Expanding the genetic architecture of nicotine dependence and its shared genetics with multiple traits

[1]  M. Pirinen,et al.  Genome-wide association meta-analysis of nicotine metabolism and cigarette consumption measures in smokers of European descent , 2020, Molecular Psychiatry.

[2]  A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles , 2020, Nature Neuroscience.

[3]  Christopher R. Erbes,et al.  International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci , 2019, Nature Communications.

[4]  S. H. Lee,et al.  Prevalence and analysis of tobacco use disorder in patients diagnosed with lung cancer , 2019, PloS one.

[5]  Emily E. Burke,et al.  Regional Heterogeneity in Gene Expression, Regulation, and Coherence in the Frontal Cortex and Hippocampus across Development and Schizophrenia , 2019, Neuron.

[6]  P. Tsao,et al.  Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations , 2019, Nature Communications.

[7]  X.-D. Chen,et al.  Expression of long non-coding RNA MAGI2‑AS3 in human gliomas and its prognostic significance. , 2019, European review for medical and pharmacological sciences.

[8]  Jonathan P. Beauchamp,et al.  Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences , 2019, Nature Genetics.

[9]  Dajiang J. Liu,et al.  Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci , 2019, Molecular Psychiatry.

[10]  Dajiang J. Liu,et al.  Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use , 2018, Nature Genetics.

[11]  R. Marioni,et al.  Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions , 2018, Nature Neuroscience.

[12]  Prashant S. Emani,et al.  Comprehensive functional genomic resource and integrative model for the human brain , 2018, Science.

[13]  Alicia R. Martin,et al.  Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder , 2018, Nature Genetics.

[14]  Jonathan P. Beauchamp,et al.  Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals , 2018, Nature Genetics.

[15]  K. Ong,et al.  Elucidating the genetic basis of social interaction and isolation , 2018, Nature Communications.

[16]  S. Linnarsson,et al.  Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways , 2018, Nature Genetics.

[17]  L. Bierut,et al.  Is the Fagerström test for nicotine dependence invariant across secular trends in smoking? A question for cross-birth cohort analysis of nicotine dependence. , 2018, Drug and alcohol dependence.

[18]  Milton Pividori,et al.  Integrating predicted transcriptome from multiple tissues improves association detection , 2018, bioRxiv.

[19]  Sarah M. Hartz,et al.  Trans-ancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders , 2018, Nature Neuroscience.

[20]  L. Bierut,et al.  Human Genetics of Addiction: New Insights and Future Directions , 2018, Current Psychiatry Reports.

[21]  Warren W. Kretzschmar,et al.  Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression , 2017, Nature Genetics.

[22]  Michael C. Neale,et al.  Genome-wide association study across European and African American ancestries identifies a SNP in DNMT3B contributing to nicotine dependence , 2017, Molecular Psychiatry.

[23]  Sarah M. Hartz,et al.  Genetic correlation between smoking behaviors and schizophrenia , 2017, Schizophrenia Research.

[24]  H. Stefánsson,et al.  Polygenic risk scores for schizophrenia and bipolar disorder associate with addiction , 2017, Addiction biology.

[25]  Alex A. Pollen,et al.  Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex , 2017, Science.

[26]  P. Lichtenstein,et al.  Assessing the evidence for shared genetic risks across psychiatric disorders and traits , 2017, Psychological Medicine.

[27]  Kathryn S. Burch,et al.  Leveraging polygenic functional enrichment to improve GWAS power , 2017, bioRxiv.

[28]  Nicola J. Rinaldi,et al.  Genetic effects on gene expression across human tissues , 2017, Nature.

[29]  Mary Goldman,et al.  Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics , 2016, Nature Communications.

[30]  Jakob Grove,et al.  Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder , 2017, bioRxiv.

[31]  William S. Bush,et al.  Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes , 2017, Nature Genetics.

[32]  Evan Z. Macosko,et al.  Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types , 2017, Nature Genetics.

[33]  K. Czene,et al.  Lung cancer, genetic predisposition and smoking: the Nordic Twin Study of Cancer , 2016, Thorax.

[34]  Joseph K. Pickrell,et al.  Detection and interpretation of shared genetic influences on 42 human traits , 2015, Nature Genetics.

[35]  Tom R. Gaunt,et al.  LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis , 2016, bioRxiv.

