How can I buy cocaine online in Taichung

__________________________

📍 Verified store!

📍 Guarantees! Quality! Reviews!

__________________________

▼▼ ▼▼ ▼▼ ▼▼ ▼▼ ▼▼ ▼▼

▲▲ ▲▲ ▲▲ ▲▲ ▲▲ ▲▲ ▲▲

How can I buy cocaine online in Taichung

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. These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries 5. Here we leveraged global genetic diversity across 3. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3, associated variants from 2, loci showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction. We developed a multi-ancestry meta-regression method to meta-analyse ancestrally diverse genome-wide association study GWAS summary statistics from 60 cohorts with 3,, individuals Supplementary Table 1 ; see Supplementary Fig. The meta-analytic method used here uses meta-regression to account for per study axes of genetic ancestry variation combined with a random effect to capture further unexplained heterogeneity in the effect of a given genetic variant. Smoking phenotypes were selected to represent different stages of tobacco use and addiction, including initiation, the onset of regular use, amount smoked and cessation. Colours are coded by primary ancestry of individuals in the cohort. Ancestry component 3 was a north—south EUR cline, which was omitted here as we did not conduct meta-analyses stratified by northern versus southern Europe. The full moderation results are in Supplementary Table 2. The grey circles indicate variants showing little to no evidence of effect size heterogeneity across ancestry, whereas the coloured circles represent variants with adequate evidence of effect size heterogeneity. The plots highlight that the majority of variants have similar effect sizes across all ancestry clines, with some potentially interesting exceptions in which the variant effects sizes differ substantially between ancestry clines. Of these, 1, loci and 2, independent variants were associated with SmkInit, 33 loci 39 variants with AgeSmk, loci variants with CigDay, loci variants with SmkCes and loci variants with DrnkWk. All sentinel variants within identified loci had high posterior probabilities that their effect would replicate in a sufficiently powered study according to a trans-ancestry extension of our GWAS cross-validation technique 6. Only 17 sentinel variants 0. Inclusion of diverse ancestry may improve the discovery of new variants through a combination of increased genetic variation, larger sample sizes and improved fine-mapping due to diverse patterns of linkage disequilibrium LD. We quantified gains in power from the use of our multi-ancestry model over a simpler ancestry-naive fixed-effects model excluding the ancestry meta-regression. Comparing the number of associated variants, we found additional independent variants that were identified only by the multi-ancestry meta-regression analysis. Both sets of models were fit to the same data, such that the larger number of significantly associated variants identified with the multi-ancestry model indicates increased power from accounting for axes of genetic variation and residual heterogeneity. Included among these were newly associated variants in genes related to nervous system function for example, NRXN1 including glutamatergic GRIN2A neurotransmission, which is of relevance to neurocircuitry in addiction 7 , 8. We identified loci Across all 2, loci, 1, These findings highlight the utility of both increased sample size and diverse ancestry in fine-mapping variants for these complex behavioural phenotypes. To characterize genes prioritized from fine-mapping, we conducted a series of functional enrichment analyses. Within the brain, cell-type enrichment of the high-priority genes was observed for projecting glutamatergic neurons from the cortex, hippocampus and amygdala telencephalon excitatory projection neurons and projection GABA neurons from medium spiny neurons of the striatum telencephalon inhibitory projecting neurons , along with neurons in various subcortical structures such as the hypothalamus and midbrain, consistent with aspects of the mesolimbic theory of addiction 7 , 8 Extended Data Fig. Finally, these high-priority genes that were strongly associated with substance use were enriched in gene pathways related to neurogenesis, neuronal development, neuronal differentiation and synaptic function. The multi-ancestry meta-analysis method also allowed for tests of whether a variant effect size differed that is, was moderated by ancestry along four ancestry dimensions estimated from multidimensional scaling MDS of allele frequencies from each participating study Fig. There was minimal evidence of effect size moderation by ancestry for most independent variants, ranging from Another 7. Finally, roughly 3. Comparisons between the variants with strong evidence for effect size moderation by ancestry and those with no evidence suggested that the identification of these variants was not driven to a large extent by differences in imputation quality, LD scores or Fst fixation index across ancestries. Across phenotypes, 88 of these variants showed moderation by the first axis of ancestry variation approximate EAS cline; Fig. Nine variants showed differences in effect size moderated by the third axis EUR cline. Only the effect of one variant was moderated by three or more ancestry clines EAS, AFR and AMR : rs, a missense variant in the alcohol dehydrogenase gene ADH1B , which has been shown to be protective against alcohol consumption 9. An increase on any of these clines was associated with a reduced effect size of this allele, on average. To further evaluate causal genes and relevant tissues through which associated variants may be operating, we applied a trans-ancestry transcriptome-wide association study TWAS analysis to each phenotype across 49 tissues derived from the GTEx Consortium Using a P value threshold Bonferroni-corrected for the total number of genes and tissues within a phenotype, we found 1, genes significantly associated with SmkInit, 21 genes with AgeSmk, genes with CigDay, genes with SmkCes and genes with DrnkWk resulting in 1, unique genes across phenotypes; Supplementary Table 6. For each of our five phenotypes, matrix decomposition parallel analysis 11 of the per-tissue P value correlation matrix suggested two components: one explaining Pathway enrichment analyses