SCGB1D2 inhibits growth of Borrelia burgdorferi and affects susceptibility to Lyme disease.

Lyme disease is a tick-borne disease caused by bacteria of the genus Borrelia. The host factors that modulate susceptibility for Lyme disease have remained mostly unknown. Using epidemiological and genetic data from FinnGen and Estonian Biobank, we identify two previously known variants and an unknown common missense variant at the gene encoding for Secretoglobin family 1D member 2 (SCGB1D2) protein that increases the susceptibility for Lyme disease. Using live Borrelia burgdorferi (Bb) we find that recombinant reference SCGB1D2 protein inhibits the growth of Bb in vitro more efficiently than the recombinant protein with SCGB1D2 P53L deleterious missense variant. Finally, using an in vivo murine infection model we show that recombinant SCGB1D2 prevents infection by Borrelia in vivo. Together, these data suggest that SCGB1D2 is a host defense factor present in the skin, sweat, and other secretions which protects against Bb infection and opens an exciting therapeutic avenue for Lyme disease.

Genome-wide association studies highlight novel risk loci for septal defects and left-sided congenital heart defects.

BACKGROUND: Congenital heart defects (CHD) are structural defects of the heart affecting approximately 1% of newborns. They exhibit low penetrance and non-Mendelian patterns of inheritance as varied and complex traits. While genetic factors are known to play an important role in the development of CHD, the specific genetics remain unknown for the majority of patients. To elucidate the underlying genetic risk, we performed a genome wide association study (GWAS) of CHDs in general and specific CHD subgroups using the FinnGen Release 10 (R10) (N > 393,000), followed by functional fine-mapping through eQTL and co-localization analyses using the GTEx database. RESULTS: We discovered three genome-wide significant loci associated with general CHD. Two of them were located in chromosome 17: 17q21.32 (rs2316327, intronic: LRRC37A2, Odds ratio (OR) [95% Confidence Interval (CI)] = 1.17[1.12-1.23], p = 1.5 × 10-9) and 17q25.3 (rs1293973611, nearest: BAHCC1, OR[95%CI] = 4.48[2.80-7.17], p = 7.0 × 10-10), respectively, and in addition to general CHD, the rs1293973611 locus was associated with the septal defect subtype. The third locus was in band 1p21.2 (rs35046143, nearest: PALMD, OR[95%CI] = 1.15[1.09-1.21], p = 7.1 × 10-9), and it was associated with general CHD and left-sided lesions. In the subgroup analysis, two additional loci were associated with septal defects (rs75230966 and rs6824295), and one with left-sided lesions (rs1305393195). In the eQTL analysis the variants rs2316327 (general CHD), and rs75230966 (septal defects) both located in 17q21.32 (with a LD r2 of 0.41) were both predicted to significantly associate with the expression of WNT9B in the atrial appendage tissue category. This effect was further confirmed by co-localization analysis, which also implicated WNT3 expression in the atrial appendage. A meta-analysis of general CHD together with the UK Biobank (combined N = 881,678) provided a different genome-wide significant locus in LRRC37A2; rs16941382 (OR[95%CI] = 1.15[1.11-1.20], p = 1.5 × 10-9) which is in significant LD with rs2316327. CONCLUSIONS: Our results of general CHD and different CHD subcategories identified a complex risk locus on chromosome 17 near BAHCC1 and LRRC37A2, interacting with the genes WNT9B, WNT3 and MYL4, may constitute potential novel CHD risk associated loci, warranting future experimental tests to determine their role.

An immunogenetic basis for lung cancer risk.

