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Human Molecular Genetics Advance Access originally published online on March 28, 2006
Human Molecular Genetics 2006 15(9):1539-1549; doi:10.1093/hmg/ddl073
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© The Author 2006. Published by Oxford University Press. All rights reserved. The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact: journals.permissions@oxfordjournals.org

Association of alcohol dehydrogenase genes with alcohol dependence: a comprehensive analysis

Howard J. Edenberg1,*, Xiaoling Xuei1, Hui-Ju Chen1, Huijun Tian1, Leah Flury Wetherill1, Danielle M. Dick2, Laura Almasy3, Laura Bierut2, Kathleen K. Bucholz2, Alison Goate2, Victor Hesselbrock4, Samuel Kuperman5, John Nurnberger1, Bernice Porjesz6, John Rice2, Marc Schuckit7, Jay Tischfield8, Henri Begleiter6 and Tatiana Foroud1

1Indiana University School of Medicine, Indianapolis, IN, USA, 2Washington University School of Medicine, St Louis, MO, USA, 3Southwest Foundation for Biomedical Research, San Antonio, TX, USA, 4University of Connecticut, Farmington, CT, USA, 5University of Iowa Carver College of Medicine, Iowa City, IA, USA, 6SUNY Health Sciences Center, Brooklyn, NY, USA, 7University of California, San Diego, CA, USA and 8Rutgers University, Piscataway, NJ, USA

* To whom correspondence should be addressed. Tel: +1 3172742353; Fax: +1 3172744686; Email: edenberg{at}iupui.edu

Received January 18, 2006; Accepted March 22, 2006


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Linkage evidence indicated that gene(s) located on chromosome 4q, in the region of the alcohol dehydrogenase (ADH) genes, affected risk for alcoholism. We genotyped 110 single nucleotide polymorphisms (SNPs) across the seven ADH genes and analyzed their association with alcoholism in a set of families with multiple alcoholic members, using the pedigree disequilibrium test. There was strong evidence that variations in ADH4 are associated with alcoholism: 12 SNPs were significantly associated. The region of strongest association ran from intron 1 to 19.5 kb beyond the 3' end of the gene. Haplotype tag SNPs were selected for the block in the ADH4 gene that provided evidence of association and subsequently used in association analysis; the haplotype was significantly associated with alcoholism (P=0.01) There was weaker evidence that variations in ADH1A and ADH1B might also play a role in modifying risk. Among African-Americans, there was evidence that the ADH1B*3 allele was protective.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Alcoholism (alcohol dependence) is a common, complex disease, with significant genetic contributions to the risk. Because drinking ethanol (beverage alcohol) is a necessary environmental condition for manifestation of this disease, the metabolism of ethanol is clearly relevant to the risk. The primary pathway of ethanol metabolism involves oxidation to acetaldehyde, catalyzed by alcohol dehydrogenases (ADHs), followed by further oxidation to acetate, catalyzed by aldehyde dehydrogenases (ALDHs) (1Go).

There is evidence for linkage of a region on chromosome 4q to the risk for alcoholism. The Collaborative Study on the Genetics of Alcoholism (COGA), a large study of families in which at least three individuals met diagnostic criteria for alcohol dependence, provided evidence that a broad region on chromosome 4q was linked to the risk for alcoholism (2Go–4Go). The evidence came primarily from the unaffected members of these families (2Go) and from reduced allele sharing among siblings discordant for the alcohol-dependence phenotype (3Go). Variance-component analysis of the COGA sample showed that the strongest evidence for linkage to alcoholism was in a broad region of chromosome 4q centered near the ADH gene cluster (4Go). A bivariate analysis of alcoholism and event-related potentials increased the evidence for linkage in this region (4Go). Analysis of a related quantitative trait, the maximum number of drinks ever consumed within a 24 h period, showed strongest linkage to a narrower region of chromosome 4q, centered near a cluster of ADH genes (5Go). In a study of a Southwestern Native American population, there was also evidence for linkage of alcohol dependence to markers in the ADH region of chromosome 4 (6Go).