of the TWAS-associated genes identified 1, unique gene pathways across phenotypes that were broadly enriched across tissues Supplementary Table 7 , including many of obvious relevance to neurotransmission and neurodevelopment. To further illustrate several variants within genes of interest, we integrated findings described above to select variants for which there was evidence of association across analytic methods and for which the availability of diverse ancestries was clearly relevant. Illustrative variants were chosen in a similar way as described for the enrichment analyses above: 1 we extracted variants from multi-ancestry fine-mapped credible intervals containing less than five variants, and 2 we cross-referenced the resulting variants with the multi-ancestry TWAS cis -expression quantitative trait loci and their significantly associated genes. We highlight five of the 52 genes that resulted from this process. CACNA1B is linked to multiple psychiatric disorders, including schizophrenia, bipolar disorder and autism spectrum disorders 15 , 16 , CigDay was associated with variants in neurturin NRTN , a type of glial cell line-derived neurotrophic factor involved in the development and survival of dopamine neurons This gene has been studied in relation to Parkinson disease for its potential to restore dopamine neurocircuitry PAK6 encodes a pactivated kinase that is highly expressed in the striatum and hippocampus, has been implicated in the migration of GABAergic interneurons 20 as well as the modulation of dopaminergic neurotransmission 21 , and is involved in locomotor activity and cognitive function PAK6 has been robustly associated with schizophrenia 23 and neurodegenerative diseases 24 , 25 , such as Parkinson disease and Alzheimer disease, further highlighting its role in synaptic changes. Heritability and cross-phenotype genetic correlations were generally similar in sign and modest in magnitude in each ancestry Fig. Genetic correlations for the same phenotype between each of the largest contributing cohorts and all remaining cohorts restricted to EUR ancestries only were generally high for each smoking phenotype mean r g of 0. Note that some comparisons are underpowered to detect differences in predictive accuracy across ancestry see Supplementary Note. Significant interactions are noted with text. To characterize the multifactorial genetic aetiology of tobacco and alcohol use, we computed genetic correlations of our EUR-stratified results with 1, medical, biomarker and behavioural phenotypes from the UK Biobank 29 Supplementary Tables 10 and An affinity propagation clustering algorithm 30 was used to aid interpretability by grouping UK Biobank phenotypes such that each of the five current phenotypes were exemplars Supplementary Fig. We note, however, that genetic correlations can be difficult to interpret 33 , 34 as they may be affected by genetic confounding, mediation effects or sampling bias. To evaluate within-ancestry and across-ancestry performance of polygenic scores, we iteratively fit a multiple regression model and evaluated the incremental predictive accuracy of each ancestry-based score, over and above scores already entered into the model that is, first including the AMR-based score, then adding the AFR-based, EAS-based and EUR-based scores one at a time to evaluate incremental prediction accuracy. For each ancestry and phenotype, the EUR-based score on its own outperformed the ancestry-matched score on its own Supplementary Table These results highlight the relative utility of current polygenic scores for EUR ancestries versus all others. In interpreting these results, however, we note that some comparisons may be underpowered to identify differences in the variance explained by polygenic scores between ancestries. Finally, EUR-based scores overpredicted tobacco and alcohol use for individuals of non-EUR ancestry and underpredicted for individuals of EUR ancestry, although this prediction bias is readily eliminated through statistical correction with genetic principal components. Tobacco and alcohol use are heritable behaviours that can be radically affected by environmental factors, including cultural context 37 and public health policies 38 , Despite this, we found that a large majority of associated genetic variants showed homogeneous effect size estimates across diverse ancestries, suggesting that the genetic variants associated with substance use affect such individuals similarly. The limited extent of variant effect size heterogeneity, coupled with similar heritability estimates and cross-trait genetic correlations, indicates that the genetic architecture underlying substance use is not markedly different across ancestries. By contrast, polygenic scores generally performed well in EUR ancestries but with mixed-to-limited results in other ancestries, suggesting that portability of such scores across ancestries remains challenging, even when discovery sample sizes across all ancestries are more than , Explanations for this apparent discrepancy have been proposed 40 , but more stringent and sensitive tests will be required to draw strong conclusions about such patterns of heredity. Most individuals of EUR, AFR and AMR ancestries in the current study live in the United States and Europe and share somewhat similar environments regarding tobacco and alcohol availability and policies surrounding use of these substances, and all included individuals were adults. Further increases in genetic diversity and consideration of environmental moderators, including cultural factors, will continue to add to our understanding of the genetic architecture of both substance use and related behaviours and diseases. Here we describe an overview of the methods used to conduct the association, fine-mapping and downstream in silico functional analysis. Additional details can be found in the Supplementary Note. Except for TOPMed studies, in which the genetic data were derived from deep whole-genome sequencing, participants in all studies were genotyped on genome-wide arrays. Studies composed primarily of closely related individuals for example, family studies first regressed out covariates, inverse-normalized the residuals as necessary and then tested additive variant effects under a linear mixed model with a genetic kinship matrix for all phenotypes. Some studies of unrelated individuals followed