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Connecting the dots, genome-wide association studies in substance use
Abstract
The recent genome-wide association (GWA) meta-analysis of lifetime cannabis use by the International Cannabis Consortium marks a milestone in the study of the genetics of cannabis use.¹ Similar milestones for the genetics of substance use were the GWA meta-analyses of four smoking related traits,² of coffee consumption³ and of alcohol consumption.⁴ Combined, 315 981 partly overlapping individuals were genotyped, phenotyped and their data analyzed in genetic association studies, reflecting a huge communal effort by the substance use/addiction genetics community. These genome-wide association study (GWAS) efforts considered different stages of substance use: lifetime use (ever versus never use) was analyzed for cannabis and smoking, quantity of use (in users) was analyzed for coffee, alcohol, and smoking and age of initiation and cessation were analyzed for smoking.
... They have found that the polygenic architecture underlying alcohol consumption is shared with tobacco, caffeine and cannabis use. 21,22 In the present study we perform the largest GWAS of selfreported alcohol consumption in 112 117 individuals of European ancestry from the UK Biobank (UKB). We also estimate the SNPbased heritability of alcohol consumption and perform sexspecific analyses to investigate whether the phenotypic differences in alcohol consumption in males and females have a genetic basis. ...
... The overlap between alcohol consumption and smoking is well documented and other studies have shown polygenic overlap between weekly alcohol intake and the number of cigarettes smoked (rG = 0.44). 21 A positive genetic correlation between HDL cholesterol and alcohol consumption was also found. Increased alcohol consumption is associated with increased HDL levels 52 and a Mendelian randomization study of alcohol consumption and lipid profiles found a causal effect of alcohol on increased HDL levels in the low to moderate intake range. ...
Alcohol consumption has been linked to over 200 diseases and is responsible for over 5% of the global disease burden. Well-known genetic variants in alcohol metabolizing genes, for example, ALDH2 and ADH1B, are strongly associated with alcohol consumption but have limited impact in European populations where they are found at low frequency. We performed a genome-wide association study (GWAS) of self-reported alcohol consumption in 112 117 individuals in the UK Biobank (UKB) sample of white British individuals. We report significant genome-wide associations at 14 loci. These include single-nucleotide polymorphisms (SNPs) in alcohol metabolizing genes (ADH1B/ADH1C/ADH5) and two loci in KLB, a gene recently associated with alcohol consumption. We also identify SNPs at novel loci including GCKR, CADM2 and FAM69C. Gene-based analyses found significant associations with genes implicated in the neurobiology of substance use (DRD2, PDE4B). GCTA analyses found a significant SNP-based heritability of self-reported alcohol consumption of 13% (se=0.01). Sex-specific analyses found largely overlapping GWAS loci and the genetic correlation (rG) between male and female alcohol consumption was 0.90 (s.e.=0.09, P-value=7.16 × 10−23). Using LD score regression, genetic overlap was found between alcohol consumption and years of schooling (rG=0.18, s.e.=0.03), high-density lipoprotein cholesterol (rG=0.28, s.e.=0.05), smoking (rG=0.40, s.e.=0.06) and various anthropometric traits (for example, overweight, rG=−0.19, s.e.=0.05). This study replicates the association between alcohol consumption and alcohol metabolizing genes and KLB, and identifies novel gene associations that should be the focus of future studies investigating the neurobiology of alcohol consumption.
... Vink et al. [18] found that polygenic scores for cigarettes smoked per day (CPD) predicted alcohol consumption and cannabis use. Nivard et al. [19] used genome-wide association [GWA; 20] summary data for smoking, alcohol, cannabis, and caffeine use and showed there are substantial genetic correlations between them. ...
... In conclusion, a previous study [19] found substantial (genome-wide) genetic correlations between use of different substances without differentiating between causal versus pleiotropic effects. Here, we performed the first two-sample MR study to estimate causality in the relationships between use of nicotine, alcohol, caffeine, and cannabis. ...
Background and aims:
Epidemiological studies consistently show co-occurrence of use of different addictive substances. Whether these associations are causal or due to overlapping underlying influences remains an important question in addiction research. Methodological advances have made it possible to use published genetic associations to infer causal relationships between phenotypes. In this exploratory study, we used Mendelian randomization (MR) to examine the causality of well-established associations between nicotine, alcohol, caffeine, and cannabis use.
Methods:
Two-sample MR was employed to estimate bi-directional causal effects between four addictive substances: nicotine (smoking initiation and cigarettes smoked per day), caffeine (cups of coffee per day), alcohol (units per week), and cannabis (initiation). Based on existing genome-wide association results we selected genetic variants associated with the exposure measure as an instrument to estimate causal effects. Where possible we applied sensitivity analyses (MR-Egger and weighted median) more robust to horizontal pleiotropy.
Results:
Most MR tests did not reveal causal associations. There was some weak evidence for a causal positive effect of genetically instrumented alcohol use on smoking initiation and of cigarettes per day on caffeine use, but these did not hold up with the sensitivity analyses. There was also some suggestive evidence for a positive effect of alcohol use on caffeine use (only with MR-Egger) and smoking initiation on cannabis initiation (only with weighted median). None of the suggestive causal associations survived corrections for multiple testing.
Conclusions:
Two-sample Mendelian randomization analyses found little evidence for causal relationships between nicotine, alcohol, caffeine, and cannabis use.
... Although most genetic studies tend to separately address substance abuse (usually focusing on a single substance) and behavioral addictions, there are some studies available that examined multiple types of addictions. Twin (and other) studies suggested that most shared genetic and environmental factors may not be substance-specific [45][46][47][48][49], although genome-wide association studies often implicate genes involved in substance metabolism and subjective responses to specific drugs [50,51]. Identified genetic variants in addiction-related genes (e.g., aldehyde dehydrogenases [ALDHs], Gamma-Aminobutyric Acid Type A Receptor Alpha2 Subunit [GABRA2], and DRD2/Ankyrin repeat and kinase domain containing 1 [ANKK1]) were linked to depen-dence on various substances [52][53][54][55]. ...
Epidemiological and phenomenological studies suggest shared underpinnings between multiple addictive behaviors. The present genetic association study was conducted as part of the Psychological and Genetic Factors of Addictions study (n = 3003) and aimed to investigate genetic overlaps between different substance use, addictive, and other compulsive behaviors. Association analyses targeted 32 single-nucleotide polymorphisms, potentially addictive substances (alcohol, tobacco, cannabis, and other drugs), and potentially addictive or compulsive behaviors (internet use, gaming, social networking site use, gambling, exercise, hair-pulling, and eating). Analyses revealed 29 nominally significant associations, from which, nine survived an FDRbl correction. Four associations were observed between FOXN3 rs759364 and potentially addictive behaviors: rs759364 showed an association with the frequency of alcohol consumption and mean scores of scales assessing internet addiction, gaming disorder, and exercise addiction. Significant associations were found between GDNF rs1549250, rs2973033, CNR1 rs806380, DRD2/ANKK1 rs1800497 variants, and the “lifetime other drugs” variable. These suggested that genetic factors may contribute similarly to specific substance use and addictive behaviors. Specifically, FOXN3 rs759364 and GDNF rs1549250 and rs2973033 may constitute genetic risk factors for multiple addictive behaviors. Due to limitations (e.g., convenience sampling, lack of structured scales for substance use), further studies are needed. Functional correlates and mechanisms underlying these relationships should also be investigated.
... low educational attainment) 16,17 . Results from twin 3,18 and genomic studies 19,20 further indicate that the correlation between the use of different substances stems from a common liability that is largely genetic in nature. ...
