
==== Front
Saudi Pharm J
Saudi Pharm J
Saudi Pharmaceutical Journal : SPJ
1319-0164
2213-7475
Elsevier

S1319-0164(24)00221-4
10.1016/j.jsps.2024.102171
102171
Article
SNP analysis of stress-related genes reveals significant correlations with drug addiction in Jordan
AL-Eitan Laith lneitan@just.edu.jo
a⁎
Abu Kharmah Hana a
Alghamdi Mansour bc
a Department of Biotechnology and Genetic Engineering, Jordan University of Science and Technology, 22110 Irbid, Jordan
b Department of Anatomy, College of Medicine, King Khalid University, Abha 62529, Saudi Arabia
c Genomics and Personalized Medicine Unit, College of Medicine, King Khalid University, Abha 62529, Saudi Arabia
⁎ Corresponding author at: Laith AL-Eitan, 22110 Irbid, Jordan. lneitan@just.edu.jo
13 9 2024
11 2024
13 9 2024
32 11 102171© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Objective

Drug addiction is a complex disorder caused by multiple factors, including environmental and genetic factors. Stress-related genes such as Galanin (GAL) and Oxytocin (OXT) have been linked to the reward pathways that contribute to the development and progression of substance addiction. This study aimed to explore the correlation between several polymorphisms of stress-related genes and drug addiction among Jordanian males.

Methods

The study included 500 participants, consisting of both healthy controls and drug-addicted Jordanian males. The genetic material and clinical data were collected, and 18 SNPs in four candidate genes were genotyped using the Sequenom MassARRAY® system. Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 25.0 and the SNPStats website.

Results

The study identified a significant correlation between three SNPs of the GAL gene and drug addiction, specifically rs3136544, rs3136541, and rs694066. The study also found that different genotypes of these variants were significantly associated with drug addiction. Furthermore, different haplotypes of the GAL, GALR1, and OXTR polymorphisms were also significantly correlated with drug addiction. The study also identified a correlation between several drug addiction features and the studied variants, including the association of rs2717162 of Galanin receptor 1 (GALR1) with age at use onset and the association of rs3136541 of GAL with the type of substance and number of substances used.

Conclusion

Stress-related genes can play a significant role in the development and progression of addiction among the Jordanian population, and further investigations are necessary to understand the underlying mechanisms better and improve future treatment strategies.

Keywords

Galanin
Oxytocin
SNPs
Stress
Substance use disorder
==== Body
pmc1 Introduction

Addiction is a chronic neuropsychiatric disorder worldwide that is known as the failure to control drug consumption or cessation (Ducci and Goldman, 2008, Buisman-Pijlman et al., 2014, Torres-Berrio et al., 2018). It is presumed that alcohol, followed by cannabis, nicotine, cocaine, and amphetamine, are the most recurrently consumed substances globally, contributing to 0.4 % of annual deaths (Ducci and Goldman, 2008), (Torres-Berrio et al., 2018, Kim et al., 2015, Al-Eitan et al., 2012c, Al-Eitan et al., 2024). Addiction consequences conflict with a person's psychological and social life (Buisman-Pijlman et al., 2014, Levran et al., 2014b, McGregor and Bowen, 2012, Love et al., 2018). Addiction is affected by a complex interaction between environmental, genetic, and drug-induced factors (Levran et al., 2014b, Levran et al., 2014a, Levran et al., 2008). Stress is a key factor that may induce individuals to drug addiction and prolong the addiction cycle, including initiation, maintenance, and relapse (Kim et al., 2015, Levran et al., 2014b, Al-Eitan et al., 2012b). Stress is a behavioral and physiological response that lets organisms respond to the challenges of the environment perceived as threatening (Torres-Berrio et al., 2018, Levran et al., 2014b, Randesi et al., 2020). Several molecular genetic studies have proved the relationship between various polymorphisms in stress-related genes, such as the neuropeptide galanin (GAL) and Oxytocin (OXT), among other genes and affective disorders (Levran et al., 2014a, Belfer et al., 2006, Domschke and Reif, 2012).1

Previous studies conducted an association of heroin addiction involving several genes associated with stress response (Levran et al., 2008, Proudnikov et al., 2008, Levran et al., 2009). These investigations found a correlation between polymorphisms in the adrenocorticotropic hormone ACTH receptor gene (melanocortin 2 receptor MC2R) in Hispanics, the arginine vasopressin AVP receptor gene (AVPR1A) in African Americans, and the galanin gene (GAL) in European Americans.

A case-control hypothesis-driven association study investigated 112 SNPs in 26 genes related to stress response and heroin addiction in 1090 European subjects. Nineteen single nucleotide polymorphisms (SNPs) in 9 genes (AVP, corticotropin-releasing hormone receptor 1 (CRHR1), corticotropin-releasing hormone receptor 2 (CRHR2), FK506-binding protein 51 (FKBP5), nuclear receptor subfamily 3, group C, member 2 (mineralocorticoid receptor (NR3C2)), AVPR1A, GAL, galanin receptor 1 (GLRA1), and neuropeptide Y receptor Y1 (NPY1R)) showed nominally significant association with heroin addiction (Levran et al., 2014a). Another study found that a different AVPR1A SNP was linked to general drug use disorders (Maher et al., 2011), and neuropeptide Y receptor Y2 (NPY2R) SNPs have been associated with alcohol and cocaine dependence (Wetherill et al., 2008).

Another study examined the association between 120 variants in 27 stress-related genes to determine the role of these variants in opioid dependence in Caucasian subjects from the Netherlands. They found that 12 SNPs in seven genes showed a nominally significant association with opioid dependence OD. Experiment-wise significant associations were found for three SNP pairs through an interaction effect: GALR1/GAL rs9807208/rs3136541, NPY1R/GAL rs4691910/rs1893679, NPY1R/GAL, and rs4691910/rs3136541. This corroborates evidence from prior reports on the association between stress-related variables and heroin dependence (Randesi et al., 2020).

Brain-derived neurotrophic factor (BDNF) levels have been associated with several aspects of cocaine addiction (Corominas-Roso et al., 2013). Therefore, BDNF levels have been proposed as a biomarker of cocaine use-related outcomes (von Diemen et al., 2016). These outcomes and even susceptibility to drug addiction are similarly predicted by the functioning of the hypothalamic–pituitary–adrenal (HPA) axis (Sinha, 2013). Interestingly, existing literature links the activation of the HPA axis with altered BDNF expression in the brain, suggesting that variants in stress-related genes may modulate peripheral BDNF and drug abuse risk (Suri and Vaidya, 2013).

The neuropeptide galanin (GAL) is broadly expressed in the Central Nervous System (CNS), including limbic areas that control emotionality and contribute to drug addiction and reward (Belfer et al., 2006, Jackson et al., 2011, Unschuld et al., 2010, Hawes et al., 2008, Fagerberg et al., 2014). Human galanin (GAL), which is located on chromosome 11q13.3, is composed of six exons and 30 amino-acid neuropeptides (Levran et al., 2008, Belfer et al., 2006, Unschuld et al., 2010, Juhasz et al., 2014, Holmes and Picciotto, 2006).