the same analysis for quasi-continuous phenotypes AgeSmk, CigDay and DrnkWk , but estimated additive genetic effects under a logistic model for binary phenotypes SmkInit and SmkCes. We used terminology and acronyms from the Genomes Project 42 to describe ancestry. The majority of participating cohorts stratified their sample by ancestry before generation of summary statistics. Cohorts composed of substantial samples of multiple ancestry groups provided summary statistics stratified by ancestry, as well as results based on all individuals regardless of ancestry for use in the multi-ancestry meta-analyses. For example, for both ancestry-stratified and multi-ancestry conditional analysis, we created TOPMed reference panels for estimating LD. Extensive quality control and filtering were performed on the summary statistics from each cohort. We removed studies with a sample size of less than , and those with genomic control values greater than 1. Specifically, the method aggregated weighted Z -score statistics, that is,. This method accounts for between-study heterogeneity in phenotype measures, imputation accuracy, allele frequencies and sample sizes. This method models heterogeneity of effects attributable to ancestry as well as a random effect to capture residual heterogeneity. The MEMO model contains fixed-effect, random-effect and meta-regression models as special cases. These filters reduce potential artefacts arising from sparse data or poor imputation and retain variants with reasonable statistical power. With increasing imputation accuracy and the inclusion of variants with MAF down to 0. The threshold was chosen based on previous work on low-frequency variants 5 , 46 , All statistical tests are two-sided unless otherwise stated. Genomic control correction for common variants was not applied given that elevation of genomic control values is expected with high polygenicity that is, it assumes sparsity and very large sample sizes 48 ; such a correction may be overly conservative. To evaluate this decision, we estimated the replicability of associated loci using a trans-ancestry extension of an existing method 6. To further evaluate robustness of our results, we estimated LD score regression LDSC intercepts and attenuation ratios to account for bias in the intercept test when sample sizes become extreme, as in the present case. Results were within expected limits and consistent with a limited effect of population stratification on the meta-analysis results 44 Supplementary Table 8. Finally, given reduced power in the within-sibling GWAS, we additionally compared the sign of SNP effect size estimates between EUR-stratified 23andMe summary statistics the largest participating cohort and EUR-stratified summary statistics with all cohorts except 23andMe, finding sign concordance estimates of See the Supplementary Note for further details on the methods and full results, including the list of excluded variants and loci. We performed sequential forward selection to identify independently associated variants in each locus 50 for ancestry-stratified and multi-ancestry results. The process iterates until there are no remaining significantly associated variants. The method requires an external genomic reference panel to estimate LD patterns. For ancestry-stratified conditional analyses, we used ancestry-matched individuals from TOPMed to estimate LD sample sizes given previously. Loci were defined based in part on the conditional analysis, using a multi-step approach. First, consistent with previous GWAS meta-analysis 5 in EUR ancestries, we identified all 1-Mb windows surrounding sentinel variants and collapsed overlapping windows. This resulted in a total of 1, such windows. For each window, we then used our ancestry-aware conditional analysis 51 described previously with an ancestry-matched reference panel from TOPMed to enumerate all independent variants within each window. Overlapping loci were then collapsed. This procedure avoids conventional definitions of a locus based on work in EUR ancestries and is tailored to the multi-ancestry data at hand. We evaluated evidence of effect size moderation by ancestry in the multi-ancestry model for each independent variant. To do so, we extended the MEMO model into a mixture model that separated variants with homogenous effects models with only an intercept term from those with possible heterogeneous effects on at least one axis of genetic variation. We considered six sub-models including the null model, and the models in which the number of included components varied from 0 to 4. We selected the model with the largest posterior probability for each variant as the best-fitting model to capture the genetic effect heterogeneity. Variants in which the zero component model was selected that is, all models with at least one component were rejected were considered to have homogeneous effects across ancestry. Among the remaining variants, we considered which one of the meta-regression models that is, 1—4 components best described the extent of effect heterogeneities based on the posterior probabilities for each model. In addition, we required that strongly heterogeneous variants had an MDS component effect that was significantly different from zero and were polymorphic in two or more ancestry-stratified cohorts to ease interpretation of heterogeneous effects. For example, a variant in which the model with two components best fit the data was considered at least weakly heterogeneous. Fine-mapping was conducted in EUR-stratified results, using identical loci as in multi-ancestry fine-mapping, to describe the increased resolution attributable to diverse ancestry inclusion and differences in sample size. Functional enrichment analysis was conducted to test whether high-priority genes identified in the fine-mapping results were expressed in specific tissue types or enriched in certain cell types or gene pathways. High-priority genes were defined as those mapped from variants in credible intervals containing less than five variants. That is, for each variant in credible intervals with less than five variants, we used the UCSC genome annotation database to assign genes. We assigned intergenic variants to the nearest gene. Annotation categories were derived from GTEx tissue expression 53 , central nervous systems cell types 50 and gene pathways TWAS were performed using a