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

Genome-wide association studies of lifetime and frequency cannabis use in 131,895 individuals

Cannabis is one of the most widely used drugs globally. Decriminalization of cannabis is further increasing cannabis consumption. We performed genome-wide association studies (GWASs) of lifetime (N=131,895) and frequency (N=73,374) of cannabis use. Lifetime cannabis use GWAS identified two loci, one near CADM2 (rs11922956, p=2.40E-11) and another near GRM3 (rs12673181, p=6.90E-09). Frequency of use GWAS identified one locus near CADM2 (rs4856591, p=8.10E-09; r2 =0.76 with rs11922956). Both traits were heritable and genetically correlated with previous GWASs of lifetime use and cannabis use disorder (CUD), as well as other substance use and cognitive traits. Polygenic scores (PGSs) for lifetime and frequency of cannabis use associated cannabis use phenotypes in AllofUs participants. Phenome-wide association study of lifetime cannabis use PGS in a hospital cohort replicated associations with substance use and mood disorders, and uncovered associations with celiac and infectious diseases. This work demonstrates the value of GWASs of CUD transition risk factors.

The genetic landscape of substance use disorders

Substance use disorders represent a significant public health concern with considerable socioeconomic implications worldwide. Twin and family-based studies have long established a heritable component underlying these disorders. In recent years, genome-wide association studies of large, broadly phenotyped samples have identified regions of the genome that harbour genetic risk variants associated with substance use disorders. These regions have enabled the discovery of putative causal genes and improved our understanding of genetic relationships among substance use disorders and other traits. Furthermore, the integration of these data with clinical information has yielded promising insights into how individuals respond to medications, allowing for the development of personalized treatment approaches based on an individual's genetic profile. This review article provides an overview of recent advances in the genetics of substance use disorders and demonstrates how genetic data may be used to reduce the burden of disease and improve public health outcomes.

Genetics of cannabis use in opioid use disorder: A genome-wide association and polygenic risk score study

BACKGROUND

Individuals with an Opioid Use Disorder (OUD) have increased rates of cannabis use in comparison to the general population. Research on the short- and long-term impacts of cannabis use in OUD patients has been inconclusive. A genetic component may contribute to cannabis cravings.

AIMS

Identify genetic variants associated with cannabis use through Genome-wide Association Study (GWAS) methods and investigate a Polygenic Risk Score (PRS). In addition, we aim to identify any sex differences in effect size for genetic variants reaching or nearing genome-wide significance in the GWAS.

METHODS

The study outcomes of interest were: regular cannabis use (yes/no) (n = 2616), heaviness of cannabis use (n = 1293) and cannabis cravings (n = 836). Logistic and linear regressions were preformed, respectively, to test the association between genetic variants and each outcome, regular cannabis use and heaviness of cannabis use. GWAS summary statistics from a recent large meta-GWAS investigating cannabis use disorder were used to conduct PRS's. Findings are limited to a European ancestry sample.

RESULTS

No genome-wide significant associations were found. Rs1813412 (chromosome 17) for regular cannabis use and rs62378502 (chromosome 5) for heaviness of cannabis use were approaching genome-wide significance. Both these SNPs were nominally significant (p<0.05) within males and females, however sex did not modify the association. The PRS identified statistically significant association with cannabis cravings. The variance explained by all PRSs were less than 1.02x10-2.

CONCLUSION

This study provides promising results in understanding the genetic contribution to cannabis use in individuals living with OUD.

A latent class analysis of patterns of tobacco and cannabis use in Australia and their health-related correlates

INTRODUCTION

The shifting landscape in Australia's tobacco and cannabis policies and emerging new products and modes of administration may increase experimentation and the risks of addiction to these drugs.

METHODS

We analysed cross-sectional data from the 2019 National Drug Strategy and Household Survey (n = 22,015) of Australians aged 14 and above. Latent class analysis was used to identify distinct groups based on types of tobacco and cannabis products used. The socio-demographic, health-rated correlates and past-year substance use of each latent class was examined.

RESULTS

A four-class solution was identified: co-use of tobacco and cannabis (2.4%), cannabis-only (5.5%), tobacco-only (8.0%) and non-user (84.0%). Males (odds ratio [OR] range 1.5-2.9), younger age (OR range 2.4-8.4), moderate to high psychological distress (OR range 1.3-3.0), using illicit substances in the last year (OR range 1.41-22.87) and high risk of alcohol use disorder (OR range 2.0-21.7) were more likely to be in the tobacco/cannabis use classes than non-users. Within the co-use class, 78.4% mixed tobacco with cannabis and 89.4% had used alcohol with cannabis at least once.

DISCUSSION AND CONCLUSIONS

Approximately 16% of respondents used tobacco or cannabis, or both substances, and no major distinct subgroups were identified by the use of different product types. Mental health issues and the poly-substance use were more common in the class who were co-users of cannabis and tobacco. Existing policies need to minimise cannabis and tobacco-related harms to reduce the societal burden associated with both substances.

Cannabis Pharmacogenomics: A Path to Personalized Medicine

Cannabis and related compounds have created significant research interest as a promising therapy in many disorders. However, the individual therapeutic effects of cannabinoids and the incidence of side effects are still difficult to determine. Pharmacogenomics may provide the answers to many questions and concerns regarding the cannabis/cannabinoid treatment and help us to understand the variability in individual responses and associated risks. Pharmacogenomics research has made meaningful progress in identifying genetic variations that play a critical role in interpatient variability in response to cannabis. This review classifies the current knowledge of pharmacogenomics associated with medical marijuana and related compounds and can assist in improving the outcomes of cannabinoid therapy and to minimize the adverse effects of cannabis use. Specific examples of pharmacogenomics informing pharmacotherapy as a path to personalized medicine are discussed.