It has been demonstrated that GAL regulates numerous activities such as feeding, intestinal secretion, learning, memory, mood behavior, neurological diseases, and nociception, as well as the neuroendocrine control of systems like the hypothalamic–pituitary–adrenal axis (Butzkueven and Gundlach, 2010, Ogren et al., 2010, Shen and Gundlach, 2010, Autio et al., 2020, Zhu et al., 2022).

G protein-coupled receptors (GPCRs) bind to Galanin and modify the intracellular signaling pathways that participate in the addiction (Hawes et al., 2008, Gold et al., 2012). Galanin applies its action via three G-protein-coupled receptors: Galanin receptor 1 (GALR1), Galanin receptor 2 (GALR2), and Galanin receptor 3 (GALR3) that are expressed in the brain areas that are involved in drug dependence, including the locus coeruleus (LC), nucleus accumbens (NAc), and ventral midbrain (VMB) (Jackson et al., 2011, Unschuld et al., 2010, Juhasz et al., 2014, Šípková et al., 2017).

Galanin modifies the mesolimbic dopamine system through dopaminergic transmission inhibition that is necessary for the rewarding properties of drug abuse (McGregor and Bowen, 2012, Jackson et al., 2011, King et al., 2020). Galanin has been implicated in alcohol addiction (Genders et al., 2020), and in population susceptibility to alcoholism (Belfer et al., 2006).

The central nervous system contains a variety of neuromodulators that can alter processes involved in arginine vasopressin (AVP) and oxytocin (OXT) synthesis and release (Ciosek and Drobnik, 2013). Two members of the galanin neuropeptide family: “parental” galanin (GAL) and its cousin galanin-like peptide (GALP), are potential modulators (Wodowska and Ciosek, 2014). Justyna Wodowska et al. found that Galanin (GAL) and galanin-like peptide GALP modulate the release of AVP and OXT at every level of the hypothalamo-neurohypophysial system. Gal acts in the rat central nervous system as an inhibitory neuromodulator for AVP and OXT release via galanin receptors (Wodowska and Ciosek, 2014).

Oxytocin (OXT) is a small neuropeptide hormone that is produced in CNS and various peripheral tissues and organs, including the uterine epithelium, vascular endothelium, heart, and genital organs (Buisman-Pijlman et al., 2014, Love et al., 2018). Oxytocin is released due to different situations, such as close physical contact in a safe environment, conditioned fear, and novel environments (Buisman-Pijlman et al., 2014, N Al-Eitan et al., 2012, Al-Eitan et al., 2014, Al-Eitan et al., 2021). OXT inhibits stress-induced activity in the HPA axis and performs a vital role in response to stress (Neumann, 2008). OXT regulates many social behaviors, including anxiety reduction, inflammatory and immune responses, pain reduction, and information and memory processing (Buisman-Pijlman et al., 2014, Love et al., 2018, King et al., 2020).

OXT and its receptor (OXTR) are known to be associated mainly with stress reactivity and empathy and play an important role in numerous neuropsychiatric disorders, including alcohol and drug addiction (Buisman-Pijlman et al., 2014).

The OXTR, which is a member of the rhodopsin-type 1 G protein-coupled receptor (GPCR) (Buisman-Pijlman et al., 2014, Levran et al., 2008) is activated by the binding of OXT to its outer membrane domain, then phospholipase C, the G-protein alpha subunit, and protein kinase C are activated, finally leading to several downstream cellular responses that regulate the social and emotional behaviors and rewards (Buisman-Pijlman et al., 2014, King et al., 2020, Enck and Klosterhalfen, 2009). We are not aware of reports on the association between polymorphisms in stress-related genes, especially oxytocin and galanin, and drug addiction in the Jordanian population. Therefore, this study aimed to investigate the association, if any, between four stress-related genes (GAL and OXT and their receptors) polymorphisms and addiction in the Jordanian Arab population.

2 Materials and methods

2.1 Study subjects

This study has been approved by the Institutional Review Board (IRB) of Jordan University of Science and Technology (43/114/ 2018). This study was also approved by the Ministry of Health (MOH/ REC/180057), the Public Security Directorate (C/2/46/21546), and King Abdullah University Hospital (43/114/2018). This study consisted of one thousand Jordanian Arab participants; 500 Jordanian-addicted males were recruited from the National Centre for Rehabilitation of Addicts (NCRA) of the Ministry of Health and the Drug Rehabilitation Centre of the Jordanian Public Security Directorate (DRC-PSD), and 500 healthy controls were selected based on the criteria of being healthy individuals with no history of substance use or psychiatric disorders. The average age (Mean ± SD) was 28.4 ± 6.88 and 30.1 ± 6.81 for patients and controls, respectively. Demographic data of the participants are shown in Table 1.Table 1 Demographic data of the participants.

	Cases n (%)	Controls n (%)	
Employment			
 Yes	341 (70.3 %)	397 (79.4 %)	
 No	144 (29.7)	103 (20.6 %)	
Marital status			
 Single	346 (71 %)	205 (41 %)	
 Divorced	14 (2.9 %)	2 (0.4 %)	
 Widowed	0 (0 %)	1 (0.2 %)	
 Married	127 (26.1)	292 (58 %)	
Smoking			
 Yes	426 (87.8 %)	331 (66.2 %)	
 No	59 (12.2 %)	169 (33.8 %)	
Age (Mean ± SD)	28.4 ± 6.88	30.1 ± 6.81	
Age at the first use (Mean ± SD)	23.9 ± 6.56	−	
Cases with family history of addiction	29 (5.8 %)	−	

The addicted individuals were selected depending on the substance use criteria according to the Manual of Mental Disorders (DSM-IV) criteria (APA, 2013) (Online, 2013). Inclusion criteria were that participants had to be residents of Jordan, be 18 years of age or older, provide written informed consent, and meet DSM-IV criteria for a substance abuse problem (Online, 2013). Exclusion criteria were the presence of severe psychiatric disorders (unless a substance use disorder was the underlying cause), neurological diseases (e.g., epilepsy, multiple sclerosis, Parkinson's disease), pregnancy, dementia, severe somatic conditions like cardiovascular disorders, and current participation in another clinical study.

In Jordan, about 134,947 adult males exhibit substance use disorder, constituting a prevalence rate of 2.5 % among the total population of 9,531,712 (https://www.who.int/publications/m/item/jordan---who-special-initiative-for-mental-health). The sample size required was computed using the OpenEpi program, version 3.01, with a 95 % confidence interval. Given a substance use disorder prevalence of 2.5 % in Jordan, a precision of 3 %, and a design effect of 1, the estimated sample size required was 105 participants. The case sample in our study comprised 500 individuals, which exceeded the specified sample size.

2.2 DNA extraction, genotyping, and SNP selection

We used the Sequenom MassARRAY iPLEX Gold system to customize the SNP genotyping of the selected SNPs. Genomic DNA was extracted from the whole blood of each participant using the standard kit procedure (the Gentra ® Puregene ® Blood Kit, Qiagen, Germany). The extracted DNA's quantity and quality were measured using the Nano-Drop ND-1000 (Bio Drop, UK) and gel electrophoresis. The DNA samples were sequenced in the Australian Genome Research Facility (AGRF; Melbourne Node, Melbourne, Australia) using the Sequenom MassARRAY® system (iPLEX GOLD) (Sequenom, San Diego, CA, USA) in this study. Eighteen polymorphisms were investigated in this study: rs2740210, rs3761248, rs4813625, rs877172, rs237887, rs237902, rs4686301, rs7632287, rs2513304, rs3136544, rs1546309, rs3136541, rs694066, rs2717162, rs5374, rs5376, rs1942578 and rs9807208, as shown in Table 2.Table 2 The characteristics of the studied polymorphisms within the candidate genes.

