Abstract
Objective
Genetic factors are known to contribute to the development and progression of substance use disorder (SUD). Genes associated with dopamine-mediated reward pathways play a critical role in SUD. This study aimed to investigate the association of specific single nucleotide polymorphisms (SNPs) within the DRD1, DRD2, DRD3, DRD4, and DRD5 genes with SUD in a population of Jordanian males.
Results
A significant association was observed between the rs686 SNP and SUD, with the GG genotype showing a statistically significant association with SUD onset. The rs6280 (T > C) SNP in the DRD3 gene was significantly associated with SUD, as evidenced by both genotypic and allelic frequency analyses. Furthermore, the CG haplotype (rs4532, rs686) of DRD1 and the ACG haplotype (rs936461, rs936460, rs936465) of DRD4 were significantly associated with SUD onset, suggesting their potential roles as genetic risk factors. Multinomial logistic regression analysis identified smoking and marital status as factors significantly associated with the studied genotypes, further increasing the risk of SUD.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13104-025-07523-6.
Keywords: Addiction, Dopamine, Arab, SNP, DRD
Introduction
Substance use disorder (SUD) is defined by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) as a cluster of behavioral symptoms, including compulsive drug-seeking, impaired control over substance use, and disruptions in social functioning [1, 2]. It is a complex and multifactorial condition influenced by both genetic predispositions and environmental factors [3, 4]. Evidence from multiple studies suggests that genetic factors may account for approximately 40% to 80% of an individual’s susceptibility to addiction [5–8].
Increased dopamine release in the striatum is associated with addictive substances, especially stimulants [9, 10]. Several studies have demonstrated that dopaminergic transmission within the central nervous system (CNS) plays a key role in modulating susceptibility to addiction [11–14]. Dopamine is the primary neurotransmitter that influences the activity of other neurons and modulates sensitivity to different neurotransmitters [15].
The neurological functions of dopamine are mediated by two families of G protein-coupled receptors known as D1-like and D2-like receptors [16]. The D1-like family includes the D1 and D5 receptors, which share high sequence homology. In contrast, the D2-like family comprises the D2, D3, and D4 receptors [16, 17]. The DRD gene family plays a critical role in various neurobehavioral signaling pathways and is therefore considered a strong candidate for influencing substance use behaviors [18].
Numerous studies have highlighted the significance of the DRD gene family, particularly DRD2, which is widely recognized as a general risk factor for SUD. Polymorphisms within this gene have been associated with substance abuse across diverse populations [19, 20]. Investigating genetic variations that influence dopaminergic interaction is essential for understanding the genetic basis of SUD [21].
SUD is influenced by complex gene–environment interactions; however, few genetic studies have focused on Middle Eastern populations, resulting in a significant gap in understanding the genetic risk factors for SUD among Arabs, including Jordanians. To address this gap, the present study investigates the association between selected single nucleotide polymorphisms (SNPs) in dopamine receptor genes (DRD1, DRD2, DRD3, DRD4, and DRD5) and susceptibility to SUD in a Jordanian male population.
Materials and methods
Study population and recruitment
The study cohort consisted of 500 patients with substance use disorder (SUD) and 500 healthy control subjects, all of whom were male Jordanians of Arab origin. The diagnosis of SUD was established in accordance with the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria. All cases underwent clinical assessment by licensed psychiatrists utilizing structured interviews to confirm SUD diagnosis based on DSM-5 standards. Participants were recruited from two governmental treatment centers for SUD in Amman, Jordan.
Exclusion criteria for the study included individuals with comorbid Axis I disorders such as bipolar I or II disorder, major depressive disorder, or schizophrenia unless the SUD was the primary cause. Additionally, patients on psychotropic medications or those with severe medical conditions like cardiovascular, metabolic, neuroendocrine, neurodegenerative diseases, or epilepsy were excluded. Control participants were selected through convenience sampling from the community and were matched to cases based on demographic variables such as gender. Prior to inclusion, all control subjects were screened using standardized questionnaires and structured clinical interviews to confirm the absence of SUD and other major psychiatric conditions. Individuals with a family history of SUD in first-degree relatives were also excluded to minimize potential genetic or environmental confounding.