The co-occurrence of abuse of multiple substances is thought to stem from a common liability that is partly genetic in origin. Genetic risk may indirectly contribute to a common liability through genetically influenced individual vulnerabilities and traits. To disentangle the aetiology of common versus specific liabilities to substance abuse, polygenic scores can be used as genetic proxies indexing such risk and protective individual vulnerabilities or traits. In this study, we used genomic data from a UK birth cohort study (ALSPAC, N=4218) to generate 18 polygenic scores indexing mental health vulnerabilities, personality traits, cognition, physical traits, and substance abuse. Common and substance-specific factors were identified based on four classes of substance abuse (alcohol, cigarettes, cannabis, other illicit substances) assessed over time (age 17, 20, and 22). In multivariable regressions, we then tested the independent contribution of selected polygenic scores to the common and substance-specific factors. Our findings implicated several genetically influenced traits and vulnerabilities in the common liability to substance abuse, most notably risk taking (b standardized = 0.14; 95%CI: 0.10,0.17), followed by extraversion (b standardized =-0.10; 95%CI: - 0.13,-0.06), and schizophrenia risk (b standardized = 0.06; 95%CI: 0.02;0.09). Educational attainment (EA) and body mass index (BMI) had opposite effects on substance-specific liabilities such as cigarettes (b standardized-EA = -0.15; 95%CI: -0.19,-0.12; b standardized-BMI = 0.05; 95%CI: 0.02,0.09), alcohol (b standardized-EA = 0.07; 95%CI: 0.03,0.11; b standardized-BMI = -0.06; 95%CI: -0.10,-0.02), and other illicit substances (b standardized-EA = 0.12; 95%CI: 0.07,0.17; b standardized-BMI = -0.08; 95%CI:-0.13,-0.04). This is the first study based on genomic data that clarifies the aetiological architecture underlying the common versus substance-specific liabilities, providing novel insights for the prevention and treatment of substance abuse.
... We hypothesized that the genetic risk for AUD is likely to overlap with numerous traits relevant to addiction and psychiatric phenotypes, based on previous epidemiological data (Compton et al. 2007), twin studies (Kendler et al. 1993;Pickens et al. 1995;Knopik et al. 2009) and recent genetic correlations between alcohol consumption and neuropsychiatric traits (Clarke et al. 2017). We showed positive genetic correlations between AUDIT and lifetime cigarette smoking, as previously observed between alcohol consumption and daily cigarettes and tobacco initiation (Vink et al. 2014;Nivard et al. 2016). We also observed shared genetic architecture across AUDIT and other alcohol-related traits from independent cohorts: alcohol consumption (Schumann et al. 2016;Clarke et al. 2017) and AUD diagnosis (P = 0.062; Gelernter et al. 2014). ...
Genetic factors contribute to the risk for developing alcohol use disorder (AUD). In collaboration with the genetics company 23andMe, Inc., we performed a genome-wide association study of the alcohol use disorder identification test (AUDIT), an instrument designed to screen for alcohol misuse over the past year. Our final sample consisted of 20 328 research participants of European ancestry (55.3% females; mean age = 53.8, SD = 16.1) who reported ever using alcohol. Our results showed that the ‘chip-heritability’ of AUDIT score, when treated as a continuous phenotype, was 12%. No loci reached genome-wide significance. The gene ADH1C, which has been previously implicated in AUD, was among our most significant associations (4.4 × 10⁻⁷; rs141973904). We also detected a suggestive association on chromosome 1 (2.1 × 10⁻⁷; rs182344113) near the gene KCNJ9, which has been implicated in mouse models of high ethanol drinking. Using linkage disequilibrium score regression, we identified positive genetic correlations between AUDIT score, high alcohol consumption and cigarette smoking. We also observed an unexpected positive genetic correlation between AUDIT and educational attainment and additional unexpected negative correlations with body mass index/obesity and attention-deficit/hyperactivity disorder. We conclude that conducting a genetic study using responses to an online questionnaire in a population not ascertained for AUD may represent a cost-effective strategy for elucidating aspects of the etiology of AUD.
... Molecular genetic studies show that variants associated with the use of one substance also show a relation with the use of other substances, at least when looking at the same stage of use. For example, there are substantial genetic correlations between smoking initiation and cannabis initiation and between glasses of alcohol per week and number of cigarettes per day [7]. Likewise, genetic risk factors for smoking quantity predicted drinking quantity [8]. ...
Twin studies have shown substantial heritability for polysubstance use. Previous research has
sought to pinpoint this genetic influence to variants in genes related to dopamine signaling,
that are known to lower baseline dopamine levels (hypodopaminergic function). Candidategene
studies often used single-gene designs and have yielded inconsistent results. Genome-wide
association studies mainly include Single Nucleotide Polymorphisms (SNPs). In this study, a
risk score was calculated based on both SNPs as well as Variable Number of Tandem Repeats
(VNTRs).
Survey data on nicotine, alcohol and cannabis use from two family samples were analysed
(N=2435 and N=1173). Moderate and problematic polysubstance uses were explored. A polygenic
risk score was calculated by averaging the number of hypodopaminergic variants in three
polymorphisms. Polysubstance use was regressed on this score with sex and age as covariates.
Power was sufficient to detect small effect sizes (R2
=0.4-0.8%).
The hypodopaminergic polygenic risk score (HPRS) was not related to polysubstance use in
either sample. There were some indications for opposing effects of individual polymorphisms
and separate substance use outcomes and for an interaction of the polygenic risk score with
education level. There were no effects of a score extended with extra polymorphisms, and there
were no quadratic effects of the HPRS.
The HPRS did not predict polysubstance use. Several explanations for these findings were ruled
out. Future research might employ more comprehensive genetic models, thereby including geneenvironment
interaction.
... Our findings reveal that SNP-based heritabilities of 16 mental health traits, and the genetic correlations between them, are influenced by genetic overlap with SES traits. Our findings suggest that the genetic overlap between substance use traits and psychiatric disorders 1,14,27,60 is in part due to their shared genetic overlap with SES. These findings provide important insights into the complexity of these associations and highlight the need to consider the role of SES in future studies investigating the genetic basis of mental health traits. ...
Epidemiological studies show high comorbidity between different mental health problems, indicating that individuals with a diagnosis of one disorder are more likely to develop other mental health problems. Genetic studies reveal substantial sharing of genetic risk factors across mental health traits. However, mental health is genetically correlated with socio-economic status (SES) and it is therefore important to investigate and disentangle the genetic relationship between mental health and SES. We used summary statistics from large genome-wide association studies (average N~160,000) to estimate the genetic overlap across nine psychiatric disorders and seven substance use traits and explored the genetic influence of three different indicators of SES. Using Genomic SEM, we show significant changes in patterns of genetic correlations after partialling out SES-associated genetic variation. Our approach allows the separation of disease-specific genetic variation and genetic variation shared with SES, thereby improving our understanding of the genetic architecture of mental health.
... CPD and overall smoking initiation (smoking 100+ cigarettes over the lifetime) may be too similar to each other (as one is nested within the other). Although the two PRSs share few genetic variants between them (see TAG 2010, supplemental tables 4 and 5), and the PRS were only minimally correlated, both sets of genes may share similar associations with "active" smoking behaviors, as studies indicate partial shared genetic influence between multiple smoking stages (Kendler et al., 1999;Madden et al., 1999), and have a moderate genetic correlation (r = 0.37; Nivard et al., 2016) It is possible that these PRSs both contribute to genetic liability for a broader smoking risk phenotype. Highpowered studies and enhanced genomic methods may build more comprehensive genetic profiles of risk that can distinguish between different, but related, smoking stages. ...
