Opioid receptors and their endogenous peptide ligands play important roles in the reward and reinforcement of drugs such as heroin, cocaine, and alcohol. The binding of dynorphins to the κ-opioid receptor has been shown to produce aversive states, which may prevent the development of reinforcement. We genotyped SNPs throughout OPRK1, encoding the κ-opioid receptor, and PDYN, which encodes its ligand prodynorphin, in a group of 1860 European American individuals from 219 multiplex alcohol dependent families. Family-based analyses demonstrated associations between alcohol dependence and multiple SNPs in the promoter and 3′ end of PDYN, and in intron 2 of OPRK1. Haplotype analyses further supported the association of PDYN. Thus, variations in the genes encoding both the κ-opioid receptor and its ligand, OPRK1 and PDYN, are associated with the risk for alcohol dependence; this makes biological sense as variations in either should affect signaling through the κ-opioid system.
Opioids function as neurotransmitters or neuromodulators regulating many brain functions, including psychomotor stimulation, reward and reinforcement, mood and homeostatic adaptive processes like drinking, eating and thermoregulation.1, 2, 3, 4, 5 The opioid system appears to play an important role in the reward system by influencing dopamine release in the nucleus accumbens.6 There are three major groups of endogenous opioid peptides (dynorphins, endorphins and enkephalins) and three corresponding opioid receptors, κ-opioid receptor (KOR), μ-opioid receptor (MOR) and δ-opioid receptor (DOR).7 Stimulation of MOR and DOR in mouse brain increases the release of dopamine in the nucleus accumbens, whereas stimulation of KOR reduces the release of dopamine and generates aversive states.8, 9, 10, 11
Alcoholism (alcohol dependence) is a common complex genetic disease. Both twin and adoption studies show a strong heritable component involved in the risk for alcoholism.12, 13, 14, 15, 16 The lack of a clear pattern of inheritance suggests that multiple genes contribute to the risk for alcoholism. Several genes have already been identified by the Collaborative Study on the Genetics of Alcoholism (COGA), including GABRA2,17, 18 GABRG3,19 CHRM2,20, 21 and hTAS2R16.22 Each of these genes has a modest effect on the risk for alcohol dependence and it appears likely that additional genes contribute to the genetic susceptibility for alcohol dependence.
There is evidence that the opioid system is involved in the vulnerability to various drug addictions, including alcoholism.23, 24, 25 Naltrexone, a nonselective opioid receptor antagonist, decreases ethanol consumption in human alcoholics.26, 27 Naltrexone-treated alcohol-dependent subjects have lower levels of craving before and during ethanol self-administration compared to placebo-treated subjects.28 Nonalcoholic offspring with a family history of alcohol dependence have greater adrenocorticotropin (ACTH) and cortisol responses to opioid receptor blockade induced by naloxone than do nonalcoholic offspring without a family history of alcohol dependence.29, 30, 31 Treatment with buprenorphine, a partial agonist-antagonist of opioid receptors, in combination with naloxone, effectively reduces withdrawal symptoms in opioid-dependent individuals.32 Evidence from animal models also suggests a possible relationship between the opioid system and alcohol preference.33, 34, 35, 36
The dynorphin/KOR system may serve as a novel target for therapeutic treatment of alcohol and drug dependence. A selective KOR agonist U50,488H attenuates voluntary ethanol intake in rats.36 Blocking the KOR in wild-type C57BL/6 mice with nor-binaltorphimine (nor-BNI), a κ-receptor-specific antagonist, increases alcohol self-administration.37 Similarly, the endogenous KOR agonist dynorphin A (1–17) attenuates the cocaine-induced increases in dopamine levels in C57BL/6 mice brain, and this effect can be blocked by preinjection with the antagonist nor-BNI.38 OPRK1 knockout mice demonstrate an increased ethanol-evoked dopamine response in the nucleus accumbens37 and show less oral alcohol self-administration than wild-type mice.39
The OPRK1 gene is located on human chromosome 8. The 22 kb OPRK1 gene contains four exons, including a newly identified exon that encodes the 5′ untranslated region.40, 41 Yuferov et al.41 suggested possible association of a coding SNP (G36T) in exon 2 with opiate addiction. Loh et al.42 genotyped three OPRK1 coding SNPs, G36T, C459T and A843G, in 307 Taiwanese Han subjects, but found no association between the SNPs and alcohol dependence.
