For the identification of genes associated with smoking initiation and current smoking, genome-wide association analyses were carried out in 3497 subjects. Significant genes that replicated in three independent samples (n = 405, 5810, and 1648) were visualized into a biologically meaningful network showing cellular location and direct interaction of their proteins. Several interesting groups of proteins stood out, including glutamate receptors (e.g., GRIN2B, GRIN2A, GRIK2, GRM8), proteins involved in tyrosine kinase receptor signaling (e.g., NTRK2, GRB14), transporters (e.g., SLC1A2, SLC9A9) and cell-adhesion molecules (e.g., CDH23). We conclude that a network-based genome-wide association approach can identify genes influencing smoking behavior.

The discordant twin pair study design is powerful to control for familial confounding. We employed this approach to investigate the associations of smoking with several cancers. The NorTwinCan study combines data from the Danish, Finnish, Norwegian and Swedish twin and cancer registries. Follow-up started when smoking status was determined and ended at cancer diagnosis confirmed by information in the cancer registry, death or end of follow-up. We classified the participants as never (n = 59 093), former (n = 21 168) or current (n = 47 314) smokers. We pooled data from twin pairs where one co-twin was diagnosed with any of the following tobacco-related cancers: esophagus, kidney, larynx, liver, oral cavity, pancreas, pharynx or urinary bladder, while their co-twin had none of those. Lung cancer was included in further analysis. We used Cox regression allowing for pair-specific baseline functions to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). For tobacco-related cancer sites, we recorded 7379 cases during median 27 years of follow-up. The analyses based on individual twins showed that former (HR 1.31, 95% CI: 1.17-1.48) and current (HR 2.14 [1.95-2.34]) smokers are at increased risk to develop one of cancers listed above, compared to never smokers. Among 109 monozygotic twin pairs discordant for cancer and smoking, the HR was 1.85 (95% CI: 1.15-2.98) among current smokers and 1.69 (1.00-2.87) among former smokers when compared to their never smoking co-twin. Thus, associations of smoking with several cancers were replicated for discordant identical twin pairs. Analyses based on genetically informative data provide evidence consistent with smoking causing multiple cancers.

Smoking is associated with a moderate increased risk of Hodgkin and follicular lymphoma. To understand why, we examined lymphoma-related biomarker levels among 134 smoking and non-smoking twins (67 pairs) ascertained from the Finnish Twin Cohort. Previously collected frozen serum samples were tested for cotinine to validate self-reported smoking history. In total, 27 immune biomarkers were assayed using the Luminex Multiplex platform (R & D Systems). Current and non-current smokers were defined by a serum cotinine concentration of >3.08 ng/mL and ≤3.08 ng/mL, respectively. Associations between biomarkers and smoking were assessed using linear mixed models to estimate beta coefficients and standard errors, adjusting for age, sex and twin pair as a random effect. There were 55 never smokers, 43 current smokers and 36 former smokers. CCL17/TARC, sgp130, haptoglobin, B-cell activating factor (BAFF) and monocyte chemoattractant protein-1 (MCP1) were significantly (p < 0.05) associated with current smoking and correlated with increasing cotinine concentrations (Ptrend < 0.05). The strongest association was observed for CCL17/TARC (Ptrend = 0.0001). Immune biomarker levels were similar in former and never smokers. Current smoking is associated with increased levels of lymphoma-associated biomarkers, suggesting a possible mechanism for the link between smoking and risk of these two B-cell lymphomas.

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.

Various pivotal stages in smoking behavior can be identified, including initiation, conversion from experimenting to established use, development of tolerance, and cessation. Previous studies have shown high heritability for age of smoking initiation and cessation; however, time-to-event genome-wide association studies aiming to identify underpinning genes that accelerate or delay these transitions are missing to date.