The genetic aetiology of cannabis use: from twin models to genome-wide association studies and beyond

Cannabis is among the most widely consumed psychoactive substances worldwide. Individual differences in cannabis use phenotypes can partly be explained by genetic differences. Technical and methodological advances have increased our understanding of the genetic aetiology of cannabis use. This narrative review discusses the genetic literature on cannabis use, covering twin, linkage, and candidate-gene studies, and the more recent genome-wide association studies (GWASs), as well as the interplay between genetic and environmental factors. Not only do we focus on the insights that these methods have provided on the genetic aetiology of cannabis use, but also on how they have helped to clarify the relationship between cannabis use and co-occurring traits, such as the use of other substances and mental health disorders. Twin studies have shown that cannabis use is moderately heritable, with higher heritability estimates for more severe phases of use. Linkage and candidate-gene studies have been largely unsuccessful, while GWASs so far only explain a small portion of the heritability. Dozens of genetic variants predictive of cannabis use have been identified, located in genes such as CADM2, FOXP2, and CHRNA2. Studies that applied multivariate methods (twin models, genetic correlation analysis, polygenic score analysis, genomic structural equation modelling, Mendelian randomisation) indicate that there is considerable genetic overlap between cannabis use and other traits (especially other substances and externalising disorders) and some evidence for causal relationships (most convincingly for schizophrenia). We end our review by discussing implications of these findings and suggestions for future work.

Common and distinguishing genetic factors for substance use behavior and disorder: an integrated analysis of genomic and transcriptomic studies from both human and animal studies

BACKGROUND AND AIMS

Genomic and transcriptomic findings greatly broaden the biological knowledge regarding substance use. However, systematic convergence and comparison evidence of genome-wide findings is lacking for substance use. Here, we combined all the genome-wide findings from both substance use behavior and disorder (SUBD) and identified common and distinguishing genetic factors for different SUBDs.

METHODS

Systemic literature search for genome-wide association (GWAS) and RNA-seq studies of alcohol/nicotine/drug use behavior (partially meets or not reported diagnostic criteria) and alcohol use behavior and disorder (AUBD), nicotine use behavior and disorder (NUBD) and drug use behavior and disorder (DUBD) was performed using PubMed and the GWAS catalog. Drug use was focused upon cannabis, opioid, cocaine and methamphetamine use. GWAS studies required case-control or case/cohort samples. RNA-seq studies were based on brain tissues. The genes which contained significant single nucleotide polymorphism (P ≤ 1 × 10^-6 ) in GWAS and reported as significant in RNA-seq studies were extracted. Pathway enrichment was performed by using Metascape. Gene interaction networks were identified by using the Protein Interaction Network Analysis database.

RESULTS

Total SUBD-related 2910 genes were extracted from 75 GWAS studies (2 773 889 participants) and 17 RNA-seq studies. By overlapping the genes and pathways of AUBD, NUBD and DUBD, four shared genes (CACNB2, GRIN2B, PLXDC2 and PKNOX2), four shared pathways [two Gene Ontology (GO) terms of 'modulation of chemical synaptic transmission', 'regulation of trans-synaptic signaling', two Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of 'dopaminergic synapse', 'cocaine addiction'] were identified (significantly higher than random, P < 1 × 10^-5 ). The top shared KEGG pathways (Benjamini-Hochberg-corrected P-value < 0.05) in the pairwise comparison of AUBD versus DUBD, NUBD versus DUBD, AUBD versus NUBD were 'Epstein-Barr virus infection', 'protein processing in endoplasmic reticulum' and 'neuroactive ligand-receptor interaction', respectively. We also identified substance-specific genetic factors: i.e. ADH1B and ALDH2 were unique for AUBD, while CHRNA3 and CHRNA4 were unique for NUBD.

CONCLUSIONS

This systematic review identifies the shared and unique genes and pathways for alcohol, nicotine and drug use behaviors and disorders at the genome-wide level and highlights critical biological processes for the common and distinguishing vulnerability of substance use behaviors and disorders.

Using Mendelian randomization to explore the gateway hypothesis: possible causal effects of smoking initiation and alcohol consumption on substance use outcomes

BACKGROUND AND AIMS

Initial use of drugs such as tobacco and alcohol may lead to subsequent more problematic drug use-the 'gateway' hypothesis. However, observed associations may be due to a shared underlying risk factor, such as trait impulsivity. We used bidirectional Mendelian randomization (MR) to test the gateway hypothesis.

DESIGN

Our main method was inverse-variance weighted (IVW) MR, with other methods included as sensitivity analyses (where consistent results across methods would raise confidence in our primary results). MR is a genetic instrumental variable approach used to support stronger causal inference in observational studies.

SETTING AND PARTICIPANTS

Genome-wide association summary data among European ancestry individuals for smoking initiation, alcoholic drinks per week, cannabis use and dependence, cocaine and opioid dependence (n = 1749-1 232 091).

MEASUREMENTS

Genetic variants for exposure.

