Skip to main content

Human Genetics of Addiction: New Insights and Future Directions

Current Psychiatry Reports volume 20, Article number: 8 (2018) Cite this article

Abstract

Purpose of Review

With the advent of the genome-wide association study (GWAS), our understanding of the genetics of addiction has made significant strides forward. Here, we summarize genetic loci containing variants identified at genome-wide statistical significance (P < 5 × 10−8) and independently replicated, review evidence of functional or regulatory effects for GWAS-identified variants, and outline multi-omics approaches to enhance discovery and characterize addiction loci.

Recent Findings

Replicable GWAS findings span 11 genetic loci for smoking, eight loci for alcohol, and two loci for illicit drugs combined and include missense functional variants and noncoding variants with regulatory effects in human brain tissues traditionally viewed as addiction-relevant (e.g., prefrontal cortex [PFC]) and, more recently, tissues often overlooked (e.g., cerebellum).

Summary

GWAS analyses have discovered several novel, replicable variants contributing to addiction. Using larger sample sizes from harmonized datasets and new approaches to integrate GWAS with multiple ‘omics data across human brain tissues holds great promise to significantly advance our understanding of the biology underlying addiction.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price includes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Center for Behavioral Health Statistics and Quality. Key substance use and mental health indicators in the United States: results from the 2015 National Survey on Drug Use and Health (HHS Publication No. SMA 16-4984, NSDUH Series H-51). 2016.

  2. Babb S, Malarcher A, Schauer G, Asman K, Jamal A. Quitting smoking among adults—United States, 2000–2015. MMWR Morb Mortal Wkly Rep. 2017;65(52):1457–64. https://doi.org/10.15585/mmwr.mm6552a1.

    PubMed  Article  Google Scholar 

  3. •• Koob GF, Volkow ND. Neurobiology of addiction: a neurocircuitry analysis. Lancet Psychiatry. 2016;3(8):760–73. https://doi.org/10.1016/S2215-0366(16)00104-8. This article from the Directors of NIAAA and NIDA, respectively, provides a comprehensive review, updated from their 2010 review in Neuropsychpharmacology (see reference 4), of the key brain tissues involved at each addiction stage and their neurocircuit connections.

    PubMed  Article  Google Scholar 

  4. Koob GF, Volkow ND. Neurocircuitry of addiction. Neuropsychopharmacology. 2010;35(1):217–38. https://doi.org/10.1038/npp.2009.110.

    PubMed  Article  Google Scholar 

  5. Agrawal A, Verweij KJ, Gillespie NA, Heath AC, Lessov-Schlaggar CN, Martin NG, et al. The genetics of addiction-a translational perspective. Transl Psychiatry. 2012;2(7):e140. https://doi.org/10.1038/tp.2012.54.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. Bierut LJ, Madden PA, Breslau N, Johnson EO, Hatsukami D, Pomerleau OF, et al. Novel genes identified in a high-density genome wide association study for nicotine dependence. Hum Mol Genet. 2007;16(1):24–35. https://doi.org/10.1093/hmg/ddl441.

    CAS  PubMed  Article  Google Scholar 

  7. Saccone SF, Hinrichs AL, Saccone NL, Chase GA, Konvicka K, Madden PA, et al. Cholinergic nicotinic receptor genes implicated in a nicotine dependence association study targeting 348 candidate genes with 3713 SNPs. Hum Mol Genet. 2007;16(1):36–49. https://doi.org/10.1093/hmg/ddl438.

    CAS  PubMed  Article  Google Scholar 

  8. • Patel YM, Stram DO, Wilkens LR, Park SS, Henderson BE, Le Marchand L, et al. The contribution of common genetic variation to nicotine and cotinine glucuronidation in multiple ethnic/racial populations. Cancer Epidemiol Biomark Prev. 2015;24(1):119–27. https://doi.org/10.1158/1055-9965.EPI-14-0815. This multi-ancestry GWAS meta-analysis of cotinine and nicotine glucuronidation (total N = 2,239) was the first to report genome-wide significant associations for the UGT2B10 locus.

    CAS  Article  Google Scholar 

  9. Ware JJ, Chen X, Vink J, Loukola A, Minica C, Pool R, et al. Genome-wide meta-analysis of cotinine levels in cigarette smokers identifies locus at 4q13.2. Sci Rep. 2016;6(1):20092. https://doi.org/10.1038/srep20092.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. Thorgeirsson TE, Gudbjartsson DF, Surakka I, Vink JM, Amin N, Geller F, et al. Sequence variants at CHRNB3-CHRNA6 and CYP2A6 affect smoking behavior. Nat Genet. 2010;42(5):448–53. https://doi.org/10.1038/ng.573.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. • Wain LV, Shrine N, Miller S, Jackson VE, Ntalla I, Artigas MS, 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. Lancet Respir Med. 2015;3(10):769–81. https://doi.org/10.1016/S2213-2600(15)00283-0. This UK Biobank study included a GWAS of heavy vs. never smoking ( N = 48,931) and identified NOL4L and four other novel genome-wide significant loci and extended associations of genome-wide significant loci— PDE1C , DBH , BDNF , and CHRNA4 —from other studies with related smoking phenotypes.

    PubMed  PubMed Central  Article  Google Scholar 

  12. Rice JP, Hartz SM, Agrawal A, Almasy L, Bennett S, Breslau N, et al. CHRNB3 is more strongly associated with Fagerstrom Test for Cigarette Dependence-based nicotine dependence than cigarettes per day: phenotype definition changes genome-wide association studies results. Addiction. 2012;107(11):2019–28. https://doi.org/10.1111/j.1360-0443.2012.03922.x.

    PubMed  PubMed Central  Article  Google Scholar 

  13. Tobacco and Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat Genet. 2010;42(5):441–7. https://doi.org/10.1038/ng.571.

    Article  CAS  Google Scholar 

  14. Siedlinski M, Cho MH, Bakke P, Gulsvik A, Lomas DA, Anderson W, et al. Genome-wide association study of smoking behaviours in patients with COPD. Thorax. 2011;66(10):894–902. https://doi.org/10.1136/thoraxjnl-2011-200154.

    PubMed  PubMed Central  Article  Google Scholar 

  15. David SP, Hamidovic A, Chen GK, Bergen AW, Wessel J, Kasberger JL, et al. Genome-wide meta-analyses of smoking behaviors in African Americans. Transl Psychiatry. 2012;2(5):e119. https://doi.org/10.1038/tp.2012.41.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. Yang J, Wang S, Yang Z, Hodgkinson CA, Iarikova P, Ma JZ, et al. The contribution of rare and common variants in 30 genes to risk nicotine dependence. Mol Psychiatry. 2015;20(11):1467–78. https://doi.org/10.1038/mp.2014.156.

    CAS  PubMed  Article  Google Scholar 

  17. • Hancock DB, Guo Y, Reginsson GW, Gaddis NC, Lutz SM, Sherva R et al. Genome-wide association study across European and African American ancestries identifies a SNP in DNMT3B contributing to nicotine dependence. Mol Psychiatry. https://doi.org/10.1038/mp.2017.193. This largest GWAS meta-analysis of nicotine dependence (total N = 38,602) identified DNMT3B at genome-wide significance and extended its association to heavy vs. never smoking ( N = 48,931 from the UK Biobank). The top SNP rs910083 was indiciated as a cis -meQTL SNP in fetal brain and a cis -eQTL SNP in adult cerebellum.

