Because tolerance is an important aspect of alcohol dependence (AD) in humans, recent evidence showing that the Drosophila gene hang is critically involved in the development of alcohol tolerance in the fly suggests that variation in related human loci might be important in the etiology of alcohol-related disorders. The orthology of hang in mammals is complex, but a number of human gene products (including ZNF699) with similar levels of amino-acid identity (18–26%) and similarity (30–41%), are consistently identified as the best matches with the translated hang sequence. We tested for association between the dichotomous clinical phenotype of alcohol dependence and seven single nucleotide polymorphisms (SNPs) in ZNF699 in our sample of 565 genetically independent cases and 496 siblings diagnosed with AD, and 609 controls. In analyses of genetically independent cases and controls, four of the seven single markers show strong evidence for association with AD (0.00003<Fisher's exact P<0.001), and the most significant single marker, rs7254880, tags an associated haplotype with frequency 0.071 in cases compared to 0.034 in controls (χ2 15.563, P<0.00008, 5000 permutation P<0.001, OR 2.17); inclusion of affected siblings gives similar results. Expression analyses conducted in independent postmortem brain samples show that expression of ZNF699 mRNA is significantly reduced in the dorsolateral prefrontal cortex of individuals carrying this haplotype compared with other observed haplotype combinations.
Alcoholism is a chronic, disabling and often treatment-resistant disorder with an estimated lifetime prevalence in the US of ∼20% in males and ∼10% in females.1, 2 Family, twin and adoption studies provide substantial evidence that genetic variation is a major factor in its etiology.3 However, detection of susceptibility genes is difficult because alcoholism probably reflects a clinically and etiologically heterogeneous set of disorders.4 The results of linkage studies in a variety of populations show limited positional consistency and do not generally achieve genome-wide significance.5 As a result, the extremely rich model organism literature on the genetics of alcohol-related traits represents an excellent source of potential candidate genes for assessment in human populations.
Alcoholism is a broad term encompassing both abuse and dependence symptoms. A narrow definition of alcohol dependence (AD) is closer to a core definition of physiological dependence and is less influenced by cultural or situational factors than are abuse symptoms. It is, as a result, the preferred phenotype for detection of susceptibility loci. Under the current, widely-used Diagnostic and Statistical Manual of Mental Disorders – Fourth Edition (DSM-IV) criteria,6 symptoms of AD include direct indices of physiological dependence (withdrawal, tolerance) as well as behavioral indices of addiction (lack of control over amount consumed, binge drinking, inability to quit, continued use despite serious medical or psychiatric consequences, and drinking to the exclusion of other activities). Individuals must manifest at least three of these seven criteria to be classified as having AD.
Alcohol consumption typically leads to the development of tolerance, an acquired resistance to its physiological and behavioral effects. Tolerance allows increased consumption of alcohol, and is thought to be an important part of the risk for heavy drinking and AD.7, 8, 9 Experiments in Drosophila show that flies develop ethanol tolerance with acquisition and dissipation kinetics similar to mammals.10 A recent study11 identified one P-element insertion mutant with normal response to initial ethanol exposure but aberrant development of tolerance. Tolerance is measured as an increase in time to loss of postural control (mean elution time, MET) when exposed to ethanol vapor in an inebriometer.12, 13, 14 Initial MET and ethanol absorption and metabolism were all normal, but these mutants exhibit a 60% reduction in tolerance in single (14±3% increase in MET compared with 35±2% increase in control flies, P<0.0001) and multiple exposure paradigms (P<0.001). The P-element insertion was localized to exon 1 of the Drosophila gene hang, encoding a 1901 amino-acid zinc-finger (ZNF) protein. No detectable transcript or protein was present in the homozygous mutant fly lines.
ZNF699 (Chr. 19p13.2) is annotated in the National Center for Biotechnology Information (NCBI: http://www.ncbi.nlm.nih.gov/gquery/gquery.fcgi) as the human hang ortholog. We tested for association between single nucleotide polymorphism (SNP) markers in ZNF699 and AD in our Irish Affected Sib Pair Study of Alcohol Dependence (IASPSAD) sample.
