Connecting the dots, genome-wide association studies in substance use

Substance Use Among Middle School Adolescents: Association with Family Members' and Peers' Substance Use and the Mediating Role of School and Mental Difficulties

OBJECTIVE

We assessed the associations of substance (alcohol, tobacco, cannabis, and other illicit drugs) use of adolescents with that of their family members (father, mother, step-parent, brothers/sisters, and grandparents) and peers, and the mediating role of school and mental difficulties (SMDs) which remained insufficiently addressed.

METHODS

This cross-sectional population-based study included 1,559 middle-school adolescents in France (mean age = 13.5 ± 1.3, 778 boys, 781 girls). They completed a questionnaire including socioeconomic features (nationality, family structure and parents' education, occupation, and income), substance use, cumulative number of substance use of family members (father, mother, step-parent, brothers/sisters, and grandparents) and peers (noted familySUcn and peerSUcn), SMDs (grade repetition, suffered physical/verbal violence, sexual abuse, lack of family/peer support, depressive symptoms, suicide attempt, and age at onset). Data were analyzed using logistic regression models and Kaplan-Meier estimates.

RESULTS

Most adolescents had familySUcn 1-2, 3-5, and ≥ 6 (39.1%, 23.0%, and 4.5%, respectively) and peerSUcn 1-2 and ≥ 3 (36.1% and 13.0%, respectively). Strong dose-effect associations were found between all substance use and familySUcn and peerSUcn (odds ratio adjusted for sex, age, and socioeconomic features reaching 13.44 and 9.90, respectively, most with p < .001). SMDs explained more the associations of all substance use with familySUcn than with peerSUcn (contributions reaching 69% and 34%, respectively). The proportion of subjects without each substance use decreased with age more quickly among the adolescents with higher familySUcn or peerSUcn.

CONCLUSIONS

Early prevention reducing familySUcn, peerSUcn and SMDs among adolescents and their families may reduce efficiently initiation and regular use of substances during adolescents' life course.

FOXN3 and GDNF Polymorphisms as Common Genetic Factors of Substance Use and Addictive Behaviors

Epidemiological and phenomenological studies suggest shared underpinnings between multiple addictive behaviors. The present genetic association study was conducted as part of the Psychological and Genetic Factors of Addictions study (n = 3003) and aimed to investigate genetic overlaps between different substance use, addictive, and other compulsive behaviors. Association analyses targeted 32 single-nucleotide polymorphisms, potentially addictive substances (alcohol, tobacco, cannabis, and other drugs), and potentially addictive or compulsive behaviors (internet use, gaming, social networking site use, gambling, exercise, hair-pulling, and eating). Analyses revealed 29 nominally significant associations, from which, nine survived an FDRbl correction. Four associations were observed between FOXN3 rs759364 and potentially addictive behaviors: rs759364 showed an association with the frequency of alcohol consumption and mean scores of scales assessing internet addiction, gaming disorder, and exercise addiction. Significant associations were found between GDNF rs1549250, rs2973033, CNR1 rs806380, DRD2/ANKK1 rs1800497 variants, and the “lifetime other drugs” variable. These suggested that genetic factors may contribute similarly to specific substance use and addictive behaviors. Specifically, FOXN3 rs759364 and GDNF rs1549250 and rs2973033 may constitute genetic risk factors for multiple addictive behaviors. Due to limitations (e.g., convenience sampling, lack of structured scales for substance use), further studies are needed. Functional correlates and mechanisms underlying these relationships should also be investigated.

