Original Article | Open | Published:

Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32 330 subjects from the International Cannabis Consortium

Translational Psychiatry volume 6, page e769 (2016) | Download Citation


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.


Cannabis is the most widely produced and consumed illicit psychoactive substance worldwide.1 Following initiation, occasional cannabis use can progress to frequent use, abuse and dependence. About 1 in 10 occasional users becomes dependent, which is associated with physical, psychological, social and occupational consequences.2, 3 Despite the increasing use of cannabis for medicinal purposes,4 associations with adverse health effects have been reported.5, 6 These include increased risk for psychiatric outcomes, including psychosis, schizophrenia, schizotypal personality disorder and mania.7, 8 Early cannabis use appears to moderate relationship between polygenic risk scores for schizophrenia and brain maturation.9 In view of expanding medicalization and decriminalization, the potential consequences, and the debate surrounding the benefits versus adverse consequences associated with cannabis use,10 understanding the genetics of cannabis use should be a public health priority.11

The risk of lifetime cannabis use, defined as any use of cannabis during the lifetime, varies between individuals. Previous studies have shown that individual differences in lifetime cannabis use can be partly explained by genetic differences between individuals; a meta-analysis of twin studies reported significant heritability estimates of lifetime cannabis use of 48% for males and 40% for females.12 Shared environmental factors, such as cannabis availability and parental monitoring,13, 14 also have a role accounting for 25 and 39% of the risk for males and females, respectively.12 Also, there is substantial overlap in the genetic risks underlying lifetime cannabis use and cannabis use disorder.15

Several studies have sought to identify specific genetic risk factors associated with cannabis use phenotypes. Genome-wide linkage studies have revealed suggestive evidence for linkage across many chromosomes.16, 17, 18, 19, 20 With very little consistency across studies, nearly all findings failed to meet genome-wide significance. The one study examining lifetime cannabis use16 reported a nonsignificant linkage locus on chromosome 18 (LOD score=1.97).

Candidate gene studies, including reports examining the CNR1, GABRA2, FAAH and ABCB1 genes have detected some significant associations with cannabis use but again, replication has been inconsistent.21, 22, 23 On the basis of a sample of 7452 Caucasian individuals, Verweij et al.21 found no gene-based associations between the frequency of cannabis use and 10 candidate genes identified by Agrawal and Lynskey.24 Overall, the results of candidate-gene studies are inconclusive; some associations have been replicated a few times, but failed to replicate in other studies. Moreover, findings may be further distorted due to publication bias favouring significant results.

As an alternative to the candidate-gene approach, the genome-wide association study (GWAS) is a hypothesis-free method that aims to detect novel genetic variants involved in complex traits. To date, three GWASs of cannabis use phenotypes have been published: one GWAS of cannabis dependence in 708 cannabis-dependent individuals and 2346 controls;25 a GWAS meta-analysis of lifetime cannabis use based on two studies with a combined sample size of 10 091 individuals (40.7% users);26 and a recent GWAS of lifetime cannabis use and age of cannabis use onset based on a sample of 6744 individuals (of whom 20% were users).27 None of the studies identified any genome-wide significant associations. This was likely due to the small effect sizes typical of common variants underpinning highly polygenic traits,28 thereby indicating a need for larger sample sizes. In this context, the success of larger GWASs and international consortia examining a variety of complex traits is encouraging.29 For example, multiple large meta-analyses of GWA results for number of cigarettes smoked per day have independently identified associations on chromosome 15q25 spanning the α5, α3 and β4 nicotinic receptor subunit gene clusters (CHRNA5, CHRNA3, CHRNB4).30, 31, 32

These and other recent GWA findings29 clearly illustrate the need for larger sample sizes. In response to this need, the International Cannabis Consortium was established to combine the results of multiple GWASs to identify the genetic variants underlying individual differences in cannabis use phenotypes. Our rationale for focusing on lifetime cannabis use (yes/no) is because this phenotype is heritable and shares significant genetic risks with that risk for cannabis abuse or dependence.14, 15, 33 In contrast to frequency of use or abuse and dependence, which are not commonly assessed in large-scaled genetic studies, most general population studies have assessed lifetime cannabis use, thereby increasing our sample size and power to detect associations. Currently, the combined International Cannabis Consortium sample size for lifetime cannabis is 32 330 individuals from 13 cohorts from Europe, the United States and Australia, along with four independent replication samples comprising 5627 individuals. This sample size is considerably larger than the sample size of the previous GWAS investigating lifetime cannabis use in two samples from Australia and the UK, thereby providing substantially greater power to detect genetic variants of small effect size.

The aim of the present study is to identify genetic variants associated with lifetime cannabis use by meta-analysis of the GWAS results from all contributing International Cannabis Consortium samples. The tests of association for individual genetic variants will be complemented with gene-based tests of association. In addition, we will investigate which proportion of the heritability inferred by twin studies is explained by common SNPs captured on GWAS arrays. Finally, we will estimate the genetic correlation between lifetime cannabis and smoking initiation based on the analysis of our summary statistics and those from the publicly available Tobacco Alcohol and Genetics consortium.

Materials and methods


We performed a meta-analysis of GWA results from 13 discovery samples from Europe, USA and Australia including a total of 32 330 individuals of European ancestry. The size of the samples ranged from 721 to 6778 individuals. The age of the participants ranged from 16 to 87 years with an average of 34 years. The percentage of females ranged from 30 to 66% with an average of 53%. Owing to the differences in recruitment strategies, cultural and temporal difference, combined with likely variation in the drug availability between countries, there was a wide range in the prevalence of lifetime use (that is, never/ever used cannabis), which varied from 1 to 92% with an average of 44.5%.

Four additional independent samples with a total of 5627 subjects were used for replication. One sample (n=2660) consisted of African American subjects. The other three included subjects of European ancestry. See Table 1 for individual sample characteristics. The procedures for data collection per sample are described in the Supplementary Information 1.

Table 1: Discovery and replication sample characteristics

Phenotype and covariates

For all individuals, the data were available on whether or not the subject reported having ever used cannabis during their lifetime: yes (1) versus no (0). Although phrasing of the question slightly differed between samples (see Supplementary Information 1), our unit of analysis reflected lifetime cannabis use in all the samples.

Covariates included age at the time of phenotypic assessment, sex, birth cohort and principal components (obtained from the genome-wide genotype data). Spanning 20-year intervals, birth cohort was dummy coded, with the lowest birth cohort (that is, oldest age group) used as the reference group. The details about phenotypic assessment and individual sample characteristics for the discovery and replication samples are located in Supplementary Information 1 and Supplementary Table 1.

Genotyping and imputation

Genotype imputation was based on the 1000 Genomes phase 1 reference panel.34 Allelic dosage data were used to account for genotype uncertainties. See Supplementary Table 2 for the genotyping platform, imputation program and quality control thresholds used.

Statistical analyses

GWA analysis in each discovery cohort

The GWA analyses were performed by each group separately. Associations between the binary phenotype and the genotypes were tested genome-wide using a logistic regression model including covariates (see above). For family-based samples, familial relatedness was taken into account by using a sandwich correction as implemented in PLINK.35 The analyses plan can be found in Supplementary Information 3. It should be noted that some groups did do the analyses in a slightly different manner based on the characteristics of their sample. The analyses plan that was send to the participating groups is included in Supplementary Information 3. It should be noted that some groups did do the analyses in a slightly different manner based on the characteristics of their sample. Supplementary Table 2 lists the program used by each group.

