Genome-wide association study identifies loci associated with liability to alcohol and drug dependence that is associated with variability in reward-related ventral striatum activity in African- and European-Americans.
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- Authors
- Wetherill, Leah; Lai, Dongbing; Johnson, Emma C; Anokhin, Andrey; Bauer, Lance; Bucholz, Kathleen K; Dick, Danielle M; Hariri, Ahmad R; Hesselbrock, Victor; Kamarajan, Chella; Kramer, John; Kuperman, Samuel; Meyers, Jacquelyn L; Nurnberger, John I; Schuckit, Marc; Scott, Denise M; Taylor, Robert E; Tischfield, Jay; Porjesz, Bernice; Goate, Alison M; Edenberg, Howard J; Foroud, Tatiana; Bogdan, Ryan; Agrawal, Arpana
- Year
- 2019
- Journal
- Genes, brain, and behavior
- PMID
- 31099175
- DOI
- 10.1111/gbb.12580
- PMCID
- PMC6726116
Genetic influences on alcohol and drug dependence partially overlap, however, specific loci underlying this overlap remain unclear. We conducted a genome-wide association study (GWAS) of a phenotype representing alcohol or illicit drug dependence (ANYDEP) among 7291 European-Americans (EA; 2927 cases) and 3132 African-Americans (AA: 1315 cases) participating in the family-based Collaborative Study on the Genetics of Alcoholism. ANYDEP was heritable (h in EA = 0.60, AA = 0.37). The AA GWAS identified three regions with genome-wide significant (GWS; P < 5E-08) single nucleotide polymorphisms (SNPs) on chromosomes 3 (rs34066662, rs58801820) and 13 (rs75168521, rs78886294), and an insertion-deletion on chromosome 5 (chr5:141988181). No polymorphisms reached GWS in the EA. One GWS region (chromosome 1: rs1890881) emerged from a trans-ancestral meta-analysis (EA + AA) of ANYDEP, and was attributable to alcohol dependence in both samples. Four genes (AA: CRKL, DZIP3, SBK3; EA: P2RX6) and four sets of genes were significantly enriched within biological pathways for hemostasis and signal transduction. GWS signals did not replicate in two independent samples but there was weak evidence for association between rs1890881 and alcohol intake in the UK Biobank. Among 118 AA and 481 EA individuals from the Duke Neurogenetics Study, rs75168521 and rs1890881 genotypes were associated with variability in reward-related ventral striatum activation. This study identified novel loci for substance dependence and provides preliminary evidence that these variants are also associated with individual differences in neural reward reactivity. Gene discovery efforts in non-European samples with distinct patterns of substance use may lead to the identification of novel ancestry-specific genetic markers of risk.
Manhattan plots for the (PANEL a) European-American (EA), (Panel b) African-American (AA), and (PANEL c) trans-ancestral (EA+AA).
Regional association plots for genome-wide significant loci on (a) chromosome 3 and (b) chromosome 13 in analyses of AA families
Regional association plots for genome-wide significant loci on chromosome 1 in the trans-ancestral (EA+AA) analysis shown with (a) LD based on CEU population, and (b) LD based on AFR population
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| Citation | PMID | DOI | Status |
|---|---|---|---|
| AgrawalA, ChouYL, CareyC, Genomewide association study identifies a novel locus for cannabis dependence. Mol Psychiatry. 2016; 21(12): 1656–1669.10.1038/mp.2017.200PMC593813829112194 | — | — | — |
| AlmasyL, BlangeroJ. Multipoint quantitative-trait linkage analysis in general pedigrees. American journal of human genetics. 1998;62(5):1198–1211.954541410.1086/301844PMC1377101 | — | — | — |
