Genome-Wide Meta-Analysis of Cotinine Levels in Cigarette Smokers Identifies Locus at 4q13.2

Estimating the health impact of nicotine exposure by dissecting the effects of nicotine versus non-nicotine constituents of tobacco smoke: A multivariable Mendelian randomisation study

The detrimental health effects of smoking are well-known, but the impact of regular nicotine use without exposure to the other constituents of tobacco is less clear. Given the increasing daily use of alternative nicotine delivery systems, such as e-cigarettes, it is increasingly important to understand and separate the effects of nicotine use from the impact of tobacco smoke exposure. Using a multivariable Mendelian randomisation framework, we explored the direct effects of nicotine compared with the non-nicotine constituents of tobacco smoke on health outcomes (lung cancer, chronic obstructive pulmonary disease [COPD], forced expiratory volume in one second [FEV-1], forced vital capacity [FVC], coronary heart disease [CHD], and heart rate [HR]). We used Genome-Wide Association Study (GWAS) summary statistics from Buchwald and colleagues, the GWAS and Sequencing Consortium of Alcohol and Nicotine, the International Lung Cancer Consortium, and UK Biobank. Increased nicotine metabolism increased the risk of COPD, lung cancer, and lung function in the univariable analysis. However, when accounting for smoking heaviness in the multivariable analysis, we found that increased nicotine metabolite ratio (indicative of decreased nicotine exposure per cigarette smoked) decreases heart rate (b = -0.30, 95% CI -0.50 to -0.10) and lung function (b = -33.33, 95% CI -41.76 to -24.90). There was no clear evidence of an effect on the remaining outcomes. The results suggest that these smoking-related outcomes are not due to nicotine exposure but are caused by the other components of tobacco smoke; however, there are multiple potential sources of bias, and the results should be triangulated using evidence from a range of methodologies.

Dimensional and transdiagnostic phenotypes in psychiatric genome-wide association studies

Genome-wide association studies (GWAS) provide biological insights into disease onset and progression and have potential to produce clinically useful biomarkers. A growing body of GWAS focuses on quantitative and transdiagnostic phenotypic targets, such as symptom severity or biological markers, to enhance gene discovery and the translational utility of genetic findings. The current review discusses such phenotypic approaches in GWAS across major psychiatric disorders. We identify themes and recommendations that emerge from the literature to date, including issues of sample size, reliability, convergent validity, sources of phenotypic information, phenotypes based on biological and behavioral markers such as neuroimaging and chronotype, and longitudinal phenotypes. We also discuss insights from multi-trait methods such as genomic structural equation modelling. These provide insight into how hierarchical 'splitting' and 'lumping' approaches can be applied to both diagnostic and dimensional phenotypes to model clinical heterogeneity and comorbidity. Overall, dimensional and transdiagnostic phenotypes have enhanced gene discovery in many psychiatric conditions and promises to yield fruitful GWAS targets in the years to come.

The genetics of falling susceptibility and identification of causal risk factors

Falls represent a huge health and economic burden. Whilst many factors are associated with fall risk (e.g. obesity and physical inactivity) there is limited evidence for the causal role of these risk factors. Here, we used hospital and general practitioner records in UK Biobank, deriving a balance specific fall phenotype in 20,789 cases and 180,658 controls, performed a Genome Wide Association Study (GWAS) and used Mendelian Randomisation (MR) to test causal pathways. GWAS indicated a small but significant SNP-based heritability (4.4%), identifying one variant (rs429358) in APOE at genome-wide significance (P < 5e-8). MR provided evidence for a causal role of higher BMI on higher fall risk even in the absence of adverse metabolic consequences. Depression and neuroticism predicted higher risk of falling, whilst higher hand grip strength and physical activity were protective. Our findings suggest promoting lower BMI, higher physical activity as well as psychological health is likely to reduce falls.

Artificial image objects for classification of schizophrenia with GWAS-selected SNVs and convolutional neural network

In this article, we propose a new approach to analyze large genomics data. We considered individual genetic variants as pixels in an image and transformed a collection of variants into an artificial image object (AIO), which could be classified as a regular image by CNN algorithms. Using schizophrenia as a case study, we demonstrate the principles and their applications with 3 datasets. With 4,096 SNVs, the CNN models achieved an accuracy of 0.678 ± 0.007 and an AUC of 0.738 ± 0.008 for the diagnosis phenotype. With 44,100 SNVs, the models achieved class-specific accuracies of 0.806 ± 0.032 and 0.820 ± 0.049, and AUCs of 0.930 ± 0.017 and 0.867 ± 0.040 for the bottom and top classes stratified by the patient's polygenic risk scores. These results suggest that, once transformed to images, large genomics data can be analyzed effectively with image classification algorithms.

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Introduce a technique to transform genomics data into AIOs

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Apply CNN algorithms to classify genomics derived AIOs

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Showcase the technique with GWAS-selected SNVs to classify schizophrenia diagnosis

Genome-wide association studies have discovered many genetic variants that contribute to human diseases. However, it remains a challenge to effectively utilize these variants to facilitate early and accurate diagnosis and treatment. In this report, we propose a new approach that transforms genetic data into AIOs so that they can be classified by advanced artificial intelligence and machine learning algorithms. Using schizophrenia as a case study, we demonstrate that genetic variants can be transformed into AIOs and that the AIOs can be classified by CNN algorithms consistently. Our approach can be applied to other omics data and combine them to jointly model disease risks and treatment responses.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Genome-wide association meta-analysis of nicotine metabolism and cigarette consumption measures in smokers of European descent

