Meis1 point mutation), to determine whether it would phenotypically resemble RLS. As women are more prone to RLS, driven partly by an increased risk of developing RLS during pregnancy, we focused on female homozygous mice. We evaluated RLS-related outcomes, particularly sensorimotor behavior and sleep, in young and aged mice. Compared to noncarrier littermates, homozygous mice displayed very few differences. Significant hyperactivity occurred before the lights-on (rest) period in aged female mice, reflecting the age-dependent incidence of RLS. Sensory experiments involving tactile feedback (rotarod, wheel running, and hotplate) were only marginally different. Overall, RLS-like phenomena were not recapitulated except for the increased wake activity prior to rest. This is likely due to the focus on young mice. Nevertheless, the Meis1R272H mouse line is a potentially useful RLS model, carrying a clinically relevant variant and showing an age-dependent phenotype.

Expanding the clinical and genetic landscape of (developmental) epileptic encephalopathy with spike-and-wave activation in sleep: results from studies of a Turkish cohort.

The terms developmental epileptic encephalopathy with spike-and-wave activation in sleep (DEE-SWAS) and epileptic encephalopathy with spike-and-wave activation in sleep (EE-SWAS) designate a spectrum of conditions that are typified by different combinations of motor, cognitive, language, and behavioral regression linked to robust spike-and-wave activity during sleep. In this study, we aimed at describing the clinical and molecular findings in "(developmental) epileptic encephalopathy with spike-and-wave activation in sleep" (D)EE-SWAS) patients as well as at contributing to the genetic etiologic spectrum of (D)EE-SWAS. Single nucleotide polymorphism (SNP) array and whole-exome sequencing (WES) techniques were used to determine the underlying genetic etiologies. Of the 24 patients included in the study, 8 (33%) were female and 16 (67%) were male. The median age at onset of the first seizure was 4 years and the median age at diagnosis of (D)EE-SWAS was 5 years. Of the 24 cases included in the study, 13 were compatible with the clinical diagnosis of DEE-SWAS and 11 were compatible with the clinical diagnosis of EE-SWAS. Abnormal perinatal history was present in four cases (17%), and two cases (8%) had a family history of epilepsy. Approximately two-thirds (63%) of all patients had abnormalities detected on brain computerized tomography/magnetic resonance (CT/MR) imaging. After SNP array and WES analysis, the genetic etiology was revealed in 7 out of 24 (29%) cases. Three of the variants detected were novel (SLC12A5, DLG4, SLC9A6). This study revealed for the first time that Smith-Magenis syndrome, SCN8A-related DEE type 13 and SLC12A5 gene variation are involved in the genetic etiology of (D)EE-SWAS. (D)EE-SWAS is a genetically diverse disorder with underlying copy number variations and single-gene abnormalities. In the current investigation, rare novel variations in genes known to be related to (D)EE-SWAS and not previously reported genes to be related to (D)EE-SWAS were discovered, adding to the molecular genetic spectrum. Molecular etiology enables the patient and family to receive thorough and accurate genetic counseling as well as a personalized medicine approach.

Causal associations between leisure sedentary behaviors and sleep status with frailty: insight from Mendelian randomization study.

BACKGROUND: Observational studies have suggested that sedentary behaviors and sleep status are associated with frailty. However, it remains unclear whether these associations are causal. METHODS: Using summary statistics from genome-wide association studies, we evaluated the causal effect of modifiable risk factors, including leisure sedentary behaviors and sleep status on the frailty index (FI) using two-sample univariable and multivariable Mendelian randomization (MR) analyses. Genetic correlations were tested between the correlated traits. RESULTS: We identified potential causal associations between the time spent watching television (ß = 0.26, 95% confidence interval [CI]: 0.21-0.31, P = 3.98e-25), sleep duration (ß = -0.18, 95%CI: -0.26, -0.10; P = 6.04e-06), and daytime napping (ß = 0.29, 95%CI: 0.18-0.41, P = 2.68e-07) and the FI based on the inverse-variance-weighted method. The estimates were consistent across robust and multivariate MR analyses. Linkage disequilibrium score regression detected a genetic correlation between the time spent watching television (Rg = 0.43, P = 6.46e-48), sleep duration (Rg = -0.20, P = 5.29e-10), and daytime napping (Rg = 0.25, P = 3.34e-21) and the FI. CONCLUSIONS: Genetic predispositions to time spent watching television and daytime napping were positively associated with the FI, while sleep duration was negatively associated with the FI. Our findings offer key insights into factors influencing biological aging and suggest areas for interventions to promote healthy aging and slow down the aging process.

