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Association Analysis Between Genetic Variation in GATA Binding Protein 4 (GATA4) and Alcohol Use Disorder

Association Analysis Between Genetic Variation in GATA Binding Protein 4 (GATA4) and Alcohol Use... Aims: Previous genetic association studies have shown that variation in the GATA4 gene encoding the GATA binding protein 4, a binding protein that binds to the ANA sequence GATA, increase susceptibility for alcohol use disorder (AUD). In this study, we aimed to replicate those findings in an independent sample and analyze their association with anxiety. Methods: Overall, 1044 individuals with AUD [534 European American (EA), 510 African Americans (AA)] and 645 controls [413 EA, 232 AA] were genotyped using 34 markers. Genotype and allele frequencies were compared between cases and controls using chi-square analysis. Other phenotype data were analyzed for possible associations with GATA4 single-nucleotide poly- morphisms (SNPs) in individuals with AUD. Results: Rs6601604 was nominally significantly associated with AUD in EA, and 3 SNPs (rs6990313, rs11250159 and rs17153694) showed trend-level significance (P < 0.10) in AA. However, none of the SNPs were significant after correcting for multiple testing. Haplotype ana- lysis of the 34 SNPs did not find a significant association between haplotype blocks and AUD diag- nosis after correcting for multiple testing. From the phenotype analysis, anxiety was associated with GATA4 SNP rs10112596 among the AA group with AUD after a correction for multiple testing. Conclusions: Although previous studies have shown a relationship between variants of the GATA4 gene and a diagnosis of AUD, we did not replicate these findings in our sample. Additional studies of variation in this gene are needed to elucidate whether polymorphisms of the GATA4 gene are associated with AUD and other alcohol-related phenotypes. Short Summary: GATA4 variants were not associated with AUD in either the European ancestry or African ancestry groups after correcting for multiple comparisons. Rs10112596 demonstrated a significant relationship with an anxiety measure among the African ancestry group with AUD. Medical Council on Alcohol and Oxford University Press 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US. 361 Downloaded from https://academic.oup.com/alcalc/article/53/4/361/4838890 by DeepDyve user on 18 July 2022 362 Alcohol and Alcoholism, 2018, Vol. 53, No. 4 and this association was predictive of a lower relapse risk (Jorde INTRODUCTION et al., 2014). Zois et al. (2016) expanded on this work by identify- The prevalence, individual health risks and societal costs of exces- ing an interaction between GATA4 genotype and gray matter vol- sive alcohol use demonstrate the importance of identifying under- ume on relapse risk, such that the AA genotype group showed an lying causes of pathological alcohol consumption and developing association between greater gray matter and a reduced relapse risk. novel treatment approaches (Bouchery et al., 2011; Stahre et al., This provides further support for the possible protective nature of 2014; Grant et al., 2015). Genetic factors account for ~40–60% of the AA genotype. the variance in risk of developing alcohol use disorder (AUD) Furthermore, AUD has been shown to be associated with mood (Rietschel and Treutlein, 2013; Tawa et al., 2016); however, AUD is and anxiety disorders, neuroticism and alcohol withdrawal (Regier a complex disorder, with many factors contributing to its onset and et al., 1990; Malouff et al., 2007; Becker and Mulholland, 2014), maintenance. Identification of the underlying genetic risk variants all of which may play a role in genetic predisposition to AUD (Tawa will further our understanding of the disorder’s neurobiology and et al., 2016). In particular, much research has determined the signifi- may direct the development of novel individualized (i.e. pharmaco- cant co-occurrence of AUD and anxiety disorders, and it is likely genetic) treatment options for AUD. that genetic variation influences this comorbidity (Poikolainen, Recent case-control genome-wide association studies (GWAS) 2000; Smith and Randall, 2012). According to the common factor implicate GATA binding protein 4 (GATA4), a gene located on model proposed by Smith , a third variable (e.g. genetic susceptibil- chromosome 8, in associations with vulnerability to AUD diagnosis ity) explains the presence of both AUD and anxiety disorders. In (Treutlein et al., 2009; Edenberg et al., 2010). While a previous can- line with this model, Merikangas et al. (1996) found that relatives of didate gene study found a significant association between GATA4 patients with anxiety disorders had an increased risk for alcohol and AUD using gene-level testing (Karpyak et al., 2014), a more dependence. This result could be partly explained by shared genetic recent study by Degenhardt et al. (2016) failed to show an associ- factors influencing susceptibility to AUD and anxiety disorders. ation between rare GATA4 variants and AUD. However, it should Moreover, a review by Kenna et al. (2012) highlights more recent be noted that Degenhardt et al. (2016) attempted to identify only research that has found an association between 5-HTTLPR, a 5-HT rare risk-associated variants, which precluded them from identifying transporter polymorphism, and both alcohol dependence and anx- likely protective variants in GATA4. iety symptoms. Given the high degree of heritability of AUD and fre- The GATA4 gene encodes transcription factor GATA4, which quent comorbid occurrence of anxiety symptoms, identifying genetic regulates the expression of the atrial natriuretic peptide (ANP), risk factors that contribute to their shared pathophysiology may among other proteins (McBride and Nemer, 2001). Importantly, improve our understanding of comorbid AUD and anxiety, as well GATA4 protein is expressed throughout cells in the central nervous as inform the development of pharmacological treatments. Therefore, system (CNS). Reduced ANP expression in the CNS is associated the present study aimed to explore associations between genetic vari- with the dysregulation of stress and anxiety mechanisms in the ation in GATA4 and anxiety, as assessed by the Brief Scale for brain, suggesting a possible link between ANP and AUD (Jorde Anxiety. et al., 2014). ANP also influences hypothalamic–pituitary–adrenal In summary, previous studies indicate a possible association (HPA) axis functioning, as well as amygdala activation, further sup- between variation in the GATA4 gene and AUD. However, the porting the relationship between ANP and AUD-related phenotypes underlying mechanisms of this relationship are still relatively (McBride and Nemer, 2001). Clinically, post-detoxification patients unknown and poorly understood. Therefore, additional studies of with AUD and decreased ANP plasma levels report increased crav- SNPs within GATA4 andtheir associationwith AUD-related phe- ing and anxiety levels compared to both detoxified patients with notypes are needed. In this case-control study, we sought to (a) AUD and higher ANP plasma levels, as well as controls (Kiefer replicate findings associating variants in GATA4 with increased et al., 2002). GATA4 also acts as a transcription factor for brain