Meta-analysis of GWAS results

Before performing the meta-analysis, we applied a set of filters to each GWA results set independently. First, we removed insertions and deletions, ensuring that all base pair positions were unique and referred to the same genetic variant (that is, SNP). Second, we removed genotyped SNPs that were not in Hardy–Weinberg equilibrium (P10−5). Third, we removed SNPs with minor allele frequency (MAF) <√(5/N), which under the assumption of Hardy–Weinberg equilibrium corresponded to less than five estimated individuals in the least frequent genotype group. In the EGCUT1 sample, due to very low prevalence of lifetime cannabis use (1.3%), we excluded SNPs with MAF<0.2. Fourth, regardless of the quality score type used, we excluded SNPs with imputation quality scores below 0.6. Finally, SNPs present in only one sample and SNPs with alleles or allele frequencies inconsistent with the 1000 Genomes phase I European reference panel (absolute MAF difference >0.15) were removed.

We performed a fixed-effects meta-analysis based on the cohort’s effect sizes and standard errors using METAL.36 Our meta-analysis combined association summary statistics for 6 444 471 SNPs that passed all the filters. We applied the conventional threshold of 5 × 10−8 as an indication of genome-wide significance (see ref. 37). Although the combined sample size of the meta-analysis based on the discovery samples is 32 330, the sample size per SNP varies due to missingness across subsamples.

Gene-based test

Results of the GWAS were then used as part of gene-based tests of association in the Knowledge-based mining system for Genome-wide Genetic studies (KGG) software package Version 3.5.38, 39 This approach uses an extended Simes test that integrates prior functional information and the meta-analysis association results when combining the SNP P-values within a gene to obtain an overall association P-value for each entire gene. We conducted 24 576 gene-based tests of association. The genome-wide significance level according to the Knowledge-based mining system for Genome-wide Genetic studies default setting of Benjamini and Hochberg false discovery rate threshold of 0.05 (ref. 40) was 9.38 × 10−6.

Estimation of SNP-based heritability and genetic overlap with lifetime cigarette smoking

The proportion of phenotypic variance that could be explained by the SNPs was estimated using the density estimation method developed by So et al.41 The density estimation method estimates the genome-wide distribution of effect sizes based on the difference between the observed distribution of test statistics in the meta-analysis and the corresponding null distribution. Before estimation, the SNPs present in at least 25% of the meta-analysis samples were pruned for LD. We used the r2=0.15 pruning level as the primary result for consistency with other applications of this method. Additional details are located in the Supplementary Information 2. LD Score regression42, 43 was used as an alternative method to estimate the SNP-based heritability, as well as to estimate the degree of genetic covariance between lifetime cannabis use (present study) and lifetime cigarette smoking31 (see Supplementary Information 2).



No genome-wide significant associations between individual SNPs and lifetime cannabis use were observed (see Manhattan plot, Supplementary Figure 1a). However, the QQ plot (Supplementary Figure 1b) reveals strong enrichment of SNPs with P<10−4. Supplementary Figures 2a–m and 3a–m illustrate the Manhattan and QQ plots for each sample. Table 2 illustrates the top 10 independent (R2<0.1) SNPs associated with lifetime cannabis use. None of these 10 SNPs were replicated in the four independent replication samples (Supplementary Table 3). In a combined meta-analysis of the 10 top SNPs (that is, discovery plus replication samples), none of the SNPs reached genome-wide significance. Local plots of the most strongly associated regions, including neighboring genes, are provided in Supplementary Figures 4a–j. The most statistically significant marker (P-value =4.6 × 10−7) was rs4984460 located on chromosome 15 (see Supplementary Figure 5 for the forest plot). The SNP is located in an intergenic region between LOC400456/LOC145820 and NR2F2 and MIR1469 genes. Supplementary Table 4 includes the 153 SNPs identified with P-values <10−5. Because not all SNPs passed the post-imputation quality control steps in all the samples, this table includes the effective sample size per SNP.

Table 2: Top 10 SNPs with meta-analysis results of discovery samples, and results of combined discovery and replication samples

Gene-based tests

The gene-based tests of associations were run on 24 576 genes/genetic regions (see ‘Materials and Methods’ section for details). The Manhattan and QQ plot for this test are shown in Figures 1a and b. Results for the top 100 genes can be found in Supplementary Table 5. As shown in Table 3, four genes and one intergenic noncoding RNA region were significantly (false discovery rate-corrected P<0.05) associated with lifetime cannabis use: (i) neural cell adhesion molecule 1 (NCAM1, on 11q23); (ii) cell adhesion molecule 2 (CADM2, on 3p12); (iii) short coiled-coil protein (SCOC) and (iv) SCOC antisense RNA1 (SCOC-AS1, both located on 4q31); and (v) potassium channel, subfamily T, member 2 (KCNT2, on 1q31). Regional plots44 of these top genes are located in Supplementary Figure 6.

Figure 1
Figure 1

The Manhattan (a) and the QQ plot (b) based on results of the gene-based analysis performed in the discovery sample using HYST (hybrid set-based test).

Table 3: Top five genes from the gene-based tests of association with corrected P-values (Benjamini and Hochberg) based on the meta-analytic discovery and replication samples

The smallest gene-based P-value was found for the NCAM1 gene. Within this gene, rs4471463 had the lowest SNP P-value, and was also among the top 10 associated SNPs. The forest plot in Figure 2 illustrates the effect of this SNP in each sample. In most samples, the effect is in the same direction, such that the major (T) allele is associated with a decreased risk of lifetime cannabis use. The forest plot for two SNPs with lowest P-values in the other significant gene regions can be found in Supplementary Figure 5.

Figure 2
Figure 2

Forest plot for the top-SNP rs4471463 in the NCAM1 gene on chromosome 11. SNP, single-nucleotide polymorphism.

Of the five genes included in our replication analyses, none were replicated in two of the independent replication samples (see Table 3). In the African American replication sample, suggestive associations with SCOC-AS1 (P=0.044) and SCOC (P=0.027) were found.

SNP-based heritability and genetic overlap with lifetime cigarette smoking

Using the density estimation method (see ‘Materials and Methods’ section for a description), all the SNPs available in at least 25% of the samples when combined explained 20% of the total variance in lifetime cannabis use (P<0.001). Alternative estimation with LD score regression also yielded a significant heritable component of 13% (h2LD=0.13, s.e.=0.02, P=1.4 × 10−7). These variance estimates were robust across pruned sets with similar r2 thresholds (see Supplementary Table 6). Stricter LD pruning (that is, r2=0.05), or restricting analyses to SNPs present in all studies substantially decreased the estimate of variance explained. Both SNP heritability estimates confirmed that lifetime cannabis use has a significant heritable component (13–20%), indicating that GWAS should be able to identify these common SNPs (but effect sizes are small and large sample sizes are thus required). However, because these estimates are only based on common SNPs, the total heritability of lifetime cannabis use is likely to be higher.

The LD score regression analyses revealed a strong and highly significant genetic correlation (rg=0.83, s.e.=0.15, P=1.85 × 10−8) between lifetime cannabis use and lifetime cigarette smoking (based on the Tobacco Alcohol and Genetics consortium31 summary results), implying that SNPs for lifetime cannabis use and lifetime cigarette smoking are highly correlated.


To date, this is the largest GWA study of lifetime cannabis use. We performed meta-analysis of the GWA results based on a discovery sample comprising 32 330 individuals from 13 cohorts, and a replication sample comprising 5627 subjects from four cohorts (including one African American cohort). There were no genome-wide significant SNP associations. However, heritability analyses revealed that between 13 and 20% of the variation in lifetime cannabis use could be explained by common SNPs. Moreover, gene-based tests of association identified four protein-coding genes and one intergenic region significantly associated with lifetime cannabis use including NCAM1, which has previously been linked to substance use.45, 46, 47, 48 Finally, we revealed that the genetic liability to lifetime cannabis use correlated to a large extent (r=0.83) with the genetic liability to lifetime cigarette smoking. Our results are consistent with the hypothesis that lifetime cannabis use is a highly polygenic trait, comprising many SNPs each with small effects contributing to lifetime risk. Moreover, portions of the genetic risk in lifetime cannabis use likely correlates with other substances including cigarette smoking.