FINDINGS

We found evidence of causal effects from smoking initiation to increased drinks per week [(IVW): β = 0.06; 95% confidence interval (CI) = 0.03-0.09; P = 9.44 × 10-06 ], cannabis use [IVW: odds ratio (OR) = 1.34; 95% CI = 1.24-1.44; P = 1.95 × 10-14 ] and cannabis dependence (IVW: OR = 1.68; 95% CI = 1.12-2.51; P = 0.01). We also found evidence of an effect of cannabis use on the increased likelihood of smoking initiation (IVW: OR = 1.39; 95% CI = 1.08-1.80; P = 0.01). We did not find evidence of an effect of drinks per week on other substance use outcomes, except weak evidence of an effect on cannabis use (IVW: OR = 0.55; 95% CI = 0.16-1.93; P-value = 0.35). We found weak evidence of an effect of opioid dependence on increased drinks per week (IVW: β = 0.002; 95% CI = 0.0005-0.003; P = 8.61 × 10-03 ).

CONCLUSIONS

Bidirectional Mendelian randomization testing of the gateway hypothesis reveals that smoking initiation may lead to increased alcohol consumption, cannabis use and cannabis dependence. Cannabis use may also lead to smoking initiation and opioid dependence to alcohol consumption. However, given that tobacco and alcohol use typically begin before other drug use, these results may reflect a shared risk factor or a bidirectional effect for cannabis use and opioid dependence.

Prevalence and correlates of cannabis use disorder among Australians using cannabis products to treat a medical condition

INTRODUCTION

Prior research has examined the prevalence and correlates of cannabis use disorder (CUD) in people who use cannabis; however, these are poorly described for people using cannabis for medical reasons.

METHODS

Data came from a 2018 to 2019 online, anonymous, cross-sectional survey of Australians reporting using either illicit or licit cannabis for medical reasons within the past year. Included were questions on demographics, current and lifetime patterns of cannabis use, clinical conditions for which medical cannabis was used, and individual criteria for CUD and cannabis withdrawal syndrome. Bayesian Horseshoe logistic regression models were used to identify covariates associated with meeting CUD DSM-5 conditions for any-CUD (≥2/11 criteria) and moderate-severe-CUD (≥4/11).

RESULTS

A total of 905 participants were included in the analysis. The majority (98%) used illicit cannabis products. Criteria for any-CUD criteria were met by 290 (32.0%), and 117 (12.9%) met criteria for moderate-severe-CUD. Tolerance (21%) and withdrawal (35%) were the most commonly met criteria. Correlates with the strongest association with CUD were inhaled route of administration [odds ratio (OR) = 2.96, 95% credible interval 1.11, 7.06], frequency of cannabis use (OR = 1.24, 1.11-1.35), proportion of cannabis for medical reasons (OR = 0.83, 0.74, 0.94), frequency of tobacco use (OR = 1.10, 1.03, 1.17), age (OR = 0.75, 0.64, 0.90) and pain as main clinical indication (OR = 0.58, 0.36, 1.00).

DISCUSSION AND CONCLUSIONS

Prevalence of CUD in medical cannabis users appears comparable to 'recreational' users, with many similar correlates. CUD was associated with using cannabis to treat mental health rather than pain conditions and inhaled over other routes of administration.

Lower-Risk Cannabis Use Guidelines (LRCUG) for reducing health harms from non-medical cannabis use: A comprehensive evidence and recommendations update

BACKGROUND

Cannabis use is common, especially among young people, and is associated with risks for various health harms. Some jurisdictions have recently moved to legalization/regulation pursuing public health goals. Evidence-based 'Lower Risk Cannabis Use Guidelines' (LRCUG) and recommendations were previously developed to reduce modifiable risk factors of cannabis-related adverse health outcomes; related evidence has evolved substantially since. We aimed to review new scientific evidence and to develop comprehensively up-to-date LRCUG, including their recommendations, on this evidence basis.

METHODS

Targeted searches for literature (since 2016) on main risk factors for cannabis-related adverse health outcomes modifiable by the user-individual were conducted. Topical areas were informed by previous LRCUG content and expanded upon current evidence. Searches preferentially focused on systematic reviews, supplemented by key individual studies. The review results were evidence-graded, topically organized and narratively summarized; recommendations were developed through an iterative scientific expert consensus development process.

RESULTS

A substantial body of modifiable risk factors for cannabis use-related health harms were identified with varying evidence quality. Twelve substantive recommendation clusters and three precautionary statements were developed. In general, current evidence suggests that individuals can substantially reduce their risk for adverse health outcomes if they delay the onset of cannabis use until after adolescence, avoid the use of high-potency (THC) cannabis products and high-frequency/-intensity of use, and refrain from smoking-routes for administration. While young people are particularly vulnerable to cannabis-related harms, other sub-groups (e.g., pregnant women, drivers, older adults, those with co-morbidities) are advised to exercise particular caution with use-related risks. Legal/regulated cannabis products should be used where possible.

CONCLUSIONS

Cannabis use can result in adverse health outcomes, mostly among sub-groups with higher-risk use. Reducing the risk factors identified can help to reduce health harms from use. The LRCUG offer one targeted intervention component within a comprehensive public health approach for cannabis use. They require effective audience-tailoring and dissemination, regular updating as new evidence become available, and should be evaluated for their impact.

