NCAM1-TTC12-ANKK1-DRD2 variants and smoking motives as intermediate phenotypes for nicotine dependence

Integrative analysis of genetics, epigenetics and RNA expression data reveal three susceptibility loci for smoking behavior in Chinese Han population

Despite numerous studies demonstrate that genetics and epigenetics factors play important roles on smoking behavior, our understanding of their functional relevance and coordinated regulation remains largely unknown. Here we present a multiomics study on smoking behavior for Chinese smoker population with the goal of not only identifying smoking-associated functional variants but also deciphering the pathogenesis and mechanism underlying smoking behavior in this under-studied ethnic population. After whole-genome sequencing analysis of 1329 Chinese Han male samples in discovery phase and OpenArray analysis of 3744 samples in replication phase, we discovered that three novel variants located near FOXP1 (rs7635815), and between DGCR6 and PRODH (rs796774020), and in ARVCF (rs148582811) were significantly associated with smoking behavior. Subsequently cis-mQTL and cis-eQTL analysis indicated that these variants correlated significantly with the differential methylation regions (DMRs) or differential expressed genes (DEGs) located in the regions where these variants present. Finally, our in silico multiomics analysis revealed several hub genes, like DRD2, PTPRD, FOXP1, COMT, CTNNAP2, to be synergistic regulated each other in the etiology of smoking.

Influence of substance use on male reproductive health and offspring outcomes

The prevalence of substance use globally is rising and is highest among men of reproductive age. In Africa, and South and Central America, cannabis use disorder is most prevalent and in Eastern and South-Eastern Europe, Central America, Canada and the USA, opioid use disorder predominates. Substance use might be contributing to the ongoing global decline in male fertility, and emerging evidence has linked paternal substance use with short-term and long-term adverse effects on offspring development and outcomes. This trend is concerning given that substance use is increasing, including during the COVID-19 pandemic. Preclinical studies have shown that male preconception substance use can influence offspring brain development and neurobehaviour through epigenetic mechanisms. Additionally, human studies investigating paternal health behaviours during the prenatal period suggest that paternal tobacco, opioid, cannabis and alcohol use is associated with reduced offspring mental health, in particular hyperactivity and attention-deficit hyperactivity disorder. The potential effects of paternal substance use are areas in which to focus public health efforts and health-care provider counselling of couples or individuals interested in conceiving.

Evaluating the causal effect of tobacco smoking on white matter brain aging: a two-sample Mendelian randomization analysis in UK Biobank

BACKGROUND AND AIMS

Tobacco smoking is a risk factor for impaired brain function, but its causal effect on white matter brain aging remains unclear. This study aimed to measure the causal effect of tobacco smoking on white matter brain aging.

DESIGN

Mendelian randomization (MR) analysis using two non-overlapping data sets (with and without neuroimaging data) from UK Biobank (UKB). The group exposed to smoking and control group consisted of current smokers and never smokers, respectively. Our main method was generalized weighted linear regression with other methods also included as sensitivity analysis.

SETTING

United Kingdom.

PARTICIPANTS

The study cohort included 23 624 subjects [10 665 males and 12 959 females with a mean age of 54.18 years, 95% confidence interval (CI) = 54.08, 54.28].

MEASUREMENTS

Genetic variants were selected as instrumental variables under the MR analysis assumptions: (1) associated with the exposure; (2) influenced outcome only via exposure; and (3) not associated with confounders. The exposure smoking status (current versus never smokers) was measured by questionnaires at the initial visit (2006-10). The other exposure, cigarettes per day (CPD), measured the average number of cigarettes smoked per day for current tobacco users over the life-time. The outcome was the 'brain age gap' (BAG), the difference between predicted brain age and chronological age, computed by training machine learning model on a non-overlapping set of never smokers.

FINDINGS

The estimated BAG had a mean of 0.10 (95% CI = 0.06, 0.14) years. The MR analysis showed evidence of positive causal effect of smoking behaviors on BAG: the effect of smoking is 0.21 (in years, 95% CI = 6.5 × 10-3 , 0.41; P-value = 0.04), and the effect of CPD is 0.16 year/cigarette (UKB: 95% CI = 0.06, 0.26; P-value = 1.3 × 10-3 ; GSCAN: 95% CI = 0.02, 0.31; P-value = 0.03). The sensitivity analyses showed consistent results.

CONCLUSIONS

There appears to be a significant causal effect of smoking on the brain age gap, which suggests that smoking prevention can be an effective intervention for accelerated brain aging and the age-related decline in cognitive function.

