Systematic Review of Polygenic Gene-Environment Interaction in Tobacco, Alcohol, and Cannabis Use

Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.

Examining interactions between polygenic scores and interpersonal trauma exposure on alcohol consumption and use disorder in an ancestrally diverse college cohort

Introduction: Genetic factors impact alcohol consumption and use disorder (AUD), with large-scale genome-wide association studies (GWAS) identifying numerous associated variants. Aggregate genetic methods in combination with important environmental factors (e.g., interpersonal trauma [IPT]) can be applied to expand our understanding of the ways by which genetic and environmental variables work together to influence alcohol consumption and disordered use. The present study aimed to detail the relationships between genome-wide polygenic scores (PGS) for alcohol phenotypes (i.e., alcohol consumption and AUD status) and IPT exposure as well as the interaction between them across ancestry. Methods: Data were drawn from the Spit for Science (S4S) study, a US college student population, where participants reported on IPT exposure prior to college and alcohol consumption and problems during college (N = 9,006; ancestry: 21.3% African [AFR], 12.5% Admixed Americas [AMR], 9.6% East Asian [EAS], 48.1% European [EUR], 8.6% South Asian [SAS]). Two trans-ancestry PGS were constructed, one for alcohol consumption and another for AUD, using large-scale GWAS summary statistics from multiple ancestries weighted using PRS-CSx. Regression models were applied to test for the presence of associations between alcohol-PGS and IPT main and interaction effects. Results: In the meta-analysis across ancestry groups, IPT exposure and PGS were significantly associated with alcohol consumption (βIPT = 0.31, P IPT = 0.0002; βPGS = 0.09, P PGS = 0.004) and AUD (ORIPT = 1.12, P IPT = 3.5 × 10-8; ORPGS = 1.02, P PGS = 0.002). No statistically significant interactions were detected between IPT and sex nor between IPT and PGS. When inspecting ancestry specific results, the alcohol consumption-PGS and AUD-PGS were only statistically significant in the EUR ancestry group (βPGS = 0.09, P PGS = 0.04; ORPGS = 1.02, P PGS = 0.022, respectively). Discussion: IPT exposure prior to college was strongly associated with alcohol outcomes in this college-age sample, which could be used as a preventative measure to identify students at high risk for problematic alcohol use. Additionally, results add to developing evidence of polygenic score association in meta-analyzed samples, highlighting the importance of continued efforts to increase ancestral representation in genetic studies and inclusive analytic approaches to increase the generalizability of results from genetic association studies.

Positive personality traits moderate persistent high alcohol consumption, determined by polygenic risk in U.S. military veterans: results from a 10-year, population-based, observational cohort study

BACKGROUND

Understanding the interplay between psychosocial factors and polygenic risk scores (PRS) may help elucidate the biopsychosocial etiology of high alcohol consumption (HAC). This study examined the psychosocial moderators of HAC, determined by polygenic risk in a 10-year longitudinal study of US military veterans. We hypothesized that positive psychosocial traits (e.g. social support, personality traits, optimism, gratitude) may buffer risk of HAC in veterans with greater polygenic liability for alcohol consumption (AC).

METHODS

Data were analyzed from 1323 European-American US veterans who participated in the National Health and Resilience in Veterans Study, a 10-year, nationally representative longitudinal study of US military veterans. PRS reflecting genome-wide risk for AC (AUDIT-C) was derived from a Million Veteran Program genome-wide association study (N = 200 680).

RESULTS

Among the total sample, 328 (weighted 24.8%) had persistent HAC, 131 (weighted 9.9%) had new-onset HAC, 44 (weighted 3.3%) had remitted HAC, and 820 (weighted 62.0%) had no/low AC over the 10-year study period. AUDIT-C PRS was positively associated with persistent HAC relative to no/low AC [relative risk ratio (RRR) = 1.43, 95% confidence interval (CI) = 1.23-1.67] and remitted HAC (RRR = 1.63, 95% CI = 1.07-2.50). Among veterans with higher AUDIT-C PRS, greater baseline levels of agreeableness and greater dispositional gratitude were inversely associated with persistent HAC.

CONCLUSIONS

AUDIT-C PRS was prospectively associated with persistent HAC over a 10-year period, and agreeableness and dispositional gratitude moderated this association. Clinical interventions designed to target these modifiable psychological traits may help mitigate risk of persistent HAC in veterans with greater polygenic liability for persistent HAC.

The Associations of Adverse Childhood Experiences (ACEs) With Substance Use in Young Adults: A Systematic Review

Introduction

Young adulthood is a transitional period between adolescence and adulthood. Due to the unique pressures of taking on a new social role and associated uncertainties, young adults are at heightened risk for drug and alcohol use. Furthermore, adverse childhood experiences (ACEs) increases the likelihood of using maladaptive coping strategies such as using substances to avoid or soothe negative emotions. The current review aimed to summarize the associations between exposure to ACEs before the age of 18 years and subsequent drug or alcohol use between the ages of 18 and 25 years.