An estimated 134,947 adult males in Jordan are affected by SUD, reflecting a prevalence of 2.5% within the national population of 9,531,712 (https://www.who.int/publications/m/item/jordan---who-special-initiative-for-mental-health). The sample size was determined using OpenEpi version 3.01, applying a 95% confidence level, a 2.5% prevalence rate of SUD in Jordan, a 3% margin of error, and a design effect of 1. The resulting estimated sample size was 105 participants. A total of 500 individuals were included in the case group, exceeding the estimated sample size requirement.
This study was approved by the Institutional Review Board (IRB) of Jordan University of Science and Technology (43/114/2018). Additional approvals were obtained from the Ministry of Health (MOH/REC/180057), the Public Security Directorate (C/2/46/21546), and King Abdullah University Hospital (43/114/2018). Written informed consent was obtained from all participants for their enrollment in the study and the publication of the results.
Participant demographics and clinical characteristics
Participants in the SUD group had a mean age of 28.42 ± 6.88 years, while those in the control group averaged 30.05 ± 6.81 years. Smoking was more prevalent among cases (87.8%) than controls (66.2%). Regarding marital status, 70.9% of individuals in the SUD group were single, whereas 58.4% of controls were married. Employment status also differed between groups, with 29.7% of cases being unemployed compared to 20.6% of controls. Among the substances reported, synthetic cannabinoids were the most prevalent, accounting for 47.3% of cases (Table 1).
Table 1.
Sociodemographic and clinical characteristics of study participants
| Characteristics | Cases | Controls | |
|---|---|---|---|
| Age (mean ± SD) | 28.42 ± 6.88 | 30.05 ± 6.81 | |
| Smoking status | Smokers | 426 (87.8%) | 331 (66.2%) |
| Non-smokers | 59 (12.2%) | 169 (33.8%) | |
| Marital status | Single | 345 (70.9%) | 205 (41%) |
| Married | 127 (26.1%) | 292 (58.4%) | |
| Divorced | 14 (3%) | 2 (0.4%) | |
| Widowed | 0 (0%) | 1 (0.2%) | |
| Employment status | Employed | 314 (70.3%) | 397 (79.4%) |
| Unemployed | 144 (29.7%) | 103 (20.6%) | |
| Age at onset (mean ± SD) | 24.01 ± 6.36 | – | |
| Types of substances | Synthetic Cannabinoids | 230 (47.3%) | – |
| Cannabinoids | 96 (19.8%) | – | |
| Opiates | 37 (7.6%) | – | |
| Amphetamines | 26 (5.3%) | – | |
| Benzodiazepines | 10 (2.1%) | – | |
| Alcohol | 26 (5.3%) | – | |
| Cocaine | 5 (1.1%) | – | |
| Mixed substances | 56 (11.5%) | – | |
The candidate gene and SNPs selection
The selection of these SNPs was based on their functional and biological relevance, as well as their specific localization within the targeted gene. This selection was further supported by existing literature identifying key polymorphisms in the DRD genes among SUD patients from different populations worldwide. Comprehensive assessments of the SNPs were conducted using predictive web tools and databases, including Ensembl (http://www.ensembl.org/index.html), HaploReg v4.2 (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php), rSNPBase 3.1 (http://rsnp3.psych.ac.cn/), RegulomeDB (https://regulomedb.org/regulome-search/) v, and the NCBI SNP database (https://www.ncbi.nlm.nih.gov/snp/).
The findings from RegulomeDB assigned rs1076560, rs2283265, rs6280, rs936461, rs936460, and rs936465 a grade of 1 F, with corresponding scores of 0.22271, 0.59917, 0.22271, 0.66703, 0.66703, and 0.55436. These results imply that these SNPs have a potential role in affecting transcription factor binding and may influence the expression of target genes. Functional annotation performed using HaploReg v4.2 revealed that rs4532 and rs686 are classified as UTR variants, rs1076560 and rs2283265 as intron variants, rs936461 and rs936460 as upstream transcript variants, and rs6280 as a missense variant, which may have a significant impact on protein function. These SNPs are predicted to influence histone marks associated with promoters and enhancers and alter transcription factor binding motifs, which could play an indirect role in the development of SUD.