| AndrewsMM, MedaSA, ThomasAD, Individuals family history positive for alcoholism show functional magnetic resonance imaging differences in reward sensitivity that are related to impulsivity factors. Biological psychiatry. 2011;69(7):675–683.2112673510.1016/j.biopsych.2010.09.049PMC3677031 | — | — | — |
| Association AP. Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision (DSM-IV-TR) American Psychiatric Association; 2000, Washington, DC. | — | — | — |
| BarbeiraAN, DickinsonSP, BonazzolaR, Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nature communications. 2018;9(1):1825.10.1038/s41467-018-03621-1PMC594082529739930 | — | — | — |
| BarrettJC, FryB, MallerJ, DalyMJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics (Oxford, England). 2005;21(2):263–265.10.1093/bioinformatics/bth45715297300 | — | — | — |
| BattleA, BrownCD, EngelhardtBE, MontgomerySB. Genetic effects on gene expression across human tissues. Nature. 2017;550(7675):204–213.2902259710.1038/nature24277PMC5776756 | — | — | — |
| BiernackaJM, GeskeJ, JenkinsGD, Genome-wide gene-set analysis for identification of pathways associated with alcohol dependence. The international journal of neuropsychopharmacology. 2013;16(2):271–278.2271704710.1017/S1461145712000375PMC3854955 | — | — | — |
| BierutLJ, AgrawalA, BucholzKK, A genome-wide association study of alcohol dependence. Proceedings of the National Academy of Sciences of the United States of America. 2010;107(11):5082–5087.2020292310.1073/pnas.0911109107PMC2841942 | — | — | — |
| BierutLJ, DinwiddieSH, BegleiterH, Familial transmission of substance dependence: alcohol, marijuana, cocaine, and habitual smoking: a report from the Collaborative Study on the Genetics of Alcoholism. Archives of general psychiatry. 1998;55(11):982–988.981906610.1001/archpsyc.55.11.982 | — | — | — |
| BlumK, BravermanER, HolderJM, Reward deficiency syndrome: a biogenetic model for the diagnosis and treatment of impulsive, addictive, and compulsive behaviors. Journal of psychoactive drugs. 2000;32 Suppl:i-iv, 1–112.10.1080/02791072.2000.1073609911280926 | — | — | — |
| BoyleAP, HongEL, HariharanM, Annotation of functional variation in personal genomes using RegulomeDB. Genome research. 2012;22(9):1790–1797.2295598910.1101/gr.137323.112PMC3431494 | — | — | — |
| BucholzKK, CadoretR, CloningerCR, A new, semi-structured psychiatric interview for use in genetic linkage studies: a report on the reliability of the SSAGA. Journal of studies on alcohol. 1994;55(2):149–158.818973510.15288/jsa.1994.55.149 | — | — | — |
| Bulik-SullivanBK, LohPR, FinucaneHK, LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47(3):291–295.2564263010.1038/ng.3211PMC4495769 | — | — | — |
| BüchelC, PetersJ, BanaschewskiT, Blunted ventral striatal responses to anticipated rewards foreshadow problematic drug use in novelty-seeking adolescents. Nature communications. 2017;8:14140.10.1038/ncomms14140PMC532176228221370 | — | — | — |
| Canela-XandriO, RawlikK, TenesaA. An atlas of genetic associations in UK Biobank. Nature genetics. 2018;50(11):1593–1599.3034911810.1038/s41588-018-0248-zPMC6707814 | — | — | — |
| CareyCE, KnodtAR, ConleyED, HaririAR, BogdanR. Reward-related ventral striatum activity links polygenic risk for attention-deficit/hyperactivity disorder to problematic alcohol use in young adulthood. Biological psychiatry Cognitive neuroscience and neuroimaging. 2017;2(2):180–187.2882504810.1016/j.bpsc.2016.10.003PMC5560771 | — | — | — |
| ChenMH, YangQ. GWAF: an R package for genome-wide association analyses with family data. Bioinformatics (Oxford, England). 2010;26(4):580–581.10.1093/bioinformatics/btp710PMC285221920040588 | — | — | — |