Gene	Rs #	Chr. Position	SNP	functional consequence	
GALR1	rs9807208	18:77262299	G>A	intron variant	
GALR1	rs2717162	18:77256371	T>C	intron variant	
GALR1	rs5374	18:77250689	T>C	coding sequence variant	
GALR1	rs5376	18:77268853	G>A	missense variant	
GALR1	rs1942578	18:77481057	T>C	NA	
GAL	rs1546309	11:68687214	C>T	intron variant	
GAL	rs3136541	11:68690475	C>T	upstream transcript variant	
GAL	rs694066	11:68685517	G>A	intron variant	
GAL	rs3136544	11:68692195	A>G	NA	
GAL	rs2513304	11:68693677	T>G	NA	
OXT	rs2740210	20:3072609	C>A	downstream transcript variant	
OXT	rs3761248	20:3069747	T>C	intron variant	
OXT	rs4813625	20:3069074	G>C	intron variant	
OXT	rs877172	20:3069244	T>G	intron variant	
OXTR	rs237887	3:8755356	G>A	intron variant	
OXTR	rs237902	3:8767498	G>A	NA	
OXTR	rs4686301	3:8756900	C>T	intron variant	
OXTR	rs7632287	3:8749759	G>A	downstream transcript variant	
NA: Not Applicable.

2.3 Statistical and haplotype analyses

Genetic associations and haplotype analysis analyses were performed in this study. In addition, the genotypic and allelic distribution and the minor allele frequencies for both groups, as well as the Hardy-Weinberg Equilibrium (HWE) equation, were conducted using SNPStats web (HTTPs: //www. snpstats.net/start.htm) (2006 Institute Català d'Oncologia). P<0.05 was considered significant. The P-value of HWE is > 0.05 to be considered normally distributed. The Chi-square test and Bonferroni multivariate analysis were performed using the Statistical Package for the Social Sciences (SPSS), version 25.0 (SPSS, Inc., Chicago, IL).

3 Results

This study was conducted with five hundred participants from each group (the healthy controls and SUD patients). The genetic association between the studied polymorphisms and drug addiction revealed that three SNPs in the GAL gene (rs3136541, rs694066, and rs3136544) were significantly associated with drug addiction with P-values (0.0039, 0.0042, and 0.015), respectively (Table 3). Furthermore, none of the other fourteen SNPs were in correlation with drug addiction among Jordanians (P>0.05) (Supplementary Table 1).Table 3 The genotype association and genetic model analysis of the significant associated SNPs and SUD.

	Polymorphism	%	P value	Model	Genotype	OR (95 % CI)	P value	
Gene	Ref. SNP # / Group (n)											
	rs2513304	G/G	G/T	T/T	G	T		Codominant	G/G, G/T, T/T	0.76 (0.58–1.00)
0.69 (0.48–1.01)	0.062	
	controls	46.0	40.0	14.0	66.0	34.0	0.06	Dominant	G/G, G/T+T/T	0.74 (0.58–0.96)	0.021	
	patients	38.0	45.0	17.0	61.0	39.0		Recessive	G/G+G/T, T/T	0.80 (0.56–1.13)	0.2	
								Overdominant	G/G+T/T, G/T	0.84 (0.65–1.08)	0.17	
	rs3136544	A/A	A/G	G/G	A	G	0.015	Codominant	A/A, A/G, G/G	1.13 (0.84–1.53)
1.62 (1.15–2.29)	0.015	
	controls	26.0	45.0	29.0	49.0	51.0		Dominant	A/A, A/G+G/G	1.29 (0.98–1.70)	0.073	
	patients	31.0	47.0	21.0	55.0	45.0		Recessive	A/A+A/G, G/G	0.64 (1.13–2.01)	0.005	
								Overdominant	A/A+G/G, A/G	0.90 (0.70–1.16)	0.43	
	rs3136541	C/C	C/T	T/T	C	T	0.0039	Codominant	T/T, C/T, C/C	1.13 (0.84–1.52)
1.74 (1.23–2.45)	0.003	
GAL	controls	27.0	43.0	30.0	49.0	51.0		Dominant	T/T, C/T+C/C	1.32 (1.00–1.73)	0.05	
	patients	33.0	47.0	21.0	56.0	44.0		Recessive	T/T+C/T, C/C	1.62 (1.21–2.17)	0.002	
								Overdominant	T/T+C/C, C/T	0.88 (0.68–1.13)	0.30	
	rs694066	A/A	A/G	G/G	A	G	0.0042	Codominant	G/G, G/A, A/A	0.62 (0.46–0.84)
0.61 (0.29–1.31)	0.004	
	controls	2.0	20.0	77.0	13.0	87.0		Dominant	G/G, G/A+A/A	0.62 (0.47–0.83)	0.001	
	patients	3.0	29.0	68.0	18.0	82.0		Recessive	G/G+G/A, A/A	0.69 (0.33–1.47)	0.33	
								Overdominant	G/G+A/A, G/A	0.64 (0.47–0.85)	0.002	
	rs1546309	C/C	C/T	T/T	C	T	0.125	Codominant	T/T, C/T, C/C	0.83 (0.63–1.08)
0.68 (0.46–1.02)	0.12	
	controls	11.0	42.0	48.0	32.0	68.0		Dominant	T/T, C/T+C/C	0.79 (0.62–1.02)	0.068	
	patients	14.0	44.0	42.0	36.0	64.0		Recessive	T/T+C/T, C/C	0.75 (0.51–1.10)	0.14	
								Overdominant	T/T+C/C, C/T	0.90 (0.70–1.15)	0.39	


