Buy marijuana Kuopio

__________________________

📍 Verified store!

📍 Guarantees! Quality! Reviews!

__________________________

▼▼ ▼▼ ▼▼ ▼▼ ▼▼ ▼▼ ▼▼

▲▲ ▲▲ ▲▲ ▲▲ ▲▲ ▲▲ ▲▲

Buy marijuana Kuopio

Official websites use. Share sensitive information only on official, secure websites. Research Topics. More Research Topics. Quick Links. About NIDA.

Weed Hook up in Kuopio, Finland

Buy marijuana Kuopio

Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. 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. The International Cannabis Consortium was established with the aim to identify genetic risk variants of cannabis use. 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. 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. 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. About 1 in 10 occasional users becomes dependent, which is associated with physical, psychological, social and occupational consequences. The risk of lifetime cannabis use, defined as any use of cannabis during the lifetime, varies between individuals. Several studies have sought to identify specific genetic risk factors associated with cannabis use phenotypes. Genome-wide linkage studies have revealed suggestive evidence for linkage across many chromosomes. Moreover, findings may be further distorted due to publication bias favouring significant results. As an alternative to the candidate-gene approach, the genome-wide association study GWAS is a hypothesis-free method that aims to detect novel genetic variants involved in complex traits. This was likely due to the small effect sizes typical of common variants underpinning highly polygenic traits, 28 thereby indicating a need for larger sample sizes. In this context, the success of larger GWASs and international consortia examining a variety of complex traits is encouraging. These and other recent GWA findings 29 clearly illustrate the need for larger sample sizes. In response to this need, the International Cannabis Consortium was established to combine the results of multiple GWASs to identify the genetic variants underlying individual differences in cannabis use phenotypes. This sample size is considerably larger than the sample size of the previous GWAS investigating lifetime cannabis use in two samples from Australia and the UK, thereby providing substantially greater power to detect genetic variants of small effect size. The aim of the present study is to identify genetic variants associated with lifetime cannabis use by meta-analysis of the GWAS results from all contributing International Cannabis Consortium samples. The tests of association for individual genetic variants will be complemented with gene-based tests of association. In addition, we will investigate which proportion of the heritability inferred by twin studies is explained by common SNPs captured on GWAS arrays. Finally, we will estimate the genetic correlation between lifetime cannabis and smoking initiation based on the analysis of our summary statistics and those from the publicly available Tobacco Alcohol and Genetics consortium. The size of the samples ranged from to individuals. The age of the participants ranged from 16 to 87 years with an average of 34 years. Four additional independent samples with a total of subjects were used for replication. The other three included subjects of European ancestry. See Table 1 for individual sample characteristics. The procedures for data collection per sample are described in the Supplementary Information 1. For all individuals, the data were available on whether or not the subject reported having ever used cannabis during their lifetime: yes 1 versus no 0. Although phrasing of the question slightly differed between samples see Supplementary Information 1 , our unit of analysis reflected lifetime cannabis use in all the samples. Covariates included age at the time of phenotypic assessment, sex, birth cohort and principal components obtained from the genome-wide genotype data. Spanning year intervals, birth cohort was dummy coded, with the lowest birth cohort that is, oldest age group used as the reference group. The details about phenotypic assessment and individual sample characteristics for the discovery and replication samples are located in Supplementary Information 1 and Supplementary Table 1. Genotype imputation was based on the Genomes phase 1 reference panel. See Supplementary Table 2 for the genotyping platform, imputation program and quality control thresholds used. The GWA analyses were performed by each group separately. Associations between the binary phenotype and the genotypes were tested genome-wide using a logistic regression model