| DasS, ForerL, SchonherrS, Next-generation genotype imputation service and methods. Nature genetics. 2016;48(10):1284–1287.2757126310.1038/ng.3656PMC5157836 | — | — | — |
| DegenhardtL, HallW. Extent of illicit drug use and dependence, and their contribution to the global burden of disease. Lancet (London, England). 2012;379(9810):55–70.10.1016/S0140-6736(11)61138-022225671 | — | — | — |
| DelaneauO, HowieB, CoxAJ, ZaguryJF, MarchiniJ. Haplotype estimation using sequencing reads. American journal of human genetics. 2013;93(4):687–696.2409474510.1016/j.ajhg.2013.09.002PMC3791270 | — | — | — |
| DelgadoMR, MillerMM, InatiS, PhelpsEA. An fMRI study of reward-related probability learning. NeuroImage. 2005;24(3):862–873.1565232110.1016/j.neuroimage.2004.10.002 | — | — | — |
| DickDM, BarrP, GuyM, NasimA, ScottD. (Invited review) genetic research on alcohol use outcomes in African American populations: A review of the literature, associated challenges, and implications. The American journal on addictions / American Academy of Psychiatrists in Alcoholism and Addictions. 2017.10.1111/ajad.12495PMC588410228240821 | — | — | — |
| DuncanLE, RatanatharathornA, AielloAE, Largest GWAS of PTSD (N=20 070) yields genetic overlap with schizophrenia and sex differences in heritability. Molecular psychiatry. 2018;23(3):666–673.2843910110.1038/mp.2017.77PMC5696105 | — | — | — |
| EdenbergHJ, KollerDL, XueiX, Genome-wide association study of alcohol dependence implicates a region on chromosome 11. Alcoholism, clinical and experimental research. 2010;34(5):840–852.10.1111/j.1530-0277.2010.01156.xPMC288407320201924 | — | — | — |
| EdenbergHJ. The collaborative study on the genetics of alcoholism: an update. Alcohol research & health : the journal of the National Institute on Alcohol Abuse and Alcoholism. 2002;26(3):214–218.12875050PMC6683843 | — | — | — |
| FalkD, YiHY, Hiller-SturmhofelS. An epidemiologic analysis of co-occurring alcohol and drug use and disorders: findings from the National Epidemiologic Survey of Alcohol and Related Conditions (NESARC). Alcohol research & health : the journal of the National Institute on Alcohol Abuse and Alcoholism. 2008;31(2):100–110.23584812PMC3860461 | — | — | — |
| GamazonER, SegreAV, van de BuntM, Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nature genetics. 2018;50(7):956–967.2995518010.1038/s41588-018-0154-4PMC6248311 | — | — | — |
| GelernterJ, KranzlerHR, ShervaR, Genome-wide association study of alcohol dependence:significant findings in African- and European-Americans including novel risk loci. Molecular psychiatry. 2014;19(1):41–49.2416640910.1038/mp.2013.145PMC4165335 | — | — | — |
| GelernterJ, KranzlerHR, ShervaR, Genome-wide association study of nicotine dependence in American populations: identification of novel risk loci in both African-Americans and European-Americans. Biological psychiatry. 2015;77(5):493–503.2555548210.1016/j.biopsych.2014.08.025PMC4386684 | — | — | — |
| GelernterJ, ShervaR, KoestererR, Genome-wide association study of cocaine dependence and related traits: FAM53B identified as a risk gene. Molecular psychiatry. 2014;19(6):717–723.2395896210.1038/mp.2013.99PMC3865158 | — | — | — |
| GrantBF, SahaTD, RuanWJ, Epidemiology of DSM-5 Drug Use Disorder: Results From the National Epidemiologic Survey on Alcohol and Related Conditions-III. JAMA psychiatry. 2016;73(1):39–47.2658013610.1001/jamapsychiatry.2015.2132PMC5062605 | — | — | — |
| GruczaRA, BucholzKK, RiceJP, BierutLJ. Secular trends in the lifetime prevalence of alcohol dependence in the United States: a re-evaluation. Alcoholism, clinical and experimental research. 2008;32(5):763–770.10.1111/j.1530-0277.2008.00635.xPMC244310118336633 | — | — | — |
