Next Article in Journal
Methods for Stratification and Validation Cohorts: A Scoping Review
Next Article in Special Issue
Brain Mapping the Effects of Chronic Aerobic Exercise in the Rat Brain Using FDG PET
Previous Article in Journal
Acute Left Atrial Response to Different Eccentric Resistance Exercise Loads in Patients with Heart Failure with Middle Range Ejection Fraction: A Pilot Study
Previous Article in Special Issue
Reward Deficiency Syndrome (RDS) Surprisingly Is Evolutionary and Found Everywhere: Is It “Blowin’ in the Wind”?
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

FOXN3 and GDNF Polymorphisms as Common Genetic Factors of Substance Use and Addictive Behaviors

Department of Molecular Biology, Institute of Biochemistry and Molecular Biology, Semmelweis University, 1094 Budapest, Hungary
Institute of Psychology, ELTE Eötvös Loránd University, 1075 Budapest, Hungary
Doctoral School of Psychology, ELTE Eötvös Loránd University, 1075 Budapest, Hungary
Nyírő Gyula National Institute of Psychiatry and Addictions, 1135 Budapest, Hungary
International Gaming Research Unit, Psychology Department, Nottingham Trent University, Nottingham NG1 4FQ, UK
Departments of Psychiatry, Child Study and Neuroscience, Yale University School of Medicine, New Haven, CT 06511, USA
Connecticut Council on Problem Gambling, Wethersfield, CT 06109, USA
Connecticut Mental Health Center, New Haven, CT 06519, USA
Department of Psychiatry, Ichan School of Medicine at Mount Sinai, New York, NY 10029, USA
Division of Addiction Research & Education, Center for Psychiatry, Medicine, & Primary Care (Office of the Provost), Western University Health Sciences, Pomona, CA 91766, USA
Centre of Excellence in Responsible Gaming, University of Gibraltar, Gibraltar GX11 1AA, Gibraltar
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
These authors contributed equally to this work.
J. Pers. Med. 2022, 12(5), 690;
Submission received: 1 April 2022 / Revised: 20 April 2022 / Accepted: 21 April 2022 / Published: 26 April 2022


Epidemiological and phenomenological studies suggest shared underpinnings between multiple addictive behaviors. The present genetic association study was conducted as part of the Psychological and Genetic Factors of Addictions study (n = 3003) and aimed to investigate genetic overlaps between different substance use, addictive, and other compulsive behaviors. Association analyses targeted 32 single-nucleotide polymorphisms, potentially addictive substances (alcohol, tobacco, cannabis, and other drugs), and potentially addictive or compulsive behaviors (internet use, gaming, social networking site use, gambling, exercise, hair-pulling, and eating). Analyses revealed 29 nominally significant associations, from which, nine survived an FDRbl correction. Four associations were observed between FOXN3 rs759364 and potentially addictive behaviors: rs759364 showed an association with the frequency of alcohol consumption and mean scores of scales assessing internet addiction, gaming disorder, and exercise addiction. Significant associations were found between GDNF rs1549250, rs2973033, CNR1 rs806380, DRD2/ANKK1 rs1800497 variants, and the “lifetime other drugs” variable. These suggested that genetic factors may contribute similarly to specific substance use and addictive behaviors. Specifically, FOXN3 rs759364 and GDNF rs1549250 and rs2973033 may constitute genetic risk factors for multiple addictive behaviors. Due to limitations (e.g., convenience sampling, lack of structured scales for substance use), further studies are needed. Functional correlates and mechanisms underlying these relationships should also be investigated.