including covariates see above. For family-based samples, familial relatedness was taken into account by using a sandwich correction as implemented in PLINK. It should be noted that some groups did do the analyses in a slightly different manner based on the characteristics of their sample. The analyses plan that was send to the participating groups is included in Supplementary Information 3. Supplementary Table 2 lists the program used by each group. Before performing the meta-analysis, we applied a set of filters to each GWA results set independently. First, we removed insertions and deletions, ensuring that all base pair positions were unique and referred to the same genetic variant that is, SNP. Fourth, regardless of the quality score type used, we excluded SNPs with imputation quality scores below 0. The genome-wide significance level according to the Knowledge-based mining system for Genome-wide Genetic studies default setting of Benjamini and Hochberg false discovery rate threshold of 0. The proportion of phenotypic variance that could be explained by the SNPs was estimated using the density estimation method developed by So et al. Additional details are located in the Supplementary Information 2. LD Score regression 42 , 43 was used as an alternative method to estimate the SNP-based heritability, as well as to estimate the degree of genetic covariance between lifetime cannabis use present study and lifetime cigarette smoking 31 see Supplementary Information 2. No genome-wide significant associations between individual SNPs and lifetime cannabis use were observed see Manhattan plot, Supplementary Figure 1a. Supplementary Figures 2a—m and 3a—m illustrate the Manhattan and QQ plots for each sample. None of these 10 SNPs were replicated in the four independent replication samples Supplementary Table 3. In a combined meta-analysis of the 10 top SNPs that is, discovery plus replication samples , none of the SNPs reached genome-wide significance. Local plots of the most strongly associated regions, including neighboring genes, are provided in Supplementary Figures 4a—j. Because not all SNPs passed the post-imputation quality control steps in all the samples, this table includes the effective sample size per SNP. The Manhattan and QQ plot for this test are shown in Figures 1a and b. Results for the top genes can be found in Supplementary Table 5. Regional plots 44 of these top genes are located in Supplementary Figure 6. The Manhattan a and the QQ plot b based on results of the gene-based analysis performed in the discovery sample using HYST hybrid set-based test. The forest plot in Figure 2 illustrates the effect of this SNP in each sample. In most samples, the effect is in the same direction, such that the major T allele is associated with a decreased risk of lifetime cannabis use. The forest plot for two SNPs with lowest P -values in the other significant gene regions can be found in Supplementary Figure 5. SNP, single-nucleotide polymorphism. Of the five genes included in our replication analyses, none were replicated in two of the independent replication samples see Table 3. These variance estimates were robust across pruned sets with similar r 2 thresholds see Supplementary Table 6. However, because these estimates are only based on common SNPs, the total heritability of lifetime cannabis use is likely to be higher. To date, this is the largest GWA study of lifetime cannabis use. There were no genome-wide significant SNP associations. Moreover, gene-based tests of association identified four protein-coding genes and one intergenic region significantly associated with lifetime cannabis use including NCAM1, which has previously been linked to substance use. Our results are consistent with the hypothesis that lifetime cannabis use is a highly polygenic trait, comprising many SNPs each with small effects contributing to lifetime risk. Moreover, portions of the genetic risk in lifetime cannabis use likely correlates with other substances including cigarette smoking. Our top gene associated with lifetime cannabis use was NCAM1 , a known candidate for nicotine dependence. Importantly, the NTAD cluster has been associated with smoking behavior and nicotine dependence, 45 , 47 , 48 , 49 , 50 , 51 , 52 alcohol dependence, 53 , 54 heroin dependence, 55 as well as other substance use disorders. Variants in the CADM2 gene have been previously associated with body mass index, 56 processing speed 57 and autism disorders. It is possible that the association with lifetime cannabis use may be driven, for example, by differences in personality rather than as a direct relationship with lifetime use. The third