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. Author manuscript; available in PMC: 2013 Dec 1.
Published in final edited form as: Alcohol Clin Exp Res. 2012 May 11;36(12):2074–2085. doi: 10.1111/j.1530-0277.2012.01829.x

Association between in vivo alcohol metabolism and genetic variation in pathways that metabolize the carbon skeleton of ethanol and NADH reoxidation in the Alcohol Challenge Twin Study

Penelope A Lind 1, Stuart Macgregor 2, Andrew C Heath 3, Pamela AF Madden 3, Grant W Montgomery 1, Nicholas G Martin 4, John B Whitfield 4
PMCID: PMC3729587  NIHMSID: NIHMS458896  PMID: 22577853

Abstract

Background

Variation in alcohol metabolism affects the duration of intoxication and alcohol use. While the majority of genetic association studies investigating variation in alcohol metabolism have focused on polymorphisms in alcohol or aldehyde dehydrogenases, we have now tested for association with genes in alternative metabolic pathways that catalyze the carbon skeleton of ethanol and NADH reoxidation.

Methods

950 single nucleotide polymorphisms (SNPs) spanning 14 genes (ACN9, ACSS1, ACSS2, ALDH1A1, CAT, CYP2E1, GOT1, GOT2, MDH1, MDH2, SLC25A10, SLC25A11, SLC25A12, SLC25A13) were genotyped in 352 young adults who participated in an alcohol challenge study. Traits tested were blood and breath alcohol concentration, peak alcohol concentration and rates of alcohol absorption and elimination. Allelic association was tested using quantitative univariate and multivariate methods.

Results

A CYP2E1 promoter SNP (rs4838767, minor allele frequency 0.008) exceeded the threshold for study-wide significance (4.01 × 10−5) for two early blood alcohol concentration (BAC), eight breath alcohol concentration (BrAC) measures and the peak BrAC. For each phenotype the minor C-allele was related to a lower alcohol concentration, most strongly for the fourth BrAC (P = 2.07 × 10−7) explaining ~8% of the phenotypic variance. We also observed suggestive patterns of association with variants in ALDH1A1 and on chromosome 17 near SLC25A11 for aspects of blood and breath alcohol metabolism. A SNP upstream of GOT1 (rs2490286) reached study-wide significance for multivariate BAC metabolism (P = 0.000040).

Conclusions

Overall, we did not find strong evidence that variation in genes coding for proteins that further metabolize the carbon backbone of acetaldehyde, or contribute to mechanisms for regenerating NAD from NADH, affects alcohol metabolism in our European-descent subjects. However, based on the breath alcohol data, variation in the promoter of CYP2E1 may play a role in pre-absorptive or early hepatic alcohol metabolism, but more samples are required to validate this finding.

Keywords: alcohol metabolism, association, genetics, CYP2E1, alcohol challenge

INTRODUCTION

Inter-individual variation in the absorption and elimination of alcohol (ethanol) has been shown to be significantly heritable (Kopun and Propping, 1977; Martin et al., 1985; Vesell et al., 1971). Genes or loci affecting in vivo ethanol metabolism have been identified through linkage and candidate gene association studies. The majority of these studies have focused on enzymes that catalyse the first step of ethanol oxidation to acetaldehyde, namely alcohol dehydrogenases (ADHs) (Birley et al., 2009; Birley et al., 2008; Birley et al., 2005), and the second step in which aldehyde dehydrogenase 2 (ALDH2) converts acetaldehyde to acetate (Kiyoshi et al., 2009; Yoshida, 1992). Genetic variation in ADH1B and ALDH2 is associated with individual and ethnic differences in alcohol consumption patterns (Macgregor et al., 2009; Neumark et al., 1998; Raimondi et al., 2004; Whitfield et al., 1998) and risk for alcohol dependence (Edenberg et al., 2006; Kuo et al., 2008; Luo et al., 2006) and alcohol-related cancers (Hashibe et al., 2008), raising the possibility that genetic variation affecting alcohol metabolism through other enzymes or transporters could also affect these diseases.

Other pathways and genes that catalyze conversion of ethanol to acetaldehyde are the Microsomal Ethanol-Oxidizing System (MEOS; via CYP2E1) and catalase (CAT), but they have been estimated to account for less than 10% of total alcohol metabolism in the body (Agarwal, 2001; Deng and Deitrich, 2008; Lieber, 1999). Known variants in ADHs and ALDH2 cannot account for the heritability of metabolism, estimated at 50–60% from twin studies (Martin et al., 1985); variation in downstream events may also be significant (see Figure 1). Firstly, the carbon skeleton of acetate is further metabolised to Acetyl CoA by Acyl-CoA synthetase short-chain family member 1 or 2 (ACSS1, ACSS2) or can be incorporated into the gluconeogenesis pathway as a carbohydrate source by ACN9 (Dennis and McCammon, 1999). Secondly, the actions of both ADH (in the cytosol) and ALDH2 (in mitochondria) require the transfer of hydrogen atoms to the oxidised form of nicotinamide adenine dinucleotide (NAD+), resulting in its reduction to NADH. Since mitochondria (which regenerate NAD+ from NADH in oxidative phosphorylation) are not permeable to NADH, the bidirectional malate-aspartate shuttle transports the reducing equivalents of NADH between the cytosol and the mitochondria via aspartate-glutamate transporters (SLC25A12 and SLC25A13) and malate carriers (SLC25A10, SLC25A11) (Borst, 1963). Within the shuttle, cytoplasmic (GOT1) and mitochondrial (GOT2) aspartate aminotransferases catalyze the interconversion of aspartate and α-ketoglutarate to oxaloacetate and glutamate. Similarly, cytoplasmic (MDH1) and mitochondrial (MDH2) malate dehydrogenases catalyze the interconversion of oxaloacetate and NADH to malate and NAD+. The NADH/NAD ratio is important as excess NADH decreases ADH activity and alters the cellular redox state; reoxidation of NADH has been hypothesised as the rate-limiting step in ethanol oxidation in the liver (Lindros et al., 1972; Meijer et al., 1975). Furthermore, an increased NADH/NAD ratio affects other substrate couples and produces metabolic changes which are linked with disorders including gout and fatty liver (Lieber, 2003).

Figure 1.

Figure 1

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Alcohol (ethanol) metabolism pathways investigated. (A) The metabolism of the carbon skeleton backbone of ethanol. (B) The bidirectional malate-aspartate NADH shuttle. The fourteen candidate genes are represented by italicized gene symbols and are described in Table 2.

Rather than limiting our analysis to the well-characterized alcohol metabolizing ADH and ALDH2 genes, we have extended our previous studies on these enzymes (Birley et al., 2009; Birley et al., 2008; Dickson et al., 2006) to systematically analyse genetic variation in other genes (i) involved in the early steps of ethanol oxidation to acetaldehyde and acetate (ALDH1A1, CAT, CYP2E1); (ii) that play a role in the metabolism of the carbon skeleton of ethanol (ACN9, ACSS1, ACSS2); and (iii) within the malate-aspartate NADH shuttle (GOT1, GOT2, MDH1, MDH2, SLC25A10, SLC25A11, SLC25A12, SLC25A13). This paper reports on the associations of 950 SNPs with a series of in vivo alcohol metabolism measures in 352 participants from the Alcohol Challenge Twin Study (Martin et al., 1985), specifically blood (BAC) and breath (BrAC) alcohol concentrations, the rates of alcohol absorption and alcohol elimination in the blood and breath, and the peak BAC and BrAC achieved following alcohol challenge.

MATERIALS AND METHODS

Samples

Four hundred and twelve people (206 pairs of twins) participated in the original Alcohol Challenge Twin Study (ACTS) between 1979 and 1981 (Martin et al., 1985). Twin participants were 51.7% female and comprised 85 monozygotic (MZ) twin pairs (43 female and 42 male) and 121 dizygotic (DZ) twin pairs (44 female, 38 male and 39 opposite sex). Self-reported ancestry of the ACTS participants is predominantly Northern European (87%) with information available on the birthplace of their four grandparents. The twins ranged in age from 18 to 34 years (mean age: 23.0 ± 4.6) at the time of testing with 70% of subjects aged less than 25 years. Twins were recontacted 10–20 years after completion of the ACTS to obtain DNA for genotyping and blood samples were collected from 372 twins. Subjects gave written informed consent and genetic studies were approved by the Queensland Institute of Medical Research (QIMR) Human Research Ethics Committee. Zygosity of same-sex twin pairs was assessed using a combination of self-report, blood groups and a set of nine polymorphic DNA microsatellite markers (AmpF1STR Profiler Plus Amplification Kit, Applied Biosystems, Foster City, CA).