White Matter Integrity and Nicotine Dependence: Evaluating Vertical and Horizontal Pleiotropy

Tobacco smoking is an addictive behavior that supports nicotine dependence and is an independent risk factor for cancer and other illnesses. Its neurogenetic mechanisms are not fully understood but may act through alterations in the cerebral white matter (WM). We hypothesized that the vertical pleiotropic pathways, where genetic variants influence a trait that in turn influences another trait, link genetic factors, integrity of cerebral WM, and nicotine addiction. We tested this hypothesis using individual genetic factors, WM integrity measured by fractional anisotropy (FA), and nicotine dependence-related smoking phenotypes, including smoking status (SS) and cigarettes per day (CPDs), in a large epidemiological sample collected by the UK Biobank. We performed a genome-wide association study (GWAS) to identify previously reported loci associated with smoking behavior. Smoking was found to be associated with reduced WM integrity in multiple brain regions. We then evaluated two competing vertical pathways: Genes → WM integrity → Smoking versus Genes → Smoking → WM integrity and a horizontal pleiotropy pathway where genetic factors independently affect both smoking and WM integrity. The causal pathway analysis identified 272 pleiotropic single-nucleotide polymorphisms (SNPs) whose effects on SS were mediated by FA, as well as 22 pleiotropic SNPs whose effects on FA were mediated by CPD. These SNPs were mainly located in important susceptibility genes for smoking-induced diseases NCAM1 and IREB2. Our findings revealed the role of cerebral WM in the maintenance of the complex addiction and provided potential genetic targets for future research in examining how changes in WM integrity contribute to the nicotine effects on the brain.

SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits

Local genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions. However, accurate estimation of local genetic correlation remains challenging, due to linkage disequilibrium in local genomic regions and sample overlap across studies. We introduce SUPERGNOVA, a statistical framework to estimate local genetic correlations using summary statistics from genome-wide association studies. We demonstrate that SUPERGNOVA outperforms existing methods through simulations and analyses of 30 complex traits. In particular, we show that the positive yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically distinct genetic signatures with bidirectional local genetic correlations.

High genes: Genetic underpinnings of cannabis use phenotypes

Cannabis is one of the most widely used substances across the globe and its use has a substantial heritable component. However, the heritability of cannabis use varies according to substance use phenotype, suggesting that a unique profile of gene variants may contribute to the different stages of use, such as age of use onset, lifetime use, cannabis use disorder, and withdrawal and craving during abstinence. Herein, we review a subset of genes identified by candidate gene, family-based linkage, and genome-wide association studies related to these cannabis use phenotypes. We also describe their relationships with other substances, and their functions at the neurobiological, cognitive, and behavioral levels to hypothesize the role of these genes in cannabis use risk. Delineating genetic risk factors in the various stages of cannabis use will provide insight into the biological mechanisms related to cannabis use and highlight points of intervention prior to and following the development of dependence, as well as identify targets to aid drug development for treating problematic cannabis use.

GW-SEM 2.0: Efficient, Flexible, and Accessible Multivariate GWAS

Most genome-wide association study (GWAS) analyses test the association between single-nucleotide polymorphisms (SNPs) and a single trait or outcome. While valuable second-step analyses of these associations (e.g., calculating genetic correlations between traits) are common, single-step multivariate analyses of GWAS data are rarely performed. This is unfortunate because multivariate analyses can reveal information which is irrevocably obscured in multi-step analysis. One simple example is the distinction between variance common to a set of measures, and variance specific to each. Neither GWAS of sum- or factor-scores, nor GWAS of the individual measures will deliver a clean picture of loci associated with each measure's specific variance. While multivariate GWAS opens up a broad new landscape of feasible and informative analyses, its adoption has been slow, likely due to the heavy computational demands and difficulties specifying models it requires. Here we describe GW-SEM 2.0, which is designed to simplify model specification and overcome the inherent computational challenges associated with multivariate GWAS. In addition, GW-SEM 2.0 allows users to accurately model ordinal items, which are common in behavioral and psychological research, within a GWAS context. This new release enhances computational efficiency, allows users to select the fit function that is appropriate for their analyses, expands compatibility with standard genomic data formats, and outputs results for seamless reading into other standard post-GWAS processing software. To demonstrate GW-SEM's utility, we conducted (1) a series of GWAS using three substance use frequency items from data in the UK Biobank, (2) a timing study for several predefined GWAS functions, and (3) a Type I Error rate study. Our multivariate GWAS analyses emphasize the utility of GW-SEM for identifying novel patterns of associations that vary considerably between genomic loci for specific substances, highlighting the importance of differentiating between substance-specific use behaviors and polysubstance use. The timing studies demonstrate that the analyses take a reasonable amount of time and show the cost of including additional items. The Type I Error rate study demonstrates that hypothesis tests for genetic associations with latent variable models follow the hypothesized uniform distribution. Taken together, we suggest that GW-SEM may provide substantially deeper insights into the underlying genomic architecture for multivariate behavioral and psychological systems than is currently possible with standard GWAS methods. The current release of GW-SEM 2.0 is available on CRAN (stable release) and GitHub (beta release), and tutorials are available on our github wiki ( https://jpritikin.github.io/gwsem/ ).