Materials and methods
Subjects and phenotypes
Sample details (including clinical characteristics and evidence of diagnostic validity) have been described previously.15 Briefly, data collection occurred between 1998 and 2002 in a collaboration between Virginia Commonwealth University, the Health Research Board, Dublin, Ireland and Shaftesbury Square Hospital in Belfast, Northern Ireland. Ascertainment of probands was conducted in community alcoholism treatment facilities and public and private hospitals in the Republic of Ireland and Northern Ireland. Probands and affected siblings were eligible for study inclusion if they met DSM-IV criteria for AD, and if all four grandparents had been born in Ireland, Northern Ireland, Scotland, Wales or England. Lifetime history of DSM-IV AD was assessed using the SSAGA interview, version 1116 administered by trained research interviewers. As expected for a clinically ascertained sample, the probands and siblings were severely affected. Endorsement rates for the seven AD criteria ranged from 87 to 97%. Controls were recruited in the Republic of Ireland from the Garda Siochana (the national police force) and the Forsa Cosanta Aituil (the army reserve). In Northern Ireland, controls were recruited from volunteers donating at the Northern Ireland Blood Transfusion Service. Controls were briefly screened and their samples excluded if they reported a history of alcoholism. All participants provided informed consent before assessment and sample collection.
Our sample from families containing at least two siblings with AD (N=1414) included 591 probands and 610 affected siblings who met all eligibility criteria, and 213 additional family members. Of these, 1407 participants provided DNA samples. Eighty-five per cent (1196) provided blood samples, 15% (211) provided buccal swab samples only. Although acceptable for microsatellite genotyping, buccal swab samples do not perform reliably in our hands for SNP genotyping methods. After extensive optimization for SNP genotyping, DNA samples from 565 probands, 496 affected siblings and 609 controls gave reliable results. Individual samples (18 probands, 12 siblings, 12 controls in this study) were excluded from analysis if they were missing more than three genotypes. Our final sample for analysis of this dataset was 547 probands, 484 affected siblings and 597 controls.
Independent postmortem samples, consisting of genomic DNA and RNA isolated from dorsolateral prefrontal cortex (Brodmann's area 46) from 35 control individuals, were provided by the Stanley Foundation for Medical Research. Exclusion criteria for these control samples included: (1) significant structural brain pathology, (2) history of pre-existing central nervous system disease, (3) poor RNA quality, (4) documented IQ <70, (5) age less than 30 years and (6) substance abuse within 1 year of death.17
Single nucleotide polymorphisms
At the time we began this study, the ZNF699 locus was not covered by HapMap (http://www.hapmap.org/index.html), so we selected seven markers spread across the 10.8 Kb genomic locus from dbSNP (http://www.ncbi.nlm.nih.gov/SNP/). Relatively few SNPs have been deposited for ZNF699; three SNPs from this study overlap those included in the January 2006 HapMap data release (chr19: 9270275–9275172). Marker identities and positions, and intermarker distances are shown in Table 1. PCR and single-base extension primer sequences are available from the authors on request.
We genotyped SNPs by fluorescence polarization-template directed incorporation of dye terminators (FP-TDI).18 Genomic DNA is amplified in 10 μl reaction mixtures according to standard protocols. Exonuclease 1 and shrimp alkaline phosphatase cleanup is followed by heat inactivation. The DNA mixture is kept at 4°C and used in the FP-TDI assay without further quantification or characterization. After the primer extension reaction, FP measurement is taken on an LJL Analyst fluorescence reader. The average FP value and standard deviation of the negative control samples are determined for each set of experiments. The FP value of the test sample reactions is then compared to the average FP value of the control samples. If the net change is >40 mP (more than seven times the standard deviation of the controls), the test sample is scored as positive for the allele. Our automated procedure for FP-TDI genotype scoring19 reduces errors by minimizing data handling and scoring the genotypes using a statistical model. A mixed subset of 94 samples was genotyped independently twice for each marker to assess reliability and error rates, with no discordant replicates.
Quantitative real-time PCR
We assessed ZNF699 expression levels in RNA from dorsolateral prefrontal cortex of N=34 control samples in the Stanley Foundation postmortem brain series (one of 35 samples had very low levels of RNA and was excluded from analysis). ZNF699 cDNA was reverse transcribed using the Ready-To-Go Kit from Amersham Bioscience (Piscataway, NJ, USA) following the manufacturer's instructions. Expression was assessed in triplicate by Quantitative Real-Time PCR using SYBR Green I on a Bio-Rad (Hercules, CA, USA) iCycler platform, normalized against two reference genes20 (GAPDH and TBP) and analyzed by the algorithm21 implemented in the Relative Gene Expression Macro software. Melting curve and electrophoretic analyses in tandem for ZNF699 and both reference genes demonstrated a single sharp melting peak matched to a prominent single band of expected size.