gene, SCOC, encodes a short coiled-coil domain-containing protein that localizes to the Golgi apparatus. Many coiled-coil-type proteins are involved in important biological functions such as the regulation of gene expression through the regulation of transcription factor binding. Suggestive association for SNPs near KCNT2 have previously been found for cocaine dependence and for early-onset, highly comorbid, heavy opioid use. The lack of genome-wide significant associations for individual SNPs is consistent with previous GWA studies of lifetime cannabis use 26 , 27 and cannabis dependence. First, complex traits are known to be influenced by many variants, each with very small effect sizes. Therefore, our data suggest that the effect sizes of single variants contributing to lifetime cannabis use are likely to be smaller than 1. Combining variants within larger units that is, genes did however reveal four significant genes associated with lifetime cannabis use implying that these genes are appropriate targets for future functional studies of cannabis use. Unfortunately, our gene-based results were not replicated in the replication samples, probably due to low sample sizes and therefore low power. Speculatively, this may indicate that much of the variance explained comes from SNPs located in the regions of weak LD. Such effects are likely to be poorly tagged for the estimation of variance explained after strict LD pruning, and are likely to be more difficult to impute owing to a lack of strongly correlated genotyped SNPs and thus missing from some studies. Our SNP-based heritability estimates lie in between two previous heritability estimates for lifetime cannabis use based on the Genome-wide Complex Trait Analysis 65 software package. Verweij et al. Provided that the current sample is much larger than the samples used in the previous studies, we conclude that approximately one-third to half of the heritability is explained by common SNPs captured on a GWAS array. Other sources of variation may explain the discrepancy between SNP- and twin-based heritability estimates. Twin studies have shown moderate to high genetic correlations of 0. Our findings should be interpreted in the context of at least four potential limitations. First, our study was underpowered to detect very small effects of individual variants. Power analyses revealed that a twofold increase in sample size is required to detect SNP effect sizes with odds ratios of 1. Second, lifetime cannabis use is a dichotomous measure combining single lifetime, regular and chronic users. Consequently, our sample may compromise heterogeneous patterns of use, which has the potential to reduce the power to detect genetic association. This was likely due to differences in the sample characteristics, recruitment strategies and the political differences between countries. This indicates that the input samples were representative of the same population of users. This can decrease power, but does not invalidate our results. On the basis of our observations, the following recommendations for future studies can be made. We have identified four genes significantly associated with cannabis use, which are candidates for follow-up functional studies. The next goal of the International Cannabis Consortium is to perform a meta-analysis on GWA studies investigating the age at first cannabis use. Our rationale is based on the observation that early initiation of cannabis use is associated with rapid progression towards cannabis abuse and dependence, polysubstance use and other substance use disorders. Hopefully, the combination of advanced technologies and novel statistical approaches with larger samples will further contribute to our understanding of the genetic architecture of cannabis use. We have performed the largest meta-analysis to date of GWASs investigating cannabis use phenotypes. Our results illustrated that lifetime cannabis use is under the influence of many common genetic variants. Future studies should investigate the impact of these genes on the biological mechanisms leading to lifetime cannabis use. United Nations Office on Drugs and Crime. Hall W, Solowij N. Adverse effects of cannabis. Lancet ; : — Hall W, Babor TF. Cannabis use and public health: assessing the burden. Addiction ; 95 : — Medicinal use of cannabis in the United States: historical perspectives, current trends, and future directions. J Opioid Manag ; 5 : — Article Google Scholar. Hall W. What has research over the past two decades revealed about the adverse health effects of recreational cannabis use? Addiction ; : 19— Adverse health effects of marijuana use. N Engl J Med ; : PubMed Google