Using genetic information to inform policy on cannabis

Examining the shared etiology of psychopathology with genome-wide association studies

Genome-wide association studies (GWASs) have ushered in a new era of reproducible discovery in psychiatric genetics. The field has now identified hundreds of common genetic variants that are associated with mental disorders, and many of them influence more than one disorder. By advancing the understanding of causal biology underlying psychopathology, GWAS results are poised to inform the development of novel therapeutics, stratification of at-risk patients, and perhaps even the revision of top-down classification systems in psychiatry. Here, we provide a concise review of GWAS findings with an emphasis on findings that have elucidated the shared genetic etiology of psychopathology, summarizing insights at three levels of analysis: 1) genome-wide architecture; 2) networks, pathways, and gene sets; and 3) individual variants/genes. Three themes emerge from these efforts. First, all psychiatric phenotypes are heritable, highly polygenic, and influenced by many pleiotropic variants with incomplete penetrance. Second, GWAS results highlight the broad etiological roles of neuronal biology, system-wide effects over localized effects, and early neurodevelopment as a critical period. Third, many loci that are robustly associated with multiple forms of psychopathology harbor genes that are involved in synaptic structure and function. Finally, we conclude our review by discussing the implications that GWAS results hold for the field of psychiatry, as well as expected challenges and future directions in the next stage of psychiatric genetics.

Is there a causal relationship between executive function and liability to mental health and substance use? A Mendelian randomization approach

Poorer performance in tasks testing executive function (EF) is associated with a range of psychopathologies such as schizophrenia, major depressive disorder (MDD) and anxiety, as well as smoking and alcohol consumption. We used two-sample bidirectional Mendelian randomization to examine whether these may reflect causal relationships and the direction of causation. We used genome-wide association study summary data (N = 17 310 to 848 460) for a common EF factor score (cEF), schizophrenia, MDD, anxiety, smoking initiation, alcohol consumption, alcohol dependence and cannabis use disorder (CUD). We found evidence of increased cEF on reduced schizophrenia liability (OR = 0.10; CI: 0.05 to 0.19; p-value = 3.43 × 10^-12), MDD liability (OR = 0.52; CI: 0.38 to 0.72; p-value = 5.23 × 10^-05), drinks per week (β = -0.06; CI: -0.10 to -0.02; p-value = 0.003) and CUD liability (OR = 0.27; CI: 0.12 to 0.61; p-value = 1.58 × 10^-03). We also found evidence of increased schizophrenia liability (β = -0.04; CI: -0.04 to -0.03; p-value = 3.25 × 10^-27) and smoking initiation on decreased cEF (β = -0.06; CI: -0.09 to -0.03; p-value = 6.11 × 10^-05). Our results indicate potential causal relationships between cEF and mental health and substance use. Further studies are required to improve our understanding of the underlying mechanisms of these effects, but our results suggest that EF may be a promising intervention target for mental health and substance use.

Mapping drug epidemiology futures

Epidemiology is a core discipline generating evidence to inform and drive drug policy. In this essay, we speculate on what the future of drug epidemiology might become. We highlight for attention two areas shaping the future of drug epidemiology: nesting epidemiology within a 'syndemic' and 'relational' approach; and innovating in relation to causal inference in the face of complexity. We argue that shifts towards a more relational approach emphasise contingency, including in relation to how drugs might constitute benefit or harm. This leads us to speculate on a 'positive epidemiology'; one that is configured not merely in relation to harm but also in relation to the potential benefits of drugs in relation to well-being. In responding to the complex challenges of delineating contingent causalities, we emphasise the potential of carefully conducted observational study designs that go beyond statistical associations to test causal inference. We acknowledge that each of these developments we describe - a shift towards more relational approaches which emphasise contingent causation, and methodological innovations in relation to establishing causal inference - can be at odds with the other in how they imagine drug epidemiology futures.

Using Mendelian randomization analysis to better understand the relationship between mental health and substance use: a systematic review

BACKGROUND

Poor mental health has consistently been associated with substance use (smoking, alcohol drinking, cannabis use, and consumption of caffeinated drinks). To properly inform public health policy it is crucial to understand the mechanisms underlying these associations, and most importantly, whether or not they are causal.

METHODS

In this pre-registered systematic review, we assessed the evidence for causal relationships between mental health and substance use from Mendelian randomization (MR) studies, following PRISMA. We rated the quality of included studies using a scoring system that incorporates important indices of quality, such as the quality of phenotype measurement, instrument strength, and use of sensitivity methods.

RESULTS

Sixty-three studies were included for qualitative synthesis. The final quality rating was '-' for 16 studies, '- +' for 37 studies, and '+'for 10 studies. There was robust evidence that higher educational attainment decreases smoking and that there is a bi-directional, increasing relationship between smoking and (symptoms of) mental disorders. Another robust finding was that higher educational attainment increases alcohol use frequency, but decreases binge-drinking and alcohol use problems, and that mental disorders causally lead to more alcohol drinking without evidence for the reverse.

CONCLUSIONS

The current MR literature increases our understanding of the relationship between mental health and substance use. Bi-directional causal relationships are indicated, especially for smoking, providing further incentive to strengthen public health efforts to decrease substance use. Future MR studies should make use of large(r) samples in combination with detailed phenotypes, a wide range of sensitivity methods, and triangulate with other research methods.