Our top gene associated with lifetime cannabis use was NCAM1, a known candidate for nicotine dependence.45 The role of NCAM1 is to regulate pituitary growth hormone secretion as a membrane-bound glycoprotein that mediates cell–cell contact by hemophilic interactions.46 NCAM1 is part of the NCAM1TTC12ANKK1DRD2 (NTAD) gene cluster, which is related to neurogenesis and dopaminergic neurotransmission. Importantly, the NTAD cluster has been associated with smoking behavior and nicotine dependence,45, 47, 48, 49, 50, 51, 52 alcohol dependence,53, 54 heroin dependence,55 as well as other substance use disorders.54 Although it is plausible that NCAM1 is capturing pleiotropic risks underpinning the liability to licit and illicit substance use in general, we note that NCAM1 was not identified either by the Tobacco Alcohol and Genetics consortium or other consortia for cigarette smoking.30, 31, 32 The functions of the putative variants responsible for the associations in the candidate-gene studies remain to be determined.

The second gene, CADM2, is a synaptic cell adhesion molecule (SynCAM family) belonging to the immunoglobulin (Ig) superfamily. Variants in the CADM2 gene have been previously associated with body mass index,56 processing speed57 and autism disorders.58 Interestingly, these phenotypes were associated with cannabis use in previous studies,59, 60, 61 which together suggest that CADM2 can be considered an important gene related to a variety of complex traits. It is possible that the association with lifetime cannabis use may be driven, for example, by differences in personality rather than as a direct relationship with lifetime use.

The third gene, SCOC, encodes a short coiled-coil domain-containing protein that localizes to the Golgi apparatus. Many coiled-coil-type proteins are involved in important biological functions such as the regulation of gene expression through the regulation of transcription factor binding.62 The function of SCOC is largely unknown and no previous association studies have linked SCOC to cannabis or other substance use phenotypes. The SCOC antisense RNA1 gene is located in the same chromosomal region.

Finally, KCNT2 encodes a potassium voltage-gated channel (subfamily S, member 2). The sodium-activated potassium channels Slack and Slick are encoded by KCNT1 (potassium channel, subfamily T, member 1) and KCNT2, respectively, which are found in neurons throughout the brain. Suggestive association for SNPs near KCNT2 have previously been found for cocaine dependence and for early-onset, highly comorbid, heavy opioid use.63, 64 This suggests that potassium signaling may have a role in addiction.

The lack of genome-wide significant associations for individual SNPs is consistent with previous GWA studies of lifetime cannabis use26, 27 and cannabis dependence.25 The difficulty of identifying specific SNPs for lifetime cannabis use may be attributable to several reasons. First, complex traits are known to be influenced by many variants, each with very small effect sizes. Although power calculations reveals suitable power (96%) to detect odds ratios of 1.15 based on common SNPs (MAF=0.2), the power to detect smaller effect sizes remains lower. For example, there is only 28% power to detect effect sizes with odds ratio of 1.1 and MAF=0.2. Therefore, our data suggest that the effect sizes of single variants contributing to lifetime cannabis use are likely to be smaller than 1.15. Combining variants within larger units (that is, genes) did however reveal four significant genes associated with lifetime cannabis use implying that these genes are appropriate targets for future functional studies of cannabis use. Unfortunately, our gene-based results were not replicated in the replication samples, probably due to low sample sizes and therefore low power. In the African American replication sample, we did find suggestive association with SCOC-AS1 and SCOC.

On the basis of twin studies, the heritability of lifetime cannabis use is estimated at 40–50%.12 In our study, all common SNPs combined explained 13–20% of the variance in the liability to use cannabis depending on the method used. Stricter LD pruning (that is, r2=0.05) or restricting to SNPs observed (genotyped or imputed) in all the analyses, substantially reduces the estimate of variance explained. Speculatively, this may indicate that much of the variance explained comes from SNPs located in the regions of weak LD. Such effects are likely to be poorly tagged for the estimation of variance explained after strict LD pruning, and are likely to be more difficult to impute owing to a lack of strongly correlated genotyped SNPs (and thus missing from some studies). Our SNP-based heritability estimates lie in between two previous heritability estimates for lifetime cannabis use based on the Genome-wide Complex Trait Analysis65 software package. Verweij et al.26 estimated that 6% of the variance in lifetime cannabis use is explained by aggregated common SNPs (MAF>0.05). Minică et al.27 found an estimate of 25%. Provided that the current sample is much larger than the samples used in the previous studies, we conclude that approximately one-third to half of the heritability is explained by common SNPs captured on a GWAS array. Other sources of variation may explain the discrepancy between SNP- and twin-based heritability estimates. For example, age-related genetic differences, non-additive genetic variance, interactions between genetic variants and environmental risk factors, epistasis and/or rare mutations may also have a role.

Our results indicate a very high genetic overlap (r=0.83) between our measure of lifetime cannabis use and lifetime cigarette use when based on the SNP panel. Twin studies have shown moderate to high genetic correlations of 0.59–0.74 between lifetime cannabis and nicotine use.66 Kendler et al.67 also reported significant biometrical genetic correlations between the levels of cannabis, nicotine and alcohol use, which were increasingly influenced by common genetic risks detectable in early adulthood.

Our findings should be interpreted in the context of at least four potential limitations. First, our study was underpowered to detect very small effects of individual variants. Power analyses revealed that a twofold increase in sample size is required to detect SNP effect sizes with odds ratios of 1.1. Second, lifetime cannabis use is a dichotomous measure combining single lifetime, regular and chronic users. Consequently, our sample may compromise heterogeneous patterns of use, which has the potential to reduce the power to detect genetic association.68 Third, prevalences of lifetime cannabis use varied between 1% (EGCUT1) and 92% (Yale Penn EA). This was likely due to differences in the sample characteristics, recruitment strategies and the political differences between countries. Despite these differences, the forest plots of the key SNPs (see Figure 2; see also Supplementary Figure 5) revealed that the 95% confidence intervals surrounding the effect estimates typically included the estimated meta-analytic effect, which tends to overlap across studies. This indicates that the input samples were representative of the same population of users. Finally, the average age of participants varied between 18 (ALSPAC) and 45 (QIMR) years. Consequently, some younger participants might have initiated cannabis use at a later age, but have been classified as ‘never users’ in the current study. This can decrease power, but does not invalidate our results. In addition, we note that the average age of each sample did not correlate with sample prevalences (r=−0.04, P=0.91).

On the basis of our observations, the following recommendations for future studies can be made. We have identified four genes significantly associated with cannabis use, which are candidates for follow-up functional studies. In particular, the role of NCAM1 can be examined to determine the functional role of this gene, possibly in combination with other genes in the same gene cluster (NCAM1TTC12ANKK1DRD2).

The next goal of the International Cannabis Consortium is to perform a meta-analysis on GWA studies investigating the age at first cannabis use. Our rationale is based on the observation that early initiation of cannabis use is associated with rapid progression towards cannabis abuse and dependence, polysubstance use and other substance use disorders.69, 70, 71 Methods other than GWASs may also be used to reveal the biological pathways of cannabis use, such as rare variant association analyses. The environmental risk factors may be incorporated to investigate gene × environment interactions. Hopefully, the combination of advanced technologies and novel statistical approaches with larger samples will further contribute to our understanding of the genetic architecture of cannabis use.