Humans have seven ADH genes tightly clustered on chromosome 4q22 in a head-to-tail array extending over ~365 kb. The order of the genes (from 5' to 3') is ADH7-ADH1C-ADH1B-ADH1A-ADH6-ADH4-ADH5, running from qter toward the centromere. All of the ADH enzymes are broad substrate oxidoreductases that use NAD+/NADH as cofactors (reviewed in 1Go). ADH1A, ADH1B and ADH1C encode {alpha}, ß and {gamma} subunits, respectively; these can form heterodimers, and are defined as class I ADHs. Class I ADHs have Km for ethanol in the range of 0.05–34 mM (1Go). ADH4 encodes {pi}-ADH, a class II ADH with Km for ethanol of 34 mM. ADH5 encodes {chi}-ADH, which is also a glutathione-dependent formaldehyde dehydrogenase; {chi}-ADH has very low affinity for ethanol. ADH7 encodes {sigma}-ADH (also known as µ-ADH); it is the most efficient of these enzymes at oxidizing retinol. The protein encoded by ADH6 has not been purified from tissue.

The primary site of ethanol oxidation is the liver, in which there are high concentrations of most of the ADHs, except {sigma}-ADH (1Go,7Go). Consideration of the enzyme concentrations in liver and the kinetic properties of the ADHs suggest that the class I enzymes (encoded by ADH1A, ADH1B and ADH1C) and the class II enzyme (encoded by ADH4) make the most significant contribution to ethanol metabolism (1Go,7Go,8Go). It has been calculated that the class I enzymes contribute ~70% of the total ethanol oxidizing capacity of the liver at an ethanol concentration of 22 mM (0.1%; 0.08% is defined as legally intoxicated in virtually all states in the USA), and the class II enzyme contributes ~30% (1Go,8Go).

The pharmacokinetics of ethanol metabolism influences the risk for alcohol dependence. Many studies have shown that coding variations in the genes encoding three alcohol-metabolizing enzymes, ADH1B, ADH1C and ALDH2, are associated with risk for alcoholism (1Go,9Go). The ADH1B*2 allele in which arginine 47 is replaced with histidine encodes the ß2 subunit, which has a 40-fold higher Vmax than the ß1 subunit encoded by ADH1B*1 (1Go,8Go). ADH1B*2 is relatively common among Asians, where it has been shown to be protective against alcoholism (1Go,10Go,11Go); although rarer in Europeans, it has also been shown to be protective in that group (12Go,13Go). A different allele, ADH1B*3, encodes the ß3 subunit in which arginine 269 is replaced by cysteine; the ß3 subunit has a 30-fold higher Vmax than the ß1 subunit (1Go,8Go,14Go). ADH1B*3 is relatively common among individuals of African ancestry, and individuals carrying this polymorphism have a higher rate of metabolizing alcohol (15Go). ADH1C, which encodes the {gamma} subunit, has polymorphisms at amino acids 271 and 349; these are in high linkage disequilibrium (LD), with the 271Arg–349Ile form called {gamma}1 (encoded by ADH1C*1) and the 271Gln–349Val called {gamma}2 (encoded by ADH1C*2) (16Go). The Vmax of {gamma}1 is about twice that of {gamma}2 (1Go,8Go). Although the ADH1C*1 allele has a reported protective effect against alcohol dependence in Asian populations (1Go,10Go,17Go,18Go), the LD between ADH1C*1 and ADH1B*2 obscures the independent effect of ADH1C*1 (19Go,20Go). In a Mexican-American population, ADH1C*1 was protective (21Go), and in a Native American population, there was modest evidence of linkage between ADH1C*1 and binge drinking and of association with alcoholism (22Go).

Despite their potential contribution to alcohol metabolism, there have been fewer studies of ADH4 variants. There is a coding variation in ADH4 in which an single nucleotide polymorphism (SNP) leads to either isoleucine or valine at position 308 of the {pi}-ADH encoded by that gene (23Go). [Note that (23Go) and some other references use an alternate, non-standard nomenclature (24Go); this can be confusing. We will use the official nomenclature of the HUGO Gene Nomenclature Committee http://www.gene.ucl.ac.uk/nomenclature/genefamily/ADH.shtml and NCBI.] The kinetic properties with ethanol are very similar for the two forms, but the Val308 form is less stable than the Ile308 form (23Go). A functional mutation in the ADH4 promoter has also been reported. The –136A allele (numbered from the translational start site in the currently recommended manner; this corresponds to the –75A allele numbered from the transcription start site in the original publication) has a 2-fold higher promoter activity in transfected cells (25Go).