	
	rs2717162	C/C	C/T	T/T	C	T	0.644	Codominant	T/T, C/T, C/C	0.93 (0.71–1.21)
0.82 (0.54–1.26)	0.64	
	controls	10.0	44.0	46.0	32.0	68.0		Dominant	T/T, C/T+C/C	0.91 (0.71–1.17)	0.45	
	patients	12.0	45.0	43.0	34.0	66.0		Recessive	T/T+C/T, C/C	0.86 (0.57–1.27)	0.44	
								Overdominant	T/T+C/C, C/T	0.97 (0.75–1.24)	0.79	
	rs5374	C/C	C/T	T/T	C	T	0.910	Codominant	C/C, C/T, T/T	0.97 (0.74–1.27)
0.92 (0.62–1.36)	0.91	
	controls	41.0	47.0	13.0	64.0	36.0		Dominant	C/C,C/T+T/T	0.96 (0.74–1.24)	0.75	
	patients	40.0	47.0	14	63.0	37.0		Recessive	C/C+C/T, T/T	0.93 (0.64–1.35)	0.71	
								Overdominant	C/C+T/T, C/T	0.99 (0.77–1.27)	0.95	
	rs5376	A/A	A/G	G/G	A	G	0.597	Codominant	G/G, G/A, A/A	1.06 (0.56–2.01)
NA (0.00-NA)	0.49	
GALR1	controls	96.0	4.0	0.0	98.0	2.0		Dominant	G/G, G/A+A/A	1.11 (0.59–2.09)	0.75	
	patients	96.0	4.0	0.0	98.0	2.0		Recessive	G/G+G/A, A/A	NA (0.00-NA)	0.24	
								Overdominant	G/G+A/A, G/A	1.05 (0.56–2.00)	0.87	
	rs9807208	A/A	A/G	G/G	A	G	0.219	Codominant	G/G, G/A, A/A	0.79 (0.60–1.03)
0.91 (0.61–1.37)	0.22	
	controls	46.0	42.0	13.0	67.0	33.0		Dominant	G/G, G/A+A/A	0.82 (0.63–1.05)	0.11	
	patients	41.0	47.0	12.0	64.0	36.0		Recessive	G/G+G/A, A/A	1.03 (0.71–1.51)	0.87	
								Overdominant	G/G+A/A, G/A	0.81 (0.63–1.04)	0.091	
	rs1942578	C/C	T/C	T/T	C	T	0.746	Codominant	T/T, T/C, C/C	1.11 (0.83–1.48)
1.11 (0.77–1.59)	0.75	
	controls	20.0	50.0	30.0	45.0	55.0		Dominant	T/T, T/C+C/C	1.11 (0.85–1.46)	0.44	
	patients	19.0	49.0	32.0	44.0	56.0		Recessive	T/T+T/C, C/C	1.04 (0.76–1.42)	0.81	
								Overdominant	T/T+C/C, T/C	1.07 (0.83–1.37)	0.61	


	
	rs2740210	A/A	A/C	C/C	A	C	0.213	Codominant	C/C, A/C, A/A	1.23 (0.94–1.60)
1.35 (0.83–2.18)	0.21	
	controls	9.0	38.0	53.0	28	72		Dominant	C/C, C/A+A/A	1.25 (0.97–1.61)	0.085	
	patients	7	35.0	58	24	76		Recessive	C/C+C/A, A/A	1.24 (0.78–1.99)	0.36	
								Overdominant	C/C+A/A, C/A	1.18 (0.91–1.53)	0.2	
	rs3761248	C/C	C/T	T/T	C	T	0.568	Codominant	T/T, C/T, C/C	0.87 (0.66–1.13)
0.92 (0.54–1.55)	0.57	
	controls	6.0	21.0	62.0	22.0	78.0		Dominant	T/T, C/T+C/C	0.87 (0.68–1.13)	0.3	
	patients	6.0	35.0	59.0	24.0	76.0		Recessive	T/T+C/T, C/C	0.97 (0.58–1.62)	0.89	
OXT								Overdominant	T/T+C/C, C/T	0.87 (0.67–1.14)	0.31	
	rs4813625	C/C	C/G	G/G	C	G	0.627	Codominant	C/C, C/G, G/G	0.92 (0.70–1.23)
1.08 (0.76–1.55)	0.63	
	controls	20.0	47.0	33.0	44.0	56.0		Dominant	C/C,C/G+G/G	0.97 (0.74–1.26)	0.8	
	patients	18.0	49.0	32.0	43.0	57.0		Recessive	C/C+C/G, G/G	1.14 (0.83–1.56)	0.43	
								Overdominant	C/C+G/G, C/G	0.90 (0.70–1.15)	0.39	
	rs877172	G/G	G/T	T/T	G	T	0.279	Codominant	T/T, G/T, G/G	0.79 (0.59–1.06)
0.83 (0.59–1.17)	0.28	
	controls	23.0	43.0	34.0	44.0	56.0		Dominant	T/T, C/T+C/C	0.81 (0.62–1.05)	0.12	
	patients	24.0	47.0	30.0	47.0	53.0		Recessive	T/T+C/T, C/C	0.95 (0.70–1.27)	0.72	
								Overdominant	T/T+C/C, C/T	0.86 (0.67–1.11)	0.24	


	
	rs237887	A/A	A/G	G/G	A	G	0.273	Codominant	G/G, G/A, A/A	1.06 (0.78–1.43)
0.82 (0.57–1.17)	0.27	
	controls	26.0	54.0	21.0	53.0	47.0		Dominant	G/G, G/A+A/A	0.98 (0.73–1.30)	0.87	
	patients	25.0	50.0	25.0	50.0	50.0		Recessive	G/G+G/A, A/A	0.79 (0.58–1.06)	0.12	
								Overdominant	G/G+A/A, G/A	1.16 (0.90–1.49)	0.24	
	rs237902	A/A	A/G	G/G	A	G	0.073	Codominant	G/G, G/A, A/A	1.19 (0.91–1.56)
1.58 (1.05–2.37)	0.072	
	controls	15.0	44.0	41.0	37.0	63.0		Dominant	G/G, G/A+A/A	1.27 (0.98–1.64)	0.066	
	patients	11.0	42.0	47.0	32.0	68.0		Recessive	G/G+G/A, A/A	1.45 (0.99–2.13)	0.056	
OXTR								Overdominant	G/G+A/A, G/A	1.08 (0.83–1.39)	0.057	
	rs4686301	C/C	C/T	T/T	C	T	0.085	Codominant	C/C, C/T, T/T	0.94 (0.72–1.22)
0.59 (0.37–0.94)	0.083	
	controls	50.0	43.0	7.0	72.0	28.0		Dominant	C/C,C/T+T/T	0.87 (0.68–1.11)	0.26	
	patients	47.0	43.0	11.0	68.0	32.0		Recessive	C/C+C/T, T/T	0.61 (0.38–0.96)	0.03	
								Overdominant	C/C+T/T, C/T	1.01 (0.79–1.30)	0.92	
	rs7632287	A/A	A/G	G/G	A	G	0.395	Codominant	G/G, G/A, A/A	0.97 (0.74–1.28)
1.43 (0.83–2.47)	0.39	
	controls	7.0	31.0	62.0	22.0	78.0		Dominant	G/G, G/A+A/A	1.03 (0.80–1.34)	0.81	
	patients	5.0	32.0	63.0	21.0	79.0		Recessive	G/G+G/A, A/A	1.44 (0.84–2.47)	0.18	
								Overdominant	G/G+A/A, G/A	0.94 (0.72–1.23)	0.67	
*P-value less than 0.05 is considered significant.

The genetic model analysis (see Table 3)revealed that the codominant model of the rs3136541 SNPs (OR = 0.57, P = 0.003) and the recessive model (OR = 1.62, P = 0.002) were significantly associated with drug addiction. Regarding the rs3136541 polymorphism, 30 % of controls had the (TT) genotype compared to 21 % in addicted patients. So, according to the genotype distribution between cases and controls, we conclude that the (TT) genotype and the (T) allele could be protective factors against drug addiction. For the SNP rs694066, the different models indicated as genetic risk factors for drug addiction include the dominant model (OR = 0.62, P = 0.0010), the codominant model (OR = 1.60, P = 0.0044), and the Overdominant model (OR = 0.64, P = 0.0024) for drug addiction. However, the (A/G) genotype in controls was lower (20.0 %) than it (29 %) in the addicted patients. Therefore, the (G/A) genotype could be considered a risk factor that increases the risk of addiction among Jordanians. As illustrated, the rs3136544 polymorphism was found to have a relationship with drug addiction in this study; the codominant model (OR = 1.13, P = 0.015) and the recessive model (OR = 0.64, P = 0.0056) were significantly associated with drug addiction.