  18. Liu JZ, Tozzi F, Waterworth DM, Pillai SG, Muglia P, Middleton L, et al. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat Genet. 2010;42(5):436–40. https://doi.org/10.1038/ng.572.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. Gabrielsen ME, Romundstad P, Langhammer A, Krokan HE, Skorpen F. Association between a 15q25 gene variant, nicotine-related habits, lung cancer and COPD among 56,307 individuals from the HUNT study in Norway. Eur J Hum Genet. 2013;21(11):1293–9. https://doi.org/10.1038/ejhg.2013.26.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. Munafo MR, Timofeeva MN, Morris RW, Prieto-Merino D, Sattar N, Brennan P, et al. Association between genetic variants on chromosome 15q25 locus and objective measures of tobacco exposure. J Natl Cancer Inst. 2012;104(10):740–8. https://doi.org/10.1093/jnci/djs191.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. Richmond-Rakerd LS, Otto JM, Slutske WS, Ehlers CL, Wilhelmsen KC, Gizer IR. A novel tobacco use phenotype suggests the 15q25 and 19q13 loci may be differentially associated with cigarettes per day and tobacco-related problems. Nicotine Tob Res. 2017;19(4):426–34. https://doi.org/10.1093/ntr/ntw260.

    PubMed  Google Scholar 

  22. Chen X, Chen J, Williamson VS, An SS, Hettema JM, Aggen SH, et al. Variants in nicotinic acetylcholine receptors alpha5 and alpha3 increase risks to nicotine dependence. Am J Med Genet B Neuropsychiatr Genet. 2009;150B(7):926–33. https://doi.org/10.1002/ajmg.b.30919.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. Saccone NL, Emery LS, Sofer T, Gogarten SM, Becker DM, Bottinger EP, et al. Genome-wide association study of heavy smoking and daily/nondaily smoking in the Hispanic Community Health Study / Study of Latinos (HCHS/SOL). Nicotine Tob Res. https://doi.org/10.1093/ntr/ntx107.

  24. Chen LS, Saccone NL, Culverhouse RC, Bracci PM, Chen CH, Dueker N, et al. Smoking and genetic risk variation across populations of European, Asian, and African American ancestry—a meta-analysis of chromosome 15q25. Genet Epidemiol. 2012;36(4):340–51. https://doi.org/10.1002/gepi.21627.

    PubMed  PubMed Central  Article  Google Scholar 

  25. Buczkowski K, Sieminska A, Linkowska K, Czachowski S, Przybylski G, Jassem E, et al. Association between genetic variants on chromosome 15q25 locus and several nicotine dependence traits in Polish population: a case-control study. Biomed Res Int. 2015;2015:350348. https://doi.org/10.1155/2015/350348.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  26. Chen LS, Hung RJ, Baker T, Horton A, Culverhouse R, Saccone N, et al. CHRNA5 risk variant predicts delayed smoking cessation and earlier lung cancer diagnosis-a meta-analysis. J Natl Cancer Inst. 2015;107(5) https://doi.org/10.1093/jnci/djv100.

  27. Bloom AJ, Hartz SM, Baker TB, Chen LS, Piper ME, Fox L, et al. Beyond cigarettes per day. A genome-wide association study of the biomarker carbon monoxide. Ann Am Thorac Soc. 2014;11(7):1003–10. https://doi.org/10.1513/AnnalsATS.201401-010OC.

    PubMed  PubMed Central  Article  Google Scholar 

  28. Thorgeirsson TE, Geller F, Sulem P, Rafnar T, Wiste A, Magnusson KP, et al. A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature. 2008;452(7187):638–42. https://doi.org/10.1038/nature06846.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. Caporaso N, Gu F, Chatterjee N, Sheng-Chih J, Yu K, Yeager M, et al. Genome-wide and candidate gene association study of cigarette smoking behaviors. PLoS One. 2009;4(2):e4653. https://doi.org/10.1371/journal.pone.0004653.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  30. Kita-Milczarska K, Sieminska A, Jassem E. Association between CHRNA3 and CHRNA5 nicotine receptor subunit gene variants and nicotine dependence in an isolated population of Kashubians in Poland. Med Sci Monit. 2016;22:1442–50. https://doi.org/10.12659/MSM.895907.

    PubMed  PubMed Central  Article  Google Scholar 

  31. Sorice R, Bione S, Sansanelli S, Ulivi S, Athanasakis E, Lanzara C, et al. Association of a variant in the CHRNA5-A3-B4 gene cluster region to heavy smoking in the Italian population. Eur J Hum Genet. 2011;19(5):593–6. https://doi.org/10.1038/ejhg.2010.240.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. Ware JJ, Aveyard P, Broderick P, Houlston RS, Eisen T, Munafo MR. The association of rs1051730 genotype on adherence to and consumption of prescribed nicotine replacement therapy dose during a smoking cessation attempt. Drug Alcohol Depend. 2015;151:236–40. https://doi.org/10.1016/j.drugalcdep.2015.03.035.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Broms U, Wedenoja J, Largeau MR, Korhonen T, Pitkaniemi J, Keskitalo-Vuokko K, et al. Analysis of detailed phenotype profiles reveals CHRNA5-CHRNA3-CHRNB4 gene cluster association with several nicotine dependence traits. Nicotine Tob Res. 2012;14(6):720–33. https://doi.org/10.1093/ntr/ntr283.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. Zhu AZ, Zhou Q, Cox LS, David SP, Ahluwalia JS, Benowitz NL, et al. Association of CHRNA5-A3-B4 SNP rs2036527 with smoking cessation therapy response in African-American smokers. Clin Pharmacol Ther. 2014;96(2):256–65. https://doi.org/10.1038/clpt.2014.88.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. • Hancock DB, Reginsson GW, Gaddis NC, Chen X, Saccone NL, Lutz SM, et al. Genome-wide meta-analysis reveals common splice site acceptor variant in CHRNA4 associated with nicotine dependence. Transl Psychiatry. 2015;5(10):e651. https://doi.org/10.1038/tp.2015.149. This GWAS meta-analysis was the first to identify CHRNA4 as a genome-wide significant locus for nicotine dependence (total N = 17,074 for discovery and 7,469 for replication). The top SNP rs2273500 was indicated as a splice site acceptor SNP and a cis -eQTL SNP for CHRNA4 in postmortem human intralobular white matter.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Bloom AJ, Baker TB, Chen LS, Breslau N, Hatsukami D, Bierut LJ, et al. Variants in two adjacent genes, EGLN2 and CYP2A6, influence smoking behavior related to disease risk via different mechanisms. Hum Mol Genet. 2014;23(2):555–61. https://doi.org/10.1093/hmg/ddt432.

    CAS  PubMed  Article  Google Scholar 

  37. Timofeeva MN, McKay JD, Smith GD, Johansson M, Byrnes GB, Chabrier A, et al. Genetic polymorphisms in 15q25 and 19q13 loci, cotinine levels, and risk of lung cancer in EPIC. Cancer Epidemiol Biomark Prev. 2011;20(10):2250–61. https://doi.org/10.1158/1055-9965.EPI-11-0496.