Methods

The review was performed in accordance with the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The literature search of the Web of Science, PubMed, and PsycINFO databases was conducted in February 2022.

Results

The initial search yielded 7178 articles, with 777 duplicates. Consequently, 6401 titles were inspected for relevance. After reading the full text, 88 articles were included in the review.

Conclusion

This review provides clear evidence that exposure to multiple ACEs is a robust risk factor for the use of alcohol, cannabis and other drugs by young adults. Poor self-regulation and maladaptive coping strategies were identified as mechanisms explaining this link; however, further detailed research is needed.

Candidate gene-environment interactions in substance abuse: A systematic review

BACKGROUND

The abuse of psychogenic drugs can lead to multiple health-related problems. Genetic and environmental vulnerabilities are factors in the emergence of substance use disorders. Empirical evidence regarding the gene-environment interaction in substance use is mixed. Summaries of the latest findings from a candidate gene approach will be useful for revealing the significance of particular gene contributions. Thus, we aim to identify different gene-environment interactions in patterns of substance use and investigate whether any effects trend notably across different genders and races.

METHODS

We reviewed published studies, until March 1, 2022, on substance use for candidate gene-environment interaction. Basic demographics of the included studies, target genes, environmental factors, main findings, patterns of gene-environment interaction, and other relevant information were collected and summarized.

RESULTS

Among a total of 44 studies, 38 demonstrated at least one significant interaction effect. About 61.5% of studies on the 5-HTTLPR gene, 100% on the MAOA gene, 42.9% on the DRD2 gene, 50% on the DRD4 gene, 50% on the DAT gene, 80% on the CRHR1 gene, 100% on the OPRM1 gene, 100% on the GABRA1 gene, and 50% on the CHRNA gene had a significant gene-environment interaction effect. The diathesis-stress model represents a dominant interaction pattern (89.5%) in the studies with a significant interaction effect; the remaining significant effect on substance use is found in the differential susceptibility model. The social push and swing model were not reported in the included studies.

CONCLUSION

The gene-environment interaction research on substance use behavior is methodologically multidimensional, which causes difficulty in conducting pooled analysis, or stated differently-making it hard to identify single sources of significant influence over maladaptive patterns of drug taking. In decreasing the heterogeneity and facilitating future pooled analysis, researchers must (1) replicate the existing studies with consistent study designs and measures, (2) conduct power calculations to report gene-environment correlations, (3) control for covariates, and (4) generate theory-based hypotheses with factorial based experiments when designing future studies.

Association between genetic risk of alcohol consumption and alcohol-related morbidity and mortality under different alcohol policy conditions: Evidence from the Finnish alcohol price reduction of 2004

AIMS

Harmful alcohol consumption is influenced by both genetic susceptibility and the price of alcohol. Many previous studies have observed that genetic susceptibility to consumption of alcohol is more predictive in less restrictive drinking conditions. We assess whether such a pattern applies when the prices of alcoholic beverages are decreased.

DESIGN

Data consist of genetically informed population-representative surveys (FINRISK 1992, 1997, 2002 and Health 2000) linked to administrative registers. We analysed the interaction between a polygenic score (PGS) for alcoholic drinks per week consumed and price reduction in predicting the incidence of alcohol-related hospitalizations and deaths in difference-in-difference and interrupted time-series frameworks.

SETTING

Individuals in Finland were followed quarter-yearly from 1 March 2000 to 31 May 2008.

PARTICIPANTS

A total of 22 152 individuals (607 132-person quarter-years, 1399 outcome events) aged 30-79 years.

INTERVENTION

A natural experiment stemming from the alcohol tax reduction in March 2004 and import deregulation in May 2004.

MEASUREMENTS

Outcome was quarter-yearly-measured alcohol-related death or hospitalization. The independent variables of main interest were PGS and a price reform indicator. We adjusted for gender, age, age squared, season, 10 first principal components of the genome, data collection round and genotyping batch.

FINDINGS

Both alcohol price reduction and one standard deviation change in PGS were associated with alcohol-related health outcomes; odds ratios (ORs) were 1.32, 95% confidence interval (CI) = 1.13, 1.53 and 1.26, 95% CI = 1.12, 1.42 in the 8-year follow-up, respectively. The association between PGS and alcohol-related morbidity was similar before and after the alcohol price reform (PGS × price reform interaction OR = 0.96, 95% CI = 0.81, 1.14). These results were robust across different follow-up periods and measurement and analysis strategies.