1. Introduction

Psychological and biological overlaps between different types of addictions have long been noted. At the phenomenological and diagnostic levels, the American Psychiatric Association (APA) defines substance use disorders as complex conditions “in which there is uncontrolled use of a substance despite harmful consequence.” The APA also states that individuals can develop addictions not only to substances but to different behaviors, such as gambling. The classification and the diagnostic criteria of addictive behaviors in the fifth edition of the Diagnostic and Statistical Manual for Mental Disorders ([1] and the eleventh revision of the International Classification of Diseases [2] also reflect phenomenological similarities of addictive behaviors. The DSM-5 includes the updated category of “Substance-related and Addictive Disorders” replacing the former “Substance-related Disorders” category from the DSM-IV-TR. Although only gambling disorder is officially included in the DSM-5 (under the Non-Substance-Related Disorders category of Substance-Related and Addictive Disorders; internet gaming disorder was included in Section 3 as a tentative disorder requiring more research before official inclusion), the new “Substance-related and Addictive Disorders” terminology in the DSM-5 reflects a formal acknowledgment of behavioral addictions that is based on empirical data [3]. Furthermore, in ICD-11, gambling and gaming disorders are both included and classified as disorders due to addictive behaviors [4,5,6].
Co-occurrences and comorbidities are common, as many individuals with addiction-related diagnoses have more than one psychiatric diagnosis [7]. In a large US-population-based study, 96% of individuals with gambling disorder experienced one or more co-occurring psychiatric disorders, with DSM-IV nicotine dependence, substance abuse, and substance dependence found in over 60%, 40%, and 30%, respectively ([8,9]. The co-occurrence of substance and behavioral addictions is well documented in other studies [10,11,12,13,14,15,16,17,18].
As for the neurobiological systems involved in addiction, the basal ganglia, the ventral tegmental area, the striatum [19], and the prefrontal prelimbic cortex have been implicated in previous studies [20,21]. Furthermore, these neurocircuits interact with other circuits, such as motivational, stress, and mood regulation circuits, involving the hypothalamus, amygdala, and habenula [22]. The main neurotransmitter is dopamine, showing a large and fast increase related to feelings of being “high” in, for example, stimulant use disorders [23,24]. Furthermore, opioid peptides, serotonin, acetylcholine, GABA (gamma aminobutyric acid), glutamate, endocannabinoid systems, orexin, and corticotropin-releasing factor have also been implicated [20,21].
These systems were the focus of multiple studies on substance abuse, non-substance addictions, and various risk tendencies, such as novelty seeking, impulsivity, or aggressive behaviors; see reviews from our group [25,26,27] and others [28,29,30]. For example, common molecular pathways of substance and behavioral addictions were suggested between cocaine and alcohol addictions and compulsive running [31,32]. Overall, neurobiological research suggested that different substances and behaviors may impact both similar and distinct neurobiological pathways [26,27,33,34].
From a genetic perspective, family, twin, and adoption studies estimated the heritability (i.e., the overall genetic contribution) of addictions to be 30–70% [35,36,37,38]. Studies also suggested genetic overlaps between behavioral (e.g., gambling) and substance (e.g., alcohol, tobacco, cannabis, and stimulant) addictions [39,40,41,42]. Furthermore, the genetic and environmental overlap between different types of substance and behavioral addictions has long been suggested [43,44]. Although most genetic studies tend to separately address substance abuse (usually focusing on a single substance) and behavioral addictions, there are some studies available that examined multiple types of addictions. Twin (and other) studies suggested that most shared genetic and environmental factors may not be substance-specific [45,46,47,48,49], although genome-wide association studies often implicate genes involved in substance metabolism and subjective responses to specific drugs [50,51]. Identified genetic variants in addiction-related genes (e.g., aldehyde dehydrogenases [ALDHs], Gamma-Aminobutyric Acid Type A Receptor Alpha2 Subunit [GABRA2], and DRD2/Ankyrin repeat and kinase domain containing 1 [ANKK1]) were linked to dependence on various substances [52,53,54,55]. Gene network analysis showed immune signaling and extracellular signal-regulated protein kinases 1 and 2 (ERK1/2) as novel genetic markers for multiple addiction phenotypes including alcohol, tobacco, and opioid use disorders [56]. Moreover, shared genetic contributions to gambling and substance use disorders were described [39,40,41,57,58].
Twin research has observed a genetic overlap between substance use and gambling frequency [59]. A genome-wide association study (GWAS) provided a linked polygenic risk score for alcoholism and gambling disorder [60], although no findings from this and another GWAS identified any region that reached genome-wide significance for gambling disorder [61]. Some common allelic variants were also implicated in personality traits, such as risk-taking, which were linked to alcohol and drug use [62]. As such, some research findings suggested that specific genetic markers may increase the likelihood of addictions generally. Pharmacological studies have shown treatment non-specificity, suggesting similar underlying neurobiological pathways across addictions [63,64,65,66]. The theory of addiction as a substance-independent disease is further supported by data that individuals recovering from a given substance may switch one substance or behavior for another (e.g., from opiates to cocaine, alcohol, gambling) before successfully recovering from addiction [67,68,69,70].
In summary, co-occurrences of different types of addictive behaviors, twin studies, similarities in phenomenological and behavioral characteristics, and empirical studies of psychological and molecular mechanisms suggest commonalities across substance and behavioral addictions. The aim of the present study was to investigate possible genetic overlaps between different types of substance-related, addictive, and compulsive behaviors utilizing the large sample of Psychological and Genetic Factors of Addictions (PGA) study [71]. The uniqueness of this study is the genetic analysis of both substance and behavioral addictions within the same cohort. The sample was assessed for a wide range of potentially addictive substances (nicotine, alcohol, cannabis, and other drugs) and potentially addictive behaviors (internet use, gaming, social networking site use, gambling, exercising, hair-pulling, and eating). The present genetic association analysis was conducted by focusing on 32 single-nucleotide polymorphisms (SNPs) in candidate genes that were formerly implicated in studies of various addictions.