Genomic and Personalized Medicine Approaches for Substance Use Disorders (SUDs) Looking at Genome-Wide Association Studies

Drug addiction, or substance use disorder (SUD), is a chronic, relapsing disorder in which compulsive drug-seeking and drug-taking behaviour persist despite serious negative consequences. Drug abuse represents a problem that deserves great attention from a social point of view, and focuses on the importance of genetic studies to help in understanding the genetic basis of addiction and its medical treatment. Despite the complexity of drug addiction disorders, and the high number of environmental variables playing a role in the onset, recurrence, and duration of the symptoms, several studies have highlighted the non-negligible role of genetics, as demonstrated by heritability and genome-wide association studies. A correlation between the relative risk of addiction to specific substances and heritability has been recently observed, suggesting that neurobiological mechanisms may be, at least in part, inherited. All these observations point towards a scenario where the core neurobiological factors of addiction, involving the reward system, impulsivity, compulsivity, stress, and anxiety response, are transmitted, and therefore, genes and mutations underlying their variation might be detected. In the last few years, the development of new and more efficient sequencing technologies has paved the way for large-scale studies in searching for genetic and epigenetic factors affecting drug addiction disorders and their treatments. These studies have been crucial to pinpoint single nucleotide polymorphisms (SNPs) in genes that affect the reaction to medical treatments. This is critically important to identify pharmacogenomic approaches for substance use disorder, such as OPRM1 SNPs and methadone required doses for maintenance treatment (MMT). Nevertheless, despite the promising results obtained by genome-wide association and pharmacogenomic studies, specific studies related to population genetics diversity are lacking, undermining the overall applicability of the preliminary findings, and thus potentially affecting the portability and the accuracy of the genetic studies. In this review, focusing on cannabis, cocaine and heroin use, we report the state-of-the-art genomics and pharmacogenomics of SUDs, and the possible future perspectives related to medical treatment response in people that ask for assistance in solving drug-related problems.

Genetics of substance use disorders in the era of big data

Substance use disorders (SUDs) are conditions in which the use of legal or illegal substances, such as nicotine, alcohol or opioids, results in clinical and functional impairment. SUDs and, more generally, substance use are genetically complex traits that are enormously costly on an individual and societal basis. The past few years have seen remarkable progress in our understanding of the genetics, and therefore the biology, of substance use and abuse. Various studies - including of well-defined phenotypes in deeply phenotyped samples, as well as broadly defined phenotypes in meta-analysis and biobank samples - have revealed multiple risk loci for these common traits. A key emerging insight from this work establishes a biological and genetic distinction between quantity and/or frequency measures of substance use (which may involve low levels of use without dependence), versus symptoms related to physical dependence.

Genetic basis of cannabis use: a systematic review

BACKGROUND

With the increase in cannabis use rates, cannabis use disorder is being reported as one of the most common drug use disorders globally. Cannabis use has several known physical, psychological, and social adverse events, such as altered judgement, poor educational outcomes, and respiratory symptoms. The propensity for taking cannabis and the development of a cannabis use disorder may be genetically influenced for some individuals. Heritability estimates suggest a genetic basis for cannabis use, and several genome-wide association studies (GWASs) have identified possible regions of association, albeit with inconsistent findings. This systematic review aims to summarize the findings from GWASs investigating cannabis use and cannabis use disorder.

METHODS

This systematic review incorporates articles that have performed a GWAS investigating cannabis use or cannabis use disorder. MEDLINE, Web of Science, EMBASE, CINAHL, GWAS Catalog, GWAS Central, and NIH Database of Genotype and Phenotype were searched using a comprehensive search strategy. All studies were screened in duplicate, and the quality of evidence was assessed using the quality of genetic association studies (Q-Genie) tool. All studies underwent qualitative synthesis; however, quantitative analysis was not feasible.

RESULTS

Our search identified 5984 articles. Six studies met our eligibility criteria and were included in this review. All six studies reported results that met our significance threshold of p ≤ 1.0 × 10-7. In total 96 genetic variants were identified. While meta-analysis was not possible, this review identified the following genes, ANKFN1, INTS7, PI4K2B, CSMD1, CST7, ACSS1, and SCN9A, to be associated with cannabis use. These regions were previously reported in different mental health conditions, however not in relation to cannabis use.

CONCLUSION

This systematic review summarized GWAS findings within the field of cannabis research. While a meta-analysis was not possible, the summary of findings serves to inform future candidate gene studies and replication efforts. Systematic Review Registration PROSPERO CRD42020176016.

Influence of Cannabinoid Receptor 1 Genetic Variants on the Subjective Effects of Smoked Cannabis

As many jurisdictions consider relaxing cannabis legislation and usage is increasing in North America and other parts of the world, there is a need to explore the possible genetic differences underlying the subjective effects of cannabis. This pilot study investigated specific genetic variations within the cannabinoid receptor 1 (CNR1) gene for association with the subjective effects of smoked cannabis. Data were obtained from a double-blinded, placebo-controlled clinical trial studying the impact of cannabis intoxication on driving performance. Participants randomized to the active cannabis group who consented to secondary genetic analysis (n = 52) were genotyped at the CNR1 rs1049353 and rs2023239 polymorphic areas. Maximum value and area under the curve (AUC) analyses were performed on subjective measures data. Analysis of subjective effects by genotype uncovered a global trend towards greater subjective effects for rs1049353 T-allele- and rs2023239 C-allele-carrying subjects. However, significant differences attributed to allelic identity were only documented for a subset of subjective effects. Our findings suggest that rs1049353 and rs2023239 minor allele carriers experience augmented subjective effects during acute cannabis intoxication.