We have performed the largest meta-analysis to date of GWASs investigating cannabis use phenotypes. With a sample of over 32 000 individuals, our results implicate four genes as involved in lifetime cannabis use: NCAM1, CADM2, SCOC and KCNT2. Our results illustrated that lifetime cannabis use is under the influence of many common genetic variants. The combined SNPs explained 13–20% of the phenotypic variation, and revealed a high degree of genetic sharing (r=0.83) with lifetime cigarette smoking. Future studies should investigate the impact of these genes on the biological mechanisms leading to lifetime cannabis use.


  1. 1.

    United Nations Office on Drugs and Crime. 2010 World Drug Report, New York, USA, 2010.

  2. 2.

    , . Adverse effects of cannabis. Lancet 1998; 352: 1611–1616.

  3. 3.

    , . Cannabis use and public health: assessing the burden. Addiction 2000; 95: 485–490.

  4. 4.

    , , , , , . Medicinal use of cannabis in the United States: historical perspectives, current trends, and future directions. J Opioid Manag 2009; 5: 153–168.

  5. 5.

    . What has research over the past two decades revealed about the adverse health effects of recreational cannabis use? Addiction 2014; 110: 19–35.

  6. 6.

    , , . Adverse health effects of marijuana use. N Engl J Med 2014; 371: 879.

  7. 7.

    , , , , , . Cannabis use and mania symptoms: a systematic review and meta-analysis. J Affect Disord 2014; 171c: 39–47.

  8. 8.

    , , . Gone to pot: a review of the association between cannabis and psychosis. Front Psychiatry 2014; 5: 54.

  9. 9.

    , , , , , et al. Early cannabis use, polygenic risk score for schizophrenia and brain maturation in adolescence. JAMA Psychiatry 2015; 72: 1002–1011.

  10. 10.

    , , , , , et al. Cannabinoids for medical use: a systematic review and meta-analysis. JAMA 2015; 313: 2456–2473.

  11. 11.

    . The cannabis experiment. Nature 2015; 524: 280–283.

  12. 12.

    , , , , , et al. Genetic and environmental influences on cannabis use initiation and problematic use: a meta-analysis of twin studies. Addiction 2010; 105: 417–430.

  13. 13.

    , , , , . Two-part random effects growth modeling to identify risks associated with alcohol and cannabis initiation, initial average use and changes in drug consumption in a sample of adult, male twins. Drug Alcohol Depend 2012; 123: 220–228.

  14. 14.

    , , . Pathways to cannabis abuse: a multi-stage model from cannabis availability, cannabis initiation and progression to abuse. Addiction 2009; 104: 430–438.

  15. 15.

    , , , , . Illicit drug use and abuse/dependence: modeling of two-stage variables using the CCC approach. Addict Behav 2005; 30: 1043–1048.

  16. 16.

    , , , , , et al. Autosomal linkage analysis for cannabis use behaviors in Australian adults. Drug Alcohol Depend 2008; 98: 185–190.

  17. 17.

    , , , , , et al. An autosomal linkage scan for cannabis use disorders in the nicotine addiction genetics project. Arch Gen Psychiatry 2008; 65: 713–722.

  18. 18.

    , , , . Heritability and a genome-wide linkage analysis of a Type II/B cluster construct for cannabis dependence in an American Indian community. Addict Biol 2009; 14: 338–348.

  19. 19.

    , , , . Linkage analyses of cannabis dependence, craving, and withdrawal in the San Francisco Family Study. Am J Med Genet B Neuropsychiatr Genet 2010; 153B: 802–811.

  20. 20.

    , , , , , et al. A genome-wide scan for loci influencing adolescent cannabis dependence symptoms: evidence for linkage on chromosomes 3 and 9. Drug Alcohol Depend 2007; 89: 34–41.

  21. 21.

    , , , , , et al. No association of candidate genes with cannabis use in a large sample of Australian twin families. Addict Biol 2012; 17: 687–690.

  22. 22.

    , , , , . Marijuana withdrawal and craving: influence of the cannabinoid receptor 1 (CNR1) and fatty acid amide hydrolase (FAAH) genes. Addiction 2008; 103: 1678–1686.

  23. 23.

    , , , , , et al. The role of GABRA2 in alcohol dependence, smoking, and illicit drug use in an Australian population sample. Alcohol Clin Exp Res 2008; 32: 1721–1731.

  24. 24.

    , . Candidate genes for cannabis use disorders: findings, challenges and directions. Addiction 2009; 104: 518–532.

  25. 25.

    , , , , , et al. A genome-wide association study of DSM-IV cannabis dependence. Addict Biol 2011; 16: 514–518.

  26. 26.

    , , , , , et al. The genetic aetiology of cannabis use initiation: a meta-analysis of genome-wide association studies and a SNP-based heritability estimation. Addict Biol 2013; 18: 846–850.

  27. 27.

    , , , , , et al. Heritability, SNP-and gene-based analyses of cannabis use initiation and age at onset. Behav Genet 2015; 45: 503–513, 1-11.

  28. 28.

    , , , , , et al. Finding the missing heritability of complex diseases. Nature 2009; 461: 747–753.

  29. 29.

    , , . Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet 2012; 13: 537–551.

  30. 30.

    , , , , , et al. Sequence variants at CHRNB3-CHRNA6 and CYP2A6 affect smoking behavior. Nat Genet 2010; 42: 448–U135.

  31. 31.

    , , , , , et al. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat Genet 2010; 42: 441–U134.

  32. 32.

    , , , , , et al. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat Genet 2010; 42: 366–368.

  33. 33.

    , , , , . Genetic and environmental risk factors in the aetiology of illicit drug initiation and subsequent misuse in women. Br J Psychiatry 1999; 175: 351–356.

  34. 34.

    , , , , , et al. An integrated map of genetic variation from 1092 human genomes. Nature 2012; 491: 56–65.

  35. 35.

    , , , , , et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559–575.

  36. 36.

    , , . METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 2010; 26: 2190–2191.

  37. 37.

    , . Statistical power and significance testing in large-scale genetic studies. Nat Rev Genet 2014; 15: 335–346.

  38. 38.

    , , , . GATES: a rapid and powerful gene-based association test using extended Simes procedure. Am J Hum Genet 2011; 88: 283–293.

  39. 39.

    , , . HYST: a hybrid set-based test for genome-wide association studies, with application to protein-protein interaction-based association analysis. Am J Hum Genet 2012; 91: 478–488.

  40. 40.

    , . Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 1995; 57: 289–300.

  41. 41.

    , , . Uncovering the total heritability explained by all true susceptibility variants in a genome-wide association study. Genet Epidemiol 2011; 35: 447–456.

  42. 42.

    , , , , . LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 2015; 47: 291–295.

  43. 43.

    , , , , , et al. An atlas of genetic correlations across human diseases and traits. Nat Genet 2015; 47: 1236–1241.

  44. 44.

    . Conducting meta-analyses in R with the metafor package. J Stat Softw 2010; 36: 1–48.

  45. 45.

    , , , , , et al. Haplotype spanning TTC12 and ANKK1, flanked by the DRD2 and NCAM1 loci, is strongly associated to nicotine dependence in two distinct American populations. Hum Mol Genet 2006; 15: 3498–3507.

  46. 46.

    , , , , , et al. The cell adhesion molecules N-cadherin and neural cell adhesion molecule regulate human growth hormone: a novel mechanism for regulating pituitary hormone secretion. J Clin Endocrinol Metab 2003; 88: 3724–3730.

  47. 47.