Measures

Blood and breath alcohol concentration (expressed as mg per 100 ml of blood) was recorded following ingestion of a weight-related dose of ethanol (0.75 g/kg) over 20 minutes. Full details are given in Martin et al. (1985) and a summary of the phenotypes analysed in the current study is given in Table 1. Six timed measurements of BAC (LP1-LP6; over 3.5-hours) and ten of BrAC (RP01-RP10; over 5.5-hours) were obtained starting 40 minutes after the commencement of ethanol intake. Due to changes in the sampling schedule over the duration of the ACTS, occasional delays in taking blood samples and missing samplings, values were predicted for each individual at the six mean observation times for BAC and ten mean observation times for BrAC. A curve was fitted to the predicted BACs and BrACs for each subject and the rate of absorption, peak concentration and rate of elimination were calculated for blood (BLAP, PKLP, BLEP respectively) and breath (BRAP, PKRP, BREP) separately. Correlations between observed and predicted BACs and BrACs at all sampling times were greater than 0.92 and variances of predicted BACs and BrACs were lower than those of the corresponding observed readings consistent with a reduction in error variance.

Table 1.

Summary of predicted blood and breath alcohol phenotypes

Phenotype Measurement Time (minutes)a Abbreviation
Predicted blood alcohol concentration 1 56 LP1
2 68 LP2
3 83 LP3
4 123 LP4
5 143 LP5
6 182 LP6
Rate of blood alcohol absorption BLAP
Peak blood alcohol concentration PKLP
Rate of blood alcohol elimination BLEP
Predicted breath alcohol concentration 1 40 RP01
2 56 RP02
3 68 RP03
4 83 RP04
5 100 RP05
6 123 RP06
7 143 RP07
8 160 RP08
9 182 RP09
10 213 RP10
Rate of breath alcohol absorption BRAP
Peak breath alcohol concentration PKRP
Rate of breath alcohol elimination BREP
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a

Minutes following alcohol ingestion.

SNP Selection

Data for 950 SNPs from fourteen candidate genes were obtained from two datasets: a candidate gene study (using Sequenom iPLEX genotyping chemistry) and a genome-wide association study (GWAS) using Illumina BeadChips. The genes and number of SNPs genotyped and analyzed are summarized in Figure 1 and Table 2, respectively.

Table 2.

Summary of study design

Candidate Region (Gene Symbol) Candidate Gene Chr Band Transcript Positionb Gene Size (bp) Number of SNPs
Sequenom (N = 132) Illumina (N = 818) Total (N = 950) In Geneb (N = 571) Intergenicc (N = 379)
MDH1 Cytosolic malate dehydrogenase 2 2p15 63,669,626–63,687,833 18,208 6 25 31 20 11
SLC25A12 Solute carrier family 25 (mitochondrial carrier, Aralar), member 12 2 2q31.1 172,349,127–172,458,979 109,853 13 63 76 62 14
MDH2 Mitochondrial malate dehydrogenase precursor 7 7q11.23 75,515,329–75,533,865 18,537 9 23 32 20 12
SLC25A13 Solute carrier family 25, member 13 (citrin) 7 7q21.3 95,587,469–95,789,341 201,873 13 101 114 99 15
ACN9 Homolog of the Saccharomyces cerevisiae acetate nonutilizing 9 gene 7 7q21.3 96,584,956–96,649,010 64,055 13 34 47 29 18
ALDH1A1 Aldehyde dehydrogenase 1 family, member A1 9 9q21.13 74,705,408–74,757,789 52,382 0 115 115 74 41
GOT1 Glutamic-oxaloacetic transaminase 1, soluble (aspartate aminotransferase 1) 10 10q24.2 101,146,618–101,180,336 33,719 13 51 64 46 18
CYP2E1 Cytochrome P450, family 2, subfamily E, 10 10q26.3 135,190,857–135,202,611 11,755 15 82 97 35 62
CAT Catalase 11 11p13 34,417,054–34,450,180 33,127 0 138 138 70 68
GOT2 Glutamic-oxaloacetic transaminase 2, mitochondrial (aspartate aminotransferase 2) 16 16q21 57,298,536–57,325,747 27,212 12 33 45 34 11
SLC25A11 Solute carrier family 25 (mitochondrial carrier; oxoglutarate carrier), member 11 17 17p13.2 4,781,349–4,784,063 2,715 4 21 25 0 25
SLC25A10 Solute carrier family 25 (mitochondrial carrier; dicarboxylate transporter), member 10 17 17q25.3 77,289,776–77,298,447 8,672 7 8 15 5 10
ACSS1 Acyl-CoA synthetase short-chain family member 1 20 20p11.21 24,934,874–24,987,616 52,743 20 89 109 59 50
ACSS2 Acyl-CoA synthetase short-chain family member 2 20 20q11.22 32,926,502–32,979,422 52,921 7 35 42 18 24
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Note: Abbreviations: bp, base-pair; Chr, chromosome; SNP, single nucleotide polymorphism.

a

Base-pair position of the transcription start and stop site of the candidate gene based on NCBI dbSNP build 130.

b

Number of SNPs physically located in the candidate gene (promoter, intronic, exonic and 3′untranslated region SNPs).

c

Number of SNPs physically located outside of the candidate gene.

The candidate gene studywas conceived in December 2007, carried out in two stages and completed in 2009. A total of 132 SNPs from 12 genes were selected based on the information available at the time from published literature (Dick et al., 2008; Webb et al., 2011), in addition to common (MAF > 5%) synonymous or non-synonymous SNPs and tag SNPs selected from genotype data downloaded from the International HapMap Project public database using NCBI Build 35 (Stage 1; ACN9, ACSS2, CYP2E1, SLC25A13) and NCBI Build 36 (Stage 2; ACSS1, GOT1, GOT2, MDH1, MDH2, SLC25A10, SLC25A11, SLC25A12) and Haploview version 4.0 software (Barrett et al., 2005).

Imputed genotypes from the GWAS dataset were extracted for all SNPs (N = 936) within a fixed 20 Kb gene border for each of the 12 candidate genes above plus ALDH1A1 and CAT using gene co-ordinates downloaded from the UCSC table browser for all RefSeq genes on July 24th 2008.

Genotyping

Sequenom Platform

Assays were designed using the MassARRAY Assay Design (version 3.0) software (Sequenom Inc., San Diego, CA) and typed using iPLEX chemistry on a Compact MALDI-TOF Mass Spectrometer (Sequenom Inc.). Primers were purchased from Bioneer Corporation (Daejeon, Korea). Genotyping was carried out in standard 384-well plates with 12.5 ng genomic DNA used per sample and allele calls were reviewed using the cluster tool in the SpectroTyper software (Sequenom Inc.).

Illumina Platform

Genotype data for 275 ACTS twins (352 if including 77 imputed MZ co-twins) was drawn from 4 of 9 GWAS subsamples (N = 17,862 individuals) genotyped by the Genetic Epidemiology laboratory at QIMR using Illumina HumanCNV370-Quadv3 (N = 223), Human610-Quadv1 (N = 36) and 317 K (N = 16) BeadChips. Standard QC filters were applied and have been described elsewhere (Medland et al., 2009). A consensus marker set (N=269,840 SNPs) was imputed up to 2,380,486 HapMap SNPs (The International HapMap Consortium., 2003) using the Mach (Li and Abecasis, 2006) program, as described by Medland et al. (2009). Samples were screened for ancestry outliers and individuals were excluded who were >2 standard deviations from the PC1 and PC2 centroid derived from European populations following Eigenstrat analysis (McEvoy et al., 2009).

Imputation QC measures for 818 of 936 SNPs are summarized in Supplementary Table S1. Imputed genotypes for 118 SNPs typed in the candidate gene study were not included in the joint dataset. On average the genotype discordance rate for the 118 duplicated SNPs was 2.4% and is summarized in Supplementary Table S2. The genotype data for ACSS1 and SLC25A10 SNPs showed the strongest discordance rates on average with 4.39% (range, 0–7.9%) and 4.33% (range, 0–24.5%), respectively.