Sample preparation and genotyping
DNA was extracted from fresh whole blood samples using the Gentra Puregene Blood Kit (Qiagen, CA, USA), following the manufacturer’s instructions. Nine single nucleotide polymorphisms (SNPs) across five dopamine receptor genes were analyzed: rs4532 and rs686 in DRD1; rs1076560 and rs2283265 in DRD2; rs6280 in DRD3; rs936461, rs936460, and rs936465 in DRD4; and rs7690455 in DRD5. Genotyping was performed using the Sequenom MassARRAY® system (iPLEX GOLD) (Sequenom, San Diego, CA, USA) at the Australian Genome Research Facility (AGRF; Melbourne Node, Melbourne, Australia). Primer sequences used for SNP amplification are provided in Additional File 1. A summary of the studied SNPs is included in Additional File 2, and comprehensive genotypic data are presented in Additional File 3.
Statistical analysis
Demographic, allelic, and genotypic data were analyzed using the Statistical Package for the Social Sciences (SPSS), version 25.0 (SPSS Inc., Chicago, IL, USA). The distribution of DRD receptor SNPs was examined using chi-square tests and one-way ANOVA. Genetic association and haplotype analyses were conducted using the SNPStats web tool (https://www.snpstats.net/start.htm). Multinomial logistic regression analysis was employed to assess the relationship between DRD SNPs and SUD-related features. Results were reported as odds ratios (ORs), and a p-value < 0.05 was considered statistically significant. Hardy-Weinberg equilibrium (HWE) was evaluated using the chi-square test, with p > 0.05 indicating equilibrium.
Results
Hardy-Weinberg equilibrium assessment
A total of 1000 Jordanian males were screened for dopamine receptor gene (DRD) SNPs to analyze their prevalence. The Hardy–Weinberg Equilibrium (HWE) was assessed for all single nucleotide polymorphisms (SNPs) in both case and control groups, with a p-value > 0.05 indicating equilibrium. All SNPs were in HWE, except for rs2283265, which showed a significant deviation in the control group (p < 0.05).
Associations of DRD polymorphisms with SUD
The allelic and genotypic distributions of the SNPs were assessed between the case and control groups. A significant genotypic difference was observed for rs686 (A > G) in the DRD1 gene (overall p = 0.034), with the GG genotype being notably more frequent among cases compared to controls (p = 4.2e-6). However, the allelic frequency of rs686 did not differ significantly between groups (p = 0.066). For the rs6280 (T > C) SNP in DRD3, the TT genotype was significantly more prevalent in the case group (44%) than in controls (35%) (p = 4.2e-6), while the CC genotype was more frequent in controls (18%) compared to SUD patients (14%) (p = 0.00046). Overall, both genotypic and allelic distributions of rs6280 differed significantly between the groups (p = 0.030 and p = 0.016, respectively), with the T allele being more prevalent among individuals with SUD and the C allele more frequent in controls. The other studied SNPs across all genes showed no significant differences in genotype or allele frequency between the study groups (Table 2).
Table 2.
Association of genotypes and alleles of the studied SNPs with SUD susceptibility
| Gene | SNP_ID | Allele/ Genotype | Controls (N = 500) | Cases (N = 500) |
P –value (posthoc analysis) |
Overall p-value |
|---|---|---|---|---|---|---|
| DRD1 | rs4532 | TT | 0.61 | 0.65 | 0.0026 | 0.065 |