    , , , , , et al. TTC12-ANKK1-DRD2 and CHRNA5-CHRNA3-CHRNB4 influence different pathways leading to smoking behavior from adolescence to mid-adulthood. Biol Psychiatry 2011; 69: 650–660.

  48. 48.

    , , , , , . NCAM1-TTC12-ANKK1-DRD2 variants and smoking motives as intermediate phenotypes for nicotine dependence. Psychopharmacology 2015; 232: 1177–1186.

  49. 49.

    , , , , , et al. Genomewide linkage scan for nicotine dependence: identification of a chromosome 5 risk locus. Biol Psychiatry 2007; 61: 119–126.

  50. 50.

    , , , , , et al. Genetic variation in dopamine pathways differentially associated with smoking progression in adolescence. J Am Acad Child Adolesc Psychiatry 2008; 47: 673–681.

  51. 51.

    , , , , , et al. Cholinergic nicotinic receptor genes implicated in a nicotine dependence association study targeting 348 candidate genes with 3713 SNPs. Hum Mol Genet 2007; 16: 36–49.

  52. 52.

    , , , , . The genetic basis for smoking behavior: a systematic review and meta-analysis. Nicotine Tob Res 2004; 6: 583–597.

  53. 53.

    , , , , , . Association of haplotypic variants in DRD2, ANKK1, TTC12 and NCAM1 to alcohol dependence in independent case-control and family samples. Hum Mol Genet 2007; 16: 2844–2853.

  54. 54.

    , , , , , . Haplotypic variants in DRD2, ANKK1, TTC12, and NCAM1 are associated with comorbid alcohol and drug dependence. Alcohol Clin Exp Res 2008; 32: 2117–2127.

  55. 55.

    , , , , , et al. ANKK1, TTC12, and NCAM1 polymorphisms and heroin dependence: importance of considering drug exposure. JAMA Psychiatry 2013; 70: 325–333.

  56. 56.

    , , , , , et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 2010; 42: 937–948.

  57. 57.

    , , , , , et al. GWAS for executive function and processing speed suggests involvement of the CADM2 gene. Mol Psychiatry 2015; 21: 189–197.

  58. 58.

    , , , , , et al. A novel approach of homozygous haplotype sharing identifies candidate genes in autism spectrum disorder. Hum Genet 2012; 131: 565–579.

  59. 59.

    , , , , , . Cannabis use and obesity and young adults. Am J Drug Alcohol Abuse 2010; 36: 350–356.

  60. 60.

    , , , . The effects of cannabis on information-processing speed. Addict Behav 2004; 29: 1213–1219.

  61. 61.

    , , , , , et al. ADHD symptoms, autistic traits, and substance use and misuse in adult Australian twins. J Stud Alcohol Drugs 2014; 75: 211–221.

  62. 62.

    , . Coiled coil domains: stability, specificity, and biological implications. Chembiochem 2004; 5: 170–176.

  63. 63.

    , , , , , et al. Genome-wide association study of opioid dependence: multiple associations mapped to calcium and potassium pathways. Biol Psychiatry 2014; 76: 66–74.

  64. 64.

    , , , , , et al. Genome-wide association study of cocaine dependence and related traits: FAM53B identified as a risk gene. Mol Psychiatry 2014; 19: 717–723.

  65. 65.

    , , , . GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 2011; 88: 76–82.

  66. 66.

    , , , , . Mechanisms underlying the lifetime co-occurrence of tobacco and cannabis use in adolescent and young adult twins. Drug Alcohol Depend 2010; 108: 49–55.

  67. 67.

    , , , . Genetic and environmental influences on alcohol, caffeine, cannabis, and nicotine use from early adolescence to middle adulthood. Arch Gen Psychiatry 2008; 65: 674–682.

  68. 68.

    , , , , , . The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases. PLoS One 2013; 8: e76295.

  69. 69.

    , , , , , . A cotwin-control analysis of drug use and abuse/dependence risk associated with early-onset cannabis use. Addict Behav 2010; 35: 35–41.

  70. 70.

    , , , , , et al. Early cannabis use and DSM-IV nicotine dependence: a twin study. Addiction 2008; 103: 1896–1904.

  71. 71.

    , , . Early onset cannabis use and progression to other drug use in a sample of Dutch twins. Behav Genet 2006; 36: 195–200.