Purpose
Polygenic risk scores (PRSs) for smoking behavior largely fail to consider the demonstrated developmental change in genetic influence over age and stage of smoking behaviors. Additionally, few studies have examined how stage-specific smoking PRSs (e.g. for initiation vs. smoking heaviness) generalize to other stages of risk. The current study examines the stability of PRS effects over age, and how specifically calibrated PRSs associate with other smoking phenotypes.
Methods
7228 participants were from the National Longitudinal Study of Adolescent to Adult Health, who had calculated PRSs for two smoking phenotypes, Centers for Disease Control and Prevention (CDC) smoking initiation status, and cigarettes per day (CPD). Four time-varying effects models estimated associations between both PRSs and four smoking phenotypes (CDC status, cigarettes/day on smoking days, any past-30 day smoking, and past-30 day daily smoking) over adolescence and young adulthood.
Findings
The time-varying effects models demonstrated that both PRSs significantly associated with all four phenotypes age. PRS effects were similar, in both odds ratios and the overlap of 95% confidence intervals. There were increases in PRS associations with quantity of smoking over age, and a decrease in PRS effects over age for the CDC smoking status phenotype over early to late adolescence.
Conclusions
Smoking PRSs can be robust predictors of smoking behavior over age. However, the lack of differentiation between specific PRSs and multiple smoking phenotypes, as well as the added contribution of both PRSs to explaining genetic variance, indicates a need to reconceptualize phenotypic measurement used to calibrate smoking PRSs.
... For example, in observational studies, the use of different classes of substances is typically associated with a range of shared individual factors such as mental health vulnerabilities (e.g., schizophrenia, attention deficit and hyperactivity disorder [ADHD]), 8,9 personality traits (e.g., risk taking), 10,11 cognitive factors (e.g., educational attainment), 12 and physical characteristics (e.g., body mass index [BMI]). 13 Results from twin 4,14 and genomic studies 15,16 further indicate that the correlation between the use of different substances stems from a common liability that is largely genetic in nature. ...
Individuals most often use several rather than one substance among alcohol, cigarettes or cannabis. This widespread co‐occurring use of multiple substances is thought to stem from a common liability that is partly genetic in origin. Genetic risk may indirectly contribute to a common liability to substance use through genetically influenced mental health vulnerabilities and individual traits. To test this possibility, we used polygenic scores indexing mental health and individual traits and examined their association with the common versus specific liabilities to substance use.
We used data from the Avon Longitudinal Study of Parents and Children (N = 4218) and applied trait‐state‐occasion models to delineate the common and substance‐specific factors based on four classes of substances (alcohol, cigarettes, cannabis and other illicit substances) assessed over time (ages 17, 20 and 22). We generated 18 polygenic scores indexing genetically influenced mental health vulnerabilities and individual traits. In multivariable regression, we then tested the independent contribution of selected polygenic scores to the common and substance‐specific factors.
Our results implicated several genetically influenced traits and vulnerabilities in the common liability to substance use, most notably risk taking (b standardised = 0.14; 95% confidence interval [CI] [0.10, 0.17]), followed by extraversion (b standardised = −0.10; 95% CI [−0.13, −0.06]), and schizophrenia risk (b standardised = 0.06; 95% CI [0.02, 0.09]). Educational attainment (EA) and body mass index (BMI) had opposite effects on substance‐specific liabilities such as cigarette use (b standardised‐EA = −0.15; 95% CI [−0.19, −0.12]; b standardised‐BMI = 0.05; 95% CI [0.02, 0.09]) and alcohol use (b standardised‐EA = 0.07; 95% CI [0.03, 0.11]; b standardised‐BMI = −0.06; 95% CI [−0.10, −0.02]). These findings point towards largely distinct sets of genetic influences on the common versus specific liabilities.
... Genetic overlap was observed for all measures of AUDIT and other substance use traits, including lifetime tobacco and cannabis use, as we previously reported (10,36,37), demonstrating that genetic risk factors for high AUDIT scores overlap with increased consumption of multiple drug types. ...
Objective::
Alcohol use disorders are common conditions that have enormous social and economic consequences. Genome-wide association analyses were performed to identify genetic variants associated with a proxy measure of alcohol consumption and alcohol misuse and to explore the shared genetic basis between these measures and other substance use, psychiatric, and behavioral traits.
Method::
This study used quantitative measures from the Alcohol Use Disorders Identification Test (AUDIT) from two population-based cohorts of European ancestry (UK Biobank [N=121,604] and 23andMe [N=20,328]) and performed a genome-wide association study (GWAS) meta-analysis. Two additional GWAS analyses were performed, a GWAS for AUDIT scores on items 1-3, which focus on consumption (AUDIT-C), and for scores on items 4-10, which focus on the problematic consequences of drinking (AUDIT-P).
Results::
The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; this study also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (rg=0.76-0.92) and DSM-IV alcohol dependence (rg=0.33-0.63). AUDIT-P and AUDIT-C scores showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P score was significantly positively genetically correlated with schizophrenia (rg=0.22), major depressive disorder (rg=0.26), and attention deficit hyperactivity disorder (rg=0.23), whereas AUDIT-C score was significantly negatively genetically correlated with major depressive disorder (rg=-0.24) and ADHD (rg=-0.10). This study also used the AUDIT data in the UK Biobank to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total scores ≤4 as control subjects and those with scores ≥12 as case subjects produced a significant high genetic correlation with DSM-IV alcohol dependence (rg=0.82) while retaining most subjects.
Conclusions::
AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and alcohol use disorders.
... Genetic overlap was observed for all measures of AUDIT and other substance use traits, including lifetime tobacco and cannabis use, as we previously reported (10,36,37), demonstrating that genetic risk factors for high AUDIT scores overlap with increased consumption of multiple drug types. ...
Alcohol use disorders (AUD) are common conditions that have enormous social and economic consequences. We obtained quantitative measures using the Alcohol Use Disorder Identification Test (AUDIT) from two population-based cohorts of European ancestry: UK Biobank (UKB; N=121,604) and 23andMe (N=20,328) and performed a genome-wide association study (GWAS) meta-analysis. We also performed GWAS for AUDIT items 1-3, which focus on consumption (AUDIT-C), and for items 4-10, which focus on the problematic consequences of drinking (AUDIT-P). The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; we also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (rg=0.76-0.92) and Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) alcohol dependence (rg=0.33-0.63). AUDIT-P and AUDIT-C showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P was positively genetically correlated with schizophrenia (rg=0.22, p=3.0×10−10), major depressive disorder (rg=0.26, p=5.6×10−3), and attention-deficit/hyperactivity disorder (ADHD; rg=0.23, p=1.1×10−5), whereas AUDIT-C was negatively genetically correlated with major depressive disorder (rg=−0.24, p=3.7×10−3) and ADHD (rg=−0.10, p=1.8×10−2). We also used the AUDIT data in the UKB to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total score of ≤4 as controls and ≥12 as cases produced a high genetic correlation with DSM-IV alcohol dependence (rg=0.82, p=3.2×10−6) while retaining most subjects. We conclude that AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and AUD.