Smoking behaviors, including amount smoked, smoking cessation, and tobacco-related diseases, are altered by the rate of nicotine clearance. Nicotine clearance can be estimated using the nicotine metabolite ratio (NMR) (ratio of 3'hydroxycotinine/cotinine), but only in current smokers. Advancing the genomics of this highly heritable biomarker of CYP2A6, the main metabolic enzyme for nicotine, will also enable investigation of never and former smokers. We performed the largest genome-wide association study (GWAS) to date of the NMR in European ancestry current smokers (n = 5185), found 1255 genome-wide significant variants, and replicated the chromosome 19 locus. Fine-mapping of chromosome 19 revealed 13 putatively causal variants, with nine of these being highly putatively causal and mapping to CYP2A6, MAP3K10, ADCK4, and CYP2B6. We also identified a putatively causal variant on chromosome 4 mapping to TMPRSS11E and demonstrated an association between TMPRSS11E variation and a UGT2B17 activity phenotype. Together the 14 putatively causal SNPs explained ~38% of NMR variation, a substantial increase from the ~20 to 30% previously explained. Our additional GWASs of nicotine intake biomarkers showed that cotinine and smoking intensity (cotinine/cigarettes per day (CPD)) shared chromosome 19 and chromosome 4 loci with the NMR, and that cotinine and a more accurate biomarker, cotinine + 3'hydroxycotinine, shared a chromosome 15 locus near CHRNA5 with CPD and Pack-Years (i.e., cumulative exposure). Understanding the genetic factors influencing smoking-related traits facilitates epidemiological studies of smoking and disease, as well as assists in optimizing smoking cessation support, which in turn will reduce the enormous personal and societal costs associated with smoking.

Multi-polygenic Analysis of Nicotine Dependence in Individuals of European Ancestry

INTRODUCTION

Heritability estimates of nicotine dependence (ND) range from 40-70%, but discovery GWAS of ND are underpowered and have limited predictive utility. In this work, we leverage genetically correlated traits and diseases to increase the accuracy of polygenic risk prediction.

METHODS

We employed a multi-trait model using summary statistic-based best linear unbiased predictors (SBLUP) of genetic correlates of DSM-IV diagnosis of ND in 6,394 individuals of European Ancestry (prevalence = 45.3%, %female = 46.8%, µage = 40.08 [s.d. = 10.43]) and 3,061 individuals from a nationally-representative sample with Fagerström Test for Nicotine Dependence symptom count (FTND; 51.32% female, mean age = 28.9 [s.d. = 1.70]). Polygenic predictors were derived from GWASs known to be phenotypically and genetically correlated with ND (i.e., Cigarettes per Day (CPD), the Alcohol Use Disorders Identification Test (AUDIT-Consumption and AUDIT-Problems), Neuroticism, Depression, Schizophrenia, Educational Attainment, Body Mass Index (BMI), and Self-Perceived Risk-Taking); including Height as a negative control. Analyses controlled for age, gender, study site, and the first 10 ancestral principal components.

RESULTS

The multi-trait model accounted for 3.6% of the total trait variance in DSM-IV ND. Educational Attainment (β=-0.125; 95% confidence interval (CI): [-0.149,-0.101]), CPD (0.071 [0.047,0.095]), and Self-Perceived Risk-Taking (0.051 [0.026,0.075]) were the most robust predictors. PGS effects on FTND were limited.

CONCLUSIONS

Risk for ND is not only polygenic, but also pleiotropic. Polygenic effects on ND that are accessible by these traits are limited in size and act additively to explain risk.

Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals

Here we report a large genome-wide association study (GWAS) for longitudinal smoking phenotypes in 286,118 individuals from the Million Veteran Program (MVP) where we identified 18 loci for smoking trajectory of current versus never in European Americans, one locus in African Americans, and one in Hispanic Americans. Functional annotations prioritized several dozen genes where significant loci co-localized with either expression quantitative trait loci or chromatin interactions. The smoking trajectories were genetically correlated with 209 complex traits, for 33 of which smoking was either a causal or a consequential factor. We also performed European-ancestry meta-analyses for smoking status in the MVP and GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN) (N_total = 842,717) and identified 99 loci for smoking initiation and 13 loci for smoking cessation. Overall, this large GWAS of longitudinal smoking phenotype in multiple populations, combined with a meta-GWAS for smoking status, adds new insights into the genetic vulnerability for smoking behavior.

Genome-wide association studies (GWASs) for cigarette smoking have identified several hundred loci that account for a small proportion of the overall genetic risk. Here, the authors report a large GWAS for smoking trajectories and meta-analysis for smoking status, finding multiple plausible loci.

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci

Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10-8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10-8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10-3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.

Depressive symptoms predict smoking cessation in a 20-year longitudinal study of adult twins

Depression has been suggested to hinder smoking cessation, especially when co-occurring with nicotine dependence. The study aimed to examine the longitudinal association of depressive symptoms with smoking cessation among daily smokers. The study utilized adult Finnish twin cohort where 1438 daily smokers (mean age: 38.3, range: 33-45) in 1990 were re-examined for their smoking status in 2011. We assessed baseline depressive symptoms with the Beck Depression Inventory, and the self-reported smoking status at follow-up. The methods included multinomial logistic regression and time to event analyses, adjusted for multiple covariates (age, sex, marital status, social class, heavy drinking occasions, and health status) and smoking heaviness at baseline assessed by cigarettes per day (CPD). Additionally, within-twin-pair analyses were conducted. Results indicated that moderate/severe depressive symptoms at baseline were associated with a lower likelihood of smoking cessation two decades later. Adjusting for covariates, those with moderate/severe depressive symptoms (vs. no/minimal depressive symptoms) had 46% lower likelihood of quitting (relative risk ratio, RRR = 0.54, 95% CI: 0.30-0.96). After including CPD, the association of depressive symptoms with smoking cessation attenuated modestly (RRR = 0.62, 95% CI: 0.34-1.12). Further, time to event analysis for quitting year since baseline yielded similar findings. In the within-pair analysis, depressive symptoms were not associated with quitting smoking. The results suggest that reporting more depressive symptoms is associated with a lower likelihood of smoking cessation during a 20-year period. The baseline amount of smoking and familial factors partly explain the observed association. Smoking cessation programs should monitor depressive symptoms.

UGT2B10 Genotype Influences Serum Cotinine Levels and Is a Primary Determinant of Higher Cotinine in African American Smokers

BACKGROUND

Cotinine is the most widely used biomarker of tobacco exposure. At similar smoking levels, African Americans have higher serum cotinine than Whites. UGT2B10-catalyzed cotinine glucuronidation impacts these levels, and African Americans often have low UGT2B10 activity due to a high prevalence of a UGT2B10 splice variant (rs2942857).