The role of ADH enzymes in the pharmacokinetics of alcohol metabolism, along with the linkage results for alcoholism and alcoholism-related phenotypes, make the ADH genes prime candidates for the genes in the chromosome 4q region that influence risk for alcoholism. Rather than limiting our analysis to the previously reported coding and promoter variants in a few of the genes, we systematically analyzed the association of all seven ADH genes with alcoholism, selecting multiple SNPs to query each of the genes.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
One hundred and ten SNPs were genotyped in the 417 kb region containing the 364 kb ADH gene cluster plus 21.5 kb downstream and 31.2 kb upstream; the genes in this cluster are all transcribed in the direction from qter to pter. Genotyping was concentrated within and flanking the genes and at lower density between them (Table 1, Fig. 1). Over 80% of the SNPs had minor allele frequencies (MAFs)>0.2 in the COGA European American sample, and over 88% had MAF>0.15. There were differences between the European-American and African-American samples in the MAFs; in 88 of the 110 SNPs, the difference was greater than 0.05 (Table 1). The extent of LD among the SNPs genotyped across the ADH gene cluster was examined in the European-American samples (Fig. 1). Overall, there was high LD within each gene and lower LD between genes. Within each gene, D' estimates for adjacent SNPs were greater than 0.80 for 87.6% of the adjacent comparisons, excluding those comparisons which included an SNP with MAF<0.05. The extent of coverage was compared with the data in HapMap using Tagger (26Go). Tagger only analyzed 69 of the SNPs we used (others were not genotyped in HapMap or had MAF<0.05). This subset of SNPs showed an average r2 of 0.79 with the 388 SNPs (MAF>0.05) in a 378 kb region; 68% of the SNPs in the region had r2>0.8 with one of our SNPs. Within genes (and the 10 kb flanking each end), the coverage is even better. In ADH4, the 11 SNPs Tagger analyzed (out of 18 genotyped) had mean r2 of 0.91 with the HapMap SNPs and had r2>0.8 with 87% of the 76 known SNPs. Therefore, the association analyses of the tested SNPs carry information on a very large fraction of untested variations within each gene. Similar patterns of LD were observed in the smaller number of African-American samples, although the blocks of LD tended to be smaller.


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  		Table 1. SNPs, positions and MAFs
 

Figure 0731
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  		Figure 1. LD (D') between the SNPs genotyped in the ADH cluster: Haploview. Blue lines at top show positions of the ADH genes (ADH7, ADH1C, ADH1B, ADH1A, ADH6, ADH4, ADH5 in order from qter toward cen); transcription runs from left to right.

 
The pedigree disequilibrium test (PDT) (27Go) was employed to test for association between each ADH SNP and the phenotype of alcohol dependence as defined by the DSM-IV criteria (28Go); DSM-IV criteria gave the strongest linkage signal in prior experiments (4Go). The most consistent evidence of association (P<0.05) was observed using the PDTsum option for the SNPs in the ADH4 gene, extending into the intergenic region between ADH4 and ADH5 (Fig. 2). SNPs extending over 39 kb, from rs4148886 (in intron 1) through rs2602846 (19.5 kb downstream of exon 9), were individually associated with alcohol dependence, as were three SNPs in the upstream region of ADH4 (Figs 2 and 3, Table 1). The SNP showing the greatest evidence of association, rs4148886, yielded a P-value of 0.0042; permutation testing resulted in a global significance of 0.036. Among these SNPs are two non-synonymous SNPs in the coding region, which were associated with alcohol dependence: rs1126671 (I308V, P=0.06) and rs1126673 (I373V, P=0.06); rs1042364 is listed as a non-synonymous SNP in dbSNP, but lies in the 3'-non-translated region of mRNA encoding the normal peptide; it was also significant (P=0.02). These were not significant when analyzed by PDTaverage. The functional promoter SNP (25Go), rs1800759, was not significantly associated with alcohol dependence. In the remainder of the ADH region, only one other SNP (in ADH7) was significant.


Figure 0732
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  		Figure 2. Association of SNPs across the ADH gene cluster with alcohol dependence. Results of the PDTsum are plotted as –log (P-value). Diamonds represent results with DSM-IV definition of alcohol dependence and circles represent results using COGA criteria. Locations of the ADH genes are shown as lines across the top; genes are transcribed from left to right; abscissa runs from qter toward cen.