Heritability of tea drinking and its relationship with cigarette smoking in the Chinese male adult twins

The aims of this study are to estimate the contributions of genetic factors to the variation of tea drinking and cigarette smoking, to examine the roles of genetic factors in their correlation and further to investigate underlying causation between them. We included 11 625 male twin pairs from the Chinese National Twin Registry (CNTR). Bivariate genetic modelling was fitted to explore the genetic influences on tea drinking, cigarette smoking and their correlation. Inference about Causation through Examination of FAmiliaL CONfounding (ICE FALCON) was further used to explore the causal relationship between them. We found that genetic factors explained 17% and 23% of the variation in tea drinking and cigarette smoking, respectively. A low phenotypic association between them was reported (r_ph  = 0.21, 95% confidence interval [CI]: [0.19, 0.24]), which was partly attributed to common genetic factors (r_A  = 0.45, 95% CI [0.19, 1.00]). In the ICE FALCON analysis with current smoking as the exposure, tea drinking was associated with his own (βself = 0.39, 95% CI [0.23, 0.55]) and his co-twin's smoking status (βco-twin = 0.25, 95% CI [0.10, 0.41]). Their association attenuated with borderline significance conditioning on his own smoking status (p = 0.045), indicating a suggestive causal effect of smoking status on tea drinking. On the contrary, when we used tea drinking as the predictor, we found familial confounding between them only. In conclusion, both tea drinking and cigarette smoking were influenced by genetic factors, and their correlation was partly explained by common genetic factors. In addition, our finding suggests that familial confounders account for the relationship between tea drinking and cigarette smoking. And current smoking might have a causal effect on weekly tea drinking, but not vice versa.

Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits

Epidemiological studies show high comorbidity between different mental health problems, indicating that individuals with a diagnosis of one disorder are more likely to develop other mental health problems. Genetic studies reveal substantial sharing of genetic factors across mental health traits. However, mental health is also genetically correlated with socio-economic status (SES), and it is therefore important to investigate and disentangle the genetic relationship between mental health and SES. We used summary statistics from large genome-wide association studies (average N ~ 160,000) to estimate the genetic overlap across nine psychiatric disorders and seven substance use traits and explored the genetic influence of three different indicators of SES. Using genomic structural equation modelling, we show significant changes in patterns of genetic correlations after partialling out SES-associated genetic variation. Our approach allows the separation of disease-specific genetic variation and genetic variation shared with SES, thereby improving our understanding of the genetic architecture of mental health.

Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits

Epidemiological studies show high comorbidity between different mental health problems, indicating that individuals with a diagnosis of one disorder are more likely to develop other mental health problems. Genetic studies reveal substantial sharing of genetic factors across mental health traits. However, mental health is also genetically correlated with socio-economic status (SES), and it is therefore important to investigate and disentangle the genetic relationship between mental health and SES. We used summary statistics from large genome-wide association studies (average N ~ 160,000) to estimate the genetic overlap across nine psychiatric disorders and seven substance use traits and explored the genetic influence of three different indicators of SES. Using genomic structural equation modelling, we show significant changes in patterns of genetic correlations after partialling out SES-associated genetic variation. Our approach allows the separation of disease-specific genetic variation and genetic variation shared with SES, thereby improving our understanding of the genetic architecture of mental health.

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.

Genetic overlap and causal associations between smoking behaviours and mental health

Cigarette smoking is a modifiable behaviour associated with mental health. We investigated the degree of genetic overlap between smoking behaviours and psychiatric traits and disorders, and whether genetic associations exist beyond genetic influences shared with confounding variables (cannabis and alcohol use, risk-taking and insomnia). Second, we investigated the presence of causal associations between smoking initiation and psychiatric traits and disorders. We found significant genetic correlations between smoking and psychiatric disorders and adult psychotic experiences. When genetic influences on known covariates were controlled for, genetic associations between most smoking behaviours and schizophrenia and depression endured (but not with bipolar disorder or most psychotic experiences). Mendelian randomization results supported a causal role of smoking initiation on psychiatric disorders and adolescent cognitive and negative psychotic experiences, although not consistently across all sensitivity analyses. In conclusion, smoking and psychiatric disorders share genetic influences that cannot be attributed to covariates such as risk-taking, insomnia or other substance use. As such, there may be some common genetic pathways underlying smoking and psychiatric disorders. In addition, smoking may play a causal role in vulnerability for mental illness.

