Genome-wide association analysis of insomnia using data from Partners Biobank

Identifying links between cardiovascular disease and insomnia by modeling genes from a pleiotropic locus

Insomnia symptoms double the risk of cardiovascular disease (CVD), yet shared genetic pathways remain unclear. Genome-wide association studies identified a genetic locus (near ATP5G1, UBE2Z, SNF8, IGF2BP1 and GIP) linked to insomnia and CVD. We used Drosophila models to perform tissue-specific RNA interference knockdowns of four conserved orthologs (ATPsynC, lsn, Bruce and Imp) in neurons and the heart. Neuronal-specific knockdown of ATPsynC, Imp and lsn impaired sleep quantity and quality. In contrast, cardiac knockdown of ATPsynC and lsn reduced cardiac function and lifespan, with lsn knockdown also causing cardiac dilation and myofibrillar disorganization. Cross-tissue effects were evident: neuronal Imp knockdown compromised cardiac function, whereas cardiac ATPsynC and lsn knockdown increased sleep fragmentation and inflammation (marked by Upd3 elevation in the heart or head). Overexpression of Upd3 in neurons impaired cardiac function, and its overexpression in the heart disrupted sleep. Our findings reveal conserved genes mediating tissue-specific and cross-tissue interactions between sleep and cardiac function, providing novel insights into the genetic mechanisms linking insomnia and CVD through inflammation.

A gap analysis of UK biobank publications reveals SNPs associated with intrinsic subtypes of breast cancer

Breast cancer is a multifaceted disease and a leading cause of cancer morbidity and mortality in females across the globe. In 2020 alone, 2.3 million women were diagnosed and 685,000 died of breast cancer worldwide. With the number of diagnoses projected to increase to 3 million per year by 2040 it is essential that new methods of detection and disease stratification are sought to decrease this global cancer burden. Although significant improvements have been made in breast cancer diagnosis and treatment, the prognosis of breast cancer remains poor in some patient groups (i.e. triple negative breast cancer), necessitating research into better patient stratification, diagnosis and drug discovery. The UK Biobank, a comprehensive biomedical and epidemiological database with a wide variety of multiomics data (genomics, proteomics, metabolomics) offers huge potential to uncover groundbreaking discoveries in breast cancer research leading to improved patient stratification. Combining genomic, proteomic, and metabolic profiles of breast cancer in combination with histological classification, can aid treatment decisions through accurate diagnosis and prognosis prediction of tumor behaviour. Here, we systematically reviewed PubMed publications reporting the analysis of UK Biobank data in breast cancer research. Our analysis of UK Biobank studies in the past five years identified 125 publications, of which 76 focussed on genomic data analysis. Interestingly, only two studies reported the analysis of metabolomics and proteomics data, with none performing multiomics analysis of breast cancer. A meta-analysis of the 76 publications identified 2870 genetic variants associated with breast cancer across 445 genes. Subtype analysis revealed differential genetic alteration in 13 of the 445 genes and the identification of 59 well-established breast cancer genes. in differential pathways. Pathway interaction analyses illuminated their involvement in general cancer biomolecular pathways (e.g. DNA damage repair, Gene expression). While our meta-analysis only measured genetic differences in breast cancer due to current usage of UK Biobank data, minimal multi-omics analyses have been performed and the potential for harnessing multi-omics strategies within the UK Biobank cohort holds promise for unravelling the biological signatures of distinct breast cancer subtypes further in the future.

Genetics of posttraumatic stress disorder and cardiovascular conditions using Life's Essential 8, Electronic Health Records, and Heart Imaging

BACKGROUND

Patients with post-traumatic stress disorder (PTSD) experience higher risk of adverse cardiovascular (CV) outcomes. This study explores shared loci, and genes between PTSD and CV conditions from three major domains: CV diagnoses from electronic health records (CV-EHR), cardiac and aortic imaging, and CV health behaviors defined in Life's Essential 8 (LE8).

