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. Author manuscript; available in PMC: 2010 Feb 10.
Published in final edited form as: Alcohol Clin Exp Res. 2009 Jan 21;33(4):712. doi: 10.1111/j.1530-0277.2008.00888.x

Sequence Variations of the Human MPDZ Gene and Association With Alcoholism in Subjects With European Ancestry

Victor M Karpyak 1, Jeong-Hyun Kim 1, Joanna M Biernacka 1, Eric D Wieben 1, David A Mrazek 1, John L Black 1, Doo-Sup Choi 1
PMCID: PMC2819379  NIHMSID: NIHMS173437  PMID: 19175764

Abstract

Background

Mpdz gene variations are known contributors of acute alcohol withdrawal severity and seizures in mice.

Methods

To investigate the relevance of these findings for human alcoholism, we resequenced 46 exons, exon–intron boundaries, and 2 kilobases in the 5′ region of the human MPDZ gene in 61 subjects with a history of alcohol withdrawal seizures (AWS), 59 subjects with a history of alcohol withdrawal without AWS, and 64 Coriell samples from self-reported nonalcoholic subjects [all European American (EA) ancestry] and compared with the Mpdz sequences of 3 mouse strains with different propensity to AWS. To explore potential associations of the human MPDZ gene with alcoholism and AWS, single SNP and haplotype analyses were performed using 13 common variants.

Results

Sixty-seven new, mostly rare variants were discovered in the human MPDZ gene. Sequence comparison revealed that the human gene does not have variations identical to those comprising Mpdz gene haplotype associated with AWS in mice. We also found no significant association between MPDZ haplotypes and AWS in humans. However, a global test of haplotype association revealed a significant difference in haplotype frequencies between alcohol-dependent subjects without AWS and Coriell controls (p = 0.015), suggesting a potential role of MPDZ in alcoholism and or related phenotypes other than AWS. Haplotype-specific tests for the most common haplotypes (frequency > 0.05), revealed a specific high-risk haplotype (p = 0.006, maximum statistic p = 0.051), containing rs13297480G allele also found to be significantly more prevalent in alcoholics without AWS compared with nonalcoholic Coriell subjects (p = 0.019).

Conclusions

Sequencing of MPDZ gene in individuals with EA ancestry revealed no variations in the sites identical to those associated with AWS in mice. Exploratory haplotype and single SNP association analyses suggest a possible association between the MPDZ gene and alcohol dependence but not AWS. Further functional genomic analysis of MPDZ variants and investigation of their association with a broader array of alcoholism-related phenotypes could reveal additional genetic markers of alcoholism.

Keywords: Genomics, Alcoholism, MPDZ, Withdrawal, Seizures

Twin And Linkage studies have revealed that alcohol dependence is highly heritable and allelic variants of several candidate genes have been found to be associated with altered risks for alcohol dependence and withdrawal (Higuchi et al., 2006; Liu et al., 2004; Prescott and Kendler, 1999; Schuckit et al., 1972; Tyndale, 2003). Using a combination of interval-specific congenic strains and recombinant progeny tests in mice (Buck et al., 1997; Fehr et al., 2002; Shirley et al., 2004), it was shown that the Mpdz gene and its variants could affect alcohol and barbiturate withdrawal liability measured by presence and severity of seizures. The human analog, MPDZ, is located on chromosome 9p24-p22 and encodes for the Multiple-PDZ-Domain protein, which is also known as MUPP1 and contains 13 PDZ domains (Ullmer et al., 1998). PDZ domains are protein-interaction domains and are often part of the multi-domain scaffolding proteins that assemble large molecular complexes, control synaptic protein composition and structure, and allow the dynamic trafficking of synaptic proteins in specific cell locations (Kim and Sheng, 2004). The MPDZ protein is known to be involved in learning and memory-related synaptic plasticity as well as seizures and epilepsy through its interaction with the serotonin 5-HT2C receptor, glutamate NMDA receptors, GABAB receptor, and dopamine D2, D3, and D4 receptors (Bettler et al., 2004; Griffon et al., 2003; Krapivinsky et al., 2004; Parker et al., 2003).

Specific haplotypes associated with the production of the Mpdz protein variants II and III (NCBI# AAL37379.1 and AAL37390.1, respectively), differentiate strains of mice with severe withdrawal (including C57LJ and DBA/2J) from strains that have less severe withdrawal (including wild type C57BL/6J) and possess a haplotype that produces protein variant I (NCBI# AAL37377.2) (Fehr et al., 2002). Therefore, we hypothesized that genetic variability of the human MPDZ gene may also be associated with the risk of alcohol withdrawal seizures (AWS). This association may be due to variations in the coding sequence of the human MPDZ gene that are analogous to those described in mice (Fehr et al., 2002). Alternatively, variations in the regulatory regions could potentially affect protein production and contribute to the risk of alcohol withdrawal and AWS (Shirley et al., 2004).

To address these hypotheses, we performed direct comparisons between the human MPDZ gene and its mouse homolog Mpdz to identify potential variability in the human DNA sites that are homologous to the variability sites described as being part of the withdrawal seizure-related haplotype in mice (Fehr et al., 2002). The current version of the dbSNP (build 129) lists a total of 589 single nucleotide polymorphisms (SNPs) in the human MPDZ gene including 23 SNPs (12 nonsynonymous, 10 synonymous, and 1 frame shift) in coding regions (NCBI). In order to ensure the inclusion of all relevant variations, we performed resequencing of the MPDZ gene in a sample of alcohol-dependent subjects with European Ancestry and a sample of DNA from self reported nonalcoholic European Americans (EA) obtained from the Coriell Institute.