Download references


JMV, CCM and HM are supported by the European Research Council (Beyond the Genetics of Addiction ERC-284167, PI JMV). SS and EMD are supported by the Foundation Volksbond Rotterdam. KJHV is supported in part by the Netherlands Organization for Health Research and Development (ZonMW 31160212) and in part by a 2014 NARSAD Young Investigator Grant from the Brain and Behavior Research Foundation. NAG is supported by US National Institutes of Health, National Institute on Drug Abuse R00DA023549. GHL is supported by NIDA R37 DA-018673 and NSF BCS-1229450. RW is supported by NIH U01 MH094432 and NSF BCS-1229450. Statistical analyses were carried out on the Genetic Cluster Computer (http://www.geneticcluster.org) hosted by SURFsara and financially supported by the Netherlands Organization for Scientific Research (NWO 480-05-003 PI: Posthuma) along with a supplement from the Dutch Brain Foundation and the VU University Amsterdam. The study site acknowledgments are as follows: ALSPAC—We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The UK Medical Research Council and the Wellcome Trust (Grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. GWAS data were generated by Sample Logistics and Genotyping Facilities at the Wellcome Trust Sanger Institute and LabCorp (Laboratory Corportation of America) using support from 23andMe. AIS is supported by a Wellcome Trust 4-year PhD studentship in molecular, genetic and lifecourse epidemiology (WT083431MA). JJW is supported by a Postdoctoral Research Fellowship from the Oak Foundation. JJW and MRM are members of the MRC Integrative Epidemiology Unit at the University of Bristol, funded by the UK Medical Research Council (MC_UU_12013/6) and the University of Bristol. JJW and MRM are also members of the UK Centre for Tobacco and Alcohol Studies, a UKCRC Public Health Research: Centre of Excellence. Funding from British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council and the National Institute for Health Research, under the auspices of the UK Clinical Research Collaboration is gratefully acknowledged. BLTS—The BLTS was supported by grants from the United States National Institute on Drug Abuse (R00DA023549) awarded to NAG, by the Australian Research Council to MJW (Nos. DP0343921, DP0664638 and DP1093900) and by Australian National Health and Medical Research Council Australia Fellowships awarded to Ian Hickie (No. 464914) and GWM (No. 619667). We acknowledge and thank the following project staff: AKH, Leanne Wallace and Lisa Bowdler for the laboratory processing, genotyping and QC; Soad Hancock as Project Coordinator; Lenore Sullivan as Research Editor; our research interviewers Pieta-Marie Shertock and Jill Wood; and David Smyth for IT. We also thank the twins and their siblings for their willing cooperation. SEM was supported by an ARC future fellowship FT110100548. CADD—The Center on Antisocial Drug Dependence (CADD) data reported here were funded by grants from the National Institute on Drug Abuse (P60 DA011015, R01 DA012845, R01 DA021913, R01 DA021905, R01 DA035804). EGCUT—received financing by FP7 grants (278913, 306031, 313010), Center of Excellence in Genomics (EXCEGEN) and University of Tartu (SP1GVARENG). We acknowledge EGCUT technical personnel, especially Mr V Soo and S Smit. Data analyses were carried out in part in the High Performance Computing Center of University of Tartu. FinnTwin—We warmly thank the participating twin pairs and their family members for their contribution. We express our appreciation to the skilled study interviewers A-M Livonen, K Karhu, H-M Kuha, U Kulmala-Gråhn, M Mantere, K Saanakorpi, M Saarinen, R Sipilä, L Viljanen and E Voipio. Anja Häppölä and Kauko Heikkilä are acknowledged for their valuable contribution in recruitment, data collection and data management. Phenotyping and genotyping of the Finnish twin cohorts has been supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (grants 213506, 129680), the Academy of Finland (grants 100499, 205585, 118555, 141054, 265240, 263278 and 264146 to JK), National Institute of Alcohol Abuse and Alcoholism (grants AA-12502, AA-00145, and AA-09203 to RJR and AA15416 and K02AA018755 to DM Dick), Sigrid Juselius Foundation (to JK), and the Welcome Trust Sanger Institute, UK. Antti-Pekka Sarin and Samuli Ripatti are acknowledged for genotype data quality controls and imputation. GWAS analyses were run at the ELIXIR Finland node hosted at CSC—IT Center for Science for ICT resources. HUVH—Financial support was received from ‘Instituto de Salud Carlos III-FIS’ (PI11/00571, PI11/01629, PI12/01139), ‘Plan Nacional Sobre Drogas’ (PNSD#2011-0080), ‘Agència de Gestió d’Ajuts Universitaris i de Recerca-AGAUR, Generalitat de Catalunya ‘ (2014SGR1357) and ‘Departament de Salut’, Government of Catalonia, Spain. MR is a recipient of a Miguel de Servet contract from the ‘Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación’, Spain. MCTFR—This research was supported in part by USPHS Grants from the National Institute on Alcohol Abuse and Alcoholism (AA09367 and AA11886), the National Institute on Drug Abuse (DA05147, DA13240 and DA024417) and the National Institute on Mental Health (MH066140). NTR—We thank the Netherlands Twin Register participants whose data we analyzed in this study. This work was supported by grants from the Netherlands Organization for Scientific Research (ZonMW Addiction 31160008; ZonMW 940-37-024; NWO/SPI 56-464-14192; NWO-400-05-717; NWO-MW 904-61-19; NWO-MagW 480-04-004; NWO-Veni 016-115-035), the European Research Council (Beyond the Genetics of Addiction ERC-284167; Genetics of Mental Illness: ERC-230374), the Centre for Medical Systems Biology (NWO Genomics), Netherlands Bioinformatics Center/BioAssist/RK/2008.024. We acknowledge the EMGO+ Institute for Health and Care Research, the Neuroscience Campus Amsterdam, BBMRI-NL (184.021.007: Biobanking and Biomolecular Resources Research Infrastructure), the Avera Institute, Sioux Falls, South Dakota (USA) for support. Genotyping was funded in part by grants from the National Institutes of Health (4R37DA018673-06, RC2 MH089951), Rutgers University Cell and DNA Repository cooperative agreement (National Institute of Mental Health U24 MH068457-06), and the National Institutes of Health (NIH R01 HD042157-01A1, MH081802, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995) and the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health. The statistical analyses were carried out on the Genetic Cluster Computer (http://www.geneticcluster.org) which is supported by the Netherlands Scientific Organization (NWO 480-05-003), the Dutch Brain Foundation and the Department of Psychology and Education of the VU University Amsterdam. QIMR—This is supported by National Institutes of Health Grants AA07535, AA0758O, AA07728, AA10249, AA13320, AA13321, AA14041, AA11998, AA17688, DA012854, DA018267, DA018660, DA23668 and DA019951; by Grants from the Australian National Health and Medical Research Council (241944, 339462, 389927, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 552485, 552498 and 628911); by Grants from the Australian Research Council (A7960034, A79906588, A79801419, DP0770096, DP0212016 and DP0343921); and by the 5th Framework Programme (FP-5) GenomEUtwin Project (QLG2-CT-2002-01254). This research was further supported by the Centre for Research Excellence on Suicide Prevention (CRESP—Australia). We thank AKH, Richard Parker, Soad Hancock, Judith Moir, Sally Rodda, Pieta-Maree Shertock, Heather Park, Jill Wood, Pam Barton, Fran Husband, Adele Somerville, Ann Eldridge, Marlene Grace, Kerrie McAloney, Lisa Bowdler, Alexandre Todorov, Steven Crooks, David Smyth, Harry Beeby and Daniel Park. Last, we thank the twins and their families for their participation. RADAR—We thank all the adolescents and their families and friends for their participation. Moreover, we thank the various assistants that helped in recruiting participants as well as collecting and cleaning the data. The research was funded partly by the Netherlands Organisation for Scientific Research (Brain and Cognition, 056-21-010). Data of the RADAR study were used. RADAR has been financially supported by main grants from the Netherlands Organisation for Scientific Research (GB-MAGW 480-03-005), and Stichting Achmea Slachtoffer en Samenleving (SASS) and various other grants from the Netherlands Organisation for Scientific Research, the VU University Amsterdam and Utrecht University. ACH is supported by the Netherlands Organization for Health Research and Development, ZonMW 31160212. Saguenay Youth Study—The Canadian Institutes of Health Research and the Heart and Stroke Foundation of Canada fund the SYS (TP, ZP). TP is the Tanenbaum Chair in Population Neuroscience (University of Toronto) and the Dr John and Consuela Phelan Scholar (Child Mind Institute). TRAILS—TRAILS (TRacking Adolescents’ Individual Lives Survey) is a collaborative project involving various departments of the University Medical Center and University of Groningen, the Erasmus University Medical Center Rotterdam, the University of Utrecht, the Radboud Medical Center Nijmegen and the Parnassia Bavo group, all in the Netherlands. TRAILS has been financially supported by grants from the Netherlands Organization for Scientific Research NWO (Medical Research Council program grant GB-MW 940-38-011; ZonMW Brainpower grant 100-001-004; ZonMw Risk Behavior and Dependence grant 60-60600-97-118; ZonMw Culture and Health grant 261-98-710; Social Sciences Council medium-sized investment grants GB-MaGW 480-01-006 and GB-MaGW 480-07-001; Social Sciences Council project grants GB-MaGW 452-04-314 and GB-MaGW 452-06-004; NWO large-sized investment grant 175.010.2003.005; NWO Longitudinal Survey and Panel Funding 481-08-013); the Dutch Ministry of Justice (WODC), the European Science Foundation (EuroSTRESS project FP-006), Biobanking and Biomolecular Resources Research Infrastructure BBMRI-NL (CP 32), the participating universities and Accare Center for Child and Adolescent Psychiatry. We are grateful to all the adolescents, their parents and teachers who participated in this research and to everyone who worked on this project and made it possible. Statistical analyses were carried out on the Genetic Cluster Computer (http://www.geneticcluster.org), which is financially supported by the Netherlands Scientific Organization (NWO 480-05-003) along with a supplement from the Dutch Brain Foundation. TwinsUK—The study was funded by the Wellcome Trust; European Community’s Seventh Framework Programme (FP7/2007-2013). The study also receives support from the National Institute for Health Research (NIHR)- funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. SNP Genotyping was performed by The Wellcome Trust Sanger Institute and National Eye Institute via NIH/C. Utrecht—We are grateful to Chris Schubart and Willemijn van Gastel and numerous students for their work in the study. Foremost, we thank our study participants. This study was financially supported by a grant of the NWO (Netherlands Organization for Scientific Research), grant no. 91207039. The study was performed at the University Medical Centre Utrecht, The Netherlands. Yale Penn—Genotyping services for a part of our GWAS study were provided by the Center for Inherited Disease Research (CIDR) and Yale University (Center for Genome Analysis). CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University (contract number N01-HG-65403). This study was supported by the National Institutes of Health grants RC2 DA028909, R01 DA12690, R01 DA12849, R01 DA18432, R01 AA11330, R01 AA017535, and the VA Connecticut and Philadelphia VA MIRECCs.