    CAS  Article  Google Scholar 

  38. Kumasaka N, Aoki M, Okada Y, Takahashi A, Ozaki K, Mushiroda T, et al. Haplotypes with copy number and single nucleotide polymorphisms in CYP2A6 locus are associated with smoking quantity in a Japanese population. PLoS One. 2012;7(9):e44507. https://doi.org/10.1371/journal.pone.0044507.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. • Loukola A, Buchwald J, Gupta R, Palviainen T, Hallfors J, Tikkanen E, et al. A genome-wide association study of a biomarker of nicotine metabolism. PLoS Genet. 2015;11(9):e1005498. https://doi.org/10.1371/journal.pgen.1005498. This first GWAS of NMR was conducted using total N = 1,518 and identified genome-wide significant associations of chromosome 9q13, which contained three independent signals all located in the vicinity of CYP2A6 .

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  40. Baurley JW, Edlund CK, Pardamean CI, Conti DV, Krasnow R, Javitz HS, et al. Genome-wide association of the Laboratory-Based Nicotine Metabolite Ratio in three ancestries. Nicotine Tob Res. 2016;18(9):1837–44. https://doi.org/10.1093/ntr/ntw117.

    PubMed  PubMed Central  Article  Google Scholar 

  41. Machiela MJ, Chanock SJ. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics. 2015;31(21):3555–7. https://doi.org/10.1093/bioinformatics/btv402.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. Lind PA, Macgregor S, Vink JM, Pergadia ML, Hansell NK, de Moor MH, et al. A genomewide association study of nicotine and alcohol dependence in Australian and Dutch populations. Twin Res Hum Genet. 2010;13(1):10–29. https://doi.org/10.1017/S183242740002003X.

    PubMed  PubMed Central  Article  Google Scholar 

  43. McGue M, Zhang Y, Miller MB, Basu S, Vrieze S, Hicks B, et al. A genome-wide association study of behavioral disinhibition. Behav Genet. 2013;43(5):363–73. https://doi.org/10.1007/s10519-013-9606-x.

    PubMed  Article  Google Scholar 

  44. Loukola A, Wedenoja J, Keskitalo-Vuokko K, Broms U, Korhonen T, Ripatti S, et al. Genome-wide association study on detailed profiles of smoking behavior and nicotine dependence in a twin sample. Mol Psychiatry. 2014;19(5):615–24. https://doi.org/10.1038/mp.2013.72.

    CAS  PubMed  Article  Google Scholar 

  45. Gelernter J, Kranzler HR, Sherva R, Almasy L, Herman AI, Koesterer R, et al. Genome-wide association study of nicotine dependence in American populations: identification of novel risk loci in both African-Americans and European-Americans. Biol Psychiatry. 2015;77(5):493–503. https://doi.org/10.1016/j.biopsych.2014.08.025.

    CAS  PubMed  Article  Google Scholar 

  46. Begum F, Ruczinski I, Hokanson JE, Lutz SM, Parker MM, Cho MH, et al. Hemizygous deletion on chromosome 3p26.1 is associated with heavy smoking among African American subjects in the COPDGene Study. PLoS One. 2016;11(10):e0164134. https://doi.org/10.1371/journal.pone.0164134.

    PubMed  PubMed Central  Article  Google Scholar 

  47. Yin X, Bizon C, Tilson J, Lin Y, Gizer IR, Ehlers CL, et al. Genome-wide meta-analysis identifies a novel susceptibility signal at CACNA2D3 for nicotine dependence. Am J Med Genet B Neuropsychiatr Genet. 2017;174(5):557–67. https://doi.org/10.1002/ajmg.b.32540.

    CAS  Article  Google Scholar 

  48. Jensen KP, Smith AH, Herman AI, Farrer LA, Kranzler HR, Sofuoglu M, et al. A protocadherin gene cluster regulatory variant is associated with nicotine withdrawal and the urge to smoke. Mol Psychiatry. 2017;22(2):242–9. https://doi.org/10.1038/mp.2016.43.

    CAS  PubMed  Article  Google Scholar 

  49. PhenX Toolkit [database on the Internet]. Available from: https://www.phenxtoolkit.org/index.php?pageLink=browse.protocols&filter=1&id=031000. Accessed: October 23, 2017.

  50. McKay JD, Hung RJ, Han Y, Zong X, Carreras-Torres R, Christiani DC, et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat Genet. 2017;49(7):1126–32. https://doi.org/10.1038/ng.3892.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. Lutz SM, Cho MH, Young K, Hersh CP, Castaldi PJ, McDonald ML, et al. A genome-wide association study identifies risk loci for spirometric measures among smokers of European and African ancestry. BMC Genet. 2015;16(1):138. https://doi.org/10.1186/s12863-015-0299-4.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. Wilk JB, Shrine NR, Loehr LR, Zhao JH, Manichaikul A, Lopez LM, et al. Genome wide association studies identify CHRNA5/3 and HTR4 in the development of airflow obstruction. Am J Respir Crit Care Med. 2012;186(7):622–32. https://doi.org/10.1164/rccm.201202-0366OC.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. Hobbs BD, de Jong K, Lamontagne M, Bosse Y, Shrine N, Artigas MS, et al. Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis. Nat Genet. 2017;49(3):426–32. https://doi.org/10.1038/ng.3752.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. Castaldi PJ, Cho MH, San Jose Estepar R, ML MD, Laird N, Beaty TH, et al. Genome-wide association identifies regulatory loci associated with distinct local histogram emphysema patterns. Am J Respir Crit Care Med. 2014;190(4):399–409. https://doi.org/10.1164/rccm.201403-0569OC.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. Olfson E, Saccone NL, Johnson EO, Chen LS, Culverhouse R, Doheny K, et al. Rare, low frequency and common coding variants in CHRNA5 and their contribution to nicotine dependence in European and African Americans. Mol Psychiatry. 2016;21(5):601–7. https://doi.org/10.1038/mp.2015.105.

    CAS  PubMed  Article  Google Scholar 

  56. Wessel J, McDonald SM, Hinds DA, Stokowski RP, Javitz HS, Kennemer M, et al. Resequencing of nicotinic acetylcholine receptor genes and association of common and rare variants with the Fagerstrom test for nicotine dependence. Neuropsychopharmacology. 2010;35(12):2392–402. https://doi.org/10.1038/npp.2010.120.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. Haller G, Druley T, Vallania FL, Mitra RD, Li P, Akk G, et al. Rare missense variants in CHRNB4 are associated with reduced risk of nicotine dependence. Hum Mol Genet. 2012;21(3):647–55. https://doi.org/10.1093/hmg/ddr498.