The results of this sequence comparison between mice and humans are reported as well as comparisons of sequence variation frequencies in groups of alcohol-dependent subjects with and without AWS and in the Coriell sample.

MATERIALS AND METHODS

Study Subjects

This study was conducted in compliance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) and the standards established by the Institutional Review Board of the Mayo Clinic Rochester. Participants were recruited from subjects treated in the outpatient and residential addictions treatment programs or on the medical and surgical floors of the hospitals affiliated with the Mayo Clinic in Rochester, MN. All subjects provided informed consent. DNA samples were collected from 120 male and female EA subjects, 18 years or older, who met DSM-IVTR criteria for diagnosis of alcohol dependence (APA, 2000). Of these, 61 subjects had a history of alcohol withdrawal with seizures (AWS), including 15 subjects who also had a history of delirium tremens (DT). The remaining 59 subjects reported a history of alcohol withdrawal without seizures or DT. Potential study subjects were excluded if they had a history of: psychotic or bipolar I disorders; use of substances known to potentially cause seizures or delirium (e.g., barbiturates, benzodiazepines, opiates, hallucinogens, stimulants, cocaine, hallucinogens, PCP, anticholinergics) at the time of the first AWS; neurologic conditions with seizures and or EEG or imaging data indicating presence of the potentially epileptogenic focus prior to the first AWS; presence of somatic or metabolic conditions capable of provoking seizures and or delirium at the time of the first AWS (e.g., fever > 40°C, sodium < 120 or >160 mEq/l, glucose < 60 mg%, magnesium < 1.0 mEq/l, calcium < 6.0 mEq/l, ammonia > 50 μgN/dl, BUN > 100 mg%, creatinine > 5.0 mg%, or osmolality 350 mOsm/l). A review of the medical records and diagnostic evaluation of each study participant by a Board Certified psychiatrist (VMK) was performed to ensure that DSM IV-TR criteria for alcohol dependence are met and none of the exclusion criteria are present. Genomic DNA from 64 self-reported EA nonalcoholic subjects obtained from the Coriell Institute was used as nonalcoholic control.

DNA Resequencing

Genomic DNA was extracted from peripheral blood lymphocytes using AutoPure LS (Gentra, Minneapolis, MN), according to the manufacturer’s protocol. New pairs of primers were generated for all exons of the human MPDZ gene, including the exon–intron boundaries and 2 kilobases in the 5′ region based on the chromosome 9p24-p22 genomic sequence (NT_008413). The coding sequence of the human MPDZ gene was compared with the Ensembl transcript OTTHUMT00000055488. All exons and introns, including 75 to 150 bp of the boundary regions of the respective exons, were amplified using polymerase chain reaction and sequenced using an ABI PRISM Big Dye Terminator, Cycle sequencing assay (Applied Biosystems, Foster City, CA), and an ABI 3730xl automated sequencer (Applied Biosystems; Genomics Technology Center, Mayo Clinic, Rochester, MN). After the sequencing, sequence variants were screened using the Polyphred program.

Data Analysis and Statistics

The ALIGN software (Pearson et al., 1997) and ClustalW program (http://www.ebi.ac.uk/Tools/clustalw2/index.html) were used to compare coding DNA (cDNA) sequence of the human MPDZ gene and protein with the sequences of Mpdz gene and proteins of 3 mouse strains. The Branchpoint (BP) Analysis program of EMBL-EBI web site (http://www.ebi.ac.uk/asd-srv/wb.cgi?method=2) was used to determine if variations were located in consensus areas potentially important for alternative splicing process. The SIGSCAN (http://bimas.dcrt.nih.gov/molbio/signal/) program was used to identify potential transcription enhancer/repressor consensus sequences in the putative promotor areas.

Haploview v3.2 software (Barrett et al., 2005) was used to determine linkage disequilibrium (LD) of MPDZ gene variants. Allele frequencies of all variants with a minor allele frequency (MAF) ≥0.05 were compared in the 3 groups of subjects using a Fisher’s exact test. Associations of these variants with AWS were explored by comparing allele frequencies between alcohol-dependent subjects with a history of AWS and alcohol-dependent subjects without AWS history. Associations with alcoholism were explored by comparing allele frequencies of alcohol-dependent subjects without AWS history and frequencies in the nonalcoholic Coriell subjects.

Haplotype association tests were performed on haplotypes composed of all SNPs with a MAF ≥ 0.05, as well as tag-SNPs representative of these SNPs, using the HaploStats package (Schaid et al., 2002). First, a global test of association was performed. This test simultaneously considers all haplotypes (allelic combinations) of a specific set of tightly linked polymorphisms, uses a single statistic to test whether frequencies of any of these haplotypes differ between cases and controls and, thus, eliminates the need for correction for multiple testing. Subsequently, haplotype-specific tests for the most common haplotypes (frequency > 0.05) were performed. Each haplotype-specific test assesses whether the frequency of a particular haplotype differs between cases and controls. For the haplotype with the largest score statistic, demonstrating the strongest evidence of association with the phenotype of interest, a simulation-based p-value for the maximum score statistic was calculated. The maximum statistic simulated p-value is used to control for multiple testing in this case. It is expected that the global test may be more powerful when several haplotypes are associated with the trait, while the maximum score statistic is expected to be more powerful than the global test of association when the effect of a single haplotype is much larger than that of all the other haplotypes (see Schaid et al., 2002, for details).