| HancockDB, ReginssonGW, GaddisNC, Genome-wide meta-analysis reveals common splice site acceptor variant in CHRNA4 associated with nicotine dependence. Transl Psychiatry. 2015;5:e651. doi: 10.1038/tp.2015.149.:e651.26440539PMC4930126 | — | — | — |
| HanS, YangBZ, KranzlerHR, Integrating GWASs and human protein interaction networks identifies a gene subnetwork underlying alcohol dependence. American journal of human genetics. 2013;93(6):1027–1034.2426866010.1016/j.ajhg.2013.10.021PMC3853414 | — | — | — |
| HaririAR, BrownSM, WilliamsonDE, FloryJD, de WitH, ManuckSB. Preference for immediate over delayed rewards is associated with magnitude of ventral striatal activity. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2006;26(51):13213–13217.1718277110.1523/JNEUROSCI.3446-06.2006PMC6675016 | — | — | — |
| HasinDS, StinsonFS, OgburnE, GrantBF. Prevalence, correlates, disability, and comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions10.1001/archpsyc.64.7.83017606817 | — | — | — |
| HeitzegMM, CopeLM, MartzME, HardeeJE. Neuroimaging Risk Markers for Substance Abuse: Recent Findings on Inhibitory Control and Reward System Functioning. Curr Addict Rep. 2015;2(2):91–103.2623657510.1007/s40429-015-0048-9PMC4520315 | — | — | — |
| HesselbrockM, EastonC, BucholzKK, SchuckitM, HesselbrockV. A validity study of the SSAGA--a comparison with the SCAN. Addiction (Abingdon, England). 1999;94(9):1361–1370.10.1046/j.1360-0443.1999.94913618.x10615721 | — | — | — |
| Johnson JAG. GAS Power Calculator: web-based power calculator for genetic association studies. https://wwwbiorxivorg/content/101101/164343v1. 2017. | — | — | — |
| KanaiM, TanakaT, OkadaY. Empirical estimation of genome-wide significance thresholds based on the 1000 Genomes Project data set. Journal of human genetics. 2016;61(10):861–866.2730598110.1038/jhg.2016.72PMC5090169 | — | — | — |
| KendlerKS, JacobsonKC, PrescottCA, NealeMC. Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. Am J Psychiatry. 2003;160(4):687–695.1266835710.1176/appi.ajp.160.4.687 | — | — | — |
| KendlerKS, MyersJ, PrescottCA. Specificity of genetic and environmental risk factors for symptoms of cannabis, cocaine, alcohol, caffeine, and nicotine dependence10.1001/archpsyc.64.11.131317984400 | — | — | — |
| KoobGF, VolkowND. Neurobiology of addiction: a neurocircuitry analysis. Lancet Psychiatry. 2016;3(8):760–773.2747576910.1016/S2215-0366(16)00104-8PMC6135092 | — | — | — |
| Lopez-QuinteroC, Perez de losCobos J, HasinDS, Probability and predictors of transition from first use to dependence on nicotine, alcohol, cannabis, and cocaine: results of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Drug and alcohol dependence. 2011;115(1–2):120–130.2114517810.1016/j.drugalcdep.2010.11.004PMC3069146 | — | — | — |
| LuczakSE, KhoddamR, YuS, WallTL, SchwartzA, SussmanS. A review of the prevalence and co-occurrence of addictions in US ethnic/racial groups: Implications for genetic research. The American Journal on Addictions. 2017; 26(5):424–436.2775994410.1111/ajad.12464PMC5397372 | — | — | — |
| Marquez-LunaC, LohPR, South Asian Type 2 Diabetes C, Consortium STD, Price AL. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet Epidemiol. 2017;41(8):811–823.2911033010.1002/gepi.22083PMC5726434 | — | — | — |
| Martin-SoelchC, SzczepanikJ, NugentA, Lateralization and gender differences in the dopaminergic response to unpredictable reward in the human ventral striatum. The European journal of neuroscience. 2011;33(9):1706–1715.2145342310.1111/j.1460-9568.2011.07642.xPMC3086965 | — | — | — |