trans-ancestry method. In brief, this method fits a series of meta-regression models including the first four axes of genetic variation MDS components , similar to that of our multi-ancestry meta-analysis model minus the random-effect term. Genetic effect estimates from these four models were then used to estimate phenotypic effects of each variant. Finally, we used a Cauchy combination test 57 to combine P values across all available tissues for each gene. The final, combined P value was subjected to a Bonferroni correction for 22, genes in 49 tissues. Pathway enrichment was also conducted using a weighted regression approach 58 with the TWAS per-tissue P values to quantify the enrichment of identified genes in each pathway. Affinity propagation clustering 62 , a message-passing algorithm based on exemplars that identifies their corresponding set of clusters, was then used to further interpret the pattern of genetic correlations and multifactorial nature of substance use. A Bonferroni-corrected P value threshold for 1, UK Biobank phenotypes was used to identify genetic correlations that were significantly different from zero. Polygenic risk scores were computed using LDpred for each ancestry group separately, an approach that incorporates the correlation between genetic variants to re-weight effect size estimates We used an independent prediction cohort, Add Health 35 , to validate each score. Add Health is a nationally representative sample of US adolescents enrolled in grades 7 through 12 during the — school year. The mean birth year of respondents was s. Phenotypic descriptive statistics are given in Supplementary Table We used each Add Health ancestry group as its own LD reference panel for construction of each polygenic score, after removing related individuals, except for EAS in which we use Genomes due to the small sample size in Add Health. All scores were scaled to have a mean of zero and standard deviation of one. Ethical review and approval were provided by the University of Minnesota institutional review board. All human participants provided informed consent. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. We have provided association results for variants that passed quality-control filters in the multi-ancestry and ancestry-stratified results for each of the five substance use phenotypes, excluding data provided by 23andMe. Ancestry-stratified polygenic score weights based on ancestry-stratified summary statistics are also provided. All software used to perform these analyses is publicly available. Software tools used are listed in the main text and Methods. An amendment to the underlying article code was made to enable an author name to appear correctly in PubMed. World Health Organization. The top 10 causes of death. Griswold, M. Alcohol use and burden for countries and territories, — a systematic analysis for the Global Burden of Disease Study Lancet , — Article Google Scholar. Liu, M. Association studies of up to 1. McGuire, D. Model-based assessment of replicability for genome-wide association meta-analysis. Volkow, N. Neurobiologic advances from the brain disease model of addiction. Koob, G. Neurocircuitry of Addiction. Neuropsychopharmacology 35 , — Bierut, L. ADH1B is associated with alcohol dependence and alcohol consumption in populations of European and African ancestry. Psychiatry 17 , — The GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science , — Horn, J. A rationale and test for the number of factors in factor analysis. Psychometrika 30 , — Berrettini, W. Buchta, W. Andrade, A. Genetic associations between voltage-gated calcium channels and psychiatric disorders. Purcell, S. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature , — Moskvina, V. Gene-wide analyses of genome-wide association data sets: evidence for multiple common risk alleles for schizophrenia and bipolar disorder and for overlap in genetic risk. Psychiatry 14 , — Liao, X. Genetic associations between voltage-gated calcium channels and autism spectrum disorder: a systematic review. Brain 13 , 96 Koskela, M. Update of neurotrophic factors in neurobiology of addiction and future directions. Domanskyi, A. Gene Ther. Zhang, K. The pactivated kinases in neural cytoskeletal remodeling and related neurological disorders. Protein Cell 13 , 6—25 Civiero, L. PAKs in the brain: function and dysfunction. Acta , — Nekrasova, T. Targeted disruption of the Pak5 and Pak6 genes in mice leads to deficits in learning and locomotion. Landek-Salgado, M. Molecular substrates of schizophrenia: homeostatic signaling to connectivity. Psychiatry 21 , 10—28 Leucine-rich repeat kinase 2 interacts with pactivated kinase 6 to control neurite complexity in mammalian brain. Ma, Q. Buchsbaum, I. ECE2 regulates neurogenesis and neuronal migration during human cortical development. EMBO Rep. Mzhavia, N. Characterization of endothelin-converting enzyme Implication for a role in the nonclassical processing of regulatory peptides. Baranello, R. Alzheimer Res. Bycroft, C. The UK Biobank resource with deep phenotyping and genomic data. Bodenhofer, U. APCluster: an R package for affinity propagation clustering. Bioinformatics 27 , — Mallard, T. Item-level genome-wide association study of the alcohol use disorders identification test in three population-based cohorts. Psychiatry , 58—70 Zhou, H. Genome-wide meta-analysis of problematic alcohol use in , individuals yields insights into biology and relationships with other traits. Bulik-Sullivan, B. An atlas of genetic correlations across human diseases and traits. Kraft, P. The use of genetic correlation and Mendelian randomization studies to increase our understanding of relationships between complex traits. Harris, K. Martin, A. Human demographic history impacts genetic risk prediction across diverse populations. Hermalin, L. Flor, L. The effects of tobacco control policies on global smoking prevalence. Burton, R. A rapid evidence review of the effectiveness and cost-effectiveness of alcohol control policies: an English perspective. Mathieson, I. The omnigenic model and polygenic prediction of complex traits. McCarthy, S. A reference panel of 64, haplotypes for genotype imputation. A global reference for human genetic variation. Nature , 68—74 Zhan, X. Bioinformatics 32 , — Loh, P. Mixed-model association for biobank-scale datasets. Zhou, W. Efficiently controlling for case—control imbalance and sample relatedness in large-scale genetic association studies. Chen, Z. A new approach to account for the correlations among single nucleotide polymorphisms in genome-wide association studies. Gao, X. Avoiding the high Bonferroni penalty in genome-wide association studies. Google Scholar. Yang, J. Genomic inflation factors under polygenic inheritance. Lee, J. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1. Bryois, J. Jiang, Y. Proper conditional analysis in the presence of missing data: application to large scale meta-analysis of tobacco use phenotypes. PLoS Genet. Kanai, M. Insights from complex trait fine-mapping across diverse populations. Lonsdale, J. Gene Ontology Consortium. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res. Gamazon, E. A gene-based association method for mapping traits using reference transcriptome data. Lin, D. A general framework for detecting disease associations with rare variants in sequencing studies. Liu, Y. Cauchy combination test: a powerful test with analytic p-value calculation under arbitrary dependency structures. Leeuw, C. PLoS Comput. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Luo, Y. Estimating heritability and its enrichment in tissue-specific gene sets in admixed populations. CAS Google Scholar. Altshuler, D. Integrating common and rare genetic variation in diverse human populations. Nature , 52—58 Frey, B. Clustering by passing messages between data points. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Download references. It was conducted by using the UK Biobank Resource under application number A full list of acknowledgements is provided in the Supplementary Note. These authors contributed equally: Gretchen R. Gretchen R. Otto, William Iacono, James J. Allison E. Ashley-Koch, Melanie E. Kathleen C. Traci M. Bartz, Joshua C. Bis, Jennifer A. Brody, Sina A. Lawrence F. Bielak, Sharon L. Kardia, Patricia A. Peyser, Jennifer A. Gyda Bjornsdottir, Daniel F. John Blangero, Joanne E. Dorret I. Brian E. Zhengming Chen, Iona Y. Marilyn C. Marissa A. Ehringer, John K. Hewitt, Matthew C. Keller, Michael C. Jessica D. Faul, Jennifer A. Maiken E. Gabrielsen, Kristian Hveem, Jonas B. Scott D. Gordon, Nicholas G. Xiuqing Guo, Jerome I. Jeffrey Haessler, Robert C. John K. John E. Hokanson, Nicole E. Department of Epidemiology, Harvard T. Department of Research, Innovation and Education, St. Eric O. Department of Biostatics, Harvard T. Patrick F. McArdle, Braxton D. Mitchell, May E. Ulrike Peters, Alex P. Amy E. You can also search for this author in PubMed Google Scholar. S and X. Chen, S. Liu and C. Phenotype definitions were developed by L. McGue, M. Software development was carried out by X. Chen and C. Multi-ancestry meta-analyses were performed by X. Ancestry-stratified meta-analyses were performed by G. Conditional analyses were performed by X. Fine-mapping and allelic heterogeneity were performed by X. Replicability analyses were performed by C. Heritability and genetic correlation analyses were performed by S. Polygenic scoring analyses was performed by G. Bioinformatics analyses were performed and interpreted by F. Figures were created by M. Liu, G. Liu and S. All authors contributed to and critically reviewed the manuscript. Chen, C. Correspondence to Dajiang J. Liu or Scott Vrieze. The spouse of N. Saccone is listed as an inventor on issued U. The spouse of C. The 23andMe Research Team, including J. Moll received grant support from Bayer. All other authors declare no competing interests. Nature thanks David Balding, Ditte Demontis, Eske Derks and the other, anonymous, reviewer s for their contribution to the peer review of this work. Peer reviewer reports are available. The meta-regression within the MEMO model requires specification of ancestry clines. To ensure consistency in the meaning of ancestry clines across all five MEMO analyses one for each phenotype we created a single multidimensional scaling solution based on allele frequencies from all phenotypes in all participating cohorts. These solutions are plotted in panel a circles correspond to TOPMed cohorts, squares are all other cohorts which used imputed microarray genotypes, and triangles are Genomes ancestry groups. Each Genomes individual is colored by their known ancestry. This PC information was used in assigning ancestry to TOPMed individuals for the purpose of reference panel creation individuals of South Asian ancestry were not included in analyses. The PCs in panel b were reordered or reversed in some cases to align with panel a. These transformations are noted in the axis labels. Note that some y-axis scales are discontinuous to better illustrate variants with very small P -values e. All P -values are from two-sided statistical tests. We define high priority genes here as those located nearest to the variants in fine-mapped credible intervals containing less than five variants. The x-axis denotes GTEx tissue types. The y-axis represents relative risk estimates comparing high priority to control genes. Panel b shows similar relative risk comparisons with 39 brain cell types. This file contains Supplementary Notes, Supplementary Figs. Reprints and permissions. Saunders, G. Genetic diversity fuels gene discovery for tobacco and alcohol use. Download citation. Received : 09 March Accepted : 25 October Published : 07 December Issue Date : 22 December 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. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Skip to main content Thank you for visiting nature. Download PDF. Subjects Genome-wide association studies Human behaviour Risk factors. This article has been updated. Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing Article Open access 26 January Genome-wide association study of smoking trajectory and meta-analysis of smoking status in , individuals Article Open access 20 October Multi-ancestry meta-analysis of tobacco use disorder identifies potential risk genes and reveals associations with multiple health outcomes Article 17 April Main We developed a multi-ancestry meta-regression method to meta-analyse ancestrally diverse genome-wide association study GWAS summary statistics from 60 cohorts with 3,, individuals Supplementary Table 1 ; see Supplementary Fig. Full size image. Summary Tobacco and alcohol use are heritable behaviours that can be radically affected by environmental factors, including cultural context 37 and public health policies 38 , Methods Here we describe an overview of the methods used to conduct the association, fine-mapping and downstream in silico functional analysis. Generation of summary statistics and ancestry considerations Except for TOPMed studies, in which the genetic data were derived from deep whole-genome sequencing, participants in all studies were genotyped on genome-wide arrays. Code availability All software used to perform these analyses is publicly available. Change history 26 January An amendment to the underlying article code was made to enable an author name to appear correctly in PubMed. References World Health Organization. Article Google Scholar Liu, M. Article Google Scholar Bierut, L. Article Google Scholar Horn, J. Article Google Scholar Zhou, H. Article Google Scholar Harris, K. Article Google Scholar Martin, A. Article Google Scholar Mathieson, I. Article Google Scholar Zhan, X. Article Google Scholar Gao, X. Google Scholar Yang, J. Article Google Scholar Lee, J. Article Google Scholar Kanai, M. Article Google Scholar Gamazon, E. Article Google Scholar Download references. Author information Author notes These authors contributed equally: Gretchen R. Freedman K. Lutz Department of Biostatics, Harvard T. Saunders View author publications. View author publications. Ethics declarations Competing interests The spouse of N. Peer review Peer review information Nature thanks David Balding, Ditte Demontis, Eske Derks and the other, anonymous, reviewer s for their contribution to the peer review of this work. Extended data figures and tables. Extended Data Fig. Reporting Summary. Supplementary Tables Supplementary Tables 1— Peer Review File. About this article. Cite this article Saunders, G. Copy to clipboard. This article is cited by Gene expression differences associated with alcohol use disorder in human brain Caryn Willis Julie D. White Dana B. Hancock Molecular Psychiatry Peripheral blood transcriptomic profiling of molecular mechanisms commonly regulated by binge drinking and placebo effects Amol Carl Shetty John Sivinski Chamindi Seneviratne Scientific Reports Gastroesophageal reflux disease increases the risk of rheumatoid arthritis: a bidirectional two-sample Mendelian randomization study Quan Yuan Zixiong Shen Yang Li Scientific Reports An approach to identify gene-environment interactions and reveal new biological insight in complex traits Xiaofeng Zhu Yihe Yang Hugues Aschard Nature Communications Cracking the chicken and egg problem of schizophrenia and substance use: Genetic interplay between schizophrenia, cannabis use disorder, and tobacco smoking Meghan J. Chenoweth Neuropsychopharmacology Search Search articles by subject, keyword or author. Show results from All journals This journal. Advanced search. Close banner Close. Email address Sign up. Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing.

Search Result - All

How can I buy cocaine online in Taichung

Official websites use. Share sensitive information only on official, secure websites. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Acupuncture has been used for treating drug addiction since the s, but little is known about the mechanisms by which acupuncture affects drug cue-induced relapse. EA at LI4 and LI11 significantly prevented cue-induced cocaine CPP reinstatement, whereas needle insertion without electrical stimulation at these acupoints had no such effect. Drug abuse is a huge problem for communities and health care systems worldwide 1. Chronic abuse of addictive substances leads to neuroplasticity or neuroadaptation in brain structure and function, impairing cognitive functions, making it difficult for individuals to stop using the addictive substances and rendering them highly susceptible to relapse, even after long periods of abstinence 1 , 2. More than two-thirds of people diagnosed with substance use disorder relapse within the first year after undergoing detoxification 1 , 3 , 4. Cocaine is a highly addictive psychostimulant and no regulatory authorities worldwide have as yet approved any medications for the treatment of addiction to cocaine in adolescents and adults. The NAc has a critical role in the reward circuitry underlying addiction, as it receives dopaminergic input from the VTA and glutamatergic input from the hippocampus, amygdala and frontal cortex 8. Acupuncture needle penetration into acupuncture points or acupoints at specific locations of the body is followed by either manual manipulation traditional acupuncture or electrical current stimulation electroacupuncture \[EA\]. Acupuncture has been used to treat opiate addiction since the s, although its efficacy in drug addiction treatment needs to be confirmed by large-scale clinical trials 17 — Moreover, the mechanisms of these different acupuncture modalities that are used in cocaine addiction treatment have not been clarified. Furthermore, animal studies have shown that EA reduces the risk of relapse to drug-seeking behavior. Thus, the experimental evidence shows that acupuncture modulates molecular abnormalities induced by exposure to addictive substances, but evidence is lacking as to the effects of acupuncture on glutamatergic neurotransmission and how acupuncture reduces the reinstatement of drug-seeking behavior. The lack of standardized therapeutic protocols for acupuncture treatment of drug addiction complicates comparisons of outcomes from existing acupuncture studies using different formulas Clinical evidence suggests that the anticonvulsant gabapentin GBP may be helpful in treating alcohol dependence and for reducing the symptoms of insomnia, dysphoria and craving 22 — 24 , in opioid addiction 25 — 27 , and in treating cannabis dependence One small, open-label trial has described finding that GBP reduced cocaine use and craving 29 , although other research has reported that GBP does not affect abstinence rates, treatment retention, cravings, the subjective effects of cocaine, or likelihood of future cocaine use 30 — We therefore sought to determine whether treatment with combined GBP and EA would have any beneficial effect upon cue-induced cocaine seeking and relapse. Some researchers have suggested that some of the persistent neurobehavioral consequences of repeated exposure to psychostimulant drugs may be because these agents are capable of reorganizing synaptic