RESULTS

Study Subjects

The group of alcohol-dependent subjects who had a history of AWS included 44 males (72%) and 17 females (28%) with a mean age of 50 ± 10. The group of alcohol-dependent subjects without an AWS history included 45 males (76%) and 14 females (24%) who had a mean age of 52 ± 8. The average age of the beginning of regular use of alcohol was 18 ± 4 in the AWS group and 18 ± 3 in the group of subjects without AWS history. Maximum drinking tolerance (defined as the maximum number of drinks ever consumed per 24 hour) was 29 ± 18 in the AWS group and 23 ± 10 in the group of alcohol-dependent subjects without AWS. This difference in maximum drinking tolerance was not statistically significant (Kruskal–Wallis test p > 0.05).

Resequencing of the Human MPDZ Gene

The 46 exons, exon–intron boundaries, and proximal promoter region of the MPDZ gene were resequenced and 92 variants were identified including 67 new variants. A detailed description of the identified variations is presented in Table 1 and their locations are illustrated in Fig. 1. Primer sequences are available in the Table S1. Of the 92 variants, 13 had MAF ≥ 0.05 in the combined set of all subjects (Table 1, Fig. 1). Figure 2 shows the LD pattern of these 13 SNPs with a MAF ≥ 0.05.

Table 1.