CONCLUSIONS

Although the decrease of alcohol price in Finland in 2004 substantially increased overall alcohol-related morbidity and mortality, the genetic susceptibility to alcohol consumption did not become more manifest in predicting them.

Interplay between genetic risk and the parent environment in adolescence and substance use in young adulthood: A TRAILS study

Many adolescents start using tobacco, alcohol, and cannabis. Genetic vulnerability, parent characteristics in young adolescence, and interaction (GxE) and correlation (rGE) between these factors could contribute to the development of substance use. Using prospective data from the TRacking Adolescent Individuals' Lives Survey (TRAILS; N = 1,645), we model latent parent characteristics in young adolescence to predict young adult substance use. Polygenic scores (PGS) are created based on genome-wide association studies (GWAS) for smoking, alcohol use, and cannabis use. Using structural equation modeling we model the direct, GxE, and rGE effects of parent factors and PGS on young adult smoking, alcohol use, and cannabis initiation. The PGS, parental involvement, parental substance use, and parent-child relationship quality predicted smoking. There was GxE such that the PGS amplified the effect of parental substance use on smoking. There was rGE between all parent factors and the smoking PGS. Alcohol use was not predicted by genetic or parent factors, nor by interplay. Cannabis initiation was predicted by the PGS and parental substance use, but there was no GxE or rGE. Genetic risk and parent factors are important predictors of substance use and show GxE and rGE in smoking. These findings can act as a starting point for identifying people at risk.

Stress-related exposures amplify the effects of genetic susceptibility on depression and anxiety

It is unclear whether and to what extent stress-related exposures moderate the effects of polygenic risk scores (PRSs) on depression and anxiety. We aimed to examine such moderation effects for a variety of stress-related exposures on depression and anxiety. We included 41,810 participants with both genome-wide genetic data and measurements of depression and anxiety in the Lifelines Cohort Study. Current depression and anxiety were measured by the MINI International Neuropsychiatric Interview. Stress-related exposures included long-term difficulties, stressful life events, reduced social support, childhood trauma, and loneliness, which were measured by self-report questionnaires. PRSs were calculated based on recent large genome-wide association studies for depression and anxiety. We used linear mixed models adjusting for family relationships to estimate the interactions between PRSs and stress-related exposures. Nine of the ten investigated interactions between the five stress-related exposures and the two PRSs for depression and anxiety were significant (Ps < 0.001). Reduced social support, and higher exposure to long-term difficulties, stressful life events, and loneliness amplified the genetic effects on both depression and anxiety. As for childhood trauma exposure, its interaction with the PRS was significant for depression (P = 1.78 × 10^-05) but not for anxiety (P = 0.32). Higher levels of stress-related exposures significantly amplify the effects of genetic susceptibility on depression and anxiety. With a large sample size and a comprehensive set of stress-related exposures, our study provides powerful evidence on the presence of polygenic risk-by-environment interactions in relation to depression and anxiety.

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.

Does crime trigger genetic risk for type 2 diabetes in young adults? A G x E interaction study using national data

BACKGROUND

Living in neighborhoods perceived as disordered exacerbates genetic risk for type 2 diabetes (T2D) among older adults. It is unknown whether this gene-neighborhood interaction extends to younger adults. The present study aims to investigate whether crime, an objectively measured indicator of neighborhood disorder, triggers genetic risk for T2D among younger adults, and whether this hypothesized triggering occurs through exposure to obesity.

METHODS

Data were from the Wave I (2008) National Longitudinal Study of Adolescent to Adult Health. A standardized T2D polygenic score was created using 2014 GWAS meta-analysis results. Weighted mediation analyses using generalized structural equation models were conducted in a final sample of 7606 adults (age range: 25-34) to test the overall association of T2D polygenic scores with T2D, and the mediating path through obesity exposure in low, moderate, and high county crime-rate groups. Age, sex, ancestry, educational degree, household income, five genetic principal components, and county-level concentrated advantage and population density were adjusted.

RESULTS

The overall association between T2D polygenic score and T2D was not significant in low-crime areas (p = 0.453), marginally significant in moderate-crime areas (p = 0.064), and statistically significant in high-crime areas (p = 0.007). The mediating path through obesity was not significant in low or moderate crime areas (ps = 0.560 and 0.261, respectively), but was statistically significant in high-crime areas (p = 0.023). The indirect path through obesity accounted for 12% of the overall association in high-crime area.

CONCLUSION

A gene-crime interaction in T2D was observed among younger adults, and this association was partially explained by exposure to obesity.