METHODS

Two UGT2B10 SNPs (rs6175900 and rs2942857) were genotyped in 289 African Americans and 627 White smokers. Each smoker was assigned a genetic score of 0, 1, or 2 based on the number of variant alleles. Total nicotine equivalents (TNE), the sum of nicotine and six metabolites, and serum cotinine and 3'-hydroxycotinine were quantified. The contribution of UGT2B10 genetic score to cotinine concentration was determined.

RESULTS

Serum cotinine was significantly higher in smokers with UGT2B10 genetic scores of 2 versus 0 (327 ng/mL vs. 221 ng/mL; P < 0.001); TNEs were not different. In a linear regression model adjusted for age, gender, cigarettes per day, TNE, race, and CYP2A6 activity, geometric mean cotinine increased 43% between genetic score 2 versus 0 (P < 0.001). A 0.1 increase in the CYP2A6 activity ratio, 3'-hydroxycotinine/cotinine, resulted in a 6% decrease in cotinine. After adjustment for UGT2B10 genotype and the other covariants, there was no significant difference in serum cotinine by race.

CONCLUSIONS

UGT2B10 genotype is a major contributor to cotinine levels and explains the majority of high serum cotinine in African American smokers.

IMPACT

Cotinine levels in smokers may greatly overestimate tobacco exposure and potentially misinform our understanding of ethnic/racial difference in tobacco-related disease if UGT2B10 genotype is not taken into account.

Polygenic Risk Scores for Subtyping of Schizophrenia

Schizophrenia is a complex disorder with many comorbid conditions. In this study, we used polygenic risk scores (PRSs) from schizophrenia and comorbid traits to explore consistent cluster structure in schizophrenia patients. With 10 comorbid traits, we found a stable 4-cluster structure in two datasets (MGS and SSCCS). When the same traits and parameters were applied for the patients in a clinical trial of antipsychotics, the CATIE study, a 5-cluster structure was observed. One of the 4 clusters found in the MGS and SSCCS was further split into two clusters in CATIE, while the other 3 clusters remained unchanged. For the 5 CATIE clusters, we evaluated their association with the changes of clinical symptoms, neurocognitive functions, and laboratory tests between the enrollment baseline and the end of Phase I trial. Class I was found responsive to treatment, with significant reduction for the total, positive, and negative symptoms (p = 0.0001, 0.0099, and 0.0028, respectively), and improvement for cognitive functions (VIGILANCE, p = 0.0099; PROCESSING SPEED, p = 0.0006; WORKING MEMORY, p = 0.0023; and REASONING, p = 0.0015). Class II had modest reduction of positive symptoms (p = 0.0492) and better PROCESSING SPEED (p = 0.0071). Class IV had a specific reduction of negative symptoms (p = 0.0111) and modest cognitive improvement for all tested domains. Interestingly, Class IV was also associated with decreased lymphocyte counts and increased neutrophil counts, an indication of ongoing inflammation or immune dysfunction. In contrast, Classes III and V showed no symptom reduction but a higher level of phosphorus. Overall, our results suggest that PRSs from schizophrenia and comorbid traits can be utilized to classify patients into subtypes with distinctive clinical features. This genetic susceptibility based subtyping may be useful to facilitate more effective treatment and outcome prediction.

SNPs within CHRNA5-A3-B4 and CYP2A6/B6, nicotine metabolite concentrations and nicotine dependence treatment success in smokers

AIM

Plasma values of nicotine and its metabolites are highly variable, and this variability has a strong genetic influence. In our study, we analysed the impact of common polymorphisms associated with smoking on the plasma values of nicotine, nicotine metabolites and their ratios and investigated the potential effect of these polymorphisms and nicotine metabolite ratios on the successful treatment of tobacco dependence.

METHODS

Five variants (rs16969968, rs6474412, rs578776, rs4105144 and rs3733829) were genotyped in a group of highly dependent adult smokers (n=103). All smokers underwent intensive treatment for tobacco dependence; 33 smokers were still abstinent at the 12-month follow-up.

RESULTS

The rs4105144 (CYP2A6, P<0.005) and rs3733829 (EGLN2, P<0.05) variants were significantly associated with plasma concentrations of 3OH-cotinine and with 3OH-cotinine: cotinine ratios. Similarly, the unweighted gene score was a significant (P<0.05) predictor of both cotinine:nicotine and 3OH-cotinine:cotinine ratios. No associations between the analysed polymorphisms or nicotine metabolite ratios and nicotine abstinence rate were observed.

CONCLUSION

Although CYP2A6 and EGLN2 polymorphisms were associated with nicotine metabolism ratios, neither these polymorphisms nor the ratios were associated with abstinence rates.

Translational Molecular Approaches in Substance Abuse Research

Excessive abuse of psychoactive substances is one of the leading contributors to morbidity and mortality worldwide. In this book chapter, we review translational research strategies that are applied in the pursuit of new and more effective therapeutics for substance use disorder (SUD). The complex, multidimensional nature of psychiatric disorders like SUD presents difficult challenges to investigators. While animal models are critical for outlining the mechanistic relationships between defined behaviors and genetic and/or molecular changes, the heterogeneous pathophysiology of brain diseases is uniquely human, necessitating the use of human studies and translational research schemes. Translational research describes a cross-species approach in which findings from human patient-based data can be used to guide molecular genetic investigations in preclinical animal models in order to delineate the mechanisms of reward circuitry changes in the addicted state. Results from animal studies can then inform clinical investigations toward the development of novel treatments for SUD. Here we describe the strategies that are used to identify and functionally validate genetic variants in the human genome which may contribute to increased risk for SUD, starting from early candidate gene approaches to more recent genome-wide association studies. We will next examine studies aimed at understanding how transcriptional and epigenetic dysregulation in SUD can persistently alter cellular function in the disease state. In our discussion, we then focus on examples from the literature illustrating molecular genetic methodologies that have been applied to studies of different substances of abuse - from alcohol and nicotine to stimulants and opioids - in order to exemplify how these approaches can both delineate the underlying molecular systems driving drug addiction and provide insights into the genetic basis of SUD.

