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Meta-Analysis
. 2016 Mar;46(2):151-69.
doi: 10.1007/s10519-015-9737-3. Epub 2015 Sep 21.

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

Tae-Hwi Schwantes-An  1   2 Juan Zhang  1   3 Li-Shiun Chen  4 Sarah M Hartz  4 Robert C Culverhouse  5 Xiangning Chen  6 Hilary Coon  7 Josef Frank  8 Helen M Kamens  9   10   11 Bettina Konte  12 Leena Kovanen  13 Antti Latvala  14 Lisa N Legrand  15 Brion S Maher  16 Whitney E Melroy  9   10 Elliot C Nelson  4 Mark W Reid  17 Jason D Robinson  18 Pei-Hong Shen  19 Bao-Zhu Yang  20 Judy A Andrews  17 Paul Aveyard  21 Olga Beltcheva  22 Sandra A Brown  23 Dale S Cannon  7 Sven Cichon  24   25 Robin P Corley  9 Norbert Dahmen  26 Louisa Degenhardt  27   28 Tatiana Foroud  29 Wolfgang Gaebel  30 Ina Giegling  12 Stephen J Glatt  31 Richard A Grucza  4 Jill Hardin  32 Annette M Hartmann  12 Andrew C Heath  4 Stefan Herms  24   25 Colin A Hodgkinson  19 Per Hoffmann  24   25 Hyman Hops  17 David Huizinga  33 Marcus Ising  34 Eric O Johnson  35 Elaine Johnstone  36 Radka P Kaneva  22 Kenneth S Kendler  6 Falk Kiefer  37 Henry R Kranzler  38 Ken S Krauter  9   39 Orna Levran  40 Susanne Lucae  34 Michael T Lynskey  41 Wolfgang Maier  42 Karl Mann  43 Nicholas G Martin  44 Manuel Mattheisen  24   45   46 Grant W Montgomery  44 Bertram Müller-Myhsok  34 Michael F Murphy  47 Michael C Neale  6 Momchil A Nikolov  4   22 Denise Nishita  32 Markus M Nöthen  24 John Nurnberger  48 Timo Partonen  13 Michele L Pergadia  4 Maureen Reynolds  49 Monika Ridinger  50   51 Richard J Rose  52 Noora Rouvinen-Lagerström  13 Norbert Scherbaum  53 Christine Schmäl  43 Michael Soyka  54   55 Michael C Stallings  9   56 Michael Steffens  57 Jens Treutlein  8 Ming Tsuang  23 Tamara L Wall  23 Norbert Wodarz  50 Vadim Yuferov  40 Peter Zill  58 Andrew W Bergen  32 Jingchun Chen  6 Paul M Cinciripini  18 Howard J Edenberg  59 Marissa A Ehringer  9   10 Robert E Ferrell  60 Joel Gelernter  20   61   62 David Goldman  19 John K Hewitt  9   56 Christian J Hopfer  63 William G Iacono  15 Jaakko Kaprio  13   14   64 Mary Jeanne Kreek  40 Ivo M Kremensky  22 Pamela A F Madden  4 Matt McGue  15 Marcus R Munafò  65 Robert A Philibert  66 Marcella Rietschel  8 Alec Roy  67 Dan Rujescu  12 Sirkku T Saarikoski  13 Gary E Swan  68 Alexandre A Todorov  4 Michael M Vanyukov  49 Robert B Weiss  69 Laura J Bierut  4 Nancy L Saccone  70
Affiliations
Meta-Analysis

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

Tae-Hwi Schwantes-An et al. Behav Genet. 2016 Mar.

Abstract

The mu1 opioid receptor gene, OPRM1, has long been a high-priority candidate for human genetic studies of addiction. Because of its potential functional significance, the non-synonymous variant rs1799971 (A118G, Asn40Asp) in OPRM1 has been extensively studied, yet its role in addiction has remained unclear, with conflicting association findings. To resolve the question of what effect, if any, rs1799971 has on substance dependence risk, we conducted collaborative meta-analyses of 25 datasets with over 28,000 European-ancestry subjects. We investigated non-specific risk for "general" substance dependence, comparing cases dependent on any substance to controls who were non-dependent on all assessed substances. We also examined five specific substance dependence diagnoses: DSM-IV alcohol, opioid, cannabis, and cocaine dependence, and nicotine dependence defined by the proxy of heavy/light smoking (cigarettes-per-day >20 vs. ≤ 10). The G allele showed a modest protective effect on general substance dependence (OR = 0.90, 95% C.I. [0.83-0.97], p value = 0.0095, N = 16,908). We observed similar effects for each individual substance, although these were not statistically significant, likely because of reduced sample sizes. We conclude that rs1799971 contributes to mechanisms of addiction liability that are shared across different addictive substances. This project highlights the benefits of examining addictive behaviors collectively and the power of collaborative data sharing and meta-analyses.

Keywords: Addiction; Genetic association; OPRM1; Opioid receptor; Single nucleotide polymorphism (SNP); Substance dependence.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr. Bierut is listed as an inventor on Issued U.S. Patent 8,080,371, “Markers for Addiction” covering the used of certain SNPs in determining the diagnosis, prognosis, and treatment of addiction, and served as a consultant for Pfizer in 2008. Dr. NL Saccone is the spouse of Dr. SF Saccone, who is also listed as an inventor on the above patent. Dr. Cinciripini served on the scientific advisory board of Pfizer, conducted educational talks sponsored by Pfizer on smoking cessation (2006–2008), and has received grant support from Pfizer. Dr. Degenhardt has no relevant disclosures for this specific project; however, for general pharmaceutical company disclosures, Dr. Degenhardt has received untied educational grants from Reckitt Benckiser to conduct post-marketing surveillance of the diversion and injection of opioid substitution therapy medications in Australia. Although these activities are unrelated to the current study, Dr. Kranzler has been a consultant or advisory board member for Alkermes, Lilly, Lundbeck, Otsuka and Pfizer; he is also a member of the American Society of Clinical Psychopharmacology's Alcohol Clinical Trials Initiative, which is supported by Ethypharm, Lilly, Lundbeck, AbbVie, and Pfizer. Dr. Ridinger is member of the advisory board of Lundbeck referring to Nalmefene. Prof. Dr. N. Scherbaum received honoraria for several activities (advisory boards, lectures, manuscripts and educational material) by the factories Sanofi-Aventis, Reckitt-Benckiser, Lundbeck, and Janssen-Cilag. During the last three years he participated in clinical trials financed by the pharmaceutical industry. The remaining authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Forest plot of general substance dependence and rs1799971 across studies that had at least 5 cases and 5 controls. Summary odds ratio, 95% Confidence Interval, and p-values are based on fixed effect meta-analysis. *indicates the subset of 10 studies that had all five specific substance dependence diagnoses, examined in secondary analyses to confirm consistency of results. Estimated heterogeneity variance was Q = 20.13 with a p-value of 0.387 among all 20 studies and Q = 6.49 with a p-value of 0.69 among the subset of 10 studies.
Figure 2
Figure 2
Forest plot of general substance dependence and rs1799971 across studies that had at least 5 cases and 5 controls. Summary odds ratio, 95% Confidence Interval, and p-values are based on fixed effect meta-analysis. *indicates the subset of 10 studies that had all five specific substance dependence diagnoses, examined in secondary analyses to confirm consistency of results. Estimated heterogeneity variance was Q = 20.13 with a p-value of 0.387 among all 20 studies and Q = 6.49 with a p-value of 0.69 among the subset of 10 studies.

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