| TC | 0.33 | 0.33 | 0.045 | |||
| CC | 0.06 | 0.03 | 0.00017 | |||
| T | 0.78 | 0.81 | NS | NS | ||
| C | 0.22 | 0.19 | NS | |||
| rs686 | AA | 0.52 | 0.55 | 0.0019 | 0.034 | |
| AG | 0.38 | 0.39 | 0.021 | |||
| GG | 0.10 | 0.06 | 4.2E-06 | |||
| A | 0.71 | 0.75 | 0.0003 | 0.066 | ||
| G | 0.29 | 0.25 | 0.0003 | |||
| DRD2 | rs1076560 | AA | 0.02 | 0.02 | 0.012 | 0.208 |
| CA | 0.19 | 0.23 | 0.00046 | |||
| CC | 0.79 | 0.75 | 0.00096 | |||
| A | 0.12 | 0.13 | NS | NS | ||
| C | 0.88 | 0.87 | NS | |||
| DRD3 | rs6280 | CC | 0.18 | 0.14 | 0.00046 | 0.030 |
| CT | 0.47 | 0.42 | 0.00067 | |||
| TT | 0.35 | 0.44 | 4.2E-06 | |||
| C | 0.41 | 0.35 | 6.7E-06 | 0.016 | ||
| T | 0.59 | 0.65 | 6.7E-06 | |||
| DRD4 | rs936461 | AA | 0.3 | 0.26 | 0.00046 | 0.099 |
| AG | 0.49 | 0.48 | 0.021 | |||
| GG | 0.21 | 0.26 | 0,000063 | |||
| A | 0.55 | 0.5 | NS | NS | ||
| G | 0.45 | 0.5 | NS | |||
| rs936460 | CC | 0.08 | 0.06 | 0.00031 | 0.100 | |
| CT | 0.39 | 0.39 | 0.0026 | |||
| TT | 0.53 | 0.65 | 0.00014 | |||
| C | 0.27 | 0.23 | NS | NS | ||
| T | 0.73 | 0.77 | NS | |||
| rs936465 | CC | 0.22 | 0.26 | 0.0013 | 0.162 | |
| CG | 0.46 | 0.48 | 0.009 | |||
| GG | 0.32 | 0.27 | 0.00014 | |||
| C | 0.45 | 0.49 | NS | NS | ||
| G | 0.55 | 0.51 | NS | |||
| DRD5 | rs7690455 | AA | 0.13 | 0.14 | 0.045 | 0.999 |
| AG | 0.44 | 0.44 | 0.045 | |||
| GG | 0.43 | 0.42 | 0.045 | |||
| A | 0.35 | 0.35 | NS | NS | ||
| G | 0.65 | 0.65 | NS |
a: multivariate analysis for significant SNPs: P-value < 0.0083 for genotype association, P-value < 0.0125 for the allelic association. b: P-value < 0.05 is considered significant. NA: not available, NS: not significant
Associations of genetic models of DRD polymorphisms with SUD risk
Subsequently, we examined the association between various genetic models (codominant, dominant, recessive, and overdominant) of the studied variants and the risk of SUD. The analysis revealed significant associations between specific models and the risk of SUD. Although the overall genotypic association of the DRD1 rs4532 SNP with SUD was not significant, the dominant model (TT + TC vs. CC) showed a significant association (p = 0.02), with the CC genotype more frequently observed in controls than in cases. For DRD1 rs686, two genetic models were significantly associated with SUD: the codominant model (AA vs. AG vs. GG; p = 0.034) and the recessive model (AA + AG vs. GG; p = 0.009). The GG genotype was more common in control subjects (10.2%) than in SUD cases (5.7%). Similarly, the CC genotype of DRD3 rs6280 was less frequent among cases (14.4%) than controls (17.8%). Both the codominant (TT vs. TC vs. CC; p = 0.024) and dominant (TT vs. TC + CC; p = 0.0076) models were significantly associated with SUD. Finally, for DRD4 rs936461, the dominant model (AA + AG vs. GG) showed a significant association with SUD (p = 0.048) (Additional file 4).
Haplotype analysis of DRD genes
Haplotype association analysis was conducted for DRD1 and DRD4 SNPs in relation to SUD. The CG haplotype in the DRD1 block (rs4532 and rs686) showed a slight association with a reduced risk of SUD, with an odds ratio (OR) of 0.80 and a P-value of 0.046. Similarly, the ACG haplotype in the DRD4 block (rs936461, rs936460, and rs936465) was significantly associated with a reduced risk, showing an OR of 0.64 and P = 0.005. No significant associations were observed for the other haplotypes (Table 3).
Table 3.
