scholarly journals Evidence for genetic correlations and bidirectional, causal effects between smoking and sleep behaviours

2018 ◽  
Author(s):  
Mark Gibson ◽  
Marcus R Munafò ◽  
Amy E Taylor ◽  
Jorien L. Treur
Keyword(s):  
Smoking Cessation ◽  
Sleep Duration ◽  
Association Studies ◽  
Genetic Correlations ◽  
Smoking Initiation ◽  
Causal Effects ◽  
Poor Sleep ◽  
Genome Wide Association Studies ◽  
Level Data ◽  
Increased Risk

AbstractIntroductionCigarette smokers are at increased risk of poor sleep behaviours. However, it is largely unknown whether these associations are due to shared (genetic) risk factors and/or causal effects (which may be bi-directional).MethodsWe obtained summary-level data of genome-wide association studies of smoking (smoking initiation (n=74,035), cigarettes per day (n=38,181) and smoking cessation (n=41,278)) and sleep behaviours (sleep duration and chronotype, or ‘morningness’) (n=128,266) and insomnia (n=113,006)). Using LD score regression, we calculated genetic correlations between smoking and sleep behaviours. To investigate causal effects, we employed Mendelian randomization (MR), both with summary-level data and individual level data (n=333,581 UK Biobank participants). For MR with summary-level data, individual genetic variants were combined with inverse-variance weighted meta-analysis, weighted median regression and MR Egger regression methods.ResultsWe found positive genetic correlations between insomnia and smoking initiation (rg=0.27, 95% CI 0.06 to 0.49) and insomnia and cigarettes per day (rg=0.15, 0.01 to 0.28), and negative genetic correlations between sleep duration and smoking initiation (rg=-0.14, -0.26 to -0.01) and chronotype and smoking cessation (rg=-0.18, -0.31 to -0.06). MR analyses provided strong evidence that smoking more cigarettes per day causally decreases the odds of being a morning person, and weak evidence that insomnia causally increases smoking heaviness and decreases smoking cessation odds.ConclusionsSmoking and sleep behaviours show moderate genetic correlation. Heavier smoking seems to causally affect circadian rhythm and there is some indication that insomnia increases smoking heaviness and hampers cessation. Our findings point to sleep as a potentially interesting smoking treatment target.

scholarly journals Investigating genetic correlations and causal effects between caffeine consumption and sleep behaviours

2017 ◽  
Author(s):  
Jorien L. Treur ◽  
Mark Gibson ◽  
Amy E Taylor ◽  
Peter J Rogers ◽  
Marcus R Munafò
Keyword(s):  
Genetic Correlation ◽  
Sleep Duration ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Genetic Correlations ◽  
Causal Effects ◽  
Genome Wide Association Studies ◽  
Caffeine Consumption ◽  
Insomnia Complaints

AbstractStudy Objectives:Higher caffeine consumption has been linked to poorer sleep and insomnia complaints. We investigated whether these observational associations are the result of genetic risk factors influencing both caffeine consumption and poorer sleep, and/or whether they reflect (possibly bidirectional) causal effects.Methods:Summary-level data were available from genome-wide association studies (GWAS) on caffeine consumption (n=91,462), sleep duration, and chronotype (i.e., being a ‘morning’ versus an ‘evening’ person) (both n=128,266), and insomnia complaints (n=113,006). Linkage disequilibrium (LD) score regression was used to calculate genetic correlations, reflecting the extent to which genetic variants influencing caffeine consumption and sleep behaviours overlap. Causal effects were tested with bidirectional, two-sample Mendelian randomization (MR), an instrumental variable approach that utilizes genetic variants robustly associated with an exposure variable as an instrument to test causal effects. Estimates from individual genetic variants were combined using inverse-variance weighted meta-analysis, weighted median regression and MR Egger regression methods.Results:There was no clear evidence for genetic correlation between caffeine consumption and sleep duration (rg=0.000,p=0.998), chronotype (rg=0.086,p=0.192) or insomnia (rg=-0.034,p=0.700). Two-sample Mendelian randomization analyses did not support causal effects from caffeine consumption to sleep behaviours, or the other way around.Conclusions:We found no evidence in support of genetic correlation or causal effects between caffeine consumption and sleep. While caffeine may have acute effects on sleep when taken shortly before habitual bedtime, our findings suggest that a more sustained pattern of high caffeine consumption is likely associated with poorer sleep through shared environmental factors.