The distribution of (G/G) genotype among controls was 29.0 % compared to 21.0 % in the people with an addiction. This significant difference between the controls and patients may pose the (G/G) genotype as a protective factor for drug addiction that may decrease the risk of the disorder’s development and progression. Moreover, the rs2513304 SNP showed an association with drug addiction regarding the dominant genetic model (OR = 0.74, P = 0.021), and the (G/G) genotype could be considered as a risk factor according to the results of the genotype frequency results as shown in Table 3. For the SNP rs4686301, the polymorphism was found to be associated with drug addiction under the recessive model (OR = 0.61, P = 0.03). The remaining studied polymorphisms showed no significant association, as shown in (Table 3).

The association analysis between the clinical data of the addicted individuals and the studied SNPs revealed that four SNPs were significant; rs2717162 was associated with the age at the use onset (P = 0.02), and rs3136541 was associated with the types of substances abused and multi substance use (P = 0.03 and P = 0.04, respectively). Additionally, rs5376 and rs694066 were associated with types of substances abused (P = 0.02 and P = 6e-9, respectively), as shown in Supplementary Table 1.

Supplementary Tables 2 and 5 show that the haplotype analysis of the GAL, GALR1, and OXTR gene SNPs revealed associations of specific SNP arrangements with drug addiction. It could increase the risk of susceptibility to addiction.

These arrangements were CCAAT of the GAL gene (OR = 0.63, P = 0.001) and GACT of the GALR1 gene (OR = 0.32, P = 0.045). The last significant haplotypes were (AACG) and (AACA) of the OXYR gene (OR = 1.59, P = 0.014, and OR = 3.67, P = 0.011, respectively). The haplotype analysis for the remaining polymorphisms showed no significant association with drug addiction among Jordanians, as illustrated in Supplementary Tables 2, 3, 4, and 5.

The calculation of D′ values, a standardized measure of linkage disequilibrium (LD), and their corresponding P-values were conducted for all SNP pairs within our sample. Supplementary Tables 6, 7, 8, and 9 show that most SNPs exhibited strong and statistically significant linkage disequilibrium.

In our efforts to uncover the correlation between haplotypes and clinical and demographic conditions, we observed a correlation between the specific haplotypes and these conditions, as shown in Supplementary Table 10.

4 Discussion

Drug Addiction is a complex chronic disorder that is caused by a combination of genetic, environmental, and drug-induced factors (Buisman-Pijlman et al., 2014, Randesi et al., 2020). Stress is a severe risk factor affecting both the development and the relapse of addiction behaviour and disorders (Levran et al., 2014a, Randesi et al., 2020). Therefore, it is essential to determine the genetic vulnerability to drug addiction by investigating genetic variants of genes biologically involved in drug metabolism and transportation (Hawes et al., 2008, Baracz et al., 2020, Al-Eitan et al., 2020). The neuropeptide galanin is a well-defined stress-related gene that has been reported as a protective factor against the progression of drug abuse (Al-Eitan et al., 2012b, Hawes et al., 2008). This study aimed to identify the association between stress-related genes variants and drug addiction in Jordan. We analyzed 18 variants in four stress-related genes in 500 Jordanians addressed as addicted males in addition to 500 healthy controls; the GAL gene (rs2513304, rs3136544, rs1546309, rs3136541, and rs694066), the GALR1 gene (rs2717162, rs5374, rs5376, rs1942578, and rs9807208), the OXT gene (rs2740210, rs3761248, rs877172 and rs4813625), and the OXTR gene (rs877172, rs237887, rs237902, and rs4686301).

The genetic association between the studied polymorphisms of the GAL and GALR1 genes and drug addiction revealed that three SNPs of the GAL gene; rs3136541 P = 0.0039), rs694066 (P = 0.0042) and rs3136544 (P = 0.015) are highly associated. On the other hand, the remaining polymorphisms showed no significant association with drug addiction (P>0.05). The association analysis between the clinical data of the addicted individuals and the studied SNPs revealed an association with five SNPs; rs2717162 was associated with the age at substance use onset (P = 0.02). rs3136541 was associated with the types of substances abused and the number of used substances (P = 0.03 and P = 0.04, respectively). rs5376, rs694066, and rs3136544 were associated with the types of substances (P = 0.02, P = 6e-9, and P = 0.03, respectively) (Supplementary Table 1).

The GALR1 and GAL genes are highly expressed in the regions of the human brain that are critical for drug and cue-induced craving and implicated in emotional behavior (Hawes et al., 2008, Gold et al., 2012, ONLINE, P., 2013). Functional changes in the GALR1 gene could lead to a depletion in dopamine release, resulting in reduced drug reward (Lori et al., 2011, Richardson et al., 2014). Many studies indicated that variations in the GALR1 and GAL genes affect the probability of developing a tobacco addiction, the risk of relapse, and the efficacy of smoking cessation aids (Richardson et al., 2014).

Our study revealed that GAL SNP rs3136541 had a significant association with drug addiction, and the genotype (TT) /the (T) allele could be assigned as a protective factor against drug addiction among Jordanians. Our results are in accordance with studies in African–American and European populations (Randesi et al., 2020). A plausible correlation in this current study was detected between rs694066 and drug addiction (P = 0.0042), and our findings suggest that the GG genotype/ G allele may be considered a factor in increasing the risk of addiction. However, the variant rs694066 was implicated with weight loss from alcoholism in Finnish and Native American males (Levran et al., 2008, Belfer et al., 2006, Holmes and Picciotto, 2006). The substantial findings suggest that galanin may act as a protective or increased risk factor for drug addiction depending on the variant’s distribution within different populations (Jackson et al., 2011, Unschuld et al., 2010, Lori et al., 2011).

The association analysis in our study between the GALR1 polymorphisms studied and the substance use disorders (SUD). SUD was non-significant. In contrast to other studies, a nominal association between GALR1 SNPs rs5376 and drug addiction was estimated in the African population. At the same time, rs2717162 was claimed to be correlated with drug addiction in African–Americans (Levran et al., 2014b, Randesi et al., 2020). Different studies found that the (G) allele of rs948854 has a gender-specific association with anxiety severity (female carriers of the G-allele were more anxious) (Randesi et al., 2020). Certain studies showed an association between GALR1 SNP rs2717162, which is associated with craving for tobacco in European and African-American smokers (Levran et al., 2014b, Gold et al., 2012), and rs3136541 with alcoholism in European ancestry that could increase alcohol consumption (Levran et al., 2014b, Jackson et al., 2011, Richardson et al., 2014). Various studies have suggested that the GALR1 SNP rs9807208 reduces responses to the use of several drugs of abuse, such as cocaine, morphine, amphetamines, and opioids (Richardson et al., 2014, Al-Eitan et al., 2012a). Otherwise, several studies have reported no association between rs9807208 and drug addiction, which is consistent with this study in Jordan (Hawes et al., 2008, Richardson et al., 2014).

