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Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use

1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 4 , 9 , 10 , 2 , 3 , 1 , 7 , 8 , 2 , 3 , 11 , 12 , 13 , 23andMe Research Team 14 , HUNT All-In Psychiatry 14 , 15 , 16 , 17 , 18 , 19 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 1 , 26 , 27 , 2 , 9 , 28 , 29 , 30 , 31 , 31 , 32 , 19 , 33 , 20 , 18 , 34 , 30 , 24 , 25 , 23 , 1 , 35 , 19 , 34 , 36 , 33 , 4 , 5 , 6 , 19 , 23 , 22 , 31 , 37 , 38 , 4 , 39 , 8 , 28 , 31 , 16 , 21 , 33 , 40 , 28 , 41 , 42 , 43 , 4 , 44 , 45 , 35 , 4 , 46 , 24 , 25 , 47 , 1 , 48 , 29 , 34 , 4 , 44 , 7 , 8 , 22 , 24 , 25 , 49 , 4 , 9 , 50 , 51 , 52 , 32 , 53 , 43 , 21 , 1 , 4 , 39 , 52 , 28 , 54 , 55 , 29 , 56 , 22 , 57 , 26 , 26 , 58 , 22 , 57 , 59 , 25 , 60 , 61 , 62 , 4 , 44 , 50 , 33 , 15 , 63 , 62 , 64 , 18 , 15 , 21 , 65 , 15 , 30 , 66 , 59 , 20 , 67 , 68 , 1 , 66 , 69 , 70 , 36 , 55 , 71 , 72 , 73 , 12 , 15 , 32 , 74 , 4 , 39 , 33 , 75 , 33 , 19 , 2 , 3 , * , 1 , *

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      Abstract

      Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6–11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures.

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      Most cited references 71

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      Principal components analysis corrects for stratification in genome-wide association studies.

      Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.
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        GCTA: a tool for genome-wide complex trait analysis.

        For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the "missing heritability" problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.
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          Neurocircuitry of addiction.

          Drug addiction is a chronically relapsing disorder that has been characterized by (1) compulsion to seek and take the drug, (2) loss of control in limiting intake, and (3) emergence of a negative emotional state (eg, dysphoria, anxiety, irritability) reflecting a motivational withdrawal syndrome when access to the drug is prevented. Drug addiction has been conceptualized as a disorder that involves elements of both impulsivity and compulsivity that yield a composite addiction cycle composed of three stages: 'binge/intoxication', 'withdrawal/negative affect', and 'preoccupation/anticipation' (craving). Animal and human imaging studies have revealed discrete circuits that mediate the three stages of the addiction cycle with key elements of the ventral tegmental area and ventral striatum as a focal point for the binge/intoxication stage, a key role for the extended amygdala in the withdrawal/negative affect stage, and a key role in the preoccupation/anticipation stage for a widely distributed network involving the orbitofrontal cortex-dorsal striatum, prefrontal cortex, basolateral amygdala, hippocampus, and insula involved in craving and the cingulate gyrus, dorsolateral prefrontal, and inferior frontal cortices in disrupted inhibitory control. The transition to addiction involves neuroplasticity in all of these structures that may begin with changes in the mesolimbic dopamine system and a cascade of neuroadaptations from the ventral striatum to dorsal striatum and orbitofrontal cortex and eventually dysregulation of the prefrontal cortex, cingulate gyrus, and extended amygdala. The delineation of the neurocircuitry of the evolving stages of the addiction syndrome forms a heuristic basis for the search for the molecular, genetic, and neuropharmacological neuroadaptations that are key to vulnerability for developing and maintaining addiction.
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            Author and article information