Polymorphisms of MPDZ Gene of Alcoholic Subjects and Coriell Controls

Minor allele frequency
Chr. position Gene position dbSNP ID Variation Amino acid change Location Position Coriell n = 64 No AWS n = 60 AWS n = 60
1 13242041 −1727 G > C Upstream-1727 Upstream-1727 0.0000 0.0085 0.0000
2 13241615 −1301 C > G Upstream-1301 Upstream-1301 0.0078 0.0000 0.0000
3 13240778 −464 G > A Upstream-464 Upstream-464 0.0000 0.0085 0.0000
4 13240750 −436 insT Upstream-436 Upstream-436 0.2344 0.2458 0.1967
5 13240302 13 A > G Ile5Val Exon 1 c13 0.0000 0.0085 0.0082
6 13240274 41 C > T Intron 1 IVS1 + 25 0.0078 0.0085 0.0164
7 13237636 2679 C > G Gln61Glu Exon 2 c181 0.0000 0.0085 0.0000
8 13237519 2796 A > G Intron 2 IVS2 + 115 0.0000 0.0085 0.0082
9 13214491 25824 rs17273542 C > T Ser92Leu Exon 3 c275 0.0078 0.0169 0.0000
10 13214441 25874 G > T Gly109Cys Exon 3 c325 0.0000 0.0000 0.0082
11 13214410 25905 G > C Cys119Ser Exon 3 c356 0.0000 0.0000 0.0082
12 13214354 25961 A > C Intron 3 IVS3 + 31 0.0156 0.0000 0.0000
13 13213592 26723 C > G Gln171Glu Exon 4 c511 0.0000 0.0000 0.0082
14 13213515 26800 G > A Intron 4 IVS4 + 55 0.0000 0.0000 0.0082
15 13212200 28115 A > C Intron 5 IVS5 + 32 0.0156 0.0085 0.0082
16 13211401 28914 A > G Lys282Lys Exon 6 c846 0.0078 0.0000 0.0000
17 13209839 30476 C > T Intron 6 IVS6 + 1532 0.0078 0.0000 0.0000
18 13209797 30517–30518 delTA Intron 6 IVS6 + 1573–74 0.0156 0.0000 0.0000
19 13209603 30712 rs34911705 G > C Leu347Phe Exon 7 c1041 0.0078 0.0000 0.0000
20 13209570 30745 rs13297480 A > G Thr358Thr Exon 7 c1074 0.0938 0.2034 0.0902
21 13209459 30856 A > G Intron 7 IVS7 + 99 0.0000 0.0085 0.0000
22 13195789 44526 A > T Intron 10 IVS10 + 126 0.0313 0.0000 0.0164
23 13195264 45051 rs2039333 T > C Intron 10 IVS10 + 651 0.0313 0.0000 0.0164
24 13195020 45295 C > T Intron 11 IVS11 + 15 0.0000 0.0085 0.0000
25 13186032 54283 T > C Intron 12 IVS12 + 88 0.0078 0.0000 0.0082
26 13182360 57955 insA Intron 13 IVS13 + 806 0.0156 0.0169 0.0164
27 13182320 57995 rs1331676 G > C Intron 13 IVS13 + 846 0.0000 0.0000 0.0082
28 13182201 58114 C > T Arg633STOP Exon 14 c1897 0.0000 0.0085 0.0000
29 13182048 58267 rs13291548 A > G Intron 14 IVS14 + 82 0.0391 0.0169 0.0000
30 13182024 58291 rs16930194 C > T Intron 14 IVS14 + 106 0.0234 0.0339 0.0164
31 13180292 60023 G > A Val659Ile Exon 15 c1975 0.0078 0.0085 0.0082
32 13180272 60043 C > T Ile665Ile Exon 15 c1995 0.0078 0.0000 0.0000
33 13180235 60080 A > T Thr678Ser Exon 15 c2032 0.0078 0.0000 0.0082
34 13180163 60152 rs4741289 G > A Glu702Lys Exon 15 c2104 0.0078 0.0000 0.0164
35 13180162 60153 rs4740548 A > T Glu702Val Exon 15 c2105 0.0078 0.0000 0.0164
36 13179093 61222 C > T Intron 15 IVS15 + 1020 0.0000 0.0000 0.0082
37 13179017 61298 rs41265290 A > G Intron 15 IVS15 + 1096 0.0313 0.0000 0.0082
38 13178754 61561 T > G Intron 16 IVS16 + 29 0.0078 0.0000 0.0000
39 13178719 61596 T > C Intron 16 IVS16 + 64 0.0000 0.0085 0.0000
40 13176355 63960 A > G Lys799Glu Exon 17 c2395 0.0078 0.0000 0.0082
41 13173701 66614 A > G Intron 17 IVS17 + 2568 0.0000 0.0085 0.0000
42 13173486 66829 rs34704118 A > G Leu860Leu Exon 18 c2580 0.0156 0.0000 0.0082
43 13166312 74003 rs2274856 G > A Ser918Ser Exon 19 c2754 0.2734 0.3051 0.2049
44 13165987 74328 rs1331674 G > A Intron 19 IVS19 + 148 0.2734 0.2966 0.2131
45 13165908 74407 C > T Intron 19 IVS19 + 227 0.0391 0.0339 0.0164
46 13165719 74594–74596 rs3831219 delCTC Intron 20 IVS20 + 30–32 0.3359 0.3390 0.2541
47 13165639 74676 G > T Intron 20 IVS20 + 112 0.0000 0.0085 0.0000
48 13158588 81727 T > C Intron 20 IVS20 + 7163 0.0000 0.0000 0.0082
49 13158251 82064 insT Intron 21 IVS21 + 114 0.0000 0.0085 0.0000
50 13153010 87305 rs1331672 C > G Intron 21 IVS21 + 5355 0.3359 0.3220 0.2295
51 13148261 92054 delC Intron 22 IVS22 + 4429 0.0156 0.0085 0.0164
52 13148062 92253 rs41265286 G > A Ser1136Asn Exon 23 c3407 0.0078 0.0085 0.0000
53 13140714 99601 G > A Intron 23 IVS23 + 7303 0.0313 0.0000 0.0082
54 13140558 99757 T > G Ser1194Arg Exon 24 c3582 0.0078 0.0085 0.0000
55 13140531 99784 rs10756457 A > G Lys1203Lys Exon 24 c3609 0.3359 0.3475 0.2377
56 13140506 99809 C > T Intron 24 IVS24 + 4 0.0000 0.0000 0.0082
57 13137723 102592 rs7024892 A > G Intron 24 IVS24 + 2787 0.3281 0.3644 0.2377
58 13137602 102713 G > A Arg1229Gln Exon 25 c3686 0.0000 0.0085 0.0000
59 13137569 102746 A > T Gln1240Leu Exon 25 c3719 0.0000 0.0000 0.0082
60 13133591 106724 G > A Intron 25 IVS25 + 3956 0.0000 0.0000 0.0082
61 13133470 106845 G > T Asp1279Tyr Exon 26 c3835 0.0000 0.0085 0.0000
62 13133254 107061 T > C Intron 26 IVS26 + 211 0.0078 0.0000 0.0000
63 13130070 110245 G > A Glu1307Lys Exon 27 c3919 0.0000 0.0085 0.0000
64 13127982 112333 delT Leu1392a Exon 28 c4174 0.0078 0.0000 0.0000
65 13127889 112426 A > G Intron 28 IVS28 + 67 0.0000 0.0085 0.0082
66 13126160 114155 G > C Gln1438His Exon 30 c4314 0.0000 0.0000 0.0082
67 13123801 116514 A > G Intron 31 IVS31 + 22 0.0000 0.0000 0.0082
68 13116836 123479 G > T Intron 31 IVS31 + 6987 0.0078 0.0085 0.0000
69 13115415 124900 T > C Intron 33 IVS33 + 1100 0.0078 0.0000 0.0000
70 13115201 125114 rs2297003 A > G Intron 34 IVS34 + 14 0.3359 0.3390 0.2459
71 13113330 126985 T > C Intron 34 IVS34 + 1885 0.0000 0.0000 0.0082
72 13109766 130549 A > G Intron 37 IVS37 + 1972 0.0000 0.0085 0.0082
73 13105422 134893 rs2274647 A > C Intron 38 IVS38 + 4079 0.3125 0.3644 0.2705
74 13105350 134965 insC Intron 38 IVS38 + 4151 0.0078 0.0085 0.0082
75 13105349 134966 rs41265282 C > G Intron 38 IVS38 + 4152 0.0313 0.0000 0.0082
76 13103159 137156 delA Intron 40 IVS40 + 771 0.0313 0.0000 0.0082
77 13103006 137309 A > G Intron 41 IVS41 + 4 0.0000 0.0085 0.0000
78 13102228 138087 C > G Intron 41 IVS41 + 782 0.0078 0.0000 0.0000
79 13102024 138291 rs34605667 G > A Arg1880Lys Exon 42 c5639 0.0547 0.0339 0.0492
80 13102015 138300 G > A Intron 42 IVS42 + 8 0.0000 0.0000 0.0082
81 13100138 140177 T > A Intron 43 IVS43 + 497 0.0078 0.0000 0.0000
82 13100068 140247 C > T Intron 43 IVS43 + 567 0.0078 0.0000 0.0000
83 13099969 140346 T > C Ile1947Thr Exon 44 c5840 0.0000 0.0085 0.0000
84 13099015 141300 G > T Gly1968Cys Exon 45 c5902 0.0000 0.0000 0.0082
85 13098915 141400 C > A Intron 45 IVS45 + 20 0.0000 0.0000 0.0082
86 13098824 141491 G > A Intron 45 IVS45 + 111 0.0078 0.0000 0.0000
87 13098794 141521 rs3765550 A > G Intron 45 IVS45 + 141 0.4063 0.3729 0.4426
88 13097222 143093 C > T Intron 45 IVS45 + 1713 0.0000 0.0000 0.0082
89 13097121 143194 A > G Intron 45 IVS45 + 1814 0.0078 0.0085 0.0000
90 13096598 143717 rs3264 A > G Exon 46 3′-UTR + 366 0.3359 0.4237 0.3689
91 13096469 143846 A > G Intron 46 Intergenic + 109 0.0000 0.0000 0.0082
92 13096335 143980 rs722651 C > T Intron 46 Intergenic + 243 0.3828 0.2627 0.3607
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AWS, alcohol withdrawal seizure.