Association of genetic haplotypes with SUD
| Gene | Haplotype Block | Freq. Case | Freq. Control | Odds ratio (95%) CI | P-value* | |||
|---|---|---|---|---|---|---|---|---|
| DRD1 | T | A | 0.7477 | 0.7075 | 1.00 | – | ||
| C | G | 0.193 | 0.2288 | 0.80 (0.64–1.00) | 0.046 | |||
| T | G | 0.0593 | 0.0637 | 0.89 (0.62–1.29) | 0.54 | |||
| DRD4 | G | T | C | 0.2912 | 0.2624 | 1.00 | – | |
| A | T | G | 0.2066 | 0.2076 | 0.89 (0.68–1.17) | 0.4 | ||
| A | T | C | 0.1535 | 0.1495 | 0.90 (0.63–1.27) | 0.53 | ||
| A | C | G | 0.1187 | 0.1662 | 0.64 (0.46–0.88) | 0.0057 | ||
| G | T | G | 0.1181 | 0.1057 | 0.98 (0.68–1.40) | 0.9 | ||
| G | C | G | 0.0616 | 0.0707 | 0.80 (0.49–1.30) | 0.37 | ||
| A | C | C | 0.0304 | 0.0126 | 0.73 (0.30–1.76) | 0.48 | ||
| G | C | C | 0.0199 | 0.0253 | 1.77 (0.66–4.72) | 0.25 | ||
*p-value < 0.05 is considered significant
Logistic regression analysis of SUD features and DRD gene polymorphisms
We attempted to examine the association between various SUD features, including age, smoking, marital status, and employment, with our target polymorphisms using multinomial logistic regression analysis. Significant associations were observed among SUD cases with the (CC) genotype of rs4532 (odds ratio = 0.419, p = 0.018) and the (GG) genotype of rs686 (odds ratio = 1.786, p = 0.032) in the DRD1 gene, suggesting a reduced likelihood of substance addiction. Furthermore, the (CC) (odds ratio = 0.639, p = 0.030) and (CT) (odds ratio = 0.729, p = 0.038) genotypes of the DRD3 gene were significantly associated with lower chance of addiction. In addition, the (GG) genotype of rs936461 in the DRD4 gene (odds ratio = 0.675, p = 0.045) was associated with a reduced likelihood of addiction. Smoking and marital status demonstrated significant effects on SUD incidence across all genotypes (Table 4).
Table 4.
The association between SUD features and the studied SNPs using multinomial logistic regression analysis
| Model** | Covariate | Odd ratio | Confidence interval 95% | p-value* | ||||
|---|---|---|---|---|---|---|---|---|
| DRD1 | rs4532 | Age | 1.246 | (0.789–1.965) | 0.346 | |||
| Smoking | 0.287 | (0.201–0.410) | < 0.0001 | |||||
| Marital status | 0.223 | (0.158–0.315) | < 0.0001 | |||||
| Job | 1.139 | (0.812–1.589) | 0.449 | |||||
| CC | 0.419 | (0.205–0.859) | 0.018 | |||||
| CT | 1.056 | (0.786–1.423) | 0.723 | |||||
| TT *** | NA | |||||||
| rs686 | Age | 0.808 | (0.527–1.239) | 0.329 | ||||
| Smoking | 3.616 | (2.554–5.139) | < 0.0001 | |||||
| Marital status | 3.846 | (2.773–5.334) | < 0.0001 | |||||
| Job | 0.882 | (0.632–1.232) | 0.462 | |||||
| GG | 1.786 | (1.051–3.036) | 0.032 | |||||
| GA | 0.915 | (0.686–1.221) | 0.545 | |||||
| AA | NA | |||||||
| DRD2 | rs1076560 | Age | 0.824 | (0.536–1.266) | 0.376 | |||
| Smoking | 3.614 | (2.544–5.135) | < 0.0001 | |||||
| Marital status | 3.903 | (2.815–5.412) | < 0.0001 | |||||
| job | 0.923 | (0.660–1.289) | 0.636 | |||||
| CC | 0.833 | (0.316–2.198) | 0.712 | |||||
| CA | 0.650 | (0.238–1.775) | 0.401 | |||||
| AA | NA | |||||||
| DRD3 | rs6280 | Age | 1.262 | (0.820–1.941) | 0.290 | |||
| Smoking | 0.280 | (0.197–0.399) | < 0.0001 | |||||
| Job | 1.114 | (0.797–1.558) | 0.526 | |||||
| Marital status | 0.251 | (0.181–0.349) | < 0.0001 | |||||
| CC | 0.639 | (0.427–0.957) | 0.030 | |||||
| CT | 0.729 | (0.541–0.982) | 0.038 | |||||
| TT | NA | |||||||
| DRD4 | rs936461 | Age | 0.807 | (0.526–1.238) | 0.326 | |||
| Smoking | 3.953 | (2.609–5.288) | < 0.0001 | |||||
| Marital status | 3.714 | (2.851–5.282) | < 0.0001 | |||||
| Job | 0.849 | (0.640–1.251) | 0.453 | |||||
| GG | 0.675 | (0.460–0.995) | 0.045 | |||||
| GA | 0.908 | (0.657–1.255) | 0.559 | |||||