[36]  C. Spencer,et al.  A contribution of novel CNVs to schizophrenia from a genome-wide study of 41,321 subjects: CNV Analysis Group and the Schizophrenia Working Group of the Psychiatric Genomics Consortium , 2016, bioRxiv.

[37]  M. Miquel,et al.  Have we been ignoring the elephant in the room? Seven arguments for considering the cerebellum as part of addiction circuitry , 2016, Neuroscience & Biobehavioral Reviews.

[38]  Brian J. Eastwood,et al.  BrainSeq: Neurogenomics to Drive Novel Target Discovery for Neuropsychiatric Disorders , 2015, Neuron.

[39]  N. Laird,et al.  A genome-wide association study identifies risk loci for spirometric measures among smokers of European and African ancestry , 2015, BMC Genetics.

[40]  Jonathan P. Beauchamp,et al.  Genetic Associations with Subjective Well-Being Also Implicate Depression and Neuroticism , 2015, bioRxiv.

[41]  Mitchell J. Machiela,et al.  LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants , 2015, Bioinform..

[42]  L. Wain,et al.  Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank , 2015, The Lancet. Respiratory medicine.

[43]  S. Steinberg,et al.  Genome-wide meta-analysis reveals common splice site acceptor variant in CHRNA4 associated with nicotine dependence , 2015, Translational Psychiatry.

[44]  J. Danesh,et al.  A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease , 2016 .

[45]  S. Wacholder,et al.  Time to First Morning Cigarette and Risk of Chronic Obstructive Pulmonary Disease: Smokers in the PLCO Cancer Screening Trial , 2015, PloS one.

[46]  G. Kempermann Faculty Opinions recommendation of Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. , 2015 .

[47]  Jun S. Liu,et al.  The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans , 2015, Science.

[48]  M. Daly,et al.  An Atlas of Genetic Correlations across Human Diseases and Traits , 2015, Nature Genetics.

[49]  C. Wijmenga,et al.  Gene expression analysis identifies global gene dosage sensitivity in cancer , 2015, Nature Genetics.

[50]  J. Hirschhorn,et al.  Biological interpretation of genome-wide association studies using predicted gene functions , 2015, Nature Communications.

[51]  Huaqin Pan,et al.  Using the PhenX Toolkit to Add Standard Measures to a Study , 2011, Current protocols in human genetics.

[52]  S. Wacholder,et al.  Time to smoke first morning cigarette and lung cancer in a case-control study. , 2014, Journal of the National Cancer Institute.

[53]  Joseph Pratt,et al.  Data compatibility in the addiction sciences: an examination of measure commonality. , 2014, Drug and alcohol dependence.

[54]  C. Spencer,et al.  Biological Insights From 108 Schizophrenia-Associated Genetic Loci , 2014, Nature.

[55]  Peter Szolovits,et al.  Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records , 2014, Human Genetics.

[56]  Andres Metspalu,et al.  Distribution and Medical Impact of Loss-of-Function Variants in the Finnish Founder Population , 2014, PLoS genetics.

[57]  Andrew D. Johnson,et al.  Parent-of-origin specific allelic associations among 106 genomic loci for age at menarche , 2014, Nature.

[58]  David Borsook,et al.  The cerebellum and addiction: insights gained from neuroimaging research , 2014, Addiction biology.

[59]  Peggy Hall,et al.  The NHGRI GWAS Catalog, a curated resource of SNP-trait associations , 2013, Nucleic Acids Res..

[60]  B. Lushniak,et al.  The Health consequences of smoking—50 years of progress : a report of the Surgeon General , 2014 .

[61]  R. McMillen,et al.  What Aspect of Dependence Does the Fagerström Test for Nicotine Dependence Measure? , 2012, ISRN Addiction.

[62]  Rachel L. Denlinger,et al.  Dependence and withdrawal-induced craving predict abstinence in an incentive-based model of smoking relapse. , 2013, Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco.

[63]  Jake K. Byrnes,et al.  Bayesian refinement of association signals for 14 loci in 3 common diseases , 2012, Nature Genetics.

[64]  N. Martin,et al.  The genetics of addiction—a translational perspective , 2012, Translational Psychiatry.

[65]  W. Loh,et al.  Are tobacco dependence and withdrawal related amongst heavy smokers? Relevance to conceptualizations of dependence. , 2012, Journal of abnormal psychology.

[66]  G. Coetzee,et al.  A Noncoding RNA Antisense to Moesin at 5p14.1 in Autism , 2012, Science Translational Medicine.