    CAS  PubMed  Article  Google Scholar 

  58. Doyle GA, Chou AD, Saung WT, Lai AT, Lohoff FW, Berrettini WH. Identification of CHRNA5 rare variants in African-American heavy smokers. Psychiatr Genet. 2014;24(3):102–9. https://doi.org/10.1097/YPG.0000000000000029.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. Thorgeirsson TE, Steinberg S, Reginsson GW, Bjornsdottir G, Rafnar T, Jonsdottir I, et al. A rare missense mutation in CHRNA4 associates with smoking behavior and its consequences. Mol Psychiatry. 2016;21(5):594–600. https://doi.org/10.1038/mp.2016.13.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. McClure-Begley TD, Papke RL, Stone KL, Stokes C, Levy AD, Gelernter J, et al. Rare human nicotinic acetylcholine receptor alpha4 subunit (CHRNA4) variants affect expression and function of high-affinity nicotinic acetylcholine receptors. J Pharmacol Exp Ther. 2014;348(3):410–20. https://doi.org/10.1124/jpet.113.209767.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. Bierut LJ, Stitzel JA, Wang JC, Hinrichs AL, Grucza RA, Xuei X, et al. Variants in nicotinic receptors and risk for nicotine dependence. Am J Psychiatry. 2008;165(9):1163–71. https://doi.org/10.1176/appi.ajp.2008.07111711.

    PubMed  PubMed Central  Article  Google Scholar 

  62. Chen LS, Baker TB, Piper ME, Breslau N, Cannon DS, Doheny KF, et al. Interplay of genetic risk factors (CHRNA5-CHRNA3-CHRNB4) and cessation treatments in smoking cessation success. Am J Psychiatry. 2012;169(7):735–42. https://doi.org/10.1176/appi.ajp.2012.11101545.

    PubMed  PubMed Central  Article  Google Scholar 

  63. Wang JC, Cruchaga C, Saccone NL, Bertelsen S, Liu P, Budde JP, et al. Risk for nicotine dependence and lung cancer is conferred by mRNA expression levels and amino acid change in CHRNA5. Hum Mol Genet. 2009;18(16):3125–35. https://doi.org/10.1093/hmg/ddp231.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. Wang JC, Spiegel N, Bertelsen S, Le N, McKenna N, Budde JP, et al. Cis-regulatory variants affect CHRNA5 mRNA expression in populations of African and European ancestry. PLoS One. 2013;8(11):e80204. https://doi.org/10.1371/journal.pone.0080204.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  65. Hancock DB, Wang JC, Gaddis NC, Levy JL, Saccone NL, Stitzel JA, et al. A multiancestry study identifies novel genetic associations with CHRNA5 methylation in human brain and risk of nicotine dependence. Hum Mol Genet. 2015;24(20):5940–4. https://doi.org/10.1093/hmg/ddv303.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. Gallego X, Molas S, Amador-Arjona A, Marks MJ, Robles N, Murtra P, et al. Overexpression of the CHRNA5/A3/B4 genomic cluster in mice increases the sensitivity to nicotine and modifies its reinforcing effects. Amino Acids. 2012;43(2):897–909. https://doi.org/10.1007/s00726-011-1149-y.

    CAS  PubMed  Article  Google Scholar 

  67. Picciotto MR, Kenny PJ. Molecular mechanisms underlying behaviors related to nicotine addiction. Cold Spring Harb Perspect Med. 2013;3(1):a012112. https://doi.org/10.1101/cshperspect.a012112.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  68. Wilking JA, Stitzel JA. Natural genetic variability of the neuronal nicotinic acetylcholine receptor subunit genes in mice: consequences and confounds. Neuropharmacology. 2015;96(Pt B):205–12. https://doi.org/10.1016/j.neuropharm.2014.11.022.

    CAS  PubMed  Article  Google Scholar 

  69. Goldstein RZ, Volkow ND. Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat Rev Neurosci. 2011;12(11):652–69. https://doi.org/10.1038/nrn3119.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. Miquel M, Vazquez-Sanroman D, Carbo-Gas M, Gil-Miravet I, Sanchis-Segura C, Carulli D, et al. Have we been ignoring the elephant in the room? Seven arguments for considering the cerebellum as part of addiction circuitry. Neurosci Biobehav Rev. 2016;60:1–11. https://doi.org/10.1016/j.neubiorev.2015.11.005.

    PubMed  Article  Google Scholar 

  71. Moulton EA, Elman I, Becerra LR, Goldstein RZ, Borsook D. The cerebellum and addiction: insights gained from neuroimaging research. Addict Biol. 2014;19(3):317–31. https://doi.org/10.1111/adb.12101.

    PubMed  PubMed Central  Article  Google Scholar 

  72. Strick PL, Dum RP, Fiez JA. Cerebellum and nonmotor function. Annu Rev Neurosci. 2009;32(1):413–34. https://doi.org/10.1146/annurev.neuro.31.060407.125606.

    CAS  PubMed  Article  Google Scholar 

  73. • Clarke TK, Adams MJ, Davies G, Howard DM, Hall LS, Padmanabhan S, et al. Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N=112 117). Mol Psychiatry. 2017;22(10):1376–84. https://doi.org/10.1038/mp.2017.153. This largest ever GWAS for any alcohol phenotype reported genome-wide significant loci at more than one independent association signal at two previously implicated loci, ADH1B-ADH1C-ADH5 and KLB , and at three novel loci, GCKR , CADM2 , and FAM69C .

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. Treutlein J, Cichon S, Ridinger M, Wodarz N, Soyka M, Zill P, et al. Genome-wide association study of alcohol dependence. Arch Gen Psychiatry. 2009;66(7):773–84. https://doi.org/10.1001/archgenpsychiatry.2009.83.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. Schumann G, Coin LJ, Lourdusamy A, Charoen P, Berger KH, Stacey D, et al. Genome-wide association and genetic functional studies identify autism susceptibility candidate 2 gene (AUTS2) in the regulation of alcohol consumption. Proc Natl Acad Sci U S A. 2011;108(17):7119–24. https://doi.org/10.1073/pnas.1017288108.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. Wang KS, Liu X, Zhang Q, Pan Y, Aragam N, Zeng M. A meta-analysis of two genome-wide association studies identifies 3 new loci for alcohol dependence. J Psychiatr Res. 2011;45(11):1419–25. https://doi.org/10.1016/j.jpsychires.2011.06.005.

    PubMed  Article  Google Scholar 

  77. Heath AC, Whitfield JB, Martin NG, Pergadia ML, Goate AM, Lind PA, et al. A quantitative-trait genome-wide association study of alcoholism risk in the community: findings and implications. Biol Psychiatry. 2011;70(6):513–8. https://doi.org/10.1016/j.biopsych.2011.02.028.

    PubMed  PubMed Central  Article  Google Scholar 

  78. Lydall GJ, Bass NJ, McQuillin A, Lawrence J, Anjorin A, Kandaswamy R, et al. Confirmation of prior evidence of genetic susceptibility to alcoholism in a genome-wide association study of comorbid alcoholism and bipolar disorder. Psychiatr Genet. 2011;21(6):294–306. https://doi.org/10.1097/YPG.0b013e32834915c2.

    PubMed  PubMed Central  Article  Google Scholar 

  79. Kutalik Z, Benyamin B, Bergmann S, Mooser V, Waeber G, Montgomery GW, et al. Genome-wide association study identifies two loci strongly affecting transferrin glycosylation. Hum Mol Genet. 2011;20(18):3710–7. https://doi.org/10.1093/hmg/ddr272.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  80. Frank J, Cichon S, Treutlein J, Ridinger M, Mattheisen M, Hoffmann P, et al. Genome-wide significant association between alcohol dependence and a variant in the ADH gene cluster. Addict Biol. 2012;17(1):171–80. https://doi.org/10.1111/j.1369-1600.2011.00395.x.