Dynorphins are derived proteolytically from prodynorphin, which is encoded by PDYN.43 Dynorphins bind selectively to the KOR, encoded by OPRK1.44, 45, 46 The human PDYN gene contains four exons, covering 15.3 kb on chromosome 20. Variation in PDYN has been studied for its role in addiction to opiates, cocaine and alcohol. A 68-bp repeat variant located about 1.5 kb upstream of the promoter region was identified in the initial sequencing of the gene.43 The level of gene expression appeared to be affected by the number of copies of the 68-bp repeats.47, 48 Longer alleles appear to increase gene expression while the shorter alleles appear to decrease expression. Longer alleles may be protective against cocaine addiction; alleles containing three or four copies of the repeat were more common in control subjects than in cocaine-addicted subjects.47 Geijer et al.49 identified a GC/AT base pair exchange 301 bp upstream of the exon 1/intron 1 boundary of PDYN, but found no association of the variant with alcoholism in a Scandinavian population.
Based on the biological evidence noted above, we hypothesized that genetic differences in the κ-opioid system may be linked to differences in the risk for alcoholism. The present study is designed to test that hypothesis in a large sample of families selected for the presence of multiple alcoholic members.
Materials and methods
Sample and phenotype
The Collaborative Study on the Genetics of Alcoholism (COGA) is a multi-site study recruiting families at six centers across the US: Indiana University, State University of New York Downstate Medical Center, University of Connecticut, University of Iowa, University of California/San Diego, and Washington University, St Louis. The institutional review boards of all participating institutions approved the study. The ascertainment and assessment of this sample has previously been described.50, 51 Briefly, probands were identified through alcohol treatment programs; families with at least three first-degree relatives with alcohol dependence participated in the genetic part of this study. A sample of 1860 European American individuals from 219 alcoholic families was used in this study.
Phenotypic diagnoses were based on interview data from the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA).52, 53 Alcoholism was defined as meeting criteria for both DSM-IIIR alcohol dependence54 and Feighner definition of alcoholism.55 As a component of the genetic risk might be common to other drugs, individuals who did not meet criteria for alcoholism but did meet criteria for dependence on an illicit drug (marijuana, cocaine, stimulant, sedative or opioid) were defined as unknown rather than unaffected.
SNP selection and genotyping
SNPs distributed throughout the OPRK1 and PDYN genes and extending to the 5′ and 3′ flanking regions were selected from public databases, primarily dbSNP (http://www.ncbi.nlm.nih.gov/SNP/). At the time SNPs were selected, allele frequencies were not usually available. To determine allele frequencies, SNPs were genotyped in two sets of samples, each consisting of 40 unrelated individuals from the Coriell European- and African-American diversity samples. SNPs with greater than 10% heterozygosity were preferentially genotyped; all genotyped SNPs were in Hardy–Weinberg equilibrium in both test populations. Most SNPs were located in non-coding regions of the genes. Key SNPs encoding PDYN and OPRK1 coding variants and the PDYN promoter variants were genotyped regardless of their minor allele frequency (Tables 1 and 2). Locations of the SNPs were determined from the annotations in the NCBI human genome assembly Build 35.1.
Genotyping was done using a modified single nucleotide extension reaction with allele detection by mass spectrometry (Sequenom MassArray system; Sequenom, San Diego, CA, USA). The success rate of all genotypes was 95% or higher. All SNP genotypes were checked for Mendelian inheritance using the program PEDCHECK.56 Marker allele frequencies and heterozygosities were computed in the COGA sample using the program USERM13, part of the MENDEL linkage computer programs.57 Only SNPs with a minimal allele frequency of 4% (Tables 1 and 2) were included in subsequent analyses.
To determine if there were any additional coding variations within the two genes, all exons of OPRK1 and PDYN were sequenced in 16 individuals. For each gene, eight individuals with the high-risk haplotype and eight individuals with the low-risk haplotype were chosen based on the tag SNPs from the most significant haplotype block of each gene; four of the individuals were sequenced for both genes.
Sequencing was performed using the ABI PRISM 3100 Genetic Analyzer capillary DNA sequencer with Big Dye chemistry (Applied Biosystems). The sequencing covered the exons plus approximately 200 bp flanking intronic regions on each side, plus 1.1–1.6 kb upstream and 600–900 bp downstream. A total of 12 new SNPs were found, seven in OPRK1 and five in PDYN. These were submitted to dbSNP (see Electronic-Database Information).