susceptibility to AUD and (b) determine associations between natriuretic peptide (BNP), a peptide involved in the regulation of the GATA4 variants and alcohol-related clinical phenotypes, specific- cardiovascular system. Interestingly, BNP is also involved in stress ally anxiety, as assessed by the Brief Scale for Anxiety. Identifying responses when found in the CNS (Amir et al., 2010). One study genetic variants associated with AUD and related clinical pheno- demonstrated a relationship between GATA4 binding site methyla- types could be used to identify individuals at risk of developing tion and BNP expression among alcohol-dependent patients experi- AUD. Ultimately, this could inform the development of more encing alcohol withdrawal (Glahn et al., 2016). targeted pharmacological prevention and treatment approaches Previous GWAS highlight an association between the specific for AUD. intronic single-nucleotide polymorphism (SNP) rs13273672 in the GATA4 gene and AUD-related phenotypes (Kiefer et al., 2011; Jorde et al.,2014). In a randomized, double-blind, placebo- MATERIALS AND METHODS controlled study, Kiefer et al. (2011) showed that alcohol-dependent individuals with the rs13273672 G allele had a decreased time to Participants relapse following Acamprosate treatment compared to A allele car- This study was approved by the Institutional Review Board at the riers with AUD. Furthermore, the G allele of this SNP was predictive National Institutes of Health (NIH). All participants provided writ- of a significant decrease in variance in ANP plasma concentration ten informed consent and permission to use collected samples. Out compared to the A allele. Acamprosate is an FDA-approved pharma- of 1778 individuals with collected samples, 1044 individuals with cological intervention for AUD that decreases cravings to reduce AUD [534 European Americans (EA), 510 African Americans relapse risk; it is thought to primarily work through the glutamatergic (AA)] and 645 controls [413 EA and 232 AA] took part in this system, although the exact mechanism of action remains unclear study. The 89 missing participants were excluded because they did (Kiefer et al., 2011). Further research identified theAA genotypeas not have a completed SCID diagnosis. Study participants were recruited associated with stronger alcohol cue-induced amygdala activation, between 2005 and 2016 from the inpatient unit and outpatient clinic Downloaded from https://academic.oup.com/alcalc/article/53/4/361/4838890 by DeepDyve user on 18 July 2022 Alcohol and Alcoholism, 2018, Vol. 53, No. 4 363 of the Laboratory of Clinical and Translational Studies at the Analysis National Institute on Alcohol Abuse and Alcoholism (NIAAA), NIH Allele frequencies for each SNP were determined using PLINK for (Bethesda, MD). Participants were recruited from three screening the sample as a whole, and then separately for subjects of European protocols, all of which excluded those under 18 years of age. Two and African ancestry (based on self-report). Due to multiple differ- of the screening protocols included only those in good health with- ences in allele frequency across the 34 SNPs, subsequent analyses out major medical problems, and excluded individuals that were were conducted separately in each group. Single marker association under court-mandated or involuntary treatment. The third protocol analyses were conducted using frequency comparison by chi-square excluded prisoners, as well as pregnant women. Alcohol-dependent test, which is the standard case-control method in PLINK, with subjects were diagnosed with alcohol dependence according to the adjustment for multiple comparisons performed using the Benjamini– Diagnostic and Statistical Manuel for Mental Disorders, 4th edn, Hochberg method for false discovery rate (FDR) (Benjamini and Text-revised (DSM-IV-TR) (American Psychiatric Association, Hochberg, 1995). The threshold for FDR was set at q = 0.05. Haplotype 2000). Participants were diagnosed using the Structured Clinical blocks were determined using HaploView software (Barrett et al., 2005), Interview (SCID-I) for DSM-IV-TR (First, et al., 2002). Given the with haplotype blocks defined using the default D’/LOD method. overlap between the DSM-IV alcohol dependence criteria and the Haplotype association tests using these defined blocks were con- Diagnostic and Statistical Manual of Mental Disorders, 5th edn ducted in PLINK, and were corrected for multiple comparisons using (DSM-5) (American Psychiatric Association, 2013) AUD criteria, permutation tests (5000 permutations). all participants also met criteria for AUD; however, a separate clin- Participants also completed a variety of clinical assessments, ical interview was not conducted. Informed consent was obtained including the Alcohol Dependence Scale (ADS; Skinner and Allen, from all subjects who participated in accordance with the Declaration 1982), Montgomery Asberg Depression Rating Scale (MADRS; of Helsinki. Montgomery and Asberg, 1979), Brief Scale for Anxiety (BSA; Tyrer et al., 1984), State-Trait Anxiety Inventory (STAI; Spielberger et al., 1970), Clinical Institute Withdrawal Assessment for Alcohol (CIWA; Sullivan et al., 1989) and NEO-PI-R (Costa and McCrae, Genotyping and SNP selection 2002). Sample sizes for these assessments are inconsistent due to Large-scale genotyping was performed at the NIAAA Laboratory of missing data, particularly among the control group who were not Neurogenetics using the Illumina OmniExpress BeadChip (Illumina, administered these assessments until later in the study’s recruitment. San Diego, CA). Data for all SNPs located within the GATA4 gene Single marker association and haplotype analyses were conducted that were genotyped on the array were extracted using PLINK ver- for these continuous outcomes using linear regression models in sion 1.07 (Purcell et al., 2007)(http://pngu.mgh.harvard.edu/purcell/ PLINK. These analyses controlled for age, gender, and African and plink/), based on start and end base pair positions for the gene European ancestry via the AIMS scores for Africa and Europe, located on chromosome 8 (11561716, 1161750; GRCh37/hg19 based on research showing age, gender and ethnicity differences in assembly). This procedure resulted in genotype data for 34 SNPs. alcohol consumption and its related consequences (Delker et al. Ancestry informative markers (AIMs; n = 2500) were also extracted 2016). from the Illumina array to calculate ancestral proportions for all study participants. Using methods described previously for an AIM panel including 186 markers (Hodgkinson et al., 2008), which were RESULTS not available for the current data set, the ancestry assessment identi- fied six ethnic factors (Africa, Europe, Asia, Far East Asia, Oceania Table 1 shows the demographic information of participants, as well and Americas). An analysis of the 34 SNPs among the full sample as differences in the clinical assessments between groups. As (n = 1778) found that all were in Hardy–Weinberg equilibrium expected, AUD participants had significantly greater scores in all (HWE) except rs12550668 (P < 0.005) and rs3729856 (P < 0.033) alcohol-related phenotype measures when compared to controls in in the EA group. In the AA group, all SNPs were in HWE except both the EA and AA group. To analyze