    CAS  PubMed  Article  Google Scholar 

  81. Wang KS, Liu X, Zhang Q, Wu LY, Zeng M. Genome-wide association study identifies 5q21 and 9p24.1 (KDM4C) loci associated with alcohol withdrawal symptoms. J Neural Transm (Vienna). 2012;119(4):425–33. https://doi.org/10.1007/s00702-011-0729-z.

    CAS  Article  Google Scholar 

  82. Edwards AC, Aliev F, Bierut LJ, Bucholz KK, Edenberg H, Hesselbrock V, et al. Genome-wide association study of comorbid depressive syndrome and alcohol dependence. Psychiatr Genet. 2012;22(1):31–41. https://doi.org/10.1097/YPG.0b013e32834acd07.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  83. Zuo L, Wang K, Zhang XY, Krystal JH, Li CS, Zhang F, et al. NKAIN1-SERINC2 is a functional, replicable and genome-wide significant risk gene region specific for alcohol dependence in subjects of European descent. Drug Alcohol Depend. 2013;129(3):254–64. https://doi.org/10.1016/j.drugalcdep.2013.02.006.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  84. Pan Y, Luo X, Liu X, Wu LY, Zhang Q, Wang L, et al. Genome-wide association studies of maximum number of drinks. J Psychiatr Res. 2013;47(11):1717–24. https://doi.org/10.1016/j.jpsychires.2013.07.013.

    PubMed  PubMed Central  Article  Google Scholar 

  85. Wetherill L, Kapoor M, Agrawal A, Bucholz K, Koller D, Bertelsen SE, et al. Family-based association analysis of alcohol dependence criteria and severity. Alcohol Clin Exp Res. 2014;38(2):354–66. https://doi.org/10.1111/acer.12251.

    CAS  PubMed  Article  Google Scholar 

  86. Mbarek H, Milaneschi Y, Fedko IO, Hottenga JJ, de Moor MH, Jansen R, et al. The genetics of alcohol dependence: twin and SNP-based heritability, and genome-wide association study based on AUDIT scores. Am J Med Genet B Neuropsychiatr Genet. 2015;168(8):739–48. https://doi.org/10.1002/ajmg.b.32379.

    CAS  PubMed  Article  Google Scholar 

  87. Adkins DE, Clark SL, Copeland WE, Kennedy M, Conway K, Angold A, et al. Genome-wide meta-analysis of longitudinal alcohol consumption across youth and early adulthood. Twin Res Hum Genet. 2015;18(4):335–47. https://doi.org/10.1017/thg.2015.36.

    PubMed  PubMed Central  Article  Google Scholar 

  88. • Schumann G, Liu C, O'Reilly P, Gao H, Song P, Xu B, et al. KLB is associated with alcohol drinking, and its gene product beta-Klotho is necessary for FGF21 regulation of alcohol preference. Proc Natl Acad Sci U S A. 2016;113(50):14372–7. https://doi.org/10.1073/pnas.1611243113. This GWAS meta-analysis was the first to report KLB as a genome-wide significant locus for daily alcohol intake (total N = 98,477).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  89. Lu S, Zhao LJ, Chen XD, Papasian CJ, Wu KH, Tan LJ, et al. Bivariate genome-wide association analyses identified genetic pleiotropic effects for bone mineral density and alcohol drinking in Caucasians. J Bone Miner Metab. 2016;35(6):649–58. https://doi.org/10.1007/s00774-016-0802-7.

    PubMed  PubMed Central  Article  Google Scholar 

  90. Chen XD, Xiong DH, Yang TL, Pei YF, Guo YF, Li J, et al. ANKRD7 and CYTL1 are novel risk genes for alcohol drinking behavior. Chin Med J. 2012;125(6):1127–34.

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Sanchez-Roige S, Fontanillas P, Elson SL, 23 and Me Research Team, Gray JC, de Wit H, et al. Genome-wide association study of alcohol use disorder identification test (AUDIT) scores in 20 328 research participants of European ancestry. Addict Biol. https://doi.org/10.1111/adb.12574.

  92. Bierut LJ, Agrawal A, Bucholz KK, Doheny KF, Laurie C, Pugh E, et al. A genome-wide association study of alcohol dependence. Proc Natl Acad Sci U S A. 2010;107(11):5082–7. https://doi.org/10.1073/pnas.0911109107.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  93. Edenberg HJ, Koller DL, Xuei X, Wetherill L, McClintick JN, Almasy L, et al. Genome-wide association study of alcohol dependence implicates a region on chromosome 11. Alcohol Clin Exp Res. 2010;34(5):840–52. https://doi.org/10.1111/j.1530-0277.2010.01156.x.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  94. Kendler KS, Kalsi G, Holmans PA, Sanders AR, Aggen SH, Dick DM, et al. Genomewide association analysis of symptoms of alcohol dependence in the molecular genetics of schizophrenia (MGS2) control sample. Alcohol Clin Exp Res. 2011;35(5):963–75. https://doi.org/10.1111/j.1530-0277.2010.01427.x.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  95. Zuo L, Gelernter J, Zhang CK, Zhao H, Lu L, Kranzler HR, et al. Genome-wide association study of alcohol dependence implicates KIAA0040 on chromosome 1q. Neuropsychopharmacology. 2012;37(2):557–66. https://doi.org/10.1038/npp.2011.229.

    CAS  PubMed  Article  Google Scholar 

  96. Gelernter J, Kranzler HR, Sherva R, Almasy L, Koesterer R, Smith AH, et al. Genome-wide association study of alcohol dependence:significant findings in African- and European-Americans including novel risk loci. Mol Psychiatry. 2014;19(1):41–9. https://doi.org/10.1038/mp.2013.145.

    CAS  PubMed  Article  Google Scholar 

  97. Xu K, Kranzler HR, Sherva R, Sartor CE, Almasy L, Koesterer R, et al. Genomewide association study for maximum number of alcoholic drinks in European Americans and African Americans. Alcohol Clin Exp Res. 2015;39(7):1137–47. https://doi.org/10.1111/acer.12751.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  98. Chen G, Zhang F, Xue W, Wu R, Xu H, Wang K, et al. An association study revealed substantial effects of dominance, epistasis and substance dependence co-morbidity on alcohol dependence symptom count. Addict Biol. 2016;22(6):1475–85. https://doi.org/10.1111/adb.12402.

    PubMed  Article  CAS  Google Scholar 

  99. Baik I, Cho NH, Kim SH, Han BG, Shin C. Genome-wide association studies identify genetic loci related to alcohol consumption in Korean men. Am J Clin Nutr. 2011;93(4):809–16. https://doi.org/10.3945/ajcn.110.001776.

    CAS  PubMed  Article  Google Scholar 

  100. Takeuchi F, Isono M, Nabika T, Katsuya T, Sugiyama T, Yamaguchi S, et al. Confirmation of ALDH2 as a major locus of drinking behavior and of its variants regulating multiple metabolic phenotypes in a Japanese population. Circ J. 2011;75(4):911–8. https://doi.org/10.1253/circj.CJ-10-0774.