To generate the standard curves, the cDNAs for ZNF699, GAPDH and TBP were cloned in the pcDNA3.1 expression vector and used as standards in the subsequent experiments. Primer sequences were designed using the Beacon Designer software version 4.02, and are available from the authors on request. The threshold cycles (CT) were determined automatically, and were subsequently used to calculate and plot the linear regression line by plotting the logarithm of template concentration against the corresponding CT. The standard points were made using five 1:5 serial dilutions and the quality of the standard curve was judged from the slope and the correlation coefficient (r). Correlation coefficients r>0.99 and PCR efficiency >98% were observed for the experimental and both reference loci.
For the association study, single-marker and haplotype analyses were performed using HAPLOVIEW v3.2.24 In addition to reconstructing haplotypes and providing case and control frequencies, the newest implementation includes a permutation test (set at 5000 for these analyses) to assess empirical significance. Single-marker significance levels were independently calculated using Fisher's exact test implemented in SAS.25
Haplotype-specific expression differences in the postmortem samples were assessed by the Mann–Whitney U test, and the effects of potential confounder variables (age, postmortem interval, refrigeration interval, brain pH and smoking) were assessed by ANOVA, both implemented in Prism v4.0 (GraphPad Software, San Diego, CA, USA).
Orthology of hang in other species
Many Drosophila genes have obvious mammalian and human orthologs.26 ZNF699 (Chr. 19p13.2) is annotated in the National Center for Biotechnology Information (NCBI) as the human hang ortholog, which was our original motivation to examine this gene. However, during the course of the association study below, several features raised questions about this annotation. First, ZNF699 contains a Krüppel-associated box (KRAB) domain, found only in tetrapod vertebrates.27 Second, the polypeptide lengths of ZNF699 and hang (642 and 1901 amino acids, respectively) and the arrangement of ZNF domains are very different. Additionally, ZNF521 (Chr. 18q11.2) is also annotated in some parts of NCBI as the human hang ortholog. These features led us to examine the sequence homology in detail.
Comparisons using the reference hang protein sequence (NP_727980) as a BLAST28 query show that the orthology of hang in other species is not straightforward. Across species, a number of ZNF proteins (including orthologs of ZNF699, PRDM15, ZBTB40, ZBTB11, PRDM5 and ZNF658) are consistently identified as among the best matches with hang (Supplementary Tables 1 and 2). These proteins show low but similar levels of identity (18–26%) and similarity (30–41%) driven by the zinc-finger domains. In humans, there is some clustering of these loci on chromosomes 19p13.2, 19q13.2–q13.44 and 9p12–p13.1. ZNF521 orthologs are not identified as among the best matches with hang except in Gallus gallus. There thus appears to be no clear ortholog of hang in any species examined.
In dog, cattle and nematode, hang (NP_727980) and ZNF699 (XP_371132) reference protein sequence BLAST searches jointly identify the same best match sequences (Supplementary Table 3); other species show much greater divergence between the results of these parallel searches. Although the relationship between hang and ZNF699 is a distant one, the presence of similar relationships in other species makes these more likely to reflect meaningful functional conservation. There are two reasons to suspect that identity and similarity in ZNF genes across divergent species may be lower than is observed in, for example, highly conserved enzymes. First, zinc-fingers function by binding specific DNA target sequences. If these target sequences diverge across evolution, the ZNF proteins must co-evolve to maintain function. Second, the presence of KRAB or other N-terminal domains that are limited to certain organisms automatically reduce the identity and similarity of proteins in wider comparisons.
All seven markers are in Hardy–Weinberg equilibrium and in high LD, with contiguous marker pairwise D′ values all 0.98 or greater (Figure 1a). D′ values for non-contiguous markers range from 0.72 to 1.0. Our data are in good agreement with the current release of HapMap (chromosome 19: 9270275–9275172, Figure 1b). Because of our increased SNP number, we observe a larger number of haplotypes (N=7) with frequency ⩾0.01 compared to HapMap (N=3), but when the results of the three overlapping markers are examined, the same haplotypes are observed with differences in the frequency estimates <0.02. We observe one haplotype with frequency 0.01 that is not detected in the HapMap data. Both datasets indicate lower LD between rs7254880 and rs7252865 (confidence interval method29 shown in Figure 1).