Cancer risk is influenced by inherited mutations, DNA replication errors, and environmental factors. However, the influence of genetic variation in immunosurveillance on cancer risk is not well understood. Leveraging population-level data from the UK Biobank and FinnGen, we show that heterozygosity at the human leukocyte antigen (HLA)-II loci is associated with reduced lung cancer risk in smokers. Fine-mapping implicated amino acid heterozygosity in the HLA-II peptide binding groove in reduced lung cancer risk, and single-cell analyses showed that smoking drives enrichment of proinflammatory lung macrophages and HLA-II+ epithelial cells. In lung cancer, widespread loss of HLA-II heterozygosity (LOH) favored loss of alleles with larger neopeptide repertoires. Thus, our findings nominate genetic variation in immunosurveillance as a critical risk factor for lung cancer.

Genetic Analysis of Obstructive Sleep Apnea and Its Relationship with Severe COVID-19.

RATIONALE: While patients with obstructive sleep apnea (OSA) have a higher risk for COVID-19 hospitalization, the causal relationship has remained unexplored. OBJECTIVES: To understand the causal relationship between OSA and COVID-19 leveraging data from vaccination and electronic health records, genetic risk factors from genome-wide association studies (GWAS) and Mendelian randomization. METHODS: We elucidated genetic risk factors for OSA using FinnGen (N total = 377,277 individuals) performing genome-wide association. We used the associated variants as instruments for univariate and multivariate Mendelian randomization (MR) analyses and computed absolute risk reduction (ARR) against COVID-19 hospitalization with or without vaccination. MEASUREMENTS AND MAIN RESULTS: We identified 9 novel loci for OSA and replicated our findings in the Million Veterans Program. Furthermore, MR analysis showed that OSA was a causal risk factor for severe COVID-19 (P=9.41x10-4). Probabilistic modelling showed that the strongest genetic risk factor for OSA at the FTO locus reflected a signal of higher BMI, whereas BMI independent association was seen with the earlier reported SLC9A4 locus and a MECOM locus which is a transcriptional regulator with 210-fold enrichment in the Finnish population. Similarly, Multivariate MR (MVMR) analysis showed that the causality for severe COVID-19 was driven by body mass index (BMI), (P MVMR = 5.97x10-6, beta=0.47). Finally, vaccination reduced the risk for COVID-19 hospitalization more in the OSA patients than in the non-OSA controls: ARR = 13.3% vs. ARR = 6.3% in the OSA vs. non-OSA population. CONCLUSIONS: Our analysis identified novel genetic risk factors for OSA and showed that OSA is a causal risk factor for severe COVID-19. The effect is predominantly explained by higher BMI and suggests BMI-dependent effects at the level of individual variants and at the level of comorbid causality.

Nightmares share genetic risk factors with sleep and psychiatric traits.

Nightmares are vivid, extended, and emotionally negative or negative dreams that awaken the dreamer. While sporadic nightmares and bad dreams are common and generally harmless, frequent nightmares often reflect underlying pathologies of emotional regulation. Indeed, insomnia, depression, anxiety, or alcohol use have been associated with nightmares in epidemiological and clinical studies. However, the connection between nightmares and their comorbidities are poorly understood. Our goal was to examine the genetic risk factors for nightmares and estimate correlation or causality between nightmares and comorbidities. We performed a genome-wide association study (GWAS) in 45,255 individuals using a questionnaire-based assessment on the frequency of nightmares during the past month and genome-wide genotyping data. While the GWAS did not reveal individual risk variants, heritability was estimated at 5%. In addition, the genetic correlation analysis showed a robust correlation (rg > 0.4) of nightmares with anxiety (rg = 0.671, p = 7.507e-06), depressive (rg = 0.562, p = 1.282e-07) and posttraumatic stress disorders (rg = 0.4083, p = 0.0152), and personality trait neuroticism (rg = 0.667, p = 4.516e-07). Furthermore, Mendelian randomization suggested causality from insomnia to nightmares (beta = 0.027, p = 0.0002). Our findings suggest that nightmares share genetic background with psychiatric traits and that insomnia may increase an individual's liability to experience frequent nightmares. Given the significant correlations with psychiatric and psychological traits, it is essential to grow awareness of how nightmares affect health and disease and systematically collect information about nightmares, especially from clinical samples and larger cohorts.