Airway inflammation in adolescents and elderly women: Chronic air pollution exposure and polygenic susceptibility

BACKGROUND AND AIM

The fractional exhaled nitric oxide (FeNO) concentration in the exhaled breath is a biomarker for eosinophilic airway inflammation. We explored the interplay between chronic air pollution exposure and polygenic susceptibility to airway inflammation at different critical age stages.

METHODS

Adolescents (15 yr) enrolled in the GINIplus/LISA birth cohorts (n = 2434) and 220 elderly women (75 yr on average) enrolled in the SALIA cohort with FeNO measurements available were investigated. Environmental main effects of the mean of ESCAPE land-use regression air pollutant concentrations within a time window of 15 years and main effects of the polygenic risk scores (PRS) using internal weights from elastic net regression of genome-wide derived single nucleotide polymorphisms were investigated. Furthermore, we examined gene-environment interaction (GxE) effects on natural log-transformed FeNO levels by adjusted linear regression models.

RESULTS

While we observed no significant environmental and polygenic main effects on airway inflammation in either age group, we found robust harmful effects of chronic nitrogen dioxide (NO₂) exposure in the GxE models for elderly women (16.2 % increase in FeNO, p-value = 0.027). Stratified analyses found GxE effects between the PRS and chronic NO₂ exposure in never-smoker elderly women and in adolescents without any inflammatory respiratory conditions.

CONCLUSIONS

FeNO measurement is a useful biomarker to detect higher risk of NO₂-induced eosinophilic airway inflammation in the elderly. There was limited evidence for GxE effects on airway inflammation in adolescents or the elderly. Further GxE studies in subpopulations should be conducted to investigate the assumption that susceptibility to airway inflammation differs between age stages.

Principal Component Analysis Reduces Collider Bias in Polygenic Score Effect Size Estimation

In this study, we test principal component analysis (PCA) of measured confounders as a method to reduce collider bias in polygenic association models. We present results from simulations and application of the method in the Collaborative Study of the Genetics of Alcoholism (COGA) sample with a polygenic score for alcohol problems, DSM-5 alcohol use disorder as the target phenotype, and two collider variables: tobacco use and educational attainment. Simulation results suggest that assumptions regarding the correlation structure and availability of measured confounders are complementary, such that meeting one assumption relaxes the other. Application of the method in COGA shows that PC covariates reduce collider bias when tobacco use is used as the collider variable. Application of this method may improve PRS effect size estimation in some cases by reducing the effect of collider bias, making efficient use of data resources that are available in many studies.

Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status

This study aims to disentangle the contribution of genetic liability, educational attainment (EA), and their overlap and interaction in lifetime smoking. We conducted genome-wide association studies (GWASs) in UK Biobank (N = 394,718) to (i) capture variants for lifetime smoking, (ii) variants for EA, and (iii) variants that contribute to lifetime smoking independently from EA ('smoking-without-EA'). Based on the GWASs, three polygenic scores (PGSs) were created for individuals from the Netherlands Twin Register (NTR, N = 17,805) and the Netherlands Mental Health Survey and Incidence Study-2 (NEMESIS-2, N = 3090). We tested gene-environment (G × E) interactions between each PGS, neighborhood socioeconomic status (SES) and EA on lifetime smoking. To assess if the PGS effects were specific to smoking or had broader implications, we repeated the analyses with measures of mental health. After subtracting EA effects from the smoking GWAS, the SNP-based heritability decreased from 9.2 to 7.2%. The genetic correlation between smoking and SES characteristics was reduced, whereas overlap with smoking traits was less affected by subtracting EA. The PGSs for smoking, EA, and smoking-without-EA all predicted smoking. For mental health, only the PGS for EA was a reliable predictor. There were suggestions for G × E for some relationships, but there were no clear patterns per PGS type. This study showed that the genetic architecture of smoking has an EA component in addition to other, possibly more direct components. PGSs based on EA and smoking-without-EA had distinct predictive profiles. This study shows how disentangling different models of genetic liability and interplay can contribute to our understanding of the etiology of smoking.