High genes: Genetic underpinnings of cannabis use phenotypes

Cannabis is one of the most widely used substances across the globe and its use has a substantial heritable component. However, the heritability of cannabis use varies according to substance use phenotype, suggesting that a unique profile of gene variants may contribute to the different stages of use, such as age of use onset, lifetime use, cannabis use disorder, and withdrawal and craving during abstinence. Herein, we review a subset of genes identified by candidate gene, family-based linkage, and genome-wide association studies related to these cannabis use phenotypes. We also describe their relationships with other substances, and their functions at the neurobiological, cognitive, and behavioral levels to hypothesize the role of these genes in cannabis use risk. Delineating genetic risk factors in the various stages of cannabis use will provide insight into the biological mechanisms related to cannabis use and highlight points of intervention prior to and following the development of dependence, as well as identify targets to aid drug development for treating problematic cannabis use.

Cannabis use and cannabis use disorder

Cannabis use disorder (CUD) is an underappreciated risk of using cannabis that affects ~10% of the 193 million cannabis users worldwide. The individual and public health burdens are less than those of other forms of drug use, but CUD accounts for a substantial proportion of persons seeking treatment for drug use disorders owing to the high global prevalence of cannabis use. Cognitive behavioural therapy, motivational enhancement therapy and contingency management can substantially reduce cannabis use and cannabis-related problems, but enduring abstinence is not a common outcome. No pharmacotherapies have been approved for cannabis use or CUD, although a number of drug classes (such as cannabinoid agonists) have shown promise and require more rigorous evaluation. Treatment of cannabis use and CUD is often complicated by comorbid mental health and other substance use disorders. The legalization of non-medical cannabis use in some high-income countries may increase the prevalence of CUD by making more potent cannabis products more readily available at a lower price. States that legalize medical and non-medical cannabis use should inform users about the risks of CUD and provide information on how to obtain assistance if they develop cannabis-related mental and/or physical health problems.

Molecular genetics of substance use disorders: An umbrella review

BACKGROUND

Substance use disorders (SUD) are a category of psychiatric disorders with a large epidemiological and societal impact around the world. In the last decades, a large number of genetic studies have been published for SUDs.

METHODS

With the objective of having an overview and summarizing the evidence published up to date, we carried out an umbrella review of all the meta-analyses of genetic studies for the following substances: alcohol, tobacco, cannabis, cocaine, opioids, heroin and methamphetamines. Meta-analyses for candidate gene studies and genome-wide association studies (GWAS) were included.

RESULTS

Alcohol and tobacco were the substances with the largest number of meta-analyses, and cannabis, opioids and cocaine the least studied. The following genes were associated with two or more SUDs: OPRM1, DRD2, DRD4, BDNF and SL6A4. The only genes that had an OR higher than two were the SLC6A4 for all addictions, the ADH1B for alcohol dependence, and BDNF for methamphetamine dependence. GWAS confirmed the possible role of CHRNA5 gene in nicotine dependence and identified novel candidate genes in other SUDs, such as FOXP2, PEX and, AUTS2, which need further functional analyses.

CONCLUSIONS

This umbrella review summarizes the evidence of 16 years of research on the genetics of SUDs and provides a broad and detailed overview of results from more than 150 meta-analyses for SUD. The results of this umbrella review will guide the need for future genetic studies geared toward understanding, preventing and treating SUDs.

Adverse Effects of Recreational and Medical Cannabis

PURPOSE OF REVIEW

This comprehensive review discusses the adverse effects known today about marijuana, for either medical or recreational use. It reviews the role of cannabis in the treatment of chronic pain, cognitive and neurological adverse effects, special cases and addiction.

RECENT FINDINGS

Cannabinoids work through the endocannabinoids system and inhibit the release of GABA and glutamate in the brain, impact neuromodulation, as well as dopamine, acetylcholine and norepinephrine release. They affect reward, learning and pain. The use of cannabis is increasing nationally and world-wide for both recreational and medicinal purposes, however, there is relatively only low quality evidence to the efficacy and adverse effects of this. Cannabis and its derivatives may be used for treatment of chronic pain. They are via CB1 receptors that are thought to modulate nociceptive signals in the brain. CB2 receptors in the DRG likely affect pain integration in the afferent pathways, and peripherally CB2 also affects noradrenergic pathways influencing pain. A large proportion of users may see more than 50% of chronic pain alleviation compared with placebo. Cannabis affects cognition, most notably executive function, memory and attention, and may deteriorate the boundary between emotional and executive processing. Cannabis impairs memory in the short run, which become more significant with chronic use, and may also be accompanied by poorer effort, slower processing and impacted attention. It is generally believed that long-term use and earlier age are risk factor for neurocognitive deficits; neuroimaging studies have shown reduced hippocampal volume and density. Executive functions and memory are worse in adolescent users versus adults. Cannabis addiction is different and likely less common than other addictive substances, but up to 10% of users meet criteria for lifetime cannabis dependence. Addiction patterns may be linked to genetic and epigenetic differences. It is still unclear whether abstinence reverses patterns of addiction, and more research is required into this topic.

SUMMARY

Cannabis use has become more abundant for both medical and recreational use. It carries likely benefits in the form of analgesia, anti-emesis and improved appetite in chronic patients. The evidence reviewing adverse effects of this use are still limited, however, exiting data points to a clear link with neurocognitive deterioration, backed by loss of brain volume and density. Addiction is likely complex and variable, and no good data exists to support treatment at this point. It is becoming clear that use in earlier ages carries a higher risk for long-term deficits. As with any other drug, these risks should be considered alongside benefits prior to a decision on cannabis use.