 

Figure 0733
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  		Figure 3. Association of SNPs across ADH4 with alcohol dependence defined by DSM-IV. Results of the PDTsum are plotted as –log (P-value). The exons are indicated across the top. Nineteen SNPs in and immediately flanking ADH4 are shown (78–96 in Table 1; three SNPs further upstream were not significant). Circles mark the three SNPs that tag the associated haplotype, which runs from the second SNP through the last. SNP 84 is noted; this is the promoter SNP at –136 bp.

 
Because the evidence of association with alcohol dependence was greatest in and around the ADH4 gene, we evaluated the evidence of association of haplotypes in this region. The block structure of the SNPs that spanned the 90 kb region extending from rs2097122 to rs2602846 (SNPs 75–96, Table 1), which includes the entire ADH4 gene plus 19.5 kb downstream and 49.8 kb upstream, was examined. One block containing 18 SNPs (rs4699718 to rs2602846) included 11 of the 12 significant (P≤0.05) individual SNPs and three marginally significant ones (P<0.07). Three haplotype tag SNPs (htSNPs) were identified (rs4699714, rs3762894 and rs4148884), which successfully tagged the four haplotypes with a frequency of ≥5% in this block. Only one of the observed htSNP haplotypes, with a frequency of 0.35%, could not be uniquely tagged with these three htSNPs. Three haplotypes were not observed in our sample. There was significant evidence for association with the htSNP haplotype (P=0.01).

Exploratory genetic analyses were performed using two other definitions of alcohol dependence using a similar analytic approach to that described above. Analyses using the narrower ICD-10 criteria did not produce consistent, significant results. Analyses using the broader COGA definition of alcohol dependence [DSM-IIIR (29Go) plus Feighner definite alcoholism (30Go)] resulted in evidence of association (P<0.05) with scattered SNPs within and upstream of ADH1A (rs2866151, rs4147531 and rs1826909) and with a series of three consecutive SNPs within the 5' and upstream region of ADH1B (rs1353621, rs1159918 and rs1229982). One of these, rs1159918, was also significant with the ICD-10 definition (P=0.04). A single SNP in ADH7 (rs284786) was significant with the COGA definition.

We genotyped several coding SNPs that have been analyzed in different populations (10Go–13Go,31Go).The SNP that distinguishes ADH1B*1 from ADH1B*2 (rs1229984; Arg47 or His47 in the mature protein) has MAFs in both European-American and African-American populations <0.04 (Table 1); we found no association with alcohol dependence. Rs2066702, the SNP that distinguishes ADH1B*1 from ADH1B*3 (Arg369 or His369 in the mature protein) has an extremely low MAF in European-Americans and could not effectively be tested in that population. In African-Americans, rs2066702 is associated with alcohol dependence defined by ICD-10 (P=0.029 and 0.046 with PDTaverage and PDTsum, respectively) and with DSM-IV (P=0.039 and 0.105 with PDTaverage and PDTsum, respectively); the allele encoding ADH1B*3 was the low-risk allele. In ADH1C, there are two coding SNPs in very high LD; most previous studies have only measured one (rs698) as a proxy for the pair. In our data on unrelated European-Americans (data not shown) or the European-American subset of COGA families, the LD between rs698 and rs1693482 is nearly complete (D'=1.0, r2=0.97–1.0, based on Haploview). Neither of these SNPs was significantly associated with alcohol dependence under any of the three definitions we examined, in either European-American or African-American families. ADH1C-P351T was reported in Native American populations (32Go); we found MAF of 0.005 and 0 in European-Americans and African-Americans, respectively.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 REFERENCES
 
This paper presents the most comprehensive testing to date of the association between alcohol dependence and SNPs across the entire ADH gene cluster. We examined 110 SNPs covering a 417 kb region, with concentrations in and near all seven of the ADH genes (Table 1, Fig. 1). Previous studies have focussed on a few functional SNPs in ADH1B and ADH1C. Osier et al. (33Go). reported moderate LD in a small part of this region extending from the class I genes to ADH7. Our data show that there is much LD between the SNPs in this region, which lie in blocks of restricted haplotypes (Fig. 1); each of the seven ADH genes lie in one to three blocks, as defined by Gabriel et al. (34Go). This pattern of LD affects interpretations of earlier work on coding SNPs; the coding SNPs not only affect the function of the ADH proteins, but are also in LD with potential regulatory SNPs that might affect gene expression.