Parenting in the Context of the Child: Genetic and Social Processes

The focus on the role of parenting in child development has a long-standing history. When measures of parenting precede changes in child development, researchers typically infer a causal role of parenting practices and attitudes on child development. However, this research is usually conducted with parents raising their own biological offspring. Such research designs cannot account for the effects of genes that are common to parents and children, nor for genetically influenced traits in children that influence how they are parented and how parenting affects them. The aim of this monograph is to provide a clearer view of parenting by synthesizing findings from the Early Growth and Development Study (EGDS). EGDS is a longitudinal study of adopted children, their birth parents, and their rearing parents studied across infancy and childhood. Families (N = 561) were recruited in the United States through adoption agencies between 2000 and 2010. Data collection began when adoptees were 9 months old (males = 57.2%; White 54.5%, Black 13.2%, Hispanic/Latinx 13.4%, Multiracial 17.8%, other 1.1%). The median child age at adoption placement was 2 days (M = 5.58, SD = 11.32). Adoptive parents were predominantly in their 30s, White, and coming from upper-middle- or upper-class backgrounds with high educational attainment (a mode at 4-year college or graduate degree). Most adoptive parents were heterosexual couples, and were married at the beginning of the project. The birth parent sample was more racially and ethnically diverse, but the majority (70%) were White. At the beginning of the study, most birth mothers and fathers were in their 20s, with a mode of educational attainment at high school degree, and few of them were married. We have been following these family members over time, assessing their genetic influences, prenatal environment, rearing environment, and child development. Controlling for effects of genes common to parents and children, we confirmed some previously reported associations between parenting, parent psychopathology, and marital adjustment in relation to child problematic and prosocial behavior. We also observed effects of children's heritable characteristics, characteristics thought to be transmitted from parent to child by genetic means, on their parents and how those effects contributed to subsequent child development. For example, we found that genetically influenced child impulsivity and social withdrawal both elicited harsh parenting, whereas a genetically influenced sunny disposition elicited parental warmth. We found numerous instances of children's genetically influenced characteristics that enhanced positive parental influences on child development or that protected them from harsh parenting. Integrating our findings, we propose a new, genetically informed process model of parenting. We posit that parents implicitly or explicitly detect genetically influenced liabilities and assets in their children. We also suggest future research into factors such as marital adjustment, that favor parents responding with appropriate protection or enhancement. Our findings illustrate a productive use of genetic information in prevention research: helping parents respond effectively to a profile of child strengths and challenges rather than using genetic information simply to identify some children unresponsive to current preventive interventions.

Differential Relations of Parental Behavior to Children's Early Executive Function as a Function of Child Genotype: A Systematic Review

Child genotype is an important biologically based indicator of sensitivity to the effects of parental behavior on children's executive function (EF) in early childhood, birth to age 5. While evidence for gene × parental behavior interactions on children's early EF is growing, researchers have called the quality of evidence provided by gene × environment interaction studies into question. For this reason, this review comprehensively examined the literature and evaluated the evidence for gene × parental behavior interactions on children's early EF abilities. Psychology and psychiatry databases were searched for published peer-reviewed studies. A total of 18 studies met inclusion criteria. Twenty-nine of 89 (33%) examined interactions were significant. However, a p-curve analysis did not find the significant interactions to be of evidential value. A high rate of false positives, due to the continued use of candidate gene and haplotype measures of child genotype and small sample sizes, likely contributed to the high rate of significant interactions and low evidential value. The use of contemporary molecular genetic measures and larger sample sizes are necessary to advance our understanding of child genotype as a moderator of parental effects on children's EF during early childhood and the biopsychosocial mechanisms underlying children's EF development during this critical period. Without these changes, future research is likely to be stymied by the same limitations as current research.

Predicting Alcohol Use From Genome-Wide Polygenic Scores, Environmental Factors, and Their Interactions in Young Adulthood

OBJECTIVE

Alcohol use during emerging adulthood is associated with adverse life outcomes, but its risk factors are not well known. Here, we predicted alcohol use in 3153 young adults aged 22 years from a) genome-wide polygenic scores (GPS) based on genome-wide association studies for the target phenotypes number of drinks per week and Alcohol Use Disorders Identification Test scores, b) 30 environmental factors, and c) their interactions (i.e., G × E effects).

METHODS

Data were collected from 1994 to 2018 as a part of the UK Twins Early Development Study.

RESULTS

GPS accounted for up to 1.9% of the variance in alcohol use (i.e., Alcohol Use Disorders Identification Test score), whereas the 30 measures of environmental factors together accounted for 21.1%. The 30 GPS by environment interactions did not explain any additional variance, and none of the interaction terms exceeded the significance threshold after correcting for multiple testing.

CONCLUSIONS

GPS and some environmental factors significantly predicted alcohol use in young adulthood, but we observed no GPS by environment interactions in our study.

Interactions between genetic, prenatal, cortisol, and parenting influences on adolescent substance use and frequency: A TRAILS study

INTRODUCTION

Dynamic relations between genetic, hormone, and pre- and postnatal environments are theorized as critically important for adolescent substance use but are rarely tested in multifactorial models. This study assessed the impact of interactions of genetic risk and cortisol reactivity with prenatal and parenting influences on both any and frequency of adolescent substance use.