connectivity patterns in the NAc and PFC For instance, cocaine doubles the numbers of branched spines and increases dendritic spine density on medium spiny neurons in rats To the best of our knowledge, no studies have reported the effects of acupuncture on dendritic spine morphology in drug addiction. We speculated that exploring the effects of acupuncture treatment on dendritic spine morphology may lead to more effective treatment of drug addiction disorders. The timeline for the experiment is shown in Fig. No significant differences were seen with any other pairs of treatment groups. A Timeline for the CPP testing, training, cocaine administration, and experimental interventions. Factor analysis revealed significant between-phase differences in all groups CO group: F \[1. GBP significantly reversed the effects of EA on cocaine-induced relapse behavior. A Timeline of CPP testing, training, cocaine administration, and experimental interventions. In Fig. S3 and S4. S5 and by GBP treatment in controls Fig. Figure 2 A shows the experimental timeline. The NC group without any treatment was used to confirm the basal levels of protein. The original Western blot images are available in Supplementary Figure S10, with photographs of each membrane taken after cropping. C Quantification of GluR2 expression from Fig. A Timeline for the experimental procedures. The bar graphs show the quantification of protein from B , C. The same control group was used for both Western blotting assays. S7 and S8. EA pretreatment prevented increases in dendritic spine density induced by 2 weeks of cocaine administration. A Timeline for the experimental procedure. B On Day 15, the mice were sacrificed for Golgi-Cox staining. Representative laser confocal photomicrographs of dendritic processes were obtained from the NAc of all mice in each group. C , D Bar graphs showing levels of spine density in the NAc core and shell. All groups were anesthetized and maintained under 1. Scant information is available as to the mechanisms underlying the effects of acupuncture in the treatment of cocaine reinstatement. This study investigated the effects of EA treatment in a murine model of cocaine reinstatement by assessing changes in behavior and specific biomarkers that predict the risk of cocaine reinstatement. LI4 is an acupoint that is frequently used for treatment of drug addiction 17 , 44 , LI11, another acupoint on the radial nerve, is commonly combined with LI4 in clinical trials 46 , We therefore selected the combination of LI4 and LI11 for this EA treatment investigation; both acupoints are positioned near the radial nerve in humans and mice. Previous studies have indicated that different frequencies of EA may have different effects on the release of neuropeptides and neurotransmitters For instance, EA 2 Hz stimulates the release of endomorphin, enkephalin and endorphin, while EA Hz stimulates the release of dynorphin As to cocaine addiction in basic research, EA Hz has been shown to suppress cocaine-induced CPP 54 , while more recent studies have suggested that EA 2 Hz at acupoint HT7 may suppress cocaine-seeking behavior in rats 20 , as well as methamphetamine-induced affective states and locomotor activity Thus, the evidence suggests that EA at 2 Hz is effective for inhibiting both morphine- and cocaine-induced addiction. In the present study, we selected EA 2 Hz to examine the effects of EA on cocaine-induced reinstatement. In experimental studies, acupuncture with manual twisting for 1 min 56 or acupuncture for 20 s 57 at HT7, but not at LI5, reduced foot shock-induced reinstatement cocaine-seeking behavior 56 or cocaine-induced locomotion activity Similarly, acupuncture at HT7, but not at LI5, markedly reduced reinstatement of cocaine-seeking, c-Fos expression and pCREB activation in the NAc shell 56 , and reduced self-administration behavior LI4 and LI5 are located on the same meridian and near the radial nerve. In the present study, we compared the effects of EA and manual acupuncture without twisting at LI4 and LI11, for longer periods of time than those recorded in previous studies. We found that simple needling at LI4 and LI11 did not produce any significant effects on cocaine reinstatement, consistent with previous findings, while electrical stimulation at LI4 and LI11 prevented the cocaine reinstatement of CPP. In our investigation, our results Figs. GBP may have some therapeutic potential in the treatment of opioid addiction and cannabis dependence, but there is no significant evidence in support of its use for cocaine and amphetamine abuse One review has noted that GBP has the potential for misuse when patients consume a larger dose than has been prescribed or they use GBP without prescription Such findings suggest that if GBP is prescribed to treat drug addiction, patients could misuse the drug in combination with opioids and stimulants and thereby increase the risk of abuse, rather than enhance the rates of successful rehabilitation. Furthermore, our study suggests that if the patient receives EA for detoxification from illicit substances or alcohol addiction and is prescribed concomitant GBP, the effects of EA might be negated by GBP. Further preclinical research is needed to clarify the molecular mechanisms underlying the effects of GBP in combination with EA, such as whether the inhibition of VDCCs might reverse the effects of EA when used to treat psychostimulant addiction, and to determine which brain area is affected most in addiction by GBP and its involvement in the addiction cycle. Other researchers have observed an increase in AMPA receptor function after psychostimulant exposure 68 , which is supported by research showing that repeated exposure to cocaine followed by a period of withdrawal increases levels of synaptic AMPA receptors in the NAc region In another rat study, 7 days of cocaine administration followed by 14 days of withdrawal and then a challenge dose of cocaine increased the surface expression of AMPA GluR1 and GluR2 in the NAc Chronic administration of cocaine and also methamphetamine increases dendritic branching and density in the prefrontal cortex and the NAc Changes in the size and shape of individual dendritic spines correlate with long-term potentiation and long-term depression 72 , The induction of long-term potentiation is associated with the formation of new spines These alterations