The strongest finding in this analysis is the association between many SNPs and the htSNP haplotype in ADH4 with alcohol dependence. The strongest region of associated SNPs extends 39.5 kb from intron 1 through 19.5 kb downstream of exon 9. There are also associated SNPs in the 5' region of ADH4, extending 5.6 kb upstream from the initiation codon. This association was detected using the PDTsum option, in which large families contribute more to the statistic. PDTaverage did not show this association.

We reported preliminary results on the first 58 SNPs, showing that eight SNPs in ADH4 were associated with alcohol dependence (35Go). Prior to that time, associations with ADH1B and ADH1C had been reported, but ADH4 had not been studied. Edenberg et al. (25Go). reported that SNP rs1800759 (then described as at –75 bp numbered from the transcription start site; now numbered –136 bp based on the translation start site) was functional, with A in that position having twice the promoter activity of C. Two other nearby SNPs, rs1800761 (then –159, now –220) and rs1800760 (then –192, now –253) did not detectably affect promoter activity (25Go). Edenberg et al. (25Go) predicted that the lower activity allele at rs1800759, C, increased the risk for alcohol dependence; this prediction was based on the fact that lower activity coding variations in ADH1B and ADH1C are the high-risk alleles. Guindalini et al. (36Go) reported that two of the three ADH4 promoter SNPs that had been reported by Edenberg et al. (25Go). (rs1800759 and rs1800761) and several three-SNP haplotypes were associated with alcohol dependence in both European-Brazilians and African-Brazilians. The effect of rs1800759 was greater in their sample; however, rs1800759 was in strong Hardy–Weinberg disequilibrium in the European-Brazilian controls, with a large excess of heterozygotes. SNPs rs1800759, rs1800761 and rs1800760 are within 117 bp, so we did not genotype rs1800761 (the middle SNP); the LD (D') between rs1800759 and rs1800760 (33 bp beyond rs1800761 was 1.0 in both European-American and African-American COGA samples and both were in Hardy–Weinberg equilibrium in our population. In our study, neither of these alleles was significantly associated with alcohol dependence.

While this paper was being prepared, there have been two reports of association of SNPs in ADH4 with alcohol dependence and drug dependence (37Go,38Go). These studies genotyped and analyzed seven SNPs across ADH4 in a case–control population that included many people with both alcohol and drug (primarily cocaine and opioids) dependence and analyzed the data both as case–control and cases only (the latter by Hardy–Weinberg disequilibrium). Their data did not show significant differences in allele frequencies or haplotype frequencies between cases and controls, but did show differences in genotype frequencies and highly significant Hardy–Weinberg disequilibrium in the cases-only analysis that suggested a recessive effect on both alcohol dependence and cocaine dependence in this population (37Go). Six of the seven SNPs they analyzed were in our preliminary analysis (35Go) and are reported here, along with many other SNPs that more thoroughly cover the gene (Table 1). Of these, we found four (rs1042364, rs1126671, rs7694646, rs1984362) were nominally significant and one (rs1126670) marginal using the PDT; surprisingly, the SNP they found closest to the ‘functional risk locus’, rs1800759, was not significant. The greater density of SNPs we used to examine ADH4, and the larger number of individuals, showed that what Luo et al. (37Go) considered one haplotype block appears to be split into three. Following up on their initial analysis, Luo et al. (38Go) carried out a structured association analysis of the case–control sample and a TDT analysis of a small set of nuclear families; both supported the association between alcohol dependence and SNPs in ADH4. The fact that their very different sample and study design showed significant association between ADH4 and alcohol dependence strongly supports our results.

We did not find significant association with the coding SNPs in ADH1B in the European-American families; these SNPs had very low allele frequencies in those families (Table 1). We did find association with three adjacent SNPs in ADH1B, extending from intron 1 through the promoter region to 1.5 kb upstream of the initiation codon; association in this region was with the COGA definition of alcoholism rather than DSM-IV. Thus, there is evidence that variations in ADH1B affect the risk for alcoholism in a population in which the coding polymorphisms are uncommon.