To ensure that the SNP density was sufficient to evaluate the evidence of association between each of the genes and substance dependence, the program HAPLOVIEW58 was employed to examine the extent of linkage disequilibrium between pairs of SNPs as well as the haplotype block structure within each gene. Blocks were defined using the criteria proposed by Gabriel et al.59 Tag SNPs were selected to capture haplotypes with frequency of ⩾5%.60
The Pedigree Disequilibrium Test (PDT),61 as implemented in the program UNPHASED,62 was used to test for association in the extended, multiplex COGA pedigrees. The PDT utilizes data from all available trios in a family, as well as discordant sibships. Evidence for association is assessed based on overtransmission of a particular allele to affected individuals, and greater frequency of the allele in affected individuals as compared to their unaffected siblings. The ‘avg-PDT’ statistic, which averages the association statistic across all families, was used to test for association between the SNPs and alcohol dependence.63
Association results were compared to the SNP disequilibrium pattern of each gene. Haplotypes were constructed using consecutive sets of three adjacent SNPs17 as well as employing the tag SNPs of the haplotype blocks containing SNPs associated with alcohol dependence. PDT analysis results using these two complementary haplotyping methods were compared to evaluate the pattern of association.
Eighteen SNPs were genotyped across PDYN from 4 kb upstream of the transcription initiation site to 3 kb downstream of the 3′ end (Table 1; Figure 1). The extent of linkage disequilibrium as measured by D′ between pairs of adjacent SNPs ranged from 0.77 to 1.00. The overall coverage for the gene was compared to the data in HapMap using Tagger.64 Fifteen of the 18 SNPs showed an average r2 of 0.95 with 40 SNPs (MAF>0.05) in a region of 30 kb; 88% of the HapMap alleles had r2>0.8 with at least one of our SNPs. Therefore, the association analyses of the selected SNPs on PDYN captured a very large fraction of untested variations for the gene and its immediate flanking regions. Three haplotype blocks were found (Figure 1); blocks 1 and 2 were similar to those defined by HapMap for the CEU population (www.HapMap.com). Block 3, identified in our data, appeared to be part of block 2 in the HapMap data.
There was significant evidence of association between alcohol dependence and multiple SNPs in PDYN (Table 1). Association was observed for multiple SNPs in a 7 kb region at the 3′ end of the gene, including rs6045819, a synonymous variation in exon 4, and additional SNPs at the promoter region. The haplotype constructed from the two SNPs that tag block 1 (rs2235749 and rs10485703; Figure 1) provided further support for the association with alcohol dependence (global χ2 P=0.008). Haplotype block 2, tagged by rs1883723, rs2281285 and rs1997794, was also significantly associated (global P-value=0.004). To further examine the association results, haplotypes were constructed using consecutive sets of 3-SNP sliding windows; five of six consecutive haplotypes (P⩽0.05) in the 3′ region of the gene (Table 3) supported association with alcohol dependence. The haplotype C-A-G-G-G-A (SNPs 1–6) was overtransmitted (P=0.045) to alcohol-dependent individuals, while its complementary haplotype T-G-A-A-A-G was overtransmitted (P=0.038) to nonalcohol-dependent individuals (global P=0.075).
Resequencing the 4 PDYN exons uncovered five new variations in the region (see Electronic-Database Information). However, none of the new variations altered amino-acid sequence.
Thirteen SNPs were genotyped across OPRK1, extending from 800 bp into the 5′ promoter region to the 3′ end (Table 2; Figure 2). Estimates of D′ were >0.89 for 92% of the adjacent SNPs. The overall coverage of the gene was compared to the data in HapMap using Tagger.64 Twelve of the 13 SNPs showed an average r2 of 0.94 with 40 SNPs (MAF>0.05) in a region of 30 kb; 95% of the HapMap alleles in the region had r2>0.8 with at least one of our SNPs. Thus, the selected SNPs also captured a very large fraction of untested variations contained within OPRK1 and its promoter region. The OPRK1 gene resides in two haplotype blocks, which encompassed 12 SNPs (Figure 2); SNP 5, rs6473797, was not included in either block. The haplotype block pattern was similar to that derived by HapMap for the CEU population (www.HapMap.com).