2. Materials and Methods

2.1. Data Collection and Analyzed Phenotypes

The present genetic association analysis was conducted as part of the PGA Study [71]. Data were collected between 2011 and 2015 at Hungarian high schools, colleges, and universities. In total, 3003 young adults participated in the study (mean age was 21 years; SD ±2.8 years). All participants signed written informed consent, provided buccal samples, and were administered self-report questionnaires. Participation was anonymous, and questionnaire data and DNA information were paired using a unique identification number for each participant. The study protocol was designed in accordance with the guidelines of the Declaration of Helsinki and was approved by the Scientific and Research Ethics Committee of the Medical Research Council (ETT TUKEB).
Substance consumption/use behaviors were assessed using questionnaires. Since the aim of the present genetic association analysis was to explore the genetic background of addictions, we included “never” users and regular users of specific substances. Smoking (tobacco) use was assessed with the question, “Do you smoke regularly or occasionally?”, with possible responses as “never”, “occasionally”, and “regularly”. Genetic association analysis was conducted on the “never” and “regular” cigarette-smoking groups (excluding individuals who occasionally smoked from analyses). Alcohol consumption was assessed with the question, “How many times did you drink 6 or more alcoholic drinks in the past 30 days?”, with possible answers being “never”, “1–3 times”, “4–9 times”, “10–19 times”, “not every day, but 20 or more times”, and “every day”. In order to define the regular alcohol user group, participants drinking 4–9 or more times in the past 30 days were categorized as alcohol-using. Cannabis consumption was assessed with the question, “How often did you consume marijuana in the past 30 days?”, with possible responses of “I did not use marijuana in the past 30 days”, “1–3 times”, “4–9 times”, “10–19 times”, “not every day, but 20 or more times”, and “every day”. In order to define the regular-marijuana-consuming group, participants consuming marijuana 4–9 or more times in the past 30 days were categorized as cannabis-using. With regard to other drugs (synthetic marijuana, amphetamine, cocaine, heroin, lysergic acid diethylamide [LSD], magic mushrooms, gamma hydroxybutyrate [GHB], mephedrone, steroids, alcohol with drugs, sedatives, other drugs), positive responses were rare; for detailed prevalence data see [14]. Therefore, a “lifetime other drugs” variable was created comprising participants who had ever used any of these drugs versus those who had never used any of them. In this classification of the “lifetime other drugs”, missing answers were excluded. However, since there was a high rate of missing data regarding synthetic marijuana (the number of missing answers was 1280), those who did not answer this question but answered the questions regarding the use of all other substances were also included in the appropriate never or ever other drug use groups.
With regard to behavioral addictions, they were assessed using the Problematic Internet Use Questionnaire [72] (PIUQ), Problematic Online Gaming Questionnaire Short-Form [73] (POGQ-SF), Bergen Social Media Addiction Scale [74,75] (BSMAS), Exercise Addiction Inventory [76,77] (EAI), Massachusetts General Hospital Hairpulling Scale [78] (MGH-HPS), Diagnostic Statistical Manual-IV-Adapted for Juveniles gambling criteria [79] (DSM-IV-MR-J), and the SCOFF eating disorders questionnaire [80]. For further details on the study design, materials, and participant description, see [71]. The data that support the findings of this study are available from the corresponding author upon request.