might reflect stable changes of neurons that are associated with the long-term behavioral changes seen with addiction Our study followed a previously used method of repeated cocaine administration in rodents to induce increases in dendritic spine density on medium spiny neurons in the NAc In our study, after 2 weeks of cocaine administration, dendritic spine density was significantly increased in both the NAc core and shell regions. These increases were prevented to a significant extent by EA pretreatment at LI4 and LI11, apparently by maintaining dendritic spine density despite chronic cocaine administration. A schematic diagram Fig. Schematic diagram showing synaptic events that occur after exposure to cocaine and subsequent events occurring in the NAc. Chronic cocaine increases dendritic spine density in the NAc. Golgi staining data have revealed that repeated cocaine injections stimulate spinogenesis of NAc medium spiny neurons We used the same methodology in this study to analyze the effects of cocaine upon dendritic morphology, as Golgi staining is a commonly used method to visualize changes in neuronal morphology without requiring specific protein labeling. However, it is difficult to use an ordinary Golgi staining method to evaluate the precise molecular processes involved in spinogenesis after lower dose, shorter durations of cocaine regimens, as discussed by other researchers They had free access to pellets and drinking water. Mice were bred in a similar environment to avoid unintended consequences of environmental enrichment. All experimental procedures were conducted between and h. On each day of behavioral testing, the order of testing was by random assignment. The injection procedures were carried out in the home cage. Drugs were dissolved in sterile saline distilled 0. Before entering the experimental phase, mice were placed in the animal behavior room for 1 h of acclimation. The conditioning compartments were separated by a guillotine trap door, which was open on Day 1. Each mouse was randomly placed into one of the compartments and allowed to move freely between them for 20 min to determine PRE place preference. For the min training sessions, the guillotine trap door was closed. After a single saline injection in the afternoon from PM , the mouse was placed in the preferred black saline-paired chamber. This procedure was modified from a previous study 39 and repeated for 3 consecutive days Days 2, 3 and 4. On Day 5, the mice were allowed to freely access the two chambers and the time spent in individual chambers was monitored for 20 min. The CPP score was calculated as the time spent in the cocaine-paired end chamber 36 , The mice were trained on two days Days 14 and 15 without cocaine injections and were randomly placed in either chamber, then tested for preference on Day 16 EXT testing. One day after EXT, the mice underwent acute cocaine challenge with a priming dose of i. All mice were anesthetized and maintained under 1. LI4 is located on the first dorsal interossei, medial to the middle of the second metacarpal bone. The LI11 is located at the depression medial to the extensor carpi radialis, at the lateral end of the cubital crease 79 , S9 using an electrical stimulator Trio , Ito, Japan , as per the methodology described in a previous study The cocaine-induced CPP condition involved daily i. They received a priming dose i. During the study treatment period Days 6—13 , all interventions were performed once daily. Tissue samples were homogenized in solution containing lysis buffer, protease inhibitors and phosphatase inhibitors All antibodies were diluted in Tris-buffered saline TBS buffer for use. Sixteen male ICR mice aged 4 weeks were randomly grouped into 4 groups: i. The experiment was designed as a two-stage procedure see Fig. On each experimental day, mice were anesthetized for 15 min before being subjected to 20 min of anesthesia with EA or no EA. Two h after treatment, mice received i. The mice received i. The mice were sacrificed and their brains collected for Golgi-Cox staining on Day Dendritic images of medium spiny neurons were acquired at high resolution to ensure sufficient resolution to conduct spine counting. All measurements were made manually and quantified as previously described 12 , Dendritic spines were counted along dendritic processes extending from the soma of fully impregnated medium spiny neurons in both the shell and core of the NAc, as in a previous study Quantitative analysis included only those spines appearing continuous with their parent dendrite shaft in maximum-intensity z projection. These measures were obtained from 3—5 neurons in each cerebral hemisphere. For behavior data, the two-way mixed-model ANOVA tested for possible interaction or main effects on group and phase factors. In the event of significant interaction effects, subsequent simple main effects on group and time factors were tested by one-way ANOVA and one-way ANOVA with repeated measurements, respectively. In the event of significant simple main effects, post-hoc analysis was conducted with the LSD test. In the event of a significant main effect, post-hoc analysis was conducted with the LSD test. For dendritic spine density data, two-way ANOVA tested for possible interactions or main effects after cocaine administration and EA treatment. We thank Ms. Sih-Ting Luo for her assistance in producing the manuscript. The methodology was performed by A. The original draft was prepared by A. All the data generated during this study have been statistically analyzed and are illustrated as figures. The dataset could be available on request from the corresponding author. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This section collects any data citations, data availability statements, or supplementary materials included in this article. As a library, NLM provides access to scientific literature. Sci Rep. Find articles by Ai T M Nguyen. Find articles by Tran V B Quach. Find articles by Peddanna Kotha. Find articles by Szu-Yu Chien. Find articles by Iona J MacDonald. Find articles by Hsien-Yuan Lane. Find articles by Cheng-Hao Tu. Find articles by Jaung-Geng Lin. Find articles by Yi-Hung Chen. Received Aug 13; Accepted Jun 10; Collection date Open in a new tab. Supplementary Information. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel.

How can I buy cocaine online in Taichung