    CAS  PubMed  Article  Google Scholar 

  101. Park BL, Kim JW, Cheong HS, Kim LH, Lee BC, Seo CH, et al. Extended genetic effects of ADH cluster genes on the risk of alcohol dependence: from GWAS to replication. Hum Genet. 2013;132(6):657–68. https://doi.org/10.1007/s00439-013-1281-8.

    CAS  PubMed  Article  Google Scholar 

  102. Yang X, Lu X, Wang L, Chen S, Li J, Cao J, et al. Common variants at 12q24 are associated with drinking behavior in Han Chinese. Am J Clin Nutr. 2013;97(3):545–51. https://doi.org/10.3945/ajcn.112.046482.

    CAS  PubMed  Article  Google Scholar 

  103. Quillen EE, Chen XD, Almasy L, Yang F, He H, Li X, et al. ALDH2 is associated to alcohol dependence and is the major genetic determinant of “daily maximum drinks” in a GWAS study of an isolated rural Chinese sample. Am J Med Genet B Neuropsychiatr Genet. 2014;165B(2):103–10. https://doi.org/10.1002/ajmg.b.32213.

    PubMed  Article  CAS  Google Scholar 

  104. • Jorgenson E, Thai KK, Hoffmann TJ, Sakoda LC, Kvale MN, Banda Y, et al. Genetic contributors to variation in alcohol consumption vary by race/ethnicity in a large multi-ethnic genome-wide association study. Mol Psychiatry. 2017;22(9):1359–67. https://doi.org/10.1038/mp.2017.101. This GWAS of alcohol consumption in the trans-ethnic Genetic Epidemiology Research in Adult Health and Aging cohort ( N = 86,627 non-Hispanic whites, Hispanic/Latinos, East Asians and African Americans) provided genome-wide significant evidence in known loci ALDH2 and ADH1B and replicable evidence for the prior GWAS-identified KLB and GCKR loci.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  105. Way M, McQuillin A, Saini J, Ruparelia K, Lydall GJ, Guerrini I, et al. Genetic variants in or near ADH1B and ADH1C affect susceptibility to alcohol dependence in a British and Irish population. Addict Biol. 2015;20(3):594–604. https://doi.org/10.1111/adb.12141.

    CAS  PubMed  Article  Google Scholar 

  106. Li D, Zhao H, Gelernter J. Strong association of the alcohol dehydrogenase 1B gene (ADH1B) with alcohol dependence and alcohol-induced medical diseases. Biol Psychiatry. 2011;70(6):504–12. https://doi.org/10.1016/j.biopsych.2011.02.024.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  107. Bierut LJ, Goate AM, Breslau N, Johnson EO, Bertelsen S, Fox L, et al. ADH1B is associated with alcohol dependence and alcohol consumption in populations of European and African ancestry. Mol Psychiatry. 2012;17(4):445–50. https://doi.org/10.1038/mp.2011.124.

    CAS  PubMed  Article  Google Scholar 

  108. Peng Y, Shi H, Qi XB, Xiao CJ, Zhong H, Ma RL, et al. The ADH1B Arg47His polymorphism in east Asian populations and expansion of rice domestication in history. BMC Evol Biol. 2010;10(1):15. https://doi.org/10.1186/1471-2148-10-15.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  109. Li R, Zhao Z, Sun M, Luo J, Xiao Y. ALDH2 gene polymorphism in different types of cancers and its clinical significance. Life Sci. 2016;147:59–66. https://doi.org/10.1016/j.lfs.2016.01.028.

    CAS  PubMed  Article  Google Scholar 

  110. Zhao T, Wang C, Shen L, Gu D, Xu Z, Zhang X, et al. Clinical significance of ALDH2 rs671 polymorphism in esophageal cancer: evidence from 31 case-control studies. Onco Targets Ther. 2015;8:649–59. https://doi.org/10.2147/OTT.S76526.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  111. Okada Y, Sim X, Go MJ, Wu JY, Gu D, Takeuchi F, et al. Meta-analysis identifies multiple loci associated with kidney function-related traits in east Asian populations. Nat Genet. 2012;44(8):904–9. https://doi.org/10.1038/ng.2352.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  112. Kato N, Loh M, Takeuchi F, Verweij N, Wang X, Zhang W, et al. Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation. Nat Genet. 2015;47(11):1282–93. https://doi.org/10.1038/ng.3405.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  113. Cordell HJ, Han Y, Mells GF, Li Y, Hirschfield GM, Greene CS, et al. International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways. Nat Commun. 2015;6:8019. https://doi.org/10.1038/ncomms9019.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  114. Agrawal A, Lynskey MT, Bucholz KK, Kapoor M, Almasy L, Dick DM, et al. DSM-5 cannabis use disorder: a phenotypic and genomic perspective. Drug Alcohol Depend. 2014;134:362–9. https://doi.org/10.1016/j.drugalcdep.2013.11.008.

    PubMed  Article  Google Scholar 

  115. Verweij KJ, Vinkhuyzen AA, Benyamin B, Lynskey MT, Quaye L, Agrawal A, et al. The genetic aetiology of cannabis use initiation: a meta-analysis of genome-wide association studies and a SNP-based heritability estimation. Addict Biol. 2013;18(5):846–50. https://doi.org/10.1111/j.1369-1600.2012.00478.x.

    CAS  PubMed  Article  Google Scholar 

  116. • Stringer S, Minica CC, Verweij KJ, Mbarek H, Bernard M, Derringer J, et al. Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32 330 subjects from the International Cannabis Consortium. Transl Psychiatry. 2016;6(3):e769. https://doi.org/10.1038/tp.2016.36. This meta-analysis was the largest GWAS conducted for any cannabis phenotype (total N = 32,330). No genome-wide significant SNP associations were identified.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  117. Minica CC, Dolan CV, Hottenga JJ, Pool R, Genome of the Netherlands C, Fedko IO et al. Heritability, SNP- and gene-based analyses of cannabis use initiation and age at onset. Behav Genet 2015;45(5):503–513. https://doi.org/10.1007/s10519-015-9723-9.

  118. Sherva R, Wang Q, Kranzler H, Zhao H, Koesterer R, Herman A, et al. Genome-wide association study of cannabis dependence severity, novel risk variants, and shared genetic risks. JAMA Psychiatry. 2016;73(5):472–80. https://doi.org/10.1001/jamapsychiatry.2016.0036.

    PubMed  PubMed Central  Article  Google Scholar 

  119. Uhl GR, Drgon T, Liu QR, Johnson C, Walther D, Komiyama T, et al. Genome-wide association for methamphetamine dependence: convergent results from 2 samples. Arch Gen Psychiatry. 2008;65(3):345–55. https://doi.org/10.1001/archpsyc.65.3.345.