2.2. SNP Selection Criteria

Due to power considerations based on the sample size, as well as technical limitations of the genotyping apparatus at our institution (see below), we decided to concentrate our association study on 32 single-nucleotide polymorphisms (SNPs). Genes and their polymorphisms were selected from earlier GWASs and candidate gene association studies in the literature to best represent the expected distribution pattern of the various phenotypes assessed in the sample [81,82,83,84]. For example, nicotinic acetylcholine receptor gene clusters implicated by earlier GWASs [85,86,87] or polymorphisms proposed in the Genetic Addiction Risk Severity (GARS) by [88], along with some conventional receptors and metabolizing enzymes, were selected and a few novel genetic targets were also considered (see Supplementary Table S1).

2.3. DNA Preparation and SNP Genotyping

A non-invasive DNA sampling method using buccal swabs was utilized to obtain a sufficient amount of buccal cells for the genetic analysis, followed by DNA isolation (for a slightly modified method, see [89]). The swabs were incubated overnight at 56 °C in a lysis solution containing 0.2 g/L Proteinase K, 0.1 M NaCl, 0.5% SDS, and 0.01 M pH = 8 Tris buffer. After an RNase treatment, proteins were removed via salting out with saturated NaCl solution. DNA precipitation with isopropanol and ethanol was followed by the resuspension of the samples in 5 mM pH = 8 Tris and 0.5 mM EDTA. The concentration was measured using a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). A DNA concentration range of 15–300 ng/µL was considered sufficient for further analysis and samples with lower concentrations were excluded.
Genotyping was performed using the Applied Biosystems™ QuantStudio™ 12K Flex system (Thermo Fisher Scientific). This high-throughput method is based on the application of pre-designed TaqMan® fluorescent probes that are highly specific for the variable sequence of interest, which is immobilized on an array along with target-specific primers. The reaction mixture consists of the DNA sample (approximately 30–150 ng) and genotyping master mix (each deoxyribonucleotide triphosphate and the AmpliTaq Gold® DNA polymerase, provided by the manufacturer) was loaded on the genotyping plates by the Applied Biosystems™ OpenArray™ AccuFill™ System (Thermo Fisher Scientific). Following the endpoint detection, the allele-specific FAM and VIC fluorescent intensities were evaluated using the QuantStudio 12 K Flex Software and the Thermo Fisher Cloud service. The Open Array format enables simultaneous genotyping of 32 SNPs on 96 samples on a single chip. Approximately 10% of the samples were measured in duplicates on the OpenArray system for quality control, where the reproducibility was higher than 98%.
The genotype distribution, as well as the Hardy–Weinberg equilibrium, and call rate data for the studied 32 SNPs are presented in Supplementary Table S1. Genotype distribution showed a significant deviation from the theoretically expected Hardy–Weinberg equilibrium in the case of FOXN3 rs759364, CNR1 rs2023239, and CHRNA5/A3 rs1051730 variants. The observed genotype distributions of all other SNPs were in Hardy–Weinberg equilibrium.

2.4. Statistical Analysis

Genetic association analysis was conducted using an allele-wise design. In the case of the genetic association analysis of substance-use behaviors, allele frequencies of using and non-using groups (as defined in Section 2) were compared using Chi-square tests. In the case of potentially addictive behaviors, mean scores of the scales were compared using the alleles of the 32 SNPs. The number of participants slightly differed across the analysis based on the call rate of the genotypes. For the correction for multiple testing, the FDRbl adjustment of false discovery rate correction was assessed [90,91], where the level of significance for 32 tests was p < 0.01221.