Single-marker and haplotype association
Results of single-marker analyses in independent cases (N=547) and controls (N=597) are shown in Table 1. Four of the seven markers show strong evidence of association with AD. Asymptotic χ2 and Fisher's exact test P-values are identical in this analysis, and are in the range 0.00003<P<0.001. All four SNPs remain significant in the 5000 permutation test, with significance levels in the range 0.00001<P<0.003. Odds ratios (OR) between the associated alleles of these four markers and AD range from 1.37 to 2.33. One additional SNP, rs12150875, is marginally associated with AD, but does not remain significant in the permutation test. In order to confirm the direction and magnitude of the independent case sample results, we also analyzed the full affected sib-pair sample. These analyses yield identical patterns of association, and critically the OR for associated alleles range from 1.30 to 2.33 (data not shown).
Results of haplotype analyses in the independent case and control sample are shown in Table 2. The most significantly associated single marker, rs7254880, tags an associated haplotype with frequency 0.071 in cases compared to 0.034 in controls (asymptotic P=0.00008, 5000 permutation P=0.001, OR 2.17). Another haplotype is significantly less common in cases compared to controls (asymptotic P=0.0001, 5000 permutation P<0.001, OR 0.72). The six common haplotypes account for 98.6% of haplotypes observed (98.5% of case and 98.8% of control haplotypes). The associated allele of all four markers associated in single-marker tests is present on the associated haplotype. For rs12150875, allele 1 was moderately associated with AD in single-marker tests, but allele 2 is present on the risk haplotype. The single-marker association is substantially weaker than those seen for other SNPs in this study, and critically, allele 2 is also present on the much more common haplotype over-represented in controls. Analyses of the full affected sib-pair sample again yield identical patterns of association, with the same specific haplotypes over-represented in cases (OR 2.08) and controls (OR 0.76), respectively (data not shown).
We were interested to test whether we could detect any functional effect of the associated haplotype. We assessed ZNF699 expression levels in 34 postmortem control samples. These samples were also genotyped for four of the seven ZNF699 SNPs which carry complete haplotype tagging information (rs7254880, rs12460279, rs10854142 and rs12150874, positions 1, 3, 5 and 6 in the haplotypes shown in Table 2). Haplotype reconstruction identified N=5 samples carrying one copy of the associated haplotype. We tested for expression level differences between the five heterozygotes and all other haplotype pairings. Data are expressed as mean ±95% CI (Figure 2). ZNF699 expression in the five individuals heterozygous for the associated haplotype (2-1-11-/het) was significantly reduced compared to the 29 individuals with no copy of this haplotype (all others) in a Mann–Whitney U test (two-tailed P=0.0115). This difference was not due to effects of potential confounder variables assessed by ANOVA (age, postmortem interval, refrigeration interval, brain pH or smoking, F=1.651; df=5; P=0.216). These data support the existence of functional variation on the associated haplotype.
The expression data suggest a specific hypothesis about possible mechanisms involved in the association of ZNF699 with AD. ZNF699 contains a KRAB domain (positions 19–79), a domain strongly associated with transcriptional repression.27 Previous work in Drosophila has shown that the development of chronic tolerance is blocked by cyclohexamide treatment, suggesting that it requires protein synthesis.30 Our observation of reduced expression from the associated haplotype suggests that this downregulation of a likely transcriptional repressor may relax the control of expression of downstream loci, facilitating neuroplastic changes in response to ethanol exposure.
ZNF699 is one member of a cluster of ZNF genes on Chr. 19p13.2. One limitation of this study is that the current (January 06) HapMap data show long-range LD extending ∼157 Kb centromeric from ZNF699 and covering three other members of this cluster (ZNF559, ZNF177 and ZNF266). It is therefore possible that the association observed with ZNF699 is actually arising from variants in one of these three genes, but we think this is unlikely. The associated ZNF699 haplotype shows significantly reduced expression relative to other haplotypes. If the association was due to variation in one of the loci in LD with ZNF699, then our expression data would only be expected if clustered ZNF genes were under coordinated regulation, and this has not been demonstrated.27 These data suggest that, of the loci in this region of extended LD, ZNF699 is the most likely functional candidate. We are collecting additional data currently to test for long-range LD and association in these other three loci in our sample.