METHODS

Data are from the TRacking Adolescents' Individual Lives Survey (TRAILS), a prospective longitudinal, multi-rater study of 2,230 Dutch adolescents. Genetic risk was assessed via 3 substance-specific polygenic scores. Mothers retrospectively reported prenatal risk when adolescents were 11 years old. Adolescents rated their parents' warmth and hostility at age 11. Salivary cortisol reactivity was measured in response to a social stress task at age 16. Adolescents' self-reported cigarette, alcohol, and cannabis use frequency at age 16.

RESULTS

A multivariate hurdle regression model showed that polygenic risk for smoking, alcohol, and cannabis predicted any use of each substance, respectively, but predicted more frequent use only for smoking. Blunted cortisol reactivity predicted any use and more frequent use for all 3 outcomes. There were 2 interactions: blunted cortisol reactivity exacerbated the association of polygenic risk with any smoking and the association of prenatal risk with any alcohol use.

CONCLUSION

Polygenic risk seems of importance for early use but less so for frequency of use, whereas blunted cortisol reactivity was correlated with both. Blunted cortisol reactivity may also catalyze early risks for substance use, though to a limited degree. Gene-environment interactions play no role in the context of this multifactorial model.

Genetic predispositions moderate the effectiveness of tobacco excise taxes

BACKGROUND

Tobacco consumption is one of the leading causes of preventable death. In this study, we analyze whether someone's genetic predisposition to smoking moderates the response to tobacco excise taxes.

METHODS

We interact polygenic scores for smoking behavior with state-level tobacco excise taxes in longitudinal data (1992-2016) from the US Health and Retirement Study (N = 12,058).

RESULTS

Someone's genetic propensity to smoking moderates the effect of tobacco excise taxes on smoking behavior along the extensive margin (smoking vs. not smoking) and the intensive margin (the amount of tobacco consumed). In our analysis sample, we do not find a significant gene-environment interaction effect on smoking cessation.

CONCLUSIONS

When tobacco excise taxes are relatively high, those with a high genetic predisposition to smoking are less likely (i) to smoke, and (ii) to smoke heavily. While tobacco excise taxes have been effective in reducing smoking, the gene-environment interaction effects we observe in our sample suggest that policy makers could benefit from taking into account the moderating role of genes in the design of future tobacco control policies.

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.

Deciphering pathogenicity of variants of uncertain significance with CRISPR-edited iPSCs

Genetic variants play an important role in conferring risk for cardiovascular diseases (CVDs). With the rapid development of next-generation sequencing (NGS), thousands of genetic variants associated with CVDs have been identified by genome-wide association studies (GWAS), but the function of more than 40% of genetic variants is still unknown. This gap of knowledge is a barrier to the clinical application of the genetic information. However, determining the pathogenicity of a variant of uncertain significance (VUS) is challenging due to the lack of suitable model systems and accessible technologies. By combining clustered regularly interspaced short palindromic repeats (CRISPR) and human induced pluripotent stem cells (iPSCs), unprecedented advances are now possible in determining the pathogenicity of VUS in CVDs. Here, we summarize recent progress and new strategies in deciphering pathogenic variants for CVDs using CRISPR-edited human iPSCs.

Precision Mental Health Care for Cannabis Use Disorder: Utility of a bioSocial Cognitive Theory to Inform Treatment

Globally, cannabis is the most frequently used controlled substance after alcohol and tobacco. Rates of cannabis use are steadily increasing in many countries and there is emerging evidence that there is likely to be greater risk due to increased concentrations of delta-9-tetrahydrocannabinol (THC). Cannabis use and Cannabis Use Disorder (CUD) has been linked to a wide range of adverse health outcomes. Several biological, psychological, and social risk factors are potential targets for effective evidence-based treatments for CUD. There are no effective medications for CUD and psychological interventions are the main form of treatment. Psychological treatments based on Social Cognitive Theory (SCT) emphasize the importance of targeting 2 keys psychological mechanisms: drug outcome expectancies and low drug refusal self-efficacy. This mini-review summarizes the evidence on the role of these mechanisms in the initiation, maintenance, and cessation of cannabis use. It also reviews recent evidence showing how these psychological mechanisms are affected by social and biologically-based risk factors. A new bioSocial Cognitive Theory (bSCT) is outlined that integrates these findings and implications for psychological cannabis interventions are discussed. Preliminary evidence supports the application of bSCT to improve intervention outcomes through better targeted treatment.