... Mogelijk kan meer variantie worden verklaard door een polygenetische risicoscore te berekenen; hierbij wordt het gewogen effect van veel verschillende varianten bij elkaar opgeteld. Genoombrede associatiestudies laten verder een grote genetische correlatie zien tussen de hoeveelheid sigaretten, alcohol en koffie (r: 0,38-0,44), 21 wat suggereert dat veel van de genetische risicovarianten voor roken overlappen met risicovarianten voor andere middelen. Dit duidt weer op een genetische gevoeligheid voor verslavend gedrag. ...
... In this study we employed a range of methods utilizing genetic data to systematically investigate the association between smoking behaviours and psychotic experiences across adolescence and adulthood as well as with psychiatric disorders. We assessed the degree to which smoking behaviours are genetically correlated with major psychiatric disorders (schizophrenia, major depression and bipolar disorder), with psychotic experiences during adolescence (paranoia and hallucinations, cognitive disorganization, anhedonia and negative symptoms), psychotic experiences in adults (auditory hallucinations, visual hallucinations, delusions of persecution and delusions of reference), and with schizotypy in adults (hypomania, perceptual aberrations, physical anhedonia and social anhedonia) after controlling for genetic overlap with cannabis and alcohol use, risk taking behaviour and sleep disturbances [27][28][29][30][31] . Furthermore, we aimed to assess causal associations between smoking initiation and psychotic experiences across development and confirm previous reports of causal associations with psychiatric disorders. ...
Cigarette smoking is a modifiable behaviour associated with mental health. We investigated the degree of genetic overlap between smoking behaviours and psychiatric traits and disorders, and whether genetic associations exist beyond genetic influences shared with confounding variables (cannabis and alcohol use, risk-taking and insomnia). Second, we investigated the presence of causal associations between smoking initiation and psychiatric traits and disorders. We found significant genetic correlations between smoking and psychiatric disorders and adult psychotic experiences. When genetic influences on known covariates were controlled for, genetic associations between most smoking behaviours and schizophrenia and depression endured (but not with bipolar disorder or most psychotic experiences). Mendelian randomization results supported a causal role of smoking initiation on psychiatric disorders and adolescent cognitive and negative psychotic experiences, although not consistently across all sensitivity analyses. In conclusion, smoking and psychiatric disorders share genetic influences that cannot be attributed to covariates such as risk-taking, insomnia or other substance use. As such, there may be some common genetic pathways underlying smoking and psychiatric disorders. In addition, smoking may play a causal role in vulnerability for mental illness.
... With LD score regression, the degree of genetic covariance between two phenotypes (from GWA meta-analyses ) can be estimated (Bulik-Sullivan et al., 2015). By using this method, we observed (a) high, within-substance genetic correlations over different stages of smoking (i.e., initiation, cessation, quantity, but only limited for age at onset); and (b) stage-specific genetic effects across substances: A high genetic correlation was observed between the two variables reflecting the initiation of substance use (cannabis initiation and smoking initiation) and between the three variables reflecting quantity of substance use (cigarettes per day, alcohol per week, and coffee per day) (Nivard et al., 2016). Overall, the results suggest that there are sets of " substance-specific " genes and sets of genes contributing to a " vulnerability for addictive behavior " in general. ...
The heritability of substance use is moderate to high. Successful efforts to find genetic variants associated with substance use (smoking, alcohol, cannabis) have been undertaken by large consortia. However, the proportion of phenotypic variance explained by the identified genetic variants is small. Interestingly, there is overlap between the genetic variants that influence different substances. Moreover, there are sets of "substance-specific" genes and sets of genes contributing to a "vulnerability for addictive behavior" in general. It is important to recognize that genes alone do not determine addiction phenotypes: Environmental factors such as parental monitoring, peer pressure, or socioeconomic status also play an important role. Despite a rich epidemiologic literature focused on the social determinants of substance use, few studies have examined the moderation of genetic influences like gene-environment (G × E) interactions. Understanding this balance may hold the key to understanding the individual differences in substance use, abuse, and addictive behavior. Recommendations for future research are described in this commentary and include increasing the power of G × E studies by using state-of-the-art methods such as polygenic risk scores instead of single genetic variants and taking genetic overlap between substances into account. Future genetic studies should also investigate environmental risk factors for addictive behavior more extensively to unravel the interaction between nature and nurture. Focusing on G × E interactions not only will give insight into the underlying biological mechanism but will also characterize subgroups (based on environmental factors) at high risk for addictive behaviors. With this information, we could bridge the gap between fundamental research and applications for society.
... Aetiological models posit that addiction to multiple substances stems from a common liability to addiction 4,5 -a latent continuous trait accounting for the shared risk of developing addiction to different substances. Based on findings from genomic [6][7][8] and behavioural genetic studies 9,10 , it is assumed that this common liability includes a genetic component. Indeed, genetic correlations between use of different classes of psychoactive substances are substantial, as estimated in twin (up to rg~0.89 [11][12][13] ) and genome-wide association (GWA) studies (up to rg~0. ...
Addiction to nicotine, alcohol and cannabis commonly co-occurs, which is thought to partly stem from a common heritable liability. To elucidate its genetic architecture, we modelled the common liability to addiction, inferred from genetic correlations among six measures of dependence and frequency of use of nicotine, alcohol and cannabis. Forty-two genetic variants were identified in the multivariate genome-wide association study on the common liability to addiction, of which 67% were novel and not associated with the six phenotypes. Mapped genes highlighted the role of dopamine (e.g., dopamine D2 gene), and showed enrichment for a several components of the central nervous systems (e.g., mesocorticolimbic brain regions) and molecular pathways (dopaminergic, glutamatergic, GABAergic) that are thought to modulate drug reinforcement. Genetic correlations with other traits were most prominent for reward-related behaviours (e.g., risk-taking, cocaine and heroin use) and mood (e.g., depression, insomnia). These genome-wide results triangulate and expand previous preclinical and human studies focusing on the neurobiological substrates of addiction, and help to elucidate the common genetic architecture underlying addiction.
This dissertation contributes to our understanding of the nature, development, and significance of mood variability during adolescence. The results of the reported studies suggest that daily emotions can be reliably assessed and compared across adolescence. The studies further demonstrate that adolescence is a period in which most boys and girls come to grips with their moods, although some adolescents struggle with increasingly instable moods. This emphasizes the importance of addressing interindividual differences in adolescent mood development. Lastly, heightened mood
variability is shown to contribute to the development of both internalizing and externalizing problems as well as difficulties in parent-child relationships during adolescence. Overall, the results of this dissertation indicate that adolescents deal with
emotional challenges during this transformational period and about one in ten may need help to regulate fluctuating emotions to set the stage for a healthy development.
Background
Global scale brain research collaborations such as the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium are beginning to collect data in large quantity and to conduct meta-analyses using uniformed protocols. It becomes strategically important that the results can be communicated among brain scientists effectively. Traditional graphs and charts failed to convey the complex shapes of brain structures which are essential to the understanding of the result statistics from the analyses. These problems could be addressed using interactive visualization strategies that can link those statistics with brain structures in order to provide a better interface to understand brain research results.
Results
We present ENIGMA-Viewer, an interactive web-based visualization tool for brain scientists to compare statistics such as effect sizes from meta-analysis results on standardized ROIs (regions-of-interest) across multiple studies. The tool incorporates visualization design principles such as focus+context and visual data fusion to enable users to better understand the statistics on brain structures. To demonstrate the usability of the tool, three examples using recent research data are discussed via case studies.
Conclusions
ENIGMA-Viewer supports presentations and communications of brain research results through effective visualization designs. By linking visualizations of both statistics and structures, users can gain more insights into the presented data that are otherwise difficult to obtain. ENIGMA-Viewer is an open-source tool, the source code and sample data are publicly accessible through the NITRC website (http://www.nitrc.org/projects/enigmaviewer_20). The tool can also be directly accessed online (http://enigma-viewer.org).