METHODS

We used genome-wide association study (GWAS) of PTSD (N=1,222,882), 246 CV diagnoses based on EHR data from Million Veteran Program (MVP; N=458,061), UK Biobank (UKBB; N=420,531), 82 cardiac and aortic imaging traits (N=26,893), and GWAS of traits defined in the LE8 (N = 282,271 ~ 1,320,016). Shared loci between PTSD and CV conditions were identified using local genetic correlations (rg), and colocalization (shared causal variants). Overlapping genes between PTSD and CV conditions were identified from genetically regulated proteome expression in brain and blood tissues, and subsequently tested to identify functional pathways and gene-drug targets. Epidemiological replication of EHR-CV diagnoses was performed in AllofUS cohort (AoU; N=249,906).

RESULTS

Among the 76 PTSD-susceptibility risk loci, 33 loci exhibited local rg with 45 CV-EHR traits (|rg|≥0.4), four loci with eight heart imaging traits(|rg|≥0.5), and 44 loci with LE8 factors (|rg|≥0.36) in MVP. Among significantly correlated loci, we found shared causal variants (colocalization probability > 80%) between PTSD and 17 CV-EHR (in MVP) at 11 loci in MVP, that also replicated in UKBB and/or other cohorts. Of the 17 traits, the observational analysis in the AoU showed PTSD was associated with 13 CV-EHR traits after accounting for socioeconomic factors and depression diagnosis. PTSD colocalized with eight heart imaging traits on 2 loci and with LE8 factors on 31 loci. Leveraging blood and brain proteome expression, we found 33 and 122 genes, respectively, shared between PTSD and CVD. Blood proteome genes were related to neuronal and immune processes, while the brain proteome genes converged on metabolic and calcium-modulating pathways (FDR p <0.05). Drug repurposing analysis highlighted DRD2, NOS1, GFAP, and POR as common targets of psychiatric and CV drugs.

CONCLUSION

PTSD-CV comorbidities exhibit shared risk loci, and genes involved in tissue-specific regulatory mechanisms.

Revolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine

Personalized sleep medicine represents a transformative shift in healthcare, emphasizing individualized approaches to optimizing sleep health, considering the bidirectional relationship between sleep and health. This field moves beyond conventional methods, tailoring care to the unique physiological and psychological needs of individuals to improve sleep quality and manage disorders. Key to this approach is the consideration of diverse factors like genetic predispositions, lifestyle habits, environmental factors, and underlying health conditions. This enables more accurate diagnoses, targeted treatments, and proactive management. Technological advancements play a pivotal role in this field: wearable devices, mobile health applications, and advanced diagnostic tools collect detailed sleep data for continuous monitoring and analysis. The integration of machine learning and artificial intelligence enhances data interpretation, offering personalized treatment plans based on individual sleep profiles. Moreover, research on circadian rhythms and sleep physiology is advancing our understanding of sleep's impact on overall health. The next generation of wearable technology will integrate more seamlessly with IoT and smart home systems, facilitating holistic sleep environment management. Telemedicine and virtual healthcare platforms will increase accessibility to specialized care, especially in remote areas. Advancements will also focus on integrating various data sources for comprehensive assessments and treatments. Genomic and molecular research could lead to breakthroughs in understanding individual sleep disorders, informing highly personalized treatment plans. Sophisticated methods for sleep stage estimation, including machine learning techniques, are improving diagnostic precision. Computational models, particularly for conditions like obstructive sleep apnea, are enabling patient-specific treatment strategies. The future of personalized sleep medicine will likely involve cross-disciplinary collaborations, integrating cognitive behavioral therapy and mental health interventions. Public awareness and education about personalized sleep approaches, alongside updated regulatory frameworks for data security and privacy, are essential. Longitudinal studies will provide insights into evolving sleep patterns, further refining treatment approaches. In conclusion, personalized sleep medicine is revolutionizing sleep disorder treatment, leveraging individual characteristics and advanced technologies for improved diagnosis, treatment, and management. This shift towards individualized care marks a significant advancement in healthcare, enhancing life quality for those with sleep disorders.