Identifying risk factors involved in the common versus specific liabilities to substance use: A genetically informed approach

Individuals most often use several rather than one substance among alcohol, cigarettes or cannabis. This widespread co-occurring use of multiple substances is thought to stem from a common liability that is partly genetic in origin. Genetic risk may indirectly contribute to a common liability to substance use through genetically influenced mental health vulnerabilities and individual traits. To test this possibility, we used polygenic scores indexing mental health and individual traits and examined their association with the common versus specific liabilities to substance use. We used data from the Avon Longitudinal Study of Parents and Children (N = 4218) and applied trait-state-occasion models to delineate the common and substance-specific factors based on four classes of substances (alcohol, cigarettes, cannabis and other illicit substances) assessed over time (ages 17, 20 and 22). We generated 18 polygenic scores indexing genetically influenced mental health vulnerabilities and individual traits. In multivariable regression, we then tested the independent contribution of selected polygenic scores to the common and substance-specific factors. Our results implicated several genetically influenced traits and vulnerabilities in the common liability to substance use, most notably risk taking (bstandardised = 0.14; 95% confidence interval [CI] [0.10, 0.17]), followed by extraversion (bstandardised = -0.10; 95% CI [-0.13, -0.06]), and schizophrenia risk (bstandardised = 0.06; 95% CI [0.02, 0.09]). Educational attainment (EA) and body mass index (BMI) had opposite effects on substance-specific liabilities such as cigarette use (bstandardised-EA = -0.15; 95% CI [-0.19, -0.12]; bstandardised-BMI = 0.05; 95% CI [0.02, 0.09]) and alcohol use (bstandardised-EA = 0.07; 95% CI [0.03, 0.11]; bstandardised-BMI = -0.06; 95% CI [-0.10, -0.02]). These findings point towards largely distinct sets of genetic influences on the common versus specific liabilities.

An integrative systems-based analysis of substance use: eQTL-informed gene-based tests, gene networks, and biological mechanisms

Genome-wide association studies have identified multiple genetic risk factors underlying susceptibility to substance use, however, the functional genes and biological mechanisms remain poorly understood. The discovery and characterization of risk genes can be facilitated by the integration of genome-wide association data and gene expression data across biologically relevant tissues and/or cell types to identify genes whose expression is altered by DNA sequence variation (expression quantitative trait loci; eQTLs). The integration of gene expression data can be extended to the study of genetic co-expression, under the biologically valid assumption that genes form co-expression networks to influence the manifestation of a disease or trait. Here, we integrate genome-wide association data with gene expression data from 13 brain tissues to identify candidate risk genes for 8 substance use phenotypes. We then test for the enrichment of candidate risk genes within tissue-specific gene co-expression networks to identify modules (or groups) of functionally related genes whose dysregulation is associated with variation in substance use. We identified eight gene modules in brain that were enriched with gene-based association signals for substance use phenotypes. For example, a single module of 40 co-expressed genes was enriched with gene-based associations for drinks per week and biological pathways involved in GABA synthesis, release, reuptake and degradation. Our study demonstrates the utility of eQTL and gene co-expression analysis to uncover novel biological mechanisms for substance use traits.

Cannabis Use Is Associated With Increased Risk of Cigarette Smoking Initiation, Persistence, and Relapse Among Adults in the United States

INTRODUCTION

Despite increasing use of cannabis, it is unclear how cannabis use is related to cigarette transitions. This study examined cannabis use and smoking initiation, persistence, and relapse over 1 year among a nationally representative sample of US adults.