Variations included in the haplotype analysis are highlighted. AWS group included alcohol-dependent subjects with a history of withdrawal seizures. No AWS group included alcohol-dependent subjects without history of withdrawal seizures.

a

A frameshift.

Fig. 1.

Fig. 1

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Schematic representation of the known and newly discovered sequence variations in the human MPDZ gene. The arrows indicate approximate locations of the variant alleles. Pale arrows indicate variants with minor allele frequency (MAF) < 0.05 and dark arrows indicate variants with MAF > 0.05. The numbering of cSNPs corresponds to the start codon, ATG. Detailed information about each variant is presented in Table 1. Positions in the human MPDZ sequence homologous to 18 sites in mouse Mpdz sequence associated with variable propensity to alcohol-withdrawal seizures are indicated by *. Detailed information about each of these positions in the cDNA sequence is presented in Table 2.

Fig. 2.

Fig. 2

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LD and haplotype block analysis of 13 MPDZ variations with MAF ≥ 0.05. LD plot prepared using Haploview (Barrett et al., 2005). Colors are used to display pairwise LD as follows: bright red, LOD ≥ 2 and D′ = 1; shades of pink/red, LOD ≥ 2 and D′ < 1; white, LOD < 2 and D′ < 1. The numbers indicate pairwise r2 values shown as a percentage. Blocks are defined based on the confidence interval method as described by Gabriel and colleagues (2002).

Comparison of the Human MPDZ Gene With the Mouse Homolog

As allelic variation in specific gene locations was associated with differences in severity of alcohol withdrawal as defined by presence of seizures in inbred strains of mice (Fehr et al., 2002), sequence similarity between human and mouse variants was explored. The human gene sequence (Ensembl transcript OTTHUMT00000055488 updated with our resequencing data) was compared with the gene sequence of 3 strains of mice: the wild type C57BL/6J (NCBI# AF326531), the C57L/J (NCBI# AF326533), and the DBA/2J (NCBI# AF326544). The alignment showed 76% global sequence identity between human MPDZ gene and its mouse homolog Mpdz. As shown in Table 2, at 12 of 18 cDNA positions forming withdrawal seizures-related haplotype in mice, the nucleotides in human sequence were identical with C57L/J strain and 10 of 18 of these haplotype-forming sites were the same in human and the DBA/2J strain. Both of these strains are prone to severe withdrawal and seizures. In contrast, only in 5 of 18 haplotype-forming sites nucleotides were identical in humans and the withdrawal seizure-resistant C57BL/6J strain. However, no genetic variability of these sites in human MPDZ gene was found in our sample of 240 chromosomes from alcoholics and 128 chromosomes from Coriell subjects (all with EA ancestry). This analysis had sufficient power (97.5%) to reveal rare polymorphisms and mutations (MAF = 0.01).

Table 2.

Comparison Between cDNA Sequence Positions of the Nucleotides FormingWithdrawal Seizures-Related Haplotype in Mouse Mpdz Gene and Nucleotides in the Homologues Positions in the Human MPDZ Gene

graphic file with name nihms173437f3.jpg
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The amino acid sequence of the human MPDZ protein (NP_003820) was compared to those of the inbred lines, C57BL/6J (AAL37377), C57L/J (AAL37379), and DBA/2J (AAL37390). Alignment showed 83% global sequence identity between the human MPDZ protein and its mouse homolog. As shown in Table 2, of the 18 amino acids corresponding to the SNPs described as being part of the seizure-prone haplotype in mice, 6 were completely conserved between human and mice. Of the remaining 12 amino acids, 5 amino acids in C57L/J strain, 4 amino acids in DBA/2J strain, and 4 in C57BL/6J strain were identical to the human sequence (Table 2).