There was significant evidence of association between alcohol dependence and multiple SNPs across a 9 kb region located in intron 2 of OPRK1 (P-value⩽0.05; Table 2). None of the three coding SNPs (rs702764, rs7815824, and rs1051660) demonstrated evidence of association (P>0.11). Haplotype analyses employing tag SNPs (Figure 2) were not significant, nor were haplotypes constructed with three consecutive SNPs.
Resequencing the four OPRK1 exons uncovered seven new variations in the region (see Electronic-Database Information). None of the new variations altered amino-acid sequence. The only known coding SNP that alters amino-acid sequence (rs9282808; Asn374Asp) had zero allele frequency in a sample of 40 unrelated European-American samples from Coriell and was not genotyped.
We have demonstrated in a large, family-based study of European-Americans that variations in both parts of the κ-opioid system, the OPRK1 gene that encodes the receptor and the PDYN gene that encodes its ligand prodynorphin, are associated with alcohol dependence. The fact that both receptor and its ligand are associated makes biological sense, as variations in either should affect the overall level of signaling through the system.
For both OPRK1 and PDYN the evidence for association was from SNPs that did not change the encoded protein (Tables 1 and 2). In neither case did resequencing of individuals with high- and low-risk haplotypes reveal additional coding variations, suggesting that it is differences in some aspect of gene regulation that contribute to the risk for alcoholism. The strongest association in PDYN came from SNPs in the 3′ region, including exons 3 and 4, as well as two markers at the 5′ promoter region. The strongest association in OPRK1 was with multiple SNPs in intron 2. In PDYN, analyses of haplotypes provided additional consistent evidence of association with alcohol dependence. In contrast, haplotype analyses in OPRK1 did not yield further support for association. There are several possible explanations for these differing results. For example, the variation(s) associated with alcohol dependence in PDYN may have a single ancestral origin while the variation(s) in OPRK1 associated with alcohol dependence may have multiple ancestral origins. In the former instance, haplotypic analyses would provide greater genetic information while in the latter case, they would not.
In a study among the Taiwanese Han population,42 no association between alcoholism and OPRK1 was detected; the study examined only the three synonymous variations in the coding region (G36T, rs1051660; C459T, rs7815824; A843G, rs702764). Our results for these three markers were also negative (Table 2). However, we hypothesized that regulatory variations were likely to influence complex genetic diseases, so we genotyped many more SNPs to ensure coverage throughout OPRK1, including the promoter and intronic regions. We found the strongest association was in intron 2.
The variation GC/AT in the 5′ promoter region of PDYN, reported by Geijer et al.,49 which showed no evidence of association with alcoholism in the Scandinavian population, was significantly associated with alcoholism in our study (rs1997794; P=0.004). Bakalkin et al.65 described an 11-nucleotide consensus sequence for NF-κB (IndexTermGGGGGCTTCT) located in exon 4 of PDYN, within the coding sequence of the dynorphin peptide, and found that the expression of the gene was regulated by binding of NF-κB to the intragenic sequences. Interestingly, the synonymous marker rs6045819 in exon 4, which was significantly associated with alcohol dependence in our data (P=0.007), was only 22 bp upstream of the κB-element; the same SNP was also within several nucleotides of the initiating Met198 of an N-terminally truncated T2 transcript66 that encodes dynorphin A and B.
In our sample, there were only 85 individuals who were dependent on one or more illicit drugs (cocaine, opiate, marijuana, stimulants or sedatives) but who did not meet criteria for alcohol dependence. Therefore, there was insufficient power to test the hypothesis that SNPs in these genes are associated with illicit drug dependence independent of the association observed with alcohol dependence. Adding these 85 individuals to the 869 alcohol-dependent individuals to analyze a phenotype of dependence on alcohol or illicit drugs would not substantially change the results because virtually all of the power in the sample would still be from the alcohol dependence phenotype, and therefore such an analysis would not effectively address issues of general drug dependence.