the association between the rs6601604 (P < 0.03), rs804280 (P < 0.004) and rs867858 (P < 34 GATA4 SNPs and AUD diagnosis, single marker association 0.003).The same analysis found that rs10105409 in the EA group analyses were conducted using frequency comparison by chi-square and rs13275657, rs17153747, rs3729856, rs804290 and rs11785481 test with adjustment for multiple comparisons using the Benjamini– in the AA group all had minor allele frequencies (MAF) < 5%. All Hochberg method for FDR. Results revealed that one SNP other SNPs had a MAF > 5%. (rs6601604) was nominally significantly associated with AUD in the Table 1. Demographic and clinical assessment information European ancestry African ancestry a a AUD (n = 534) Controls (n = 413) P-value AUD (n = 510) Controls (n = 232) P-value Gender count (female) 166 (31.1%) 175 (42.4%) 0.0003 140 (27.5%) 107 (46.1%) <0.0001 Mean age (SD) 42.5 (11.4) 32.0 (12.1) <0.0001 43.2 (10.1) 35.6 (11.0) <0.0001 Mean ADS score (SD) 21.8 (8.3) [n = 451] 2.1 (4.1) [n = 95] <0.0001 17.7 (8.6) [n = 345] 1.3 (3.9) [n = 94] <0.0001 Mean MADRS score (SD) 15.3 (9.7) [n = 444] 1.5 (3.5) [n = 166] <0.0001 11.2 (9.4) [n = 441] 1.2 (3.1) [n = 155] <0.0001 Mean BSA score (SD) 11.1 (7.0) [n = 446] 1.3 (2.6) [n = 166] <0.0001 8.7 (7.2) [n = 441] 1.1 (2.6) [n = 155] <0.0001 Mean STAI score (SD) 43.2 (13.6) [n = 199] 33.6 (11.5) [n = 185] <0.0001 41.1 (12.4) [n = 235] 32.4 (11.9) [n = 145] <0.0001 Mean neuroticism score (SD) 56.3 (11.5) [n = 483] 44.8 (10.0) [n = 377] <0.0001 54.6 (9.7) [n = 444] 44.9 (8.4) [n = 202] <0.0001 Chi-square test for gender; t-test for all remaining continuous variables. Downloaded from https://academic.oup.com/alcalc/article/53/4/361/4838890 by DeepDyve user on 18 July 2022 364 Alcohol and Alcoholism, 2018, Vol. 53, No. 4 EA group (P = 0.036). However, this SNP was not significant after both rs11250159 and rs17153694 (Supplementary Table S2), both of adjusting for multiple comparisons (Table 2). Three SNPs (rs6990313, which trended towards significance in the single SNP association rs11250159 and rs17153694) trended towards a significant associ- (Table 3). There was no significant association between haplotype ation with AUD in the AA group (Ps ≤ 0.065) (Table 3), but these blocks and AUD diagnosis after correction for multiple testing. SNPs failed to reach trend-level significance after adjusting for mul- Further analyses of continuous phenotype outcomes co-varied tiple comparisons. The a priori SNP of interest, rs13273672, was for age, gender, and African and European ancestry. These analyses not significantly associated with AUD for either ancestry group in showed no significant relationships that survived multiple compari- our sample. sons between the GATA4 SNPs and scores on the ADS, MADRS, In addition to tests of single SNP associations, we ran haplotype STAI, CIWA or NEO Neuroticism (data not shown). When analyz- analyses of the 34 SNPs. Interestingly, the haplotype structure dif- ing only those with current AUD, one phenotype, anxiety, as mea- fered in the EA and AA populations. There were nine haplotype sured by the BSA was significantly associated with GATA4 SNP blocks in the EA group (Supplementary Fig. S1), and seven haplo- rs10112596 when adjusting for multiple comparisons (P = 0.032) in type bocks in the AA group (Supplementary Fig. S2). the AA ancestry only (Table 4). This SNP was not in a haplotype Two haplotype blocks (Blocks 1 and 5) were nominally signifi- block. cantly related to AUD in the EA group (P = 0.037, P = 0.015, respectively). Block 1 included rs6601604 (Supplementary Table S1), DISCUSSION which was nominally significantly associated with AUD in the single SNP association (Table 2). One block (Block 9) reached trend-level Previous studies have found evidence that implicates the GATA4 significance in this group (P = 0.087) (Supplementary Table S1). Two gene in susceptibility to alcohol dependence (Treutlein et al., 2009; haplotype blocks (Block 2 and Block 3) trended towards a significant Edenberg et al., 2010; Karpyak et al., 2014). In particular, the SNP relationship with AUD in the AA group (Ps ≤ 0.068). Block 3 contained rs13273672 has been found to be related to variance in ANP Table 2. Associations between GATA4 gene SNPs and AUD in EA sample b c SNP A1/A2 MAF cases MAF controls Chi square Odds ratio P-value FDR rs6990313 A/C 0.10 0.09 1.37 1.21 0.243 0.836 rs10105409 G/A 0.01 0.00 0.05 1.16 0.818 0.904 rs6601604 A/G 0.29 0.34 4.42 0.81 0.036* 0.836 rs10112596 A/G 0.17 0.19 1.33 0.87 0.248 0.836 rs12550668 A/G 0.40 0.43 1.74 0.88 0.188 0.836 rs2898292 G/A 0.10 0.10 0.00 1.00 0.995 0.995 rs4840579 G/A 0.39 0.40 0.28 0.95 0.597 0.836 rs11250159 A/C 0.08 0.09 0.65 0.87 0.421 0.836 rs17153694 A/G 0.07 0.08 0.18 0.93 0.670 0.836 rs17153698 A/G 0.16 0.15 0.24 1.06 0.625 0.836 rs6983129 C/A 0.47 0.48 0.59 0.93 0.444 0.836 rs2898295 A/G 0.50 0.47 1.06 1.10 0.304 0.836 rs11250163 C/A 0.47 0.45 0.69 1.08 0.407 0.836 rs13275657 A/G 0.19 0.20 0.64 0.91 0.423 0.836 rs2029969 G/A 0.38 0.36 0.53 1.07 0.465 0.836 rs2173117 A/C 0.33 0.30 1.78 1.14 0.182 0.836 rs3779664 A/G 0.14 0.16 1.08 0.87 0.299 0.836 rs3735814 A/G 0.48 0.49 0.18 0.96 0.671 0.836 rs2740434 A/G 0.33 0.34 0.13 0.97 0.720 0.844 rs2645399 A/G 0.34 0.36 0.49 0.93 0.486 0.836 rs11784693 A/G 0.29 0.32 1.44 0.89 0.231 0.836 rs804283 G/A 0.29 0.31 1.24 0.89 0.265 0.836 rs17153747 G/A 0.13 0.12 0.75 1.13 0.386 0.836 rs804282 C/A 0.45 0.46 0.40 0.94 0.529 0.836 rs13264774 A/G 0.15 0.15 0.02 1.02 0.880 0.935 rs13273672 G/A 0.30 0.29 0.26 1.05 0.610 0.836 rs804280 C/A 0.43 0.44 0.32 0.95 0.574 0.836 rs3729856 G/A 0.14 0.14 0.16 1.06 0.689 0.836 rs867858 C/A 0.31 0.31 0.05 1.02 0.824 0.904 rs1062219 A/G 0.45 0.46 0.20 0.96 0.655 0.836 rs804290 A/G 0.24 0.22 1.31 1.13 0.253 0.836 rs11785481 A/G 0.14 0.15 0.26 0.93 0.607 0.836 rs12458 T/A 0.33 0.31 0.36 1.06 0.548 0.836 rs3203358 C/G 0.33 0.32 0.01 1.01 0.917 0.944 N = 534 cases, 413 controls. Alleles 1 and 2 refer to minor and major allele, respectively. MAF = minor allele frequency. *P < 0.05. Downloaded from https://academic.oup.com/alcalc/article/53/4/361/4838890 by DeepDyve user on 18 July 2022 Alcohol and Alcoholism, 2018, Vol. 53, No. 4 365 Table 3. Associations between GATA4 gene SNPs and AUD in AA sample b c SNP A1/A2 MAF cases MAF controls Chi square Odds ratio P-value FDR rs6990313 A/C 0.28 0.32 3.39 0.80 0.065 0.742 rs10105409 G/A 0.17 0.19 0.96 0.87 0.327 0.930 rs6601604 A/G 0.43 0.41 0.62 1.09 0.431 0.930 rs10112596 A/G 0.16 0.14 0.86 1.16 0.353 0.930 rs12550668 G/A 0.11 0.10 0.66 1.16 0.417 0.930 rs2898292 G/A 0.30 0.27 2.06 1.20 0.151 0.921 rs4840579 A/G 0.47 0.46 0.40 1.07 0.527 0.930 rs11250159 A/C 0.21 0.17 3.41 1.31 0.065 0.742 rs17153694 A/G 0.09 0.06 3.79 1.54 0.051 0.742 rs17153698 A/G 0.32 0.31 0.29 1.07 0.588 0.930 rs6983129 C/A 0.38 0.35 1.46 1.15 0.226 0.930 rs2898295 A/G 0.39 0.38 0.12 1.04 0.728 0.930 rs11250163 C/A 0.10 0.10 0.02 0.98 0.898 0.930 rs13275657 A/G 0.04 0.03 0.64 1.27 0.425 0.930 rs2029969 G/A 0.24 0.24 0.01 0.99 0.927 0.930 rs2173117 A/C 0.24 0.23 0.34 1.08 0.559 0.930 rs3779664 A/G 0.06 0.06 0.13 1.09 0.721 0.930 rs3735814 A/G 0.49 0.49 0.06 1.03 0.802 0.930 rs2740434 A/G 0.31 0.31 0.01 1.01 0.930 0.930 rs2645399 A/G 0.48 0.51 0.69 0.91 0.408 0.930 rs11784693 A/G 0.22 0.21 0.12 1.05 0.726 0.930 