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Published in final edited form as: Addict Biol. 2012 Aug 2;19(2):312–315. doi: 10.1111/j.1369-1600.2012.00482.x

Association of GATA4 sequence variation with alcohol dependence

Victor M Karpyak 1,*,#, Stacey J Winham 2,#, Joanna M Biernacka 1,2, Julie M Cunningham 3, Kriste A Lewis 1, Jennifer R Geske 2, Colin L Colby 2, Osama A Abulseoud 1, Daniel K Hall-Flavin 1, Larissa L Loukianova 1, Terry D Schneekloth 1, Mark A Frye 1, John A Heit 4, David A Mrazek 1
PMCID: PMC3504631  NIHMSID: NIHMS393292  PMID: 22862823

Abstract

To further explore reports of association of alcohol dependence and response to acamprosate treatment with the GATA4 rs13273672 single nucleotide polymorphism (SNP), we genotyped this and 10 other GATA4 SNPs in 816 alcohol dependent cases and 1248 controls. We tested for association of alcohol dependence with the 11 SNPs individually and performed a global test for association using a principle components analysis (PCA). Our analyses demonstrate significant association between GATA4 and alcohol dependence at the gene-level (p=0.009) but no association with rs13273672. Further studies are needed to identify potential causal GATA4 variation(s) and the functional mechanism(s) contributing to this association.

Keywords: GATA4, Alcohol dependence, Genetic association, Gene level test

The GATA4 gene encodes GATA-motif binding protein type 4 (GATA4) – a transcription factor controlling expression of multiple proteins including drug metabolizing cytochrome P450 2C9 (CYP2C9) and cytochrome P450 2C19 (CYP2C19) enzymes, human brain natriuretic peptide (hBNP) and atrial natriuretic peptide (ANP) (He, Mendez and LaPointe, 2002; Mwinyi et al., 2010). Recent reports found association between the GATA4 rs13273672 G allele and decreased variance in ANP expression with an increased propensity to relapse in alcohol dependent subjects treated with acamprosate (Kiefer et al., 2011). Rs13273672 was associated with alcohol dependence in two out of four GWAS completed so far (Edenberg et al., 2010; Treutlein et al., 2009) (Bierut et al., 2010; Zuo et al., 2012) and model studies demonstrate that high doses of ethanol augment the expression of GATA4 (Zhong et al., 2010).

Yet the physiological mechanisms underlying potential association of alcohol dependence with sequence variation in GATA4 gene and expression of the GATA4 protein remain unknown. Moreover, the association of alcohol dependence with variation in GATA4 gene has not been comprehensively investigated and it remains unknown whether rs13273672 has a functional role or is in linkage disequilibrium (LD) with other functional SNP(s). Therefore, investigation of the association between alcohol dependence and other variations in the GATA4 gene is necessary. In this study we investigated the association between alcohol dependence and 11 SNPs within GATA4, including rs13273672, and evaluated the association of alcohol dependence with total variation in the GATA4 gene.

This study was approved by the Institutional Review Board of Mayo Clinic Rochester. All subjects provided informed consent and permission to use collected information in studies of alcohol dependence and related phenotypes. Alcohol dependent subjects (n=936) were diagnosed according to DSM-IV-TR criteria and participated in ongoing or completed studies focused on clinical and genetic predictors of alcohol dependence and related phenotypes (Karpyak et al., 2010; Karpyak et al., 2009; Schneekloth et al., accepted for publication 2012). Control subjects (n=1302) were selected from a group of controls that previously participated in a GWAS of venous thrombosis carried out at Mayo Clinic and gave consent for general research use (Heit et al., 2011). The case subjects were younger (49.15 +/−12.13 vs. 57.22 +/−15.93 years, p<0.0001) than the control subjects and included more males (67.9% vs. 48.3%, p<0.0001).

