57 research outputs found
Data Related to Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci
No full textHere we have included four sets of meta-analysis results: Meta-analysis of discovery and replication cohorts, combining genotyped Exome-chip and Axiom array content for (i) Smoking Initiation, (ii) Cigarettes per day, and (iii) Smoking Cessation, and (iv) meta-analysis of discovery cohorts for Pack Years.Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. 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). 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. These 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.GSCAN; Consortium for Genetics of Smoking Behaviour; CHD Exome+ consortium. (2019). Data Related to Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci. Retrieved from the University Digital Conservancy, https://doi.org/10.13020/qfwg-tn13
Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci
No full textSmoking 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
Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use
No full textBackground: 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. Methods: We analyzed ∼250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci. Results: Meta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals. Conclusions: Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior
Human exome-chip meta-analysis identifies novel genetic loci associated with smoking behaviour
No full textSmoking is a major risk factor for many diseases, including common respiratory disorders such as chronic obstructive pulmonary disease. Previous GWASs have been successful in identifying 13 common SNPs associated with smoking behaviour. Meta-analysing summary data from up to 33 cohorts, we have carried out an association study between the SNPs found on the exome-chip (n ≈250k SNPs), which predominantly assays rare putatively functional variants, and four traits: Smoking Initiation (n≈80k), Smoking Cessation (n≈41.5k), Cigarettes Per Day (n≈26.5k) and Pack Years (n≈33k). In addition to identifying previously reported signals with regards to smoking behaviour, we have identified SNPs in 5 novel regions, including a rare variant on chromosome 16 (MAF=0.01%). The previously identified SNPs fall within CHRNA5 (rs16969968, missense), CHRNA3 (rs938682, intronic) and IREB2 (rs13180, synonymous) – which are all located at the 15q25 locus. We are currently initiating follow-up studies; and if replicated, the novel loci identified in this study will facilitate understanding the genetic aetiology of smoking behaviour and may lead to identification of drug targets of potential relevance to smoking cessation
Human exome-chip meta-analysis identifies novel genetic loci associated with smoking behaviour
No full textSmoking is a major risk factor for many diseases, including common respiratory disorders such as chronic obstructive pulmonary disease. Previous GWASs have been successful in identifying 13 common SNPs associated with smoking behaviour. Meta-analysing summary data from up to 33 cohorts, we have carried out an association study between the SNPs found on the exome-chip (n ≈250k SNPs), which predominantly assays rare putatively functional variants, and four traits: Smoking Initiation (n≈80k), Smoking Cessation (n≈41.5k), Cigarettes Per Day (n≈26.5k) and Pack Years (n≈33k). In addition to identifying previously reported signals with regards to smoking behaviour, we have identified SNPs in 5 novel regions, including a rare variant on chromosome 16 (MAF=0.01%). The previously identified SNPs fall within CHRNA5 (rs16969968, missense), CHRNA3 (rs938682, intronic) and IREB2 (rs13180, synonymous) – which are all located at the 15q25 locus. We are currently initiating follow-up studies; and if replicated, the novel loci identified in this study will facilitate understanding the genetic aetiology of smoking behaviour and may lead to identification of drug targets of potential relevance to smoking cessation
Genome-wide meta-analyses identify multiple loci associated with smoking behaviour
No full textConsistent but indirect evidence has implicated genetic factors in smoking behavior1,2. We report meta-analyses of several smoking phenotypes within cohorts of the Tobacco and Genetics Consortium (n = 74,053). We also partnered with the European Network of Genetic and Genomic Epidemiology (ENGAGE) and Oxford-GlaxoSmithKline (Ox-GSK) consortia to follow up the 15 most significant regions (n > 140,000). We identified three loci associated with number of cigarettes smoked per day. The strongest association was a synonymous 15q25 SNP in the nicotinic receptor gene CHRNA3 (rs1051730[A], β = 1.03, standard error (s.e.) = 0.053, P = 2.8 × 10−73). Two 10q25 SNPs (rs1329650[G], β = 0.367, s.e. = 0.059, P = 5.7 × 10−10; and rs1028936[A], β = 0.446, s.e. = 0.074, P = 1.3 × 10−9) and one 9q13 SNP in EGLN2 (rs3733829[G], β = 0.333, s.e. = 0.058, P = 1.0 × 10−8) also exceeded genome-wide significance for cigarettes per day. For smoking initiation, eight SNPs exceeded genome-wide significance, with the strongest association at a nonsynonymous SNP in BDNF on chromosome 11 (rs6265[C], odds ratio (OR) = 1.06, 95% confidence interval (Cl) 1.04–1.08, P = 1.8 × 10−8). One SNP located near DBH on chromosome 9 (rs3025343[G], OR = 1.12, 95% Cl 1.08–1.18, P = 3.6 × 10−8) was significantly associated with smoking cessation
