Presently, Montana and Michigan are the only MML states in the continental United States that do not share a border with another MML state.28 In addition, Figure E.1 shows that Montana and its bordering states are relatively unimportant trafficking routes for marijuana; cannabis smuggled through these states is primarily sourced from Washington and Oregon and is destined for larger, more urban centers in the midwest. Therefore, growth in the legal medical marijuana market in Montana is unlikely to have significant supply spillovers to its neighboring states. Table E.1 compares estimates of the effects of growth in the legal market on past month use restricting the sample to only include Montana and its bordering states in Panel A, to the estimates using the entire sample of states in Panel B. If there are substantial supply spillovers from MML states with large markets to other states, then we would expect the effect sizes in Panel A to exceed those in Panel B. From Table E.1, for youths aged 12-25, the effects of legal market size on past-month use are larger in Panel A than in Panel B, but for adults aged 26 and older they are quite similar. This is consistent with growth in the legal medical marijuana market having supply spillovers across states in the black market, where adolescents and young adults have substantially greater access than older adults. Table E.2 replicates the analysis of Table E.1 using prevalence of past-year initiation as the outcome variable. The results are similar. Thus, there appear to be supply spillovers from medical marijuana markets to recreational marijuana markets used by youths in other states. The differences between the estimates from Montana’s case study to those using the entire sample suggests that the effects of medical marijuana market growth on adolescent and young adult use may be twice as large as shown in the primary results if cross-state supply spillovers are accounted for.
If the decision to report marijuana use is more closely related to beliefs about legal penalties or social disapproval compared to availability,cannabis drying racks then the results from Table 2.8 suggest that the effects of legal market growth on adolescent marijuana use are a true measure of consumption changes and not of reporting behavior. Tables F.1-F.2 provide additional supporting evidence that the primary results of this paper are not driven by reporting bias. Table F.1 reports estimates for the effects of registration rates on the prevalence of past-month marijuana use by adolescents separately for the time period before the Ogden Memo and after the Cole Memo. If changes in reporting behavior are more likely to be driven by law passage than by legal market size, then registration rates should have no effect on reported past-month except due to the federal government’s memos. As evidenced in Table F.1, the coefficient estimates for adolescent past-month use are not significantly different if examined before the federal policy reduced enforcement with the Ogden Memo, or after the federal government increased enforcement with the Cole Memo. However, adolescent reporting behavior may be more sensitive to changes in risks from social or community disapproval than to changes in perceived disapproval from law enforcement. If this were the case, then changes in state marijuana policy or changes in federal enforcement policy may have less effect on adolescent reporting behavior than changes in perceived social stigma associated with cannabis consumption, which is likely highly correlated with the number of legal users and suppliers visible in the community. To address this potential concern, estimates of the effects of legal medical marijuana market size on juvenile arrests for marijuana possession are shown in Table F.2. Since adolescents for the most part do not qualify as medical marijuana patients, it is unlikely that there were significant state enforcement changes regarding juvenile arrests for marijuana-related crimes, and thus effects of legal market size on adolescent marijuana arrests are likely highly correlated with effects of legal market size on adolescent cannabis use.
Annual data on juvenile arrests from 1994-2012 were obtained from the Uniform Crime Reports County-Level Detailed Arrest Files compiled by the Inter-University Consortium for Political and Social Research. County data were aggregated up to the state level. Table F.2 reports coefficient estimates for the effect of registration rates on the juvenile marijuana possession arrest rates. In Columns -, a log-linear ordinary least-squares specification is employed, with the dependent variable constructed as the natural log of the number of juvenile arrests for marijuana possession per 100,000 of the relevant-aged population for Columns -, or the natural log of the number of juvenile marijuana possession arrests in Columns -. Columns – employ a negative binomial specification. For all model specifications, growth in the legal market size has a positive effect on juvenile arrests for marijuana possession of similar effect size to that found for the effects on adolescent past-month use. This suggests that the observed effects on self-reported use are not driven solely by changes in reporting behavior. Alcohol abuse is a global problem, constituting the seventh leading risk factor for death and disability . Worldwide, over 100 million people had an alcohol use disorder in 2016. Statistics from the National Survey on Drug Use and Health show that >85% of adults in the United States report ever having consumed alcohol, with >25% reporting binge drinking in the past month . The proportion of adults in the United States with an AUD is estimated to be 6.2% . Alcohol use behaviors are complex, and how and why people drink is partially influenced by genetic factors. However, identifying the genetic factors that increase the risk for harmful drinking has been challenging, partially because patterns of alcohol use are dynamic across the lifespan. The terms used to describe alcohol use and abuse are as diverse as the behaviors themselves. Hazardous drinking describes heavy drinking that places an individual at risk for future harm. Harmful drinking and alcohol abuse are defined as drinking that causes mental or physical damage to the individual.