3. Results

3.1. Descriptive Statistics of Substance Use and Behavioral Addiction Measures

Of the 3003 participants, 59.7% never smoked cigarettes (n = 1794) and 17.1% regularly smoked cigarettes (n = 513). Regarding alcohol consumption, 60.2% did not consume (n = 1769) and 7.3% reported drinking six or more alcoholic drinks in the past 30 days four or more times (n = 220). Regarding cannabis use, 70% did not use (n = 2102) and 2.7% reported using cannabis in the past 30 days four or more times (n = 80). Descriptive statistics of the behavioral addiction scales are described in detail elsewhere [14].

3.2. Genetic Association Analysis

The genetic association analysis results are presented in Table 1 for potentially addictive substances and Table 2 for potentially addictive behaviors. Twenty-nine nominally significant associations were observed, from which, nine associations remained significant after correcting for multiple testing. Regarding substance use associations, five remained significant after correcting for multiple testing (Table 1). The association between FOXN3 rs759364 and an alcohol consumption frequency of six or more times a month was significant (χ2 = 9.116, df = 1, p = 0.0025), where the A allele increased the odds of alcohol consumption frequency of six or more times a month (OR = 1.34 [1.11–1.63]) compared to the G allele. The minor allele (A) was more frequent in the using (37.5%, n = 147) as compared to the non-using (30.0%, n = 935) group. The number of participants in the G allele using group was 246 and the number in the G allele non-using group was 2179. The analysis also showed a significant association between DRD2/ANKK1 rs1800497 and consuming cannabis at least four times during the past 30 days (χ2 = 6.424, df = 1, p = 0.0113), where the A allele increased the odds of cannabis consumption frequency of four or more times a month (OR = 1.60 [1.11–2.30]). The association showed that the minor allele (A) was more frequent among the using (27.1%, n = 38) as compared to the non-using (18.6%, n = 699) group. The number of participants in the G allele using group was 102, and the number in the G allele non-using group was 3059. The chi-square analysis also showed a significant difference between the allele frequencies of GDNF rs1549250 for any other drug use (χ2 = 8.845, df = 1, p = 0.0029, OR = 0.85 [0.77–0.95]). The minor allele (C) was more frequent in the using (46.0%, n = 502) as compared to the non-using (40.9%, n = 1474) group. The number of participants in the A allele using group was 590, and the number in the A allele non-using group was 2130. An association was also observed between GDNF rs2973033 and drug use (χ2 = 7.060, df = 1, p = 0.0079, OR = 1.16 [1.04–1.30]). The minor (C) allele was more frequent among the using (31.5%, n = 343) as compared to the non-using (27.3%, n = 980) group. The number of participants in the T allele using group was 747, and the number in the T allele non-using group was 2606. Drug use also showed an association with CNR1 rs806380 (χ2 = 7.095, df = 1, p = 0.0077, OR = 0.87 [0.78–0.96]). The minor allele (G) was more frequent among the using (36.4%, n = 407) as compared to the non-using (32.1%, n = 1173) group. The number of participants in the A allele using group was 711, and the number in the A allele non-using group was 2479.
When comparing the mean scores of the behavioral addiction scales in ANOVAs, four associations survived correction for multiple testing (Table 2). The FOXN3 rs759364 showed a significant association with scores on the Problematic Internet Use Questionnaire (F[1, 5214] = 12.846, p = 0.0003, Cohen’s d = 0.11). The minor allele was associated with higher mean scores on the PIUQ: 10.53 ± 3.67 (n = 1594) vs. 10.13 ± 3.63 (n = 3622). The FOXN3 rs759364 also showed a significant association with scores on the Problematic Online Gaming Questionnaire (F[1, 5000] = 8.788, p = 0.0030, Cohen’s d = 0.09). The minor allele was associated with higher mean scores on the POGQ: 16.11 ± 6.40 (n = 1552) vs. 15.55 ± 6.05 (n = 3450). Therefore, in both cases, the FOXN3 rs759364 A allele was associated with higher mean scores on the scales. FOXN3 rs759364 also showed a significant association with scores on the Exercise Addiction Inventory (F[1, 5208] = 9.105, p = 0.0026, Cohen’s d = 0.09) with a higher mean score for the major allele (12.63 ± 5.04, n = 3612) as compared to the minor allele (12.18 ± 4.83, n = 1598). A significant association was also found between eating disorder scores and DRD4 rs1800955 (F[1, 4972] = 9.184, p = 0.0025, Cohen’s d = 0.09), where the minor allele was associated with lower mean scores on the EAI (0.67 ± 0.92, n = 2298) as compared to the major (T) allele (0.76 ± 0.98, n = 2676).