Recent mouse studies have provided parallel evidence implicating ZNF genes in ethanol-related traits and ethanol response. Zfp142 and Zfp133 are implicated in quantitative trait locus (QTL) studies of initial sensitivity to alcohol. QTL intervals were mapped in inbred lines31, 32 and coding differences between lines were identified in eight genes in the QTL intervals. Mapping in interval-specific congenic recombinant mice,33 which carry smaller, overlapping sections of the QTL intervals, showed that only the observed coding sequence changes in Zfp142 or Ptprn in one interval, and Zfp133 in another, could account for the phenotypic difference.34
An expression-array study of multiple trait-selected and isogenic mouse lines found that the target binding sequence for Zfp143 was over-represented in the promoter regions of genes showing increased expression in high ethanol-consuming lines, and that expression of Zfp143, a known transcriptional activator,35 was also increased in these lines. In a separate comparison of expression data between a B6.D2 congenic line and the B6 background, Zfp291 showed reduced expression in high ethanol-consuming lines.36 Zfp291 contains a U1 subclass ZNF domain characteristic of RNA binding proteins37 and maps to the strongest alcohol preference QTL in B6- and D2-derived populations, found on mouse chromosome 9.
We are following up the present study in several ways. We are cloning the case- and control-associated haplotypes from this study into cell lines for expression array analyses and carrying out work to identify the target binding sequence of ZNF699 to identify genes under its direct transcriptional control. Quantitative phenotype data38 were collected in the IASPSAD for use in linkage analyses. As this is a clinically ascertained sib-pair sample collected for linkage studies, subjects tend to be severely affected. A consequence of this sampling design is a restriction in range for quantitative traits, resulting in low power to detect QTL association. We are therefore currently designing a replication study in an epidemiological sample appropriate for the assessment of either dichotomous or quantitative phenotypes. Finally, the diversity of human proteins with similar relationships to hang suggests that a number of these loci may be involved in alcohol-related traits. We are studying additional loci identified by the present work currently.
Kessler RC, McGonagle KA, Zhao S, Nelson CB, Hughes M, Eshleman S et al. Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States. Results from the National Comorbidity Survey. Arch Gen Psychiatry 1994; 51: 8–19.
Grant BF . Prevalence and correlates of alcohol use and DSM-IV alcohol dependence in the United States: results of the National Longitudinal Alcohol Epidemiologic Survey. J Stud Alcohol 1997; 58: 464–473.
Prescott CA . The genetic epidemiology of alcoholism: sex differences and future directions. In: Agarwal DP, Seitz HK (eds). Alcohol in Health and Disease. Marcel Dekker: New York, 2001, pp 125–149.
Zucker RA, Gomberg ES . Etiology of alcoholism reconsidered. The case for a biopsychosocial process. Am Psychol 1986; 41: 783–793.
Prescott CA, Sullivan PF, Kuo PH, Webb BT, Vittum J, Patterson DG et al. Genomewide linkage study in the Irish affected sib pair study of alcohol dependence: evidence for a susceptibility region for symptoms of alcohol dependence on chromosome 4. Mol Psychiatry 2006; 11: 603–611.
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders – Fourth Edition. American Psychiatric Association: Washington, DC, 1994.
Tabakoff B, Cornell N, Hoffman PL . Alcohol tolerance. Ann Emerg Med 1986; 15: 1005–1012.
Le AD, Mayer JM . Acute tolerance. In: Dietrich RA, Erwin VG (eds). Pharmacological Effects of Ethanol on the Nervous System. CRC Press: Boca Raton, 1996, pp 251–268.
Fadda F, Rossetti ZL . Chronic ethanol consumption: from neuroadaptation to neurodegeneration. Prog Neurobiol 1998; 56: 385–431.
Scholz H, Ramond J, Singh CM, Heberlein U . Functional ethanol tolerance in Drosophila. Neuron 2000; 28: 261–271.
Scholz H, Franz M, Heberlein U . The hangover gene defines a stress pathway required for ethanol tolerance development. Nature 2005; 436: 845–847.
Weber KE, Diggins LT . Increased selection response in larger populations. II. Selection for ethanol vapor resistance in Drosophila melanogaster at two population sizes. Genetics 1990; 125: 585–597.
Moore MS, DeZazzo J, Luk AY, Tully T, Singh CM, Heberlein U . Ethanol intoxication in Drosophila: genetic and pharmacological evidence for regulation by the cAMP signaling pathway. Cell 1998; 93: 997–1007.
Singh CM, Heberlein U . Genetic control of acute ethanol-induced behaviors in Drosophila. Alcohol Clin Exp Res 2000; 24: 1127–1136.
Prescott CA, Sullivan PF, Myers JM, Patterson DG, Devitt M, Halberstadt LJ et al. The Irish affected sib pair study of alcohol dependence: study methodology and validation of diagnosis by interview and family history. Alcohol Clin Exp Res 2005; 29: 417–429.