Neuropsychiatric disorders, chronotype and sleep: A narrative review of GWAS findings and the application of Mendelian randomization to investigate causal relationships.

Genome-wide association studies (GWAS) have been important for characterizing the genetic component and enhancing our understanding of the biological aetiology of both neuropsychiatric disorders and sleep-related phenotypes such as chronotype, which is our preference for morning or evening time. Mendelian randomization (MR) is a post-GWAS analysis that is used to infer causal relationships between potential risk factors and outcomes. MR uses genetic variants as instrumental variants for exposures to study the effect on outcomes. This review details the main results from GWAS of neuropsychiatric disorders and sleep-related phenotypes, and the application of MR to investigate their bidirectional relationship. The main results from MR studies of neuropsychiatric disorders and sleep-related phenotypes are summarized. These MR studies have identified 37 causal relationships between neuropsychiatric disorders and sleep-related phenotypes. MR studies identified evidence of a causal role for five neuropsychiatric disorders and symptoms (attention deficit hyperactivity disorder, bipolar disorder, depressive symptoms, major depressive disorder and schizophrenia) on sleep-related phenotypes and evidence of a causal role for five sleep-related phenotypes (daytime napping, insomnia, morning person, long sleep duration and sleep duration) on risk for neuropsychiatric disorders. These MR results show a bidirectional relationship between neuropsychiatric disorders and sleep-related phenotypes and identify potential risk factors for follow-up studies.

Waking Up the Sleep Field: An Overview on the Implications of Genetics and Bioinformatics of Sleep.

Sleep genetics is an intriguing, as yet less understood, understudied, emerging area of biological and medical discipline. A generalist may not be aware of the current status of the field given the variety of journals that have published studies on the genetics of sleep and the circadian clock over the years. For researchers venturing into this fascinating area, this review thus includes fundamental features of circadian rhythm and genetic variables impacting sleep-wake cycles. Sleep/wake pathway medication exposure and susceptibility are influenced by genetic variations, and the responsiveness of sleep-related medicines is influenced by several functional polymorphisms. This review highlights the features of the circadian timing system and then a genetic perspective on wakefulness and sleep, as well as the relationship between sleep genetics and sleep disorders. Neurotransmission genes, as well as circadian and sleep/wake receptors, exhibit functional variability. Experiments on animals and humans have shown that these genetic variants impact clock systems, signaling pathways, nature, amount, duration, type, intensity, quality, and quantity of sleep. In this regard, the overview covers research on sleep genetics, the genomic properties of several popular model species used in sleep studies, homologs of mammalian genes, sleep disorders, and related genes. In addition, the study includes a brief discussion of sleep, narcolepsy, and restless legs syndrome from the viewpoint of a model organism. It is suggested that the understanding of genetic clues on sleep function and sleep disorders may, in future, result in an evidence-based, personalized treatment of sleep disorders.

Single-cell transcriptomics reveals that glial cells integrate homeostatic and circadian processes to drive sleep-wake cycles.

The sleep-wake cycle is determined by circadian and sleep homeostatic processes. However, the molecular impact of these processes and their interaction in different brain cell populations are unknown. To fill this gap, we profiled the single-cell transcriptome of adult Drosophila brains across the sleep-wake cycle and four circadian times. We show cell type-specific transcriptomic changes, with glia displaying the largest variation. Glia are also among the few cell types whose gene expression correlates with both sleep homeostat and circadian clock. The sleep-wake cycle and sleep drive level affect the expression of clock gene regulators in glia, and disrupting clock genes specifically in glia impairs homeostatic sleep rebound after sleep deprivation. These findings provide a comprehensive view of the effects of sleep homeostatic and circadian processes on distinct cell types in an entire animal brain and reveal glia as an interaction site of these two processes to determine sleep-wake dynamics.