Introduction:
Cigarette smoking and cannabis use are heritable traits and share, at least in part, a common genetic substrate. In recent years the prevalence of alternative methods of nicotine intakes, such as e-cigarette and water pipe use, has risen substantially. We tested whether the genetic vulnerability underlying cigarettes smoking and cannabis use explained variability in e-cigarette and water pipe use phenotypes, as these vaping methods are alternatives for smoking tobacco cigarettes and joints.
Methods:
Based on the summary statistics of the International Cannabis Consortium and the Tobacco and Genetics Consortium we generated polygenic risk scores (PRSs) for smoking and cannabis use traits, and used these to predict e-cigarette and water pipe use phenotypes in a sample of 5,025 individuals from the Netherlands Twin Register.
Results:
PRSs for cigarettes per day (CPD) were positively associated with lifetime e-cigarette use and early initiation of water pipe use, but only in ex-smokers (OR=1.43, R2=1.56%, p=.011) and never cigarette smokers (OR = 1.35, R2 = 1.60%, p = .013) respectively.
Conclusions:
Most associations of PRSs for cigarette smoking and cannabis use with e-cigarette and water pipe use were not significant, potentially due to a lack of power. The significant associations between genetic liability to smoking heaviness with e-cigarette and water pipe phenotypes are in line with studies indicating a common genetic background for substance use phenotypes. These associations emerged only in non-smokers, and future studies should investigate the nature of this observation.
Introduction:
Despite increasing use of cannabis, it is unclear how cannabis use is related to cigarette transitions. This study examined cannabis use and smoking initiation, persistence, and relapse over one year among a nationally representative sample of United States (US) adults.
Methods:
Data were from US adults (18+) who completed two waves of longitudinal data from the Population Assessment of Tobacco and Health (PATH) Study (Wave 1, 2013-2014; Wave 2, 2014-2015; n=26,341). Logistic regression models were used to calculate the risk of Wave 2 incident smoking among Wave 1 never smokers, smoking cessation among Wave 1 smokers, and smoking relapse among Wave 1 former smokers by Wave 1 cannabis use. Analyses were adjusted for age, gender, race/ethnicity, income, and education.
Results:
Among Wave 1 never smokers, cannabis use was associated with increased odds of initiation of non-daily (adjusted odds ratio (AOR)=5.50, 95% confidence limits (CL)= 4.02-7.55) and daily cigarette smoking (AOR=6.70, 95% CL=4.75-9.46) one year later. Among Wave 1 daily smokers, cannabis use was associated with reduced odds of smoking cessation (AOR=0.36, 95% CL=0.20-0.65). Among Wave 1 former smokers, cannabis use was associated with increased odds of relapse to daily and non-daily cigarette smoking (daily AOR=1.90, 95% CL=1.11-3.26; non-daily AOR=2.33, 95% CL=1.61-3.39).
Conclusions:
Cannabis use was associated with increased cigarette smoking initiation, decreased smoking cessation, and increased smoking relapse among adults in the US. Increased public education about the relationship between cannabis use and cigarette smoking transitions may be needed as cannabis use becomes more common among US adults.
Implications:
As cannabis use increases in the US and other countries, an evaluation of the relationships of cannabis use to other health-related behaviors (e.g., cigarette smoking) is needed to understand the population-level impact of legalization. Little is known about associations between cannabis use and cigarette smoking transitions (1) using recent longitudinal data, (2) among adults, and (3) examining transitions other than smoking initiation (e.g., smoking relapse). Our results suggest that among US adults, cannabis use was associated with increased cigarette smoking initiation among never smokers, decreased cigarette smoking cessation among current smokers, and increased cigarette smoking relapse among former smokers.
The literature on the behavioral genetics of disordered gambling is reviewed in this chapter. This includes studies that have focused on estimating the aggregate influence of genetic and environmental factors and studies that have focused on identifying the specific genes that account for the aggregate genetic risk. Topics also covered are disordered gambling comorbidity, developmentally relevant studies, identifying the specific environments that account for the aggregate environmental risk, and gene-environment interplay. The chapter is concluded with a summary of the general conclusions, areas in need of more research, and recommendations for the way forward.
Background. Frequency and quantity of alcohol consumption are metrics commonly used to measure alcohol consumption behaviors. Epidemiological studies indicate that these alcohol consumption measures are differentially associated with (mental) health outcomes and socioeconomic status (SES). The current study aims to elucidate to what extent genetic risk factors are shared between frequency and quantity of alcohol consumption, and how these alcohol consumption measures are genetically associated with four broad phenotypic categories: (i) SES; (ii) substance use disorders; (iii) other psychiatric disorders; and (iv) psychological/personality traits.
Methods. Genome-Wide Association analyses were conducted to test genetic associations with alcohol consumption frequency ( N = 438 308) and alcohol consumption quantity ( N = 307 098 regular alcohol drinkers) within UK Biobank. For the other phenotypes, we used genome-wide association studies summary statistics. Genetic correlations ( rg ) between the alcohol measures and other phenotypes were estimated using LD score regression.
Results. We found a substantial genetic correlation between the frequency and quantity of alcohol consumption ( rg = 0.52). Nevertheless, both measures consistently showed opposite genetic correlations with SES traits, and many substance use, psychiatric, and psychological/personality traits. High alcohol consumption frequency was genetically associated with high SES and low risk of substance use disorders and other psychiatric disorders, whereas the opposite applies for high alcohol consumption quantity.
Conclusions. Although the frequency and quantity of alcohol consumption show substantial genetic overlap, they consistently show opposite patterns of genetic associations with SES-related phenotypes. Future studies should carefully consider the potential influence of SES on the shared genetic etiology between alcohol and adverse (mental) health outcomes.
The aims of this study are to estimate the contributions of genetic factors to the variation of tea drinking and cigarette smoking, to examine the roles of genetic factors in their correlation and further to investigate underlying causation between them. We included 11 625 male twin pairs from the Chinese National Twin Registry (CNTR). Bivariate genetic modelling was fitted to explore the genetic influences on tea drinking, cigarette smoking and their correlation. Inference about Causation through Examination of FAmiliaL CONfounding (ICE FALCON) was further used to explore the causal relationship between them. We found that genetic factors explained 17% and 23% of the variation in tea drinking and cigarette smoking, respectively. A low phenotypic association between them was reported (rph = 0.21, 95% confidence interval [CI]: [0.19, 0.24]), which was partly attributed to common genetic factors (rA = 0.45, 95% CI [0.19, 1.00]). In the ICE FALCON analysis with current smoking as the exposure, tea drinking was associated with his own (βself = 0.39, 95% CI [0.23, 0.55]) and his co-twin's smoking status (βco-twin = 0.25, 95% CI [0.10, 0.41]). Their association attenuated with borderline significance conditioning on his own smoking status (p = 0.045), indicating a suggestive causal effect of smoking status on tea drinking. On the contrary, when we used tea drinking as the predictor, we found familial confounding between them only. In conclusion, both tea drinking and cigarette smoking were influenced by genetic factors, and their correlation was partly explained by common genetic factors. In addition, our finding suggests that familial confounders account for the relationship between tea drinking and cigarette smoking. And current smoking might have a causal effect on weekly tea drinking, but not vice versa.