[67]  P. Visscher,et al.  Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits , 2012, Nature Genetics.

[68]  Manolis Kellis,et al.  HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants , 2011, Nucleic Acids Res..

[69]  K. Fagerström Determinants of tobacco use and renaming the FTND to the Fagerstrom Test for Cigarette Dependence. , 2012, Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco.

[70]  H. Stefánsson,et al.  Identification of low-frequency variants associated with gout and serum uric acid levels , 2011, Nature Genetics.

[71]  K. Bucholz,et al.  A latent class analysis of DSM-IV and Fagerström (FTND) criteria for nicotine dependence. , 2011, Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco.

[72]  Andrew A. Rooney,et al.  Forest Plot Viewer: a new graphing tool. , 2011, Epidemiology.

[73]  Per Magne Ueland,et al.  Genetic Polymorphisms in 15q25 and 19q13 Loci, Cotinine Levels, and Risk of Lung Cancer in EPIC , 2011, Cancer Epidemiology, Biomarkers & Prevention.

[74]  A. Tonevitsky,et al.  Latrophilin 1 and its endogenous ligand Lasso/teneurin-2 form a high-affinity transsynaptic receptor pair with signaling capabilities , 2011, Proceedings of the National Academy of Sciences.

[75]  P. Visscher,et al.  GCTA: a tool for genome-wide complex trait analysis. , 2011, American journal of human genetics.

[76]  Megan E. Piper,et al.  WISDM primary and secondary dependence motives: associations with self-monitored motives for smoking in two college samples. , 2010, Drug and alcohol dependence.

[77]  T. Baker,et al.  Tobacco Dependence , 2010, Current directions in psychological science.

[78]  T. Baker,et al.  Refining the tobacco dependence phenotype using the Wisconsin Inventory of Smoking Dependence Motives: II. Evidence from a laboratory self-administration assay. , 2010, Journal of abnormal psychology.

[79]  Michael Boehnke,et al.  LocusZoom: regional visualization of genome-wide association scan results , 2010, Bioinform..

[80]  Yun Li,et al.  METAL: fast and efficient meta-analysis of genomewide association scans , 2010, Bioinform..

[81]  Robert T. Schultz,et al.  Common genetic variants on 5p14.1 associate with autism spectrum disorders , 2009, Nature.

[82]  Megan E. Piper,et al.  The Wisconsin Predicting Patients' Relapse questionnaire. , 2009, Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco.

[83]  Megan E. Piper,et al.  Refining the tobacco dependence phenotype using the Wisconsin Inventory of Smoking Dependence Motives. , 2008, Journal of abnormal psychology.

[84]  Tatiana Foroud,et al.  Variants in nicotinic receptors and risk for nicotine dependence. , 2008, The American journal of psychiatry.

[85]  Daniel F. Gudbjartsson,et al.  A variant associated with nicotine dependence, lung cancer and peripheral arterial disease , 2008, Nature.

[86]  Megan E. Piper,et al.  Time to first cigarette in the morning as an index of ability to quit smoking: implications for nicotine dependence. , 2007, Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco.

[87]  J. Hewitt,et al.  Genes, time to first cigarette and nicotine dependence in a general population sample of young adults. , 2007, Addiction.

[88]  Tania B. Huedo-Medina,et al.  Assessing heterogeneity in meta-analysis: Q statistic or I2 index? , 2006, Psychological methods.

[89]  Roberto Lent,et al.  Isotropic Fractionator: A Simple, Rapid Method for the Quantification of Total Cell and Neuron Numbers in the Brain , 2005, The Journal of Neuroscience.

[90]  J. Ott,et al.  Power and Sample Size Calculations for Case-Control Genetic Association Tests when Errors Are Present: Application to Single Nucleotide Polymorphisms , 2002, Human Heredity.

[91]  N. Breslau,et al.  Predicting smoking cessation and major depression in nicotine-dependent smokers. , 2000, American journal of public health.

[92]  P. Sullivan,et al.  The genetic epidemiology of smoking. , 1999, Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco.

[93]  T. Baker,et al.  Measures of affect and nicotine dependence predict differential response to smoking cessation treatments. , 1992, Journal of consulting and clinical psychology.

[94]  L. Kozlowski,et al.  The Fagerström Test for Nicotine Dependence: a revision of the Fagerström Tolerance Questionnaire. , 1991, British journal of addiction.