Adaptive changes in BMAL2 with increased locomotion associated with the evolution of unihemispheric slow-wave sleep in mammals.

Marine mammals, especially cetaceans, have evolved a very special form of sleep characterized by unihemispheric slow-wave sleep (USWS) and a negligible amount or complete absence of rapid-eye-movement sleep; however, the underlying genetic mechanisms remain unclear. Here, we detected unique, significant selection signatures in basic helix-loop-helix ARNT like 2 (BMAL2; also called ARNTL2), a key circadian regulator, in marine mammal lineages, and identified two nonsynonymous amino acid substitutions (K204E and K346Q) in the important PER-ARNT-SIM domain of cetacean BMAL2 via sequence comparison with other mammals. In vitro assays revealed that these cetacean-specific mutations specifically enhanced the response to E-box-like enhancer and consequently promoted the transcriptional activation of PER2, which is closely linked to sleep regulation. The increased PER2 expression, which was further confirmed both in vitro and in vivo, is beneficial for allowing cetaceans to maintain continuous movement and alertness during sleep. Concordantly, the locomotor activities of zebrafish overexpressing the cetacean-specific mutant bmal2 were significantly higher than the zebrafish overexpressing the wild-type gene. Subsequently, transcriptome analyses revealed that cetacean-specific mutations caused the upregulation of arousal-related genes and the downregulation of several sleep-promoting genes, which is consistent with the need to maintain hemispheric arousal during USWS. Our findings suggest a potential close relationship between adaptive changes in BMAL2 and the remarkable adaptation of USWS and may provide novel insights into the genetic basis of the evolution of animal sleep.

Exome sequencing identifies genes associated with sleep-related traits.

Sleep is vital for human health and has a moderate heritability. Previous genome-wide association studies have limitations in capturing the role of rare genetic variants in sleep-related traits. Here we conducted a large-scale exome-wide association study of eight sleep-related traits (sleep duration, insomnia symptoms, chronotype, daytime sleepiness, daytime napping, ease of getting up in the morning, snoring and sleep apnoea) among 450,000 participants from UK Biobank. We identified 22 new genes associated with chronotype (ADGRL4, COL6A3, CLK4 and KRTAP3-3), daytime sleepiness (ST3GAL1 and ANKRD12), daytime napping (PLEKHM1, ANKRD12 and ZBTB21), snoring (WDR59) and sleep apnoea (13 genes). Notably, 20 of these genes were confirmed to be significantly associated with sleep disorders in the FinnGen cohort. Enrichment analysis revealed that these discovered genes were enriched in circadian rhythm and central nervous system neurons. Phenotypic association analysis showed that ANKRD12 was associated with cognition and inflammatory traits. Our results demonstrate the value of large-scale whole-exome analysis in understanding the genetic architecture of sleep-related traits and potential biological mechanisms.

Causal association between sleep traits and autoimmune arthritis: Evidence from a bidirectional Mendelian randomization study.

OBJECTIVE: To explore whether there is a genetic causal relationship between sleep traits and the risk of autoimmune arthritis (AA). METHODS: Univariable and multivariable Mendelian randomization was employed using genome-wide association studies data to assess sleep traits' associations with AAs, including rheumatoid arthritis (RA), ankylosing spondylitis, and psoriatic arthritis. The inverse-variance weighted method served as the primary analysis, supplemented by the CAUSE method to improve power and mitigate false positives. Mediation Mendelian randomization was used to quantify direct and indirect effects. RESULTS: Significant associations were shown between insomnia symptoms and increased risk of overall RA (odds ratio = 2.75, 95% confidence interval 1.45-5.22) and seronegative RA (odds ratio = 6.95, 95% confidence interval 2.47-19.56). CAUSE results revealed an association of insomnia symptoms with overall RA and seronegative RA, as well as the sleep duration with overall RA. After the adjustment for body mass index, alcohol status, smoking status, and physical activity levels, multivariable analyses revealed that genetic predisposition to insomnia symptoms and prolonged sleep duration showed independent negative associations with the risk of overall RA and seropositive RA. In the reversed multivariable analyses, a borderline negative association was shown in the overall RA-sleep duration and a positive association of seropositive RA with the risk of insomnia symptoms. CONCLUSION: This study demonstrated a potential bidirectional causal relationship that genetic predisposition to insomnia symptoms and shorter sleep duration was associated with the risk of AA, especially RA. Genetic predisposition to RA was also associated with decreased sleep duration, as well as increased insomnia symptom risk.