Genetics of sleep medication purchases suggests causality from sleep problems to psychiatric traits.

STUDY OBJECTIVES: Over 10% of the population in Europe and in the United States use sleep medication to manage sleep problems. Our objective was to elucidate genetic risk factors and clinical correlates that contribute to sleep medication purchase and estimate the comorbid impact of sleep problems. METHODS: We performed epidemiological analysis for psychiatric diagnoses, and genetic association studies of sleep medication purchase in 797 714 individuals from FinnGen Release 7 (N = 311 892) and from the UK Biobank (N = 485 822). Post-association analyses included genetic correlation, co-localization, Mendelian randomization (MR), and polygenic risk estimation. RESULTS: In a GWAS we identified 27 genetic loci significantly associated with sleep medication, located in genes associated with sleep; AUTS2, CACNA1C, MEIS1, KIRREL3, PAX8, GABRA2, psychiatric traits; CACNA1C, HIST1H2BD, NUDT12. TOPAZ1 and TSNARE1. Co-localization and expression analysis emphasized effects on the KPNA2, GABRA2, and CACNA1C expression in the brain. Sleep medications use was epidemiologically related to psychiatric traits in FinnGen (OR [95% (CI)] = 3.86 [3.78 to 3.94], p < 2 × 10-16), and the association was accentuated by genetic correlation and MR; depression (rg = 0.55 (0.027), p = 2.86 × 10-89, p MR = 4.5 × 10-5), schizophrenia (rg = 0.25 (0.026), p = 2.52 × 10-21, p MR = 2 × 10-4), and anxiety (rg = 0.44 (0.047), p = 2.88 × 10-27, p MR = 8.6 × 10-12). CONCLUSIONS: These results demonstrate the genetics behind sleep problems and the association between sleep problems and psychiatric traits. Our results highlight the scientific basis for sleep management in treating the impact of psychiatric diseases.

Causal Association Between Subtypes of Excessive Daytime Sleepiness and Risk of Cardiovascular Diseases.

BACKGROUND: Excessive daytime sleepiness (EDS), experienced in 10% to 20% of the population, has been associated with cardiovascular disease and death. However, the condition is heterogeneous and is prevalent in individuals having short and long sleep duration. We sought to clarify the relationship between sleep duration subtypes of EDS with cardiovascular outcomes, accounting for these subtypes. METHODS AND RESULTS: We defined 3 sleep duration subtypes of excessive daytime sleepiness: normal (6-9 hours), short (<6 hours), and long (>9 hours), and compared these with a nonsleepy, normal-sleep-duration reference group. We analyzed their associations with incident myocardial infarction (MI) and stroke using medical records of 355 901 UK Biobank participants and performed 2-sample Mendelian randomization for each outcome. Compared with healthy sleep, long-sleep EDS was associated with an 83% increased rate of MI (hazard ratio, 1.83 [95% CI, 1.21-2.77]) during 8.2-year median follow-up, adjusting for multiple health and sociodemographic factors. Mendelian randomization analysis provided supporting evidence of a causal role for a genetic long-sleep EDS subtype in MI (inverse-variance weighted ß=1.995, P=0.001). In contrast, we did not find evidence that other subtypes of EDS were associated with incident MI or any associations with stroke (P>0.05). CONCLUSIONS: Our study suggests the previous evidence linking EDS with increased cardiovascular disease risk may be primarily driven by the effect of its long-sleep subtype on higher risk of MI. Underlying mechanisms remain to be investigated but may involve sleep irregularity and circadian disruption, suggesting a need for novel interventions in this population.

Associations between sleep medication use and alcohol consumption over 36 years in Finnish twins.