Scholar. Cannabis use and mania symptoms: a systematic review and meta-analysis. J Affect Disord ; c : 39— Google Scholar. Gone to pot: a review of the association between cannabis and psychosis. Front Psychiatry ; 5 : Early cannabis use, polygenic risk score for schizophrenia and brain maturation in adolescence. JAMA Psychiatry ; 72 : — Cannabinoids for medical use: a systematic review and meta-analysis. JAMA ; : — Cressey D. The cannabis experiment. Nature ; : — Genetic and environmental influences on cannabis use initiation and problematic use: a meta-analysis of twin studies. Addiction ; : — Two-part random effects growth modeling to identify risks associated with alcohol and cannabis initiation, initial average use and changes in drug consumption in a sample of adult, male twins. Drug Alcohol Depend ; : — Pathways to cannabis abuse: a multi-stage model from cannabis availability, cannabis initiation and progression to abuse. Addict Behav ; 30 : — Autosomal linkage analysis for cannabis use behaviors in Australian adults. Drug Alcohol Depend ; 98 : — An autosomal linkage scan for cannabis use disorders in the nicotine addiction genetics project. Arch Gen Psychiatry ; 65 : — Addict Biol ; 14 : — Linkage analyses of cannabis dependence, craving, and withdrawal in the San Francisco Family Study. A genome-wide scan for loci influencing adolescent cannabis dependence symptoms: evidence for linkage on chromosomes 3 and 9. Drug Alcohol Depend ; 89 : 34— No association of candidate genes with cannabis use in a large sample of Australian twin families. Addict Biol ; 17 : — Alcohol Clin Exp Res ; 32 : — Agrawal A, Lynskey MT. Candidate genes for cannabis use disorders: findings, challenges and directions. Addict Biol ; 16 : — The genetic aetiology of cannabis use initiation: a meta-analysis of genome-wide association studies and a SNP-based heritability estimation. Addict Biol ; 18 : — Heritability, SNP-and gene-based analyses of cannabis use initiation and age at onset. Behav Genet ; 45 : —, Finding the missing heritability of complex diseases. Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet ; 13 : — Nat Genet ; 42 : —U Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat Genet ; 42 : — Genetic and environmental risk factors in the aetiology of illicit drug initiation and subsequent misuse in women. Br J Psychiatry ; : — An integrated map of genetic variation from human genomes. Nature ; : 56— PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet ; 81 : — METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics ; 26 : — Statistical power and significance testing in large-scale genetic studies. Nat Rev Genet ; 15 : — Am J Hum Genet ; 88 : — HYST: a hybrid set-based test for genome-wide association studies, with application to protein-protein interaction-based association analysis. Am J Hum Genet ; 91 : — Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Uncovering the total heritability explained by all true susceptibility variants in a genome-wide association study. Genet Epidemiol ; 35 : — LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet ; 47 : — An atlas of genetic correlations across human diseases and traits. Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw ; 36 : 1— Hum Mol Genet ; 15 : — The cell adhesion molecules N-cadherin and neural cell adhesion molecule regulate human growth hormone: a novel mechanism for regulating pituitary hormone secretion. J Clin Endocrinol Metab ; 88 : — Biol Psychiatry ; 69 : — Psychopharmacology ; : — Genomewide linkage scan for nicotine dependence: identification of a chromosome 5 risk locus. Biol Psychiatry ; 61 : — Genetic variation in dopamine pathways differentially associated with smoking progression in adolescence. Cholinergic nicotinic receptor genes implicated in a nicotine dependence association study targeting candidate genes with SNPs. Hum Mol Genet ; 16 : 36— The genetic basis for smoking behavior: a systematic review and meta-analysis. Nicotine Tob Res ; 6 : — Hum Mol Genet ; 16 : — JAMA Psychiatry ; 70 : — Association analyses of , individuals reveal 18 new loci associated with body mass index. Mol Psychiatry ; 21 : — A novel approach of homozygous haplotype sharing identifies candidate genes in autism spectrum disorder. Hum Genet ; : — Cannabis use and obesity and young adults. Am J Drug Alcohol Abuse ; 36 : — The effects of cannabis on information-processing speed. Addict Behav ; 29 : — ADHD symptoms, autistic traits, and substance use and misuse in adult Australian twins. J Stud Alcohol Drugs ; 75 : — Coiled coil domains: stability, specificity, and biological implications. Chembiochem ; 5 : — Genome-wide association study