Cross-ancestry genetic investigation of schizophrenia, cannabis use disorder, and tobacco smoking.

Individuals with schizophrenia frequently experience co-occurring substance use, including tobacco smoking and heavy cannabis use, and substance use disorders. There is interest in understanding the extent to which these relationships are causal, and to what extent shared genetic factors play a role. We explored the relationships between schizophrenia (Scz; European ancestry N = 161,405; African ancestry N = 15,846), cannabis use disorder (CanUD; European ancestry N = 886,025; African ancestry N = 120,208), and ever-regular tobacco smoking (Smk; European ancestry N = 805,431; African ancestry N = 24,278) using the largest available genome-wide studies of these phenotypes in individuals of African and European ancestries. All three phenotypes were positively genetically correlated (rgs = 0.17-0.62). Genetic instrumental variable analyses suggested the presence of shared heritable factors, but evidence for bidirectional causal relationships was also found between all three phenotypes even after correcting for these shared genetic factors. We identified 327 pleiotropic loci with 439 lead SNPs in the European ancestry data, 150 of which were novel (i.e., not genome-wide significant in the original studies). Of these pleiotropic loci, 202 had lead variants which showed convergent effects (i.e., same direction of effect) on Scz, CanUD, and Smk. Genetic variants convergent across all three phenotypes showed strong genetic correlations with risk-taking, executive function, and several mental health conditions. Our results suggest that both shared genetic factors and causal mechanisms may play a role in the relationship between CanUD, Smk, and Scz, but longitudinal, prospective studies are needed to confirm a causal relationship.

A phenome-wide association and Mendelian randomisation study of alcohol use variants in a diverse cohort comprising over 3 million individuals.

BACKGROUND: Alcohol consumption is associated with numerous negative social and health outcomes. These associations may be direct consequences of drinking, or they may reflect common genetic factors that influence both alcohol consumption and other outcomes. METHODS: We performed exploratory phenome-wide association studies (PheWAS) of three of the best studied protective single nucleotide polymorphisms (SNPs) in genes encoding ethanol metabolising enzymes (ADH1B: rs1229984-T, rs2066702-A; ADH1C: rs698-T) using up to 1109 health outcomes across 28 phenotypic categories (e.g., substance-use, mental health, sleep, immune, cardiovascular, metabolic) from a diverse 23andMe cohort, including European (N ≤ 2,619,939), Latin American (N ≤ 446,646) and African American (N ≤ 146,776) populations to uncover new and perhaps unexpected associations. These SNPs have been consistently implicated by both candidate gene studies and genome-wide association studies of alcohol-related behaviours but have not been investigated in detail for other relevant phenotypes in a hypothesis-free approach in such a large cohort of multiple ancestries. To provide insight into potential causal effects of alcohol consumption on the outcomes significant in the PheWAS, we performed univariable two-sample and one-sample Mendelian randomisation (MR) analyses. FINDINGS: The minor allele rs1229984-T, which is protective against alcohol behaviours, showed the highest number of PheWAS associations across the three cohorts (N = 232, European; N = 29, Latin American; N = 7, African American). rs1229984-T influenced multiple domains of health. We replicated associations with alcohol-related behaviours, mental and sleep conditions, and cardio-metabolic health. We also found associations with understudied traits related to neurological (migraines, epilepsy), immune (allergies), musculoskeletal (fibromyalgia), and reproductive health (preeclampsia). MR analyses identified evidence of causal effects of alcohol consumption on liability for 35 of these outcomes in the European cohort. INTERPRETATION: Our work demonstrates that polymorphisms in genes encoding alcohol metabolising enzymes affect multiple domains of health beyond alcohol-related behaviours. Understanding the underlying mechanisms of these effects could have implications for treatments and preventative medicine. FUNDING: MVJ, NCK, SBB, SSR and AAP were supported by T32IR5226 and 28IR-0070. SSR was also supported by NIDA DP1DA054394. NCK and RBC were also supported by R25MH081482. ASH was supported by funds from NIAAA K01AA030083. JLMO was supported by VA 1IK2CX002095. JLMO and JJMM were also supported by NIDA R21DA050160. JJMM was also supported by the Kavli Postdoctoral Award for Academic Diversity. EGA was supported by K01MH121659 from the NIMH/NIH, the Caroline Wiess Law Fund for Research in Molecular Medicine and the ARCO Foundation Young Teacher-Investigator Fund at Baylor College of Medicine. MSA was supported by the Instituto de Salud Carlos III and co-funded by the European Union Found: Fondo Social Europeo Plus (FSE+) (P19/01224, PI22/00464 and CP22/00128).