BACKGROUND: Sleep medication use is an indicator of underlying sleep problems that might be induced by various factors such as alcohol use. However, the longitudinal relationship between drinking and sleep problems remains poorly understood. We investigated associations between sleep medication and alcohol use throughout adulthood, and examined the role of familial and potential confounding factors contributing to these associations. METHODS: We used information of zygosity and self-report questionnaire data over a follow-up period of 36 years from the Older Finnish Twin Cohort (N=13,851). RESULTS: Logistic regression analyses suggested consistent associations between sleep medication use and heavy/binge drinking at all four time points (OR range =1.36-3.18, P <0.05), implying that increased drinking is associated with increased sleep medication use over time. Cross-lagged path analyses suggested that moderate/heavy and binge drinking predict sleep medication use at most time points (OR range = 1.15-1.94, P <0.05), whilst sleep medication use predicts subsequent abstaining from alcohol (OR range =2.26-2.47, P <0.05). Within-pair analyses implied that familial factors play a role, and quantitative genetic modelling estimated genetic factors to explain approximately 80% of the lifetime association of sleep medication use with moderate/heavy and binge drinking. CONCLUSIONS: Drinking is associated with sleep medication use throughout adulthood. Further, our results suggest that drinking is likely to predict sleep medication use, thereby potentially constituting a risk factor for sleep problems, and that genetic factors contribute to the association. These findings are important in terms of better understanding the development of sleep and alcohol use disorders.

SIRPα controls CD47-dependent platelet clearance in mice and humans.

Over the last decade, more data has revealed that increased surface expression of the "don't eat me" CD47 protein on cancer cells plays a role in immune evasion and tumor progression, with CD47 blockade emerging as a new therapy in immuno-oncology. CD47 is critical in regulating cell homeostasis and clearance, as binding of CD47 to the inhibitory receptor SIRPα can prevent phagocytosis and macrophage-mediated cell clearance. The purpose of this study was to examine the role of the CD47-SIRPα signal in platelet homeostasis and clearance. Therapeutic reagents targeting the CD47-SIRPα axis are very promising for treatment of hematologic malignancies and solid tumors, but lead to transient anemia or thrombocytopenia in a subset of patients. We found that platelet homeostatic clearance is regulated through the CD47-SIRPα axis and that therapeutic blockade to disrupt this interaction in mice and in humans has a significant impact on platelet levels. Furthermore, we identified genetic variations at the SIRPA locus that impact platelet levels in humans such that higher SIRPA gene expression is associated with higher platelet levels. SIRPA expression at either end of the normal range may affect clinical outcomes of treatment with anti-CD47 therapy.

BMAL1 loss in oligodendroglia contributes to abnormal myelination and sleep.

Myelination depends on the maintenance of oligodendrocytes that arise from oligodendrocyte precursor cells (OPCs). We show that OPC-specific proliferation, morphology, and BMAL1 are time-of-day dependent. Knockout of Bmal1 in mouse OPCs during development disrupts the expression of genes associated with circadian rhythms, proliferation, density, morphology, and migration, leading to changes in OPC dynamics in a spatiotemporal manner. Furthermore, these deficits translate into thinner myelin, dysregulated cognitive and motor functions, and sleep fragmentation. OPC-specific Bmal1 loss in adulthood does not alter OPC density at baseline but impairs the remyelination of a demyelinated lesion driven by changes in OPC morphology and migration. Lastly, we show that sleep fragmentation is associated with increased prevalence of the demyelinating disorder multiple sclerosis (MS), suggesting a link between MS and sleep that requires further investigation. These findings have broad mechanistic and therapeutic implications for brain disorders that include both myelin and sleep phenotypes.

The public health impact of poor sleep on severe COVID-19, influenza and upper respiratory infections.