Linking genetic foundations of sleep disturbances to personality traits: a study of mid-life twins

Risk of sleep disturbances depends on individuals' personality, and a large body of evidence indicates that individuals prone to neuroticism, impulsivity, and (low) extraversion are more likely to experience them. Origins of these associations are unclear, but common genetic background may play an important role. Participants included 405 twin pairs (mean age of 54 years; 59% female) from the National Survey of Midlife Development in the United States (MIDUS) who reported on their personality traits (broad and specific), as well as sleep disturbances (problems with falling asleep, staying asleep, waking early, and feeling unrested). Uni- and bivariate biometric decompositions evaluated contributions of genetic and environmental factors to associations between personality and poor sleep, as well as unique contributions from individual traits. Neuroticism, extraversion, conscientiousness, and aggressiveness were the strongest phenotypic predictors of poor sleep. Genetic sources of covariance were about twice as large as non-shared environmental sources, and only shared genetic background accounted for links between aggressiveness and poor sleep. Neuroticism and extraversion accounted for most of the genetic overlap between personality and sleep disturbances. The findings shed light on developmental antecedents of ties between personality and poor sleep, suggesting a larger role of common genetic background than idiosyncratic life experiences. The results also suggest that emotion-related traits play the most important role for poor sleep, compared to other personality traits, and may partially account for genetic associations with other traits.

Potential genetic and epigenetic mechanisms in insomnia: A systematic review

Insomnia is a stress-related sleep disorder conceptualised within a diathesis-stress framework, which it is thought to result from predisposing factors interacting with precipitating stressful events that trigger the development of insomnia. Among predisposing factors genetics and epigenetics may play a role. A systematic review of the current evidence for the genetic and epigenetic basis of insomnia was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) system. A total of 24 studies were collected for twins and family heritability, 55 for genome-wide association studies, 26 about candidate genes for insomnia, and eight for epigenetics. Data showed that insomnia is a complex polygenic stress-related disorder, and it is likely to be caused by a synergy of genetic and environmental factors, with stress-related sleep reactivity being the important trait. Even if few studies have been conducted to date on insomnia, epigenetics may be the framework to understand long-lasting consequences of the interaction between genetic and environmental factors and effects of stress on the brain in insomnia. Interestingly, polygenic risk for insomnia has been causally linked to different mental and medical disorders. Probably, by treating insomnia it would be possible to intervene on the effect of stress on the brain and prevent some medical and mental conditions.

Selection of genetic instruments in Mendelian randomisation studies of sleep traits

This review explores the criteria used for the selection of genetic instruments of sleep traits in the context of Mendelian randomisation studies. This work was motivated by the fact that instrument selection is the most important decision when designing a Mendelian randomisation study. As far as we are aware, no review has sought to address this to date, even though the number of these studies is growing rapidly. The review is divided into the following sections which are essential for genetic instrument selection: 1) Single-gene region vs polygenic analysis; 2) Polygenic analysis: biologically-vs statistically-driven approaches; 3) P-value; 4) Linkage disequilibrium clumping; 5) Sample overlap; 6) Type of exposure; 7) Total (R2) and average strength (F-statistic) metrics; 8) Number of single-nucleotide polymorphisms; 9) Minor allele frequency and palindromic variants; 10) Confounding. Our main aim is to discuss how instrumental choice impacts analysis and compare the strategies that Mendelian randomisation studies of sleep traits have used. We hope that our review will enable more researchers to take a more considered approach when selecting genetic instruments for sleep exposures.