Functional 3'-UTR Variants Identify Regulatory Mechanisms Impacting Alcohol Use Disorder and Related Traits.

Although genome-wide association studies (GWAS) have identified loci associated with alcohol consumption and alcohol use disorder (AUD), they do not identify which variants are functional. To approach this, we evaluated the impact of variants in 3' untranslated regions (3'-UTRs) of genes in loci associated with substance use and neurological disorders using a massively parallel reporter assay (MPRA) in neuroblastoma and microglia cells. Functionally impactful variants explained a higher proportion of heritability of alcohol traits than non-functional variants. We identified genes whose 3'UTR activities are associated with AUD and alcohol consumption by combining variant effects from MPRA with GWAS results. We examined their effects by evaluating gene expression after CRISPR inhibition of neuronal cells and stratifying brain tissue samples by MPRA-derived 3'-UTR activity. A pathway analysis of differentially expressed genes identified inflammation response pathways. These analyses suggest that variation in response to inflammation contributes to the propensity to increase alcohol consumption.

Cross-ancestry genetic investigation of schizophrenia, cannabis use disorder, and tobacco smoking.

Individuals with schizophrenia frequently experience co-occurring substance use, including tobacco smoking and heavy cannabis use, and substance use disorders. There is interest in understanding the extent to which these relationships are causal, and to what extent shared genetic factors play a role. We explored the relationships between schizophrenia (Scz), cannabis use disorder (CanUD), and ever-regular tobacco smoking (Smk) using the largest available genome-wide studies of these phenotypes in individuals of African and European ancestries. All three phenotypes were positively genetically correlated (rgs = 0.17 - 0.62). Causal inference analyses suggested the presence of horizontal pleiotropy, but evidence for bidirectional causal relationships was also found between all three phenotypes even after correcting for horizontal pleiotropy. We identified 439 pleiotropic loci in the European ancestry data, 150 of which were novel (i.e., not genome-wide significant in the original studies). Of these pleiotropic loci, 202 had lead variants which showed convergent effects (i.e., same direction of effect) on Scz, CanUD, and Smk. Genetic variants convergent across all three phenotypes showed strong genetic correlations with risk-taking, executive function, and several mental health conditions. Our results suggest that both horizontal pleiotropy and causal mechanisms may play a role in the relationship between CanUD, Smk, and Scz, but longitudinal, prospective studies are needed to confirm a causal relationship.

Multi-ancestry genome-wide association study of cannabis use disorder yields insight into disease biology and public health implications.

As recreational use of cannabis is being decriminalized in many places and medical use widely sanctioned, there are growing concerns about increases in cannabis use disorder (CanUD), which is associated with numerous medical comorbidities. Here we performed a genome-wide association study of CanUD in the Million Veteran Program (MVP), followed by meta-analysis in 1,054,365 individuals (ncases = 64,314) from four broad ancestries designated by the reference panel used for assignment (European n = 886,025, African n = 123,208, admixed American n = 38,289 and East Asian n = 6,843). Population-specific methods were applied to calculate single nucleotide polymorphism-based heritability within each ancestry. Statistically significant single nucleotide polymorphism-based heritability for CanUD was observed in all but the smallest population (East Asian). We discovered genome-wide significant loci unique to each ancestry: 22 in European, 2 each in African and East Asian, and 1 in admixed American ancestries. A genetically informed causal relationship analysis indicated a possible effect of genetic liability for CanUD on lung cancer risk, suggesting potential unanticipated future medical and psychiatric public health consequences that require further study to disentangle from other known risk factors such as cigarette smoking.

GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors.

OBJECTIVE: Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures. METHODS: This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses. RESULTS: Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values <5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors. CONCLUSIONS: This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.

Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders.

