scholarly journals Investigating causal pathways between liability to ADHD and substance use, and liability to substance use and ADHD risk, using Mendelian randomization

2019 ◽  
Author(s):  
Jorien L Treur ◽  
Ditte Demontis ◽  
George Davey Smith ◽  
Hannah Sallis ◽  
Tom G Richardson ◽  
...  
Keyword(s):  
Substance Use ◽  
Alcohol Use ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Smoking Initiation ◽  
Cannabis Use ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Causal Pathways

ABSTRACTBackgroundAttention-deficit hyperactivity disorder (ADHD) has consistently been associated with substance (ab)use, but the nature of this association is not fully understood. In view of preventive efforts, a vital question is whether there are causal effects, from ADHD to substance use and/or from substance use to ADHD.MethodsWe applied bidirectional Mendelian randomization using summary-level data from the largest available genome-wide association studies (GWASs) on ADHD, smoking (initiation, cigarettes/day, cessation, and a compound measure of lifetime smoking), alcohol use (drinks/week and alcohol use disorder), cannabis use (initiation and cannabis use disorder (CUD)) and coffee consumption (cups/day). Genetic variants robustly associated with the ‘exposure’ were selected as instruments and then identified in the ‘outcome’ GWAS. Effect estimates from individual genetic variants were combined with inverse-variance weighted regression and five sensitivity analyses were applied (weighted median, weighted mode, MR-Egger, generalized summary-data-based MR, and Steiger filtering).ResultsWe found strong evidence that liability to ADHD increases likelihood of smoking initiation and also cigarettes per day among smokers, decreases likelihood of smoking cessation, and increases likelihood of cannabis initiation and CUD. In the other direction, there was evidence that liability to smoking initiation and CUD increase ADHD risk. There was no clear evidence of causal effects between liability to ADHD and alcohol or caffeine consumption.ConclusionsWe find evidence for causal effects of liability to ADHD on smoking and cannabis use, and of liability to smoking and cannabis use on ADHD risk, indicating bidirectional pathways. Further work is needed to explore causal mechanisms.

scholarly journals Causal relationships between substance use and insomnia

2020 ◽  
Author(s):  
Joëlle A. Pasman ◽  
Dirk J.A. Smit ◽  
Lilian Kingma ◽  
Jacqueline M. Vink ◽  
Jorien L. Treur ◽  
...  
Keyword(s):  
Substance Use ◽  
Alcohol Use ◽  
Alcohol Dependence ◽  
Strong Evidence ◽  
Smoking Initiation ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Poor Sleep ◽  
A Genome ◽  
Bidirectional Effects

AbstractBackgroundPoor sleep quality and insomnia have been associated with the use of tobacco, alcohol, and cannabis, but it is unclear if there is a causal link. In this Mendelian Randomization (MR) study we examine if insomnia causes substance use and/or if substance use causes insomnia.MethodsMR uses summary effect estimates from a genome-wide association study (GWAS) to create a genetic instrumental variable for a proposed ‘exposure’ variable and then identifies that same genetic instrument in an ‘outcome’ GWAS. With data of GWAS of insomnia, smoking (initiation, heaviness, cessation), alcohol use (drinks per week, dependence), and cannabis initiation, bi-directional causal effects were tested. Multiple sensitivity analyses were applied to assess the robustness of the findings.ResultsThere was strong evidence for positive causal effects of insomnia on all substance use phenotypes (smoking traits, alcohol dependence, cannabis initiation), except alcohol per week. The effects on alcohol dependence and cannabis initiation were attenuated after filtering out pleiotropic SNPs. In the other direction, there was strong evidence that smoking initiation increased chances of insomnia (smoking heaviness and cessation could not be tested as exposures). We found no evidence that alcohol use per week, alcohol dependence, or cannabis initiation causally affect insomnia.ConclusionsThere were unidirectional effects of insomnia on alcohol dependence and cannabis initiation, and bidirectional effects between insomnia and smoking measures. Bidirectional effects between smoking and insomnia might give rise to a vicious circle. Future research should investigate if interventions aimed at insomnia are beneficial for substance use treatment.

scholarly journals Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations

2021 ◽  
Author(s):  
Fernando Pires Hartwig ◽  
Kate Tilling ◽  
George Davey Smith ◽  
Deborah A Lawlor ◽  
Maria Carolina Borges
Keyword(s):  
Waist Circumference ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Real Data ◽  
Sensitivity Analyses ◽  
Effect Estimate ◽  
Genome Wide Association Studies ◽  
Residual Confounding

Abstract Background Two-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables. Methods We performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR. Results In the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index. Conclusions Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution.