To facilitate smoking genetics research we determined whether a screen of mutagenized zebrafish for nicotine preference could predict loci affecting smoking behaviour. From 30 screened F3 sibling groups, where each was derived from an individual ethyl-nitrosurea mutagenized F0 fish, two showed increased or decreased nicotine preference. Out of 25 inactivating mutations carried by the F3 fish, one in the slit3 gene segregated with increased nicotine preference in heterozygous individuals. Focussed SNP analysis of the human SLIT3 locus in cohorts from UK (n=863) and Finland (n=1715) identified two variants associated with cigarette consumption and likelihood of cessation. Characterisation of slit3 mutant larvae and adult fish revealed decreased sensitivity to the dopaminergic and serotonergic antagonist amisulpride, known to affect startle reflex that is correlated with addiction in humans, and increased htr1aa mRNA expression in mutant larvae. No effect on neuronal pathfinding was detected. These findings reveal a role for SLIT3 in development of pathways affecting responses to nicotine in zebrafish and smoking in humans.

We conducted a genomewide linkage screen of a simple heavy-smoking quantitative trait, the maximum number of cigarettes smoked in a 24-h period, using two independent samples: 289 Australian and 155 Finnish nuclear multiplex families, all of which were of European ancestry and were targeted for DNA analysis by use of probands with a heavy-smoking phenotype. We analyzed the trait, using a regression of identity-by-descent allele sharing on the sum and difference of the trait values for relative pairs. Suggestive linkage was detected on chromosome 22 at 27-29 cM in each sample, with a LOD score of 5.98 at 26.96 cM in the combined sample. After additional markers were used to localize the signal, the LOD score was 5.21 at 25.46 cM. To assess the statistical significance of the LOD score in the combined sample, 1,000 simulated genomewide screens were conducted, resulting in an empirical P value of .006 for the LOD score of 5.21. This linkage signal is driven mainly by the microsatellite marker D22S315 (22.59 cM), which had a single-point LOD score of 5.41 in the combined sample and an empirical P value <.001 from 1,000 simulated genomewide screens. This marker is located within an intron of the gene ADRBK2, encoding the beta-adrenergic receptor kinase 2. Fine mapping of this linkage region may reveal variants contributing to heaviness of smoking, which will lead to a better understanding of the genetic mechanisms underlying nicotine dependence.

Recently, genetic association findings for nicotine dependence, smoking behavior, and smoking-related diseases converged to implicate the chromosome 15q25.1 region, which includes the CHRNA5-CHRNA3-CHRNB4 cholinergic nicotinic receptor subunit genes. In particular, association with the nonsynonymous CHRNA5 SNP rs16969968 and correlates has been replicated in several independent studies. Extensive genotyping of this region has suggested additional statistically distinct signals for nicotine dependence, tagged by rs578776 and rs588765. One goal of the Consortium for the Genetic Analysis of Smoking Phenotypes (CGASP) is to elucidate the associations among these markers and dichotomous smoking quantity (heavy versus light smoking), lung cancer, and chronic obstructive pulmonary disease (COPD). We performed a meta-analysis across 34 datasets of European-ancestry subjects, including 38,617 smokers who were assessed for cigarettes-per-day, 7,700 lung cancer cases and 5,914 lung-cancer-free controls (all smokers), and 2,614 COPD cases and 3,568 COPD-free controls (all smokers). We demonstrate statistically independent associations of rs16969968 and rs588765 with smoking (mutually adjusted p-values<10(-35) and <10(-8) respectively). Because the risk alleles at these loci are negatively correlated, their association with smoking is stronger in the joint model than when each SNP is analyzed alone. Rs578776 also demonstrates association with smoking after adjustment for rs16969968 (p<10(-6)). In models adjusting for cigarettes-per-day, we confirm the association between rs16969968 and lung cancer (p<10(-20)) and observe a nominally significant association with COPD (p = 0.01); the other loci are not significantly associated with either lung cancer or COPD after adjusting for rs16969968. This study provides strong evidence that multiple statistically distinct loci in this region affect smoking behavior. This study is also the first report of association between rs588765 (and correlates) and smoking that achieves genome-wide significance; these SNPs have previously been associated with mRNA levels of CHRNA5 in brain and lung tissue.