Genetic liability to substance use disorders can be parsed into loci that confer general or substance-specific addiction risk. We report a multivariate genome-wide association meta-analysis that disaggregates general and substance-specific loci for published summary statistics of problematic alcohol use, problematic tobacco use, cannabis use disorder, and opioid use disorder in a sample of 1,025,550 individuals of European descent and 92,630 individuals of African descent. Nineteen independent SNPs were genome-wide significant (P < 5e-8) for the general addiction risk factor (addiction-rf), which showed high polygenicity. Across ancestries, PDE4B was significant (among other genes), suggesting dopamine regulation as a cross-substance vulnerability. An addiction-rf polygenic risk score was associated with substance use disorders, psychopathologies, somatic conditions, and environments associated with the onset of addictions. Substance-specific loci (9 for alcohol, 32 for tobacco, 5 for cannabis, 1 for opioids) included metabolic and receptor genes. These findings provide insight into genetic risk loci for substance use disorders that could be leveraged as treatment targets.

Alcohol use disorder is associated with higher risks of Alzheimer's and Parkinson's diseases: A study of US insurance claims data.

Introduction: Alcohol use disorder (AUD) is on the ascendancy in the US older adult population. The association between AUD and adverse brain outcomes remains inconclusive. Method: In a retrospective cohort design using US insurance claim data (2007-2020), 129,182 individuals with AUD were matched with 129,182 controls by age, sex, race, and clinical characteristics. We investigated the association between AUD and adverse brain outcomes using Cox analysis, Kaplan-Meier analysis, and log-rank test. Results: After adjusting for covariates, AUD was associated with a higher risk of Alzheimer's disease (female adjusted hazard ratio [HR] = 1.78, 95% confidence interval [CI]: 1.68-1.90, p < 0.001; male adjusted HR = 1.80, 95% CI: 1.71-1.91, p < 0.001) and a higher risk of Parkinson's disease (female adjusted HR = 1.49, 95% CI: 1.32-1.68, p < 0.001; male adjusted HR = 1.42, 95% CI: 1.32-1.52, p < 0.001) in the overall sample. In separate analyses of Black, White, and Hispanic individuals, those with AUD had higher risk of Alzheimer's disease (adjusted HRs ≥1.58; Ps ≤ 0.001). A significantly elevated risk for Parkinson's disease was found only in the White subpopulation (female adjusted HR = 1.55, 95% CI: 1.36-1.77, p < 0.001; male adjusted HR = 1.45, 95% CI: 1.33-1.57, p < 0.001). Conclusions: AUD is associated with Alzheimer's disease. AUD is associated with Parkinson's disease in White people. Cognitive screening and neurological examination among older adults with AUD hold the promise for early detection of Alzheimer's disease and Parkinson's disease. HIGHLIGHTS: Alcohol use disorder is associated with Alzheimer's disease and dementia.Alcohol use disorder is associated with Parkinson's disease in White people.

Clinical, environmental, and genetic risk factors for substance use disorders: characterizing combined effects across multiple cohorts.

Substance use disorders (SUDs) incur serious social and personal costs. The risk for SUDs is complex, with risk factors ranging from social conditions to individual genetic variation. We examined whether models that include a clinical/environmental risk index (CERI) and polygenic scores (PGS) are able to identify individuals at increased risk of SUD in young adulthood across four longitudinal cohorts for a combined sample of N = 15,134. Our analyses included participants of European (NEUR = 12,659) and African (NAFR = 2475) ancestries. SUD outcomes included: (1) alcohol dependence, (2) nicotine dependence; (3) drug dependence, and (4) any substance dependence. In the models containing the PGS and CERI, the CERI was associated with all three outcomes (ORs = 01.37-1.67). PGS for problematic alcohol use, externalizing, and smoking quantity were associated with alcohol dependence, drug dependence, and nicotine dependence, respectively (OR = 1.11-1.33). PGS for problematic alcohol use and externalizing were also associated with any substance dependence (ORs = 1.09-1.18). The full model explained 6-13% of the variance in SUDs. Those in the top 10% of CERI and PGS had relative risk ratios of 3.86-8.04 for each SUD relative to the bottom 90%. Overall, the combined measures of clinical, environmental, and genetic risk demonstrated modest ability to distinguish between affected and unaffected individuals in young adulthood. PGS were significant but added little in addition to the clinical/environmental risk index. Results from our analysis demonstrate there is still considerable work to be done before tools such as these are ready for clinical applications.