Statistical Analysis

We tested whether 950 SNPs were associated with (i) six blood [LP1-LP6] and ten breath [RP01-RP10] alcohol concentration measurements; (ii) rate of alcohol absorption (BLAP, BRAP); (iii) rate of alcohol elimination (BLEP, BREP); and (iv) peak alcohol concentration in the blood and breath (PKLP, PKRP). Tests of total association with each quantitative trait at each marker were performed in QTDT (Abecasis et al., 2000). Total association considers transmission within and between families, specifying an additive model against the null hypothesis of no linkage and no association (Supplementary Table S3). The between family association component is not robust to population stratification. Therefore, additional analyses in QTDT were performed to check for population stratification by using a variant of the orthogonal model which evaluates population stratification by comparing the between and within-family components of association (Supplementary Table S4). QTDT takes in to account familial relatedness and zygosity, with the trait values of MZ twins were averaged across the pair. All quantitative traits were transformed to normality using inverse rank normal transformation. Correction for sex, age, age2, sex*age and sex*age2 was performed by fitting covariates in the regression model. Phenotypes were also adjusted for possible effects of population stratification in our sample by fitting the first ten eigenvectors (PC1-PC10) from European-only principal components analysis of ancestry in the regression model (McEvoy et al., 2009). Sequenom genotype data and phenotypes were available for 366 twins (40.0% male) from 187 families comprising 77 MZ pairs (42 female and 35 male), 102 DZ pairs (37 female, 27 male and 38 opposite sex) and 8 unpaired DZ twins. Imputed GWAS data and phenotypes were available for 275 twins (45.5% male) from 191 families comprising 77 MZ twins (40 female and 37 male), 84 DZ pairs (34 female, 23 male and 27 opposite sex) and 30 unpaired DZ twins. Two SNPs (rs11190090, rs17850882) were found to be monomorphic.

To calculate the study-wide threshold of significance, the multiple correlated SNPs and phenotypes tested must first be corrected for. Reflecting moderate linkage disequilibrium (LD) across many of the candidate genes, the effective number of independent SNPs tested was 208, as determined by SNPSpD (Nyholt, 2004). Likewise, the effective number of independent phenotypes was calculated to be 6.0003 in matSpD (http://gump.qimr.edu.au/general/daleN/matSpD/) (Nyholt, 2004). Therefore, a conservative P-value < 4.01 × 10−5 (0.05/208 SNPs/6 traits) would be required for study-wide significance. We have 96% and 37.0% power (α = 0.05) to detect overall association with a SNP (with MAF above 0.01) which explains 5% and 1%, respectively, of variance in our trait under an additive model and against a background sibling correlation of 0.30 (Purcell et al., 2003). Power is reduced to 33.5% for a SNP explaining 5% of variance when α =4.01 × 10−5 (the study-wide significance threshold); to achieve 80% power a sample size of 477 would be required. The sample size required to achieve 80% power increases to 1,304 for a SNP explaining 1% of variance.

To account for the multiple correlated blood and breath alcohol concentration phenotypes (see Supplementary Table S5 for correlations), we also employed the MQFAM multivariate extension in PLINK 1.06 (Ferreira and Purcell, 2009) to test for association between individual SNPs and (i) all BAC levels; (ii) early BACs (LP1-LP3); (iii) late BACs (LP4-LP6); (iv) all BrAC levels; (v) early BrAC levels (RP01-RP05); and (vi) late BrACs (RP06-RP10) (Supplementary Table S6). For these analyses, standardized residuals adjusting for sex, age, sex*age, age2 and sex*age2 and PC1-PC10 eigenvectors for each measurement were calculated before submission to MQFAM. Trait values of MZ twins were averaged across the pair. Permutation testing within MQFAM was used to correct for family structure. For the multivariate analyses, the study-wide level of significance is 4.01 × 10−5 (0.05/208 SNPs x 6 traits).

RESULTS

Descriptive

With a combined set of in-house genotyping and imputed GWAS markers, a total of 950 SNPs spanning 14 candidate genes were analysed. No marker showed significant departures from Hardy–Weinberg equilibrium (P < 0.001). We first conducted analyses of blood alcohol measures and then breath alcohol measures obtained during the Alcohol Challenge. Total association results for all SNPs and blood and breath phenotypes are presented in Supplementary Table S3 and Supplementary Figures S1-S3.

Allelic Effects on Blood Alcohol Concentration

Tests of total association were conducted for each SNP with all six BAC levels (LP1-LP6), as well as the rate of alcohol absorption into the blood (BLAP), rate of alcohol elimination (BLEP) and the peak BAC (PKLP). Twenty three SNPs associated with one or more of these blood alcohol phenotypes at a significance level of P < 0.005 are summarized in Table 3 (rs348447 in the ALDH1A1 promoter was affected by population stratification). The strongest evidence of association with blood alcohol metabolism was observed with SNPs in CYP2E1 and ALDH1A1. One SNP, rs4838767 in the promoter of CYP2E1 exceeded the threshold for study-wide significance (4.01 × 10−5) for two blood alcohol measures tested (LP1 and LP2). Six study participants (one male MZ twin pair, one female DZ twin pair and two unrelated twins) were observed to be heterozygous (A/C) for this rare CYP2E1 promoter SNP (MAF = 0.008) that was primarily associated with the earlier times LP1-LP3 (P < 0.00007) and the peak blood alcohol concentration (P = 0.0007). A second CYP2E1 SNP (rs4646976) in moderate LD (r2 = 0.748) with rs4838767 was most associated with LP1-LP3 measures (P < 0.001) and the rate of blood alcohol elimination (BLEP, P = 0.0004). Four intronic ALDH1A1 SNPs in moderate to high LD (rs2017362, rs348461, rs348463 and rs2210103) were strongly associated with late BAC readings LP4-LP6 (P < 0.003). A non-synonymous coding polymorphism, rs238239, in exon 5 of enolase 3 (ENO3), near SLC25A11, was associated with LP1 (P = 0.0043), as well as the rate of blood alcohol absorption (BLAP; P = 0.0029).

Table 3.

Blood alcohol metabolism results where total association P-values < 0.005 were observed for at least one trait.