BACKGROUND: Poor sleep is associated with an increased risk of infections and all-cause mortality but the causal direction between poor sleep and respiratory infections has remained unclear. We examined if poor sleep contributes as a causal risk factor to respiratory infections. METHODS: We used data on insomnia, influenza and upper respiratory infections (URIs) from primary care and hospital records in the UK Biobank (N ≈ 231,000) and FinnGen (N ≈ 392,000). We computed logistic regression to assess association between poor sleep and infections, disease free survival hazard ratios, and performed Mendelian randomization analyses to assess causality. FINDINGS: Utilizing 23 years of registry data and follow-up, we discovered that insomnia diagnosis associated with increased risk for infections (FinnGen influenza Cox's proportional hazard (CPH) HR = 4.34 [3.90, 4.83], P = 4.16 × 10-159, UK Biobank influenza CPH HR = 1.54 [1.37, 1.73], P = 2.49 × 10-13). Mendelian randomization indicated that insomnia causally predisposed to influenza (inverse-variance weighted (IVW) OR = 1.65, P = 5.86 × 10-7), URI (IVW OR = 1.94, P = 8.14 × 10-31), COVID-19 infection (IVW OR = 1.08, P = 0.037) and risk of hospitalization from COVID-19 (IVW OR = 1.47, P = 4.96 × 10-5). INTERPRETATION: Our findings indicate that chronic poor sleep is a causal risk factor for contracting respiratory infections, and in addition contributes to the severity of respiratory infections. These findings highlight the role of sleep in maintaining sufficient immune response against pathogens. FUNDING: Instrumentarium Science Foundation, Academy of Finland, Signe and Ane Gyllenberg Foundation, National Institutes of Health.

Genetic analysis of probable sleep bruxism and its associations with clinical and behavioral traits.

STUDY OBJECTIVES: Sleep bruxism (SB) can cause damage on teeth, headache and severe pain affecting both sleep and daily functioning. Yet despite the growing interest into bruxism, the underlying clinically relevant biological mechanisms remain unresolved. The aim of our study was to understand biological mechanisms and clinical correlates of SB including previously reported disease associations. METHODS: We used data from the FinnGen release R9 (N = 377 277 individuals) that are linked with Finnish hospital and primary care registries. We identified 12 297 (3.26%) individuals with International Classification of Diseases (ICD)-10 codes used for SB. In addition, we used logistic regression to examine the association between probable SB and its clinically diagnosed risk factors and comorbidities using ICD-10 codes. Furthermore, we examined medication purchases using prescription registry. Finally, we performed the first genome-wide association analysis for probable SB and computed genetic correlations using questionnaire, lifestyle, and clinical traits. RESULTS: The genome-wide association analysis revealed one significant association: rs10193179 intronic to Myosin IIIB (MYO3B) gene. In addition, we observed phenotypic associations and high genetic correlations with pain diagnoses, sleep apnea, reflux disease, upper respiratory diseases, psychiatric traits, and also their related medications such as antidepressants and sleep medication (p < 1e-4 for each trait). CONCLUSIONS: Our study provides a large-scale genetic framework to understand risk factors for SB and suggests potential biological mechanisms. Furthermore, our work strengthens the important earlier work that highlights SB as a trait that is associated with multiple axes of health. As part of this study, we provide genome-wide summary statistics that we hope will be useful for the scientific community studying SB.

Narcolepsy risk loci outline role of T cell autoimmunity and infectious triggers in narcolepsy.

Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with Pandemrix®. Here, we dissect disease mechanisms and interactions with environmental triggers in a multi-ethnic sample of 6,073 cases and 84,856 controls. We fine-mapped GWAS signals within HLA (DQ0602, DQB1*03:01 and DPB1*04:02) and discovered seven novel associations (CD207, NAB1, IKZF4-ERBB3, CTSC, DENND1B, SIRPG, PRF1). Significant signals at TRA and DQB1*06:02 loci were found in 245 vaccination-related cases, who also shared polygenic risk. T cell receptor associations in NT1 modulated TRAJ*24, TRAJ*28 and TRBV*4-2 chain-usage. Partitioned heritability and immune cell enrichment analyses found genetic signals to be driven by dendritic and helper T cells. Lastly comorbidity analysis using data from FinnGen, suggests shared effects between NT1 and other autoimmune diseases. NT1 genetic variants shape autoimmunity and response to environmental triggers, including influenza A infection and immunization with Pandemrix®.