Excessive alcohol consumption is one of the main causes of death and disability worldwide. Alcohol consumption is a heritable complex trait. Here we conducted a meta-analysis of genome-wide association studies of alcohol consumption (g d⁻¹) from the UK Biobank, the Alcohol Genome-Wide Consortium and the Cohorts for Heart and Aging Research in Genomic Epidemiology Plus consortia, collecting data from 480,842 people of European descent to decipher the genetic architecture of alcohol intake. We identified 46 new common loci and investigated their potential functional importance using magnetic resonance imaging data and gene expression studies. We identify genetic pathways associated with alcohol consumption and suggest genetic mechanisms that are shared with neuropsychiatric disorders such as schizophrenia.
Background:
Substance use, substance use disorders (SUDs), and psychiatric disorders commonly co-occur. Genetic risk common to these complex traits is an important explanation; however, little is known about how polygenic risk for tobacco or alcohol use overlaps the genetic risk for the comorbid SUDs and psychiatric disorders.
Methods:
We constructed polygenic risk scores (PRSs) using GWAS meta-analysis summary statistics from a large discovery sample, GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN), for smoking initiation (SI; N = 631,564), age of initiating regular smoking (AI; N = 258,251), cigarettes per day (CPD; N = 258,999), smoking cessation (SC; N = 312,273), and drinks per week (DPW; N = 527,402). We then estimated the fixed effect of these PRSs on the liability to 15 phenotypes related to tobacco and alcohol use, substance use disorders, and psychiatric disorders in an independent target sample of Australian adults.
Results:
After adjusting for multiple testing, 10 of 75 combinations of discovery and target phenotypes remained significant. PRS-SI (R2 range: 1.98%-5.09 %) was positively associated with SI, DPW, and with DSM-IV and FTND nicotine dependence, and conduct disorder. PRS-AI (R2: 3.91 %) negatively associated with DPW. PRS-CPD (R2: 1.56 %-1.77 %) positively associated with DSM-IV nicotine dependence and conduct disorder. PRS-DPW (R2: 3.39 %-6.26 %) positively associated with only DPW. The variation of DPW was significantly influenced by sex*PRS-SI, sex*PRS-AI and sex*PRS-DPW. Such interaction effect was not detected in the other 14 phenotypes.
Conclusions:
Polygenic risks associated with tobacco use are also associated with liability to alcohol consumption, nicotine dependence, and conduct disorder.
Background
Caffeine, alcohol, nicotine and cannabis are commonly used psychoactive substances. While the use of these substances has been previously shown to be genetically correlated, causality between these substance use traits remains unclear. We aimed to revisit the genetic relationships among different measures of SU using genome-wide association study (GWAS) summary statistics from the UK Biobank, International Cannabis Consortium, and GWAS & Sequencing Consortium of Alcohol and Nicotine use.
Methods
We obtained GWAS summary statistics from the aforementioned consortia for ten substance use traits including various measures of alcohol consumption, caffeine consumption, cannabis initiation and smoking behaviours. We then conducted SNP-heritability ( h²) estimation for individual SU traits, followed by genetic correlation analyses and two-sample Mendelian randomisation (MR) studies between substance use trait pairs.
Results
SNP h² of the ten traits ranged from 0.03 to 0.11. After multiple testing correction, 29 of the 45 trait pairs showed evidence of being genetically correlated. MR analyses revealed that most SU traits were not causally associated with each other. However, we found evidence for an MR association between regular smoking initiation and caffeine consumption 40.17 mg; 95% CI: [ 24.01, 56.33] increase in caffeine intake per doubling of odds in smoking initiation). Our findings were robust against horizontal pleiotropy, SNP-outliers, and the direction of causality was consistent in all MR analyses.
Conclusions
Most of the substance traits were genetically correlated but there is little evidence to establish causality apart from the relationship between smoking initiation and caffeine consumption.
Introduction:
Cigarette use is declining among youth in the United States (US), whereas cannabis use and e-cigarette use are increasing. Cannabis use has been linked with increased uptake and persistence of cigarette smoking among adults. The goal of this study was to examine whether cannabis use was associated with the prevalence and incidence of cigarette, e-cigarette, and dual product use among US youth.
Methods:
Data included US youth ages 12 to 17 from two waves of the Population Assessment of Tobacco and Health (PATH) Study (Wave 1 youth n=13,651; Wave 1 tobacco-naïve youth n=10,081). Weighted logistic regression models were used to examine the association between Wave 1 cannabis use and (1) Wave 1 prevalence of cigarette/e-cigarette use among Wave 1 youth, and (2) Wave 2 incidence of cigarette/e-cigarette use among Wave 1 tobacco-naïve youth. Analyses were run unadjusted and adjusted for demographics and internalizing/externalizing problem symptoms.
Results:
Wave 1 cigarette and e-cigarette use were significantly more common among youth who used versus did not use cannabis. Among Wave 1 tobacco-naïve youth, Wave 1 cannabis use was associated with significantly increased incidence of cigarette and e-cigarette use by Wave 2.
Conclusions:
Youth who use cannabis are more likely to report cigarette and e-cigarette use and cannabis use is associated with increased risk of initiation of cigarette and e-cigarette use over one year. Continued success in tobacco control-specifically toward reducing smoking among adolescents-may require focusing on vaping, cannabis, and cigarette use in public health education, outreach, and intervention efforts.
Implications:
These data extend our knowledge of cigarette and e-cigarette use among youth by showing that cannabis use is associated with increased prevalence and incidence of cigarette and e-cigarette use among youth, relative to youth who do not use cannabis. The increasing popularity of cannabis use among youth and diminished perceptions of risk, coupled with the strong link between cannabis use and tobacco use, may have unintended consequences for cigarette control efforts among youth. Continued success in tobacco control among youth may require focusing on cannabis, e-cigarette, and cigarette use in public health, outreach, and intervention efforts.
Genome‐wide association studies have identified multiple genetic risk factors underlying susceptibility to substance use, however, the functional genes and biological mechanisms remain poorly understood. The discovery and characterization of risk genes can be facilitated by the integration of genome‐wide association data and gene expression data across biologically relevant tissues and/or cell types to identify genes whose expression is altered by DNA sequence variation (expression quantitative trait loci; eQTLs). The integration of gene expression data can be extended to the study of genetic co‐expression, under the biologically valid assumption that genes form co‐expression networks to influence the manifestation of a disease or trait. Here, we integrate genome‐wide association data with gene expression data from 13 brain tissues to identify candidate risk genes for 8 substance use phenotypes. We then test for the enrichment of candidate risk genes within tissue‐specific gene co‐expression networks to identify modules (or groups) of functionally related genes whose dysregulation is associated with variation in substance use. We identified eight gene modules in brain that were enriched with gene‐based association signals for substance use phenotypes. For example, a single module of 40 co‐expressed genes was enriched with gene‐based associations for drinks per week and biological pathways involved in GABA synthesis, release, reuptake and degradation. Our study demonstrates the utility of eQTL and gene co‐expression analysis to uncover novel biological mechanisms for substance use traits.
Epidemiological studies show high comorbidity between different mental health problems, indicating that individuals with a diagnosis of one disorder are more likely to develop other mental health problems. Genetic studies reveal substantial sharing of genetic factors across mental health traits. However, mental health is also genetically correlated with socio-economic status (SES), and it is therefore important to investigate and disentangle the genetic relationship between mental health and SES. We used summary statistics from large genome-wide association studies (average N ~ 160,000) to estimate the genetic overlap across nine psychiatric disorders and seven substance use traits and explored the genetic influence of three different indicators of SES. Using genomic structural equation modelling, we show significant changes in patterns of genetic correlations after partialling out SES-associated genetic variation. Our approach allows the separation of disease-specific genetic variation and genetic variation shared with SES, thereby improving our understanding of the genetic architecture of mental health. Marees et al. show changes in patterns of heritability and genetic overlap between mental health problems after removing genetic variation associated with socio-economic status.