Ankyrin Repeat and Kinase Domain Containing 1 Gene, and Addiction Vulnerability

The TaqIA single nucleotide variant (SNV) has been tested for association with addictions in a huge number of studies. TaqIA is located in the ankyrin repeat and kinase domain containing 1 gene (ANKK1) that codes for a receptor interacting protein kinase. ANKK1 maps on the NTAD cluster along with the dopamine receptor D2 (DRD2), the tetratricopeptide repeat domain 12 (TTC12) and the neural cell adhesion molecule 1 (NCAM1) genes. The four genes have been associated with addictions, although TTC12 and ANKK1 showed the strongest associations. In silico and in vitro studies revealed that ANKK1 is functionally related to the dopaminergic system, in particular with DRD2. In antisocial alcoholism, epistasis between ANKK1 TaqIA and DRD2 C957T SNVs has been described. This clinical finding has been supported by the study of ANKK1 expression in peripheral blood mononuclear cells of alcoholic patients and controls. Regarding the ANKK1 protein, there is direct evidence of its location in adult and developing central nervous system. Together, these findings of the ANKK1 gene and its protein suggest that the TaqIA SNV is a marker of brain differences, both in structure and in dopaminergic function, that increase individual risk to addiction development.

GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia

Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. In the largest genome-wide association study (GWAS) for lifetime cannabis use to date (N = 184,765), we identified eight genome-wide significant independent single nucleotide polymorphisms in six regions. All measured genetic variants combined explained 11% of the variance. Gene-based tests revealed 35 significant genes in 16 regions, and S-PrediXcan analyses showed that 21 genes had different expression levels for cannabis users versus nonusers. The strongest finding across the different analyses was CADM2, which has been associated with substance use and risk-taking. Significant genetic correlations were found with 14 of 25 tested substance use and mental health-related traits, including smoking, alcohol use, schizophrenia and risk-taking. Mendelian randomization analysis showed evidence for a causal positive influence of schizophrenia risk on cannabis use. Overall, our study provides new insights into the etiology of cannabis use and its relation with mental health.

Earlier smoking after waking and the risk of asthma: a cross-sectional study using NHANES data

Background

Recent research shows that nicotine dependence conveys additional health risks above and beyond smoking behavior. The current study examines whether smoking within 5 min of waking, an indicator of nicotine dependence, is independently associated with asthma outcomes.

Methods

Data were drawn from five pooled cross-sectional waves (2005–14) of NHANES, and the final sample consisted of N = 4081 current adult smokers. Weighted logistic regressions were run examining the relationship between smoking within 5 min of waking and outcomes of lifetime asthma, past-year asthma, and having had an asthma attack in the past year. Control variables included demographics, smoking behavior, family history of asthma, depression, obesity, and secondhand smoking exposure.

Results

After adjusting for smoking behavior, smoking within 5 min was associated with an approximately 50% increase in the odds of lifetime asthma (OR = 1.46, p = .008) and past-year asthma (OR = 1.47, p = .024), respectively. After additionally adjusting for demographics and other asthma risk factors, smoking within 5 min of waking was associated with a four-fold increase in the odds of lifetime asthma (OR = 4.05, p = .015).

Conclusions

Smoking within 5 min of waking, an indicator of nicotine dependence, is associated with a significantly increased risk of lifetime asthma in smokers. These findings could be utilized in refining risk assessment of asthma among smokers.

Electronic supplementary material

The online version of this article (10.1186/s12890-018-0672-y) contains supplementary material, which is available to authorized users.