Validated inference of smoking habits from blood with a finite DNA methylation marker set

Inferring a person’s smoking habit and history from blood is relevant for complementing or replacing self-reports in epidemiological and public health research, and for forensic applications. However, a finite DNA methylation marker set and a validated statistical model based on a large dataset are not yet available. Employing 14 epigenome-wide association studies for marker discovery, and using data from six population-based cohorts (N = 3764) for model building, we identified 13 CpGs most suitable for inferring smoking versus non-smoking status from blood with a cumulative Area Under the Curve (AUC) of 0.901. Internal fivefold cross-validation yielded an average AUC of 0.897 ± 0.137, while external model validation in an independent population-based cohort (N = 1608) achieved an AUC of 0.911. These 13 CpGs also provided accurate inference of current (average AUC_{crossvalidation} 0.925 ± 0.021, AUC_{externalvalidation}0.914), former (0.766 ± 0.023, 0.699) and never smoking (0.830 ± 0.019, 0.781) status, allowed inferring pack-years in current smokers (10 pack-years 0.800 ± 0.068, 0.796; 15 pack-years 0.767 ± 0.102, 0.752) and inferring smoking cessation time in former smokers (5 years 0.774 ± 0.024, 0.760; 10 years 0.766 ± 0.033, 0.764; 15 years 0.767 ± 0.020, 0.754). Model application to children revealed highly accurate inference of the true non-smoking status (6 years of age: accuracy 0.994, N = 355; 10 years: 0.994, N = 309), suggesting prenatal and passive smoking exposure having no impact on model applications in adults. The finite set of DNA methylation markers allow accurate inference of smoking habit, with comparable accuracy as plasma cotinine use, and smoking history from blood, which we envision becoming useful in epidemiology and public health research, and in medical and forensic applications.

Genome-wide association study in Finnish twins highlights the connection between nicotine addiction and neurotrophin signaling pathway

The heritability of nicotine dependence based on family studies is substantial. Nevertheless, knowledge of the underlying genetic architecture remains meager. Our aim was to identify novel genetic variants responsible for interindividual differences in smoking behavior. We performed a genome-wide association study on 1715 ever smokers ascertained from the population-based Finnish Twin Cohort enriched for heavy smoking. Data imputation used the 1000 Genomes Phase I reference panel together with a whole genome sequence-based Finnish reference panel. We analyzed three measures of nicotine addiction-smoking quantity, nicotine dependence and nicotine withdrawal. We annotated all genome-wide significant SNPs for their functional potential. First, we detected genome-wide significant association on 16p12 with smoking quantity (P = 8.5 × 10^-9 ), near CLEC19A. The lead-SNP stands 22 kb from a binding site for NF-κB transcription factors, which play a role in the neurotrophin signaling pathway. However, the signal was not replicated in an independent Finnish population-based sample, FINRISK (n = 6763). Second, nicotine withdrawal showed association on 2q21 in an intron of TMEM163 (P = 2.1 × 10^-9 ), and on 11p15 (P = 6.6 × 10^-8 ) in an intron of AP2A2, and P = 4.2 × 10^-7 for a missense variant in MUC6, both involved in the neurotrophin signaling pathway). Third, association was detected on 3p22.3 for maximum number of cigarettes smoked per day (P = 3.1 × 10^-8 ) near STAC. Associating CLEC19A and TMEM163 SNPs were annotated to influence gene expression or methylation. The neurotrophin signaling pathway has previously been associated with smoking behavior. Our findings further support the role in nicotine addiction.

The effect of body mass index on smoking behaviour and nicotine metabolism: a Mendelian randomization study

Given clear evidence that smoking lowers weight, it is possible that individuals with higher body mass index (BMI) smoke in order to lose or maintain their weight. We performed Mendelian randomization (MR) analyses of the effects of BMI on smoking behaviour in UK Biobank and the Tobacco and Genetics Consortium genome-wide association study (GWAS), on cotinine levels and nicotine metabolite ratio (NMR) in published GWAS and on DNA methylation in the Avon Longitudinal Study of Parents and Children. Our results indicate that higher BMI causally influences lifetime smoking, smoking initiation, smoking heaviness and also DNA methylation at the aryl-hydrocarbon receptor repressor (AHRR) locus, but we do not see evidence for an effect on smoking cessation. While there is no strong evidence that BMI causally influences cotinine levels, suggestive evidence for a negative causal influence on NMR may explain this. There is a causal effect of BMI on smoking, but the relationship is likely to be complex due to opposing effects on behaviour and metabolism.

Epigenome-wide association study of serum cotinine in current smokers reveals novel genetically driven loci

Background

DNA methylation alteration extensively associates with smoking and is a plausible link between smoking and adverse health. We examined the association between epigenome-wide DNA methylation and serum cotinine levels as a proxy of nicotine exposure and smoking quantity, assessed the role of SNPs in these associations, and evaluated molecular mediation by methylation in a sample of biochemically verified current smokers (N = 310).

Results

DNA methylation at 50 CpG sites was associated (FDR < 0.05) with cotinine levels, 17 of which are novel associations. As cotinine levels are influenced not only by nicotine intake but also by CYP2A6-mediated nicotine metabolism rate, we performed secondary analyses adjusting for genetic risk score of nicotine metabolism rate and identified five additional novel associations. We further assessed the potential role of genetic variants in the detected association between methylation and cotinine levels observing 124 cis and 3898 trans methylation quantitative trait loci (meQTLs). Nineteen of these SNPs were also associated with cotinine levels (FDR < 0.05). Further, at seven CpG sites, we observed a trend (P < 0.05) that altered DNA methylation mediates the effect of SNPs on nicotine exposure rather than a direct consequence of smoking. Finally, we performed replication of our findings in two independent cohorts of biochemically verified smokers (N = 450 and N = 79).

Conclusions

Using cotinine, a biomarker of nicotine exposure, we replicated and extended identification of novel epigenetic associations in smoking-related genes. We also demonstrated that DNA methylation in some of the identified loci is driven by the underlying genotype and may mediate the causal effect of genotype on cotinine levels.

Electronic supplementary material

The online version of this article (10.1186/s13148-018-0606-9) contains supplementary material, which is available to authorized users.