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.

The addiction risk factor: A unitary genetic vulnerability characterizes substance use disorders and their associations with common correlates.

Substance use disorders commonly co-occur with one another and with other psychiatric disorders. They share common features including high impulsivity, negative affect, and lower executive function. We tested whether a common genetic factor undergirds liability to problematic alcohol use (PAU), problematic tobacco use (PTU), cannabis use disorder (CUD), and opioid use disorder (OUD) by applying genomic structural equation modeling to genome-wide association study summary statistics for individuals of European ancestry (Total N = 1,019,521; substance-specific Ns range: 82,707-435,563) while adjusting for the genetics of substance use (Ns = 184,765-632,802). We also tested whether shared liability across SUDs is associated with behavioral constructs (risk-taking, executive function, neuroticism; Ns = 328,339-427,037) and non-substance use psychopathology (psychotic, compulsive, and early neurodevelopmental disorders). Shared genetic liability to PAU, PTU, CUD, and OUD was characterized by a unidimensional addiction risk factor (termed The Addiction-Risk-Factor, independent of substance use. OUD and CUD demonstrated the largest loadings, while problematic tobacco use showed the lowest loading. The Addiction-Risk-Factor was associated with risk-taking, neuroticism, executive function, and non-substance psychopathology, but retained specific variance before and after accounting for the genetics of substance use. Thus, a common genetic factor partly explains susceptibility for alcohol, tobacco, cannabis, and opioid use disorder. The Addiction-Risk-Factor has a unique genetic architecture that is not shared with normative substance use or non-substance psychopathology, suggesting that addiction is not the linear combination of substance use and psychopathology.

Genes identified in rodent studies of alcohol intake are enriched for heritability of human substance use.

BACKGROUND: Rodent paradigms and human genome-wide association studies (GWAS) on drug use have the potential to provide biological insight into the pathophysiology of addiction. METHODS: Using GeneWeaver, we created rodent alcohol and nicotine gene-sets derived from 19 gene expression studies on alcohol and nicotine outcomes. We partitioned the SNP heritability of these gene-sets using four large human GWAS: (1) alcoholic drinks per week, (2) problematic alcohol use, (3) cigarettes per day, and (4) smoking cessation. We benchmarked our findings with curated human alcohol and nicotine addiction gene-sets and performed specificity analyses using other rodent gene-sets (e.g., locomotor behavior) and other human GWAS (e.g., height). RESULTS: The rodent alcohol gene-set was enriched for heritability of drinks per week, cigarettes per day, and smoking cessation, but not problematic alcohol use. However, the rodent nicotine gene-set was not significantly associated with any of these traits. Both rodent gene-sets showed enrichment for several non-substance-use GWAS, and the extent of this relationship tended to increase as a function of trait heritability. In general, larger gene-sets demonstrated more significant enrichment. Finally, when evaluating human traits with similar heritabilities, both rodent gene-sets showed greater enrichment for substance use traits. CONCLUSION: Our results suggest that rodent gene expression studies can help to identify genes that contribute to the heritability of some substance use traits in humans, yet there was less specificity than expected. We outline various limitations, interpretations, and considerations for future research.

Ancestry may confound genetic machine learning: Candidate-gene prediction of opioid use disorder as an example.