4. Discussion

Previous candidate gene and genome-wide association studies of addictions have mostly focused on investigating possible relationships between genetic variants and one specific type of substance use disorder or behavioral addiction. The present study examined the possible associations between 32 polymorphisms and a wide spectrum of substance and non-substance addictions in a large sample consisting of high school and university students to identify possible common genetic factors underlying addictions.
The genetic association analysis presented here was conducted as part of the PGA study [71] assessing multiple addictive behaviors in 3003 young adult participants. The associated analyses of 32 SNPs regarding the use of four potentially addictive substances (alcohol, tobacco, cannabis, and other drugs) and engagement in seven potentially addictive behaviors (internet use, gaming, social networking sites use, gambling, exercising, hair-pulling, and eating) found 29 nominally significant associations, from which, 9 remained significant after an FDRbl correction for multiple testing.
Four of the nine significant associations were observed between a FOXN3 SNP and various addictive behaviors: rs759364 showed an association with the frequency of alcohol consumption and the mean behavior addiction scores (based on internet addiction, gaming addiction, and exercise addiction scales). The genotype frequencies of the FOXN3 rs759364 SNP observed in the present sample showed a significant deviation from the expected genotype frequencies according to the Hardy–Weinberg equilibrium (Supplementary Table S1). This deviation from the theoretically expected frequencies was probably due to the low frequency of the AA genotype, but further studies are needed to verify this association. Forkhead box protein N3 (FOXN3) is a member of the forkhead/winged-helix transcription factor family and has been shown to act both as a transcriptional repressor [92,93,94,95] and activator [96]. Forkhead transcription factors belong to one of the major transcription factor families in eukaryotes and contribute to development, immunity, metabolism, and cell cycle control [97]. Along with the transcription repressor role, interaction with histone deacetylase complexes involved in the DNA damage response was also shown [92,93,95], as well as possible gene regulatory functions [98].
FOXN3 has been previously associated with suicidal behavior in a GWAS [99] and the expression of FOXN3 mRNA was also increased in the brains of individuals who had committed suicide [100,101]. Behavioral and molecular genetic studies have demonstrated the heritability of suicide behavior alongside substance abuse phenotypes [102]. The presence or absence of alcohol use was previously associated with suicide-specific genes in Polish populations [103]. The rs759364 SNP is an intronic variant of FOXN3, which has been associated with alcoholism and cigarette smoking [104]. Quantitative linkage analyses of 1717 SNPs showed a linkage peak for cannabis dependence on chromosome 14 bounded by rs759364 [105]. This peak includes candidate genes, such as SERPINA1 and SERPINA2 (serine-peptidase inhibitor, clade A, members 1 and 2), which were previously implicated in a GWAS for substance abuse vulnerability [106]. An association between FOXN3 expression and areca nut chewing habits in patients with oral cancer has also been shown [107].
In the present study, we found two associations between GDNF variants: the intronic rs1549250 and rs2973033 in the 5′ untranslated region of the gene) and the “lifetime other drugs” variable. Glial-cell-line-derived neurotrophic factor (GDNF) plays a vital role in peripheral neuronal development [108], including axon guidance [109]. GDNF is produced by striatal neurons [110] and transported via dopaminergic neurons to the substantia nigra [111] and ventral tegmental area (VTA) [112]. GDNF positively regulates dopaminergic activity in nigrostriatal and mesolimbic projections [112,113].
Drugs of abuse can affect the expression of neurotrophic factors. Manipulation of neurotrophic factor levels can modify drug-seeking behavior [114]. The administration of GDNF in the mesocorticolimbic system has been linked to increased craving [115]. In contrast, several alcohol studies showed acute inhibitory effects of GDNF on drug-seeking behavior [116,117]. Upregulation in endogenous levels of GDNF mRNA and protein was found in rat VTA ten hours after a single administration of 20% ethanol, while no change in the nucleus accumbens was observed, suggesting that the ethanol-mediated effects on GDNF expression may be restricted to the VTA [118]. GDNF expression and downstream signaling were also found to be modulated by cocaine and amphetamines [119].