SNPa Chr Position MAF Alleles Candidate Regionb Genec Function Codon Predicted Blood Alcohol Concentration Summary Parameters
LP1 LP2 LP3 LP4 LP5 LP6 BLAP BLEP PKLP
rs2017362 9 74,734,211 0.384 C/T ALDH1A1 ALDH1A1 Intron 0.0079 0.0035 0.0020 0.0011 0.0006 0.0010 0.0040
rs348461 9 74,734,890 0.384 A/T ALDH1A1 ALDH1A1 Intron 0.0079 0.0035 0.0020 0.0011 0.0006 0.0010 0.0040
rs348462 9 74,736,989 0.313 C/G ALDH1A1 ALDH1A1 Intron 0.0096 0.0045 0.0021 0.0023 0.0019 0.0100 0.0030
rs348463 9 74,737,432 0.291 C/T ALDH1A1 ALDH1A1 Intron 0.0120 0.0049 0.0021 0.0012 0.0007 0.0022 0.0041
rs2210103 9 74,741,953 0.291 C/T ALDH1A1 ALDH1A1 Intron 0.0120 0.0049 0.0021 0.0012 0.0007 0.0022 0.0041
rs1330286 9 74,742,773 0.355 C/G ALDH1A1 ALDH1A1 Intron 0.0212 0.0106 0.0045 0.0028 0.0018 0.0055 0.0074
rs647880 9 74,749,638 0.305 A/G ALDH1A1 ALDH1A1 Intron 0.0024 0.0017 0.0010 0.0022 0.0021 0.0118 0.0411 0.0802 0.0012
rs1424482 9 74,753,377 0.365 C/T ALDH1A1 ALDH1A1 Intron 0.0147 0.0086 0.0039 0.0031 0.0022 0.0086 0.0053
rs348447d 9 74,765,983 0.253 C/G ALDH1A1 ALDH1A1 Promoter 0.0043 0.0044 0.0053 0.0203 0.0300 0.0427 0.0317 0.0072
rs6560309 9 74,769,224 0.327 C/T ALDH1A1 0.0018 0.0014 0.0012 0.0034 0.0036 0.0198 0.0241 0.0664 0.0016
rs4745204 9 74,771,920 0.327 A/G ALDH1A1 0.0018 0.0014 0.0012 0.0034 0.0036 0.0198 0.0241 0.0664 0.0016
rs918836 9 74,777,539 0.327 C/G ALDH1A1 0.0018 0.0014 0.0012 0.0034 0.0036 0.0198 0.0241 0.0664 0.0016
rs4838767t 10 135,183,608 0.008 A/C CYP2E1 CYP2E1 Promoter 0.00004 0.00003 0.00006 0.001 0.0042 0.0269 0.0017 0.0109 0.0007
rs4646976 10 135,197,717 0.011 A/G CYP2E1 CYP2E1 Intron 0.0008 0.0007 0.0009 0.0139 0.0622 0.0075 0.0004 0.0034
rs4756146 11 34,420,315 0.145 C/T CAT CAT Intron 0.0196 0.0023
rs2300182 11 34,424,424 0.145 A/T CAT CAT Intron 0.0196 0.0023
rs2076556 11 34,429,998 0.145 C/T CAT CAT Intron 0.0196 0.0023
rs4755374 11 34,443,180 0.145 A/C CAT CAT Intron 0.0196 0.0023
rs16925614 11 34,448,885 0.145 C/T CAT CAT Intron 0.0196 0.0023
rs17269847 11 34,467,622 0.124 C/T CAT ELF5 Intron 0.0495 0.0187 0.0034 0.0031 0.0030 0.0623
rs4784971 16 57,285,181 0.162 C/T GOT2 0.0535 0.0142 0.0033 0.0038 0.0105 0.0533
rs366577 17 4,795,225 0.375 C/T SLC25A11 ENO3 Intron 0.0042 0.0170 0.0628 0.0038 0.0419 0.0196
rs238239 17 4,797,326 0.395 C/T SLC25A11 ENO3 Coding exon A/V85 0.0043 0.0183 0.0693 0.0029 0.0155 0.0194
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Note: Abbreviations: BLAP, rate of alcohol absorption; BLEP, rate of alcohol elimination; LP1-LP6, six predicted blood alcohol concentration levels; MAF, minor allele frequency; PKLP, peak blood alcohol concentration. The P-value of total association for the SNP is given controlling for sex, age, age2, sex*age, sex*age2 and the first ten eigenvectors (PC1-PC10) from European-only principal components analysis of ancestry. P-values < 0.001 are highlighted by shading, values < 0.005 are in bold and values > 0.10 are not shown.

a

SNPs genotyped in-house end with the letter ‘t’.

b

Candidate gene that SNPs were originally selected to represent.

c

Gene that SNP is physically located in (may not be the candidate gene).

d

Showed evidence of population stratification with BLAP (P = 0.032).

Allelic Effects on Breath Alcohol Concentration

Total association analyses were conducted for ten BrAC levels (RP01-RP10), as well as the /rate of alcohol absorption in the breath (BRAP), rate of alcohol elimination (BREP) and the peak BrAC (PKRP). Seventeen SNPs associated with one or more of these phenotypes at a significance level of P < 0.005 are summarized in Table 4. The rs238247 SNP in the SLC25A11 candidate region showed evidence of population stratification for five BrAC levels (RP06-RP10).

Table 4.

Breath alcohol metabolism results where total association P-values < 0.005 were observed for at least one trait.

SNPa Chr Location MAF Alleles Candidate Regionb Genec Function Codon Predicted Breath Alcohol Concentration Summary Parameters
RP01 RP02 RP03 RP04 RP05 RP06 RP07 RP08 RP09 RP10 BRAP BREP PKRP
rs7804781 7 75,540,655 0.111 C/T MDH2 MDH2 Downstream 0.0762 0.0754 0.0043
rs11787668 9 74,763,412 0.462 A/C ALDH1A1 ALDH1A1 Promoter 0.0315 0.0209 0.0149 0.0174 0.0269 0.0407 0.0669 0.0906 0.0742 0.0574 0.0046
rs2298316 10 101,137,682 0.105 A/G GOT1 CNNM1 Coding exon R/Q454 0.0501 0.0170 0.0099 0.0074 0.0074 0.0089 0.0220 0.0519 0.0927 0.0194 0.0030
rs4838767t 10 135,183,608 0.008 A/C CYP2E1 CYP2E1 Promoter 0.000008 0.0000007 0.0000003 0.0000002 0.0000005 0.000003 0.00001 0.0001 0.0003 0.0019 0.00009 0.0172 0.000001
rs4646976 10 135,197,717 0.011 A/G CYP2E1 CYP2E1 Intron 0.0011 0.0003 0.0003 0.0002 0.0005 0.0018 0.0064 0.0302 0.0606 0.0027 0.0023 0.0005
rs11032682 11 34,400,329 0.442 G/T CAT 0.0186 0.0235 0.0313 0.0338 0.0373 0.0690 0.0697 0.0883 0.0156 0.0048
rs7118388 11 34,410,723 0.482 A/G CAT CAT Promoter 0.0056 0.0123 0.0262 0.0428 0.0740 0.0012 0.0476 0.0027
rs4784975 16 57,305,160 0.004 A/G GOT2 GOT2 Intron 0.0582 0.0291 0.0115 0.0024 0.0039
rs4349206 17 4,770,779 0.369 A/G SLC25A11 GP1BA Promoter 0.0063 0.0110 0.0243 0.0733 0.0085 0.0024 0.0142
rs9914087 17 4,772,572 0.288 A/G SLC25A11 GP1BA Promoter 0.0043 0.0064 0.0135 0.0460 0.0099 0.0013 0.0078
rs9903826 17 4,774,260 0.288 A/G SLC25A11 GP1BA Promoter 0.0043 0.0064 0.0135 0.0460 0.0099 0.0013 0.0078
rs2243102 17 4,779,893 0.365 C/T SLC25A11 GP1BA Downstream 0.0075 0.0142 0.0329 0.0966 0.0084 0.0034 0.0197
rs238247td 17 4,786,513 0.377 A/G SLC25A11 RNF167 Intron 0.0024 0.0052 0.0150 0.0600 0.0033 0.0036 0.0107
rs366577 17 4,795,225 0.375 C/T SLC25A11 ENO3 Intron 0.0089 0.0210 0.0538 0.0076 0.0038 0.0395
rs238239 17 4,797,326 0.395 C/T SLC25A11 ENO3 Coding exon A/V85 0.0100 0.0174 0.0387 0.0115 0.0026 0.029
rs8184053 20 24,921,005 0.182 C/T ACSS1 C20orf3 Intron 0.0136 0.0395 0.0864 0.0022 0.0591 0.0478
rs1985485 20 24,963,492 0.115 C/T ACSS1 ACSS1 Intron 0.0611 0.0227 0.0108 0.0127 0.0063 0.0047
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Note: Abbreviations: BRAP, rate of alcohol absorption; BREP, rate of alcohol elimination; MAF, minor allele frequency; RP01-RP10, ten predicted breath alcohol concentration levels; PKRP, peak blood alcohol concentration. The P-value of total association for the SNP is given controlling for sex, age, age2, sex*age, sex*age2 and the first ten eigenvectors (PC1-PC10) from European-only principal components analysis of ancestry. P-values < 0.001 are highlighted by shading, values < 0.005 are in bold and values > 0.10 are not shown.

a

SNPs genotyped in-house end with the letter ‘t’.

b

Candidate gene that SNPs were originally selected to represent.

c

Gene that SNP is physically located in (may not be the candidate gene).

d

Showed evidence of population stratification with RP06-RP10 series of BrACs (P-values range from 0.0187 to 0.0385).