Dynorphin primarily modulates KOR responses, although it also acts through MOR in alcohol and drug addictions. Significantly higher basal levels of KOR mRNA were found in alcohol-avoiding mice (DBA/2) than in alcohol-preferring mice (C57BL/6) in septum and hypothalamus.67 Similar results were also described in rats: the density of KOR in the ventromedial hypothalamus was much lower in alcohol-avoiding AA rats than in alcohol-preferring ANA rats.35 Difference in basal amount of KOR mRNA could lead to differences in KOR activity and subsequently to behavioral differences between the strains of animals in alcohol preferences. The application of a KOR-specific agonist U50,488H dose-dependently decreased voluntary ethanol intake in AA rat.36 A complete knockout of OPRK1 significantly reduced alcohol preference and consumption in mice.39
Alteration of the κ-opioid system expression was further investigated in Withdrawal Seizure-Prone (WSP) and Withdrawal Seizure-Resistant (WSR) mice,68 using in situ hybridization (ISH) and KOR autoradiography. Ethanol withdrawal increased PDYN mRNA in multiple brain regions of WSP, but not WSR mice;68 basal KOR binding was higher in WSR mice than in WSP mice. Furthermore, increased KOR density was shown to be present during ethanol withdrawal in WSP mice.68 Thus differences in the κ-opioid system might contribute to the selected differences in ethanol withdrawal severity. This evidence supports the role of differential expression of PDYN and its receptor OPRK1 in alcohol dependence.
In summary, we have demonstrated that variations in the κ-opioid system, in genes encoding both the receptor (OPRK1) and its ligand (PDYN), are associated with risk for alcoholism. Association of variations in both receptor and its ligand makes biological sense, since both should affect the overall signaling. There is considerable biological evidence for the involvement of the κ-opioid system in alcohol preference, alcohol intake, and withdrawal. Genes encoding other opioid receptors and their endogenous ligands have also been implicated as playing a role in alcohol dependence, and we are in the process of conducting further genotyping and analysis to explore possible association of these genes.
Amalric M, Cline EJ, Marinez JL, Bloom FE, Koob GF . Rewarding properties of β-endorphin as measured by conditioned place preference. Psychopharmacology 1987; 91: 14–19.
Mezey E, Kiss J, Mueller GP, Eskay R, O'Donohue TL, Palkovits M . Distribution of the pro-opiomelanocortin derived peptides (ACTH, alpha-MSH, beta-endorphin) in the rat hypothalamus. Brain Res 1985; 328: 341–347.
Spanagel R, Herz A, Bals-Kubik R, Shippenberg TS . β-Endorphin-induced locomotor stimulation and reinforcement are associated with an increase in dopamine release in the nucleus accumbens. Psychopharmacology (Berlin) 1991; 104: 51–56.
Wilcox JN, Roberts JL, Chronwall BM, Bishop JF, O'Donohue T . Localization of proopiomelanocortin mRNA in functional subsets of neurons defined by their axonal projections. J Neurosci Res 1986; 16: 89–96.
Olson GA, Olson RD, Kastin AB . Endogenous opioids (review). Peptides 1990; 11: 1277–1304.
Herz A . Endogenous opioid systems and alcohol addiction. Psychopharmacology (Berlin) 1997; 129: 99–111.
Reisine T, Bell GI . Molecular biology of opioid receptors. Trends Neurol Sci 1993; 16: 506–510.
Spanagel R, Herz A, Shippenberg TS . The influence of opioid peptides on dopamine release in the nucleus accumbens: an in-vitro microdialysis study. J Neurochem 1990; 55: 1734–1740.
Spanagel R, Herz A, Shippenberg TS . Identification of the opioid receptor types mediating β-endorphin-induced alterations in dopamine release in the nucleus accumbens. Eur J Pharmacol 1990; 190: 177–184.
Koob GF . Drugs of abuse: Anatomy, pharmacology and function of reward pathways. Trands Pharmacol Sci 1992; 13: 177–193.
Shippenberg TS, Bals-Kubik R, Herz A . Examination of the neurochemical substrates mediating the motivational effects of opioids: role of the mesolimbic dopamine system and D-1 vs D-2 dopamine receptors. J Pharmacol Exp Ther 1993; 265: 53–59.
Goodwin DW . The cause of alcoholism and why it runs in families. Br J Addict Alcohol Other Drugs 1979; 74: 161–164.
Heath AC, Bucholz KK, Madden PA, Dinwiddie SH, Slutske WS, Bierut LJ et al. Genetic and environmental contributions to alcohol dependence risk in a national twin sample: consistency of findings in women and men. Psychol Med 1997; 27: 1381–1396.
Kendler KS, Neale MC, Heath AC, Kessler RC, Eaves LJ . A twin-family study of alcoholism in women. Am J Psych 1994; 151: 707–715.
Pickens RW, Svikis DS, McGue M, Lykken DT, Heston LL, Clayton PJ . Heterogeneity in the inheritance of alcoholism: a study of male and female twins. Arch Gen Psych 1991; 48: 19–28.