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 Mendelian randomization studies of biomarkers and type 2 diabetes

2015 ◽  
Vol 4 (4) ◽  
pp. 249-260 ◽  
Author(s):  
Ali Abbasi
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Variants ◽  
Large Scale ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Causal Effects ◽  
Current Evidence ◽  
Genome Wide Association Studies ◽  
Glycaemic Traits

Many biomarkers are associated with type 2 diabetes (T2D) risk in epidemiological observations. The aim of this study was to identify and summarize current evidence for causal effects of biomarkers on T2D. A systematic literature search in PubMed and EMBASE (until April 2015) was done to identify Mendelian randomization studies that examined potential causal effects of biomarkers on T2D. To replicate the findings of identified studies, data from two large-scale, genome-wide association studies (GWAS) were used: DIAbetes Genetics Replication And Meta-analysis (DIAGRAMv3) for T2D and the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) for glycaemic traits. GWAS summary statistics were extracted for the same genetic variants (or proxy variants), which were used in the original Mendelian randomization studies. Of the 21 biomarkers (from 28 studies), ten have been reported to be causally associated with T2D in Mendelian randomization. Most biomarkers were investigated in a single cohort study or population. Of the ten biomarkers that were identified, nominally significant associations with T2D or glycaemic traits were reached for those genetic variants related to bilirubin, pro-B-type natriuretic peptide, delta-6 desaturase and dimethylglycine based on the summary data from DIAGRAMv3 or MAGIC. Several Mendelian randomization studies investigated the nature of associations of biomarkers with T2D. However, there were only a few biomarkers that may have causal effects on T2D. Further research is needed to broadly evaluate the causal effects of multiple biomarkers on T2D and glycaemic traits using data from large-scale cohorts or GWAS including many different genetic variants.

scholarly journals Cannabis use and risk of schizophrenia: a Mendelian randomization study

2016 ◽  
Author(s):  
Julien Vaucher ◽  
Brendan J. Keating ◽  
Aurélie M. Lasserre ◽  
Wei Gan ◽  
Donald M. Lyall ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Mendelian Randomization ◽  
Cannabis Use ◽  
Sensitivity Analyses ◽  
P Value ◽  
Tobacco Exposure ◽  
Increased Risk ◽  
Show Evidence ◽  
Public Health Message ◽  
Genetically Determined

ABSTRACTCannabis use is observationally associated with an increased risk of schizophrenia, however whether the relationship is causal is not known. To determine the nature of the association between cannabis use on risk of schizophrenia using Mendelian randomization (MR) analysis, we used ten genetic variants previously identified to associate with cannabis use in 32,330 individuals. Genetic variants were used in a MR analyses of the association of genetically determined cannabis on risk of schizophrenia in 34,241 cases and 45,604 controls from predominantly European descent. Estimates from MR were compared to a metaanalysis of observational studies reporting effect estimates for ever use of cannabis and risk of schizophrenia or related disorders. Genetically determined use of cannabis was associated with increased risk of schizophrenia (OR of schizophrenia for users vs. non-users of cannabis: 1.37; 95%CI, 1.09 to 1.67; P-value=0.007). The corresponding estimate from observational analysis was 1.50 (95% CI, 1.10 to 2.00; P-value for heterogeneity = 0.88). The genetic instrument did not show evidence of pleiotropy on MR-Egger (Egger test, P-value=0.292) nor on multivariable MR accounting for tobacco exposure (OR of schizophrenia for users vs. nonusers of cannabis, adjusted for ever vs. never smoker: 1.41; 95% CI, 1.09-1.83). Furthermore, the causal estimate remained robust to sensitivity analyses. These findings strongly support a causal association between genetically determined use of cannabis and risk of schizophrenia. Such robust evidence may inform public health message about the risks of cannabis use, especially regarding its potential mental health consequences.

scholarly journals Evidence for Causal Effects of Smoking Initiation and Alcohol Consumption on Substance Use Outcomes

2021 ◽  
Author(s):  
Zoe E. Reed ◽  
Robyn E. Wootton ◽  
Marcus R. Munafò
Keyword(s):  
Substance Use ◽  
Risk Factor ◽  
Alcohol Consumption ◽  
Opioid Dependence ◽  
Smoking Initiation ◽  
Cannabis Use ◽  
Causal Effects ◽  
P Value ◽  
Gateway Hypothesis ◽  
Shared Risk

AbstractBackground and AimsThe ‘gateway’ hypothesis proposes that initial use of drugs such as tobacco and alcohol can lead to subsequent more problematic drug use. However, it is unclear whether true casual pathways exist, or whether there is instead a shared underlying risk factor. We used bidirectional Mendelian Randomisation (MR) to test these two competing hypotheses.MethodsWe conducted two-sample MR analyses, using genome-wide association data for smoking initiation, alcoholic drinks per week, cannabis use and dependence, cocaine and opioid dependence. We used several MR methods that rely on different assumptions: inverse-variance weighted (IVW), MR-Egger, weighted median, simple mode and weighted mode. Consistent results across these methods would support stronger inference.ResultsWe found evidence of causal effects from smoking initiation to increased drinks per week (IVW: β=0.06; 95% CI 0.03 to 0.09; p-value=9.44×10-06), cannabis use (IVW: OR=1.34; 95% CI 1.24 to 1.44; p-value=1.95×10-14), and cannabis dependence (IVW: OR=1.68; 95% CI 1.12 to 2.51; p-value=0.01). We also found evidence of an effect of cannabis use on increased likelihood of smoking initiation (IVW: OR=1.39; 95% CI=1.08 to 1.80; p-value=0.01). We did not find evidence of an effect of drinks per week on substance use outcomes, except for weak evidence of an effect on cannabis use. We also found evidence of an effect of opioid dependence on increased drinks per week (IVW: β=0.002; 95% CI=0.0005 to 0.003; p-value=8.61×10-03).ConclusionsOverall, we found evidence suggesting a causal pathway from smoking initiation to alcohol consumption, and both cannabis use and dependence, which may support the gateway hypothesis. However, we also found causal effects of cannabis use on smoking initiation, and opioid dependence on alcohol consumption, which suggests the existence of a shared risk factor. Further research should explore whether this is the case, and in particular the nature of any shared risk factors.