Nationwide health, socio-economic and genetic predictors of COVID-19 vaccination status in Finland.

Understanding factors associated with COVID-19 vaccination can highlight issues in public health systems. Using machine learning, we considered the effects of 2,890 health, socio-economic and demographic factors in the entire Finnish population aged 30-80 and genome-wide information from 273,765 individuals. The strongest predictors of vaccination status were labour income and medication purchase history. Mental health conditions and having unvaccinated first-degree relatives were associated with reduced vaccination. A prediction model combining all predictors achieved good discrimination (area under the receiver operating characteristic curve, 0.801; 95% confidence interval, 0.799-0.803). The 1% of individuals with the highest predicted risk of not vaccinating had an observed vaccination rate of 18.8%, compared with 90.3% in the study population. We identified eight genetic loci associated with vaccination uptake and derived a polygenic score, which was a weak predictor in an independent subset. Our results suggest that individuals at higher risk of suffering the worst consequences of COVID-19 are also less likely to vaccinate.

Population analyses of mosaic X chromosome loss identify genetic drivers and widespread signatures of cellular selection.

Mosaic loss of the X chromosome (mLOX) is the most commonly occurring clonal somatic alteration detected in the leukocytes of women, yet little is known about its genetic determinants or phenotypic consequences. To address this, we estimated mLOX in >900,000 women across eight biobanks, identifying 10% of women with detectable X loss in approximately 2% of their leukocytes. Out of 1,253 diseases examined, women with mLOX had an elevated risk of myeloid and lymphoid leukemias and pneumonia. Genetic analyses identified 49 common variants influencing mLOX, implicating genes with established roles in chromosomal missegregation, cancer predisposition, and autoimmune diseases. Complementary exome-sequence analyses identified rare missense variants in FBXO10 which confer a two-fold increased risk of mLOX. A small fraction of these associations were shared with mosaic Y chromosome loss in men, suggesting different biological processes drive the formation and clonal expansion of sex chromosome missegregation events. Allelic shift analyses identified alleles on the X chromosome which are preferentially retained, demonstrating that variation at many loci across the X chromosome is under cellular selection. A novel polygenic score including 44 independent X chromosome allelic shift loci correctly inferred the retained X chromosomes in 80.7% of mLOX cases in the top decile. Collectively our results support a model where germline variants predispose women to acquiring mLOX, with the allelic content of the X chromosome possibly shaping the magnitude of subsequent clonal expansion.

Large registry-based analysis of genetic predisposition to tuberculosis identifies genetic risk factors at HLA.

Tuberculosis is a significant public health concern resulting in the death of over 1 million individuals each year worldwide. While treatment options and vaccines exist, a substantial number of infections still remain untreated or are caused by treatment resistant strains. Therefore, it is important to identify mechanisms that contribute to risk and prognosis of tuberculosis as this may provide tools to understand disease mechanisms and provide novel treatment options for those with severe infection. Our goal was to identify genetic risk factors that contribute to the risk of tuberculosis and to understand biological mechanisms and causality behind the risk of tuberculosis. A total of 1895 individuals in the FinnGen study had International Classification of Diseases-based tuberculosis diagnosis. Genome-wide association study analysis identified genetic variants with statistically significant association with tuberculosis at the human leukocyte antigen (HLA) region (P < 5e-8). Fine mapping of the HLA association provided evidence for one protective haplotype tagged by HLA DQB1*05:01 (P = 1.82E-06, OR = 0.81 [CI 95% 0.74-0.88]), and predisposing alleles tagged by HLA DRB1*13:02 (P = 0.00011, OR = 1.35 [CI 95% 1.16-1.57]). Furthermore, genetic correlation analysis showed association with earlier reported risk factors including smoking (P < 0.05). Mendelian randomization supported smoking as a risk factor for tuberculosis (inverse-variance weighted P < 0.05, OR = 1.83 [CI 95% 1.15-2.93]) with no significant evidence of pleiotropy. Our findings indicate that specific HLA alleles associate with the risk of tuberculosis. In addition, lifestyle risk factors such as smoking contribute to the risk of developing tuberculosis.