McGue M, Bouchard Jr TJ . Genetic and environmental influences on human behavioral differences. Annu Rev Neurosci 1998; 21: 1–24.
Edenberg HJ, Dick DM, Xuei X, Tian H, Almasy L, Bauer LO et al. Variations in GABRA2, encoding the α2 subunit of the GABAA receptor, are associated with alcohol dependence and with brain oscillations. Am J Hum Genet 2004; 74: 705–714.
Porjesz B, Almasy L, Edenberg HJ, Wang K, Chorlian DB, Foroud T et al. Linkage disequilibrium between the beta frequency of the human EEG and a GABAA receptor gene locus. Proc Natl Acad Sci USA 2002; 99: 3729–3733.
Dick DM, Edenberg HJ, Xuei X, Goate A, Kuperman S, Schuckit M et al. Association of GABRG3 with alcohol dependence. Alcohol Clin Exp Res 2004; 28: 4–9.
Jones KA, Porjesz B, Almasy L, Bierut L, Goate A, Wang JC et al. Linkage and linkage disequilibrium of evoked EEG oscillations with CHRM2 receptor gene polymorphisms: implications for human brain dynamics and cognition. Int J Psychophysiol 2004; 53: 75–90.
Wang JC, Hinrichs AL, Stock H, Budde J, Allen R, Bertelsen S et al. Evidence of common and specific genetic effects: association of the muscarinic acetylcholine receptor M2 (CHRM2) gene with alcohol dependence and major depressive syndrome. Human Mol Genet 2004; 13: 1903–1911.
Hinrichs A, Wang J, Bufe B, Kwon J, Budde J, Allen R et al. Functional variant in a bitter taste receptor (hTAS2R16) influences risk for alcohol dependence. Am J Hum Genet 2006; 78: 103–111.
Herz A . Opioid reward mechanisms: a key role in drug abuse. Can J Physiol Pharmacol 1998; 76: 252–258.
LaForge KS, Yuferov V, Kreek MJ . Opioid receptor and peptide gene polymorphisms: potential implications for addictions. Eur J Pharmacol 2000; 410: 249–268.
Kreek MJ, Bart G, Lilly C, LaForge KS, Nielsen DA . Pharmacogenetics and human molecular genetics of opiate and cocaine addictions and their treatments. Pharmacol Rev 2005; 57: 1–26.
Volpicelli JR, Alterman AI, Hayashida M, O'Brien CP . Naltrexone in the treatment of alcohol dependence. Arch Gen Psych 1992; 49: 876–880.
Jaffe AJ, Rounsaville B, Chang G, Shottenfeld RS, Meyer RE, O'Malley SS . Naltrexone, relapse prevention and supportive therapy with alcoholics: an analysis of patient treatment matching. J Consult Clin Psychol 1996; 64: 1044–1053.
O'Malley SS, Krishnan-Sarin S, Farren C, Sinha R, Kreek MJ . Naltrexone decreases craving and alcohol self-administration in alcohol-dependent subjects and activates the hypothalamo-pituitary-adrenocortical axis. Psychopharmacology 2002; 160: 19–29.
Wand GS, Mangold D, El Deiry S, McCaul ME, Hoover D . Family history of alcoholism and hypothalamic opioidergic activity. Arch Gen Psych 1998; 55: 1114–1119.
Wand GS, Mangold D, Ali M . Adrenocortical responses and family history of alcoholism. Alcol Clin Exp Res 1999; 23: 1185–1190.
Wand GS, McCaul ME, Gotjen D, Reynolds J, Lee S . Confirmation that offspring from families with alcohol-dependent individuals have greater hypothalamic-pituitary-adrenal axis activation induced by naloxone compound with offspring without a family history of alcohol dependence. Alcol Clin Exp Res 2001; 25: 1134–1139.
Stoller KB, Bigelow GE, Walsh SL, Strain EC . Effects of buprenorphine/naloxone in opioid-dependent humans. Psychopharmacology 2001; 154: 230–242.
Crabbe Jr JC, Li T-K . Strategies in preclinical substance abuse research. In: Bloom FE, Kupfer DJ (eds). Psychopharmacology: The fourth generation of progress, 4th edn. Raven Press: New York, 1995, pp 799–811.
Jamensky NT, Gianoulakis C . Content of dynorphins and κ-opioid receptors in distinct brain regions of C57BL/and DBA/2 mice. Alcohol Clin Exp Res 1997; 21: 1455–1464.