The most associated SNP (rs4838767) with the breath alcohol metabolism variables is the CYP2E1 promoter SNP, rs4838767 (Table 4). P-values for eight traits (RP01-RP07 and peak BrAC [PKRP]) reached a study-wide level of significance (P < 4.10 × 10−5). The observed effect of rs4838767 was strongest for RP04 (P = 2.00 × 10−7) with the minor-C allele relating to a lower BrAC; the beta calculated in QTDT = 2.205 and explained approximately 8% of variance in the RP04 breath alcohol concentration. The effect of rs4838767 genotype on blood and breath alcohol concentrations is illustrated in Figure 2. This SNP was also associated with later BrAC measurements (RP08, RP09) and breath alcohol absorption (BRAP). A second SNP in CYP2E1 (rs4646976) was associated with eight of these traits but to a lesser degree. A grouping of six SNPs on chromosome 17 near SLC25A11 in moderate to complete LD (r2 = 0.7 – 1.0) were related to early breath alcohol readings and all the breath alcohol summary measures. This grouping included the non-synonymous rs238239 polymorphism in ENO3 that codes for an alanine to valine codon change. Two SNPs in or near CAT and ACSS1 and one SNP each in ALDH1A1, MDH2 and GOT2 were associated with at least one breath alcohol trait with a P-value < 0.005. Finally, the non-synonymous SNP rs2298316 codes for an arginine to glutamate codon change in CNNM1 (near GOT1) and was most associated with peak BrAC (P = 0.003).

Figure 2.

Figure 2

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Effect of CYP2E1 promoter SNP rs4838767 genotypes on (A) blood and (B) breath alcohol concentration levels following alcohol ingestion. Mean (with standard error bars) alcohol concentrations for male and female homozygous A/A twins, plus alcohol concentrations for six heterozygous A/C twins are shown. Twins 1a and 1b are a male MZ twin pair. Twins 2a and 3a are unpaired female DZ twins. Twins 4a and 4b are a female DZ twin pair.

Multivariate Analyses

Given the (i) high correlation between the blood and breath alcohol concentration readings throughout the alcohol challenge and (ii) that the strongest allelic associations with variation in blood or breath alcohol concentrations tended to be either with early or late stages of alcohol metabolism, we used a multivariate approach to search for SNPs that acted on general blood or breath alcohol metabolism (all times, LP1-LP6 or RP01-RP10) or SNPs that acted early (LP1-LP3 or RP01-RP05) or late (LP4-LP6 or RP06-RP10) in the time course of blood or breath alcohol metabolism. The strongest evidence of association with blood alcohol phenotypes was observed with SNPs located near or within GOT1 on chromosome 10. One SNP upstream of GOT1 (rs2490286) reached study-wide significance for general blood alcohol metabolism (multivariate P = 0.000040) and was also associated with late blood alcohol metabolism but to a lesser extent (multivariate P = 0.0019). Similar patterns of association were observed with the rs2494654 (0.000057 and 0.00026, respectively) and rs2494652 (0.00018 and 0.0011). Two SNPs downstream of GOT1 (intergenic rs10748774 and rs6584273 in CNNM1) and an intronic SNP in ALDH1A1 (rs647880) were also nominally associated with general blood alcohol metabolism. With respect to breath alcohol metabolism, the most associated SNPs were located in CYP2E1 and ACSS1: rs4838767 in the promoter of CYP2E1 was associated with general BrAC levels (multivariate P = 0.00075), the intronic CYP2E1 SNP rs4646976 was most associated with late BrAC levels (multivariate P = 0.00092), and the intronic ACSS1 SNP (rs4813543) was strongly associated with late BrACs (multivariate P = 0.000041) but not associated with early BrAC levels (multivariate P = 0.714). Multivariate association results for all SNPs and phenotypes are presented in Supplementary Table S6 and Supplementary Figure S4.

DISCUSSION

The object of our study was to identify genetic variants that modulate inter-individual variation in the absorption and elimination of ethanol. This is done in the context of an alcohol metabolism pathway that includes both the metabolism of the carbon skeleton of ethanol and includes gene products that influence the activity of the malate-aspartate shuttle and rates of mitochondrial NADH reoxidation (Figure 1). We have previously shown that significant heritability exists for peak blood alcohol (0.62) and rate of elimination (0.49) in our sample (Martin et al., 1985). Linkage and functional studies have pointed towards the role of genetic variation in the alcohol dehydrogenase cluster and aldehyde dehydrogenases (Agarwal and Goedde, 1992; Birley et al., 2005) and association studies have followed up on these findings indirectly by studying the consequences of differing alcohol metabolism, namely subjective feeling of drunkenness, excessive alcohol consumption or alcohol dependence (Chen et al., 2009; Dickson et al., 2006; Edenberg et al., 2006; Macgregor et al., 2009; Quertemont, 2004). However, most pharmacogenetic studies of alcohol metabolism have focused on a small set of variants in ADH1B (Arg48His/rs1229984 and Arg369Cys/rs2066702), ADH1C (Ile349Val/rs698 and Arg271Gln/rs1693482) and ALDH2 (ALDH2*2/rs671). The role of genes that function in parallel (CYP2E1 and CAT) or downstream of ADHs and ALDH have, for the most part, not been studied with respect to variation in alcohol metabolism. Therefore, we have comprehensively tested for allelic association between 950 SNPs spanning 14 genes in the alcohol metabolism pathways with a timed series of blood and breath alcohol concentration measurements in 352 twins who participated in an alcohol challenge experiment. SNP genotype data was generated from both in-house genotyping and imputation of GWAS genotype data. It is noted that in our dataset of 118 SNPs with both in-house generated and imputed GWAS genotypes, discordance rates were on average 2.4% with four SNPs (one in CYP2E1 and three in ACSS1) showing discordance rates higher than 10%.

While multiple allelic associations (P < 0.005) between the tested SNPs and blood or breath alcohol phenotypes were observed in our sample, the only study-wide significant associations were observed with a promoter SNP in CYP2E1 (rs4838767). This SNP was genotyped on the Sequenom MassARRAY platform and the assay cluster plot and representative genotype spectrums are given in Supplementary Figure S5. Association with rs4838767 was strongest with earlier BrACs (RP01-RP06) and peak BrAC (PKRP), with only suggestive or nominal association with later BrACs and the rate of breath alcohol elimination (BREP). The strongest association was between rs4838767 and RP04 (P = 2.0 × 10−7) explaining ~8% of RP04 phenotypic variance. This SNP was also most strongly associated with early blood alcohol concentrations (LP1-LP3) and the peak BAC (PKLP).

The rs4838767 result, however, should be interpreted in the context of several limitations. First, the MAF for rs4838767 is low (~0.9%) in the twin sample where all phenotypes and covariates are available; only six heterozygote A/C twins and no homozygote C/C individuals were observed. However, this issue will be encountered in any allelic association analysis of low-MAF SNPs. We therefore obtained empirical P-values for rs4838767 at each phenotype by conducting 10,000 or 100,000 simulations in MERLIN (Supplemental Table S7). The --simulate option in MERLIN results in datasets in which SNP data is simulated under the null hypothesis of no linkage or association to observed phenotypes while phenotypic measurements, including covariates, are preserved. We report study-wide significant empirical P-values for RP01, RP02, RP05 and RP06 but P-values smaller that those reported for RP03, RP04 and PKRP were not observed following 100,000 simulations. Second, the association finding may be affected by one opposite sex DZ female twin (‘Twin 2a DZOS’ in Figure 2) who exhibited low alcohol concentrations throughout the course of the experiment. When this twin was excluded the best P-value of association was 0.0002 with RP04. Third, the MAF is higher in non-European populations (ranging from 8–17% in the Asian and African HapMap samples). In the ACTS sample, the heterozygote MZ twin pair (‘Twin 1a MZM’ and ‘Twin 1b MZM’ in Figure 2) reported Egyptian ancestry for all grandparents. When association analyses were re-run excluding this twin pair, the significance of association remained study-wide significant for RP01-RP05 and PKRP (the P-value for RP04 was 2.0 × 10−5).