We found significant association with rs2066702, the SNP that defines ADH1B*3, in African-American families. Association was strongest with the ICD-10 definitions of alcoholism, with P=0.046 for PDTsum and 0.029 for PDTaverage; it was also significant for DSM-IV for PDTaverage (P=0.039). The high-risk allele was the C that encodes ADH1B*1, and the low-risk allele encodes ADH1B*3. The fact that the allele encoding the higher Vmax enzyme was protective is consistent with the pattern seen for ADH1B*2. This supports the role of ADH1B*3 as a functional polymorphism in affecting risk for alcoholism.

In addition, we found association with three SNPs distributed across ADH1A from the upstream region through exon 8, also with the COGA definition of alcoholism. These SNPs span two haplotype blocks and the intermediate SNPs do not show association. There have been no previous reports of association of ADH1A with alcoholism and no reports of coding variation in the protein it encodes (although a non-synonymous SNP is listed in dbSNP, rs1041977, this non-validated SNP lies in a region identical in ADH1A and ADH1B and nearly identical in ADH1C, suggesting that it might be a sequencing error).

Surprisingly, we did not find association with any SNPs in or near ADH1C, despite evidence that coding variations in this gene are associated with risk for alcoholism in several populations (21Go,22Go). We analyzed the coding SNP most frequently studied (rs698, Ile349Val) and the coding SNP in very high LD with it (rs1693482, Gln271Arg), both of which have high MAF. The effect of these coding SNPs has always been relatively small in other populations; this might explain our failure to find association in our families.

Osier et al. (39Go) confirmed the protective effect associated with the ADH1B*2 allele in a Taiwanese sample, but found that in their sample the effect was restricted to one of two common haplotypes identical at ADH1B but differing at a StyI intronic SNP (rs1154458) in ADH7. They did not find evidence of LD across the segment between the ADH1B site and the ADH7 site in that Asian population. Our data on European-Americans (and on African-Americans) also show little LD between SNPs in ADH1B and ADH7 (Fig. 1). Osier et al. (39Go) suggested that their result might be due to epistasis between a variation in strong LD with the ADH7 SNP and the ADH1B*2 allele or to the possibility that the combination of SNPs demarks a chromosome containing protective alleles.

Birley et al. (40Go) analyzed the time course of blood and breath alcohol levels in a set of monozygotic and dizygotic twins and found that the ADH region of chromosome 4 contained a quantitative trait locus that accounted for 64% of the additive genetic covariation common to blood and breath alcohol at the first time point measured. They did not detect more than a very minor contribution of haplotypes for ADH1B and ADH1C to the genetic variation in metabolism at this initial time point; their population, of European descent, has a very low frequency of ADH1B*2 alleles. They suggested that other genetic variation in the ADH region must explain the genetic effects on metabolism and suggested both ADH4 and regulatory variation in general as possibilities.

Our comprehensive assessment of SNPs across the ADH region shows that the strongest association with alcoholism is due to variations in the ADH4 gene and weaker association with SNPs in ADH1A and ADH1B. The associations are with non-coding SNPs. In the case of ADH4, the association is strongest with the SNPs extending from intron 1 to 19.5 kb past the 3' end of the gene. Within the blocks of restricted haplotypes, it is difficult to assign which SNP(s) are ‘functional’ and which are merely traveling on the same chromosomes. These findings, along with the potential for epistatic interactions among SNPs, must be considered in analyzing and interpreting association data. The breadth of the original linkage peak (2Go–4Go,6Go) suggests that additional genes might also affect risk for alcoholism.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
REFERENCES
 
Sample
The COGA is a multi-site study recruiting families at six centers across the USA: Indiana University, State University of New York Health Science Center, University of Connecticut, University of Iowa, University of California/San Diego and Washington University, St Louis (41Go–43Go). The institutional review boards of all participating institutions approved the study. Probands were identified through inpatient or outpatient alcohol treatment programs. Probands and their families were administered a poly-diagnostic instrument, the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) interview (44Go,45Go). The families that participated in the genetic phase of this study included a proband and at least two first-degree relatives who met both DSM-IIIR criteria (29Go) for alcohol dependence and Feighner et al. (30Go). criteria for definite alcoholism; this combination is called COGA criteria. The SSAGA also allows derivation of diagnoses based on DSM-IV (28Go) and ICD-10 (46Go) criteria. Details of the ascertainment and assessment have previously been published (41Go–43Go).