Marinelli PW, Kiianmaa K, Gianoulakis C . Opioid propeptide mRNA content and receptor density in the brains of AA and ANA rats. Life Sci 2000; 66: 1915–1927.
Lindholm S, Werme M, Brene S, Franck J . The selective kappa-opioid receptor agonist U50, 488 H attenuates voluntary ethanol intake in the rat. Behav Brain Res 2001; 120: 137–146.
Zapata A, Chefer R, Gonzales R, Shippberg TS . Kappa opioid receptor modulation of the neurochemical effects of ethanol in the nucleus accumbens and central amygdale. Alcohol Clin Exp Res (Suppl) 2005; 29: 181A.
Zhang Y, Butelman ER, Schlussman SD, Ho A, Kreek MJ . Effect of the endogenous κ opioid agonist dynorphin A(1–17) on cocaine-evoked increases in striatal dopamine levels and cocaine-induced place preference in C57BL/6J mice. Psychopharmacology 2004; 172: 422–429.
Kovacs KM, Szakall I, O'Brien D, Wang R, Vinod KY, Saito M et al. Decreased oral self-administration of alcohol in κ-opioid receptor knock-out mice. Alcohol Clin Exp Res 2005; 29: 730–738.
Simonin F, Gaveriaux-Ruff C, Befort K, Matthes H, Lannes B, Micheletti G et al. Opioid receptor in humans: cDNA and genomic cloning, chromosomal assignment, functional expression, pharmacology, and expression pattern in the central nervous systems. Proc Natl Acad Sci USA 1995; 92: 7006–7010.
Yuferov V, Fussell D, LaForge KS, Nielsen DA, Gordon D, Ho A et al. Redefinition of the human pappa opioid receptor gene (OPRK1) structure and association of haplotypes with opiate addiction. Pharmacogenetics 2004; 14: 793–804.
Loh EW, Fann CSJ, Chang YT, Chang CJ, Cheng ATA . Endogenous opioid receptor genes and alcohol dependence among Taiwanese Han. Alcohol Clin Exp Res 2004; 28: 15–19.
Horikawa S, Takai T, Toyosato M, Takahashi H, Noda M, Kakidani H et al. Isolation and structural organization of the human preproenkephalin B gene. Nature 1983; 306: 611–614.
Charnes ME . Ethanol and opioid receptor signaling. Experientia 1989; 45: 518–528.
Clarke S, Zimmer A, Zimmer AM, Hill RG, Kitchen L . Region selective up-regulation of μ-, δ- and κ-opioid receptors but not opioid receptor-like 1 receptors in the brains of enkephalin and dynorphin knockout mice. Neuroscience 2003; 122: 479–489.
Zhang S, Tong Y, Tian M, Dehaven RN, Cortesburgos L, Mansson E et al. Dynorphin A as a potential endogenous ligand for four members of the opioid receptor gene family. J Pharmacol Exp Ther 1998; 286: 136–141.
Chen ACH, LaForge KS, Ho A, McHugh PF, Kellogg S, Bell K et al. Potentially functional polymorphism in the promoter region of prodynorphin gene may be associated with protection against cocaine dependence or abuse. Am J Med Genet (Neuropsychiatric Genetics) 2002; 114: 429–435.
Zimprich A, Kraus J, Wöltje M, Mayer P, Rauch E, Höllt V . An allele variation in the human prodynorphin gene promoter alters stimulus-induced expression. J Neurochem 2000; 74: 474–477.
Geijer T, Jonsson E, Neiman J, Gyllander A, Sedvall G, Rydberg U et al. Prodynorphin allelic distribution in Scandinavian chronic alcoholics. Alcohol Clin Exp Res 1997; 21: 1333–1336.
Foroud T, Edenberg HJ, Goate A, Rice J, Flury L, Koller DL et al. Alcoholism susceptibility loci: confirmation studies in a replicate sample and further mapping. Alcohol Clin Exp Res 2000; 24: 933–945.
Reich T, Edenberg HJ, Goate A, Williams JT, Rice JP, Van Eerdewegh P et al. Genome-wide search fro genes affecting the risk for alcohol dependence. Am J Med Genet (Neuropsychiatric Genetics) 1998; 81: 207–215.