Bidirectional mechanism of comorbidity of depression and insomnia based on synaptic plasticity

Insomnia is one of the most common accompanying symptoms of depression, with both sharing highly overlapping molecular pathways. The same pathological changes can trigger comorbidity of insomnia and depression, which further forms a vicious cycle with the involvement of more mechanisms and disease progression. Thus, understanding the potential interaction mechanisms between insomnia and depression is critical for clinical diagnosis and treatment. Comorbidity genetic factors, the hypothalamic-pituitary-adrenal axis, along with circadian rhythms of cortisol and the brain reward mechanism, are important ways in contributing to the comorbidity occurrence and development. However, owing to lack of pertinent investigational data, intricate molecular mechanisms necessitate further elaboration. Synaptic plasticity is a solid foundation for neural homeostasis. Pathological alterations of depression and insomnia may perturb the production and release of neurotransmitter, dendritic spine remodeling and elimination, which converges and reflects in aberrant synaptic dynamics. Hence, the introduction of synaptic plasticity research route and the construction of a comprehensive model of depression and insomnia comorbidity can provide new ideas for clinical depression insomnia comorbidity treatment plans.

Neuroprotective effect of Kurarinone against corticosterone-induced cytotoxicity on rat hippocampal neurons by targeting BACE1 to activate P13K-AKT signaling - A potential treatment in insomnia disorder

The hippocampus has been implicated in the pathogenesis of insomnia disorder (ID) and the purpose of this study was to investigate the neuroprotective mechanism of the natural flavone Kurarinone (Kur) on hippocampal neurotoxicity as a potential treatment of ID. The effect of Kur on hippocampal neuronal cell (HNC) viability and apoptosis were assessed by Cell counting kit-8 (CCK-8) assay and flow cytometry, respectively. Then, the effect of Kur on β-site amyloid precursor protein-cleaving enzyme 1 (BACE1), brain-derived neurotrophic factor (BDNF), and phosphatidylinositol-3-kinase (PI3K)/protein kinase B (AKT) phosphorylation level were measured by Western blot. Further, SwissTargetPrediction analysis and molecular docking experiments were used to detect a potential target of Kur. Then, the p-chlorophenylalanine (PCPA) model was established in vivo to further study the effect of BACE1 expression on Kur and HNC. As a result, HNC viability was only significantly decreased by 2 μM of Kur. Kur reversed the impacts of corticosterone upon inhibiting viability (0.25-1 μM), PI3K (0.5-1 μM)/AKT phosphorylation, and BDNF (1 μM) level, and enhancing the apoptosis (0.25-1 μM) and BACE1 expression (1 μM) in HNCs. BACE1 was a potential target of Kur. Notably, Kur (150 mg/kg) attenuated PCPA-induced upregulation of BACE1 expression in rat hippocampal tissues as ZRAS (0.8 g/kg). The effects of Kur (1 μM) on corticosterone-treated HNCs were reversed by BACE1 overexpression. Collectively, Kur downregulates BACE1 level to activate PI3K/AKT, thereby attenuating corticosterone-induced toxicity in HNCs, indicating that Kur possibly exerted a neuroprotective effect, which providing a new perspective for the treatment of insomnia disorders.

Genome-wide association studies and cross-population meta-analyses investigating short and long sleep duration

Sleep duration has been linked to a wide range of negative health outcomes and to reduced life expectancy. We present genome-wide association studies of short ( ≤ 5 h) and long ( ≥ 10 h) sleep duration in adults of European (N = 445,966), African (N = 27,785), East Asian (N = 3141), and admixed-American (N = 16,250) ancestry from UK Biobank and the Million Veteran Programme. In a cross-population meta-analysis, we identify 84 independent loci for short sleep and 1 for long sleep. We estimate SNP-based heritability for both sleep traits in each ancestry based on population derived linkage disequilibrium (LD) scores using cov-LDSC. We identify positive genetic correlation between short and long sleep traits (rg = 0.16 ± 0.04; p = 0.0002), as well as similar patterns of genetic correlation with other psychiatric and cardiometabolic phenotypes. Mendelian randomisation reveals a directional causal relationship between short sleep and depression, and a bidirectional causal relationship between long sleep and depression.