Clinical Trials of Cannabidiol for Substance Use Disorders: Outcome Measures, Surrogate Endpoints, and Biomarkers

Background: Cannabidiol (CBD) is a cannabinoid of potential interest for the treatment of substance use disorders. Our aim was to review the outcome measures, surrogate endpoints, and biomarkers in published and ongoing randomized clinical trials. Methods: We conducted a search in PubMed, Web of Science, PMC, PsycINFO, EMBASE, CENTRAL Cochrane Library, "clinicalTrials.gov," "clinicaltrialsregister.eu," and "anzctr.org.au" for published and ongoing studies. Inclusion criteria were randomized clinical trials (RCTs) examining the use of CBD alone or in association with other cannabinoids, in all substance use disorders. The included studies were analyzed in detail and their qualities assessed by a standardized tool (CONSORT 2010). A short description of excluded studies, consisting in controlled short-term or single administration in non-treatment-seeking drug users, is provided. Findings: The screening retrieved 207 published studies, including only 3 RCTs in cannabis use disorder. Furthermore, 12 excluded studies in cannabis, tobacco, and opioid use disorders are described. Interpretation: Primary outcomes were validated withdrawal symptoms scales and drug use reduction in the three RCTs. In the short-term or crossover studies, the outcome measures were visual analog scales for subjective states; self-rated scales for withdrawal, craving, anxiety, or psychotomimetic symptoms; and laboratory tasks of drug-induced craving, effort expenditure, attentional bias for substance, impulsivity, or anxiety to serve as surrogate endpoints for treatment efficacy. Of note, ongoing studies are now adding peripheral biomarkers of the endocannabinoid system status to predict treatment response. Conclusion: The outcome measures and biomarkers assessed in the ongoing CBD trials for substance use disorders are improving.

Testing structural models of psychopathology at the genomic level

Genome-wide association studies (GWAS) have revealed hundreds of genetic loci associated with the vulnerability to major psychiatric disorders, and post-GWAS analyses have shown substantial genetic correlations among these disorders. This evidence supports the existence of a higher-order structure of psychopathology at both the genetic and phenotypic levels. Despite recent efforts by collaborative consortia such as the Hierarchical Taxonomy of Psychopathology (HiTOP), this structure remains unclear. In this study, we tested multiple alternative structural models of psychopathology at the genomic level, using the genetic correlations among fourteen psychiatric disorders and related psychological traits estimated from GWAS summary statistics. The best-fitting model included four correlated higher-order factors - externalizing, internalizing, thought problems, and neurodevelopmental disorders - which showed distinct patterns of genetic correlations with external validity variables and accounted for substantial genetic variance in their constituent disorders. A bifactor model including a general factor of psychopathology as well as the four specific factors fit worse than the above model. Several model modifications were tested to explore the placement of some disorders - such as bipolar disorder, obsessive-compulsive disorder, and eating disorders - within the broader psychopathology structure. The best-fitting model indicated that eating disorders and obsessive-compulsive disorder, on the one hand, and bipolar disorder and schizophrenia, on the other, load together on the same thought problems factor. These findings provide support for several of the HiTOP higher-order dimensions and suggest a similar structure of psychopathology at the genomic and phenotypic levels.

Does Cannabis Intake Protect Against Non-alcoholic Fatty Liver Disease? A Two-Sample Mendelian Randomization Study

Background and Aim

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. Previous observational studies suggested that cannabis use may be associated with a lower risk for NAFLD but the causal relationship remains unclear. We aim in this study to examine the causal effect of cannabis consumption on the risk of NAFLD using a Mendelian randomization analysis. Clarifying this causal effect is important for cannabis-based drug discovery for NAFLD.

Methods

We used data from the largest-to-date GWAS meta-analysis on lifetime use of cannabis (yes or no) consisting of three cohorts [International Cannabis Consortium (ICC), 23andMe, and the UK Biobank] of European ancestry (total N = 184,765). We also used other GWAS data on cannabis use dependence and cannabis use disorder (CUD). The NAFLD GWAS data were generated from the UK Biobank population (1,122 cases and 399,900 controls). The inverse variance weighted (IVW) method was used to assess the causal impact of cannabis lifetime use on the risk of NAFLD. We also performed a sensitivity analysis using weighted median estimator and MR-Egger.

Results

There was no statistically significant causal effect between either the lifetime cannabis use, cannabis use dependence or CUD and the risk for NAFLD (p > 0.05 for all tests). No significant pleotropic effect was observed based on both MR-PRESSO global test (p = 0.99) and the modified Q’ statistics. However, the study may be underpowered.

Conclusion

Our results demonstrated no evidence that cannabis consumption has a causal effect of protection against the development of NAFLD.

Genetic determinants of cannabis use: a systematic review protocol

BACKGROUND

With the legalization of cannabis in Canada, there is an increase trend in use. Cannabis has been known to have several health implications, one of which is the development of cannabis use disorder (CUD). CUD is more common in males than females, as well as in certain ethnic groups such as Native Americans. Additionally, both environmental and genetic risk factors have been found for cannabis use. The objective of this systematic review will be to summarize the genetic variants associated with cannabis use which have reached borderline genome-wide significance.

METHODS

This systematic review will incorporate articles that have performed a genome-wide association study (GWAS) investigating cannabis use. MEDLINE, Web of Science, EMBASE, GWAS Catalog, GWAS Central, and NIH Database of Genotype and Phenotype will be searched using a comprehensive search strategy. The quality of genetic association studies (Q-Genie) tool will be utilized to assess the quality of the included studies. All screening and data extraction will occur independently by two authors. If feasible, a random-effects meta-analysis will be conducted on pooled odds ratios of single nucleotide polymorphisms reaching borderline genome-wide significance.