    CAS  PubMed  Article  Google Scholar 

  120. Ikeda M, Okahisa Y, Aleksic B, Won M, Kondo N, Naruse N, et al. Evidence for shared genetic risk between methamphetamine-induced psychosis and schizophrenia. Neuropsychopharmacology. 2013;38(10):1864–70. https://doi.org/10.1038/npp.2013.94.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  121. Hart AB, Engelhardt BE, Wardle MC, Sokoloff G, Stephens M, de Wit H, et al. Genome-wide association study of d-amphetamine response in healthy volunteers identifies putative associations, including cadherin 13 (CDH13). PLoS One. 2012;7(8):e42646. https://doi.org/10.1371/journal.pone.0042646.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  122. Gelernter J, Sherva R, Koesterer R, Almasy L, Zhao H, Kranzler HR, et al. Genome-wide association study of cocaine dependence and related traits: FAM53B identified as a risk gene. Mol Psychiatry. 2014;19(6):717–23. https://doi.org/10.1038/mp.2013.99.

    CAS  PubMed  Article  Google Scholar 

  123. Nielsen DA, Ji F, Yuferov V, Ho A, Chen A, Levran O, et al. Genotype patterns that contribute to increased risk for or protection from developing heroin addiction. Mol Psychiatry. 2008;13(4):417–28. https://doi.org/10.1038/sj.mp.4002147.

    CAS  PubMed  Article  Google Scholar 

  124. Nielsen DA, Ji F, Yuferov V, Ho A, He C, Ott J, et al. Genome-wide association study identifies genes that may contribute to risk for developing heroin addiction. Psychiatr Genet. 2010;20(5):207–14. https://doi.org/10.1097/YPG.0b013e32833a2106.

    PubMed  Article  Google Scholar 

  125. Kalsi G, Euesden J, Coleman JR, Ducci F, Aliev F, Newhouse SJ, et al. Genome-wide association of heroin dependence in Han Chinese. PLoS One. 2016;11(12):e0167388. https://doi.org/10.1371/journal.pone.0167388.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  126. Gelernter J, Kranzler HR, Sherva R, Koesterer R, Almasy L, Zhao H, et al. Genome-wide association study of opioid dependence: multiple associations mapped to calcium and potassium pathways. Biol Psychiatry. 2014;76(1):66–74. https://doi.org/10.1016/j.biopsych.2013.08.034.

    CAS  PubMed  Article  Google Scholar 

  127. Nelson EC, Agrawal A, Heath AC, Bogdan R, Sherva R, Zhang B, et al. Evidence of CNIH3 involvement in opioid dependence. Mol Psychiatry. 2016;21(5):608–14. https://doi.org/10.1038/mp.2015.102.

    CAS  PubMed  Article  Google Scholar 

  128. Li D, Zhao H, Kranzler HR, Li MD, Jensen KP, Zayats T, et al. Genome-wide association study of copy number variations (CNVs) with opioid dependence. Neuropsychopharmacology. 2014;40(4):1016–26. https://doi.org/10.1038/npp.2014.290.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  129. • Hancock DB, Levy JL, Gaddis NC, Glasheen C, Saccone NL, Page GP, et al. Cis-expression quantitative trait loci mapping reveals replicable associations with heroin addiction in OPRM1. Biol Psychiatry. 2015;78(7):474–84. https://doi.org/10.1016/j.biopsych.2015.01.003. This study mapped prefrontal cortex cis -eQTL SNPs for the long-studied candidate gene OPRM1 , and in finding an association between the cis -eQTL SNP rs3778150 and heroin addiction, it was the first to report genome-wide significant association evidence for any OPRM1 SNP.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  130. • Schwantes-An TH, Zhang J, Chen LS, Hartz SM, Culverhouse RC, Chen X, et al. Association of the OPRM1 variant rs1799971 (A118G) with non-specific liability to substance dependence in a collaborative de novo meta-analysis of European-ancestry cohorts. Behav Genet. 2016;46(2):151–69. https://doi.org/10.1007/s10519-015-9737-3. Using the largest sample size ( N = 28,689) ever to investigate the association between the long-studied missense OPRM1 SNP rs1799971 and substance dependence, no significant association was observed with heroin specifically, but a modest association was found with general substance dependence.

    PubMed  Article  Google Scholar 

  131. Otto JM, Gizer IR, Deak JD, Fleming KA, Bartholow BD. A cis-eQTL in OPRM1 is associated with subjective response to alcohol and alcohol use. Alcohol Clin Exp Res. 2017;41(5):929–38. https://doi.org/10.1111/acer.13369.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  132. Nishizawa D, Fukuda K, Kasai S, Hasegawa J, Aoki Y, Nishi A, et al. Genome-wide association study identifies a potent locus associated with human opioid sensitivity. Mol Psychiatry. 2014;19(1):55–62. https://doi.org/10.1038/mp.2012.164.

    CAS  PubMed  Article  Google Scholar 

  133. • Smith AH, Jensen KP, Li J, Nunez Y, Farrer LA, Hakonarson H, et al. Genome-wide association study of therapeutic opioid dosing identifies a novel locus upstream of OPRM1. Mol Psychiatry. 2017;22(3):346–52. https://doi.org/10.1038/mp.2016.257. This GWAS identified a novel genome-wide signficant SNP association in OPRM1 with therapeutical methadone dose in 383 African Americans with opioid dependence. The association was extended to morphine dosage for treating surgical pain in an independent study sample of 241 African American children.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  134. Wetherill L, Agrawal A, Kapoor M, Bertelsen S, Bierut LJ, Brooks A, et al. Association of substance dependence phenotypes in the COGA sample. Addict Biol. 2015;20(3):617–27. https://doi.org/10.1111/adb.12153.

    CAS  PubMed  Article  Google Scholar 

  135. • Johnson EO, Hancock DB, Levy JL, Gaddis NC, Page GP, Glasheen C, et al. KAT2B polymorphism identified for drug abuse in African Americans with regulatory links to drug abuse pathways in human prefrontal cortex. Addict Biol. 2016;21(6):1217–32. https://doi.org/10.1111/adb.12286. This GWAS compared people who inject drugs with population controls and found a genome-wide significant and replicable association for the KAT2B SNP rs9829896 among African Americans ( N = 3,742 for discovery and 755 for replication). Rs9829896 was also associated with KAT2B RNA expression in postmortem prefrontal cortex of African Americans.

    CAS  PubMed  Article  Google Scholar 

  136. Aschard H, Vilhjalmsson BJ, Joshi AD, Price AL, Kraft P. Adjusting for heritable covariates can bias effect estimates in genome-wide association studies. Am J Hum Genet. 2015;96(2):329–39. https://doi.org/10.1016/j.ajhg.2014.12.021.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  137. Hartz SM, Horton AC, Hancock DB, Baker TB, Caporaso NE, Chen LS, et al. Genetic correlation between smoking behaviors and schizophrenia. Schizophr Res. 2017; https://doi.org/10.1016/j.schres.2017.02.022.

  138. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47(11):1236–41. https://doi.org/10.1038/ng.3406.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  139. Power RA, Verweij KJ, Zuhair M, Montgomery GW, Henders AK, Heath AC, et al. Genetic predisposition to schizophrenia associated with increased use of cannabis. Mol Psychiatry. 2014;19(11):1201–4. https://doi.org/10.1038/mp.2014.51.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  140. Reginsson GW, Ingason A, Euesden J, Bjornsdottir G, Olafsson S, Sigurdsson E, et al. Polygenic risk scores for schizophrenia and bipolar disorder associate with addiction. Addict Biol. 2018;23(1):485–92. https://doi.org/10.1111/adb.12496.