Genome-wide association in Drosophila identifies a role for Piezo and Proc-R in sleep latency.

Sleep latency, the amount of time that it takes an individual to fall asleep, is a key indicator of sleep need. Sleep latency varies considerably both among and within species and is heritable, but lacks a comprehensive description of its underlying genetic network. Here we conduct a genome-wide association study of sleep latency. Using previously collected sleep and activity data on a wild-derived population of flies, we calculate sleep latency, confirming significant, heritable genetic variation for this complex trait. We identify 520 polymorphisms in 248 genes contributing to variability in sleep latency. Tests of mutations in 23 candidate genes and additional putative pan-neuronal knockdown of 9 of them implicated CG44153, Piezo, Proc-R and Rbp6 in sleep latency. Two large-effect mutations in the genes Proc-R and Piezo were further confirmed via genetic rescue. This work greatly enhances our understanding of the genetic factors that influence variation in sleep latency.

Associations between sleep-behavioral traits and healthspan: A one-sample Mendelian randomization study based on 388,909 participants of the UK-Biobank.

BACKGROUND: Although the association between sleep behavior and morbidity and mortality risk has been reported before, there is still uncertainty whether the observed associations are causal or confounding. Therefore, we investigated the causal relationships between sleep-behavioral traits and terminated healthspan risk using Mendelian randomization (MR). METHODS: We conducted a one-sample MR analysis to evaluate causality between six sleep-behavioral traits (sleep duration, chronotype/morningness, napping, sleeplessness/insomnia, and getting up from bed) and risk of healthspan termination among 388, 909 UK Biobank (UKB) participants. Instrumental variables for sleep behaviors (N = 590) were obtained from recent genome-wide association studies (GWAS). We defined healthspan based on eight predominant health-terminating events associated with longevity (congestive heart failure, myocardial infarction, chronic obstructive pulmonary disease, stroke, dementia, diabetes, cancer, and death). We further constructed a sleep score and a weighted genetic risk score to increase the predictive ability of the sleep-behavioral traits. Cox regression models and Inverse Probability Treatment Weighting (IPTW) were implemented, followed by MR to assess causation. We used inverse-variance-weighted MR to estimate causal effects, and weighted-median and MR-egger for sensitivity analysis to test the pleiotropic effects. RESULTS: In IPTW, we observed a decreased risk of terminated healthspan for healthy sleep behaviors such as 'sleep duration 7-8h/d' (Hazard ratio, HR = 0.93; 95 % confidence interval, CI: 0.92-0.96; P < 0.001); 'morningness' (HR = 0.95; 95%CI: 0.93-0.98; P < 0.01); 'napping' (HR = 0.93; 95%CI: 0.91-0.94; P < 0.001); 'easy getting up from bed' (HR = 0.91; 95%CI: 0.88-0.93; P < 0.001); and, 'never/rarely experience sleeplessness/insomnia' (HR = 0.94; 95%CI: 0.92-0.96; P < 0.001). MR results further indicated causal associations between healthy sleep duration (OR = 0.98; 95%CI: 0.97-1.00; P = 0.036) and insomnia (OR = 1.02; 95%CI: 1.01-1.03; P < 0.001) with terminated healthspan. MR-egger did not suggest any potential pleiotropy. CONCLUSION: This study supports abnormal sleep duration and insomnia as potential causal risk factors for terminated healthspan. Thus, healthy sleep behavior is valuable for the extension of healthspan, and well-designed and tailored sleep health interventions are warranted.