For cost savings and efficiency, previously genotyped controls were used for this study, and thus genotyping was performed separately in cases and controls. As part of a GWAS of venous thrombosis (Heit et al., 2011), 1302 controls were previously genotyped with the Illumina 660 genome-wide SNP array, which contained eleven SNPs within the GATA4 gene (Table 1), including rs13273672. The LD pattern of these SNPs is shown in Supporting Figure 1. To investigate the association with alcohol dependence, these 11 GATA4 SNPs were subsequently genotyped in 936 alcohol dependent cases using the Illumina BeadXpress platform with Illumina GoldenGate SNP array. Forty-three ancestry informative markers (Supporting Table 1) were also genotyped to allow for verification of self-reported ancestry.

Table 1.

Association between alcohol dependence and GATA4 SNPs

SNP BP position (Chromoso me 8) Major/ Minor Allele MAF in cases MAF in controls OR (95% CI) P-value uncorrected P-value corrected
rs13273672 11649790 A/G 0.332 0.325 1.04 (0.91, 1.18) 0.59 1.00
rs6601604 11612927 G/A 0.278 0.307 0.87 (0.76, 1.00) 0.044 0.30
rs10112596 11617211 G/A 0.151 0.179 0.82 (0.69, 0.97) 0.018 0.14
rs12550668 11617363 G/A 0.363 0.393 0.88 (0.77, 1.00) 0.049 0.32
rs1390950 11633238 A/G 0.374 0.366 1.03 (0.91, 1.18) 0.61 1.00
rs3735814 11644805 A/G 0.450 0.480 0.93 (0.82, 1.06) 0.28 0.91
rs7006733 11645399 A/G 0.325 0.350 0.90 (0.79, 1.03) 0.113 0.60
rs804283 11648452 A/G 0.257 0.286 0.87 (0.75, 1.00) 0.044 0.30
rs17153747 11648762 A/G 0.123 0.123 1.00 (0.82, 1.21) 0.962 1.00
rs867858 11653747 A/G 0.302 0.317 0.93 (0.82, 1.07) 0.317 0.94
rs809204 11656572 A/C 0.388 0.353 1.15 (1.02, 1.31) 0.027 0.20
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MAF, minor allele frequency; OR, odds ratio.

Nominally significant p-values (at p<0.05 level) presented in bold.

As part of case genotyping quality control, 4 duplicate samples were included with 100% concordance. One CEPH parent-child trio was genotyped six times with 100% concordance and no Mendelian errors. Furthermore, one of the CEPH controls genotyped with the cases was also genotyped with the control samples. The genotypes from the two platforms were 100% concordant. Twenty-one case samples failed genotyping. All SNPs had call rates greater than 97% (mean=99.9%). All SNPs had minor allele frequencies greater than 0.10 and none exhibited departures from Hardy-Weinberg Equilibrium (all p>0.01).

Thirteen potential control subjects had a documented history of alcohol dependence in the medical records and were excluded from analyses. To avoid the confounding effects of population stratification, only subjects with self-reported “white, non-Hispanic” race were included for analyses, and STRUCTURE (Pritchard, Stephens and Donnelly, 2000) analysis of 43 ancestry informative markers was used to verify genetic ancestry. One case and one control subject were excluded because they were estimated to have greater than 30% African ancestry, leading to a final sample of 816 cases and 1248 controls.

Associations between the 11 GATA4 SNPs and alcohol dependence were assessed using single SNP and gene-level tests of association. For each SNP, the association with alcohol dependence was evaluated using univariate logistic regression, with SNP genotypes coded as 0, 1 or 2 - the number of copies of the minor allele. Statistical significance was determined with a likelihood ratio test and permutation testing (10,000 permutations) was used for multiple testing correction.