rs804283 G/A 0.13 0.11 1.72 1.26 0.190 0.921 rs17153747 G/A 0.05 0.04 0.27 1.15 0.605 0.930 rs804282 C/A 0.46 0.48 0.18 0.95 0.671 0.930 rs13264774 A/G 0.22 0.26 1.82 0.84 0.177 0.921 rs13273672 G/A 0.37 0.38 0.20 0.95 0.657 0.930 rs804280 C/A 0.42 0.41 0.20 1.05 0.655 0.930 rs3729856 G/A 0.02 0.02 0.63 0.74 0.428 0.930 rs867858 C/A 0.23 0.23 0.06 0.97 0.810 0.930 rs1062219 A/G 0.17 0.17 0.03 1.03 0.867 0.930 rs804290 A/G 0.05 0.03 2.20 1.54 0.138 0.921 rs11785481 A/G 0.03 0.03 0.03 0.94 0.852 0.930 rs12458 T/A 0.39 0.39 0.02 0.98 0.892 0.930 rs3203358 C/G 0.06 0.06 0.19 1.11 0.667 0.930 N = 510 cases, 232 controls. Alleles 1 and 2 refer to minor and major allele, respectively. MAF = minor allele frequency. expression, alcohol-induced cue reactivity and relapse risk (Kiefer have confounded the analysis. Furthermore, it is likely that multiple et al., 2011; Jorde et al., 2014; Zois et al., 2016). genes are involved in AUD, with only their interaction accumulating This case-control study aimed to replicate previous GWAS and to account for a significant proportion of the variance. Therefore, candidate gene studies relating GATA4 and SNP rs13273672 with additional studies of genetic variation are needed to elucidate AUD and alcohol-related phenotypes. Although previous studies whether polymorphisms of the GATA4 gene interact with other have shown a relationship between variants of this gene and a diag- genes to contribute to the genetic risk for AUD and other alcohol- nosis of AUD, we did not replicate these findings in our sample. related phenotypes. Given that one SNP in the EA group There are several explanations for these discrepant results. First, (rs6601604) and three SNPs in the AA group (rs6990313, as our study consisted of a relatively small sample size, we may have rs11250159 and rs17153694) did not survive correction for mul- lacked adequate power to detect small effects, which is a limitation tiple comparisons, our data indicate a need for further replication of the current study. This limitation might be particularly relevant studies with larger sample sizes. Karpyak et al. (2014) used gene- given the number of SNPs that did not have a MAF > 5%. While level testing to identify an association between AUD diagnoses and Karpyak et al. (2014) used a sample of over 800 AD cases, we were GATA4 variation at the gene-level. Future studies should use this limited to 534 and 510 AUD cases in the EA and AA subgroups, additional analysis to replicate these findings and identify any gene- respectively. Clinical heterogeneity, such as differences in anxiety or level association between GATA4 variants and alcohol-related clin- participant status, may also account for our inability to replicate ical phenotypes. Confirming a gene-level association between past studies. Edenberg et al. (2010) and Treutlein et al. (2009) used GATA4 and AUD would provide a target for identifying and treat- a sample of participants receiving treatment for their alcohol use, ing maladaptive alcohol use. while our cohort included both treatment-seeking and non- Our finding of a relationship between SNP rs10112596 and an treatment-seeking individuals. Although all AUD patients in the pre- anxiety measure in the AA group with AUD is novel, as this marker sent study received a diagnosis based on the DSM-IV, it is possible has not yet been associated with any alcohol-related phenotype. that the two cohorts represent different phenotypes, which may ANP levels might underlie this correlation, as decreased ANP levels Downloaded from https://academic.oup.com/alcalc/article/53/4/361/4838890 by DeepDyve user on 18 July 2022 366 Alcohol and Alcoholism, 2018, Vol. 53, No. 4 Table 4. Associations between GATA4 gene SNPs and Brief Scale that found an association between GATA4 variants and risk for for Anxiety (BSA) scores in AA sample with AUD AUD; however, further studies with larger samples and gene-level testing techniques are needed. We did find an association between SNP A1 N BETA STAT P-value FDR rs10112596 and anxiety in the AA group, suggesting that this SNP may contribute to risk for AUD and anxiety in individuals of AA rs6990313 A 414 1.14 2.09 0.037 0.418 rs10105409 G 415 1.55 2.43 0.015 0.262 but not EA. This finding also implicates GATA4 in the relationship rs6601604 A 415 0.24 0.47 0.642 0.845 between AUD and anxiety, indicating a possible protective effect of rs10112596 A 415 −2.24 −3.33 0.001* 0.032* the rs10112596 A minor allele. This investigation contributes mean- rs12550668 G 415 −0.76 −0.95 0.344 0.828 ingfully to the field because it extends the discovery of certain geno- rs2898292 G 415 0.94 1.72 0.086 0.418 types that may be associated with a higher risk of developing and rs4840579 A 415 0.30 0.58 0.560 0.828 maintaining AUD, as well as those genotypes that constitute part of rs11250159 A 415 1.07 1.73 0.085 0.418 a genetic ‘protective’ factor. As medicine and treatment plans are rs17153694 A 412 1.03 1.17 0.241 0.746 becoming more personalized and patient-specific, it becomes crucial rs17153698 A 415 0.83 1.58 0.115 0.489 to elucidate the mechanisms behind the genetic contribution to com- rs6983129 C 414 0.34 0.65 0.516 0.828 plex disorders. Ideally, genetic information will provide us with the rs2898295 A 415 0.22 0.43 0.671 0.845 rs11250163 C 415 −0.27 −0.32 0.753 0.883 tools to better diagnose and prevent psychiatric disorders, including rs13275657 A 411 −1.33 −0.94 0.346 0.828 AUD. Genetics can also provide meaningful information regarding rs2029969 G 415 −0.38 −0.63 0.532 0.828 the underlying biological basis of a disease when designing treat- rs2173117 A 415 −0.44 −0.74 0.461 0.828 ment strategies. An understanding of genetic susceptibility to AUD rs3779664 A 415 −0.44 −0.43 0.665 0.845 may inform the development of individualized pharmacological rs3735814 A 415 0.92 1.86 0.064 0.418 interventions that may provide patient-specific drug efficacy. rs2740434 A 415 0.42 0.77 0.440 0.828 rs2645399 A 414 0.42 0.83 0.405 0.828 rs11784693 A 414 0.33 0.53 0.598 0.845 SUPPLEMENTARY MATERIAL rs804283 G 415 0.06 0.08 0.940 0.940 Supplementary data are available at Alcohol And Alcoholism rs17153747 G 415 −0.72 −0.61 0.545 0.828 rs804282 C 414 −0.04 −0.09 0.927 0.940 online. rs13264774 A 415 0.42 0.67 0.506 0.828 rs13273672 G 415 0.20 0.36 0.717 0.870 CONFLICT OF INTEREST STATEMENT rs804280 C 415 0.46 0.87 0.384 0.828 rs3729856 G 415 0.17 0.10 0.924 0.940 The authors declare no conflict of interest. rs867858 C 415 −0.10 −0.15 0.877 0.940 rs1062219 A 412 −1.15 −1.78 0.076 0.418 rs804290 A 415 −1.60 −1.40 0.161 0.548 FUNDING rs11785481 A 415 0.33 0.22 0.825 0.935 This work was supported by the National Institutes of Health (NIH) rs12458 T 413 −0.32 −0.61 0.541 0.828 intramural funding [ZIA-AA000242; Section on Clinical Genomics rs3203358 C 415 −1.57 −1.43 0.153 0.548 and Experimental Therapeutics; to F.W.L.; Division of Intramural Allele 1 refers to minor allele. 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Association Analysis Between Genetic Variation in GATA Binding Protein 4 (GATA4) and Alcohol Use Disorder

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Oxford University Press
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Copyright © 2022 Medical Council on Alcohol and Oxford University Press