We previously used a single nucleotide polymorphism (SNP) in the CHRNA5-A3-B4 gene cluster associated with heaviness of smoking within smokers to confirm the causal effect of smoking in reducing body mass index (BMI) in a Mendelian randomisation analysis. While seeking to extend these findings in a larger sample we found that this SNP is associated with 0.74% lower body mass index (BMI) per minor allele in current smokers (95% CI -0.97 to -0.51, P = 2.00 × 10(-10)), but also unexpectedly found that it was associated with 0.35% higher BMI in never smokers (95% CI +0.18 to +0.52, P = 6.38 × 10(-5)). An interaction test confirmed that these estimates differed from each other (P = 4.95 × 10(-13)). This difference in effects suggests the variant influences BMI both via pathways unrelated to smoking, and via the weight-reducing effects of smoking. It would therefore be essentially undetectable in an unstratified genome-wide association study of BMI, given the opposite association with BMI in never and current smokers. This demonstrates that novel associations may be obscured by hidden population sub-structure. Stratification on well-characterized environmental factors known to impact on health outcomes may therefore reveal novel genetic associations.

Observational studies on smoking and risk of hay fever and asthma have shown inconsistent results. However, observational studies may be biased by confounding and reverse causation. Mendelian randomization uses genetic variants as markers of exposures to examine causal effects. We examined the causal effect of smoking on hay fever and asthma by using the smoking-associated single nucleotide polymorphism (SNP) rs16969968/rs1051730. We included 231,020 participants from 22 population-based studies. Observational analyses showed that current vs never smokers had lower risk of hay fever (odds ratio (OR) = 0·68, 95% confidence interval (CI): 0·61, 0·76; P < 0·001) and allergic sensitization (OR = 0·74, 95% CI: 0·64, 0·86; P < 0·001), but similar asthma risk (OR = 1·00, 95% CI: 0·91, 1·09; P = 0·967). Mendelian randomization analyses in current smokers showed a slightly lower risk of hay fever (OR = 0·958, 95% CI: 0·920, 0·998; P = 0·041), a lower risk of allergic sensitization (OR = 0·92, 95% CI: 0·84, 1·02; P = 0·117), but higher risk of asthma (OR = 1·06, 95% CI: 1·01, 1·11; P = 0·020) per smoking-increasing allele. Our results suggest that smoking may be causally related to a higher risk of asthma and a slightly lower risk of hay fever. However, the adverse events associated with smoking limit its clinical significance.

Maternal smoking during pregnancy (MSDP) has been linked to offspring's externalizing problems. It has been argued that socio-demographic factors (e.g. maternal age and education), co-occurring environmental risk factors, or pleiotropic genetic effects may account for the association between MSDP and later outcomes. This study provides a comprehensive investigation of the association between MSDP and a single harmonized component of externalizing: aggressive behaviour, measured throughout childhood and adolescence.

Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk.

To investigate, using a Mendelian randomisation approach, whether heavier smoking is associated with a range of regional adiposity phenotypes, in particular those related to abdominal adiposity.

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).

Cigarette smoking is the leading cause of preventable morbidity and mortality. Genetic variation contributes to initiation, regular smoking, nicotine dependence, and cessation. We present a Fagerström Test for Nicotine Dependence (FTND)-based genome-wide association study in 58,000 European or African ancestry smokers. We observe five genome-wide significant loci, including previously unreported loci MAGI2/GNAI1 (rs2714700) and TENM2 (rs1862416), and extend loci reported for other smoking traits to nicotine dependence. Using the heaviness of smoking index from UK Biobank (N = 33,791), rs2714700 is consistently associated; rs1862416 is not associated, likely reflecting nicotine dependence features not captured by the heaviness of smoking index. Both variants influence nearby gene expression (rs2714700/MAGI2-AS3 in hippocampus; rs1862416/TENM2 in lung), and expression of genes spanning nicotine dependence-associated variants is enriched in cerebellum. Nicotine dependence (SNP-based heritability = 8.6%) is genetically correlated with 18 other smoking traits (rg = 0.40-1.09) and co-morbidities. Our results highlight nicotine dependence-specific loci, emphasizing the FTND as a composite phenotype that expands genetic knowledge of smoking.

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.

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.