Causal Link between Gut Microbiota, Neurophysiological States, and Bone Diseases: A Comprehensive Mendelian Randomization Study

Increasing evidence highlights a robust correlation between the gut microbiota and bone diseases; however, the existence of a causal relationship between them remains unclear. In this study, we thoroughly examined the correlation between gut microbiota and skeletal diseases using genome-wide association studies. Linkage disequilibrium score regression and Mendelian randomization were used to probe genetic causality. Furthermore, the potential mediating role of neuropsychological states (i.e., cognition, depression, and insomnia) between the gut microbiota and bone diseases was evaluated using mediation analysis, with genetic colocalization analysis revealing potential targets. These findings suggest a direct causal relationship between Ruminococcaceae and knee osteoarthritis (OA), which appears to be mediated by cognitive performance and insomnia. Similarly, a causal association was observed between Burkholderiales and lumbar pelvic fractures, mediated by cognitive performance. Colocalization analysis identified a shared causal variant (rs2352974) at the TRAF-interacting protein locus for cognitive ability and knee OA. This study provides compelling evidence that alterations in the gut microbiota can enhance cognitive ability, ameliorate insomnia, and potentially reduce the risk of site-specific fractures and OA. Therefore, strategies targeting gut microbiota optimization could serve as novel and effective preventive measures against fractures and OA.

Genetic Variation in the Mitochondrial Glycerol-3-Phosphate Acyltransferase Is Associated With Liver Injury

BACKGROUND AND AIMS

Most of the genetic basis of chronic liver disease remains undiscovered.

APPROACH AND RESULTS

To identify genetic loci that modulate the risk of liver injury, we performed genome-wide association studies on circulating levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and total bilirubin across 312,671 White British participants in the UK Biobank. We focused on variants associated with elevations in all four liver biochemistries at genome-wide significance (P < 5 × 10-8 ) and that replicated using Mass General Brigham Biobank in 19,323 European ancestry individuals. We identified a genetic locus in mitochondrial glycerol-3-phosphate acyltransferase (GPAM rs10787429) associated with increased levels of ALT (P = 1.4 × 10-30 ), AST (P = 3.6 × 10-10 ), ALP (P = 9.5 × 10-30 ), and total bilirubin (P = 2.9 × 10-12 ). This common genetic variant was also associated with an allele dose-dependent risk of alcohol-associated liver disease (odd ratio [OR] = 1.34, P = 2.6 × 10-5 ) and fatty liver disease (OR = 1.18, P = 5.8 × 10-4 ) by International Classification of Diseases, 10th Revision codes. We identified significant interactions between GPAM rs10787429 and elevated body mass index in association with ALT and AST (P = 7.1 × 10-9 and 3.95 × 10-8 , respectively), as well as between GPAM rs10787429 and weekly alcohol consumption in association with ALT, AST, and alcohol-associated liver disease (P = 4.0 × 10-2 , 1.6 × 10-2 , and 1.3 × 10-2 , respectively). Unlike previously described genetic variants that are associated with an increased risk of liver injury but confer a protective effect on circulating lipids, GPAM rs10787429 was associated with an increase in total cholesterol (P = 2.0 × 10-17 ), LDL cholesterol (P = 2.0 × 10-10 ), and HDL cholesterol (P = 6.6 × 10-37 ). Single-cell RNA-sequencing data demonstrated hepatocyte-predominant expression of GPAM in cells that co-express genes related to VLDL production (P = 9.4 × 10-103 ).

CONCLUSIONS

Genetic variation in GPAM is associated with susceptibility to liver injury. GPAM may represent a therapeutic target in chronic liver disease.