Nevertheless, CYP2E1 is a major component of the MEOS which oxidises up to 10% of ethanol consumed. Furthermore, CYP2E1 contributes up to 60% of ethanol metabolism in heavy drinkers since the enzyme has a higher Km for alcohol than ADH and its activity is induced by chronic alcohol ingestion (Lands, 1998; Lieber, 1994; Tanaka et al., 2000). A recent study (Webb et al., 2011) reported both linkage and association between CYP2E1 and the level of response to alcohol with the strongest association observed between a promoter SNP, rs10776687, and Subjective High Assessment Scale score. Two other CYP2E1 SNPs that many association studies have genotyped were included in the current study; CYP2E1*5B (constituting rs2031920 and rs3813867) in the promoter and CYP2E1*1B/rs2070676 in intron 7. CYP2E1*5B has been reported to affect transcription activity of the gene (Hayashi et al., 1991; Watanabe et al., 1994) and enhance both alcohol metabolism and risk for alcohol dependence in Mexican-Americans (Konishi et al., 2003). Similarly, rs2070676 has been associated with increased activity of CYP2E1 in vivo (Haufroid et al., 2001). We did not observe association between the four SNPs described above and any phenotypes tested in our study (P-values > 0.05), with low LD (rs2070676, r2 = 0.116) or no LD observed between rs4838767 and these SNPs. However, previously reported associations with these SNPs have been inconsistent and inconclusive, possibly due to phenotypic and population differences. It has also been suggested that CYP2E1 activity is higher in smokers than nonsmokers (Lucas et al., 1995; Schoedel and Tyndale, 2003). While participants were not prevented from smoking during the experimental period, smoking history at the time of the alcohol challenge is available for 411 of the 412 twins: 154 were current smokers, 68 non-smokers and 189 ex-smokers. To test for a possible affect of current smoking on alcohol metabolism, secondary analyses of the CYP2E1 SNPs were performed in which current smoker status (Yes/No) was also included as a covariate. No change in the level or pattern of association was observed (data not shown).

While no other SNP-phenotype associations were study-wide significant, there are suggestive patterns of findings that should be followed-up in other cohorts as it would be unwise to dismiss the possibility of a true effect. We have low statistical power to detect an association at the study-wide level in our small sample; and the significance threshold of 4.01 × 10−5 does not take in to account the strong prior probability of a true association between some of our genes (in particular CYP2E1) and traits of interest. The overall lack of study-wide significant findings may be because there is an upper limit in the effect size associated with any single SNP that can be detected with our comparatively small sample (N = 352). While we have 80% statistical power to detect the observed association between rs4838767 and RP04 in our study using an α = 4.01 × 10−5 (the study-wide significance threshold), the required sample size almost triples (N = 804) when the SNP effect size falls to 3% of the trait variance. Therefore, we cannot exclude associations of smaller effect. However, it should be noted that few alcohol challenge studies currently exist in the literature and also have small sample sizes due to the practical difficulty in conducting large-scale experiments. Other alcohol challenge studies include 297 adult males (Schuckit et al., 2004), 7 MZ and 7 DZ twin pairs (Vesell, 1972), 19 MZ and 21 DZ male twin pairs (Kopun and Propping, 1977).

Turning to the suggestive associations, there is some evidence for a role of cytosolic ALDH (ALDH1A1) in alcohol metabolism, with SNPs in this gene influencing all measurements of BAC and the peak BAC. ALDH1A1 has previously been reported to be associated with several alcohol-related phenotypes including alcohol consumption levels and alcohol dependence risk in Finns (Lind et al., 2008; Liu et al., 2010) and Southwest California Indians (Ehlers et al., 2004). Next, five intronic SNPs in catalase (CAT) (effectively one SNP due to being in complete LD) were the only SNPs tested that achieved a level of significance less than 0.005 with the rate of blood alcohol elimination in our study. A CAT promoter SNP was most associated with the rate of breath alcohol absorption and peak BrAC. Finally, a series of SNPs located near SLC25A11 (a malate carrier in the malate-aspartate NADH shuttle) on chromosome 17 are associated with aspects of blood and breath alcohol metabolism; including a non-synonymous coding SNP in EN03 that is primarily associated with the rate of blood alcohol absorption and breath alcohol elimination. The role of ENO3 in alcohol metabolism is unclear as it functions in skeletal muscle and glycogen storage, and it was not selected a priori as a candidate gene for this study(Comi et al., 2001).

Finally we used a multivariate approach (Ferreira and Purcell, 2009) to search for polymorphisms with effects on multiple blood or breath alcohol traits that could have been missed in our main analysis. One SNP, rs2490286, upstream of the glutamic-oxaloacetic transaminase 1 (GOT1) reached study-wide significance for overall blood alcohol metabolism (Supplementary Table S6). This SNP was not associated with any univariate blood or breath alcohol concentration measure in the main analysis but did show borderline association with the rate of blood alcohol elimination (P = 0.0068). GOT1 is an intriguing candidate gene which has recently been implicated in the level of response to alcohol in a genome-wide association studyutilising three variables measured in an alcohol challenge (Joslyn et al., 2010).

In conclusion, to study the genetics of alcohol metabolism it is important to test for effects of variation not only in the well-studied ADH and ALDH2 genes, but in additional genes/pathways involved in the metabolism of ethanol, co-factors (NAD+/NADH) and breakdown products (acetaldehyde). We hypothesize that variants that influence alcohol metabolism can be further studied in other alcohol cohorts where their affect on alcohol consumption, and subsequently risk for alcohol dependence, can be investigated. We did not find strong evidence that variation in genes metabolizing the carbon backbone of acetate or in mechanisms for regenerating NAD from NADH or the metabolism of acetaldehyde is rate-limiting in alcohol metabolism in our population. However, we did observe suggestive patterns of association with variants in ALDH1A1 and on chromosome 17 near SLC25A11 for aspects of blood and breath alcohol metabolism. Finally, while we lack power to observe small sizes of effect in our sample, we can conclude that regulatory sequences in the promoter of CYP2E1 may play a role in the pre-absorptive metabolism of alcohol; however, more samples are required to validate this finding.

Supplementary Material

Figures
Tables

Acknowledgments

Twin subjects were recruited from the Australian National Health and Medical Research Council Twin Registry. We thank the twins for their cooperation in a trying protocol. Statistical analyses were carried out on the Genetic Cluster Computer (http://www.geneticcluster.org) which is financially supported by the Netherlands Scientific Organization (NWO 480-05-003). The Australian studies were supported by NIH grants AA07535, AA07728, AA13320, AA13321, AA14041, AA11998, AA17688, DA012854, DA019951; by grants from the Australian National Health and Medical Research Council (241944, 339462, 389927, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 552485, 552498); from the Australian Research Council (A7960034, A79906588, A79801419, DP0770096, DP0212016, DP0343921); and the FP-5 GenomEUtwin Project (QLG2-CT-2002-01254). Genotyping at CIDR was supported by grant AA13320 to the late Richard Todd, PhD, MD.