Numerous studies have revealed that OXT gene expression in the brain is altered due to exposure to alcohol and drugs like nicotine, cocaine, methamphetamine, and morphine (King et al., 2020). A variant in the OXT gene (rs4813625) was found in association with adolescent alcohol drinking, which is mediated by an interaction with the dopamine system and is sex dependent (Buisman-Pijlman et al., 2014). Furthermore, two polymorphisms (rs4564970 and rs1488467) of the OXTR gene appeared to moderate the aggressive behaviour of alcohol in adult men (Buisman-Pijlman et al., 2014, Baracz et al., 2020). In contrast to our study, which showed a non-significant association between the studied polymorphisms of oxytocin and oxytocin receptor genes and drug addiction in Jordanian males. As appeared from the haplotype analysis for the studied polymorphisms of the GAL and OXT genes and their receptors, there was no association with drug addiction except for four arrangements that could increase the risk of addiction in the Jordanian population; (CCAAT) of the GAL gene (OR = 0.63, P = 0.001), (GACT) of the GALR1 gene (OR = 0.32, P = 0.045), and (AACG) and (AACA) of the OXYR gene (OR = 1.59, P = 0.014, and OR = 3.67, P = 0.011, respectively).

The relationship between SNPs and addiction might be non-linear. Traditional correlation measures capture linear relationships and might miss complex, non-linear interactions (Moore and Williams, 2002). Interactions with other genetic, environmental, or lifestyle factors could influence the effect of SNPs on addiction. These interactions might mask a direct correlation but still result in significant associations when analyzed through more complex statistical models that account for these interactions (Moore and Williams, 2002, PHILLIPS, P. C. , 2008). In addition, significant associations might be present in specific population subgroups, such as individuals with certain genetic backgrounds or environmental exposures. These subgroup-specific effects could lead to significant overall associations without a clear correlation in the general population (Marchini et al., 2004, Thomas and Witte, 2002). Confounding factors might also play a role. There could be other variables that influence both the SNPs and addiction, creating an indirect pathway that leads to significant associations (Thomas and Witte, 2002, Smith and Ebrahim, 2003). Advanced statistical techniques can adjust for these confounders to reveal true associations. Addiction is a complex trait influenced by multiple genetic and environmental factors. The genetic architecture of addiction might involve many small-effect SNPs contributing to the risk in an intricate manner that is not captured by simple correlation measures (Manolio et al., 2009, Kreek et al., 2005).

5 Conclusion and future directions

Drug addiction is a multifactorial disorder worldwide. Stress-related genes such as GAL play a crucial role in the development and risk of addiction, thus improving diagnosis and treatment. Our study investigated the association between several SNPs in four stress genes (GAL, OXY, and their receptors). Our analysis revealed that several SNPs in stress-related genes, especially in the GAL gene, were strongly associated with drug addiction among Jordanians. This may contribute to an increased or decreased risk of developing the disorder. To the best of our knowledge, no studies have identified a direct link between the GAL gene and OXT in terms of stress and drug addiction. Additional research is needed to investigate this area. The present study had other limitations that should be acknowledged. First is the omission to assess psychiatric comorbidity in patients with substance use disorders (SUD). We were unable to collect data on dual diagnoses in addicted patients. Future studies should address this area to provide a more comprehensive understanding of SUD. Integrating genetic findings into the pharmacological treatment of SUD and dual diagnosis holds significant promise. Identifying genetic variations in stress-related genes associated with addiction may have clinical implications. It determines therapy targets and supports treatment alternatives proposed by animal studies. It also offers the potential to improve therapeutic interventions by identifying patients at specific risk for stress-related relapse and patients who may benefit from particular interventions depending on their genotype, particularly early in the addiction cycle(Al-Eitan and L. & A HADDAD, Y. , 2014, Al-Eitan and L. & H TARKHAN, A. , 2016, AL-EITAN, L. N. , 2020).

Genome-wide association study (GWAS) is a successful candidate gene study approach. However, a critical question is whether GWAS can be effectively used in diverse populations to understand the associations between genotype and substance use disorder more comprehensively. Therefore, further association and functional studies, a much larger sample size, including patients from different ethnic groups, and broader SNP coverage are needed to confirm these findings further and determine the role of GAL, OXY, and their receptors as well as the other genes variations indicated, as contributing factors to drug addictions.

Funding

This project was funded by the Deanship of Research at Jordan University of Science and Technology under grant number MPH/43/114/ 2018 .

CRediT authorship contribution statement

Laith AL-Eitan: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Hana Abu Kharmah: Writing – review & editing, Writing – original draft, Investigation, Formal analysis. Mansour Alghamdi: Writing – review & editing, Writing – original draft, Resources, Investigation, Formal analysis.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the Supplementary data to this article:Supplementary Data 1

Supplementary Data 2

Supplementary Data 3

Supplementary Data 4

Supplementary Data 5

Supplementary Data 6

Supplementary Data 7

Supplementary Data 8

Acknowledgments

The authors thank 10.13039/501100004035 Jordan University of Science and Technology , Jordan, for providing administrative and technical support.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jsps.2024.102171.

1 FKBP5: FK506-binding protein 51; GAL: Galanin; GALP:galanin-like peptide;GALR1:Galanin receptor 1;GALR2:Galanin receptor 2;GALR3:Galanin receptor 3; GPCRs: G protein-coupled receptors;GWAS: Genome-wide association study;HPA; hypothalamic–pituitary–adrenal; HWE: Hardy-Weinberg Equilibrium;IRP: Institutional Review Board; LC: locus coeruleus; MC2R:melanocortin 2 receptor;NAc: nucleus accumbens; NCRA: National Centre for Rehabilitation of Addicts;NPY1R:neuropeptide Y receptor Y1, PY2R; neuropeptide Y receptor Y2;R3C2: nuclear receptor subfamily 3, group C, member 2;OD: opioid dependence;OXT: Oxytocin; SNP: single nucleotide polymorphism; SNPs: single nucleotide polymorphism; SPSS: Statistical Package for the Social Sciences; SUD; substance use disorders;; SNPs: single nucleotide polymorphism; VMB: ventral midbrain;
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References