The effect of body mass index on smoking behaviour and nicotine metabolism:a Mendelian randomization study
No full textGiven 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 GWAS, on cotinine levels and nicotine metabolite ratio 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 not smoking cessation. While there is no strong evidence that BMI causally influences cotinine levels, suggestive evidence for a negative causal influence on nicotine metabolite ratio 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.<br/
Polygenic risk scores for nicotine use and family history of smoking are associated with smoking behaviour
No full textIntroduction: Formal genetics studies show that smoking is influenced by genetic factors; exploring this on the molecular level can offer deeper insight into the etiology of smoking behaviours.Methods: Summary statistics from the latest wave of the GWAS and Sequencing Consortium of Alcohol and Nicotine (GSCAN) were used to calculate polygenic risk scores (PRS) in a sample of ~2200 individuals who smoke/individuals who never smoked. The associations of smoking status with PRS for Smoking Initiation (i.e., Lifetime Smoking; SI-PRS), and Fagerström Test for Nicotine Dependence (FTND) score with PRS for Cigarettes per Day (CpD-PRS) were examined, as were distinct/additive effects of parental smoking on smoking status.Results: SI-PRS explained 10.56% of variance (Nagelkerke-R2) in smoking status (p=6.45x10-30). In individuals who smoke, CpD-PRS was associated with FTND score (R2=5.03%, p=1.88x10-12). Parental smoking alone explained R2=3.06% (p=2.43×10-12) of smoking status, and 0.96% when added to the most informative SI-PRS model (total R²=11.52%).Conclusion: These results show the potential utility of molecular genetic data for research investigating smoking prevention. The fact that PRS explains more variance than family history highlights progress from formal to molecular genetics; the partial overlap and increased predictive value when using both suggests the importance of combining these approaches.<br
Little evidence for an effect of smoking on multiple sclerosis risk:A Mendelian Randomization study
No full textThe causes of multiple sclerosis (MS) remain unknown. Smoking has been associated with MS in observational studies and is often thought of as an environmental risk factor. We used two-sample Mendelian randomization (MR) to examine whether this association is causal using genetic variants identified in genome-wide association studies (GWASs) as associated with smoking. We assessed both smoking initiation and lifetime smoking behaviour (which captures smoking duration, heaviness, and cessation). There was very limited evidence for a meaningful effect of smoking on MS susceptibility as measured using summary statistics from the International Multiple Sclerosis Genetics Consortium (IMSGC) meta-analysis, including 14,802 cases and 26,703 controls. There was no clear evidence for an effect of smoking on the risk of developing MS (smoking initiation: odds ratio [OR] 1.03, 95% confidence interval [CI] 0.92-1.61; lifetime smoking: OR 1.10, 95% CI 0.87-1.40). These findings suggest that smoking does not have a detrimental consequence on MS susceptibility. Further work is needed to determine the causal effect of smoking on MS progression.</p
The effect of body mass index on smoking behaviour and nicotine metabolism: a Mendelian randomization study
No full textAbstractBackgroundGiven 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.Methods and FindingsWe undertook Mendelian randomization analyses using 97 genetic variants associated with BMI. We performed two sample Mendelian randomization analyses of the effects of BMI on smoking behaviour in UK Biobank (N=335,921) and the Tobacco and Genetics consortium genomewide association study (GWAS) (N≤74,035) respectively, and two sample Mendelian randomization analyses of the effects of BMI on cotinine levels (N≤4,548) and nicotine metabolite ratio (N≤1,518) in published GWAS, and smoking-related DNA methylation in the Avon Longitudinal Study of Parents and Children (N≤846).In inverse variance weighted Mendelian randomization analysis, there was evidence that higher BMI was causally associated with smoking initiation (OR for ever vs never smoking per one SD increase in BMI: 1.19, 95% CI: 1.11 to 1.27) and smoking heaviness (1.45 additional cigarettes smoked per day per SD increase in BMI, 95% CI: 1.03 to 1.86), but little evidence for a causal effect with smoking cessation. Results were broadly similar using pleiotropy robust methods (MR-Egger, median and weighted mode regression). These results were supported by evidence for a causal effect of BMI on DNA methylation at the aryl-hydrocarbon receptor repressor (AHRR) locus. There was no strong evidence that BMI was causally associated with cotinine, but suggestive evidence for a causal negative association with the nicotine metabolite ratio.ConclusionsThere is a causal bidirectional association between BMI and smoking, but the relationship is likely to be complex due to opposing effects on behaviour and metabolism. It may be useful to consider BMI
and smoking together when designing prevention strategies to minimise the effects of these risk factors on health outcomes.</jats:sec
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