| AA | NA | |||||||
| rs936460 | Age | 1.357 | (0.872–2.114) | 0.177 | ||||
| Smoking | 0.273 | (0.190–0.393) | < 0.0001 | |||||
| Marital status | 0.237 | (0.169–0.333) | < 0.0001 | |||||
| Job | 1.109 | (0.786–1.564) | 0.557 | |||||
| CC | 0.583 | (0.326–1.043) | 0.069 | |||||
| CT | 0.800 | (0.595–1.0177) | 0.141 | |||||
| TT | NA | |||||||
| rs936465 | Age | 0.826 | (0.528–1.237) | 0.324 | ||||
| Smoking | 3.674 | (2.586–5.218) | < 0.0001 | |||||
| Marital status | 3.945 | (2.846–5.467) | < 0.0001 | |||||
| Job | 0.911 | (0.652–1.275) | 0.584 | |||||
| GG | 0.478 | (0.147–1.555) | 0.22 | |||||
| GC | 0.374 | (0.111–1.260) | 0.113 | |||||
| CC | NA | |||||||
| DRD5 | rs7690455 | Age | 0.836 | (0.536–1.297) | 0.425 | |||
| Smoking | 3.595 | (2.526–5.117) | < 0.0001 | |||||
| Marital status | 4.020 | (2.889–5.595) | < 0.0001 | |||||
| Job | 0.875 | (0.624–1.227) | 0.439 | |||||
| GG | 0.885 | (0.576–1.359) | 0.576 | |||||
| GA | 0.91 | (0.593–1.397) | 0.666 | |||||
| AA | NA | |||||||
*p-value < 0.05 is considered significant. **The reference category is the control group. *** The data is not available because it is redundant
Discussion
This study investigated the association between genetic polymorphisms in dopamine receptor genes (DRD1, DRD2, DRD3, DRD4, and DRD5) and susceptibility to substance use disorder (SUD) among Jordanian males. Our results demonstrated that the rs6280 (T >C) SNP in the DRD3 gene shows a significant genotypic and allelic association with substance use disorder (SUD), with the TT genotype potentially increasing the risk of addiction. Additionally, the CC genotype of the rs4532 SNP in the DRD1 gene may act as a protective factor against SUD in Jordanians, consistent with findings from studies in Han Chinese populations. Analysis of genotypic and allelic frequencies between cases and controls revealed a significant genotypic association for rs686 (A >G) in the DRD1 gene; however, no significant allelic association was observed. The GG genotype showed a protective association compared to the AA and AG genotypes under both codominant and recessive models, where the G allele is minor and the A allele is major. These findings are consistent with studies conducted in other ethnic groups, such as Han Chinese and African American populations [12, 13, 17].
Additionally, a significant association between rs686 and nicotine dependence (ND) has been reported in both African American samples and pooled populations [22, 23]. In the present study, rs686 also demonstrated a statistically significant association with the risk of SUD. Furthermore, a previous study reported that the rs686 and rs4532 SNPs in DRD1 were overexpressed in patients with alcohol dependence compared to control groups [24]. The C allele of DRD1 rs4532 has been associated with enhanced dopamine transmission in the brain [25]. Moreover, individuals homozygous for the C allele exhibit greater D1 receptor efficiency compared to those carrying the T allele [26].
Chronic cocaine abuse has been associated with a reduction in dopamine receptor D2 (DRD2) expression. Moreover, decreased DRD2/DRD3 levels in the striatum are considered a hallmark of addiction in affected individuals. It has been hypothesized that lower DRD2 levels may contribute to an increased risk of developing addictive disorders [27, 28]. Conversely, DRD2 overexpression has been shown to reduce the risk of alcohol dependence [29]. A study investigating the genetic association of the DRD2 rs1076560 SNP in clinically diagnosed patients with SUD in the United Arab Emirates found no significant associations between this variant and SUD [30], which aligns with our findings.