METHODS

Data were from US adults (≥18 years) who completed two waves of longitudinal data from the Population Assessment of Tobacco and Health Study (Wave 1, 2013-2014; Wave 2, 2014-2015; n = 26 341). Logistic regression models were used to calculate the risk of Wave 2 incident smoking among Wave 1 never-smokers, smoking cessation among Wave 1 smokers, and smoking relapse among Wave 1 former smokers by Wave 1 cannabis use. Analyses were adjusted for age, gender, race/ethnicity, income, and education.

RESULTS

Among Wave 1 never-smokers, cannabis use was associated with increased odds of initiation of nondaily (adjusted odds ratio [AOR] = 5.50, 95% confidence limits [CL] = 4.02-7.55) and daily cigarette smoking (AOR = 6.70, 95% CL = 4.75-9.46) 1 year later. Among Wave 1 daily smokers, cannabis use was associated with reduced odds of smoking cessation (AOR = 0.36, 95% CL = 0.20-0.65). Among Wave 1 former smokers, cannabis use was associated with increased odds of relapse to daily and nondaily cigarette smoking (daily AOR = 1.90, 95% CL = 1.11-3.26; nondaily AOR = 2.33, 95% CL = 1.61-3.39).

CONCLUSIONS

Cannabis use was associated with increased cigarette smoking initiation, decreased smoking cessation, and increased smoking relapse among adults in the United States. Increased public education about the relationship between cannabis use and cigarette smoking transitions may be needed as cannabis use becomes more common among US adults.

IMPLICATIONS

As cannabis use increases in the United States and other countries, an evaluation of the relationships of cannabis use to other health-related behaviors (eg, cigarette smoking) is needed to understand the population-level impact of legalization. Little is known about associations between cannabis use and cigarette smoking transitions (1) using recent longitudinal data, (2) among adults, and (3) examining transitions other than smoking initiation (eg, smoking relapse). Our results suggest that among US adults, cannabis use was associated with increased cigarette smoking initiation among never-smokers, decreased cigarette smoking cessation among current smokers, and increased cigarette smoking relapse among former smokers.

Stability in effects of different smoking-related polygenic risk scores over age and smoking phenotypes

PURPOSE

Polygenic risk scores (PRSs) for smoking behavior largely fail to consider the demonstrated developmental change in genetic influence over age and stage of smoking behaviors. Additionally, few studies have examined how stage-specific smoking PRSs (e.g. for initiation vs. smoking heaviness) generalize to other stages of risk. The current study examines the stability of PRS effects over age, and how specifically calibrated PRSs associate with other smoking phenotypes.

METHODS

7228 participants were from the National Longitudinal Study of Adolescent to Adult Health, who had calculated PRSs for two smoking phenotypes, Centers for Disease Control and Prevention (CDC) smoking initiation status, and cigarettes per day (CPD). Four time-varying effects models estimated associations between both PRSs and four smoking phenotypes (CDC status, cigarettes/day on smoking days, any past-30 day smoking, and past-30 day daily smoking) over adolescence and young adulthood.

FINDINGS

The time-varying effects models demonstrated that both PRSs significantly associated with all four phenotypes age. PRS effects were similar, in both odds ratios and the overlap of 95 % confidence intervals. There were increases in PRS associations with quantity of smoking over age, and a decrease in PRS effects over age for the CDC smoking status phenotype over early to late adolescence.

CONCLUSIONS

Smoking PRSs can be robust predictors of smoking behavior over age. However, the lack of differentiation between specific PRSs and multiple smoking phenotypes, as well as the added contribution of both PRSs to explaining genetic variance, indicates a need to reconceptualize phenotypic measurement used to calibrate smoking PRSs.

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.

Investigating the genetic and causal relationship between initiation or use of alcohol, caffeine, cannabis and nicotine

BACKGROUND

Caffeine, alcohol, nicotine and cannabis are commonly used psychoactive substances. While the use of these substances has been previously shown to be genetically correlated, causality between these substance use traits remains unclear. We aimed to revisit the genetic relationships among different measures of SU using genome-wide association study (GWAS) summary statistics from the UK Biobank, International Cannabis Consortium, and GWAS & Sequencing Consortium of Alcohol and Nicotine use.