AL-Eitan L.N. Pharmacogenomic landscape of VIP genetic variants in Jordanian Arabs and comparison with worldwide populations Gene 737 2020 144408
Al-Eitan L.N. Haddad Y.A. Emergence of pharmacogenomics in academic medicine and public health in Jordan: History, present state and prospects Curr. Pharmacogenomics Personalized Medicine (formerly Current Pharmacogenomics) 12 2014 167 175
Al-Eitan L.N. Jaradat S.A. Hulse G.K. Tay G.K. Pharmacogenetic approach to treating drug dependence: serotonin transporter gene (SLC6A4) promoter polymorphisms as treatment predictors in Jordanian Arabs Curr. Pharmacogenomics Personalized Med. (formerly Current Pharmacogenomics) 10 2012 293 305
Al-Eitan L.N. Alshudaifat K.M. Anani J.Y. Association of the DRD4 exon III and 5-HTTLPR VNTR polymorphisms with substance abuse in Jordanian Arab population Gene 733 2020 144267
Al-Eitan L. Jaradat S. Dadour I. Tay G. Hulse G. Polymorphisms in the u-opioid receptor gene in Jordanian Arabs with opiate drug dependence ScienceMED 3 2012 91 97
Al-Eitan L.N. Jaradat S.A. Su S.Y. Tay G.K. Hulse G.K. Mu opioid receptor (OPRM1) as a predictor of treatment outcome in opiate-dependent individuals of Arab descent Pharmgenomics Pers. Med. 5 2012 99 111 23226066
Al-Eitan L.N. Jaradat S.A. Qin W. Wildenauer D.M. Wildenauer D.D. Hulse G.K. Tay G.K. Characterization of serotonin transporter gene (SLC6A4) polymorphisms and its association with drug dependence in a Jordanian Arab population Toxicol Ind Health 30 2014 598 610 23033208
Al-Eitan L.N. Rababa'H D.M. Alghamdi M.A. Genetic susceptibility of opioid receptor genes polymorphism to drug addiction: a candidate-gene association study BMC Psychiatry 21 2021 5 33402148
Al-Eitan L. Shatnawi M. Alghamdi M. Investigating CHRNA5, CHRNA3, and CHRNB4 variants in the genetic landscape of substance use disorder in Jordan BMC Psychiatry 24 2024 436 38862938
Al-Eitan L.N. Tarkhan H.A. Practical challenges and translational issues in pharmacogenomics and personalized medicine from 2010 onwards Curr. Pharmacogenomics Personalized Med. (formerly Current Pharmacogenomics) 14 2016 7 17
Al-Eitan L.N. Jaradat S.A. Hulse G.K. Tay G.K. Custom genotyping for substance addiction susceptibility genes in Jordanians of Arab descent BMC Res Notes 5 2012 497 22963930
Autio, J., Stenbäck, V., Gagnon, D.D., Leppäluoto, J, Herzig, K.H., 2020. (Neuro) Peptides, Physical Activity, and Cognition. J. Clin. Med, 9.
Baracz S.J. Everett N.A. Cornish J.L. The impact of early life stress on the central oxytocin system and susceptibility for drug addiction: applicability of oxytocin as a pharmacotherapy Neurosci. Biobehav. Rev. 110 2020 114 132 30172802
Belfer I. Hipp H. McKnight C. Evans C. Buzas B. Bollettino A. Albaugh B. Virkkunen M. Yuan Q. Max M.B. Goldman D. Enoch M.A. Association of galanin haplotypes with alcoholism and anxiety in two ethnically distinct populations Mol. Psychiatry 11 2006 301 311 16314872
Buisman-Pijlman F.T. Sumracki N.M. Gordon J.J. Hull P.R. Carter C.S. Tops M. Individual differences underlying susceptibility to addiction: role for the endogenous oxytocin system Pharmacol. Biochem. Behav. 119 2014 22 38 24056025
Butzkueven H. Gundlach A.L. Galanin in glia: expression and potential roles in the CNS Exp. Suppl. 102 2010 61 69 21299062
Ciosek J. Drobnik J. Galanin modulates oxytocin release from rat hypothalamo-neurohypophysial explant in vitro - the role of acute or prolonged osmotic stimulus Endokrynol. Pol. 64 2013 139 148 23653277
Corominas-Roso M. Roncero C. Eiroa-Orosa F.J. Gonzalvo B. Grau-Lopez L. Ribases M. Rodriguez-Cintas L. Sánchez-Mora C. Ramos-Quiroga J.A. Casas M. Brain-derived neurotrophic factor serum levels in cocaine-dependent patients during early abstinence Eur. Neuropsychopharmacol. 23 2013 1078 1084 23021567
Domschke K. Reif A. Behavioral genetics of affective and anxiety disorders Curr. Top. Behav. Neurosci. 12 2012 463 502 22307738
Ducci F. Goldman D. Genetic approaches to addiction: genes and alcohol Addiction 103 2008 1414 1428 18422824
Enck P. Klosterhalfen S. The story of O–is oxytocin the mediator of the placebo response? Neurogastroenterol. Motil. 21 2009 347 350 19309413
Fagerberg L. Hallström B.M. Oksvold P. Kampf C. Djureinovic D. Odeberg J. Habuka M. Tahmasebpoor S. Danielsson A. Edlund K. Asplund A. Sjöstedt E. Lundberg E. Szigyarto C.A. Skogs M. Takanen J.O. Berling H. Tegel H. Mulder J. Nilsson P. Schwenk J.M. Lindskog C. Danielsson F. Mardinoglu A. Sivertsson A. von Feilitzen K. Forsberg M. Zwahlen M. Olsson I. Navani S. Huss M. Nielsen J. Ponten F. Uhlén M. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics Mol. Cell Proteomics 13 2014 397 406 24309898
Genders S.G. Scheller K.J. Djouma E. Neuropeptide modulation of addiction: focus on galanin Neurosci. Biobehav. Rev. 110 2020 133 149 29949733
Gold A.B. Wileyto E.P. Lori A. Conti D. Cubells J.F. Lerman C. Pharmacogenetic association of the galanin receptor (GALR1) SNP rs2717162 with smoking cessation Neuropsychopharmacology 37 2012 1683 1688 22373943
Hawes J.J. Brunzell D.H. Narasimhaiah R. Langel U. Wynick D. Picciotto M.R. Galanin protects against behavioral and neurochemical correlates of opiate reward Neuropsychopharmacology 33 2008 1864 1873 17957220
Holmes A. Picciotto M.R. Galanin: a novel therapeutic target for depression, anxiety disorders and drug addiction? CNS Neurol. Disord. Drug Targets 5 2006 225 232 16611095
Jackson K.J. Chen X. Miles M.F. Harenza J. Damaj M.I. The neuropeptide galanin and variants in the GalR1 gene are associated with nicotine dependence Neuropsychopharmacology 36 2011 2339 2348 21796100
Juhasz G. Hullam G. Eszlari N. Gonda X. Antal P. Anderson I.M. Hökfelt T.G. Deakin J.F. Bagdy G. Brain galanin system genes interact with life stresses in depression-related phenotypes Proc. Natl. Acad. Sci. USA 111 2014 E1666 E1673 24706871
Kim Y.S. Yang M. Mat W.K. Tsang S.Y. Su Z. Jiang X. Ng S.K. Liu S. Hu T. Pun F. Liao Y. Tang J. Chen X. Hao W. Xue H. GABRB2 haplotype association with heroin dependence in Chinese population PLoS One 10 2015 e0142049 26561861
King C.E. Gano A. Becker H.C. The role of oxytocin in alcohol and drug abuse Brain Res. 1736 2020 146761
Kreek M.J. Nielsen D.A. Butelman E.R. Laforge K.S. Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction Nat. Neurosci. 8 2005 1450 1457 16251987