Epidemiology is a core discipline generating evidence to inform and drive drug policy. In this essay, we speculate on what the future of drug epidemiology might become. We highlight for attention two areas shaping the future of drug epidemiology: nesting epidemiology within a ‘syndemic’ and ‘relational’ approach; and innovating in relation to causal inference in the face of complexity. We argue that shifts towards a more relational approach emphasise contingency, including in relation to how drugs might constitute benefit or harm. This leads us to speculate on a ‘positive epidemiology’; one that is configured not merely in relation to harm but also in relation to the potential benefits of drugs in relation to well-being. In responding to the complex challenges of delineating contingent causalities, we emphasise the potential of carefully conducted observational study designs that go beyond statistical associations to test causal inference. We acknowledge that each of these developments we describe – a shift towards more relational approaches which emphasise contingent causation, and methodological innovations in relation to establishing causal inference – can be at odds with the other in how they imagine drug epidemiology futures.
By running gene and pathway analyses for several smoking behaviours in the Tobacco and Genetics Consortium (TAG) sample of 74 053 individuals, 21 genes and several chains of biological pathways were implicated. Analyses were carried out using the HYbrid Set-based Test (HYST) as implemented in the Knowledge-based mining system for Genome-wide Genetic studies software. Fifteen genes are novel and were not detected with the single nucleotide polymorphism-based approach in the original TAG analysis. For quantity smoked, 14 genes passed the false discovery rate of 0.05 (corrected for multiple testing), with the top association signal located at the IREB2 gene (P=1.57E-37). Three genomic loci were significantly associated with ever smoked. The top signal is located at the noncoding antisense RNA transcript BDNF-AS (P=6.25E-07) on 11p14. The SLC25A21 gene (P=2.09E-08) yielded the top association signal in the analysis of smoking cessation. The 19q13 noncoding RNA locus exceeded the genome-wide significance in the analysis of age at initiation (P=1.33E-06). Pathways belonging to the Neuronal system pathways, harbouring the nicotinic acetylcholine receptor genes expressing the α (CHRNA 1-9), β (CHRNB 1-4), γ, δ and ɛ subunits, yielded the smallest P-values in the pathway analysis of the quantity smoked (lowest P=4.90E-42). Additionally, pathways belonging to 'a subway map of cancer pathways' regulating the cell cycle, mitotic DNA replication, axon growth and synaptic plasticity were found significantly enriched for genetic variants in ever smokers relative to never smokers (lowest P=1.61E-07). In addition, these pathways were also significantly associated with the quantity smoked (lowest P=4.28E-17). Our results shed light on one of the world's leading causes of preventable death and open a path to potential therapeutic targets. These results are informative in decoding the biological bases of other disease traits, such as depression and cancers, with which smoking shares genetic vulnerabilities.Molecular Psychiatry advance online publication, 29 March 2016; doi:10.1038/mp.2016.20.
Smoking and caffeine consumption show a strong positive correlation, but the mechanism underlying this association is unclear. Explanations include shared genetic/environmental factors or causal effects. This study employed three methods to investigate the association between smoking and caffeine. First, bivariate genetic models were applied to data of 10 368 twins from the Netherlands Twin Register in order to estimate genetic and environmental correlations between smoking and caffeine use. Second, from the summary statistics of meta-analyses of genomewide association studies on smoking and caffeine, the genetic correlation was calculated by LD-score regression.
Third, causal effects were tested using Mendelian randomization analysis in 6605 Netherlands Twin Register participants and 5714 women from the Avon Longitudinal Study of Parents and Children. Through twin modelling, a genetic correlation of r0.47 and an environmental correlation of r0.30 were estimated between current smoking (yes/no) and coffee use (high/low). Between current smoking and total caffeine use, this was r0.44 and r0.00, respectively. LD-score regression also indicated sizeable genetic correlations between smoking and coffee use (r0.44 between smoking heaviness and cups of coffee per day, r0.28 between smoking initiation and coffee use and r0.25 between smoking persistence and coffee use). Consistent with the relatively high genetic correlations and
lower environmental correlations, Mendelian randomization provided no evidence for causal effects of smoking on caffeine or vice versa. Genetic factors thus explain most of the association between smoking and caffeine consumption. These findings suggest that quitting smoking may be more difficult for heavy caffeine consumers, given their genetic susceptibility.
Cannabis is the most widely produced and consumed illicit psychoactive substance worldwide. Occasional cannabis use can progress to frequent use, abuse and dependence with all known adverse physical, psychological and social consequences. Individual differences in cannabis initiation are heritable (40-48%). The International Cannabis Consortium was established with the aim to identify genetic risk variants of cannabis use. We conducted a meta-analysis of genome-wide association data of 13 cohorts (N=32 330) and four replication samples (N=5627). In addition, we performed a gene-based test of association, estimated single-nucleotide polymorphism (SNP)-based heritability and explored the genetic correlation between lifetime cannabis use and cigarette use using LD score regression. No individual SNPs reached genome-wide significance. Nonetheless, gene-based tests identified four genes significantly associated with lifetime cannabis use: NCAM1, CADM2, SCOC and KCNT2. Previous studies reported associations of NCAM1 with cigarette smoking and other substance use, and those of CADM2 with body mass index, processing speed and autism disorders, which are phenotypes previously reported to be associated with cannabis use. Furthermore, we showed that, combined across the genome, all common SNPs explained 13-20% (P<0.001) of the liability of lifetime cannabis use. Finally, there was a strong genetic correlation (rg=0.83; P=1.85 × 10(-8)) between lifetime cannabis use and lifetime cigarette smoking implying that the SNP effect sizes of the two traits are highly correlated. This is the largest meta-analysis of cannabis GWA studies to date, revealing important new insights into the genetic pathways of lifetime cannabis use. Future functional studies should explore the impact of the identified genes on the biological mechanisms of cannabis use.
Coffee, a major dietary source of caffeine, is among the most widely consumed beverages in the world and has received considerable attention regarding health risks and benefits. We conducted a genome-wide (GW) meta-analysis of predominately regular-type coffee consumption (cups per day) among up to 91 462 coffee consumers of European ancestry with top single-nucleotide polymorphisms (SNPs) followed-up in ~30 062 and 7964 coffee consumers of European and African-American ancestry, respectively. Studies from both stages were combined in a trans-ethnic meta-analysis. Confirmed loci were examined for putative functional and biological relevance. Eight loci, including six novel loci, met GW significance (log10Bayes factor (BF)>5.64) with per-allele effect sizes of 0.03-0.14 cups per day. Six are located in or near genes potentially involved in pharmacokinetics (ABCG2, AHR, POR and CYP1A2) and pharmacodynamics (BDNF and SLC6A4) of caffeine. Two map to GCKR and MLXIPL genes related to metabolic traits but lacking known roles in coffee consumption. Enhancer and promoter histone marks populate the regions of many confirmed loci and several potential regulatory SNPs are highly correlated with the lead SNP of each. SNP alleles near GCKR, MLXIPL, BDNF and CYP1A2 that were associated with higher coffee consumption have previously been associated with smoking initiation, higher adiposity and fasting insulin and glucose but lower blood pressure and favorable lipid, inflammatory and liver enzyme profiles (P<5 × 10(-8)).Our genetic findings among European and African-American adults reinforce the role of caffeine in mediating habitual coffee consumption and may point to molecular mechanisms underlying inter-individual variability in pharmacological and health effects of coffee.Molecular Psychiatry advance online publication, 7 October 2014; doi:10.1038/mp.2014.107.