Face masks to prevent transmission of respiratory infections: Systematic review and meta-analysis of randomized controlled trials on face mask use.

OBJECTIVES: To examine the use of face mask intervention in mitigating the risk of spreading respiratory infections and whether the effect of face mask intervention differs in different exposure settings and age groups. DESIGN: Systematic review and meta-analysis. We evaluated the risk of bias using the Cochrane Risk of Bias 2 tool (ROB2). DATA SOURCES: We searched PubMed, Embase, Cochrane Central Register of Controlled Trials, and Web of Science were searched for randomized controlled trials investigating the effect of face masks on respiratory infections published between 1981 and February 9, 2022. We followed the PRISMA 2020 guidelines. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included randomized controlled trials investigating the use of face mask intervention in mitigating the risk of spreading respiratory infections across different exposure settings. RESULTS: We identified 2,400 articles for screening. 18 articles passed the inclusion criteria for both evidence synthesis and meta-analysis. There were N = 189,145 individuals in the face mask intervention arm and N = 173,536 in the control arm, and the follow-up times ranged from 4 days to 19 months. Our results showed between-study heterogeneity (p < 0.0001). While there was no statistically significant association over all studies when the covariate unadjusted intervention effect estimates were used (RR = 0.977 [0.858-1.113], p = 0.728), our subgroup analyses revealed that a face mask intervention reduced respiratory infections in the adult subgroup (RR = 0.8795 [0.7861-0.9839], p = 0.0249) and in a community setting (RR = 0.890 [0.812-0.975], p = 0.0125). Furthermore, our leave-one-out analysis found that one study biased the results towards a null effect. Consequently, when using covariate adjusted odds ratio estimates to have a more precise effect estimates of the intervention effect to account for differences at the baseline, the results showed that a face mask intervention did reduce respiratory infections when the biasing study was excluded from the analysis (OR = 0.8892 [0.8061-0.9810], p = 0.0192). CONCLUSION: Our findings support the use of face masks particularly in a community setting and for adults. We also observed substantial between-study heterogeneity and varying adherence to protocol. Notably, many studies were subject to contamination bias thus affecting the efficacy of the intervention, that is when also some controls used masks or when the intervention group did not comply with mask use leading to a downward biased effect of treatment receipt and efficacy. TRIAL REGISTRATION: PROSPERO registration number CRD42020205523.

The impact of Mendelian sleep and circadian genetic variants in a population setting.

Rare variants in ten genes have been reported to cause Mendelian sleep conditions characterised by extreme sleep duration or timing. These include familial natural short sleep (ADRB1, DEC2/BHLHE41, GRM1 and NPSR1), advanced sleep phase (PER2, PER3, CRY2, CSNK1D and TIMELESS) and delayed sleep phase (CRY1). The association of variants in these genes with extreme sleep conditions were usually based on clinically ascertained families, and their effects when identified in the population are unknown. We aimed to determine the effects of these variants on sleep traits in large population-based cohorts. We performed genetic association analysis of variants previously reported to be causal for Mendelian sleep and circadian conditions. Analyses were performed using 191,929 individuals with data on sleep and whole-exome or genome-sequence data from 4 population-based studies: UK Biobank, FINRISK, Health-2000-2001, and the Multi-Ethnic Study of Atherosclerosis (MESA). We identified sleep disorders from self-report, hospital and primary care data. We estimated sleep duration and timing measures from self-report and accelerometery data. We identified carriers for 10 out of 12 previously reported pathogenic variants for 8 of the 10 genes. They ranged in frequency from 1 individual with the variant in CSNK1D to 1,574 individuals with a reported variant in the PER3 gene in the UK Biobank. No carriers for variants reported in NPSR1 or PER2 were identified. We found no association between variants analyzed and extreme sleep or circadian phenotypes. Using sleep timing as a proxy measure for sleep phase, only PER3 and CRY1 variants demonstrated association with earlier and later sleep timing, respectively; however, the magnitude of effect was smaller than previously reported (sleep midpoint ~7 mins earlier and ~5 mins later, respectively). We also performed burden tests of protein truncating (PTVs) or rare missense variants for the 10 genes. Only PTVs in PER2 and PER3 were associated with a relevant trait (for example, 64 individuals with a PTV in PER2 had an odds ratio of 4.4 for being "definitely a morning person", P = 4x10-8; and had a 57-minute earlier midpoint sleep, P = 5x10-7). Our results indicate that previously reported variants for Mendelian sleep and circadian conditions are often not highly penetrant when ascertained incidentally from the general population.