METHODS

We obtained GWAS summary statistics from the aforementioned consortia for ten substance use traits including various measures of alcohol consumption, caffeine consumption, cannabis initiation and smoking behaviours. We then conducted SNP-heritability (h²) estimation for individual SU traits, followed by genetic correlation analyses and two-sample Mendelian randomisation (MR) studies between substance use trait pairs.

RESULTS

SNP h² of the ten traits ranged from 0.03 to 0.11. After multiple testing correction, 29 of the 45 trait pairs showed evidence of being genetically correlated. MR analyses revealed that most SU traits were not causally associated with each other. However, we found evidence for an MR association between regular smoking initiation and caffeine consumption 40.17 mg; 95 % CI: [24.01, 56.33] increase in caffeine intake per doubling of odds in smoking initiation). Our findings were robust against horizontal pleiotropy, SNP-outliers, and the direction of causality was consistent in all MR analyses.

CONCLUSIONS

Most of the substance traits were genetically correlated but there is little evidence to establish causality apart from the relationship between smoking initiation and caffeine consumption.

Potential influence of socioeconomic status on genetic correlations between alcohol consumption measures and mental health

BACKGROUND

Frequency and quantity of alcohol consumption are metrics commonly used to measure alcohol consumption behaviors. Epidemiological studies indicate that these alcohol consumption measures are differentially associated with (mental) health outcomes and socioeconomic status (SES). The current study aims to elucidate to what extent genetic risk factors are shared between frequency and quantity of alcohol consumption, and how these alcohol consumption measures are genetically associated with four broad phenotypic categories: (i) SES; (ii) substance use disorders; (iii) other psychiatric disorders; and (iv) psychological/personality traits.

METHODS

Genome-Wide Association analyses were conducted to test genetic associations with alcohol consumption frequency (N = 438 308) and alcohol consumption quantity (N = 307 098 regular alcohol drinkers) within UK Biobank. For the other phenotypes, we used genome-wide association studies summary statistics. Genetic correlations (rg) between the alcohol measures and other phenotypes were estimated using LD score regression.

RESULTS

We found a substantial genetic correlation between the frequency and quantity of alcohol consumption (rg = 0.52). Nevertheless, both measures consistently showed opposite genetic correlations with SES traits, and many substance use, psychiatric, and psychological/personality traits. High alcohol consumption frequency was genetically associated with high SES and low risk of substance use disorders and other psychiatric disorders, whereas the opposite applies for high alcohol consumption quantity.

CONCLUSIONS

Although the frequency and quantity of alcohol consumption show substantial genetic overlap, they consistently show opposite patterns of genetic associations with SES-related phenotypes. Future studies should carefully consider the potential influence of SES on the shared genetic etiology between alcohol and adverse (mental) health outcomes.

Associations between polygenic risk for tobacco and alcohol use and liability to tobacco and alcohol use, and psychiatric disorders in an independent sample of 13,999 Australian adults

BACKGROUND

Substance use, substance use disorders (SUDs), and psychiatric disorders commonly co-occur. Genetic risk common to these complex traits is an important explanation; however, little is known about how polygenic risk for tobacco or alcohol use overlaps the genetic risk for the comorbid SUDs and psychiatric disorders.

METHODS

We constructed polygenic risk scores (PRSs) using GWAS meta-analysis summary statistics from a large discovery sample, GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN), for smoking initiation (SI; N = 631,564), age of initiating regular smoking (AI; N = 258,251), cigarettes per day (CPD; N = 258,999), smoking cessation (SC; N = 312,273), and drinks per week (DPW; N = 527,402). We then estimated the fixed effect of these PRSs on the liability to 15 phenotypes related to tobacco and alcohol use, substance use disorders, and psychiatric disorders in an independent target sample of Australian adults.