Levran O. Londono D. O'Hara K. Nielsen D.A. Peles E. Rotrosen J. Casadonte P. Linzy S. Randesi M. Ott J. Adelson M. Kreek M.J. Genetic susceptibility to heroin addiction: a candidate gene association study Genes Brain Behav. 7 2008 720 729 18518925
Levran O. Londono D. O'Hara K. Randesi M. Rotrosen J. Casadonte P. Linzy S. Ott J. Adelson M. Kreek M.J. Heroin addiction in African Americans: a hypothesis-driven association study Genes Brain Behav. 8 2009 531 540 19500151
Levran O. Peles E. Randesi M. Li Y. Rotrosen J. Ott J. Adelson M. Kreek M.J. Stress-related genes and heroin addiction: a role for a functional FKBP5 haplotype Psychoneuroendocrinology 45 2014 67 76 24845178
Levran O. Randesi M. Li Y. Rotrosen J. Ott J. Adelson M. Kreek M.J. Drug addiction and stress-response genetic variability: association study in African Americans Ann. Hum. Genet. 78 2014 290 298 24766650
Lori A. Tang Y. O'Malley S. Picciotto M.R. Wu R. Conneely K.N. Cubells J.F. The galanin receptor 1 gene associates with tobacco craving in smokers seeking cessation treatment Neuropsychopharmacology 36 2011 1412 1420 21430647
Love T.M. Cranford J.A. Burmeister M. Wojnar M. Zucker R.A. Brower Kirk J. Oxytocin genotype moderates the impact of social support on psychiatric distress in alcohol-dependent patients Alcohol Alcohol 53 2018 57 63 29040351
Maher B.S. Vladimirov V.I. Latendresse S.J. Thiselton D.L. McNamee R. Kang M. Bigdeli T.B. Chen X. Riley B.P. Hettema J.M. Chilcoat H. Heidbreder C. Muglia P. Murrelle E.L. Dick D.M. Aliev F. Agrawal A. Edenberg H.J. Kramer J. Nurnberger J. Tischfield J.A. Devlin B. Ferrell R.E. Kirillova G.P. Tarter R.E. Kendler K.S. Vanyukov M.M. The AVPR1A gene and substance use disorders: association, replication, and functional evidence Biol Psychiatry 70 2011 519 527 21514569
Manolio T.A. Collins F.S. Cox N.J. Goldstein D.B. Hindorff L.A. Hunter D.J. McCarthy M.I. Ramos E.M. Cardon L.R. Chakravarti A. Cho J.H. Guttmacher A.E. Kong A. Kruglyak L. Mardis E. Rotimi C.N. Slatkin M. Valle D. Whittemore A.S. Boehnke M. Clark A.G. Eichler E.E. Gibson G. Haines J.L. Mackay T.F. McCarroll S.A. Visscher P.M. Finding the missing heritability of complex diseases Nature 461 2009 747 753 19812666
Marchini J. Cardon L.R. Phillips M.S. Donnelly P. The effects of human population structure on large genetic association studies Nat Genet 36 2004 512 517 15052271
McGregor I.S. Bowen M.T. Breaking the loop: oxytocin as a potential treatment for drug addiction Horm Behav 61 2012 331 339 22198308
Moore J.H. Williams S.M. New strategies for identifying gene-gene interactions in hypertension Ann Med 34 2002 88 95 12108579
Neumann I.D. Brain oxytocin: a key regulator of emotional and social behaviours in both females and males J. Neuroendocrinol. 20 2008 858 865 18601710
Ogren S.O. Kuteeva E. Elvander-Tottie E. Hökfelt T. Neuropeptides in learning and memory processes with focus on galanin Eur. J. Pharmacol. 626 2010 9 17 19837050
ONLINE, P., 2013. Diagnostic And Statistical Manual Of Mental Disorders, Fifth Edition [Online]. Psychiatry OnlineAvailable: https://dsm.psychiatryonline.org/doi/book/10.1176/appi.books.9780890425596 [Accessed].
Phillips P.C. Epistasis–the essential role of gene interactions in the structure and evolution of genetic systems Nat. Rev. Genet 9 2008 855 867 18852697
Proudnikov D. Hamon S. Ott J. Kreek M.J. Association of polymorphisms in the melanocortin receptor type 2 (MC2R, ACTH receptor) gene with heroin addiction Neurosci. Lett. 435 2008 234 239 18359160
Randesi M. Levran O. van den Brink W. Blanken P. van Ree J.M. Ott J. Kreek M.J. Further evidence for the association of GAL, GALR1 and NPY1R variants with opioid dependence Pharmacogenomics 21 2020 903 917 32757697
Richardson T.G. Minica C. Heron J. Tavare J. Mackenzie A. Day I. Lewis G. Hickman M. Vink J.M. Gelernter J. Kranzler H.R. Farrer L.A. Munafò M. Wynick D. Evaluating the role of a galanin enhancer genotype on a range of metabolic, depressive and addictive phenotypes Am. J. Med. Genet. B Neuropsychiatr. Genet 165 2014 654 664
Shen P.J. Gundlach A.L. Galanin systems and ischemia: peptide and receptor plasticity in neurons and oligodendroglial precursors Exp Suppl 102 2010 209 221 21299071
Sinha, R., 2013. Chapter 23 - Stress and Addiction, Elsevier.
Šípková J. Kramáriková I. Hynie S. Klenerová V. The galanin and galanin receptor subtypes, its regulatory role in the biological and pathological functions Physiol. Res. 66 2017 729 740 28730831
Smith G.D. Ebrahim S. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32 2003 1 22 12689998
Suri D. Vaidya V.A. Glucocorticoid regulation of brain-derived neurotrophic factor: relevance to hippocampal structural and functional plasticity Neuroscience 239 2013 196 213 22967840
Thomas D.C. Witte J.S. Point: population stratification: a problem for case-control studies of candidate-gene associations? Cancer Epidemiol. Biomarkers Prev. 11 2002 505 512 12050090
Torres-Berrio A. Cuesta S. Lopez-Guzman S. Nava-Mesa M.O. Interaction between stress and addiction: contributions from Latin-American Neuroscience Front Psychol 9 2018 2639 30622500
Unschuld P.G. Ising M. Roeske D. Erhardt A. Specht M. Kloiber S. Uhr M. Müller-Myhsok B. Holsboer F. Binder E.B. Gender-specific association of galanin polymorphisms with HPA-axis dysregulation, symptom severity, and antidepressant treatment response Neuropsychopharmacology 35 2010 1583 1592 20237460
von Diemen L. Brolese G. Possa M. Schuch S.B. Sordi A.O. Cocaine and brain-derived neurotrophic factor. Neuropathology of Drug Addictions and substance misuse 2016 Elsevier
Wetherill L. Schuckit M.A. Hesselbrock V. Xuei X. Liang T. Dick D.M. Kramer J. Nurnberger Jr. J.I. Tischfield J.A. Porjesz B. Edenberg H.J. Foroud T. Neuropeptide Y receptor genes are associated with alcohol dependence, alcohol withdrawal phenotypes, and cocaine dependence Alcohol Clin. Exp. Res. 32 2008 2031 2040 18828811
Wodowska J. Ciosek J. Galanin and galanin-like peptide modulate vasopressin and oxytocin release in vitro: the role of galanin receptors Neuropeptides 48 2014 387 397 25464889
Zhu S. Hu X. Bennett S. Charlesworth O. Qin S. Mai Y. Dou H. Xu J. Galanin family peptides: Molecular structure, expression and roles in the neuroendocrine axis and in the spinal cord Front. Endocrinol. (Lausanne) 13 2022 1019943 36561569