In addition to tests of single SNP association, we performed global tests for association of alcohol dependence with all measured variation in the GATA4 gene using a principle components analysis (PCA) approach (Gauderman et al., 2007). Data from all 11 SNPs were transformed into 11 principle components (linear combinations of the SNPs) that explain the total SNP variance. We retained the first four components (which together explained more than 80% of the SNP variation) as predictors in a logistic regression model, and used a four degree-of-freedom likelihood ratio test to assess the joint effect of the principle components on alcohol dependence. For further verification of these results, we also performed a gene-level test using a joint SNP approach. We included all 11 SNPs simultaneously in a logistic regression model and tested for association using an 11 degree-of-freedom likelihood ratio test. As only one gene was evaluated for association with alcohol dependence, correction for multiple testing is not necessary for the gene-level tests. All statistical analyses were performed in R Statistical Software, version 2.14.0.

Although none of the SNP-specific results were significant after correction for multiple testing, 5 of the 11 GATA4 SNPs were nominally associated with alcohol dependence at the 0.05 significance level (Table 1). The minor allele of rs809204 was estimated to be a risk variant, whereas the minor alleles of rs10112596, rs804283, rs6601604, and rs12550668 were estimated to have protective effects. We did not observe significant evidence for association of alcohol dependence with candidate SNP rs13273672 (p=0.59). However, the PCA approach provided significant evidence of association between GATA4 and alcohol dependence at the gene level (p=0.009), which was also verified by joint-SNP gene-level analysis (p=0.037). Both gene-level approaches revealed statistically significant evidence of association between alcohol dependence and variation in the GATA4 gene.

The GATA4 rs13273672 SNP was among association findings in two GWAS of alcohol dependence (at the level of significance below 0.05) (Edenberg et al., 2010; Treutlein et al., 2009), where the estimated odds ratio was 1.20 and 1.19, respectively. However, we did not find this SNP to be associated with alcohol dependence and the estimated odds ratio in our study (OR=1.04) was consistent with the results reported in two other GWAS (Bierut et al., 2010; Zuo et al., 2012). None of the other GATA4 SNPs considered here have been previously reported to be significantly associated with alcohol dependence in GWAS. We also did not detect association with any of the individual GATA4 SNPs after correction for multiple testing. However, our findings provide evidence of association between alcohol dependence and variation in GATA4 at the gene level. It is possible that GATA4 sequence variation(s) other than those tested in this study are responsible for the observed association. Further investigation is necessary to identify the functional variation that may play a role in the observed association and study the underlying mechanism driving the association.

Supplementary Material

Supp Figure S1&Table S1

Acknowledgments

This study was supported by grants from the St. Marys Hospital Sponsorship Award (VMK), Samuel C. Johnson Genomics of Addiction Program (VMK, JMB, DAM, MAF), NIH/NIAAA P20 AA17830Z (VMK, JMB, MAF, DAM). Controls were recruited and genotyped as part of the GWAS of Venous Thrombosis study (NIH/NHGRI grant HG04735, PI J.A. Heit). We thank the Mayo Clinic Cancer Center for the use of the Genotyping Core, which provided genotyping services. Mayo Clinic Cancer Center is supported in part by an NCI Cancer Center Support Grant 5P30 CA15083-37. This project was also supported by NIH/NCRR/NCATS CTSA Grant Number UL1 RR024150. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

Footnotes

AUTHORS CONTRIBUTION

VMK and JMB were responsible for the study concept and design. SJW and JMB designed the analysis plan and oversaw the statistical analysis of the data, while JRG and CLC managed the data, performed the analyses and prepared the results summaries. JMC oversaw the genotyping and quality control. VMK and SJW drafted the manuscript. VMK, DKH-F, LLL and TDS evaluated study subjects. JAH designed and directed study which provided control subjects. VMK, SJW, JMB, JRG, CLC, JMC, KAL, OAA, DKH-F, LLL, TDS, MAF, JAH and DAM critically reviewed and approved the final version of the manuscript.

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Supplementary Materials

Supp Figure S1&Table S1
NIHMS393292-supplement-Supp_Figure_S1_Table_S1.doc (84.5KB, doc)

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