We performed a systematic analysis of blood DNA methylation profiles from 4483 participants from seven independent cohorts identifying differentially methylated positions (DMPs) associated with psychosis, schizophrenia, and treatment-resistant schizophrenia. Psychosis cases were characterized by significant differences in measures of blood cell proportions and elevated smoking exposure derived from the DNA methylation data, with the largest differences seen in treatment-resistant schizophrenia patients. We implemented a stringent pipeline to meta-analyze epigenome-wide association study (EWAS) results across datasets, identifying 95 DMPs associated with psychosis and 1048 DMPs associated with schizophrenia, with evidence of colocalization to regions nominated by genetic association studies of disease. Many schizophrenia-associated DNA methylation differences were only present in patients with treatment-resistant schizophrenia, potentially reflecting exposure to the atypical antipsychotic clozapine. Our results highlight how DNA methylation data can be leveraged to identify physiological (e.g., differential cell counts) and environmental (e.g., smoking) factors associated with psychosis and molecular biomarkers of treatment-resistant schizophrenia.

Individuals with fast nicotine metabolism typically smoke more and thus have a greater risk for smoking-induced diseases. Further, the efficacy of smoking cessation pharmacotherapy is dependent on the rate of nicotine metabolism. Our objective was to use nicotine metabolite ratio (NMR), an established biomarker of nicotine metabolism rate, in a genome-wide association study (GWAS) to identify novel genetic variants influencing nicotine metabolism. A heritability estimate of 0.81 (95% CI 0.70-0.88) was obtained for NMR using monozygotic and dizygotic twins of the FinnTwin cohort. We performed a GWAS in cotinine-verified current smokers of three Finnish cohorts (FinnTwin, Young Finns Study, FINRISK2007), followed by a meta-analysis of 1518 subjects, and annotated the genome-wide significant SNPs with methylation quantitative loci (meQTL) analyses. We detected association on 19q13 with 719 SNPs exceeding genome-wide significance within a 4.2 Mb region. The strongest evidence for association emerged for CYP2A6 (min p = 5.77E-86, in intron 4), the main metabolic enzyme for nicotine. Other interesting genes with genome-wide significant signals included CYP2B6, CYP2A7, EGLN2, and NUMBL. Conditional analyses revealed three independent signals on 19q13, all located within or in the immediate vicinity of CYP2A6. A genetic risk score constructed using the independent signals showed association with smoking quantity (p = 0.0019) in two independent Finnish samples. Our meQTL results showed that methylation values of 16 CpG sites within the region are affected by genotypes of the genome-wide significant SNPs, and according to causal inference test, for some of the SNPs the effect on NMR is mediated through methylation. To our knowledge, this is the first GWAS on NMR. Our results enclose three independent novel signals on 19q13.2. The detected CYP2A6 variants explain a strikingly large fraction of variance (up to 31%) in NMR in these study samples. Further, we provide evidence for plausible epigenetic mechanisms influencing NMR.

Genome-wide association studies (GWAS) of complex behavioural phenotypes such as cigarette smoking typically employ self-report phenotypes. However, precise biomarker phenotypes may afford greater statistical power and identify novel variants. Here we report the results of a GWAS meta-analysis of levels of cotinine, the primary metabolite of nicotine, in 4,548 daily smokers of European ancestry. We identified a locus close to UGT2B10 at 4q13.2 (minimum p = 5.89 × 10(-10) for rs114612145), which was consequently replicated. This variant is in high linkage disequilibrium with a known functional variant in the UGT2B10 gene which is associated with reduced nicotine and cotinine glucuronidation activity, but intriguingly is not associated with nicotine intake. Additionally, we observed association between multiple variants within the 15q25.1 region and cotinine levels, all located within the CHRNA5-A3-B4 gene cluster or adjacent genes, consistent with previous much larger GWAS using self-report measures of smoking quantity. These results clearly illustrate the increase in power afforded by using precise biomarker measures in GWAS. Perhaps more importantly however, they also highlight that biomarkers do not always mark the phenotype of interest. The use of metabolite data as a proxy for environmental exposures should be carefully considered in the context of individual differences in metabolic pathways.