DISCUSSION

This systematic review will synthesize available GWAS on cannabis use. Results from this review will inform and direct further investigation of genetic variants associated with cannabis use.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42020176016.

Illicit drug use and the genetic overlap with Cannabis use

BACKGROUND

The use of illicit substances is correlated, meaning that individuals who use one illicit substance are more likely to also use another illicit substance. This association could (partly) be explained by overlapping genetic factors. Genetic overlap may indicate a common underlying genetic predisposition, or can be the result of a causal association.

METHODS

Polygenic scores for lifetime cannabis use were generated in a sample of Dutch participants (N = 8348). We tested the association of a PGS for cannabis use with ecstasy, stimulants and a broad category of illicit drug use. To explore the nature of the relationship: (1) these analyses were repeated separately in cannabis users and non-users and (2) monozogytic twin pairs discordant for cannabis use were compared on their drug use.

RESULTS

The lifetime prevalence was 24.8 % for cannabis, 6.2 % for ecstasy, 6.5 % for stimulants and 7.1 % for any illicit drug use. Significant, positive associations were found between PGS for cannabis use with ecstasy use, stimulants and any illicit drug use. These associations seemed to be stronger in cannabis users compared to non-users for both ecstasy and stimulant use, but only in people born after 1968 and not significant after correction for multiple testing. The discordant twin pair analyses suggested that cannabis use could play a causal role in drug use.

CONCLUSIONS

The genetic liability underlying cannabis use significantly explained variability in ecstasy, stimulant and any illicit drug use. Further research should further explore the underlying mechanism to understand the nature of the association.

Deconstructing the neurobiology of cannabis use disorder

There have been dramatic changes worldwide in the attitudes toward and consumption of recreational and medical cannabis. Cannabinoid receptors, which mediate the actions of cannabis, are abundantly expressed in brain regions known to mediate neural processes underlying reward, cognition, emotional regulation and stress responsivity relevant to addiction vulnerability. Despite debates regarding potential pathological consequences of cannabis use, cannabis use disorder is a clinical diagnosis with high prevalence in the general population and that often has its genesis in adolescence and in vulnerable individuals associated with psychiatric comorbidity, genetic and environmental factors. Integrated information from human and animal studies is beginning to expand insights regarding neurobiological systems associated with cannabis use disorder, which often share common neural characteristics with other substance use disorders, that could inform prevention and treatment strategies.

Psychosocial moderation of polygenic risk for cannabis involvement: the role of trauma exposure and frequency of religious service attendance

Cannabis use and disorders (CUD) are influenced by multiple genetic variants of small effect and by the psychosocial environment. However, this information has not been effectively incorporated into studies of gene-environment interaction (GxE). Polygenic risk scores (PRS) that aggregate the effects of genetic variants can aid in identifying the links between genetic risk and psychosocial factors. Using data from the Pasman et al. GWAS of cannabis use (meta-analysis of data from the International Cannabis Consortium and UK Biobank), we constructed PRS in the Collaborative Study on the Genetics of Alcoholism (COGA) participants of European (N: 7591) and African (N: 3359) ancestry. The primary analyses included only individuals of European ancestry, reflecting the ancestral composition of the discovery GWAS from which the PRS was derived. Secondary analyses included the African ancestry sample. Associations of PRS with cannabis use and DSM-5 CUD symptom count (CUDsx) and interactions with trauma exposure and frequency of religious service attendance were examined. Models were adjusted for sex, birth cohort, genotype array, and ancestry. Robustness models were adjusted for cross-term interactions. Higher PRS were associated with a greater likelihood of cannabis use and with CUDsx among participants of European ancestry (p < 0.05 and p < 0.1 thresholds, respectively). PRS only influenced cannabis use among those exposed to trauma (R2: 0.011 among the trauma exposed vs. R2: 0.002 in unexposed). PRS less consistently influenced cannabis use among those who attend religious services less frequently; PRS × religious service attendance effects were attenuated when cross-term interactions with ancestry and sex were included in the model. Polygenic liability to cannabis use was related to cannabis use and, less robustly, progression to symptoms of CUD. This study provides the first evidence of PRS × trauma for cannabis use and demonstrates that ignoring important aspects of the psychosocial environment may mask genetic influences on polygenic traits.

Risky behaviors and Parkinson disease: A mendelian randomization study

Objective

To examine causal associations between risky behavior phenotypes and Parkinson disease using a mendelian randomization approach.

Methods

We used 2-sample mendelian randomization to generate unconfounded estimates using summary statistics from 2 independent, large meta-analyses of genome-wide association studies on risk-taking behaviors (n = 370,771–939,908) and Parkinson disease (cases n = 9,581, controls n = 33,245). We used the inverse variance weighted method as the main method for judging causality.

Results

Our results support a strong protective association between the tendency to smoke and Parkinson disease (odds ratio [OR] 0.714 per log odds of ever smoking, 95% confidence interval [CI] 0.568–0.897, p = 0.0041, Cochran Q test p = 0.238; I² index 6.3%). Furthermore, we observed risk association trends between automobile speed propensity and the number of sexual partners and Parkinson disease after removal of overlapping loci with other risky traits (OR 1.986 for each 1-SD increase in normalized automobile speed propensity, 95% CI 1.215–3.243, p = 0.0066; OR 1.635 for each 1-SD increase in number of sexual partners, 95% CI 1.165–2.293, p = 0.0049).