| MartinAR, KanaiM, KamataniY., OkadaY., NealeBM., DalyM. Hidden ‘risk” in polygenic scores: clinical use today could exacerbate health disparities. Nat Genet. 2019; 51(4):584–591.3092696610.1038/s41588-019-0379-xPMC6563838 | — | — | — |
| MerikangasKR, StolarM, StevensDE, Familial transmission of substance use disorders. Archives of general psychiatry. 1998;55(11):973–979.981906510.1001/archpsyc.55.11.973 | — | — | — |
| MeyersJL, ZhangJ, WangJC, An endophenotype approach to the genetics of alcohol dependence: a genome wide association study of fast beta EEG in families of African ancestry. Molecular psychiatry. 2017.10.1038/mp.2016.239PMC550379428070124 | — | — | — |
| MuthenLK, MuthenBO. Mplus User’s Guide. Los Angeles CA: Muthen & Muthen; 1998-2011. | — | — | — |
| NeedAC, GoldsteinDB. Next generation disparities in human genomics: concerns and remedies. Trends Genet. 2009;25(11):489–494.1983685310.1016/j.tig.2009.09.012 | — | — | — |
| NelsonEC, AgrawalA, HeathAC, Evidence of CNIH3 involvement in opioid dependence. Mol Psychiatry. 2016; 21(5):608–614.2623928910.1038/mp.2015.102PMC4740268 | — | — | — |
| NikolovaYS, FerrellRE, ManuckSB, HaririAR. Multilocus genetic profile for dopamine signaling predicts ventral striatum reactivity. Neuropsychopharmacology. 2011;36(9):1940–1947.2159373310.1038/npp.2011.82PMC3154113 | — | — | — |
| NikolovaYS, KnodtAR, RadtkeSR, HaririAR. Divergent responses of the amygdala and ventral striatum predict stress-related problem drinking in young adults: possible differential markers of affective and impulsive pathways of risk for alcohol use disorder. Molecular psychiatry. 2016;21(3):348–356.2612258410.1038/mp.2015.85PMC4696925 | — | — | — |
| O’ConnellJR, WeeksDE. PedCheck: a program for identification of genotype incompatibilities in linkage analysis. American journal of human genetics. 1998;63(1):259–266.963450510.1086/301904PMC1377228 | — | — | — |
| PasmanJA, VerweijKJH, GerringZ, GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia. Nat Neurosci. 2018;21(9):1161–1170.3015066310.1038/s41593-018-0206-1PMC6386176 | — | — | — |
| PasmanJA, VerweijKJH, GerringZ, GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia. Nature neuroscience. 2018;21(9):1161–1170.3015066310.1038/s41593-018-0206-1PMC6386176 | — | — | — |
| PeacockA, LeungJ, LarneyS, Global statistics on alcohol, tobacco and illicit drug use: 2017 status report. Addiction (Abingdon, England) 2018;113(10):1905–1926.10.1111/add.1423429749059 | — | — | — |
| PizzagalliDA, HolmesAJ, DillonDG, Reduced caudate and nucleus accumbens response to rewards in unmedicated individuals with major depressive disorder. The American journal of psychiatry. 2009;166(6):702–710.1941136810.1176/appi.ajp.2008.08081201PMC2735451 | — | — | — |
| ReichT, EdenbergHJ, GoateA, Genome-wide search for genes affecting the risk for alcohol dependence. Am J Med Genet. 1998;81(3):207–215.9603606 | — | — | — |
| RentzschP, WittenD, CooperGM, ShendureJ, KircherM. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic acids research. 2019; 8(47):D886–D894.10.1093/nar/gky1016PMC632389230371827 | — | — | — |
| SartorCE, AgrawalA, LynskeyMT, Cannabis or alcohol first? Differences by ethnicity and in risk for rapid progression to cannabis-related problems in women. Psychological medicine. 2013;43(4):813–823.2280487710.1017/S0033291712001493PMC3697079 | — | — | — |
| SchuckitMA. Low level of response to alcohol as a predictor of future alcoholism. The American journal of psychiatry. 1994;151(2):184–189.829688610.1176/ajp.151.2.184 | — | — | — |