BACKGROUND: Machine learning (ML) models are beginning to proliferate in psychiatry, however machine learning models in psychiatric genetics have not always accounted for ancestry. Using an empirical example of a proposed genetic test for OUD, and exploring a similar test for tobacco dependence and a simulated binary phenotype, we show that genetic prediction using ML is vulnerable to ancestral confounding. METHODS: We utilize five ML algorithms trained with 16 brain reward-derived "candidate" SNPs proposed for commercial use and examine their ability to predict OUD vs. ancestry in an out-of-sample test set (N = 1000, stratified into equal groups of n = 250 cases and controls each of European and African ancestry). We rerun analyses with 8 random sets of allele-frequency matched SNPs. We contrast findings with 11 genome-wide significant variants for tobacco smoking. To document generalizability, we generate and test a random phenotype. RESULTS: None of the 5 ML algorithms predict OUD better than chance when ancestry was balanced but were confounded with ancestry in an out-of-sample test. In addition, the algorithms preferentially predicted admixed subpopulations. Random sets of variants matched to the candidate SNPs by allele frequency produced similar bias. Genome-wide significant tobacco smoking variants were also confounded by ancestry. Finally, random SNPs predicting a random simulated phenotype show that the bias attributable to ancestral confounding could impact any ML-based genetic prediction. CONCLUSIONS: Researchers and clinicians are encouraged to be skeptical of claims of high prediction accuracy from ML-derived genetic algorithms for polygenic traits like addiction, particularly when using candidate variants.

The relationship between cannabis and schizophrenia: a genetically informed perspective.

BACKGROUND AND AIMS: While epidemiological studies support a role for heavy, high-potency cannabis use on first-episode psychosis, genetic models of causation suggest reverse causal effects of schizophrenia on cannabis use liability. We estimated the genetic relationship between cannabis use disorder (CUD) and schizophrenia (SCZ) and tested whether liability for CUD is causally associated with increased liability to SCZ while adjusting for tobacco smoking. DESIGN: This study used summary statistics from published genome-wide association studies (GWAS). We used genomic structural equation modeling, latent causal variable analysis, and multivariable Mendelian randomization to examine genetic relationships between CUD, cannabis ever-use, ever-smoked tobacco regularly, nicotine dependence and SCZ, and to test for a causal relationship between liability to CUD and liability to SCZ. SETTING: Genome-wide association studies were published previously as part of international consortia. PARTICIPANTS: Sample sizes of the GWAS summary statistics used in this study ranged from 161 405 to 357 806 individuals of European ancestry. MEASUREMENTS: Genome-wide summary statistics for CUD and SCZ were the primary measurements, while summary statistics for cannabis ever-use, ever-smoked tobacco regularly and nicotine dependence were included as additional variables in the genomic structural equation models and the multivariable Mendelian randomization analyses. FINDINGS: Genetic liability to CUD was significantly associated with SCZ [ß = 0.29, 95% confidence interval (CI) = 0.11, 0.46, P = 0.001], even when accounting for cannabis ever-use, ever-smoked tobacco regularly and nicotine dependence as simultaneous predictors. We found mixed evidence of a causal relationship, with the latent causal variable analysis finding no evidence of causality (genetic causality proportion = -0.08, 95% CI = -0.40, 0.23, P = 0.87) but the multivariable Mendelian randomization analyses suggesting a significant, risk-increasing effect of CUD on liability to SCZ (ß = 0.10, 95% CI = 0.02, 0.18, P = 0.02), accounting for the additional risk factors (cannabis ever-use, ever-smoked tobacco regularly and nicotine dependence). CONCLUSIONS: Genetic liability for cannabis use disorder appears to be robustly associated with schizophrenia, above and beyond tobacco smoking and cannabis ever-use, with mixed evidence to support a causal relationship between cannabis use disorder and schizophrenia.

Allele-specific expression and high-throughput reporter assay reveal functional genetic variants associated with alcohol use disorders.

Genome-wide association studies (GWAS) of complex traits, such as alcohol use disorders (AUD), usually identify variants in non-coding regions and cannot by themselves distinguish whether the associated variants are functional or in linkage disequilibrium with the functional variants. Transcriptome studies can identify genes whose expression differs between alcoholics and controls. To test which variants associated with AUD may cause expression differences, we integrated data from deep RNA-seq and GWAS of four postmortem brain regions from 30 subjects with AUD and 30 controls to analyze allele-specific expression (ASE). We identified 88 genes with differential ASE in subjects with AUD compared to controls. Next, to test one potential mechanism contributing to the differential ASE, we analyzed single nucleotide polymorphisms (SNPs) in the 3' untranslated regions (3'UTR) of these genes. Of the 88 genes with differential ASE, 61 genes contained 437 SNPs in the 3'UTR with at least one heterozygote among the subjects studied. Using a modified PASSPORT-seq (parallel assessment of polymorphisms in miRNA target-sites by sequencing) assay, we identified 25 SNPs that affected RNA levels in a consistent manner in two neuroblastoma cell lines, SH-SY5Y and SK-N-BE(2). Many of these SNPs are in binding sites of miRNAs and RNA-binding proteins, indicating that these SNPs are likely causal variants of AUD-associated differential ASE. In sum, we demonstrate that a combination of computational and experimental approaches provides a powerful strategy to uncover functionally relevant variants associated with the risk for AUD.