scholarly journals Investigating causality in associations between education and smoking: A two-sample Mendelian randomization study

2017 ◽  
Author(s):  
Suzanne H. Gage ◽  
Jack Bowden ◽  
George Davey Smith ◽  
Marcus R. Munafo
Keyword(s):  
Smoking Cessation ◽  
Educational Attainment ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Smoking Initiation ◽  
Self Report ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Increased Risk ◽  
Higher Educational Attainment

AbstractBackgroundLower educational attainment is associated with increased rates of smoking, but ascertaining causality is challenging. We used two-sample Mendelian randomization (MR) analyses of summary statistics to examine whether educational attainment is causally related to smoking.Methods and FindingsWe used summary statistics from genome-wide association studies of educational attainment and a range of smoking phenotypes (smoking initiation, cigarettes per day, cotinine levels and smoking cessation). Various complementary MR techniques (inverse-variance weighted regression, MR Egger, weighted-median regression) were used to test the robustness of our results. We found broadly consistent evidence across these techniques that higher educational attainment leads to reduced likelihood of smoking initiation, reduced heaviness of smoking among smokers (as measured via self-report and cotinine levels), and greater likelihood of smoking cessation among smokers.ConclusionsOur findings indicate a causal association between low educational attainment and increased risk of smoking, and may explain the observational associations between educational attainment and adverse health outcomes such as risk of coronary heart disease.

scholarly journals Cigarette smoking and personality: interrogating causality using Mendelian randomisation

2018 ◽  
Vol 49 (13) ◽  
pp. 2197-2205 ◽  
Author(s):  
Hannah M. Sallis ◽  
George Davey Smith ◽  
Marcus R. Munafò
Keyword(s):  
Personality Traits ◽  
Association Studies ◽  
Smoking Initiation ◽  
Mendelian Randomisation ◽  
Genome Wide Association Studies ◽  
Individual Level ◽  
Causal Pathways ◽  
Genome Wide ◽  
Level Data ◽  
Causal Nature

AbstractBackgroundDespite the well-documented association between smoking and personality traits such as neuroticism and extraversion, little is known about the potential causal nature of these findings. If it were possible to unpick the association between personality and smoking, it may be possible to develop tailored smoking interventions that could lead to both improved uptake and efficacy.MethodsRecent genome-wide association studies (GWAS) have identified variants robustly associated with both smoking phenotypes and personality traits. Here we use publicly available GWAS summary statistics in addition to individual-level data from UK Biobank to investigate the link between smoking and personality. We first estimate genetic overlap between traits using LD score regression and then use bidirectional Mendelian randomisation methods to unpick the nature of this relationship.ResultsWe found clear evidence of a modest genetic correlation between smoking behaviours and both neuroticism and extraversion. We found some evidence that personality traits are causally linked to certain smoking phenotypes: among current smokers each additional neuroticism risk allele was associated with smoking an additional 0.07 cigarettes per day (95% CI 0.02–0.12, p = 0.009), and each additional extraversion effect allele was associated with an elevated odds of smoking initiation (OR 1.015, 95% CI 1.01–1.02, p = 9.6 × 10−7).ConclusionWe found some evidence for specific causal pathways from personality to smoking phenotypes, and weaker evidence of an association from smoking initiation to personality. These findings could be used to inform future smoking interventions or to tailor existing schemes.

Daytime sleepiness, night sleep changes and ALS: a Mendelian randomization study

2020 ◽  
Author(s):  
Gan Zhang ◽  
Linjing Zhang ◽  
Tao Huang ◽  
Dongsheng Fan
Keyword(s):  
Sleep Duration ◽  
Daytime Sleepiness ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Sleep Efficiency ◽  
Genome Wide Association Studies ◽  
Night Sleep ◽  
Inverse Variance ◽  
Point Increase ◽  
Increased Risk