RESULTS

After adjusting for multiple testing, 10 of 75 combinations of discovery and target phenotypes remained significant. PRS-SI (R² range: 1.98%-5.09 %) was positively associated with SI, DPW, and with DSM-IV and FTND nicotine dependence, and conduct disorder. PRS-AI (R²: 3.91 %) negatively associated with DPW. PRS-CPD (R²: 1.56 %-1.77 %) positively associated with DSM-IV nicotine dependence and conduct disorder. PRS-DPW (R²: 3.39 %-6.26 %) positively associated with only DPW. The variation of DPW was significantly influenced by sex*PRS-SI, sex*PRS-AI and sex*PRS-DPW. Such interaction effect was not detected in the other 14 phenotypes.

CONCLUSIONS

Polygenic risks associated with tobacco use are also associated with liability to alcohol consumption, nicotine dependence, and conduct disorder.

New alcohol-related genes suggest shared genetic mechanisms with neuropsychiatric disorders

Excessive alcohol consumption is one of the main causes of death and disability worldwide. Alcohol consumption is a heritable complex trait. Here we conducted a meta-analysis of genome-wide association studies of alcohol consumption (g d-1) from the UK Biobank, the Alcohol Genome-Wide Consortium and the Cohorts for Heart and Aging Research in Genomic Epidemiology Plus consortia, collecting data from 480,842 people of European descent to decipher the genetic architecture of alcohol intake. We identified 46 new common loci and investigated their potential functional importance using magnetic resonance imaging data and gene expression studies. We identify genetic pathways associated with alcohol consumption and suggest genetic mechanisms that are shared with neuropsychiatric disorders such as schizophrenia.

Genome-Wide Association Study Meta-Analysis of the Alcohol Use Disorders Identification Test (AUDIT) in Two Population-Based Cohorts

OBJECTIVE

Alcohol use disorders are common conditions that have enormous social and economic consequences. Genome-wide association analyses were performed to identify genetic variants associated with a proxy measure of alcohol consumption and alcohol misuse and to explore the shared genetic basis between these measures and other substance use, psychiatric, and behavioral traits.

METHOD

This study used quantitative measures from the Alcohol Use Disorders Identification Test (AUDIT) from two population-based cohorts of European ancestry (UK Biobank [N=121,604] and 23andMe [N=20,328]) and performed a genome-wide association study (GWAS) meta-analysis. Two additional GWAS analyses were performed, a GWAS for AUDIT scores on items 1-3, which focus on consumption (AUDIT-C), and for scores on items 4-10, which focus on the problematic consequences of drinking (AUDIT-P).

RESULTS

The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; this study also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (rg=0.76-0.92) and DSM-IV alcohol dependence (rg=0.33-0.63). AUDIT-P and AUDIT-C scores showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P score was significantly positively genetically correlated with schizophrenia (rg=0.22), major depressive disorder (rg=0.26), and attention deficit hyperactivity disorder (rg=0.23), whereas AUDIT-C score was significantly negatively genetically correlated with major depressive disorder (rg=-0.24) and ADHD (rg=-0.10). This study also used the AUDIT data in the UK Biobank to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total scores ≤4 as control subjects and those with scores ≥12 as case subjects produced a significant high genetic correlation with DSM-IV alcohol dependence (rg=0.82) while retaining most subjects.

CONCLUSIONS

AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and alcohol use disorders.