Using Genetic Marginal Effects to Study Gene-Environment Interactions with GWAS Data

Gene-environment interactions (GxE) play a central role in the theoretical relationship between genetic factors and complex traits. While genome wide GxE studies of human behaviors remain underutilized, in part due to methodological limitations, existing GxE research in model organisms emphasizes the importance of interpreting genetic associations within environmental contexts. In this paper, we present a framework for conducting an analysis of GxE using raw data from genome wide association studies (GWAS) and applying the techniques to analyze gene-by-age interactions for alcohol use frequency. To illustrate the effectiveness of this procedure, we calculate genetic marginal effects from a GxE GWAS analysis for an ordinal measure of alcohol use frequency from the UK Biobank dataset, treating the respondent's age as the continuous moderating environment. The genetic marginal effects clarify the interpretation of the GxE associations and provide a direct and clear understanding of how the genetic associations vary across age (the environment). To highlight the advantages of our proposed methods for presenting GxE GWAS results, we compare the interpretation of marginal genetic effects with an interpretation that focuses narrowly on the significance of the interaction coefficients. The results imply that the genetic associations with alcohol use frequency vary considerably across ages, a conclusion that may not be obvious from the raw regression or interaction coefficients. GxE GWAS is less powerful than the standard "main effect" GWAS approach, and therefore require larger samples to detect significant moderated associations. Fortunately, the necessary sample sizes for a successful application of GxE GWAS can rely on the existing and on-going development of consortia and large-scale population-based studies.

No evidence of associations between genetic liability for schizophrenia and development of cannabis use disorder

BACKGROUND

Cannabis use and cannabis use disorder (CUD) is increased in patients with schizophrenia. It is important to establish if this is explained by non-causal factors, such as shared genetic vulnerability. We aimed to investigate whether the polygenic risk scores (PRS) for schizophrenia and other psychiatric disorders would predict CUD in controls, patients with schizophrenia, and patients with other psychiatric disorders.

METHODS

We linked nationwide Danish registers and genetic information obtained from dried neonatal bloodspots in an observational analysis. We included people with schizophrenia, other psychiatric disorders, and controls. The exposures of interest were the PRS for schizophrenia, attention-deficit hyperactivity disorder (ADHD) autism spectrum disorder, and anorexia nervosa. The main outcome of interest was the diagnosis of CUD.

RESULTS

The study included 88 637 individuals. PRS for schizophrenia did not predict CUD in controls [hazard ratio (HR) = 1.16, 95% CI 0.95-1.43 per standard-deviation increase in PRS, or HR = 1.47, 95% CI 0.72-3.00 comparing highest v. remaining decile], but PRS for ADHD did (HR = 1.27, 95% CI 1.08-1.50 per standard-deviation increase, or HR = 2.02, 95% CI 1.27-3.22 for the highest decile of PRS). Among cases with schizophrenia, the PRS for schizophrenia was associated with CUD. While CUD was a strong predictor of schizophrenia (HR = 4.91, 95% CI 4.36-5.53), the inclusion of various PRS did not appreciably alter this association.

CONCLUSION

The PRS for schizophrenia was not associated with CUD in controls or patients with other psychiatric disorders than schizophrenia. This speaks against the hypothesis that shared genetic vulnerability would explain the association between cannabis and schizophrenia.

Cannabis use in college: Genetic predispositions, peers, and activity participation

BACKGROUND

Among adult college students in the US, cannabis use is common and associated with considerable negative consequences to health, cognition, and academic functioning, underscoring the importance of identifying risk and protective factors. Cannabis use is influenced by genetic factors, but genetic risk is not determinative. Accordingly, it is critical to identify environments that reduce risk among those who are at elevated genetic risk. This study examined the impact of polygenic scores for cannabis initiation, various forms of social activity participation, and peer deviance on recent cannabis use. Our aim was to test whether these environments moderate genetic risk for cannabis use.

METHODS

Data came from a longitudinal sample of undergraduate college students of European American (EA; NEA = 750) and African American (AA; NAA = 405) ancestry. Generalized estimating equations with a logit link function were used to examine main effects and two-way interactions.

RESULTS

Engagement with church activities was associated with lower probability of cannabis use. Peer deviance was associated with higher probability of cannabis use. Engagement with community activities moderated the influence of the polygenic risk score in the EA sample, such that PRS was associated with recent cannabis use among those who never engaged in community activities. This effect did not replicate in AAs, which may have been due to the portability of PRS based on EA discovery samples.

CONCLUSIONS

Results suggest that community activities may limit the influence of genetic risk, as associations between PRS and cannabis use were only observed among individuals who never engaged in community activities.