scholarly journals Investigating the causal nature of the relationship of subcortical brain volume with smoking and alcohol use

2020 ◽  
Author(s):  
Emma Logtenberg ◽  
Martin F Overbeek ◽  
Joelle A Pasman ◽  
Abdel Abdellaoui ◽  
Maartje Luijten ◽  
...  
Keyword(s):  
Substance Use ◽  
Alcohol Use ◽  
Alcohol Dependence ◽  
Genetic Variants ◽  
Brain Volume ◽  
Hippocampal Volume ◽  
Causal Effects ◽  
Subcortical Brain ◽  
Wide Range ◽  
Causal Nature

Background: Structural variation in subcortical brain regions has been linked to substance use, including the most prevalent substances nicotine and alcohol. It may be that pre-existing differences in subcortical brain volume affect smoking and alcohol use, but there is also evidence that smoking and alcohol use can lead to structural changes. We assess the causal nature of this complex relationship with bi-directional Mendelian randomization (MR). Methods: MR uses genetic variants predictive of a certain trait (exposure) as instrumental variables to test causal effects on a certain outcome. Due to random assortment at meiosis, genetic variants shouldnt be associated with confounders, allowing less biased causal inference. We employed summary-level data of the largest available genome-wide association studies of subcortical brain region volumes (nucleus accumbens, amygdala, caudate nucleus, hippocampus, pallidum, putamen, and thalamus; n=50,290) and smoking and alcohol use (smoking initiation, n=848,460; cigarettes per day, n=216,590; smoking cessation, n=378,249; alcohol drinks per week, n=630,154; alcohol dependence, n=46,568). The main analysis, inverse-variance weighted regression, was verified by a wide range of sensitivity methods. Results: There was strong evidence that alcohol dependence decreased amygdala and hippocampal volume and that smoking more cigarettes per day decreased hippocampal volume. From subcortical brain volumes to substance use, there was no or weak evidence for causal effects. Conclusions: Our findings suggest that heavy alcohol use and smoking can causally reduce subcortical brain volume. This adds to accumulating evidence that alcohol and smoking affect the brain, and most likely mental health, warranting more recognition in public health efforts.

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 < 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.

scholarly journals Causal Effects of Blood Lipids on Amyotrophic Lateral Sclerosis: A Mendelian Randomization Study

2018 ◽  
Author(s):  
Ping Zeng ◽  
Xiang Zhou
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Causal Relationship ◽  
Mendelian Randomization ◽  
East Asian ◽  
Density Lipoprotein ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Asian Populations ◽  
Lateral Sclerosis

AbstractAmyotrophic lateral sclerosis (ALS) is a late-onset fatal neurodegenerative disorder that is predicted to increase across the globe by ~70% in the following decades. Understanding the disease causal mechanism underlying ALS and identifying modifiable risks factors for ALS hold the key for the development of effective preventative and treatment strategies. Here, we investigate the causal effects of four blood lipid traits that include high density lipoprotein (HDL), low density lipoprotein (LDL), total cholesterol (TC), and triglycerides (TG) on the risk of ALS. By leveraging instrument variables from multiple large-scale genome-wide association studies in both European and East Asian populations, we carry out one of the largest and most comprehensive Mendelian randomization analyses performed to date on the causal relationship between lipids and ALS. Among the four lipids, we found that only LDL is causally associated with ALS and that higher LDL level increases the risk of ALS in both the European and East Asian populations. Specifically, the odds ratio of ALS per one standard deviation (i.e. 39.0 mg/dL) increase of LDL is estimated to be 1.14 (95% CI 1.05 - 1.24, p = 1.38E-3) in the European and population and 1.06 (95% CI 1.00 - 1.12, p = 0.044) in the East Asian population. The identified causal relationship between LDL and ALS is robust with respect to the choice of statistical methods and is validated through extensive sensitivity analyses that guard against various model assumption violations. Our study provides important evidence supporting the causal role of higher LDL on increasing the risk of ALS, paving ways for the development of preventative strategies for reducing the disease burden of ALS across multiple nations.

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