Comparison of the MPDZ Variations Frequency in the Alcoholic and Coriell Samples

To explore potential associations of the human MPDZ gene variations with AWS and with alcoholism, we studied 13 variants (Table 3) with MAF ≥ 0.05. The synonymous rs13297480 G variant, in exon 7, was found to be significantly more prevalent in alcoholics without history of AWS compared to nonalcoholic Coriell subjects (p = 0.019), or to alcoholics with history of AWS (p = 0.017). Also, the intron 24 rs7024892 G variant was found to be less prevalent in AWS group compared to the group without AWS history. Given that these results are not corrected for the multiple comparisons performed, replication is necessary to confirm these findings.

Table 3.

Polymorphic Variants of the MPDZ Gene Selected for LD and Haplotype Analysis

Minor allele frequency
 No AWSa versus Coriellb
 AWS versus Alcoholicb
dbSNP ID Variant Location AWS No AWSa Coriell p value OR 95% CI p value OR 95% CI
N/A insT −436 upstream 0.197 0.246 0.234 0.882 1.064 0.567–1.994 0.437 0.752 0.388–1.450
rs13297480 A > G Exon 7 0.090 0.203 0.094 0.019 2.459 1.113–5.701 0.017 0.390 0.163–0.878
rs2274856 G > A Exon 19 0.205 0.305 0.273 0.673 0.858 0.475–1.546 0.078 1.700 0.909–3.218
rs1331674 G > A Intron 19 0.213 0.297 0.273 0.777 0.893 0.494–1.614 0.142 1.554 0.832–2.930
rs3831219 delCTC Intron 20 0.254 0.339 0.336 1.000 1.014 0.577–1.780 0.160 0.665 0.365–1.204
rs1331672 C > G Intron 21 0.230 0.322 0.336 0.892 0.939 0.532–1.655 0.114 0.628 0.339–1.154
rs10756457 A > G Exon 24 0.238 0.347 0.336 0.893 1.052 0.600–1.845 0.066 0.587 0.320–1.067
rs7024892 A > G Intron 24 0.238 0.364 0.328 0.593 1.173 0.670–2.055 0.035 0.545 0.298–0.988
rs2297003 A > G Intron 34 0.246 0.339 0.336 1.000 0.987 0.562–1.734 0.120 1.570 0.864–2.873
rs2274647 A > C Intron 38 0.270 0.364 0.313 0.420 1.260 0.718–2.217 0.129 0.648 0.360–1.159
rs3765550 A > G Intron 45 0.443 0.373 0.406 0.603 0.870 0.503–1.499 0.295 1.334 0.772–2.315
rs3264 A > G Exon 46 0.369 0.424 0.336 0.188 1.451 0.838–2.522 0.429 0.796 0.458–1.379
rs722651 C > T Intron 46 0.361 0.263 0.383 0.056 0.576 0.321–1.023 0.125 1.580 0.880–2.862
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AWS, group of alcoholic subjects with a history of withdrawal seizure; OR, odd ratio; CI, confidence intervals.

a

No AWS group included alcohol-dependent subjects without history of withdrawal seizure and/or delirium tremens.

b

Statistical analyses were performed using Fisher’s exact test.

p values below 0.05 are presented in bold.

The association between MPDZ haplotypes and alcoholism with and without AWS was further investigated by considering the haplotype that comprised all 13 SNPs with MAF ≥ 0.05. The comparisons were made using the approach proposed by Schaid and colleagues (2002), as described in the Methods section. The seven 13-SNP haplotypes with frequencies > 0.05 used for these comparisons are presented in Table 4.

Table 4.

Association of MPDZ Haplotypes With AlcoholismWithout AWS

SNP
Haplotype −436 Upstream* rs13297480* rs2274856* rs1331674 rs3831219* rs1331672 rs10756457* rs7024892 rs2297003 rs2274647* rs3765550* rs3264* rs722651* Haplotype frequency Simulated p-value Max statistic simulated p-value
1 A G G C A A G A A G C 0.0621 0.087
2 A G G C A A G A A A C 0.0725 0.164
3 A G G C A A G A G A T 0.2421 0.162
4 insT A A A delCTC G G G A C A A C 0.0520 0.916
5 insT A G G C A A G C A A C 0.0980 0.179
6 A G G C A A G A G G C 0.0774 0.063
7 G A A delCTC G G G A A A G C 0.0746 0.006 0.051
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AWS, alcohol withdrawal seizure.

Global score test statistic = 16.90, df = 7, Global simulation p-value = 0.015.

*

Tag-SNP.

p values below 0.05 are presented in bold.

First, to test for association between the MPDZ gene and AWS, the frequencies of the 13-SNP haplotypes were compared between subjects with AWS history and alcohol-dependent subjects without AWS. No significant haplotype associations were identified (data not shown).

To test for association between MPDZ gene and alcoholism without AWS, we compared frequencies of the same 13-SNP haplotypes between the alcohol-dependent subjects without AWS and Coriell samples. The results of this comparison, including frequencies of the 7 most common haplotypes (frequency > 0.05) and score test simulation p-values are shown in Table 4. The global score test (Schaid et al., 2002) led to a simulation-based p-value of 0.015. As explained in the Methods section, only one global haplotype test was performed for this comparison, and the p-value does not require correction for multiple testing. Tests of individual haplotype effects shown in the last column of Table 4 indicate that haplotype #7 had the strongest association with alcoholism (uncorrected p = 0.006). The corresponding maximum statistic simulation p-value, which corrects for the multiple tests performed on all individual haplotypes shown in Table 4, was 0.051 suggesting that haplotype #7 confers a higher risk of alcoholism than the other haplotypes. This haplotype is one of only 2 common haplotypes that carries the high risk rs13297480 G variant in the exon 7, indicating its potentially important role.