Distinguishing genetic correlation from causation across 52 diseases and complex traits

Mendelian randomization, a method to infer causal relationships, is confounded by genetic correlations reflecting shared etiology. We developed a model in which a latent causal variable mediates the genetic correlation; trait 1 is partially genetically causal for trait 2 if it is strongly genetically correlated with the latent causal variable, quantified using the genetic causality proportion. We fit this model using mixed fourth moments [Formula: see text] and [Formula: see text] of marginal effect sizes for each trait; if trait 1 is causal for trait 2, then SNPs affecting trait 1 (large [Formula: see text]) will have correlated effects on trait 2 (large α1α2), but not vice versa. In simulations, our method avoided false positives due to genetic correlations, unlike Mendelian randomization. Across 52 traits (average n = 331,000), we identified 30 causal relationships with high genetic causality proportion estimates. Novel findings included a causal effect of low-density lipoprotein on bone mineral density, consistent with clinical trials of statins in osteoporosis.

The Cholinergic System as a Treatment Target for Opioid Use Disorder

Opioid overdoses recently became the leading cause of accidental death in the US, marking an increase in the severity of the opioid use disorder (OUD) epidemic that is impacting global health. Current treatment protocols for OUD are limited to opioid medications, including methadone, buprenorphine, and naltrexone. While these medications are effective in many cases, new treatments are required to more effectively address the rising societal and interpersonal costs associated with OUD. In this article, we review the opioid and cholinergic systems, and examine the potential of acetylcholine (ACh) as a treatment target for OUD. The cholinergic system includes enzymes that synthesize and degrade ACh and receptors that mediate the effects of ACh. ACh is involved in many central nervous system functions that are critical to the development and maintenance of OUD, such as reward and cognition. Medications that target the cholinergic system have been approved for the treatment of Alzheimer's disease, tobacco use disorder, and nausea. Clinical and preclinical studies suggest that medications such as cholinesterase inhibitors and scopolamine, which target components of the cholinergic system, show promise for the treatment of OUD and further investigations are warranted.

Prediction of Schizophrenia Diagnosis by Integration of Genetically Correlated Conditions and Traits

Schizophrenia is genetically heterogeneous and comorbid with many conditions. In this study, we explored polygenic scores (PGSs) from genetically related conditions and traits to predict schizophrenia diagnosis using both logistic regression and deep neural network (DNN) models. We used the combined Molecular Genetics of Schizophrenia and Swedish Schizophrenia Case Control Study (MGS + SSCCS) data for training and testing the models, and used the Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE) data as independent validation. We screened 28 conditions and traits comorbid with schizophrenia to identify traits as potential predictors and used LASSO regression to select predictors for model construction. We investigated how PGS calculation influenced model performance. We found that the inclusion of comorbid traits improved model performance and PGSs calculated from two traits were more generalizable in independent validation. With a DNN model using 19 PGS predictors, we accomplished a prediction accuracy of 0.813 and an AUC of 0.905 in the MGS + SSCCS data. When this model was validated with the CATIE data, it achieved an accuracy of 0.721 and AUC of 0.747. Our results indicate that PGSs alone may not be sufficient to predict schizophrenia accurately and the inclusion of behavioral and clinical data may be necessary for more accurate prediction model.

Biochemical, demographic, and self-reported tobacco-related predictors of the acute heart rate response to nicotine in smokers

Understanding the stimulatory effects of nicotine on cardiovascular function in humans is of great interest given the wide-spread use of different forms of combustible and smokeless products that deliver nicotine. An intravenous nicotine infusion procedure was used to evaluate factors associated with the acute heart rate (HR) response to nicotine (0.5 mg per 70 kg bodyweight) in a sample of 213 smokers. We tested for differential response to nicotine based on demographic characteristics (race [European American vs African America], sex, body mass index and age); a set of blood-based biomarkers (baseline nicotine, cotinine and cortisol levels and nicotine metabolite ratio); and a set of self-reported measures related to tobacco use. Nicotine infusion was first noted to increase HR approximately 10 beats per minute (95% CI: 7.8-12.3) one minute post-infusion, and 13 beats per minute (95% CI: 11.0-15.2) two minutes post-infusion. Higher cortisol, lower nicotine levels, higher nicotine metabolite ratio, being female and greater withdrawal symptoms were independently associated with a potentiated increase in HR 1 or 2 min after nicotine infusion. Factors associated with the acute HR effects of nicotine warrant further investigation given their potential to inform the development of nicotine delivery systems as tobacco harm reduction approaches for smokers.

Investigating causality in associations between education and smoking: a two-sample Mendelian randomization study

Background

Lower educational attainment is associated with increased rates of smoking, but ascertaining causality is challenging. We used two-sample Mendelian randomization (MR) analyses of summary statistics to examine whether educational attainment is causally related to smoking.

Methods and Findings

We used summary statistics from genome-wide association studies (GWAS) of educational attainment and a range of smoking phenotypes (smoking initiation, cigarettes per day, cotinine levels and smoking cessation). Of 74 single nucleotide polymorphisms (SNPs) that predict educational attainment, 57 (or their highly correlated proxies) were present in the smoking initiation, cigarettes per day and smoking cessation GWAS, and 72 in the cotinine GWAS. Various complementary MR techniques (inverse variance weighted regression, MR Egger, weighted median regression) were used to test the robustness of our results. We found broadly consistent evidence across these techniques that higher educational attainment leads to reduced likelihood of smoking initiation, reduced heaviness of smoking among smokers (as measured via self-report [e.g. inverse variance weighted beta -2.25, 95% confidence interval (CI) -3.81, -0.70, P = 0.005] and cotinine levels [e.g. inverse variance weighted beta -0.34, 95% CI -0.67, -0.01, P = 0.057]), and greater likelihood of smoking cessation among smokers (inverse variance weighted beta 0.65, 95% CI 0.35, 0.95, P = 5.54 × 10-5). Less consistent across the different techniques were associations between educational attainment and smoking initiation.

Conclusions

Our findings indicate a causal association between low educational attainment and increased risk of smoking, and may explain the observational associations between educational attainment and adverse health outcomes such as risk of coronary heart disease.