Author information

Author notes

    • S Stringer
    • , C C Minică
    •  & K J H Verweij

    Shared first author.

    • N A Gillespie
    • , E M Derks
    •  & J M Vink

    Shared last author.


  1. Department of Complex Trait Genetics, VU Amsterdam, Center for Neurogenomics and Cognitive Research, Amsterdam, The Netherlands

    • S Stringer
  2. Department of Psychiatry, Academic Medical Centre, Amsterdam, The Netherlands

    • S Stringer
    •  & E M Derks
  3. Department of Biological Psychology/Netherlands Twin Register, VU University, Amsterdam, The Netherlands

    • C C Minică
    • , K J H Verweij
    • , H Mbarek
    • , A Abdellaoui
    • , J J Hottenga
    • , G H Lubke
    • , M G Nivard
    • , D I Boomsma
    •  & J M Vink
  4. Neuroscience Campus Amsterdam, Amsterdam, The Netherlands

    • K J H Verweij
    •  & D I Boomsma
  5. Department of Developmental Psychology and EMGO Institute for Health and Care Research, VU University, Amsterdam, The Netherlands

    • K J H Verweij
    • , D F Maciejewski
    • , A C Huizink
    • , P A C van Lier
    •  & H M Koot
  6. The Hospital for Sick Children Research Institute, Toronto, Canada

    • M Bernard
    • , Z Pausova
    •  & J Shin
  7. Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, USA

    • J Derringer
  8. Department of Human Neurogenetics, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands

    • K R van Eijk
    • , R S Kahn
    •  & M P Boks
  9. Department of Psychology, University of Minnesota, Minneapolis, MN, USA

    • J D Isen
    • , D E Irons
    • , M K McGue
    • , M B Miller
    •  & W G Iacono
  10. Department of Public Health, Hjelt Institute, University of Helsinki, Helsinki, Finland

    • A Loukola
    • , T Korhonen
    • , B Qaiser
    •  & J Kaprio
  11. Estonian Genome Center, University of Tartu, Tartu, Estonia

    • E Mihailov
    • , T Esko
    • , R Mägi
    •  & A Metspalu
  12. Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

    • P J van der Most
    • , I M Nolte
    •  & H Snieder
  13. Psychiatric Genetics Unit, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain

    • C Sánchez-Mora
    • , I Garcia-Martinez
    •  & M Ribasés
  14. Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Spain

    • C Sánchez-Mora
    • , M Casas
    • , I Garcia-Martinez
    • , J A Ramos-Quiroga
    • , V Richarte
    •  & M Ribasés
  15. Biomedical Network Research Centre on Mental Health (CIBERSAM), Barcelona, Spain

    • C Sánchez-Mora
    • , M Casas
    • , J A Ramos-Quiroga
    •  & M Ribasés
  16. Twin Research and Genetic Epidemiology, King's College London, London, UK

    • L Roos
    • , J M Harris
    • , L Quaye
    •  & T Spector
  17. Biomedical Genetics Department, Boston University School of Medicine, Boston, MA, USA

    • R Sherva
  18. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    • R Walters
  19. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    • R Walters
  20. Department of Medicine, Harvard Medical School, Boston, MA, USA

    • R Walters
  21. School of Social and Community Medicine, University of Bristol, Bristol, UK

    • J J Ware
    • , M Hickman
    • , A I Stiby
    •  & M R Munafò
  22. MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK

    • J J Ware
  23. Department of Psychiatry, Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA

    • T B Bigdeli
    •  & N A Gillespie
  24. Research Centre Adolescent Development, Utrecht University, Utrecht, The Netherlands

    • S J T Branje
    •  & W H J Meeus
  25. Department of Psychology and Psychiatry, University of California San Diego, La Jolla, CA, USA

    • S A Brown
  26. The LifeLines Cohort Study, University of Groningen, Groningen, The Netherlands

    • M Bruinenberg
  27. Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain

    • M Casas
    •  & J A Ramos-Quiroga
  28. Genetic Epidemiology, Molecular Epidemiology and Neurogenetics Laboratories, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia

    • S D Gordon
    • , A K Henders
    • , S E Medland
    • , G W Montgomery
    • , M J Wright
    • , N G Martin
    •  & N A Gillespie
  29. Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

    • C A Hartman
  30. Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA

    • A C Heath
    •  & P A F Madden
  31. Brain and Mind Research Institute, University of Sydney, Sydney, NSW, Australia

    • I B Hickie
  32. Department of Psychiatry, University of Colorado Denver, Aurora, CO, USA

    • C J Hopfer
  33. Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland

    • T Korhonen
  34. Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, Helsinki, Finland

    • T Korhonen
    •  & J Kaprio
  35. Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    • H R Kranzler
  36. Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA

    • K Krauter
  37. Department of Psychology, University of Notre Dame, Notre Dame, IN, USA

    • G H Lubke
  38. Developmental Psychology, Tilburg University, Tilburg, The Netherlands

    • W H J Meeus
  39. Interdisciplinary Center for Pathology and Emotion Regulation, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

    • A J Oldehinkel
  40. Department of Physiology and Nutritional Sciences, University of Toronto, Toronto, ON, Canada

    • Z Pausova
  41. Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA

    • R J Rose
  42. Department of Psychology and Neuroscience, Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA

    • M C Stallings
    •  & J K Hewitt
  43. Department of Psychiatry, University of California San Diego, La Jolla, CA, USA

    • T L Wall
  44. Rotman Research Institute, Baycrest, Toronto, ON, Canada

    • T Paus
  45. Department of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada

    • T Paus
  46. Center for the Developing Brain, Child Mind Institute, New York, NY, USA

    • T Paus
  47. Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland

    • J Kaprio
  48. UK Centre for Tobacco and Alcohol Studies and School of Experimental Psychology, University of Bristol, Bristol, UK

    • M R Munafò
  49. Department of Psychiatry, Genetics, and Neurobiology, Yale University School of Medicine and VA CT, West Haven, CT, USA