We also investigated association of the studied phenotypes with haplotypes composed of 9 tag-SNPs selected from the 13 common SNPs. The tag-SNPs are indicated in Table 4 with an asterisk (*) following the SNP rs number. Results were very similar to those described above for the 13-SNP haplotype: There was no association between the tag-SNP haplotypes and history of AWS; however, there was evidence of association of the tag-SNP haplotypes with alcoholism without AWS (global test simulation p = 0.031; uncorrected p-value for most strongly associated haplotype = 0.007; maximum statistic simulation p-value = 0.045).

DISCUSSION

Animal studies have demonstrated that genetic variations in coding sequence of Mpdz gene and changes in the expression of this gene might be associated with the severity of acute alcohol withdrawal and seizures (Fehr et al., 2002; Shirley et al., 2004). To investigate the signficance of this finding in human subjects with alcoholism, sequence variations of the human MPDZ gene were examined in alcohol-dependent subjects (EA ancestry) with and without history of AWS and in the DNA samples from EA subjects available at the Coriell Institue (as representatives of the EA population). Sequencing of 240 chromosomes from the alcohol-dependent subjects and 128 chromosomes from the Coriell subjects provided sufficient power to reveal rare polymorphisms and mutations, with 97.5% power to detect variations with a MAF of 0.01 within specific ethnic group. We identified 67 new sequence variants but found no genetic variability in the human MPDZ gene sites homologous to the variability sites described as being part of a withdrawal seizure-related haplotype in mice. We also found no significant haplotype association of MPDZ gene with a history of AWS, although a larger study would be recommended to reliably rule out this possibility.

At the same time, a statistically significant haplotype association of MPDZ gene with alcoholism was demonstrated when subjects without AWS history were compared to Coriell controls. This association disappeared when haplotype association was tested in all 120 alcoholics (with and without AWS) versus Coriell controls (data not shown). Several important issues need to be considered in order to interpret this finding. First, different alcoholism-related phenotypes may have shared as well as independent genetic risk factors. For example, it is well known that AWS-resistant C57BL/6J mice consume significantly more alcohol than the AWS-prone DBA/2J mice (Belknap et al., 1993; Metten and Crabbe, 2005). Two (or more) different genetic variations may be responsible for the increased propensity to AWS and decreased preference of alcohol. Alternatively, the pleiotropic effects of a single genetic variation may contribute to both phenotypes, leading to inverse correlation between propensity to AWS and alcohol preference phenotypes in mice. Both of these possibilities are potentially applicable to humans and need to be investigated. Second, all alcohol-dependent subjects in our study had a history of alcohol withdrawal and the number of subjects with the history of AWS was increased compared to their frequency in general population or among treatment seeking alcoholics (Caetano et al., 1998). Consequently, it is possible that increased presence of the AWS phenotype may mask the association between MPDZ gene and other alcohol-related phenotypes, including alcoholism in general and alcohol withdrawal symptoms other than seizures. Comparison of alcohol-dependent subjects with and without history of withdrawal is necessary in order to investigate this posibility. In fact, in the model studies in mice, only one alcoholism-related phenotype, defined as “acute alcohol withdrawal,” has been studied for association with Mpdz gene variations and mRNA expression (Fehr et al., 2002; Shirley et al., 2004). The results of this study indicate that the role of MPDZ gene variability in a broader array of alcohol-related phenotypes needs to be considered. As a scaffolding protein, MPDZ is known to intereact with serotonin, dopamine, glutamate, and GABA receptors in the brain (Bettler et al., 2004; Griffon et al., 2003; Krapivinsky et al., 2004; Parker et al., 2003). Therefore, if particular variations of the MPDZ gene alter its expression or protein structure, these variants could be associated with changes in addictive behaviors (e.g., impulsivity, craving, level of response and/or withdrawal), which have been associated with serotonin, dopamine, glutamate, and GABA neurotransmission and are implicated in alcoholism-related phenotypes in humans and in animal models (Dahchour and De Witte, 2003; De Witte et al., 2003; Heinz et al., 2004; Kalivas and Volkow, 2005; Krystal et al., 2003; Nakamura et al., 1999).

The discrepancy between allelic variations in the coding sequence of the mouse Mpdz gene associated with severity of alcohol withdrawal and corresponding human sequence might explain the lack of genetic association of human MPDZ haplotypes and the history of withdrawal seizures. However, this conclusion should not be extended to an association between the abundance of the Mpdz mRNA in the different strains of mice and severity of alcohol withdrawal (Shirley et al., 2004). It is possible that noncoding variations may be responsible for the difference of Mpdz mRNA expression in different strains of mice and that these variations may potentially be conserved between species. Our findings also suggest that species-specific genetic variations may contribute to the susceptibility to the disease while the role of the gene remains similar between species. Although the pattern of results emerging in psychiatric genetics is generally consistent with the findings of behavioral genetics in animal models, for some behaviors, the pathway from genes to behavior differs meaningfully between species (Kendler and Greenspan, 2006). Further research in mice and human studies are necessary to address this issue.