Reduced GDNF expression may also potentiate methamphetamine self-administration, enhance motivation to take methamphetamine, increase vulnerability to drug-primed reinstatement, and prolong cue-induced reinstatement of extinguished methamphetamine-seeking behavior [120]. Previous studies have associated GDNF polymorphisms with methamphetamine use [121] and cigarette smoking [122]. The rs2973033 variant of GDNF has been associated with a culturally distinct form of gambling in an Indian population [123].
The “lifetime other drugs” variable has also been associated with two additional polymorphisms: the rs806380 variant of the Cannabinoid Receptor 1 (CNR1) gene and rs1800497 of the DRD2/Ankyrin Repeat and Kinase Domain Containing 1 (ANKK1) gene. CNR1 codes for cannabinoid 1 receptors that showed high expression in brain regions that were linked to reward, addiction, and cognitive function [124,125]. The rs806380 SNP in intron 2 of CNR1 has been associated with cannabis dependence [126]. However, it should be noted that most participants also met the criteria for alcohol dependence. The rs806380 SNP was also associated with the development of cannabis dependence symptoms [127]. A microsatellite polymorphism of CNR1 has also been positively associated with intravenous drug use and cocaine, amphetamine, and cannabis dependence [128]. The rs1800497 SNP of ANKK1, also known as the TaqIA polymorphism, was widely studied among individuals with psychiatric disorders. Initially associated with the gene coding for the dopamine D2 receptor, the TaqIA polymorphism has been linked to reduced dopamine D2 receptor densities and binding affinity [129,130,131,132]. However, subsequent studies observed linkage disequilibrium between DRD2 and ANKK1, with some studies linking associations with substance use behaviors more to ANKK1 than DRD2 [26]. Therefore, there may be direct or indirect influences on the concentration of dopamine in the synaptic clefts. The TaqIA polymorphism has been associated with nicotine dependence [133,134], cigarette smoking cessation [135], alcohol use disorder [136,137], opioid dependence [138,139,140], and cocaine dependence [141,142].
Finally, we found an association between rs1800955 of the DRD4 gene and the mean scores on the SCOFF questionnaire assessing eating disorders. This SNP, also known as -521 C/T, is a variant in the promoter region upstream of the DRD4 gene and has a putative role in the regulation of transcriptional activity [143]. A previous study showed a significant association with anorexia nervosa in a single locus analysis of rs1800955, while additional haplotype analysis showed a significant association at a four-locus haplotype including rs1800955 [144]. Previous associations were reported between alleles at -521 C/T and novelty seeking, extraversion, and drug use [145,146]. The rs1800955 polymorphism of DRD4 was also proposed in the Genetic Addiction Risk Severity (GARS) test by Blum et al. This test identifies alleles that are proposed to impart vulnerability to addiction and makes an assessment of the degree of vulnerability of an individual to develop addictive behaviors [84]. This SNP has been associated with heroin addiction [139,147] and was reported to contribute to a “risk-taking phenotype” among skiers and snowboarders [148].
One of the aims of the present study was to investigate possible common genetic factors contributing to different substance use disorders and behavioral addictions. Based on the presented results, it appears that rs759364 of FOXN3, along with rs1549250 and rs2973033 of GDNF, may be non-specific genetic risk factors for various types of addictive behaviors. However, it should be noted that the results should be interpreted with caution due to the nature of the data (convenience sampling). Additionally, the measures of substance use behaviors are self-reported and were not assessed using structured scales. As adolescence and young adulthood are characterized by elevated levels of substance use, studies of larger samples with more diverse ages are also warranted. Furthermore, the candidate gene approach has limitations relating to a priori selection criteria and threshold levels, and the effect size values were also low. Therefore, the generalizability of the results has limitations and further studies are needed to confirm the role of these polymorphisms in addictions. Such studies may utilize multiple approaches, including haplotype analysis, polygenic risk scores, and genomic structural equation modeling. Nonetheless, as an initial study of genetic factors linked to substance use and behavioral addictions, it provides direction for future investigations to be conducted using independent samples.