A study examining the influence of 13 DRD3 SNPs on nicotine dependence (ND) identified a significant association between rs6280 and ND in both European American (EA) and pooled samples [31]. Additionally, a study by Laucht et al. reported that rs6280 moderately affects smoking initiation among adolescents of European descent [32]. In our study, rs6280 showed a significant allelic and genotypic association with substance use disorder (SUD).
DRD4, an essential component of the dopaminergic system, is expressed in the limbic regions of the brain. It harbors several variants that may influence receptor function or alter the gene’s transcriptional activity [33]. Numerous alleles have been linked to the novelty-seeking trait in humans and have also been associated with susceptibility to substance abuse, including heroin and alcohol use, particularly in Japanese populations [34]. In our study, the GG genotype of the rs936461 SNP in DRD4 was significantly associated with SUD when compared to the AA and AG genotypes. A previous study investigating three functional polymorphisms in the DRD4 promoter region also reported a significant association between the − 521 C/T polymorphism and heroin addiction susceptibility [35].
Although the association between specific DRD gene polymorphisms and SUD was statistically significant, the possibility of linkage disequilibrium with other nearby functional variants cannot be excluded. Furthermore, it is plausible that gene-environment interactions, such as exposure to early-life stressors, peer influence, or socioeconomic factors, may contribute to the observed associations. These alternative explanations warrant further investigation through larger, multiethnic studies with detailed environmental exposure assessments.
Limitations
This study provides insights into the association between DRD gene variants and SUD susceptibility in a Jordanian population; however, several limitations should be considered. The findings remain inconclusive due to the complex, multifactorial nature of SUD, where genetic influences interact with environmental and behavioral factors. The role of gene-environment interactions was not fully explored, which may affect the interpretation of genetic associations. The study was limited to a specific ethnic background, and replication in other populations is necessary to determine the broader applicability of these results. This study is the lack of correction for multiple comparisons in the statistical analyses. Given the number of SNPs and genetic models tested, the potential for Type I error (false positives) is increased. While this approach was chosen to allow for the detection of potential associations in this exploratory study, the results should be interpreted with caution. Future studies should employ appropriate corrections for multiple testing.
Supplementary Information
Additional file 1. Table S1. Primers sequence (forward, reverse, and extension) forthe studied SNPs genes.
Additional file 2. Table S2. List of genes, their SNPs, and positions based on thewhole cohort
Additional file 3. Genotype data of selected SNPs.
Additional file 4. Table S3. Genetic models and distributions of SNPs within thestudied genes in 500 cases and 500 controls.
Acknowledgements
The authors thank Jordan University of Science and Technology, Jordan, for providing administrative and technical support.
Author contributions
L.A.-E. designed the study. L.A.-E. and O.A.-S. collected the data and supervised this study. All authors conducted the experimental work, analyzed and interpreted the data, drafted the manuscript, and reviewed and approved the final version.
Funding
The Scientific Research Support Fund (SRSF) at the Jordanian Ministry of Higher Education supported this work under grant number MPH/1/43/2017.
Data availability
All data generated or analysed during this study are included in this published article and its supplementary information files. Additionally, the dataset supporting the conclusions of this article is available in the European Variation Archive (EVA) at EMBL-EBI under the accession number PRJEB89083 (https://www.ebi.ac.uk/eva/?eva-study=PRJEB89083).
Declarations
Ethics approval and consent to participate
This study was approved by the Institutional Review Board (IRB) of Jordan University of Science and Technology and King Abdullah University Hospital (43/114/2018), as well as by the Ministry of Health (MOH/REC/180057) and the Public Security Directorate (C/2/46/21546). It was conducted in accordance with the ethical principles of the Declaration of Helsinki, and written informed consent was obtained from all participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
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References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Additional file 1. Table S1. Primers sequence (forward, reverse, and extension) forthe studied SNPs genes.
Additional file 2. Table S2. List of genes, their SNPs, and positions based on thewhole cohort
Additional file 3. Genotype data of selected SNPs.
Additional file 4. Table S3. Genetic models and distributions of SNPs within thestudied genes in 500 cases and 500 controls.
Data Availability Statement
All data generated or analysed during this study are included in this published article and its supplementary information files. Additionally, the dataset supporting the conclusions of this article is available in the European Variation Archive (EVA) at EMBL-EBI under the accession number PRJEB89083 (https://www.ebi.ac.uk/eva/?eva-study=PRJEB89083).