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0735-0414
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1464-3502
DOI
10.1093/alcalc/agx120
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29415147
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Abstract

Aims: Previous genetic association studies have shown that variation in the GATA4 gene encoding the GATA binding protein 4, a binding protein that binds to the ANA sequence GATA, increase susceptibility for alcohol use disorder (AUD). In this study, we aimed to replicate those findings in an independent sample and analyze their association with anxiety. Methods: Overall, 1044 individuals with AUD [534 European American (EA), 510 African Americans (AA)] and 645 controls [413 EA, 232 AA] were genotyped using 34 markers. Genotype and allele frequencies were compared between cases and controls using chi-square analysis. Other phenotype data were analyzed for possible associations with GATA4 single-nucleotide poly- morphisms (SNPs) in individuals with AUD. Results: Rs6601604 was nominally significantly associated with AUD in EA, and 3 SNPs (rs6990313, rs11250159 and rs17153694) showed trend-level significance (P < 0.10) in AA. However, none of the SNPs were significant after correcting for multiple testing. Haplotype ana- lysis of the 34 SNPs did not find a significant association between haplotype blocks and AUD diag- nosis after correcting for multiple testing. From the phenotype analysis, anxiety was associated with GATA4 SNP rs10112596 among the AA group with AUD after a correction for multiple testing. Conclusions: Although previous studies have shown a relationship between variants of the GATA4 gene and a diagnosis of AUD, we did not replicate these findings in our sample. Additional studies of variation in this gene are needed to elucidate whether polymorphisms of the GATA4 gene are associated with AUD and other alcohol-related phenotypes. Short Summary: GATA4 variants were not associated with AUD in either the European ancestry or African ancestry groups after correcting for multiple comparisons. Rs10112596 demonstrated a significant relationship with an anxiety measure among the African ancestry group with AUD. Medical Council on Alcohol and Oxford University Press 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US. 361 Downloaded from https://academic.oup.com/alcalc/article/53/4/361/4838890 by DeepDyve user on 18 July 2022 362 Alcohol and Alcoholism, 2018, Vol. 53, No. 4 and this association was predictive of a lower relapse risk (Jorde INTRODUCTION et al., 2014). Zois et al. (2016) expanded on this work by identify- The prevalence, individual health risks and societal costs of exces- ing an interaction between GATA4 genotype and gray matter vol- sive alcohol use demonstrate the importance of identifying under- ume on relapse risk, such that the AA genotype group showed an lying causes of pathological alcohol consumption and developing association between greater gray matter and a reduced relapse risk. novel treatment approaches (Bouchery et al., 2011; Stahre et al., This provides further support for the possible protective nature of 2014; Grant et al., 2015). Genetic factors account for ~40–60% of the AA genotype. the variance in risk of developing alcohol use disorder (AUD) Furthermore, AUD has been shown to be associated with mood (Rietschel and Treutlein, 2013; Tawa et al., 2016); however, AUD is and anxiety disorders, neuroticism and alcohol withdrawal (Regier a complex disorder, with many factors contributing to its onset and et al., 1990; Malouff et al., 2007; Becker and Mulholland, 2014), maintenance. Identification of the underlying genetic risk variants all of which may play a role in genetic predisposition to AUD (Tawa will further our understanding of the disorder’s neurobiology and et al., 2016). In particular, much research has determined the signifi- may direct the development of novel individualized (i.e. pharmaco- cant co-occurrence of AUD and anxiety disorders, and it is likely genetic) treatment options for AUD. that genetic variation influences this comorbidity (Poikolainen, Recent case-control genome-wide association studies (GWAS) 2000; Smith and Randall, 2012). According to the common factor implicate GATA binding protein 4 (GATA4), a gene located on model proposed by Smith , a third variable (e.g. genetic susceptibil- chromosome 8, in associations with vulnerability to AUD diagnosis ity) explains the presence of both AUD and anxiety disorders. In (Treutlein et al., 2009; Edenberg et al., 2010). While a previous can- line with this model, Merikangas et al. (1996) found that relatives of didate gene study found a significant association between GATA4 patients with anxiety disorders had an increased risk for alcohol and AUD using gene-level testing (Karpyak et al., 2014), a more dependence. This result could be partly explained by shared genetic recent study by Degenhardt et al. (2016) failed to show an associ- factors influencing susceptibility to AUD and anxiety disorders. ation between rare GATA4 variants and AUD. However, it should Moreover, a review by Kenna et al. (2012) highlights more recent be noted that Degenhardt et al. (2016) attempted to identify only research that has found an association between 5-HTTLPR, a 5-HT rare risk-associated variants, which precluded them from identifying transporter polymorphism, and both alcohol dependence and anx- likely protective variants in GATA4. iety symptoms. Given the high degree of heritability of AUD and fre- The GATA4 gene encodes transcription factor GATA4, which quent comorbid occurrence of anxiety symptoms, identifying genetic regulates the expression of the atrial natriuretic peptide (ANP), risk factors that contribute to their shared pathophysiology may among other proteins (McBride and Nemer, 2001). Importantly, improve our understanding of comorbid AUD and anxiety, as well GATA4 protein is expressed throughout cells in the central nervous as inform the development of pharmacological treatments. Therefore, system (CNS). Reduced ANP expression in the CNS is associated the present study aimed to explore associations between genetic vari- with the dysregulation of stress and anxiety mechanisms in the ation in GATA4 and anxiety, as assessed by the Brief Scale for brain, suggesting a possible link between ANP and AUD (Jorde Anxiety. et al., 2014). ANP also influences hypothalamic–pituitary–adrenal In summary, previous studies indicate a possible association (HPA) axis functioning, as well as amygdala activation, further sup- between variation in the GATA4 gene and AUD. However, the porting the relationship between ANP and AUD-related phenotypes underlying mechanisms of this relationship are still relatively (McBride and Nemer, 2001). Clinically, post-detoxification patients unknown and poorly understood. Therefore, additional studies of with AUD and decreased ANP plasma levels report increased crav- SNPs within GATA4 andtheir associationwith AUD-related phe- ing and anxiety levels compared to both detoxified patients with notypes are needed. In this case-control study, we sought to (a) AUD and higher ANP plasma levels, as well as controls (Kiefer replicate findings associating variants in GATA4 with increased et al., 2002). GATA4 also acts as a transcription factor for brain susceptibility