Abstract Background Observational studies have indicated that there is a high prevalence of daytime sleepiness and night sleep changes in amyotrophic lateral sclerosis (ALS). However, the actual relation between these symptoms and ALS remains unclear. We aimed to determine whether daytime sleepiness and night sleep changes have an effect on ALS. Methods We used 2-sample mendelian randomization to estimate the effects of daytime sleepiness, sleep efficiency, number of sleep episodes and sleep duration on ALS. Summary statistics we used was from resent and large genome-wide association studies on the traits we chosen (n = 85,670–452,071) and ALS (cases n = 20,806, controls n = 59,804). Inverse variance weighted method was used as the main method for assessing causality. Results A genetically predicted 1-point increase in the assessment of daytime sleepiness was significantly associated with an increased risk of ALS (inverse-variance-weighted (IVW) odds ratio = 2.70, 95% confidence interval (CI): 1.27–5.76; P = 0.010). ALS was not associated with a genetically predicted 1-SD increase in sleep efficiency (IVW 1.01, 0.64–1.58; P = 0.973), Number of sleep episodes (IVW 1.02, 0.80–1.30; P = 0.859) or sleep duration (IVW 1.00, 1.00–1.01; P = 0.250). Conclusions Our results provide novel evidence that daytime sleepiness causes an increase in the risk of ALS and indicate that daytime sleepiness may be inherent in preclinical and clinical ALS patients, rather than simply affected by potential influencing factors.

scholarly journals Inference of causal relationships between sleep-related traits and 1,527 phenotypes using genetic data

SLEEP ◽  
2020 ◽  
Author(s):  
Luis M García-Marín ◽  
Adrián I Campos ◽  
Nicholas G Martin ◽  
Gabriel Cuéllar-Partida ◽  
Miguel E Rentería
Keyword(s):  
Sleep Duration ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Causal Relationships ◽  
Physical And Mental Health ◽  
Related Factors ◽  
Study Objective ◽  
Genome Wide ◽  
Increased Risk ◽  
Level Statistics

Abstract Study Objective Sleep is essential for both physical and mental health, and there is a growing interest in understanding how different factors shape individual variation in sleep duration, quality and patterns, or confer risk for sleep disorders. The present study aimed to identify novel inferred causal relationships between sleep-related traits and other phenotypes, using a genetics-driven hypothesis-free approach not requiring longitudinal data. Methods We used summary-level statistics from genome-wide association studies and the latent causal variable (LCV) method to screen the phenome and infer causal relationships between seven sleep-related traits (insomnia, daytime dozing, easiness of getting up in the morning, snoring, sleep duration, napping, and morningness) and 1,527 other phenotypes. Results We identify 84 inferred causal relationships. Among other findings, connective tissue disorders increase insomnia risk and reduce sleep duration; depression-related traits increase insomnia and daytime dozing; insomnia, napping and snoring are affected by obesity and cardiometabolic traits and diseases; and working with asbestos, thinner, or glues may increase insomnia risk, possibly through an increased risk of respiratory disease or socio-economic related factors. Conclusion Overall, our results indicate that changes in sleep variables are predominantly the consequence, rather than the cause, of other underlying phenotypes and diseases. These insights could inform the design of future epidemiological and interventional studies in sleep medicine and research.

scholarly journals Sleep Deficits and Cannabis Use Behaviors: An Analysis of Shared Genetics Using Linkage Disequilibrium Score Regression and Polygenic Risk Prediction

2020 ◽  
Author(s):  
Evan A. Winiger ◽  
Jarrod M. Ellingson ◽  
Claire L. Morrison ◽  
Robin P. Corley ◽  
Joëlle A. Pasman ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Sleep Duration ◽  
Association Studies ◽  
Genetic Correlations ◽  
Cannabis Use ◽  
European Ancestry ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Short Sleep Duration ◽  
Polygenic Risk