The genetics of smoking in individuals with chronic obstructive pulmonary disease

Background

Smoking is the principal modifiable environmental risk factor for chronic obstructive pulmonary disease (COPD) which affects 300 million people and is the 3rd leading cause of death worldwide. Most of the genetic studies of smoking have relied on self-reported smoking status which is vulnerable to reporting and recall bias. Using data from the Lung Health Study (LHS), we sought to identify genetic variants associated with quantitative smoking and cessation in individuals with mild to moderate COPD.

Methods

The LHS is a longitudinal multicenter study of mild-to-moderate COPD subjects who were all smokers at recruitment. We performed genome-wide association studies (GWASs) for salivary cotinine (n = 4024), exhaled carbon monoxide (eCO) (n = 2854), cigarettes per day (CPD) (n = 2706) and smoking cessation at year 5 follow-up (n = 717 quitters and 2175 smokers). The GWAS analyses were adjusted for age, gender, and genetic principal components.

Results

For cotinine levels, SNPs near UGT2B10 gene achieved genome-wide significance (i.e. P < 5 × 10^− 8) with top SNP rs10023464, P = 1.27 × 10^− 11. For eCO levels, one significant SNP was identified which mapped to the CHRNA3 gene (rs12914385, P = 2.38 × 10^− 8). A borderline region mapping to KCNMA1 gene was associated with smoking cessation (rs207675, P = 5.95 × 10^− 8). Of the identified loci, only the CHRNA3/5 locus showed significant associations with lung function but only in heavy smokers. No regions met genome-wide significance for CPD.

Conclusion

The study demonstrates that using objective measures of smoking such as eCO and/or salivary cotinine can more precisely capture the genetic contribution to multiple aspects of smoking behaviour. The KCNMA1 gene association with smoking cessation may represent a potential therapeutic target and warrants further studies.

Trial registration

The Lung Health Study ClinicalTrials.gov Identifier: NCT00000568. Date of registration: October 28, 1999.

Electronic supplementary material

The online version of this article (10.1186/s12931-018-0762-7) contains supplementary material, which is available to authorized users.

Pathways to precision medicine in smoking cessation treatments

Cigarette smoking is highly addictive and modern genetic research has identified robust genetic influences on nicotine dependence. An important step in translating these genetic findings to clinical practice is identifying the genetic factors affecting smoking cessation in order to enhance current smoking cessation treatments. We reviewed the significant genetic variants that predict nicotine dependence, smoking cessation, and response to cessation pharmacotherapy. These data suggest that genetic risks can predict smoking cessation outcomes and moderate the effect of pharmacological treatments. Some pharmacogenetic findings have been replicated in meta-analyses or in multiple smoking cessation trials. The variation in efficacy between smokers with different genetic markers supports the notion that personalized smoking cessation intervention based upon genotype could maximize the efficiency of such treatment while minimizing side effects, thus influencing the number needed to treat (NNT) and the number needed to harm. In summary, as precision medicine is revolutionizing healthcare, smoking cessation may be one of the first areas where genetic variants may identify individuals at increased risk. Current evidence strongly suggests that genetic variants predict cessation failure and that cessation pharmacotherapy effectiveness is modulated by biomarkers such as nicotinic cholinergic receptor α5 subunit (CHRNA5) genotypes or nicotine metabolism ratio (NMR). These findings strengthen the case for the development and rigorous testing of treatments that target patients with different biological risk profiles.

The Value of Biosamples in Smoking Cessation Trials: A Review of Genetic, Metabolomic, and Epigenetic Findings

Introduction

Human genetic research has succeeded in definitively identifying multiple genetic variants associated with risk for nicotine dependence and heavy smoking. To build on these advances, and to aid in reducing the prevalence of smoking and its consequent health harms, the next frontier is to identify genetic predictors of successful smoking cessation and also of the efficacy of smoking cessation treatments ("pharmacogenomics"). More broadly, additional biomarkers that can be quantified from biosamples also promise to aid "Precision Medicine" and the personalization of treatment, both pharmacological and behavioral.

Aims and Methods

To motivate ongoing and future efforts, here we review several compelling genetic and biomarker findings related to smoking cessation and treatment.

Results

These Key results involve genetic variants in the nicotinic receptor subunit gene CHRNA5, variants in the nicotine metabolism gene CYP2A6, and the nicotine metabolite ratio. We also summarize reports of epigenetic changes related to smoking behavior.

Conclusions

The results to date demonstrate the value and utility of data generated from biosamples in clinical treatment trial settings. This article cross-references a companion paper in this issue that provides practical guidance on how to incorporate biosample collection into a planned clinical trial and discusses avenues for harmonizing data and fostering consortium-based, collaborative research on the pharmacogenomics of smoking cessation.

Implications

Evidence is emerging that certain genotypes and biomarkers are associated with smoking cessation success and efficacy of smoking cessation treatments. We review key findings that open potential avenues for personalizing smoking cessation treatment according to an individual's genetic or metabolic profile. These results provide important incentive for smoking cessation researchers to collect biosamples and perform genotyping in research studies and clinical trials.

Human Genetics of Addiction: New Insights and Future Directions

PURPOSE OF REVIEW

With the advent of the genome-wide association study (GWAS), our understanding of the genetics of addiction has made significant strides forward. Here, we summarize genetic loci containing variants identified at genome-wide statistical significance (P < 5 × 10-8) and independently replicated, review evidence of functional or regulatory effects for GWAS-identified variants, and outline multi-omics approaches to enhance discovery and characterize addiction loci.

RECENT FINDINGS

Replicable GWAS findings span 11 genetic loci for smoking, eight loci for alcohol, and two loci for illicit drugs combined and include missense functional variants and noncoding variants with regulatory effects in human brain tissues traditionally viewed as addiction-relevant (e.g., prefrontal cortex [PFC]) and, more recently, tissues often overlooked (e.g., cerebellum). GWAS analyses have discovered several novel, replicable variants contributing to addiction. Using larger sample sizes from harmonized datasets and new approaches to integrate GWAS with multiple 'omics data across human brain tissues holds great promise to significantly advance our understanding of the biology underlying addiction.