Genome-wide association study of alcohol use disorder identification test (AUDIT) scores in 20 328 research participants of European ancestry

Genetic factors contribute to the risk for developing alcohol use disorder (AUD). In collaboration with the genetics company 23andMe, Inc., we performed a genome-wide association study of the alcohol use disorder identification test (AUDIT), an instrument designed to screen for alcohol misuse over the past year. Our final sample consisted of 20 328 research participants of European ancestry (55.3% females; mean age = 53.8, SD = 16.1) who reported ever using alcohol. Our results showed that the 'chip-heritability' of AUDIT score, when treated as a continuous phenotype, was 12%. No loci reached genome-wide significance. The gene ADH1C, which has been previously implicated in AUD, was among our most significant associations (4.4 × 10-7 ; rs141973904). We also detected a suggestive association on chromosome 1 (2.1 × 10-7 ; rs182344113) near the gene KCNJ9, which has been implicated in mouse models of high ethanol drinking. Using linkage disequilibrium score regression, we identified positive genetic correlations between AUDIT score, high alcohol consumption and cigarette smoking. We also observed an unexpected positive genetic correlation between AUDIT and educational attainment and additional unexpected negative correlations with body mass index/obesity and attention-deficit/hyperactivity disorder. We conclude that conducting a genetic study using responses to an online questionnaire in a population not ascertained for AUD may represent a cost-effective strategy for elucidating aspects of the etiology of AUD.

Investigating causal associations between use of nicotine, alcohol, caffeine and cannabis: a two-sample bidirectional Mendelian randomization study

BACKGROUND AND AIMS

Epidemiological studies consistently show co-occurrence of use of different addictive substances. Whether these associations are causal or due to overlapping underlying influences remains an important question in addiction research. Methodological advances have made it possible to use published genetic associations to infer causal relationships between phenotypes. In this exploratory study, we used Mendelian randomization (MR) to examine the causality of well-established associations between nicotine, alcohol, caffeine and cannabis use.

METHODS

Two-sample MR was employed to estimate bidirectional causal effects between four addictive substances: nicotine (smoking initiation and cigarettes smoked per day), caffeine (cups of coffee per day), alcohol (units per week) and cannabis (initiation). Based on existing genome-wide association results we selected genetic variants associated with the exposure measure as an instrument to estimate causal effects. Where possible we applied sensitivity analyses (MR-Egger and weighted median) more robust to horizontal pleiotropy.

RESULTS

Most MR tests did not reveal causal associations. There was some weak evidence for a causal positive effect of genetically instrumented alcohol use on smoking initiation and of cigarettes per day on caffeine use, but these were not supported by the sensitivity analyses. There was also some suggestive evidence for a positive effect of alcohol use on caffeine use (only with MR-Egger) and smoking initiation on cannabis initiation (only with weighted median). None of the suggestive causal associations survived corrections for multiple testing.

CONCLUSIONS

Two-sample Mendelian randomization analyses found little evidence for causal relationships between nicotine, alcohol, caffeine and cannabis use.

ENIGMA-Viewer: interactive visualization strategies for conveying effect sizes in meta-analysis

BACKGROUND

Global scale brain research collaborations such as the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium are beginning to collect data in large quantity and to conduct meta-analyses using uniformed protocols. It becomes strategically important that the results can be communicated among brain scientists effectively. Traditional graphs and charts failed to convey the complex shapes of brain structures which are essential to the understanding of the result statistics from the analyses. These problems could be addressed using interactive visualization strategies that can link those statistics with brain structures in order to provide a better interface to understand brain research results.

RESULTS

We present ENIGMA-Viewer, an interactive web-based visualization tool for brain scientists to compare statistics such as effect sizes from meta-analysis results on standardized ROIs (regions-of-interest) across multiple studies. The tool incorporates visualization design principles such as focus+context and visual data fusion to enable users to better understand the statistics on brain structures. To demonstrate the usability of the tool, three examples using recent research data are discussed via case studies.

CONCLUSIONS

ENIGMA-Viewer supports presentations and communications of brain research results through effective visualization designs. By linking visualizations of both statistics and structures, users can gain more insights into the presented data that are otherwise difficult to obtain. ENIGMA-Viewer is an open-source tool, the source code and sample data are publicly accessible through the NITRC website ( http://www.nitrc.org/projects/enigmaviewer_20 ). The tool can also be directly accessed online ( http://enigma-viewer.org ).