    • J Gelernter
  50. Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands

    • J M Vink


  1. Search for S Stringer in:

  2. Search for C C Minică in:

  3. Search for K J H Verweij in:

  4. Search for H Mbarek in:

  5. Search for M Bernard in:

  6. Search for J Derringer in:

  7. Search for K R van Eijk in:

  8. Search for J D Isen in:

  9. Search for A Loukola in:

  10. Search for D F Maciejewski in:

  11. Search for E Mihailov in:

  12. Search for P J van der Most in:

  13. Search for C Sánchez-Mora in:

  14. Search for L Roos in:

  15. Search for R Sherva in:

  16. Search for R Walters in:

  17. Search for J J Ware in:

  18. Search for A Abdellaoui in:

  19. Search for T B Bigdeli in:

  20. Search for S J T Branje in:

  21. Search for S A Brown in:

  22. Search for M Bruinenberg in:

  23. Search for M Casas in:

  24. Search for T Esko in:

  25. Search for I Garcia-Martinez in:

  26. Search for S D Gordon in:

  27. Search for J M Harris in:

  28. Search for C A Hartman in:

  29. Search for A K Henders in:

  30. Search for A C Heath in:

  31. Search for I B Hickie in:

  32. Search for M Hickman in:

  33. Search for C J Hopfer in:

  34. Search for J J Hottenga in:

  35. Search for A C Huizink in:

  36. Search for D E Irons in:

  37. Search for R S Kahn in:

  38. Search for T Korhonen in:

  39. Search for H R Kranzler in:

  40. Search for K Krauter in:

  41. Search for P A C van Lier in:

  42. Search for G H Lubke in:

  43. Search for P A F Madden in:

  44. Search for R Mägi in:

  45. Search for M K McGue in:

  46. Search for S E Medland in:

  47. Search for W H J Meeus in:

  48. Search for M B Miller in:

  49. Search for G W Montgomery in:

  50. Search for M G Nivard in:

  51. Search for I M Nolte in:

  52. Search for A J Oldehinkel in:

  53. Search for Z Pausova in:

  54. Search for B Qaiser in:

  55. Search for L Quaye in:

  56. Search for J A Ramos-Quiroga in:

  57. Search for V Richarte in:

  58. Search for R J Rose in:

  59. Search for J Shin in:

  60. Search for M C Stallings in:

  61. Search for A I Stiby in:

  62. Search for T L Wall in:

  63. Search for M J Wright in:

  64. Search for H M Koot in:

  65. Search for T Paus in:

  66. Search for J K Hewitt in:

  67. Search for M Ribasés in:

  68. Search for J Kaprio in:

  69. Search for M P Boks in:

  70. Search for H Snieder in:

  71. Search for T Spector in:

  72. Search for M R Munafò in:

  73. Search for A Metspalu in:

  74. Search for J Gelernter in:

  75. Search for D I Boomsma in:

  76. Search for W G Iacono in:

  77. Search for N G Martin in:

  78. Search for N A Gillespie in:

  79. Search for E M Derks in:

  80. Search for J M Vink in:

Competing interests

HRK is a consultant or Advisory Board Member for Alkermes, Lilly, Lundbeck, Otsuka, Pfizer, Roche; member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, supported by AbbVie, Ethypharm, Lilly, Lundbeck and Pfizer. The remaining authors declare no conflict of interest.

Corresponding author

Correspondence to J M Vink.

Supplementary information

About this article

Publication history






Supplementary Information accompanies the paper on the Translational Psychiatry website (http://www.nature.com/tp)

Further reading

  • Attention-deficit/hyperactivity disorder and lifetime cannabis use: genetic overlap and causality

    • María Soler Artigas
    • , Cristina Sánchez-Mora
    • , Paula Rovira
    • , Vanesa Richarte
    • , Iris Garcia-Martínez
    • , Mireia Pagerols
    • , Ditte Demontis
    • , Sven Stringer
    • , Jacqueline M. Vink
    • , Anders D. Børglum
    • , Benjamin M. Neale
    • , Barbara Franke
    • , Stephen V. Faraone
    • , Miguel Casas
    • , Josep Antoni Ramos-Quiroga
    •  & Marta Ribasés

    Molecular Psychiatry (2019)

  • Effects of DRD2 splicing-regulatory polymorphism and DRD4 48 bp VNTR on crack cocaine addiction

    • Anderson R. Stolf
    • , Renata B. Cupertino
    • , Diana Müller
    • , Breno Sanvicente-Vieira
    • , Tatiana Roman
    • , Eduardo S. Vitola
    • , Eugenio H. Grevet
    • , Lisia von Diemen
    • , Felix H. P. Kessler
    • , Rodrigo Grassi-Oliveira
    • , Claiton H. D. Bau
    • , Diego L. Rovaris
    • , Flavio Pechansky
    •  & Jaqueline B. Schuch

    Journal of Neural Transmission (2019)

  • Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences

    • Richard Karlsson Linnér
    • , Pietro Biroli
    • , Edward Kong
    • , S. Fleur W. Meddens
    • , Robbee Wedow
    • , Mark Alan Fontana
    • , Maël Lebreton
    • , Stephen P. Tino
    • , Abdel Abdellaoui
    • , Anke R. Hammerschlag
    • , Michel G. Nivard
    • , Aysu Okbay
    • , Cornelius A. Rietveld
    • , Pascal N. Timshel
    • , Maciej Trzaskowski
    • , Ronald de Vlaming
    • , Christian L. Zünd
    • , Yanchun Bao
    • , Laura Buzdugan
    • , Ann H. Caplin
    • , Chia-Yen Chen
    • , Peter Eibich
    • , Pierre Fontanillas
    • , Juan R. Gonzalez
    • , Peter K. Joshi
    • , Ville Karhunen
    • , Aaron Kleinman
    • , Remy Z. Levin
    • , Christina M. Lill
    • , Gerardus A. Meddens
    • , Gerard Muntané
    • , Sandra Sanchez-Roige
    • , Frank J. van Rooij
    • , Erdogan Taskesen
    • , Yang Wu
    • , Futao Zhang
    • , Adam Auton
    • , Jason D. Boardman
    • , David W. Clark
    • , Andrew Conlin
    • , Conor C. Dolan
    • , Urs Fischbacher
    • , Patrick J. F. Groenen
    • , Kathleen Mullan Harris
    • , Gregor Hasler
    • , Albert Hofman
    • , Mohammad A. Ikram
    • , Sonia Jain
    • , Robert Karlsson
    • , Ronald C. Kessler
    • , Maarten Kooyman
    • , James MacKillop
    • , Minna Männikkö
    • , Carlos Morcillo-Suarez
    • , Matthew B. McQueen
    • , Klaus M. Schmidt
    • , Melissa C. Smart
    • , Matthias Sutter
    • , A. Roy Thurik
    • , André G. Uitterlinden
    • , Jon White
    • , Harriet de Wit
    • , Jian Yang
    • , Lars Bertram
    • , Dorret I. Boomsma
    • , Tõnu Esko
    • , Ernst Fehr
    • , David A. Hinds
    • , Magnus Johannesson
    • , Meena Kumari
    • , David Laibson
    • , Patrik K. E. Magnusson
    • , Michelle N. Meyer
    • , Arcadi Navarro
    • , Abraham A. Palmer
    • , Tune H. Pers
    • , Danielle Posthuma
    • , Daniel Schunk
    • , Murray B. Stein
    • , Rauli Svento
    • , Henning Tiemeier
    • , Paul R. H. J. Timmers
    • , Patrick Turley
    • , Robert J. Ursano
    • , Gert G. Wagner
    • , James F. Wilson
    • , Jacob Gratten
    • , James J. Lee
    • , David Cesarini
    • , Daniel J. Benjamin
    • , Philipp D. Koellinger
    •  & Jonathan P. Beauchamp

    Nature Genetics (2019)

  • Gene variants and educational attainment in cannabis use: mediating role of DNA methylation

    • Maria Carla Gerra
    • , Subramaniam Jayanthi
    • , Matteo Manfredini
    • , Donna Walther
    • , Jennifer Schroeder
    • , Karran A. Phillips
    • , Jean Lud Cadet
    •  & Claudia Donnini

    Translational Psychiatry (2018)

  • Genome-wide association study identifies a novel locus for cannabis dependence

    • A Agrawal
    • , Y-L Chou
    • , C E Carey
    • , D A A Baranger
    • , B Zhang
    • , R Sherva
    • , L Wetherill
    • , M Kapoor
    • , J-C Wang
    • , S Bertelsen
    • , A P Anokhin
    • , V Hesselbrock
    • , J Kramer
    • , M T Lynskey
    • , J L Meyers
    • , J I Nurnberger
    • , J P Rice
    • , J Tischfield
    • , L J Bierut
    • , L Degenhardt
    • , L A Farrer
    • , J Gelernter
    • , A R Hariri
    • , A C Heath
    • , H R Kranzler
    • , P A F Madden
    • , N G Martin
    • , G W Montgomery
    • , B Porjesz
    • , T Wang
    • , J B Whitfield
    • , H J Edenberg
    • , T Foroud
    • , A M Goate
    • , R Bogdan
    •  & E C Nelson

    Molecular Psychiatry (2018)