to AUD and (b) determine associations between natriuretic peptide (BNP), a peptide involved in the regulation of the GATA4 variants and alcohol-related clinical phenotypes, specific- cardiovascular system. Interestingly, BNP is also involved in stress ally anxiety, as assessed by the Brief Scale for Anxiety. Identifying responses when found in the CNS (Amir et al., 2010). One study genetic variants associated with AUD and related clinical pheno- demonstrated a relationship between GATA4 binding site methyla- types could be used to identify individuals at risk of developing tion and BNP expression among alcohol-dependent patients experi- AUD. Ultimately, this could inform the development of more encing alcohol withdrawal (Glahn et al., 2016). targeted pharmacological prevention and treatment approaches Previous GWAS highlight an association between the specific for AUD. intronic single-nucleotide polymorphism (SNP) rs13273672 in the GATA4 gene and AUD-related phenotypes (Kiefer et al., 2011; Jorde et al.,2014). In a randomized, double-blind, placebo- MATERIALS AND METHODS controlled study, Kiefer et al. (2011) showed that alcohol-dependent individuals with the rs13273672 G allele had a decreased time to Participants relapse following Acamprosate treatment compared to A allele car- This study was approved by the Institutional Review Board at the riers with AUD. Furthermore, the G allele of this SNP was predictive National Institutes of Health (NIH). All participants provided writ- of a significant decrease in variance in ANP plasma concentration ten informed consent and permission to use collected samples. Out compared to the A allele. Acamprosate is an FDA-approved pharma- of 1778 individuals with collected samples, 1044 individuals with cological intervention for AUD that decreases cravings to reduce AUD [534 European Americans (EA), 510 African Americans relapse risk; it is thought to primarily work through the glutamatergic (AA)] and 645 controls [413 EA and 232 AA] took part in this system, although the exact mechanism of action remains unclear study. The 89 missing participants were excluded because they did (Kiefer et al., 2011). Further research identified theAA genotypeas not have a completed SCID diagnosis. Study participants were recruited associated with stronger alcohol cue-induced amygdala activation, between 2005 and 2016 from the inpatient unit and outpatient clinic Downloaded from https://academic.oup.com/alcalc/article/53/4/361/4838890 by DeepDyve user on 18 July 2022 Alcohol and Alcoholism, 2018, Vol. 53, No. 4 363 of the Laboratory of Clinical and Translational Studies at the Analysis National Institute on Alcohol Abuse and Alcoholism (NIAAA), NIH Allele frequencies for each SNP were determined using PLINK for (Bethesda, MD). Participants were recruited from three screening the sample as a whole, and then separately for subjects of European protocols, all of which excluded those under 18 years of age. Two and African ancestry (based on self-report). Due to multiple differ- of the screening protocols included only those in good health with- ences in allele frequency across the 34 SNPs, subsequent analyses out major medical problems, and excluded individuals that were were conducted separately in each group. Single marker association under court-mandated or involuntary treatment. The third protocol analyses were conducted using frequency comparison by chi-square excluded prisoners, as well as pregnant women. Alcohol-dependent test, which is the standard case-control method in PLINK, with subjects were diagnosed with alcohol dependence according to the adjustment for multiple comparisons performed using the Benjamini– Diagnostic and Statistical Manuel for Mental Disorders, 4th edn, Hochberg method for false discovery rate (FDR) (Benjamini and Text-revised (DSM-IV-TR) (American Psychiatric Association, Hochberg, 1995). The threshold for FDR was set at q = 0.05. Haplotype 2000). Participants were diagnosed using the Structured Clinical blocks were determined using HaploView software (Barrett et al., 2005), Interview (SCID-I) for DSM-IV-TR (First, et al., 2002). Given the with haplotype blocks defined using the default D’/LOD method. overlap between the DSM-IV alcohol dependence criteria and the Haplotype association tests using these defined blocks were con- Diagnostic and Statistical Manual of Mental Disorders, 5th edn ducted in PLINK, and were corrected for multiple comparisons using (DSM-5) (American Psychiatric Association, 2013) AUD criteria, permutation tests (5000 permutations). all participants also met criteria for AUD; however, a separate clin- Participants also completed a variety of clinical assessments, ical interview was not conducted. Informed consent was obtained including the Alcohol Dependence Scale (ADS; Skinner and Allen, from all subjects who participated in accordance with the Declaration 1982), Montgomery Asberg Depression Rating Scale (MADRS; of Helsinki. Montgomery and Asberg, 1979), Brief Scale for Anxiety (BSA; Tyrer et al., 1984), State-Trait Anxiety Inventory (STAI; Spielberger et al., 1970), Clinical Institute Withdrawal Assessment for Alcohol (CIWA; Sullivan et al., 1989) and NEO-PI-R (Costa and McCrae, Genotyping and SNP selection 2002). Sample sizes for these assessments are inconsistent due to Large-scale genotyping was performed at the NIAAA Laboratory of missing data, particularly among the control group who were not Neurogenetics using the Illumina OmniExpress BeadChip (Illumina, administered these assessments until later in the study’s recruitment. San Diego, CA). Data for all SNPs located within the GATA4 gene Single marker association and haplotype analyses were conducted that were genotyped on the array were extracted using PLINK ver- for these continuous outcomes using linear regression models in sion 1.07 (Purcell et al., 2007)(http://pngu.mgh.harvard.edu/purcell/ PLINK. These analyses controlled for age, gender, and African and plink/), based on start and end base pair positions for the gene European ancestry via the AIMS scores for Africa and Europe, located on chromosome 8 (11561716, 1161750; GRCh37/hg19 based on research showing age, gender and ethnicity differences in assembly). This procedure resulted in genotype data for 34 SNPs. alcohol consumption and its related consequences (Delker et al. Ancestry informative markers (AIMs; n = 2500) were also extracted 2016). from the Illumina array to calculate ancestral proportions for all study participants. Using methods described previously for an AIM panel including 186 markers (Hodgkinson et al., 2008), which were RESULTS not available for the current data set, the ancestry assessment identi- fied six ethnic factors (Africa, Europe, Asia, Far East Asia, Oceania Table 1 shows the demographic information of participants, as well and Americas). An analysis of the 34 SNPs among the full sample as differences in the clinical assessments between groups. As (n = 1778) found that all were in Hardy–Weinberg equilibrium expected, AUD participants had significantly greater scores in all (HWE) except rs12550668 (P < 0.005) and rs3729856 (P < 0.033) alcohol-related phenotype measures when compared to controls in in the EA group. In the AA group, all SNPs were in HWE except both the EA and AA group. To analyze the