Conclusion

These findings provide support for a causal relationship between general risk tolerance and Parkinson disease and may provide new insights into the pathogenic mechanisms leading to the development of Parkinson disease.

Genome-wide association meta-analysis of cocaine dependence: Shared genetics with comorbid conditions

Cocaine dependence is a complex psychiatric disorder that is highly comorbid with other psychiatric traits. Twin and adoption studies suggest that genetic variants contribute substantially to cocaine dependence susceptibility, which has an estimated heritability of 65-79%. Here we performed a meta-analysis of genome-wide association studies of cocaine dependence using four datasets from the dbGaP repository (2085 cases and 4293 controls, all of them selected by their European ancestry). Although no genome-wide significant hits were found in the SNP-based analysis, the gene-based analysis identified HIST1H2BD as associated with cocaine-dependence (10% FDR). This gene is located in a region on chromosome 6 enriched in histone-related genes, previously associated with schizophrenia (SCZ). Furthermore, we performed LD Score regression analysis with comorbid conditions and found significant genetic correlations between cocaine dependence and SCZ, ADHD, major depressive disorder (MDD) and risk taking. We also found, through polygenic risk score analysis, that all tested phenotypes are significantly associated with cocaine dependence status: SCZ (R² = 2.28%; P = 1.21e-26), ADHD (R² = 1.39%; P = 4.5e-17), risk taking (R² = 0.60%; P = 2.7e-08), MDD (R² = 1.21%; P = 4.35e-15), children's aggressive behavior (R² = 0.3%; P = 8.8e-05) and antisocial behavior (R² = 1.33%; P = 2.2e-16). To our knowledge, this is the largest reported cocaine dependence GWAS meta-analysis in European-ancestry individuals. We identified suggestive associations in regions that may be related to cocaine dependence and found evidence for shared genetic risk factors between cocaine dependence and several comorbid psychiatric traits. However, the sample size is limited and further studies are needed to confirm these results.

Genome-wide association study implicates CHRNA2 in cannabis use disorder

Cannabis is the most frequently used illicit psychoactive substance worldwide; around one in ten users become dependent. The risk for cannabis use disorder (CUD) has a strong genetic component, with twin heritability estimates ranging from 51 to 70%. Here we performed a genome-wide association study of CUD in 2,387 cases and 48,985 controls, followed by replication in 5,501 cases and 301,041 controls. We report a genome-wide significant risk locus for CUD (P = 9.31 × 10^-12) that replicates in an independent population (P_replication = 3.27 × 10^-3, P_{meta-analysis} = 9.09 × 10^-12). The index variant (rs56372821) is a strong expression quantitative trait locus for cholinergic receptor nicotinic α2 subunit (CHRNA2); analyses of the genetically regulated gene expression identified a significant association of CHRNA2 expression with CUD in brain tissue. At the polygenic level, analyses revealed a significant decrease in the risk of CUD with increased load of variants associated with cognitive performance. The results provide biological insights and inform on the genetic architecture of CUD.

FeatSNP: An Interactive Database for Brain-Specific Epigenetic Annotation of Human SNPs

FeatSNP is an online tool and a curated database for exploring 81 million common SNPs’ potential functional impact on the human brain. FeatSNP uses the brain transcriptomes of the human population to improve functional annotation of human SNPs by integrating transcription factor binding prediction, public eQTL information, and brain specific epigenetic landscape, as well as information of Topologically Associating Domains (TADs). FeatSNP supports both single and batched SNP searching, and its interactive user interface enables users to explore the functional annotations and generate publication-quality visualization results. FeatSNP is freely available on the internet at FeatSNP.org with all major web browsers supported.

GWAS of Behavioral Traits

Over the past decade, genome-wide association studies (GWAS) have evolved into a powerful tool to investigate genetic risk factors for human diseases via a hypothesis-free scan of the genome. The success of GWAS for psychiatric disorders and behavioral traits have been somewhat mixed, partly owing to the complexity and heterogeneity of these traits. Significant progress has been made in the last few years in the development and implementation of complex statistical methods and algorithms incorporating GWAS. Such advanced statistical methods applied to GWAS hits in combination with incorporation of different layers of genomics data have catapulted the search for novel genes for behavioral traits and improved our understanding of the complex polygenic architecture of these traits.This chapter will give a brief overview on GWAS and statistical methods currently used in GWAS. The chapter will focus on reviewing the current literature and highlight some of the most important GWAS on psychiatric and other behavioral traits and will conclude with a discussion on future directions.

Constrained maximum entropy models to select genotype interactions associated with censored failure times

We propose a novel screening method targeting genotype interactions associated with disease risks. The proposed method extends the maximum entropy conditional probability model to address disease occurrences over time. Continuous occurrence times are grouped into intervals. The model estimates the conditional distribution over the disease occurrence intervals given individual genotypes by maximizing the corresponding entropy subject to constraints linking genotype interactions to time intervals. The EM algorithm is employed to handle observations with uncertainty, for which the disease occurrence is censored. Stepwise greedy search is proposed to screen a large number of candidate constraints. The minimum description length is employed to select the optimal set of constraints. Extensive simulations show that five or so quantile-dependent intervals are sufficient to categorize disease outcomes into different risk groups. Performance depends on sample size, number of genotypes, and minor allele frequencies. The proposed method outperforms the likelihood ratio test, Lasso, and a previous maximum entropy method with only binary (disease occurrence, non-occurrence) outcomes. Finally, a GWAS study for type 1 diabetes patients is used to illustrate our method. Novel one-genotype and two-genotype interactions associated with neuropathy are identified.