  141. Hartz SM, Horton AC, Oehlert M, Carey CE, Agrawal A, Bogdan R, et al. Association between substance use disorder and polygenic liability to schizophrenia. Biol Psychiatry. 2017;82(10):709–15. https://doi.org/10.1016/j.biopsych.2017.04.020.

    PubMed  Article  Google Scholar 

  142. O'Donovan MC, Craddock N, Norton N, Williams H, Peirce T, Moskvina V, et al. Identification of loci associated with schizophrenia by genome-wide association and follow-up. Nat Genet. 2008;40(9):1053–5. https://doi.org/10.1038/ng.201.

    PubMed  Article  CAS  Google Scholar 

  143. Hancock DB, Levy JL, Gaddis NC, Glasheen C, Saccone NL, Page GP, et al. Replication of ZNF804A gene variant associations with risk of heroin addiction. Genes Brain Behav. 2015;14(8):635–40. https://doi.org/10.1111/gbb.12254.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  144. Sun Y, Zhao LY, Wang GB, Yue WH, He Y, Shu N, et al. ZNF804A variants confer risk for heroin addiction and affect decision making and gray matter volume in heroin abusers. Addict Biol. 2016;21(3):657–66. https://doi.org/10.1111/adb.12233.

    CAS  PubMed  Article  Google Scholar 

  145. Jackson KJ, Fanous AH, Chen J, Kendler KS, Chen X. Variants in the 15q25 gene cluster are associated with risk for schizophrenia and bipolar disorder. Psychiatr Genet. 2013;23(1):20–8. https://doi.org/10.1097/YPG.0b013e32835bd5f1.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  146. Notaras M, Hill R, van den Buuse M. A role for the BDNF gene Val66Met polymorphism in schizophrenia? A comprehensive review. Neurosci Biobehav Rev. 2015;51:15–30. https://doi.org/10.1016/j.neubiorev.2014.12.016.

    CAS  PubMed  Article  Google Scholar 

  147. Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337(6099):1190–5. https://doi.org/10.1126/science.1222794.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  148. Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 2010;6(4):e1000888. https://doi.org/10.1371/journal.pgen.1000888.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  149. • Markunas CA, Johnson EO, Hancock DB. Comprehensive evaluation of disease- and trait-specific enrichment for eight functional elements among GWAS-identified variants. Hum Genet. 2017;136(7):911–9. https://doi.org/10.1007/s00439-017-1815-6. Prior studies have shown that top GWAS findings are enriched for specific functional elements (e.g., eQTLs). This study assessed a broad set of eight functional elements and found significant enrichment for DNase sites, eQTLs, sequence conservation, enhancers, promoters, missense variants, and protein binding sites. Enrichment differences in blood vs. brain tissues were dependent on disease/trait and functional element.

    CAS  PubMed  Article  Google Scholar 

  150. •• Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ, et al. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet. 2015;47(9):1091–8. https://doi.org/10.1038/ng.3367. This article presents the gene-based association method PrediXcan, which integrates genome-wide SNP genotypes and RNA-sequencing (or other ‘omics) data to build genetically regulated gene expression models and applies those models to GWAS datasets.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  151. •• GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348(6235):648–60. https://doi.org/10.1126/science.1262110. The GTEx project has provided the scientific community with freely available RNA-sequencing data measured across multiple tissues from healthy individuals ( https://www.gtexportal.org/home/ ). Pilot GTEx analyses showed that blood vs. brain had the most distinct expression profiles among the tissue comparisons, emphasizing the importance of studying gene regulation specifically in brain to better understand the neurobiology of addiction and other psychiatric diseases.

    PubMed Central  Article  CAS  Google Scholar 

  152. Hernandez DG, Nalls MA, Moore M, Chong S, Dillman A, Trabzuni D, et al. Integration of GWAS SNPs and tissue specific expression profiling reveal discrete eQTLs for human traits in blood and brain. Neurobiol Dis. 2012;47(1):20–8. https://doi.org/10.1016/j.nbd.2012.03.020.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  153. McKenzie M, Henders AK, Caracella A, Wray NR, Powell JE. Overlap of expression quantitative trait loci (eQTL) in human brain and blood. BMC Med Genet. 2014;7(1):31. https://doi.org/10.1186/1755-8794-7-31.

    Google Scholar 

  154. Srivastava V, Obudulu O, Bygdell J, Lofstedt T, Ryden P, Nilsson R, et al. OnPLS integration of transcriptomic, proteomic and metabolomic data shows multi-level oxidative stress responses in the cambium of transgenic hipI-superoxide dismutase Populus plants. BMC Genomics. 2013;14(1):893. https://doi.org/10.1186/1471-2164-14-893.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  155. Kirwan GM, Johansson E, Kleemann R, Verheij ER, Wheelock AM, Goto S, et al. Building multivariate systems biology models. Anal Chem. 2012;84(16):7064–71. https://doi.org/10.1021/ac301269r.

    CAS  PubMed  Article  Google Scholar 

  156. Bouhaddani SE, Houwing-Duistermaat J, Salo P, Perola M, Jongbloed G, Uh HW. Evaluation of O2PLS in omics data integration. BMC Bioinformat. 2016;17(Suppl 2):11. https://doi.org/10.1186/s12859-015-0854-z.

    Article  CAS  Google Scholar 

  157. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10(5):e1004383. https://doi.org/10.1371/journal.pgen.1004383.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

Download references

Funding

This work was supported by NIDA grants R01s DA035825 and DA042090 (PI: Dana Hancock) and R01 DA036583 (PI: Laura Bierut).

Author information

Authors and Affiliations

  1. Behavioral and Urban Health Program, Behavioral Health and Criminal Justice Division, RTI International, 3040 East Cornwallis Road, P. O. Box 12194, Research Triangle Park, NC, 27709, USA

    Dana B. Hancock & Christina A. Markunas

  2. Department of Psychiatry, Washington University, St. Louis, MO, USA

    Laura J. Bierut

  3. Fellow Program and Behavioral Health and Criminal Justice Division, RTI International, Research Triangle Park, NC, USA

    Eric O. Johnson

Authors
  1. Dana B. Hancock
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christina A. Markunas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laura J. Bierut
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eric O. Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dana B. Hancock.

Ethics declarations

Conflict of Interest

Dana B. Hancock reports grants from National Institutes of Health.

Christina A. Markunas reports grants from National Institutes of Health.

Laura J. Bierut reports grants from National Institutes of Health. In addition, Dr. Bierut is listed as an inventor on U.S. Patent 8,080,371,“Markers for Addiction” covering the use of certain SNPs in determining the diagnosis, prognosis, and treatment of addiction.

Eric O. Johnson reports grants from National Institutes of Health.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

This article is part of the Topical Collection on Genetic Disorders

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hancock, D.B., Markunas, C.A., Bierut, L.J. et al. Human Genetics of Addiction: New Insights and Future Directions. Curr Psychiatry Rep 20, 8 (2018). https://doi.org/10.1007/s11920-018-0873-3

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s11920-018-0873-3

Keywords

  • GWAS
  • Omics
  • Brain
  • Nicotine/smoking
  • Alcohol
  • Drugs