association between the rs6601604 (P < 0.03), rs804280 (P < 0.004) and rs867858 (P < 34 GATA4 SNPs and AUD diagnosis, single marker association 0.003).The same analysis found that rs10105409 in the EA group analyses were conducted using frequency comparison by chi-square and rs13275657, rs17153747, rs3729856, rs804290 and rs11785481 test with adjustment for multiple comparisons using the Benjamini– in the AA group all had minor allele frequencies (MAF) < 5%. All Hochberg method for FDR. Results revealed that one SNP other SNPs had a MAF > 5%. (rs6601604) was nominally significantly associated with AUD in the Table 1. Demographic and clinical assessment information European ancestry African ancestry a a AUD (n = 534) Controls (n = 413) P-value AUD (n = 510) Controls (n = 232) P-value Gender count (female) 166 (31.1%) 175 (42.4%) 0.0003 140 (27.5%) 107 (46.1%) <0.0001 Mean age (SD) 42.5 (11.4) 32.0 (12.1) <0.0001 43.2 (10.1) 35.6 (11.0) <0.0001 Mean ADS score (SD) 21.8 (8.3) [n = 451] 2.1 (4.1) [n = 95] <0.0001 17.7 (8.6) [n = 345] 1.3 (3.9) [n = 94] <0.0001 Mean MADRS score (SD) 15.3 (9.7) [n = 444] 1.5 (3.5) [n = 166] <0.0001 11.2 (9.4) [n = 441] 1.2 (3.1) [n = 155] <0.0001 Mean BSA score (SD) 11.1 (7.0) [n = 446] 1.3 (2.6) [n = 166] <0.0001 8.7 (7.2) [n = 441] 1.1 (2.6) [n = 155] <0.0001 Mean STAI score (SD) 43.2 (13.6) [n = 199] 33.6 (11.5) [n = 185] <0.0001 41.1 (12.4) [n = 235] 32.4 (11.9) [n = 145] <0.0001 Mean neuroticism score (SD) 56.3 (11.5) [n = 483] 44.8 (10.0) [n = 377] <0.0001 54.6 (9.7) [n = 444] 44.9 (8.4) [n = 202] <0.0001 Chi-square test for gender; t-test for all remaining continuous variables. Downloaded from https://academic.oup.com/alcalc/article/53/4/361/4838890 by DeepDyve user on 18 July 2022 364 Alcohol and Alcoholism, 2018, Vol. 53, No. 4 EA group (P = 0.036). However, this SNP was not significant after both rs11250159 and rs17153694 (Supplementary Table S2), both of adjusting for multiple comparisons (Table 2). Three SNPs (rs6990313, which trended towards significance in the single SNP association rs11250159 and rs17153694) trended towards a significant associ- (Table 3). There was no significant association between haplotype ation with AUD in the AA group (Ps ≤ 0.065) (Table 3), but these blocks and AUD diagnosis after correction for multiple testing. SNPs failed to reach trend-level significance after adjusting for mul- Further analyses of continuous phenotype outcomes co-varied tiple comparisons. The a priori SNP of interest, rs13273672, was for age, gender, and African and European ancestry. These analyses not significantly associated with AUD for either ancestry group in showed no significant relationships that survived multiple compari- our sample. sons between the GATA4 SNPs and scores on the ADS, MADRS, In addition to tests of single SNP associations, we ran haplotype STAI, CIWA or NEO Neuroticism (data not shown). When analyz- analyses of the 34 SNPs. Interestingly, the haplotype structure dif- ing only those with current AUD, one phenotype, anxiety, as mea- fered in the EA and AA populations. There were nine haplotype sured by the BSA was significantly associated with GATA4 SNP blocks in the EA group (Supplementary Fig. S1), and seven haplo- rs10112596 when adjusting for multiple comparisons (P = 0.032) in type bocks in the AA group (Supplementary Fig. S2). the AA ancestry only (Table 4). This SNP was not in a haplotype Two haplotype blocks (Blocks 1 and 5) were nominally signifi- block. cantly related to AUD in the EA group (P = 0.037, P = 0.015, respectively). Block 1 included rs6601604 (Supplementary Table S1), DISCUSSION which was nominally significantly associated with AUD in the single SNP association (Table 2). One block (Block 9) reached trend-level Previous studies have found evidence that implicates the GATA4 significance in this group (P = 0.087) (Supplementary Table S1). Two gene in susceptibility to alcohol dependence (Treutlein et al., 2009; haplotype blocks (Block 2 and Block 3) trended towards a significant Edenberg et al., 2010; Karpyak et al., 2014). In particular, the SNP relationship with AUD in the AA group (Ps ≤ 0.068). Block 3 contained rs13273672 has been found to be related to variance in ANP Table 2. Associations between GATA4 gene SNPs and AUD in EA sample b c SNP A1/A2 MAF cases MAF controls Chi square Odds ratio P-value FDR rs6990313 A/C 0.10 0.09 1.37 1.21 0.243 0.836 rs10105409 G/A 0.01 0.00 0.05 1.16 0.818 0.904 rs6601604 A/G 0.29 0.34 4.42 0.81 0.036* 0.836 rs10112596 A/G 0.17 0.19 1.33 0.87 0.248 0.836 rs12550668 A/G 0.40 0.43 1.74 0.88 0.188 0.836 rs2898292 G/A 0.10 0.10 0.00 1.00 0.995 0.995 rs4840579 G/A 0.39 0.40 0.28 0.95 0.597 0.836 rs11250159 A/C 0.08 0.09 0.65 0.87 0.421 0.836 rs17153694 A/G 0.07 0.08 0.18 0.93 0.670 0.836 rs17153698 A/G 0.16 0.15 0.24 1.06 0.625 0.836 rs6983129 C/A 0.47 0.48 0.59 0.93 0.444 0.836 rs2898295 A/G 0.50 0.47 1.06 1.10 0.304 0.836 rs11250163 C/A 0.47 0.45 0.69 1.08 0.407 0.836 rs13275657 A/G 0.19 0.20 0.64 0.91 0.423 0.836 rs2029969 G/A 0.38 0.36 0.53 1.07 0.465 0.836 rs2173117 A/C 0.33 0.30 1.78 1.14 0.182 0.836 rs3779664 A/G 0.14 0.16 1.08 0.87 0.299 0.836 rs3735814 A/G 0.48 0.49 0.18 0.96 0.671 0.836 rs2740434 A/G 0.33 0.34 0.13 0.97 0.720 0.844 rs2645399 A/G 0.34 0.36 0.49 0.93 0.486 0.836 rs11784693 A/G 0.29 0.32 1.44 0.89 0.231 0.836 rs804283 G/A 0.29 0.31 1.24 0.89 0.265 0.836 rs17153747 G/A 0.13 0.12 0.75 1.13 0.386 0.836 rs804282 C/A 0.45 0.46 0.40 0.94 0.529 0.836 rs13264774 A/G 0.15 0.15 0.02 1.02 0.880 0.935 rs13273672 G/A 0.30 0.29 0.26 1.05 0.610 0.836 rs804280 C/A 0.43 0.44 0.32 0.95 0.574 0.836 rs3729856 G/A 0.14 0.14 0.16 1.06 0.689 0.836 rs867858 C/A 0.31 0.31 0.05 1.02 0.824 0.904 rs1062219 A/G 0.45 0.46 0.20 0.96 0.655 0.836 rs804290 A/G 0.24 0.22 1.31 1.13 0.253 0.836 rs11785481 A/G 0.14 0.15 0.26 0.93 0.607 0.836 rs12458 T/A 0.33 0.31 0.36 1.06 0.548 0.836 rs3203358 C/G 0.33 0.32 0.01 1.01 0.917 0.944 N = 534 cases, 413 controls. Alleles 1 and 2 refer to minor and major allele, respectively. MAF = minor allele frequency. *P < 0.05. Downloaded from https://academic.oup.com/alcalc/article/53/4/361/4838890 by DeepDyve user on 18 July 2022 Alcohol and Alcoholism, 2018, Vol. 53, No. 4 365 Table 3. Associations between GATA4 gene SNPs and AUD in AA sample b c SNP A1/A2 MAF cases MAF controls Chi square Odds ratio P-value FDR rs6990313 A/C 0.28 0.32 3.39 0.80 0.065 0.742 rs10105409 G/A 0.17 0.19 0.96 0.87 0.327 0.930 rs6601604 A/G 0.43 0.41 0.62 1.09 0.431 0.930 rs10112596 A/G 0.16 0.14 0.86 1.16 0.353 0.930 rs12550668 G/A 0.11 0.10 0.66 1.16 0.417 0.930 rs2898292 G/A 0.30 0.27 2.06 1.20 0.151 0.921 rs4840579 A/G 0.47 0.46 0.40 1.07 0.527 0.930 rs11250159 A/C 0.21 0.17 3.41 1.31 0.065 0.742 rs17153694 A/G 0.09 0.06 3.79 1.54 0.051 0.742 rs17153698 A/G 0.32 0.31 0.29 1.07 0.588 0.930 rs6983129 C/A 0.38 0.35 1.46 1.15 0.226 0.930 rs2898295 A/G 0.39 0.38 0.12 1.04 0.728 0.930 rs11250163 C/A 0.10 0.10 0.02 0.98 0.898 0.930 rs13275657 A/G 0.04 0.03 0.64 1.27 0.425 0.930 rs2029969 G/A 0.24 0.24 0.01 0.99 0.927 0.930 rs2173117 A/C 0.24 0.23 0.34 1.08 0.559 0.930 rs3779664 A/G 0.06 0.06 0.13 1.09 0.721 0.930 rs3735814 A/G 0.49 0.49 0.06 1.03 0.802 0.930 rs2740434 A/G 0.31 0.31 0.01 1.01 0.930 0.930 rs2645399 A/G 0.48 0.51 0.69 0.91 0.408 0.930 rs11784693 A/G 0.22 0.21 0.12 1.05 0.726 0.930 rs804283 G/A 0.13 