            Affiliations
            [1 ]Department of Psychology, University of Minnesota Twin Cities, Minneapolis, Minnesota, USA
            [2 ]Department of Public Health Sciences, College of Medicine, Pennsylvania State University, Hershey, Pennsylvania, USA
            [3 ]Institute of Personalized Medicine, College of Medicine, Pennsylvania State University, Hershey, Pennsylvania, USA
            [4 ]Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, USA
            [5 ]Department of Sociology, University of Colorado Boulder, Boulder, Colorado, USA
            [6 ]Institute of Behavioral Science, University of Colorado Boulder, Boulder, Colorado, USA
            [7 ]Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
            [8 ]The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
            [9 ]Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA
            [10 ]Interdisciplinary Quantitative Biology Graduate Group, University of Colorado Boulder, Boulder, Colorado, USA
            [11 ]23andMe, Inc., Mountain View, California, USA
            [12 ]Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA
            [13 ]Center for the Genetics of Host Defense, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA
            [14 ]A full list of members and affiliations appears at the end of the paper.
            [15 ]Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
            [16 ]Department of Psychiatry, Virginia Institute for Psychiatric & Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia, USA
            [17 ]Department of Psychiatry and Human Genetics, University of Utah, Salt Lake City, Utah, USA
            [18 ]Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, USA
            [19 ]Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
            [20 ]K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
            [21 ]Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
            [22 ]Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
            [23 ]Department of Biology Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
            [24 ]Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
            [25 ]Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
            [26 ]Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
            [27 ]Department of Child and Adolescent Psychiatry, Erasmus MC Rotterdam, Rotterdam, the Netherlands
            [28 ]Estonian Genome Center, University of Tartu, Tartu, Estonia
            [29 ]Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama City, Kanagawa, Japan
            [30 ]Department of Population Health Science, Bristol Medical School, Oakfield Grove, Bristol, United Kingdom
            [31 ]Consiglio Nazionale delle Ricerche, Istituto di Ricerca Genetica e Biomedica, Monserrato, Italy
            [32 ]Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
            [33 ]deCODE Genetics/AMGEN, Inc., Reykjavik, Iceland
            [34 ]Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA
            [35 ]Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
            [36 ]Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
            [37 ]Avera Institute for Human Genetics, Sioux Falls, SD, USA
            [38 ]Department of Family Medicine & Community Health, Alpert Medical School, Brown University, Providence, RI, USA
            [39 ]Department of Integrative Physiology, University of Colorado Boulder, Boulder, Colorado, USA
            [40 ]School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
            [41 ]Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
            [42 ]Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
            [43 ]Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
            [44 ]Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado, USA
            [45 ]Brain and Mind Centre, University of Sydney, New South Wales, Australia
            [46 ]Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
            [47 ]Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
            [48 ]Fellows Program, RTI International, Research Triangle Park, NC, USA
            [49 ]Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
            [50 ]Department of Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Finland
            [51 ]Department of Medicine, Kuopio University Hospital, Finland
            [52 ]Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
            [53 ]Department of Biostatistics and Bioinformatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
            [54 ]Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
            [55 ]Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, Michigan
            [56 ]Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
            [57 ]Department of Epidemiology, University of Washington, Seattle, Washington, USA
            [58 ]Department of Clinical Genetics, VU Medical Centre Amsterdam, Amsterdam, the Netherlands
            [59 ]Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
            [60 ]Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
            [61 ]Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA
            [62 ]SAA - National Center of Addiction Medicine, Vogur Hospital, Reykjavik, Iceland
            [63 ]Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA
            [64 ]Department of Psychiatry, University of California San Diego, San Diego, California, USA
            [65 ]FORMI and Department of Neurology, Oslo University Hospital, Oslo, Norway
            [66 ]MRC Integrative Epidemiology Unit, University of Bristol, Oakfield Grove, Bristol, United Kingdom
            [67 ]HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway
            [68 ]Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
            [69 ]UK Centre for Tobacco and Alcohol Studies, School of Psychological Science, University of Bristol, Bristol, United Kingdom
            [70 ]Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
            [71 ]Department of Human Genetics, University of Michigan, Ann Arbor, Michigan
            [72 ]Northwestern University Feinberg School of Medicine, Department of Preventative Medicine, Chicago, Ilinois, USA
            [73 ]Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
            [74 ]Department of Public Health, University of Helsinki, Helsinki, Finland
            [75 ]Faculty of Medicine, University of Iceland, Reykjavik, Iceland
            Author notes

            Dajiang Liu and Scott Vrieze jointly supervised the work.

            AUTHOR CONTRIBUTIONS: G.A., D.J.L., and S.V. designed the study. D.J.L., and S.V. led and oversaw the study. M.L. was the study’s lead analyst. She was assisted by Y.J., D.J.L., S.V., R.W., D.M.B., and G.D. Bonferroni thresholds were calculated by D.M. Phenotype definitions were developed by L.J.B., M.C.C., D.A.H., J.K., E.J., D.J.L., M.M., M.R.M., S.V., and L.Z. Software development was carried out by Y.J., D.J.L., and X.Z. Conditional analyses were performed by Y.J. and M.L. Heritability, genetic correlation, and polygenic scoring analyses were performed by R.W. Multivariate analyses were performed by Y.J., M.L. and D.J.L. Bioinformatics analyses were performed and interpreted by F.C., J.D., J.J.L, Y.L., M.L., J.A.S., S.V., and R.W. The LocusZoom website was designed by G.D. Figures were created by M.L., R.W. Y.L., and S.V. M.A.E. and M.C.K. helped with data access. R.W. coordinated authorship and acknowledgement details. M.C.C, S.P.D., E.J., J.K., and J.A.S. provided helpful advice and feedback on study design and the manuscript. All authors contributed to and critically reviewed the manuscript. Y.L., D.J.L., M.L., S.V., and R.W. made major contributions to the writing and editing.

            [* ]Correspondence to Dajiang J. Liu, dajiang.liu@ 123456psu.edu , or Scott Vrieze, vrieze@ 123456umn.edu
            Journal
            9216904
            2419
            Nat Genet
            Nat. Genet.
            Nature genetics
            1061-4036
            1546-1718
            7 November 2018
            14 January 2019
            February 2019
            14 July 2019
            : 51
            : 2
            : 237-244
            30643251
            6358542
            10.1038/s41588-018-0307-5
            NIHMS1511852

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            Genetics

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