Bucholz KK, Cadoret R, Cloninger CR, Dinwiddie SH, Hesselbrock VM, Nurnberger Jr 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.
Torrey EF, Webster M, Knable M, Johnston N, Yolken RH . The Stanley Foundation Brain Collection and Neuropathology Consortium. Schizophr Res 2000; 44: 151–155.
Chen X, Levine L, Kwok PY . Fluorescence polarization in homogeneous nucleic acid analysis. Genome Res 1999; 9: 492–498.
Van den Oord EJCG, Jiang Y, Riley BP, Kendler KS, Chen X . FP-TDI SNP genotype scoring by manual and statistical procedures: a study of error rates and types. Biotechniques 2003; 34: 610–624.
Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 2002; 3: research0034.1–research0034.11.
Livak KJ, Schmittgen TD . Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 2001; 25: 402–408.
Stephens M, Smith NJ, Donnelly P . A new statistical method for haplotype reconstruction from population data. Am J Hum Genet 2001; 68: 978–989.
Stephens M, Donnelly P . A comparison of bayesian methods for haplotype reconstruction from population genotype data. Am J Hum Genet 2003; 73: 1162–1169.
Barrett JC, Fry B, Maller J, Daly MJ . Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 2005; 21: 263–265.
SAS Institute. SAS v9, Cary, NC, 2002.
Adams MD, Celniker SE, Holt RA, Evans CA, Gocayne JD, Amanatides PG et al. The genome sequence of Drosophila melanogaster. Science 2000; 287: 2185–2195.
Urrutia R . KRAB-containing zinc-finger repressor proteins. Genome Biol 2003; 4: 231.
Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997; 25: 3389–3402.
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.
Berger KH, Heberlein U, Moore MS . Rapid and chronic: two distinct forms of ethanol tolerance in Drosophila. Alcohol Clin Exp Res 2004; 28: 1469–1480.
Markel PD, Bennett B, Beeson M, Gordon L, Johnson TE . Confirmation of quantitative trait loci for ethanol sensitivity in long-sleep and short-sleep mice. Genome Res 1997; 7: 92–99.
Bennett B, Johnson TE . Development of congenics for hypnotic sensitivity to ethanol by QTL-marker-assisted counter selection. Mamm Genome 1998; 9: 969–974.
Bennett B, Beeson M, Gordon L, Carosone-Link P, Johnson TE . Genetic dissection of quantitative trait loci specifying sedative/hypnotic sensitivity to ethanol: mapping with interval-specific congenic recombinant lines. Alcohol Clin Exp Res 2002; 26: 1615–1624.
Ehringer MA, Thompson J, Conroy O, Yang F, Hink R, Bennett B et al. Fine mapping of polymorphic alcohol-related quantitative trait loci candidate genes using interval-specific congenic recombinant mice. Alcohol Clin Exp Res 2002; 26: 1603–1608.
Kubota H, Yokota S, Yanagi H, Yura T . Transcriptional regulation of the mouse cytosolic chaperonin subunit gene Ccta/t-complex polypeptide 1 by selenocysteine tRNA gene transcription activating factor family zinc finger proteins. J Biol Chem 2000; 275: 28641–28648.
Mulligan MK, Ponomarev I, Hitzemann RJ, Belknap JK, Tabakoff B, Harris RA et al. Toward understanding the genetics of alcohol drinking through transcriptome meta-analysis. Proc Natl Acad Sci USA 2006; 103: 6368–6373.
Legrain P, Choulika A . The molecular characterization of PRP6 and PRP9 yeast genes reveals a new cysteine/histidine motif common to several splicing factors. EMBO J 1990; 9: 2775–2781.
Schuckit MA, Smith TL, Tipp JE . The self-rating of the effects of alcohol (SRE) form as a retrospective measure of the risk for alcoholism. Addiction 1997; 92: 979–988.
We thank Andrew Davies, Jill Bettinger and Michael Miles for helpful discussions of this work, and Andrew Davies for a critical reading of the manuscript and helpful comments on it. This work was supported by NIH Grant AA110408.
About this article
Cite this article
Riley, B., Kalsi, G., Kuo, PH. et al. Alcohol dependence is associated with the ZNF699 gene, a human locus related to Drosophila hangover, in the Irish affected sib pair study of alcohol dependence (IASPSAD) sample. Mol Psychiatry 11, 1025–1031 (2006). https://doi.org/10.1038/sj.mp.4001891
- alcohol dependence
- zinc finger
- transcription factor
Behavioral and Brain Functions (2019)
Human Genetics (2012)