SNP genotyping
SNPs throughout the ADH gene cluster were mainly selected from public databases, primarily dbSNP (www.ncbi.nlm.nih.gov/SNP/); several were from Alfred (47Go), Buervenich et al. (48Go) and three were identified in our laboratory (25Go,49Go). Key SNPs encoding ADH1B and ADH1C coding variants and an ADH4 promoter variant (25Go,49Go) were genotyped, despite their low MAFs. Additional SNPs were chosen throughout each of the ADH genes and at lower density in regions between the genes. At the time SNPs were selected, allele frequencies for most were not available, so SNPs were genotyped on two sets of 40 unrelated individuals from the Coriell European- and African-American samples to determine approximate allele frequencies. SNPs with high heterozygosities were preferentially genotyped in the full sample if they were in Hardy–Weinberg equilibrium in both test populations. Locations of the SNPs were in most cases determined from the annotations in the NCBI human genome assembly (build 35.1); in some cases, position was determined by BLASTing the sequence against the human genome assembly. Annotations were based on BLAST alignments; in some cases, they do not match the annotations in NCBI databases, which contain some errors. Most SNPs were not in coding regions, but rather were located in intronic, 5' and 3' regions of the genes. SNPs are presented along the coding strand (opposite to the direction on chromosome 4q).

Genotyping was done using a modified single nucleotide extension reaction, with allele detection by mass spectrometry (Sequenom MassArray system; Sequenom, San Diego, CA, USA). All SNP genotypes were checked for Mendelian inheritance using the program PEDCHECK (50Go). Marker allele frequencies and heterozygosities were computed separately in the European and African-American families using the program USERM13 (51Go).

Statistical analyses
Because of the known ethnic differences in ADH allele frequencies for the functional SNPs (1Go,33Go) and our determination of frequency differences in other SNPs, genetic analyses were separately performed in European-American families (1860 individuals, 218 families) and in African-American families (279 individuals, 35 families). Families were classified based on the racial assignment of the genetically informative portions of the pedigree. Because of the small number of African-American families, we focussed on the results in the European-Americans.

To ensure that the SNP density was sufficient to evaluate the evidence of association between the ADH gene cluster and alcohol dependence, LD between SNPs in the same gene and across genes was evaluated using the program Haploview (52Go). A gene was considered sufficiently genotyped when D' between adjacent SNPs was greater than 0.80 for at least 75% of the adjacent pairwise comparisons. The haplotype block structure in this region was examined using Haploview, with blocks defined as a set of contiguous SNPs whose average D' exceeds a predetermined threshold (34Go,53Go).

The PDT (27Go) as implemented in the program UNPHASED (54Go) 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. It produces two statistics: the ‘PDTaverage’, which averages the association statistic across all families, and the ‘PDTsum’, which gives greater weight to families with a larger number of informative trios and discordant sibships. Our primary phenotype was alcoholism as defined by DSM-IV criteria (28Go), because that gave the strongest signal in previous analyses (4Go). The permutation test implemented in UNPHASED was employed to obtain a global level of significance for the individual SNP analyses.

The block structure of 22 SNPs in and flanking ADH4 was determined using Haploview (52Go). htSNPs were selected for that block of the ADH4 gene providing evidence of association with alcoholism (SNPs 77–96, Table 1 and Fig. 3). htSNPs were selected such that haplotypes with a frequency of ≥5% could be uniquely identified. The htSNPs were then employed to perform family-based association (PDT) analysis using haplotypes rather than single SNPs.


   ACKNOWLEDGEMENTS
 
We thank Jinghua Zhao, Gayathri Rajan, Christopher Rush and Robert George for technical assistance with genotyping 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 (INGEN, supported in part by the Lilly Endowment, Inc.). The Collaborative Study on the Genetics of Alcoholism (COGA) (Principal Investigator: H. Begleiter; Co-Principal Investigators: L. Bierut, H. Edenberg, V. Hesselbrock, B. Porjesz) 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 (H.J. Edenberg, J. Nurnberger Jr, P.M. Conneally, T. Foroud); University of Iowa (S. Kuperman, R. Crowe); SUNY Downstate Medical Center (B. Porjesz, H. Begleiter); 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 Theodore Reich, M.D., Co-Principal Investigator of COGA since its inception and one of the founders of modern psychiatric genetics, we acknowledge his immeasurable and fundamental scientific contributions to COGA and the field.

Conflict of Interest statement. None declared.


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 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
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