References

  1. Abecasis GR, Cardon LR, Cookson WO. A general test of association for quantitative traits in nuclear families. Am J Hum Genet. 2000;66(1):279–292. doi: 10.1086/302698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Agarwal DP. Genetic polymorphisms of alcohol metabolizing enzymes. Pathol Biol (Paris) 2001;49(9):703–709. doi: 10.1016/s0369-8114(01)00242-5. [DOI] [PubMed] [Google Scholar]
  3. Agarwal DP, Goedde HW. Pharmacogenetics of alcohol metabolism and alcoholism. Pharmacogenetics. 1992;2(2):48–62. doi: 10.1097/00008571-199204000-00002. [DOI] [PubMed] [Google Scholar]
  4. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21(2):263–265. doi: 10.1093/bioinformatics/bth457. [DOI] [PubMed] [Google Scholar]
  5. Birley AJ, James MR, Dickson PA, Montgomery GW, Heath AC, Martin NG, Whitfield JB. ADH single nucleotide polymorphism associations with alcohol metabolism in vivo. Hum Mol Genet. 2009;18(8):1533–1542. doi: 10.1093/hmg/ddp060. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Birley AJ, James MR, Dickson PA, Montgomery GW, Heath AC, Whitfield JB, Martin NG. Association of the gastric alcohol dehydrogenase gene ADH7 with variation in alcohol metabolism. Hum Mol Genet. 2008;17(2):179–189. doi: 10.1093/hmg/ddm295. [DOI] [PubMed] [Google Scholar]
  7. Birley AJ, Whitfield JB, Neale MC, Duffy DL, Heath AC, Boomsma DI, Martin NG. Genetic time-series analysis identifies a major QTL for in vivo alcohol metabolism not predicted by in vitro studies of structural protein polymorphism at the ADH1B or ADH1C loci. Behav Genet. 2005;35(5):509–524. doi: 10.1007/s10519-005-3851-6. [DOI] [PubMed] [Google Scholar]
  8. Borst P. Functionelle und Morphologische Organisation der Zelle. Springer-Varlag; Berlin: 1963. [Google Scholar]
  9. Chen YC, Peng GS, Wang MF, Tsao TP, Yin SJ. Polymorphism of ethanol-metabolism genes and alcoholism: correlation of allelic variations with the pharmacokinetic and pharmacodynamic consequences. Chem Biol Interact. 2009;178(1–3):2–7. doi: 10.1016/j.cbi.2008.10.029. [DOI] [PubMed] [Google Scholar]
  10. Comi GP, Fortunato F, Lucchiari S, Bordoni A, Prelle A, Jann S, Keller A, Ciscato P, Galbiati S, Chiveri L, Torrente Y, Scarlato G, Bresolin N. Beta-enolase deficiency, a new metabolic myopathy of distal glycolysis. Ann Neurol. 2001;50(2):202–207. doi: 10.1002/ana.1095. [DOI] [PubMed] [Google Scholar]
  11. Deng XS, Deitrich RA. Putative role of brain acetaldehyde in ethanol addiction. Curr Drug Abuse Rev. 2008;1(1):3–8. doi: 10.2174/1874473710801010003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Dennis RA, McCammon MT. Acn9 is a novel protein of gluconeogenesis that is located in the mitochondrial intermembrane space. Eur J Biochem. 1999;261(1):236–243. doi: 10.1046/j.1432-1327.1999.00267.x. [DOI] [PubMed] [Google Scholar]
  13. Dick DM, Aliev F, Wang JC, Saccone S, Hinrichs A, Bertelsen S, Budde J, Saccone N, Foroud T, Nurnberger J, Jr, Xuei X, Conneally PM, Schuckit M, Almasy L, Crowe R, Kuperman S, Kramer J, Tischfield JA, Hesselbrock V, Edenberg HJ, Porjesz B, Rice JP, Bierut L, Goate A. A Systematic single nucleotide polymorphism screen to fine-map alcohol dependence genes on chromosome 7 identifies association with a novel susceptibility gene ACN9. Biol Psychiatry. 2008;63(11):1047–1053. doi: 10.1016/j.biopsych.2007.11.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Dickson PA, James MR, Heath AC, Montgomery GW, Martin NG, Whitfield JB, Birley AJ. Effects of variation at the ALDH2 locus on alcohol metabolism, sensitivity, consumption, and dependence in Europeans. Alcohol Clin Exp Res. 2006;30(7):1093–1100. doi: 10.1111/j.1530-0277.2006.00128.x. [DOI] [PubMed] [Google Scholar]
  15. Edenberg HJ, Xuei X, Chen HJ, Tian H, Wetherill LF, Dick DM, Almasy L, Bierut L, Bucholz KK, Goate A, Hesselbrock V, Kuperman S, Nurnberger J, Porjesz B, Rice J, Schuckit M, Tischfield J, Begleiter H, Foroud T. Association of alcohol dehydrogenase genes with alcohol dependence: a comprehensive analysis. Hum Mol Genet. 2006;15(9):1539–1549. doi: 10.1093/hmg/ddl073. [DOI] [PubMed] [Google Scholar]
  16. Ehlers CL, Spence JP, Wall TL, Gilder DA, Carr LG. Association of ALDH1 promoter polymorphisms with alcohol-related phenotypes in Southwest California Indians. Alcohol Clin Exp Res. 2004;28(10):1481–1486. doi: 10.1097/01.alc.0000141821.06062.20. [DOI] [PubMed] [Google Scholar]
  17. Ferreira MA, Purcell SM. A multivariate test of association. Bioinformatics. 2009;25(1):132–133. doi: 10.1093/bioinformatics/btn563. [DOI] [PubMed] [Google Scholar]
  18. Hashibe M, McKay JD, Curado MP, Oliveira JC, Koifman S, Koifman R, Zaridze D, Shangina O, Wunsch-Filho V, Eluf-Neto J, Levi JE, Matos E, Lagiou P, Lagiou A, Benhamou S, Bouchardy C, Szeszenia-Dabrowska N, Menezes A, Dall’Agnol MM, Merletti F, Richiardi L, Fernandez L, Lence J, Talamini R, Barzan L, Mates D, Mates IN, Kjaerheim K, Macfarlane GJ, Macfarlane TV, Simonato L, Canova C, Holcatova I, Agudo A, Castellsague X, Lowry R, Janout V, Kollarova H, Conway DI, McKinney PA, Znaor A, Fabianova E, Bencko V, Lissowska J, Chabrier A, Hung RJ, Gaborieau V, Boffetta P, Brennan P. Multiple ADH genes are associated with upper aerodigestive cancers. Nat Genet. 2008;40(6):707–709. doi: 10.1038/ng.151. [DOI] [PubMed] [Google Scholar]
  19. Haufroid V, Buchet JP, Gardinal S, Ghittori S, Imbriani M, Hirvonen A, Lison D. Importance of genetic polymorphisms of drugmetabolizing enzymes for the interpretation of biomarkers of exposure to styrene. Biomarkers. 2001;6(3):236–239. doi: 10.1080/13547500010014540. [DOI] [PubMed] [Google Scholar]
  20. Hayashi S, Watanabe J, Kawajiri K. Genetic polymorphisms in the 5′-flanking region change transcriptional regulation of the human cytochrome P450IIE1 gene. J Biochem. 1991;110(4):559–565. doi: 10.1093/oxfordjournals.jbchem.a123619. [DOI] [PubMed] [Google Scholar]
  21. Joslyn G, Ravindranathan A, Brush G, Schuckit M, White RL. Human variation in alcohol response is influenced by variation in neuronal signaling genes. Alcohol Clin Exp Res. 2010;34(5):800–812. doi: 10.1111/j.1530-0277.2010.01152.x. [DOI] [PubMed] [Google Scholar]
  22. Kiyoshi A, Weihuan W, Mostofa J, Mitsuru K, Toyoshi I, Toshihiro K, Kyoko K, Keiichi N, Iwao I, Hiroshi K. Ethanol metabolism in ALDH2 knockout mice--blood acetate levels. Leg Med (Tokyo) 2009;11(Suppl 1):S413–415. doi: 10.1016/j.legalmed.2009.02.043. [DOI] [PubMed] [Google Scholar]
  23. Konishi T, Smith JL, Lin KM, Wan YJ. Influence of genetic admixture on polymorphisms of alcohol-metabolizing enzymes: analyses of mutations on the CYP2E1, ADH2, ADH3 and ALDH2 genes in a Mexican-American population living in the Los Angeles area. Alcohol Alcohol. 2003;38(1):93–94. doi: 10.1093/alcalc/agg021. [DOI] [PubMed] [Google Scholar]
  24. Kopun M, Propping P. The kinetics of ethanol absorption and elimination in twins and supplementary repetitive experiments in singleton subjects. Eur J Clin Pharmacol. 1977;11(5):337–344. doi: 10.1007/BF00566530. [DOI] [PubMed] [Google Scholar]
  25. Kuo PH, Kalsi G, Prescott CA, Hodgkinson CA, Goldman D, van den Oord EJ, Alexander J, Jiang C, Sullivan PF, Patterson DG, Walsh D, Kendler KS, Riley BP. Association of ADH and ALDH genes with alcohol dependence in the Irish Affected Sib Pair Study of alcohol dependence (IASPSAD) sample. Alcohol Clin Exp Res. 2008;32(5):785–795. doi: 10.1111/j.1530-0277.2008.00642.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Lands WE. A review of alcohol clearance in humans. Alcohol. 1998;15(2):147–160. doi: 10.1016/s0741-8329(97)00110-9. [DOI] [PubMed] [Google Scholar]