A strong correlation exists between smoking and the use of alcohol and cannabis. This paper uses polygenic risk scores to explore the possibility of overlapping genetic factors. Those scores reflect a combined effect of selected risk alleles for smoking.
Summary-level p-values were available for smoking initiation, age at onset of smoking, cigarettes per day and smoking cessation from the Tobacco and Genetics Consortium (N between 22,000 and 70,000 subjects). Using different p-value thresholds (.1, .2 and .5) from the meta-analyses, sets of 'risk alleles' were defined and used to generate a polygenic risk score (weighted sum of the alleles) for each subject in an independent target sample from the Netherlands Twin Register (N=1583). The association between polygenic smoking scores and alcohol/cannabis use was investigated with regression analyses.
The polygenic scores for 'cigarettes per day' were significantly associated with, the number of glasses alcohol per week (p=.005, R(2) =.4-.5%) and cannabis initiation (p=.004, R(2) =0.6-.9%). The polygenic scores for 'age at onset of smoking' were significantly associated with 'age at regular drinking' (p=.001, R(2) =1.1-1.5%), while the scores for 'smoking initiation' and 'smoking cessation' did not significantly predict alcohol or cannabis use.
Smoking, alcohol and cannabis use are influenced by aggregated genetic risk factors shared between these substances. The many common genetic variants each have a very small individual effect size.
The specificity of genetic and environmental risk factors for illicit substance use and substance use disorders (SUD) was investigated by utilizing self and co-twin reports in 1,791 male twins. There was a high rate of comorbidity between both use of, and SUD from, different classes of illicit substances. For substance use, the model that included one common genetic, one shared environmental, and one individual-specific (i.e., unique) environmental factor, along with substance-specific effects that were attributed entirely to genetic factors fit the data best. For illicit SUD, one common genetic and one common unique environmental risk factor, and substance specific shared environmental and unique environmental risk factors were identified. Risk factors for illicit substance use and SUD are mainly non-specific to substance class. Co-twin rating of illicit substance use and SUD was a reliable source of information, and by taking account of random and systematic measurement error, environmental exposures unique to the individual were of lesser importance than found in earlier studies.
{Consistent but indirect evidence has implicated genetic factors in smoking behavior1,2. We report meta-analyses of several smoking phenotypes within cohorts of the Tobacco and Genetics Consortium (n = 74,053). We also partnered with the European Network of Genetic and Genomic Epidemiology (ENGAGE) and Oxford-GlaxoSmithKline (Ox-GSK) consortia to follow up the 15 most significant regions (n > 140,000). We identified three loci associated with number of cigarettes smoked per day. The strongest association was a synonymous 15q25 SNP in the nicotinic receptor gene CHRNA3 (rs1051730[A]
Alcohol consumption is a moderately heritable trait, but the genetic basis in humans is largely unknown, despite its clinical and societal importance. We report a genome-wide association study meta-analysis of approximately 2.5 million directly genotyped or imputed SNPs with alcohol consumption (gram per day per kilogram body weight) among 12 population-based samples of European ancestry, comprising 26,316 individuals, with replication genotyping in an additional 21,185 individuals. SNP rs6943555 in autism susceptibility candidate 2 gene (AUTS2) was associated with alcohol consumption at genome-wide significance (P = 4 x 10(-8) to P = 4 x 10(-9)). We found a genotype-specific expression of AUTS2 in 96 human prefrontal cortex samples (P = 0.026) and significant (P < 0.017) differences in expression of AUTS2 in whole-brain extracts of mice selected for differences in voluntary alcohol consumption. Down-regulation of an AUTS2 homolog caused reduced alcohol sensitivity in Drosophila (P < 0.001). Our finding of a regulator of alcohol consumption adds knowledge to our understanding of genetic mechanisms influencing alcohol drinking behavior
Alcohol consumption is a moderately heritable trait, but the genetic basis in humans is largely unknown, despite its clinical and societal importance. We report a genome-wide association study meta-analysis of ∼2.5 million directly genotyped or imputed SNPs with alcohol consumption (gram per day per kilogram body weight) among 12 population-based samples of European ancestry, comprising 26,316 individuals, with replication genotyping in an additional 21,185 individuals. SNP rs6943555 in autism susceptibility candidate 2 gene (AUTS2) was associated with alcohol consumption at genome-wide significance (P = 4 × 10(-8) to P = 4 × 10(-9)). We found a genotype-specific expression of AUTS2 in 96 human prefrontal cortex samples (P = 0.026) and significant (P < 0.017) differences in expression of AUTS2 in whole-brain extracts of mice selected for differences in voluntary alcohol consumption. Down-regulation of an AUTS2 homolog caused reduced alcohol sensitivity in Drosophila (P < 0.001). Our finding of a regulator of alcohol consumption adds knowledge to our understanding of genetic mechanisms influencing alcohol drinking behavior.
Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique-cross-trait LD Score regression-for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.
Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
Drug-related phenotypes are common complex and highly heritable traits. In the last few years, candidate gene (CGAS) and genome-wide association studies (GWAS) have identified a huge number of single nucleotide polymorphisms (SNPs) associated with drug use, abuse or dependence, mainly related to alcohol or nicotine. Nevertheless, few of these associations have been replicated in independent studies. The aim of this study was to provide a review of the SNPs that have been most significantly associated with alcohol-, nicotine-, cannabis- and cocaine-related phenotypes in humans between the years of 2000 and 2012. To this end, we selected CGAS, GWAS, family-based association and case-only studies published in peer-reviewed international scientific journals (using the PubMed/MEDLINE and Addiction GWAS Resource databases) in which a significant association was reported. A total of 371 studies fit the search criteria. We then filtered SNPs with at least one replication study and performed meta-analysis of the significance of the associations. SNPs in the alcohol metabolizing genes, in the cholinergic gene cluster CHRNA5-CHRNA3-CHRNB4, and in the DRD2 and ANNK1 genes, are, to date, the most replicated and significant gene variants associated with alcohol- and nicotine-related phenotypes. In the case of cannabis and cocaine, a far fewer number of studies and replications have been reported, indicating either a need for further investigation or that the genetics of cannabis/cocaine addiction are more elusive. This review brings a global state-of-the-art vision of the behavioral genetics of addiction and collaborates on formulation of new hypothesis to guide future work.
© 2015 Society for the Study of Addiction.
Polygenic risk scores for smoking: predictors for alcohol and cannabis use?
- J M Vink
- J J Hottenga
- E J De Geus
- G Willemsen
- M C Neale
- H Furberg
Vink JM, Hottenga JJ, de Geus EJ, Willemsen G, Neale MC, Furberg H et al.
Polygenic risk scores for smoking: predictors for alcohol and cannabis use?
Addiction 2014; 109: 1141-1151.
An atlas of genetic correlations across human diseases and traits
- B K Bulik-Sullivan
- H K Finucane
- V Anttila
- A Gusev
- F R Day
- Reprogen Consortium
Bulik-Sullivan BK, Finucane HK, Anttila V, Gusev A, Day FR, ReproGen
Consortiumet al. An atlas of genetic correlations across human diseases and traits.
Nat Genet 2015; 47: 1236-1241.