Bucholz KK, Cadoret R, Cloninger CR, Dinwiddie SH, Hesselbrock VM, Nurnberger JI et al. A new semi-structured psychiatric interview for use in genetic linkage studies: a report on the reliability of the SSAGA. J Stud Alcohol 1994; 55: 149–158.
Hesselbrock M, Easton C, Bucholz KK, Schuckit M, Hesselbrock V . A validity study of the SSAGA—a comparison with the SCAN. Addiction 1999; 94: 1361–1370.
American Psychiatric Association. Diagnostic and statistical manual of mental disorders, 3rd edn, revised. American Psychiatric Association Press: Washington, DC, 1987.
Feighner JP, Robins E, Guze SB, Woodruff Jr RA, Winokur G, Munoz R . Diagnostic criteria for use in psychiatric research. Arch Gen Psych 1972; 26: 57–63.
O'Connell JR, Weeks DE . PedCheck: A program for identification of genotype incompatibilities in linkage analysis. Am J Hum Genet 1998; 63: 259–266.
Boehnke M . Allele frequency estimation from data on relatives. Am J Hum Genet 1991; 48: 22–25.
Barrett JC, Fry B, Maller J, Daly MJ . Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 2005; 21: 263–265.
Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B et al. The structure of haplotype blocks in the human genome. Science 2002; 296: 2225–2229.
The International HapMap Consortium. A haplotype map of the human genome. Nature 2005; 437: 1299–1320.
Martin ER, Monks SA, Warren LL, Kaplan NL . A test for linkage and association in general pedigrees: The Pedigree Disequilibrium Test. Am J Hum Genet 2000; 67: 146–154.
Dudbridge F . Pedigree disequilibrium tests for multilocus haplotypes. Genet Epidemiol 2003; 25: 115–121.
Martin ER, Monks SA, Warren LL, Kaplan NL . Correcting for a potential bias in the pedigree disequilibrium test. Am J Hum Genet 2001; 68: 1065–1067.
de Bakker PIW, Yelensky R, Pe'er I, Gabriel SB, Daly MJ, Altshuler D . Efficiency and power in genetic association studies. Nature Genet 2005; 37: 1217–1223.
Bakalkin G, Yakovleva T, Terenius L . Prodynorphin gene expression relates to NF-κB factors. Mol Brain Res 1994; 24: 301–312.
Nikoshkov A, Hurd YL, Yakovleva T, Bazov I, Marinova Z, Cebers G et al. Prodynorphin transcripts and proteins differentially expressed and regulated in the adult human brain. FASEB J 2005; 12 July [E-pub ahead of print].
Winkler A, Spanagel R . Differences in the kappa opioid receptor mRNA content in distinct brain regions of two inbred mice strains. NeuroReport 1998; 9: 1459–1464.
Beadles-Bohling AS, Wiren KM . Alteration of kappa-opioid receptor system expression in distinct brain regions of a genetic model of enhanced ethanol withdrawal severity. Brain Res 2005; 1046: 77–89.
We thank Jinghua Zhao, Gayathri Rajan, Christopher Rush, Jun Wang and Robert George for technical assistance with genotyping, sequencing and data organization. Genotyping facilities were provided by the Center for Medical Genomics at Indiana University School of Medicine, supported in part by the Indiana 21st Century Research and Technology Fund and the Indiana Genomics Initiative at Indiana University (INGEN, supported in part by the Lilly Endowment, Inc.). The Collaborative Study on the Genetics of Alcoholism (COGA), Co-Principal Investigators B Porjesz, V Hesselbrock, H Edenberg, L Bierut, includes nine different centers where data collection, analysis, and storage take place. The nine sites and Principal Investigators and Co-Investigators are: University of Connecticut (V Hesselbrock); Indiana University (HJ Edenberg, J Nurnberger Jr, PM Conneally, T Foroud); University of Iowa (S Kuperman, R Crowe); SUNY Downstate (B Porjesz); Washington University in St Louis (L Bierut, A Goate, J Rice); University of California at San Diego (M Schuckit); Howard University (R Taylor); Rutgers University (J Tischfield); Southwest Foundation (L Almasy). Zhaoxia Ren serves as the NIAAA Staff Collaborator. This national collaborative study is supported by the NIH Grant U10AA008401 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA). In memory of Henri Begleiter and Theodore Reich, Principal and Co-Principal Investigators of COGA since its inception; we are indebted to their leadership in the establishment and nurturing of COGA, and acknowledge with great admiration their seminal scientific contributions to the field.
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