Multivariable G-E interplay in the prediction of educational achievement

Polygenic scores are increasingly powerful predictors of educational achievement. It is unclear, however, how sets of polygenic scores, which partly capture environmental effects, perform jointly with sets of environmental measures, which are themselves heritable, in prediction models of educational achievement. Here, for the first time, we systematically investigate gene-environment correlation (rGE) and interaction (GxE) in the joint analysis of multiple genome-wide polygenic scores (GPS) and multiple environmental measures as they predict tested educational achievement (EA). We predict EA in a representative sample of 7,026 16-year-olds, with 20 GPS for psychiatric, cognitive and anthropometric traits, and 13 environments (including life events, home environment, and SES) measured earlier in life. Environmental and GPS predictors were modelled, separately and jointly, in penalized regression models with out-of-sample comparisons of prediction accuracy, considering the implications that their interplay had on model performance. Jointly modelling multiple GPS and environmental factors significantly improved prediction of EA, with cognitive-related GPS adding unique independent information beyond SES, home environment and life events. We found evidence for rGE underlying variation in EA (rGE = .38; 95% CIs = .30, .45). We estimated that 40% (95% CIs = 31%, 50%) of the polygenic scores effects on EA were mediated by environmental effects, and in turn that 18% (95% CIs = 12%, 25%) of environmental effects were accounted for by the polygenic model, indicating genetic confounding. Lastly, we did not find evidence that GxE effects significantly contributed to multivariable prediction. Our multivariable polygenic and environmental prediction model suggests widespread rGE and unsystematic GxE contributions to EA in adolescence.

Our study investigates the complex interplay between genetic and environmental contributions underlying educational achievement (EA). Polygenic scores are becoming increasingly powerful predictors of EA. While emerging evidence indicates that polygenic scores are not pure measures of genetic predisposition, previous quantitative genetics findings indicate that measures of the environment are themselves heritable. In this regard it is unclear how such measures of individual predisposition jointly combine to predict EA. We investigate this question in a representative UK sample of 7,026 16-year-olds where we provide substantive results on gene-environment correlation and interaction underlying variation in EA. We show that polygenic score and environmental prediction models of EA overlap substantially. Polygenic scores effects on EA are partly accounted for by their correlation with environmental effects; similarly, environmental effects on EA are linked to polygenic scores effects. Nonetheless, jointly considering polygenic scores and measured environments significantly improves prediction of EA. We also find that, although correlation between polygenic scores and measured environments is substantial, interactions between them do not play a significant role in the prediction of EA. Our findings have relevance for genomic and environmental prediction models alike, as they show the way in which individuals’ genetic predispositions and environmental effects are intertwined. This suggests that both genetic and environmental effects must be taken into account in prediction models of complex behavioral traits such as EA.

Substance use: Interplay between polygenic risk and neighborhood environment

BACKGROUND

Tobacco, alcohol, and cannabis use are prevalent behaviors that pose considerable health risks. Genetic vulnerability and characteristics of the neighborhood of residence form important risk factors for substance use. Possibly, these factors do not act in isolation. This study tested the interaction between neighborhood characteristics and genetic risk (gene-environment interaction, GxE) and the association between these classes of risk factors (gene-environment correlation, rGE) in substance use.

METHODS

Two polygenic scores (PGS) each (based on different discovery datasets) were created for smoking initiation, cigarettes per day, and glasses of alcohol per week based on summary statistics of different genome-wide association studies (GWAS). For cannabis initiation one PGS was created. These PGS were used to predict their respective phenotype in a large population-based sample from the Netherlands Twin Register (N = 6,567). Neighborhood characteristics as retrieved from governmental registration systems were factor analyzed and resulting measures of socioeconomic status (SES) and metropolitanism were used as predictors.

RESULTS

There were (small) main effects of neighborhood characteristics and PGS on substance use. One of the 14 tested GxE effects was significant, such that the PGS was more strongly associated with alcohol use in individuals with high SES. This was effect was only significant for one out of two PGS. There were weak indications of rGE, mainly with age and cohort covariates.

CONCLUSION

We conclude that both genetic and neighborhood-level factors are predictors for substance use. More research is needed to establish the robustness of the findings on the interplay between these factors.

The Evolving Field of Genetic Epidemiology: From Familial Aggregation to Genomic Sequencing

The field of genetic epidemiology is relatively young and brings together genetics, epidemiology, and biostatistics to identify and implement the best study designs and statistical analyses for identifying genes controlling risk for complex and heterogeneous diseases (i.e., those where genes and environmental risk factors both contribute to etiology). The field has moved quickly over the past 40 years partly because the technology of genotyping and sequencing has forced it to adapt while adhering to the fundamental principles of genetics. In the last two decades, the available tools for genetic epidemiology have expanded from a genetic focus (considering 1 gene at a time) to a genomic focus (considering the entire genome), and now they must further expand to integrate information from other “-omics” (e.g., epigenomics, transcriptomics as measured by RNA expression) at both the individual and the population levels. Additionally, we can now also evaluate gene and environment interactions across populations to better understand exposure and the heterogeneity in disease risk. The future challenges facing genetic epidemiology are considerable both in scale and techniques, but the importance of the field will not diminish because by design it ties scientific goals with public health applications.