Genetic risk factors have a substantial impact on healthy life years.

The impact of genetic variation on overall disease burden has not been comprehensively evaluated. We introduce an approach to estimate the effect of genetic risk factors on disability-adjusted life years (DALYs; 'lost healthy life years'). We use genetic information from 735,748 individuals and consider 80 diseases. Rare variants had the highest effect on DALYs at the individual level. Among common variants, rs3798220 (LPA) had the strongest individual-level effect, with 1.18 DALYs from carrying 1 versus 0 copies. Being in the top 10% versus the bottom 90% of a polygenic score for multisite chronic pain had an effect of 3.63 DALYs. Some common variants had a population-level effect comparable to modifiable risk factors such as high sodium intake and low physical activity. Attributable DALYs vary between males and females for some genetic exposures. Genetic risk factors can explain a sizable number of healthy life years lost both at the individual and population level.

The public health impact of poor sleep on severe COVID-19, influenza and upper respiratory infections.

Background: Poor sleep is associated with an increased risk of infections and all-cause mortality, and acute sleep loss and disruption have been linked with inflammation and poorer immune control. Previous studies, however, have been unable to evidence causality between the chronic effects of poor sleep and respiratory infection risk. In light of the ongoing COVID-19 pandemic and potential future disease outbreaks, understanding the risk factors for these infections is of great importance. Aim: Our goal was to understand if chronic poor sleep could be identified as a causal risk factor for respiratory infections including influenza, upper respiratory infections and COVID-19. Methods: We used population cohorts from the UK Biobank (N ≈ 231,000) and FinnGen (N ≈ 327,000) with ICD-10 based electronic health records and obtained diagnoses of insomnia, influenza and upper respiratory infections (URIs) from primary care and hospital settings. We computed logistic regression to assess association between poor sleep and infections, disease free survival hazard ratios, and used summary statistics from genome-wide association studies of insomnia, influenza, URI and COVID-19 to perform Mendelian randomization analyses and assess causality. Findings: Utilizing 23 years of registry data and follow-up, we saw that insomnia diagnosis associated with increased risk for infections in FinnGen and in UK Biobank (FinnGen influenza HR = 5.32 [4.09, 6.92], P = 1.02×10-35, UK Biobank influenza HR = 1.54 [1.37, 1.73], P = 2.49×10-13). Mendelian randomization indicated that insomnia causally predisposed to influenza (OR = 1.59, P = 6.23×10-4), upper respiratory infections (OR = 1.71, P = 7.60×10-13), COVID-19 infection (OR = 1.08, P = 0.037) and risk of hospitalization from COVID-19 (OR = 1.47, P = 4.96×10-5). Conclusions: Our findings indicate that chronic poor sleep is a causal risk factor for contracting respiratory infections, and in addition contributes to the severity of respiratory infections. These findings highlight the role of sleep in maintaining sufficient immune response against pathogens as suggested by earlier work. As the current COVID-19 pandemic has increased the number of people suffering from poor sleep, safe interventions such as sleep management and treating individuals with insomnia could be promoted to reduce infections and save lives.