Shared genetic liability and causal effects between major depressive disorder and insomnia

Deciphering the genetic relationships between major depressive disorder (MDD) and insomnia may facilitate understanding biological mechanisms as well as inform more effective treatment regimens for these conditions. Here we attempted to investigate mechanisms underlying relationships between MDD and insomnia in the context of shared genetic variations. Shared genetic variation was evaluated by polygenic analysis. In two-sample bidirectional Mendelian randomization analysis, causal relationships between MDD and insomnia were investigated; the list of shared genomic loci was identified using cross-trait meta-analysis. Putatively causal genes for the two diseases were prioritized by fine-mapping of transcriptome-wide associations. Polygenic analysis identified 15.1 thousand variants as causally influencing MDD, and 10.8 thousand variants as influencing insomnia. Among these variants, 8.5 thousand were shared between the two diseases. Mendelian randomization analysis suggests that genetic liability to MDD and to insomnia have mutual causal effects (MDD on insomnia with OR = 1.25 and insomnia on MDD with OR = 2.23). Cross-trait meta-analyses identified 89 genomic loci as being shared between MDD and insomnia, with some of them being prioritized as causal in subsequent fine-mapping of transcriptome-wide association signals. Analysis highlights possible role of endogenous production of nitric oxide in the brain, and the gonadotropic secretion in the pituitary as possibly physiological connectors of MDD and insomnia. Here we show a substantial shared genetic liability and mutual causal links between MDD and insomnia. Presented findings provide novel insight into phenotypic relationship between these two interconnected conditions.

GWAS Meta-Analysis Reveals Shared Genes and Biological Pathways between Major Depressive Disorder and Insomnia

Major depressive disorder (MDD) is one of the most prevalent and disabling mental disorders worldwide. Among the symptoms of MDD, sleep disturbance such as insomnia is prominent, and the first reason patients may seek professional help. However, the underlying pathophysiology of this comorbidity is still elusive. Recently, genome-wide association studies (GWAS) have begun to unveil the genetic background of several psychiatric disorders, including MDD and insomnia. Identifying the shared genomic risk loci between comorbid psychiatric disorders could be a valuable strategy to understanding their comorbidity. This study seeks to identify the shared genes and biological pathways between MDD and insomnia based on their shared genetic variants. First, we performed a meta-analysis based on the GWAS summary statistics of MDD and insomnia obtained from Psychiatric Genomics Consortium and UK Biobank, respectively. Next, we associated shared genetic variants to genes using two gene mapping strategies: (a) positional mapping based on genomic proximity and (b) expression quantitative trait loci (eQTL) mapping based on gene expression linkage across multiple tissues. As a result, a total of 719 shared genes were identified. Over half (51%) of them are protein-coding genes. Functional enrichment analysis shows that the most enriched biological pathways are related to epigenetic modification, sensory perception, and immunologic signatures. We also identified druggable targets using a network approach. Together, these results may provide insights into understanding the genetic predisposition and underlying biological pathways of comorbid MDD and insomnia symptoms.

The heritability of insomnia: Systematic review and meta-analysis of twin studies

Twin studies have consistently found that genetic factors explain a substantial proportion of the variance for insomnia. However, studies vary widely in their heritability estimates. Therefore, this meta-analysis aimed to: 1) Estimate the mean heritability of insomnia; 2) Assess heterogeneity among twin studies of insomnia; and 3) Search and analyse characteristics of the studies (moderator variables) that may explain heterogeneity among estimates. For this purpose, separate meta-analyses were carried out for MZ and DZ correlations and for heritability estimates by assuming random-effects models. Thirteen independent samples were included in this meta-analysis. The heterogeneity index for heritability estimates was significant in both best fitting models (I² = 98.77, P < .0001) and full models (I² = 97.80, P < .0001). MZ correlations were higher (0.37; 95%CI: 0.31,.43) than DZ correlations (0.15; 95%CI: 0.12,.18). A mean heritability of 0.39 (95%CI: 0.32,.44) was found for insomnia. These results highlight the role of genetic factors in explaining differences among the population on insomnia and Emphasize heterogeneity among studies. Further research is needed to identify variables that could explain this heterogeneity.