Post-transcriptional Regulation of UGT2B10 Hepatic Expression and Activity by Alternative Splicing

The detoxification enzyme UDP-glucuronosyltransferase UGT2B10 is specialized in the N-linked glucuronidation of many drugs and xenobiotics. Preferred substrates possess tertiary aliphatic amines and heterocyclic amines, such as tobacco carcinogens and several antidepressants and antipsychotics. We hypothesized that alternative splicing (AS) constitutes a means to regulate steady-state levels of UGT2B10 and enzyme activity. We established the transcriptome of UGT2B10 in normal and tumoral tissues of multiple individuals. The highest expression was in the liver, where 10 AS transcripts represented 50% of the UGT2B10 transcriptome in 50 normal livers and 44 hepatocellular carcinomas. One abundant class of transcripts involves a novel exonic sequence and leads to two alternative (alt.) variants with novel in-frame C termini of 10 or 65 amino acids. Their hepatic expression was highly variable among individuals, correlated with canonical transcript levels, and was 3.5-fold higher in tumors. Evidence for their translation in liver tissues was acquired by mass spectrometry. In cell models, they colocalized with the enzyme and influenced the conjugation of amitriptyline and levomedetomidine by repressing or activating the enzyme (40%-70%; P < 0.01) in a cell context-specific manner. A high turnover rate for the alt. proteins, regulated by the proteasome, was observed in contrast to the more stable UGT2B10 enzyme. Moreover, a drug-induced remodeling of UGT2B10 splicing was demonstrated in the HepaRG hepatic cell model, which favored alt. variants expression over the canonical transcript. Our findings support a significant contribution of AS in the regulation of UGT2B10 expression in the liver with an impact on enzyme activity.

Effect of UGT2B10, UGT2B17, FMO3, and OCT2 genetic variation on nicotine and cotinine pharmacokinetics and smoking in African Americans

OBJECTIVES

Nicotine metabolism rates differ considerably among individuals, even after controlling for variation in the major nicotine-metabolizing enzyme, CYP2A6. In this study, the impact of genetic variation in alternative metabolic enzymes and transporters on nicotine and cotinine (COT) pharmacokinetics and smoking was investigated.

METHODS

We examined the impact of UGT2B10, UGT2B17, FMO3, NAT1, and OCT2 variation on pharmacokinetics and smoking (total nicotine equivalents and topography) before and after stratifying by CYP2A6 genotype in 60 African American (AA) smokers who received a simultaneous intravenous infusion of deuterium-labeled nicotine and COT.

RESULTS

Variants in UGT2B10 and UGT2B17 were associated with urinary glucuronidation ratios (glucuronide/free substrate). UGT2B10 rs116294140 was associated with significant alterations in COT and modest alterations in nicotine pharmacokinetics. These alterations, however, were not sufficient to change nicotine intake or topography. Neither UGT2B10 rs61750900, UGT2B17*2, FMO3 rs2266782, nor NAT1 rs13253389 altered nicotine or COT pharmacokinetics among all individuals (n=60) or among individuals with reduced CYP2A6 activity (n=23). The organic cation transporter OCT2 rs316019 significantly increased nicotine and COT Cmax (P=0.005, 0.02, respectively) and decreased nicotine clearance (P=0.05). UGT2B10 rs116294140 had no significant impact on the plasma or urinary trans-3'-hydroxycotinine/COT ratio, commonly used as a biomarker of CYP2A6 activity.

CONCLUSION

We found that polymorphisms in genes other than CYP2A6 represent minor sources of variation in nicotine pharmacokinetics, insufficient to alter smoking in AAs. The change in COT pharmacokinetics with UGT2B10 rs116294140 highlights the UGT2B10 gene as a source of variability in COT as a biomarker of tobacco exposure among AA smokers.

Does coffee consumption impact on heaviness of smoking?

BACKGROUND AND AIMS

Coffee consumption and cigarette smoking are strongly associated, but whether this association is causal remains unclear. We sought to: (1) determine whether coffee consumption influences cigarette smoking causally, (2) estimate the magnitude of any association and (3) explore potential mechanisms.

DESIGN

We used Mendelian randomization (MR) analyses of observational data, using publicly available summarized data from the Tobacco and Genetics (TAG) consortium, individual-level data from the UK Biobank and in-vitro experiments of candidate compounds.

SETTING

The TAG consortium includes data from studies in several countries. The UK Biobank includes data from men and women recruited across England, Wales and Scotland.

PARTICIPANTS

The TAG consortium provided data on n ≤ 38 181 participants. The UK Biobank provided data on 8072 participants.

MEASUREMENTS

In MR analyses, the exposure was coffee consumption (cups/day) and the outcome was heaviness of smoking (cigarettes/day). In our in-vitro experiments we assessed the effect of caffeic acid, quercetin and p-coumaric acid on the rate of nicotine metabolism in human liver microsomes and cDNA-expressed human CYP2A6.

FINDINGS

Two-sample MR analyses of TAG consortium data indicated that heavier coffee consumption might lead to reduced heaviness of smoking [beta = -1.49, 95% confidence interval (CI) = -2.88 to -0.09]. However, in-vitro experiments found that the compounds investigated are unlikely to inhibit significantly the rate of nicotine metabolism following coffee consumption. Further MR analyses in UK Biobank found no evidence of a causal relationship between coffee consumption and heaviness of smoking (beta = 0.20, 95% CI = -1.72 to 2.12).

CONCLUSIONS

Amount of coffee consumption is unlikely to have a major causal impact upon amount of cigarette smoking. If it does influence smoking, this is not likely to operate via effects of caffeic acid, quercetin or p-coumaric acid on nicotine metabolism. The observational association between coffee consumption and cigarette smoking may be due to smoking impacting on coffee consumption or confounding.

A Novel Tobacco Use Phenotype Suggests the 15q25 and 19q13 Loci May be Differentially Associated With Cigarettes per Day and Tobacco-Related Problems

INTRODUCTION

Tobacco use is associated with variation at the 15q25 gene cluster and the cytochrome P450 (CYP) genes CYP2A6 and CYP2B6. Despite the variety of outcomes associated with these genes, few studies have adopted a data-driven approach to defining tobacco use phenotypes for genetic association analyses. We used factor analysis to generate a tobacco use measure, explored its incremental validity over a simple indicator of tobacco involvement: cigarettes per day (CPD), and tested both phenotypes in a genetic association study.