of opioid dependence: multiple associations mapped to calcium and potassium pathways. Biol Psychiatry ; 76 : 66— Genome-wide association study of cocaine dependence and related traits: FAM53B identified as a risk gene. Mol Psychiatry ; 19 : — GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet ; 88 : 76— Mechanisms underlying the lifetime co-occurrence of tobacco and cannabis use in adolescent and young adult twins. Drug Alcohol Depend ; : 49— Genetic and environmental influences on alcohol, caffeine, cannabis, and nicotine use from early adolescence to middle adulthood. The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases. PLoS One ; 8 : e Addict Behav ; 35 : 35— Early onset cannabis use and progression to other drug use in a sample of Dutch twins. Behav Genet ; 36 : — Download references. The study site acknowledgments are as follows: ALSPAC—We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. We also thank the twins and their siblings for their willing cooperation. FinnTwin—We warmly thank the participating twin pairs and their family members for their contribution. Antti-Pekka Sarin and Samuli Ripatti are acknowledged for genotype data quality controls and imputation. Last, we thank the twins and their families for their participation. Moreover, we thank the various assistants that helped in recruiting participants as well as collecting and cleaning the data. The research was funded partly by the Netherlands Organisation for Scientific Research Brain and Cognition, We are grateful to all the adolescents, their parents and teachers who participated in this research and to everyone who worked on this project and made it possible. Utrecht—We are grateful to Chris Schubart and Willemijn van Gastel and numerous students for their work in the study. Foremost, we thank our study participants. You can also search for this author in PubMed Google Scholar. Correspondence to J M Vink. The remaining authors declare no conflict of interest. Supplementary Information accompanies the paper on the Translational Psychiatry website. This work is licensed under a Creative Commons Attribution 4. Reprints and permissions. Stringer, S. Transl Psychiatry 6 , e Download citation. Received : 11 December Accepted : 21 December Published : 29 March Issue Date : March Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content Thank you for visiting nature. Download PDF. Subjects Addiction Genetics Psychology. Abstract Cannabis is the most widely produced and consumed illicit psychoactive substance worldwide. Multi-ancestry genome-wide association study of cannabis use disorder yields insight into disease biology and public health implications Article Open access 20 November The genetic aetiology of cannabis use: from twin models to genome-wide association studies and beyond Article Open access 21 November Cross-ancestry genetic investigation of schizophrenia, cannabis use disorder, and tobacco smoking Article Open access 21 June Introduction Cannabis is the most widely produced and consumed illicit psychoactive substance worldwide. Table 1 Discovery and replication sample characteristics Full size table. Results Meta-analysis No genome-wide significant associations between individual SNPs and lifetime cannabis use were observed see Manhattan plot, Supplementary Figure 1a. Table 2 Top 10 SNPs with meta-analysis results of discovery samples, and results of combined discovery and replication samples Full size table. Figure 1. Full size image. Table 3 Top five genes from the gene-based tests of association with corrected P -values Benjamini and Hochberg based on the meta-analytic discovery and replication samples Full size table. Figure 2. Discussion To date, this is the largest GWA study of lifetime cannabis use. Conclusion We have performed the largest meta-analysis to date of GWASs investigating cannabis use phenotypes. Article Google Scholar Hall W. Article Google Scholar Download references. View author publications. Additional information Supplementary Information accompanies the paper on the Translational Psychiatry website. Supplementary information. Supplementary Information 1 DOC 72 kb. Supplementary Information 2 DOC 44 kb. Rights and permissions This work is licensed under a Creative Commons Attribution 4. About this article. Cite this article Stringer, S. Copy to clipboard. Nurmi C. Laughlin E. London Molecular Psychiatry Childhood maltreatment mediates the effect of the genetic background on psychosis risk in young adults Mattia Marchi Laurent Elkrief Marco P. Search Search articles by subject, keyword or author. Show results from All journals This journal. Advanced search.

Buy marijuana Kuopio