Our analyses indicated that a common rs7024892 variant in intron 24 was less frequent in the AWS group when compared to alcoholics without AWS while the synonymous rs13297480 (c1074A/G, Thr358Thr) substitution in exon 7 showed association with alcoholism (although not after correction for multiple testing). The minor 1074G allele was twice as frequent in the alcoholic group without AWS when compared to either subjects with AWS or Coriell groups. The same 1074G allele also differentiated haplotype #7, which showed strongest association with alcoholism. This warrants further investigation of its functional importance. Although synonymous single-nucleotide polymorphisms do not produce altered coding sequences, the presence of a rare codon, marked by a synonymous polymorphism, may affect the timing of cotranslational folding and alter protein translation kinetics (Kimchi-Sarfaty et al., 2007). Thus, studies investigating effects of the rs13297480 SNP on MPDZ protein translation kinetics in the context of alcohol effects could be of interest. In addition, it was demonstrated that the expression of the Thr (TGT) tRNA is higher compared to the Thr (CGT) tRNA in most body tissues (Dittmar et al., 2006). These tRNA anticodons correspond to the ACA1074ACG variation of the Exon 7 SNP in the MPDZ gene. Thus, differences in the MPDZ expression in individuals with the c1074A and c1074G alleles may be tissue specific (e.g., different in the brain vs. liver). In this case, study of the tissue-specific response to alcohol and its withdrawal in subjects with different c1074 alleles would be of interest.

Although association tests were not performed for rare SNPs, we did note that 2 rare intronic variants (rs41265290 in Intron 15 and 7303GA in Intron 23) were more common in Coriell sample (3%) than in subjects with AWS history (0.8% in AWS group and 0.0% in alcohol-dependent subjects without AWS) (Table 1). Our sample does not provide adequate power to test for statistical significance of these differences and association with the group status. However, the last 2 variants are located near splice junctions and may influence alternative splicing patterns. Genomic variants located in or near consensus splice junctions at the exon–intron boundaries are important for specifying the splice sites, and estimated to affect more than 15% of human genetic diseases (Faustino and Cooper, 2003; Garcia-Blanco et al., 2004; Pagani and Baralle, 2004). Several transcripts of the MPDZ gene were reported in humans. Thus, further investigation involving a sufficiently high number of study subjects should focus on these rare, but potentially important, variants and their contribution to the differential response to alcohol and its withdrawal in different subjects.

A limitation of this study is that only DNA from EA subjects was utilized, so it was not possible to assess variability in the human MPDZ gene in other ethnic groups. In addition, as the nonalcoholic control DNA was obtained from the Coriell Institute, rather than through random sampling from the same population as the alcoholic patients, potential effects of population stratification should be considered. As only EA subjects were enrolled in the study, we do not expect that population stratification was a serious problem. Another limitation is that there is no information about alcohol use by the subjects who provided DNA samples for the Coriell Institute. A consequence of using a control group for which detailed phenotyping is not available relates to the potential for mis-classification bias. Specifically, a proportion of the controls is likely to have the disease of interest or may develop it in the future. However, the resultant decrease in power should not be high. For example, if 5% of controls would meet the definition of cases, the loss of power is approximately the same as that due to a reduction of the sample size by 10% (Colhoun et al., 2003). As the prevalence of alcohol dependence is only 3.85% of white males and 2.37% in white females (Grant et al., 2004), the potential effect this has on the power of our study is modest. These considerations are in line with the approach used in several large case-control association studies [e.g., WTCCC Consortium (2007)]; however, a study design utilizing phenotypically screened controls would generally be desirable in future studies.

The association analyses between human MPDZ variation and alcoholism investigated here were an exploratory component of this study. A replication in an independent sample is necessary to confirm the obtained positive results. In addition, the possibility and nature of potential associations between the MPDZ gene variations, alcohol withdrawal, and/or other alcoholism-related phenotypes need to be further explored. If any of such associations were to be confirmed, it could be a useful genetic marker for increased risk of the development of alcoholism or related phenotypes.

In conclusion, our sequencing of the MPDZ gene in samples obtained from alcohol-dependent subjects and nonalcoholic control samples from Coriell Institute (all with EA ancestry) revealed 67 new, mostly rare variations. None of these variations was located in the sites identical to those associated with AWS sensitivity in mice. Exploratory haplotype and single SNP association analyses suggest possible association between the MPDZ gene and alcohol dependence and/or related phenotypes. Further investigation of MPDZ variations, including those that could alter MPDZ mRNA stability, translational efficiency, and/or alternative splicing (e.g., rs13297480 SNP in exon 7, rs41265290 in intron 15, and 7303G/A in intron 23) could provide important insights into the mechanism of this association and reveal valuable genetic markers of alcoholism and related phenotypes.

Supplementary Material

Supplement

Table S1. List of Primers for Genomic DNA Fragment Amplication and Resequencing

Acknowledgments

This study was supported by grants from the Samuel C. Johnson Genomics of Addiction Program (VMK, DSC, JMB, DAM, and EDW), the National Institutes of Health (AA015164, DSC), Mayo-Thompson Fellowship on Basic Research in Alcoholism (VMK), Decker-Denko Foundation (VMK), and Educational Foundations of America (VMK). We thank Dina Drubach, Maureen Drews, Vickie Courson, and Tracy Pietrzak for expert study coordination and data management.

Footnotes

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Table S1. List of Primers for Genomic DNA Fragment Amplication and Resequencing

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