The Moderate Alcohol and Cardiovascular Health Trial (MACH15): Design and methods for a randomized trial of moderate alcohol consumption and cardiometabolic risk.

BACKGROUND: Observational studies have documented lower risks of coronary heart disease and diabetes among moderate alcohol consumers relative to abstainers, but only a randomized clinical trial can provide conclusive evidence for or against these associations. AIM: The purpose of this study was to describe the rationale and design of the Moderate Alcohol and Cardiovascular Health Trial, aimed to assess the cardiometabolic effects of one alcoholic drink daily over an average of six years among adults 50 years or older. METHODS: This multicenter, parallel-arm randomized trial was designed to compare the effects of one standard serving (∼11-15 g) daily of a preferred alcoholic beverage to abstention. The trial aimed to enroll 7800 people at high risk of cardiovascular disease. The primary composite endpoint comprised time to the first occurrence of non-fatal myocardial infarction, non-fatal ischemic stroke, hospitalized angina, coronary/carotid revascularization, or total mortality. The trial was designed to provide >80% power to detect a 15% reduction in the risk of the primary outcome. Secondary outcomes included diabetes. Adverse effects of special interest included injuries, congestive heart failure, alcohol use disorders, and cancer. RESULTS: We describe the design, governance, masking issues, and data handling. In three months of field center activity until termination by the funder, the trial randomized 32 participants, successfully screened another 70, and identified ∼400 additional interested individuals. CONCLUSIONS: We describe a feasible design for a long-term randomized trial of moderate alcohol consumption. Such a study will provide the highest level of evidence for the effects of moderate alcohol consumption on cardiovascular disease and diabetes, and will directly inform clinical and public health guidelines.

Effects of chronic intermittent ethanol exposure and withdrawal on neuroblastoma cell transcriptome.

Cycles of heavy drinking and abstinence can lead to alcohol use disorder. We studied the effects of chronic intermittent ethanol exposure (CIE) over 3 weeks on neuroblastoma cells, using an ethanol concentration frequently attained in binge drinking (40 mM, 184 mg/dL). There were many changes in gene expression but most were small. CIE affected pathways instrumental in the development or plasticity of neurons, including axonal guidance, reelin signaling, and synaptogenesis. Genes involved in dopamine and serotonin signaling were also affected. Changes in transporters and receptors could dampen both NMDA and norepinephrine transmissions. Decreased expression of the GABA transporter SLC6A11 could increase GABA transmission and has been associated with a switch from sweet drinking to ethanol consumption in rats. Ethanol increased stress responses such as the unfolded protein response. TGF-ß and NFκB signaling were increased. Most of the genes involved in cholesterol biosynthesis were decreased in expression. Withdrawal for 24 h after CIE caused most of the CIE-induced expression changes to move back toward unexposed levels.

Sibling comparisons elucidate the associations between educational attainment polygenic scores and alcohol, nicotine and cannabis.