AbstractStudy ObjectivesEstimate the genetic relationship of cannabis use with sleep deficits and eveningness chronotype.MethodsWe used linkage disequilibrium score regression (LDSC) to analyze genetic correlations between sleep deficits and cannabis use behaviors. Secondly, we generated sleep deficit polygenic risk scores (PRSs) and estimated their ability to predict cannabis use behaviors using logistic regression. Summary statistics came from existing genome wide association studies (GWASs) of European ancestry that were focused on sleep duration, insomnia, chronotype, lifetime cannabis use, and cannabis use disorder (CUD). A target sample for PRS prediction consisted of high-risk participants and participants from twin/family community-based studies (n = 796, male = 66%; mean age = 26.81). Target data consisted of self-reported sleep (sleep duration, feeling tired, and taking naps) and cannabis use behaviors (lifetime use, number of lifetime uses, past 180-day use, age of first use, and lifetime CUD symptoms).ResultsSignificant genetic correlation between lifetime cannabis use and eveningness chronotype (rG = 0.24, p < 0.01), as well as between CUD and both short sleep duration (<7 h) (rG = 0.23, p = 0.02) and insomnia (rG = 0.20, p = 0.02). Insomnia PRS predicted earlier age of first cannabis use (β = −0.09, p = 0.02) and increased lifetime CUD symptom count use (β = 0.07, p = 0.03).ConclusionCannabis use is genetically associated with both sleep deficits and an eveningness chronotype, suggesting that there are genes that predispose individuals to both cannabis use and sleep deficits.

scholarly journals Sleep deficits and cannabis use behaviors: an analysis of shared genetics using linkage disequilibrium score regression and polygenic risk prediction

SLEEP ◽  
2020 ◽  
Author(s):  
Evan A Winiger ◽  
Jarrod M Ellingson ◽  
Claire L Morrison ◽  
Robin P Corley ◽  
Joëlle A Pasman ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Sleep Duration ◽  
Association Studies ◽  
Genetic Correlations ◽  
Cannabis Use ◽  
European Ancestry ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Short Sleep Duration ◽  
Polygenic Risk

Abstract Study Objectives Estimate the genetic relationship of cannabis use with sleep deficits and an eveningness chronotype. Methods We used linkage disequilibrium score regression (LDSC) to analyze genetic correlations between sleep deficits and cannabis use behaviors. Secondly, we generated sleep deficit polygenic risk score (PRS) and estimated their ability to predict cannabis use behaviors using linear and logistic regression. Summary statistics came from existing genome-wide association studies of European ancestry that were focused on sleep duration, insomnia, chronotype, lifetime cannabis use, and cannabis use disorder (CUD). A target sample for PRS prediction consisted of high-risk participants and participants from twin/family community-based studies (European ancestry; n = 760, male = 64%; mean age = 26.78 years). Target data consisted of self-reported sleep (sleep duration, feeling tired, and taking naps) and cannabis use behaviors (lifetime ever use, number of lifetime uses, past 180-day use, age of first use, and lifetime CUD symptoms). Results Significant genetic correlation between lifetime cannabis use and an eveningness chronotype (rG = 0.24, p &lt; 0.001), as well as between CUD and both short sleep duration (&lt;7 h; rG = 0.23, p = 0.017) and insomnia (rG = 0.20, p = 0.020). Insomnia PRS predicted earlier age of first cannabis use (OR = 0.92, p = 0.036) and increased lifetime CUD symptom count (OR = 1.09, p = 0.012). Conclusion Cannabis use is genetically associated with both sleep deficits and an eveningness chronotype, suggesting that there are genes that predispose individuals to both cannabis use and sleep deficits.

scholarly journals Shared Genetic Risk between Eating Disorder- and Substance-Use-Related Phenotypes: Evidence from Genome-Wide Association Studies

2019 ◽  
Author(s):  
Melissa A. Munn-Chernoff ◽  
Emma C. Johnson ◽  
Yi-Ling Chou ◽  
Jonathan R.I. Coleman ◽  
Laura M. Thornton ◽  
...  
Keyword(s):  
Substance Use ◽  
Eating Disorder ◽  
Binge Eating ◽  
Genetic Correlation ◽  
Association Studies ◽  
Genetic Correlations ◽  
Smoking Initiation ◽  
Genome Wide Association Studies ◽  
Current Smoking ◽  
Genome Wide

AbstractEating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa (BN) and problem alcohol use (genetic correlation [rg], twin-based=0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge-eating, AN without binge-eating, and a BN factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder (MDD). Total sample sizes per phenotype ranged from ~2,400 to ~537,000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (rg=0.18; false discovery rate q=0.0006), cannabis initiation and AN (rg=0.23; q<0.0001), and cannabis initiation and AN with binge-eating (rg=0.27; q=0.0016). Conversely, significant negative genetic correlations were observed between three non-diagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge-eating (rgs=-0.19 to −0.23; qs<0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for MDD loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships between these behaviors.