| ShervaR, WangQ, KranzlerH, Genome-wide Association Study of Cannabis Dependence Severity, Novel Risk Variants, and Shared Genetic Risks. JAMA Psychiatry. 2016:10:472–480.10.1001/jamapsychiatry.2016.0036PMC497481727028160 | — | — | — |
| ShervaR, WangQ, KranzlerH, Genome-wide Association Study of Cannabis Dependence Severity, Novel Risk Variants, and Shared Genetic Risks. JAMA psychiatry. 2016;73(5):472–480.2702816010.1001/jamapsychiatry.2016.0036PMC4974817 | — | — | — |
| SobotaRS, ShrinerD, KodamanN, Addressing population-specific multiple testing burdens in genetic association studies. Annals of human genetics. 2015;79(2):136–147.2564473610.1111/ahg.12095PMC4334751 | — | — | — |
| SpearLP. The adolescent brain and age-related behavioral manifestations. Neuroscience and biobehavioral reviews. 2000;24(4):417–463.1081784310.1016/s0149-7634(00)00014-2 | — | — | — |
| StanawayJD, AfshinA, GakidouE, Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet. 2018;392(10159):1923–1994.10.1016/S0140-6736(18)32225-6PMC622775530496105 | — | — | — |
| TsuangMT, BarJL, HarleyRM, LyonsMJ. The Harvard Twin Study of Substance Abuse: what we have learned. Harv Rev Psychiatry. 2001;9(6):267–279.11600486 | — | — | — |
| VerhulstB, NealeMC, KendlerKS. The heritability of alcohol use disorders: a meta-analysis of twin and adoption studies. Psychol Med. 2015;45(5):1061–1072.2517159610.1017/S0033291714002165PMC4345133 | — | — | — |
| VolkowND, WangG-J, FowlerJS, TomasiD, TelangF, BalerR. Addiction: decreased reward sensitivity and increased expectation sensitivity conspire to overwhelm the brain’s control circuit. BioEssays : news and reviews in molecular, cellular and developmental biology. 2010;32(9):748–755.10.1002/bies.201000042PMC294824520730946 | — | — | — |
| VriezeSI, FengS, MillerMB, Rare nonsynonymous exonic variants in addiction and behavioral disinhibition. Biol Psychiatry. 2014;75(10):783–789.2409450810.1016/j.biopsych.2013.08.027PMC3975816 | — | — | — |
| WaltersRK, PolimantiR, JohnsonEC, Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat Neurosci. 2018;21(12):1656–1669.3048294810.1038/s41593-018-0275-1PMC6430207 | — | — | — |
| WatanabeK, TaskesenE, van BochovenA, PosthumaD. Functional mapping and annotation of genetic associations with FUMA. Nature communications. 2017;8(1):1826.10.1038/s41467-017-01261-5PMC570569829184056 | — | — | — |
| WetherillL, AgrawalA, KapoorM, Association of substance dependence phenotypes in the COGA sample. Addiction biology. 2015;20(3):617–627.2483286310.1111/adb.12153PMC4233207 | — | — | — |
| WetherillL, KapoorM, AgrawalA, Family-based association analysis of alcohol dependence criteria and severity. Alcoholism, clinical and experimental research. 2014;38(2):354–366.10.1111/acer.12251PMC394679824015780 | — | — | — |
| WillerCJ, LiY, AbecasisGR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics (Oxford, England). 2010;26(17):2190–2191.10.1093/bioinformatics/btq340PMC292288720616382 | — | — | — |
| WuL-T, ZhuH, SwartzMS. Trends in cannabis use disorders among racial/ethnic population groups in the United States. Drug and alcohol dependence. 2016;165:181–190.2731704510.1016/j.drugalcdep.2016.06.002PMC4939114 | — | — | — |
| YangJ, LeeSH, GoddardME, VisscherPM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88(1):76–82.2116746810.1016/j.ajhg.2010.11.011PMC3014363 | — | — | — |
| ZhouH, PolimantiR, YangBZ, Genetic Risk Variants Associated With Comorbid Alcohol Dependence and Major Depression. JAMA psychiatry. 2017;74(12):1234–1241.2907134410.1001/jamapsychiatry.2017.3275PMC6331050 | — | — | — |
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