METHODS

Data were from the University of California, San Francisco Family Alcoholism Study (n = 1942) and a Native American sample (n = 255). Factor analyses employed a broad array of tobacco use variables to establish the candidate phenotype. Subsequently, we conducted tests for association with variants in the nicotinic acetylcholine receptor and CYP genes. We explored associations with CPD and our measure. We then examined whether the variants most strongly associated with our measure remained associated after controlling for CPD.

RESULTS

Analyses identified one factor that captured tobacco-related problems. Variants at 15q25 were significantly associated with CPD after multiple testing correction (rs938682: p = .00002, rs1051730: p = .0003, rs16969968: p = .0003). No significant associations were obtained with the tobacco use phenotype; however, suggestive associations were observed for variants in CYP2B6 near CYP2A6 (rs45482602: ps = .0082, .0075) and CYP4Z2P (rs10749865: ps = .0098, .0079).

CONCLUSIONS

CPD captures variation at 15q25. Although strong conclusions cannot be drawn, these finding suggest measuring additional dimensions of problems may detect genetic variation not accounted for by smoking quantity. Replication in independent samples will help further refine phenotype definition efforts.

IMPLICATIONS

Different facets of tobacco-related problems may index unique genetic risk. CPD, a simple measure of tobacco consumption, is associated with variants at the 15q25 gene cluster. Additional dimensions of tobacco problems may help to capture variation at 19q13. Results demonstrate the utility of adopting a data-driven approach to defining phenotypes for genetic association studies of tobacco involvement and provide results that can inform replication efforts.

Endophenotype best practices

This review examines the current state of electrophysiological endophenotype research and recommends best practices that are based on knowledge gleaned from the last decade of molecular genetic research with complex traits. Endophenotype research is being oversold for its potential to help discover psychopathology relevant genes using the types of small samples feasible for electrophysiological research. This is largely because the genetic architecture of endophenotypes appears to be very much like that of behavioral traits and disorders: they are complex, influenced by many variants (e.g., tens of thousands) within many genes, each contributing a very small effect. Out of over 40 electrophysiological endophenotypes covered by our review, only resting heart, a measure that has received scant advocacy as an endophenotype, emerges as an electrophysiological variable with verified associations with molecular genetic variants. To move the field forward, investigations designed to discover novel variants associated with endophenotypes will need extremely large samples best obtained by forming consortia and sharing data obtained from genome wide arrays. In addition, endophenotype research can benefit from successful molecular genetic studies of psychopathology by examining the degree to which these verified psychopathology-relevant variants are also associated with an endophenotype, and by using knowledge about the functional significance of these variants to generate new endophenotypes. Even without molecular genetic associations, endophenotypes still have value in studying the development of disorders in unaffected individuals at high genetic risk, constructing animal models, and gaining insight into neural mechanisms that are relevant to clinical disorder.

The CHRNA5-A3-B4 Gene Cluster and Smoking: From Discovery to Therapeutics

Genome-wide association studies (GWASs) have identified associations between the CHRNA5-CHRNA3-CHRNB4 gene cluster and smoking heaviness and nicotine dependence. Studies in rodents have described the anatomical localisation and function of the nicotinic acetylcholine receptors (nAChRs) formed by the subunits encoded by this gene cluster. Further investigations that complemented these studies highlighted the variability of individuals' smoking behaviours and their ability to adjust nicotine intake. GWASs of smoking-related health outcomes have also identified this signal in the CHRNA5-CHRNA3-CHRNB4 gene cluster. This insight underpins approaches to strengthen causal inference in observational data. Combining genetic and mechanistic studies of nicotine dependence and smoking heaviness may reveal novel targets for medication development. Validated targets can inform genetic therapeutic interventions for smoking cessation and tobacco-related diseases.

How Intravenous Nicotine Administration in Smokers Can Inform Tobacco Regulatory Science

Reducing the negative health effects caused by tobacco products continues to be a public health priority. The Family Smoking Prevention and Tobacco Control Act of 2009 gives the Food Drug Administration authority to pursue several new strategies, including regulating levels of nicotine and other ingredients in tobacco products. A nicotine reduction strategy proposed by Benowitz and Henningfield aims to reduce the nicotine content of tobacco products to an amount below a threshold that supports neither the development nor maintenance of addiction. Many factors must be considered to determine the viability and efficacy of this approach. For example, the policy should be based on precise information on the dose-dependent effects of nicotine on reinforcement and factors that contribute to individual differences in these effects. However, there have been few studies on these topics in humans. Here, we briefly review nicotine pharmacology and reinforcement then present several studies illustrating the application of intravenous (IV) nicotine delivery to study nicotine reinforcement in humans. We discuss how nicotine delivery by IV infusion may be uniquely suited for studying nicotine's dose-dependent effects, and how this can inform tobacco regulatory science to facilitate the development of effective tobacco control policies.

Genetic Relationship between Schizophrenia and Nicotine Dependence

It is well known that most schizophrenia patients smoke cigarettes. There are different hypotheses postulating the underlying mechanisms of this comorbidity. We used summary statistics from large meta-analyses of plasma cotinine concentration (COT), Fagerström test for nicotine dependence (FTND) and schizophrenia to examine the genetic relationship between these traits. We found that schizophrenia risk scores calculated at P-value thresholds of 5 × 10⁻³ and larger predicted FTND and cigarettes smoked per day (CPD), suggesting that genes most significantly associated with schizophrenia were not associated with FTND/CPD, consistent with the self-medication hypothesis. The COT risk scores predicted schizophrenia diagnosis at P-values of 5 × 10⁻³ and smaller, implying that genes most significantly associated with COT were associated with schizophrenia. These results implicated that schizophrenia and FTND/CPD/COT shared some genetic liability. Based on this shared liability, we identified multiple long non-coding RNAs and RNA binding protein genes (DA376252, BX089737, LOC101927273, LINC01029, LOC101928622, HY157071, DA902558, RBFOX1 and TINCR), protein modification genes (MANBA, UBE2D3, and RANGAP1) and energy production genes (XYLB, MTRF1 and ENOX1) that were associated with both conditions. Further analyses revealed that these shared genes were enriched in calcium signaling, long-term potentiation and neuroactive ligand-receptor interaction pathways that played a critical role in cognitive functions and neuronal plasticity.