scholarly journals Characterising the shared genetic determinants of bipolar disorder, schizophrenia and risk-taking

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Guy Hindley ◽  
Shahram Bahrami ◽  
Nils Eiel Steen ◽  
Kevin S. O’Connell ◽  
Oleksandr Frei ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Psychiatric Disorders ◽  
Risk Taking ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genome Wide Association Studies ◽  
Increased Risk ◽  
Risky Behaviours ◽  
Shared Risk ◽  
Shared Genetic

AbstractIncreased risk-taking is a central component of bipolar disorder (BIP) and is implicated in schizophrenia (SCZ). Risky behaviours, including smoking and alcohol use, are overrepresented in both disorders and associated with poor health outcomes. Positive genetic correlations are reported but an improved understanding of the shared genetic architecture between risk phenotypes and psychiatric disorders may provide insights into underlying neurobiological mechanisms. We aimed to characterise the genetic overlap between risk phenotypes and SCZ, and BIP by estimating the total number of shared variants using the bivariate causal mixture model and identifying shared genomic loci using the conjunctional false discovery rate method. Summary statistics from genome wide association studies of SCZ, BIP, risk-taking and risky behaviours were acquired (n = 82,315–466,751). Genomic loci were functionally annotated using FUMA. Of 8.6–8.7 K variants predicted to influence BIP, 6.6 K and 7.4 K were predicted to influence risk-taking and risky behaviours, respectively. Similarly, of 10.2–10.3 K variants influencing SCZ, 9.6 and 8.8 K were predicted to influence risk-taking and risky behaviours, respectively. We identified 192 loci jointly associated with SCZ and risk phenotypes and 206 associated with BIP and risk phenotypes, of which 68 were common to both risk-taking and risky behaviours and 124 were novel to SCZ or BIP. Functional annotation implicated differential expression in multiple cortical and sub-cortical regions. In conclusion, we report extensive polygenic overlap between risk phenotypes and BIP and SCZ, identify specific loci contributing to this shared risk and highlight biologically plausible mechanisms that may underlie risk-taking in severe psychiatric disorders.

scholarly journals Type 2 Diabetes and Glycemic Traits Are Not Causal Factors of Osteoarthritis: A Two-Sample Mendelian Randomization Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhiyong Cui ◽  
Hui Feng ◽  
Baichuan He ◽  
Yong Xing ◽  
Zhaorui Liu ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Knee Oa ◽  
Increased Risk

BackgroundIt remains unclear whether an increased risk of type 2 diabetes (T2D) affects the risk of osteoarthritis (OA).MethodsHere, we used two-sample Mendelian randomization (MR) to obtain non-confounded estimates of the effect of T2D and glycemic traits on hip and knee OA. We identified single-nucleotide polymorphisms (SNPs) strongly associated with T2D, fasting glucose (FG), and 2-h postprandial glucose (2hGlu) from genome-wide association studies (GWAS). We used the MR inverse variance weighted (IVW), the MR–Egger method, the weighted median (WM), and the Robust Adjusted Profile Score (MR.RAPS) to reveal the associations of T2D, FG, and 2hGlu with hip and knee OA risks. Sensitivity analyses were also conducted to verify whether heterogeneity and pleiotropy can bias the MR results.ResultsWe did not find statistically significant causal effects of genetically increased T2D risk, FG, and 2hGlu on hip and knee OA (e.g., T2D and hip OA, MR–Egger OR = 1.1708, 95% CI 0.9469–1.4476, p = 0.1547). It was confirmed that horizontal pleiotropy was unlikely to bias the causality (e.g., T2D and hip OA, MR–Egger, intercept = −0.0105, p = 0.1367). No evidence of heterogeneity was found between the genetic variants (e.g., T2D and hip OA, MR–Egger Q = 30.1362, I2 &lt; 0.0001, p = 0.6104).ConclusionOur MR study did not support causal effects of a genetically increased T2D risk, FG, and 2hGlu on hip and knee OA risk.

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