These descriptive terms were devised to identify individuals who would benefit from brief interventions and are assessed using screening questionnaires such as the Alcohol Use Disorders Identification Test . Alcohol dependence was, until recently, defined according to the DSM-IV and required the presence of 3 or more of 7 criteria in a 12-month period. The DSM-IV made a distinction between alcohol abuse and dependence that was removed under DSM-V and replaced with ‘mild’ to ‘severe’ definitions of AUD. Genetic studies encompass the wide range of alcohol use phenotypes; in this review we mirror the language used in the original studies. AUD can be viewed as the end point of a series of transitions , which begin with the initiation of use, continue with the escalation to hazardous drinking and culminate in compulsive harmful use that persists despite negative consequences. Genome-wide association studies have been instrumental in discovering novel genetic loci associated with multiple psychiatric conditions. In the field of AUD genetics, studies have mostly focused on either levels of consumption or AUD diagnosis. Recent GWAS have now begun to identify hundreds of genome-wide significant variants, and provide evidence that the components of alcohol use behavior have a distinct genetic architecture. In this review, we provide an overview of recent molecular genetic findings of alcohol use behaviors from the largest GWAS performed to date. Other reviews have elegantly summarized findings from twin and family studies of heritability, linkage, candidate gene and GWAS [e.g. ], and we extend on recent reviews of the molecular genetics of AUD by including additional GWAS of alcohol use behaviors that identify genome-wide significant hits . In addition, we discuss the application of polygenic methods, which provide mounting evidence that alcohol use and misuse are partially distinct. Finally, we delineate future directions to investigate the different etiologic sources that underlie the life course of alcohol use behaviors.For decades,pots for cannabis plants candidate gene studies were used to determine the contribution of specific genes that increase risk for AUD. Candidate gene studies tended to focus on genes that influenced pharmacokinetic and pharmacodynamic factors. One exception to this are the genes encoding ethanol metabolizing enzymes, particularly alcohol dehydrogenase and aldehyde dehydrogenase , which have repeatedly been shown to have the largest impact on alcohol consumption and risk for AUD . As study designs have evolved to incorporate GWAS, researchers have been able to scan the whole genome without any hypotheses about the underlying biology of alcohol use behaviors. Initial efforts focused on collecting clinically-defined cases of AUD, but these ascertainment strategies could not amass the large sample sizes required for GWAS . Accordingly, multi-ethnic and clinically-defined samples have been combined through the Psychiatric Genomic Consortium of Substance Use Disorders working group.
The efforts of the PGC-SUD have led to a trans-ancestral meta-analysis consisting of almost 15,000 AD cases and almost 38,000 controls from 28 independent cohorts , identifying a single locus , which was robustly associated with AD. More recently, using information from electronic health records to infer AUD status, a GWAS of 274,424 multi-ethnic individuals from the Million Veterans Program cohort identified 10 loci associated with AUD . Kranzler et al showed that alcohol consumption and AUD were genetically correlated but distinct, thus allowing them to adjust for consumption in the AUD GWAS and for AUD in the GWAS of consumption. In parallel with these efforts, which have focused on clinical diagnoses, other GWAS have incorporated continuous measures of alcohol use. These include self-reported weekly alcohol intake or the scores from screening questionnaires such as the AUDIT . The AUDIT can be decomposed to provide a measure of alcohol use from the first 3 questions and misuse from questions 4-10 . These quantitative measures are available in large population-based cohorts such as the UK Biobank , MVP and 23andMe. The GWAS meta-analysis of AUDIT identified 10 associated risk loci . Large consortia were also formed to collate quantitative measures of alcohol use, including AlcGen and the GWAS & Sequencing Consortium of Alcohol and Nicotine Use . GSCAN have recently identified nearly 100 loci associated with alcohol consumption . The MVP study also examined alcohol consumption, allowing for an explicit comparison between AUD and consumption in a single population; of the 18 loci detected in that study, 5 were common to both AUD diagnosis and alcohol consumption. As the prior two paragraphs make clear, population based cohorts have provided larger sample sizes, which are critical for obtaining adequate power for GWAS. Their use can come at the cost of missing more severe alcohol use phenotypes. For example, the frequency of AUD in the UKB is lower than the population average [7% ], indicating that certain population studies may be underpowered to detect genetic effects specific to dependence . The frequency of AUD in the MVP, on the contrary, was much higher [20%, ]. Despite these limitations, population based cohorts provide a cost-effective strategy for obtaining very large samples, compared to traditional study designs that require obtaining a diagnosis from clinically trained staff. Beyond the alcohol metabolizing genes, the region containing the genes beta-klotho and the Fibroblast growth factor 21 has been robustly associated with alcohol consumption. The AlcGen consortium was the first to show that the A allele of rs11940694 , located in the intron of KLB, was associated with reduced alcohol consumption . This finding has since been replicated – the same SNP was associated with alcohol consumption and alcohol misuse . Beta-klotho is a transmembrane protein that acts as a cofactor for the circulating hormone fibroblast growth factor 21 by facilitating its binding to FGF receptors . Interestingly the FGF21 gene, which is located on chromosome 19, was also associated with AUDIT scores at the gene-level in humans . Beta-klotho is primarily expressed in the liver, adipose tissue and pancreas , and recent studies have shown that it regulates brain specific functions related to alcohol consumption in mice. For example, mice lacking brain expressed Klb showed increased ethanol preference . Furthermore, FGF21 was found to suppress ethanol consumption in wild-type mice but had no effect on mice lacking Klb in the brain.