BACKGROUND AND AIMS: The associations between low educational attainment and substance use disorders (SUDs) may be related to a common genetic vulnerability. We aimed to elucidate the associations between polygenic scores for educational attainment and clinical criterion counts for three SUDs (alcohol, nicotine and cannabis). DESIGN: Polygenic association and sibling comparison methods. The latter strengthens inferences in observational research by controlling for confounding factors that differ between families. SETTING: Six sites in the United States. PARTICIPANTS: European ancestry participants aged 25 years and older from the Collaborative Study on the Genetics of Alcoholism (COGA). Polygenic association analyses included 5582 (54% female) participants. Sibling comparisons included 3098 (52% female) participants from 1226 sibling groups nested within the overall sample. MEASUREMENTS: Outcomes included criterion counts for DSM-5 alcohol use disorder (AUDSX), Fagerström nicotine dependence (NDSX) and DSM-5 cannabis use disorder (CUDSX). We derived polygenic scores for educational attainment (EduYears-GPS) using summary statistics from a large (> 1 million) genome-wide association study of educational attainment. FINDINGS: In polygenic association analyses, higher EduYears-GPS predicted lower AUDSX, NDSX and CUDSX [P < 0.01, effect sizes (R2 ) ranging from 0.30 to 1.84%]. These effects were robust in sibling comparisons, where sibling differences in EduYears-GPS predicted all three SUDs (P < 0.05, R2 0.13-0.20%). CONCLUSIONS: Individuals who carry more alleles associated with educational attainment tend to meet fewer clinical criteria for alcohol, nicotine and cannabis use disorders, and these effects are robust to rigorous controls for potentially confounding factors that differ between families (e.g. socio-economic status, urban-rural residency and parental education).

Lithium alters expression of RNAs in a type-specific manner in differentiated human neuroblastoma neuronal cultures, including specific genes involved in Alzheimer's disease.

Lithium (Li) is a medication long-used to treat bipolar disorder. It is currently under investigation for multiple nervous system disorders, including Alzheimer's disease (AD). While perturbation of RNA levels by Li has been previously reported, its effects on the whole transcriptome has been given little attention. We, therefore, sought to determine comprehensive effects of Li treatment on RNA levels. We cultured and differentiated human neuroblastoma (SK-N-SH) cells to neuronal cells with all-trans retinoic acid (ATRA). We exposed cultures for one week to lithium chloride or distilled water, extracted total RNA, depleted ribosomal RNA and performed whole-transcriptome RT-sequencing. We analyzed results by RNA length and type. We further analyzed expression and protein interaction networks between selected Li-altered protein-coding RNAs and common AD-associated gene products. Lithium changed expression of RNAs in both non-specific (inverse to sequence length) and specific (according to RNA type) fashions. The non-coding small nucleolar RNAs (snoRNAs) were subject to the greatest length-adjusted Li influence. When RNA length effects were taken into account, microRNAs as a group were significantly less likely to have had levels altered by Li treatment. Notably, several Li-influenced protein-coding RNAs were co-expressed or produced proteins that interacted with several common AD-associated genes and proteins. Lithium's modification of RNA levels depends on both RNA length and type. Li activity on snoRNA levels may pertain to bipolar disorders while Li modification of protein coding RNAs may be relevant to AD.

CYP2A6 metabolism in the development of smoking behaviors in young adults.

Cytochrome P450 2A6 (CYP2A6) encodes the enzyme responsible for the majority of nicotine metabolism. Previous studies support that slow metabolizers smoke fewer cigarettes once nicotine dependent but provide conflicting results on the role of CYP2A6 in the development of dependence. By focusing on the critical period of young adulthood, this study examines the relationship of CYP2A6 variation and smoking milestones. A total of 1209 European American young adults enrolled in the Collaborative Study on the Genetics of Alcoholism were genotyped for CYP2A6 variants to calculate a previously well-validated metric that estimates nicotine metabolism. This metric was not associated with the transition from never smoking to smoking initiation nor with the transition from initiation to daily smoking (P > 0.4). But among young adults who had become daily smokers (n = 506), decreased metabolism was associated with increased risk of nicotine dependence (P = 0.03) (defined as Fagerström Test for Nicotine Dependence score ≥4). This finding was replicated in the Collaborative Genetic Study of Nicotine Dependence with 335 young adult daily smokers (P = 0.02). Secondary meta-analysis indicated that slow metabolizers had a 53 percent increased odds (OR = 1.53, 95 percent CI 1.11-2.11, P = 0.009) of developing nicotine dependence compared with normal metabolizers. Furthermore, secondary analyses examining four-level response of time to first cigarette after waking (>60, 31-60, 6-30, ≤5 minutes) demonstrated a robust effect of the metabolism metric in Collaborative Study on the Genetics of Alcoholism (P = 0.03) and Collaborative Genetic Study of Nicotine Dependence (P = 0.004), illustrating the important role of this measure of dependence. These findings highlight the complex role of CYP2A6 variation across different developmental stages of smoking behaviors.