The Role of Cell Adhesion Molecule Genes Regulating Neuroplasticity in Addiction

A variety of genetic approaches, including twin studies, linkage studies, and candidate gene studies, has established a firm genetic basis for addiction. However, there has been difficulty identifying the precise genes that underlie addiction liability using these approaches. This situation became especially clear in genome-wide association studies (GWAS) of addiction. Moreover, the results of GWAS brought into clarity many of the shortcomings of those early genetic approaches. GWAS studies stripped away those preconceived notions, examining genes that would not previously have been considered in the study of addiction, consequently creating a shift in our understanding. Most importantly, those studies implicated a class of genes that had not previously been considered in the study of addiction genetics: cell adhesion molecules (CAMs). Considering the well-documented evidence supporting a role for various CAMs in synaptic plasticity, axonal growth, and regeneration, it is not surprising that allelic variation in CAM genes might also play a role in addiction liability. This review focuses on the role of various cell adhesion molecules in neuroplasticity that might contribute to addictive processes and emphasizes the importance of ongoing research on CAM genes that have been implicated in addiction by GWAS.

Gene variants and educational attainment in cannabis use: mediating role of DNA methylation

Genetic and sociodemographic risk factors potentially associated with cannabis use (CU) were investigated in 40 cannabis users and 96 control subjects. DNA methylation analyses were also performed to explore the possibility of epigenetic changes related to CU. We conducted a candidate gene association study that included variants involved in the dopaminergic (ANKK1, NCAM1 genes) and endocannabinoid (CNR1, CNR2 gene) pathways. Sociodemographic data included gender, marital status, level of education, and body mass index. We used MeDIP-qPCR to test whether variations in DNA methylation might be associated with CU. We found a significant association between SNP rs1049353 of CNR1 gene (p = 0.01) and CU. Differences were also observed related to rs2501431 of CNR2 gene (p = 0.058). A higher education level appears to decrease the risk of CU. Interestingly, females were less likely to use cannabis than males. There was a significantly higher level of DNA methylation in cannabis users compared to controls in two of the genes tested: hypermethylation at exon 8 of DRD2 gene (p = 0.034) and at the CpG-rich region in the NCAM1 gene (p = 0.0004). Both genetic variants and educational attainment were also related to CU. The higher rate of DNA methylation, evidenced among cannabis users, may be either a marker of CU or a consequence of long-term exposure to cannabis. The identified genetic variants and the differentially methylated regions may represent biomarkers and/or potential targets for designs of pharmacological therapeutic agents. Our observations also suggest that educational programs may be useful strategies for CU prevention.

Alcohol and nicotine codependence-associated DNA methylation changes in promoter regions of addiction-related genes

Altered DNA methylation in addiction-related genes may modify the susceptibility to alcohol or drug dependence (AD or ND). We profiled peripheral blood DNA methylation levels of 384 CpGs in promoter regions of 82 addiction-related genes in 256 African Americans (AAs) (117 cases with AD-ND codependence and 139 controls) and 196 European Americans (103 cases with AD-ND codependence and 93 controls) using Illumina's GoldenGate DNA methylation array assays. AD-ND codependence-associated DNA methylation changes were analyzed using linear mixed-effects models with consideration of batch effects and covariates age, sex, and ancestry proportions. Seventy CpGs (in 41 genes) showed nominally significant associations (P < 0.05) with AD-ND codependence in both AAs and EAs. One CpG (HTR2B cg27531267) was hypomethylated in AA cases (P = 7.2 × 10^-5), while 17 CpGs in 16 genes (including HTR2B cg27531267) were hypermethylated in EA cases (5.6 × 10^-9 ≤ P ≤ 9.5 × 10^-5). Nevertheless, 13 single nucleotide polymorphisms (SNPs) nearby HTR2B cg27531267 and the interaction of these SNPs and cg27531267 did not show significant effects on AD-ND codependence in either AAs or EAs. Our study demonstrated that DNA methylation changes in addiction-related genes could be potential biomarkers for AD-ND co-dependence. Future studies need to explore whether DNA methylation alterations influence the risk of AD-ND codependence or the other way around.

Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32 330 subjects from the International Cannabis Consortium

Cannabis is the most widely produced and consumed illicit psychoactive substance worldwide. Occasional cannabis use can progress to frequent use, abuse and dependence with all known adverse physical, psychological and social consequences. Individual differences in cannabis initiation are heritable (40-48%). The International Cannabis Consortium was established with the aim to identify genetic risk variants of cannabis use. We conducted a meta-analysis of genome-wide association data of 13 cohorts (N=32 330) and four replication samples (N=5627). In addition, we performed a gene-based test of association, estimated single-nucleotide polymorphism (SNP)-based heritability and explored the genetic correlation between lifetime cannabis use and cigarette use using LD score regression. No individual SNPs reached genome-wide significance. Nonetheless, gene-based tests identified four genes significantly associated with lifetime cannabis use: NCAM1, CADM2, SCOC and KCNT2. Previous studies reported associations of NCAM1 with cigarette smoking and other substance use, and those of CADM2 with body mass index, processing speed and autism disorders, which are phenotypes previously reported to be associated with cannabis use. Furthermore, we showed that, combined across the genome, all common SNPs explained 13-20% (P<0.001) of the liability of lifetime cannabis use. Finally, there was a strong genetic correlation (rg=0.83; P=1.85 × 10(-8)) between lifetime cannabis use and lifetime cigarette smoking implying that the SNP effect sizes of the two traits are highly correlated. This is the largest meta-analysis of cannabis GWA studies to date, revealing important new insights into the genetic pathways of lifetime cannabis use. Future functional studies should explore the impact of the identified genes on the biological mechanisms of cannabis use.

An initial investigation of associations between dopamine-linked genetic variation and smoking motives in African Americans

Nicotine dependence (ND) is a heterogeneous phenotype with complex genetic influences that may vary across ethnicities. The use of intermediate phenotypes may clarify genetic influences and reveal specific etiological pathways. Prior work in European Americans has found that the four Primary Dependence Motives (PDM) subscales (Automaticity, Craving, Loss of Control, and Tolerance) of the Wisconsin Inventory of Smoking Motives represent core features of nicotine dependence and are promising intermediate phenotypes for understanding genetic pathways to ND. However, no studies have examined PDM as an intermediate phenotype in African American smokers, an ethnic population that displays unique patterns of smoking and genetic variation. In the current study, 268 African American daily smokers completed a phenotypic assessment and provided a sample of DNA. Associations among haplotypes in the NCAM1-TTC12-ANKK1-DRD2 gene cluster, a dopamine-related gene region associated with ND, PDM intermediate phenotypes, and ND were examined. Dopamine-related genetic variation in the DBH and COMT genes was also considered on an exploratory basis. Mediational analysis was used to test the indirect pathway from genetic variation to smoking motives to nicotine dependence. NCAM1-TTC12-ANKK1-DRD2 region variation was significantly associated with the Automaticity subscale and, further, Automaticity significantly mediated associations among NCAM1-TTC12-ANKK1-DRD2 cluster variants and ND. DBH was also significantly associated with Automaticity, Craving, and Tolerance; Automaticity and Tolerance also served as mediators of the DBH-ND relationship. These results suggest that PDM, Automaticity in particular, may be a viable intermediate phenotype for understanding dopamine-related genetic influences on ND in African American smokers. Findings support a model in which putatively dopaminergic variants exert influence on ND through an effect on patterns of automatic routinized smoking.

NCAM1-TTC12-ANKK1-DRD2 gene cluster and the clinical and genetic heterogeneity of adults with ADHD

Dysfunctions of the dopaminergic system have been implicated on the etiology of Attention Deficit/Hyperactivity Disorder (ADHD). Meta-analyses addressing the association of the dopamine receptor D2 (DRD2) gene and ADHD were inconclusive due to excessive heterogeneity across studies. Both the great phenotypic heterogeneity of ADHD and the complexity of the genomic region where DRD2 is located could contribute to the inconsistent findings. Most previous DRD2 studies focused on the well-known Taq1A (rs1800497) SNP, which is actually placed in a neighbor gene (ANKK1). These two genes, together with NCAM1 and TTC12, form the NTAD gene cluster on Chr11q22-23. In order to address the reasons for the high heterogeneity previously reported on DRD2 effects on ADHD, this study investigates the role of NTAD variants on ADHD susceptibility in adults and on the modulation of comorbidity and personality profiles in these patients. Functional polymorphisms from NTAD were analyzed, both individually and in haplotypes, on a sample of 520 adults with ADHD and 630 non-ADHD controls. No direct association of NTAD variants with ADHD susceptibility itself was observed. However, different NTAD polymorphisms and haplotypes were associated to various phenotypes relevant to the clinical heterogeneity of ADHD, including Major Depressive Disorder, Generalized Anxiety Disorder, and Harm Avoidance and Persistence temperament scores. Therefore, these findings represent a possible explanation for the multiple conflicting findings regarding polymorphisms in this genomic region in psychiatry. The NTAD cluster may comprise a variety of independent molecular influences on various brain and behavior characteristics eventually associated with ADHD comorbidities and personality traits. © 2015 Wiley Periodicals, Inc.