  27. Li Y, Abecasis GR. Mach 1.0: Rapid Haplotype Reconstruction and Missing Genotype Inference. Am J Hum Genet. 2006;S79:2290. [Google Scholar]
  28. Lieber CS. Mechanisms of ethanol-drug-nutrition interactions. J Toxicol Clin Toxicol. 1994;32(6):631–681. doi: 10.3109/15563659409017974. [DOI] [PubMed] [Google Scholar]
  29. Lieber CS. Microsomal ethanol-oxidizing system (MEOS): the first 30 years (1968–1998)--a review. Alcohol Clin Exp Res. 1999;23(6):991–1007. [PubMed] [Google Scholar]
  30. Lieber CS. Relationships between nutrition, alcohol use, and liver disease. Alcohol Res Health. 2003;27(3):220–231. [PMC free article] [PubMed] [Google Scholar]
  31. Lind PA, Eriksson CJ, Wilhelmsen KC. The role of aldehyde dehydrogenase-1 (ALDH1A1) polymorphisms in harmful alcohol consumption in a Finnish population. Hum Genomics. 2008;3(1):24–35. doi: 10.1186/1479-7364-3-1-24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Lindros KO, Vihma R, Forsander OA. Utilization and metabolic effects of acetaldehyde and ethanol in the perfused rat liver. Biochem J. 1972;126(4):945–952. doi: 10.1042/bj1260945. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Liu J, Zhou Z, Hodgkinson CA, Yuan Q, Shen PH, Mulligan CJ, Wang A, Gray RR, Roy A, Virkkunen M, Goldman D, Enoch MA. Haplotype-Based Study of the Association of Alcohol-Metabolizing Genes With Alcohol Dependence in Four Independent Populations. Alcohol Clin Exp Res. 2010 doi: 10.1111/j.1530-0277.2010.01346.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Lucas D, Menez C, Girre C, Berthou F, Bodenez P, Joannet I, Hispard E, Bardou LG, Menez JF. Cytochrome P450 2E1 genotype and chlorzoxazone metabolism in healthy and alcoholic Caucasian subjects. Pharmacogenetics. 1995;5(5):298–304. doi: 10.1097/00008571-199510000-00005. [DOI] [PubMed] [Google Scholar]
  35. Luo X, Kranzler HR, Zuo L, Wang S, Schork NJ, Gelernter J. Diplotype trend regression analysis of the ADH gene cluster and the ALDH2 gene: multiple significant associations with alcohol dependence. Am J Hum Genet. 2006;78(6):973–987. doi: 10.1086/504113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Macgregor S, Lind PA, Bucholz KK, Hansell NK, Madden PA, Richter MM, Montgomery GW, Martin NG, Heath AC, Whitfield JB. Associations of ADH and ALDH2 gene variation with self report alcohol reactions, consumption and dependence: an integrated analysis. Hum Mol Genet. 2009;18(3):580–593. doi: 10.1093/hmg/ddn372. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Martin NG, Perl J, Oakeshott JG, Gibson JB, Starmer GA, Wilks AV. A twin study of ethanol metabolism. Behav Genet. 1985;15(2):93–109. doi: 10.1007/BF01065891. [DOI] [PubMed] [Google Scholar]
  38. McEvoy BP, Montgomery GW, McRae AF, Ripatti S, Perola M, Spector TD, Cherkas L, Ahmadi KR, Boomsma D, Willemsen G, Hottenga JJ, Pedersen NL, Magnusson PK, Kyvik KO, Christensen K, Kaprio J, Heikkila K, Palotie A, Widen E, Muilu J, Syvanen AC, Liljedahl U, Hardiman O, Cronin S, Peltonen L, Martin NG, Visscher PM. Geographical structure and differential natural selection among North European populations. Genome Res. 2009;19(5):804–814. doi: 10.1101/gr.083394.108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Medland SE, Nyholt DR, Painter JN, McEvoy BP, McRae AF, Zhu G, Gordon SD, Ferreira MA, Wright MJ, Henders AK, Campbell MJ, Duffy DL, Hansell NK, Macgregor S, Slutske WS, Heath AC, Montgomery GW, Martin NG. Common variants in the trichohyalin gene are associated with straight hair in Europeans. Am J Hum Genet. 2009;85(5):750–755. doi: 10.1016/j.ajhg.2009.10.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Meijer AJ, van Woerkom GM, Williamson JR, Tager JM. Rate-limiting factors in the oxidation of ethanol by isolated rat liver cells. Biochem J. 1975;150(2):205–209. doi: 10.1042/bj1500205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Neumark YD, Friedlander Y, Thomasson HR, Li TK. Association of the ADH2*2 allele with reduced ethanol consumption in Jewish men in Israel: a pilot study. J Stud Alcohol. 1998;59(2):133–139. doi: 10.15288/jsa.1998.59.133. [DOI] [PubMed] [Google Scholar]
  42. Nyholt DR. A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet. 2004;74(4):765–769. doi: 10.1086/383251. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Purcell S, Cherny SS, Sham PC. Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics. 2003;19(1):149–150. doi: 10.1093/bioinformatics/19.1.149. [DOI] [PubMed] [Google Scholar]
  44. Quertemont E. Genetic polymorphism in ethanol metabolism: acetaldehyde contribution to alcohol abuse and alcoholism. Mol Psychiatry. 2004;9(6):570–581. doi: 10.1038/sj.mp.4001497. [DOI] [PubMed] [Google Scholar]
  45. Raimondi S, Benhamou S, Coutelle C, Garte S, Hayes R, Kiemeney L, Lazarus P, Marchand LL, Morita S, Povey A, Romkes M, Zijno A, Taioli E. Association of metabolic gene polymorphisms with alcohol consumption in controls. Biomarkers. 2004;9(2):180–189. doi: 10.1080/13547500410001728381. [DOI] [PubMed] [Google Scholar]
  46. Schoedel KA, Tyndale RF. Induction of nicotine-metabolizing CYP2B1 by ethanol and ethanol-metabolizing CYP2E1 by nicotine: summary and implications. Biochim Biophys Acta. 2003;1619(3):283–290. doi: 10.1016/s0304-4165(02)00487-7. [DOI] [PubMed] [Google Scholar]
  47. Schuckit MA, Smith TL, Anderson KG, Brown SA. Testing the level of response to alcohol: social information processing model of alcoholism risk--a 20-year prospective study. Alcohol Clin Exp Res. 2004;28(12):1881–1889. doi: 10.1097/01.alc.0000148111.43332.a5. [DOI] [PubMed] [Google Scholar]
  48. Tanaka E, Terada M, Misawa S. Cytochrome P450 2E1: its clinical and toxicological role. J Clin Pharm Ther. 2000;25(3):165–175. doi: 10.1046/j.1365-2710.2000.00282.x. [DOI] [PubMed] [Google Scholar]
  49. The International HapMap Consortium. The International HapMap Project. Nature. 2003;426(6968):789–796. doi: 10.1038/nature02168. [DOI] [PubMed] [Google Scholar]
  50. Vesell ES. Ethanol metabolism: regulation by genetic factors in normal volunteers under a controlled environment and the effect of chronic ethanol administration. Ann N Y Acad Sci. 1972;197:79–88. doi: 10.1111/j.1749-6632.1972.tb28123.x. [DOI] [PubMed] [Google Scholar]
  51. Vesell ES, Page JG, Passananti GT. Genetic and environmental factors affecting ethanol metabolism in man. Clin Pharmacol Ther. 1971;12(2):192–201. doi: 10.1002/cpt1971122part1192. [DOI] [PubMed] [Google Scholar]
  52. Watanabe J, Hayashi S, Kawajiri K. Different regulation and expression of the human CYP2E1 gene due to the RsaI polymorphism in the 5′-flanking region. J Biochem. 1994;116(2):321–326. doi: 10.1093/oxfordjournals.jbchem.a124526. [DOI] [PubMed] [Google Scholar]
  53. Webb A, Lind PA, Kalmijn J, Feiler HS, Smith TL, Schuckit MA, Wilhelmsen K. The Investigation into CYP2E1 in Relation to the Level of Response to Alcohol Through a Combination of Linkage and Association Analysis. Alcohol Clin Exp Res. 2011;35(1):10–18. doi: 10.1111/j.1530-0277.2010.01317.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Whitfield JB, Nightingale BN, Bucholz KK, Madden PA, Heath AC, Martin NG. ADH genotypes and alcohol use and dependence in Europeans. Alcohol Clin Exp Res. 1998;22(7):1463–1469. [PubMed] [Google Scholar]
  55. Yoshida A. Molecular genetics of human aldehyde dehydrogenase. Pharmacogenetics. 1992;2(4):139–147. doi: 10.1097/00008571-199208000-00001. [DOI] [PubMed] [Google Scholar]

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