0.11 1.72 1.26 0.190 0.921 rs17153747 G/A 0.05 0.04 0.27 1.15 0.605 0.930 rs804282 C/A 0.46 0.48 0.18 0.95 0.671 0.930 rs13264774 A/G 0.22 0.26 1.82 0.84 0.177 0.921 rs13273672 G/A 0.37 0.38 0.20 0.95 0.657 0.930 rs804280 C/A 0.42 0.41 0.20 1.05 0.655 0.930 rs3729856 G/A 0.02 0.02 0.63 0.74 0.428 0.930 rs867858 C/A 0.23 0.23 0.06 0.97 0.810 0.930 rs1062219 A/G 0.17 0.17 0.03 1.03 0.867 0.930 rs804290 A/G 0.05 0.03 2.20 1.54 0.138 0.921 rs11785481 A/G 0.03 0.03 0.03 0.94 0.852 0.930 rs12458 T/A 0.39 0.39 0.02 0.98 0.892 0.930 rs3203358 C/G 0.06 0.06 0.19 1.11 0.667 0.930 N = 510 cases, 232 controls. Alleles 1 and 2 refer to minor and major allele, respectively. MAF = minor allele frequency. expression, alcohol-induced cue reactivity and relapse risk (Kiefer have confounded the analysis. Furthermore, it is likely that multiple et al., 2011; Jorde et al., 2014; Zois et al., 2016). genes are involved in AUD, with only their interaction accumulating This case-control study aimed to replicate previous GWAS and to account for a significant proportion of the variance. Therefore, candidate gene studies relating GATA4 and SNP rs13273672 with additional studies of genetic variation are needed to elucidate AUD and alcohol-related phenotypes. Although previous studies whether polymorphisms of the GATA4 gene interact with other have shown a relationship between variants of this gene and a diag- genes to contribute to the genetic risk for AUD and other alcohol- nosis of AUD, we did not replicate these findings in our sample. related phenotypes. Given that one SNP in the EA group There are several explanations for these discrepant results. First, (rs6601604) and three SNPs in the AA group (rs6990313, as our study consisted of a relatively small sample size, we may have rs11250159 and rs17153694) did not survive correction for mul- lacked adequate power to detect small effects, which is a limitation tiple comparisons, our data indicate a need for further replication of the current study. This limitation might be particularly relevant studies with larger sample sizes. Karpyak et al. (2014) used gene- given the number of SNPs that did not have a MAF > 5%. While level testing to identify an association between AUD diagnoses and Karpyak et al. (2014) used a sample of over 800 AD cases, we were GATA4 variation at the gene-level. Future studies should use this limited to 534 and 510 AUD cases in the EA and AA subgroups, additional analysis to replicate these findings and identify any gene- respectively. Clinical heterogeneity, such as differences in anxiety or level association between GATA4 variants and alcohol-related clin- participant status, may also account for our inability to replicate ical phenotypes. Confirming a gene-level association between past studies. Edenberg et al. (2010) and Treutlein et al. (2009) used GATA4 and AUD would provide a target for identifying and treat- a sample of participants receiving treatment for their alcohol use, ing maladaptive alcohol use. while our cohort included both treatment-seeking and non- Our finding of a relationship between SNP rs10112596 and an treatment-seeking individuals. Although all AUD patients in the pre- anxiety measure in the AA group with AUD is novel, as this marker sent study received a diagnosis based on the DSM-IV, it is possible has not yet been associated with any alcohol-related phenotype. that the two cohorts represent different phenotypes, which may ANP levels might underlie this correlation, as decreased ANP levels Downloaded from https://academic.oup.com/alcalc/article/53/4/361/4838890 by DeepDyve user on 18 July 2022 366 Alcohol and Alcoholism, 2018, Vol. 53, No. 4 Table 4. Associations between GATA4 gene SNPs and Brief Scale that found an association between GATA4 variants and risk for for Anxiety (BSA) scores in AA sample with AUD AUD; however, further studies with larger samples and gene-level testing techniques are needed. We did find an association between SNP A1 N BETA STAT P-value FDR rs10112596 and anxiety in the AA group, suggesting that this SNP may contribute to risk for AUD and anxiety in individuals of AA rs6990313 A 414 1.14 2.09 0.037 0.418 rs10105409 G 415 1.55 2.43 0.015 0.262 but not EA. This finding also implicates GATA4 in the relationship rs6601604 A 415 0.24 0.47 0.642 0.845 between AUD and anxiety, indicating a possible protective effect of rs10112596 A 415 −2.24 −3.33 0.001* 0.032* the rs10112596 A minor allele. This investigation contributes mean- rs12550668 G 415 −0.76 −0.95 0.344 0.828 ingfully to the field because it extends the discovery of certain geno- rs2898292 G 415 0.94 1.72 0.086 0.418 types that may be associated with a higher risk of developing and rs4840579 A 415 0.30 0.58 0.560 0.828 maintaining AUD, as well as those genotypes that constitute part of rs11250159 A 415 1.07 1.73 0.085 0.418 a genetic ‘protective’ factor. As medicine and treatment plans are rs17153694 A 412 1.03 1.17 0.241 0.746 becoming more personalized and patient-specific, it becomes crucial rs17153698 A 415 0.83 1.58 0.115 0.489 to elucidate the mechanisms behind the genetic contribution to com- rs6983129 C 414 0.34 0.65 0.516 0.828 plex disorders. Ideally, genetic information will provide us with the rs2898295 A 415 0.22 0.43 0.671 0.845 rs11250163 C 415 −0.27 −0.32 0.753 0.883 tools to better diagnose and prevent psychiatric disorders, including rs13275657 A 411 −1.33 −0.94 0.346 0.828 AUD. Genetics can also provide meaningful information regarding rs2029969 G 415 −0.38 −0.63 0.532 0.828 the underlying biological basis of a disease when designing treat- rs2173117 A 415 −0.44 −0.74 0.461 0.828 ment strategies. An understanding of genetic susceptibility to AUD rs3779664 A 415 −0.44 −0.43 0.665 0.845 may inform the development of individualized pharmacological rs3735814 A 415 0.92 1.86 0.064 0.418 interventions that may provide patient-specific drug efficacy. rs2740434 A 415 0.42 0.77 0.440 0.828 rs2645399 A 414 0.42 0.83 0.405 0.828 rs11784693 A 414 0.33 0.53 0.598 0.845 SUPPLEMENTARY MATERIAL rs804283 G 415 0.06 0.08 0.940 0.940 Supplementary data are available at Alcohol And Alcoholism rs17153747 G 415 −0.72 −0.61 0.545 0.828 rs804282 C 414 −0.04 −0.09 0.927 0.940 online. rs13264774 A 415 0.42 0.67 0.506 0.828 rs13273672 G 415 0.20 0.36 0.717 0.870 CONFLICT OF INTEREST STATEMENT rs804280 C 415 0.46 0.87 0.384 0.828 rs3729856 G 415 0.17 0.10 0.924 0.940 The authors declare no conflict of interest. rs867858 C 415 −0.10 −0.15 0.877 0.940 rs1062219 A 412 −1.15 −1.78 0.076 0.418 rs804290 A 415 −1.60 −1.40 0.161 0.548 FUNDING rs11785481 A 415 0.33 0.22 0.825 0.935 This work was supported by the National Institutes of Health (NIH) rs12458 T 413 −0.32 −0.61 0.541 0.828 intramural funding [ZIA-AA000242; Section on Clinical Genomics rs3203358 C 415 −1.57 −1.43 0.153 0.548 and Experimental Therapeutics; to F.W.L.; Division of Intramural Allele 1 refers to minor allele. 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Journal

Alcohol and AlcoholismOxford University Press

Published: Jul 1, 2018

Keywords: carrier proteins; gene frequency; genes; haplotypes; single nucleotide polymorphism; diagnosis; alcohol use disorder; anxiety; ethanol; phenotype; polymorphism; african american; binding (molecular function)

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