Supplementary Materials

The following supporting information can be downloaded at:, Table S1: Genotype distribution of the 32 analyzed SNPs.

Author Contributions

The study concept and design were defined by Z.D., C.B., M.N.P., M.D.G. and M.S.-S. Data collection was carried out by A.M., A.E., A.V., C.B. and J.F. The analysis and interpretation of the data were carried out by E.K., O.K., A.V., A.B., C.B., M.D.G., A.S., R.U., K.B. and Z.D. The study and the preparation of the manuscript were supervised by Z.D., M.N.P., R.U., K.B., R.D.B., C.B., M.D.G., A.S. and M.S.-S. All authors read and agreed to the published version of the manuscript.


This work was supported by the Hungarian National Research, Development and Innovation Office (grant number: KKP126835). Kenneth Blum was supported by R41MD012318/MD/NIMHD. Eszter Kotyuk was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Science (BO/00099/21). Orsolya Király was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences and by the ÚNKP-21-5 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund. Csaba Barta was supported by funding from the Merit-Prize Fellowship of Semmelweis University; the Bolyai János Research Fellowship of the Hungarian Academy of Sciences (BO/00987/16/5); the ÚNKP-18-4 of the new National Excellence Program of the Ministry of Human Capacities; and the Baron Munchausen Program of the Institute of Medical Chemistry, Molecular Biology and Pathobiochemistry, Semmelweis University. Marc N. Potenza received support from the Connecticut Council on Problem Gambling, the Connecticut Department of Mental Health and Addiction Services, and the National Center for Responsible Gaming. The views presented in this manuscript are those of the authors and the funding agencies did not influence the content of the manuscript beyond the provision of financial support.

Institutional Review Board Statement

The study protocol was designed in accordance with the guidelines of the Declaration of Helsinki and was approved by the Scientific and Research Ethics Committee of the Medical Research Council (ETT TUKEB).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data analyzed during the current study are available in the OSF repository,; (accessed on 6 February 2022).

Conflicts of Interest

Mark D. Griffiths’ university currently receives research funding from Norsk Tipping (the gambling operator owned by the Norwegian Government). Mark D. Griffiths has also received funding for several research projects in the area of gambling education for young people, social responsibility in gambling, and gambling treatment from GambleAware (formerly the Responsible Gambling Trust), which is a charitable body that funds its research program based on donations from the gambling industry. Mark D. Griffiths regularly undertakes consultancy for various gaming companies in the area of social responsibility in gambling. Marc N. Potenza has consulted for Opiant Therapeutics, Game Day Data, the Addiction Policy Forum, AXA, and Idorsia Pharmaceuticals; has been involved in a patent application with Yale University and Novartis; has received research support from Mohegan Sun Casino and the National Center for Responsible Gaming; has participated in surveys, mailings, or telephone consultations related to drug addiction, impulse-control disorders, or other health topics; has consulted for and/or advised gambling and legal entities on issues related to impulse-control/addictive disorders; has provided clinical care in a problem gambling services program; has performed grant reviews for research-funding agencies; has edited journals and journal sections; has given academic lectures in grand rounds, CME events, and other clinical or scientific venues; and has generated books or book chapters for publishers of mental health texts. Kenneth Blum is the inventor of the Genetic Addiction Risk Severity (GARS) test and maintains a portfolio of both USA and foreign patents. ELTE Eötvös Loránd University receives funding from Szerencsejáték Ltd. to maintain a telephone helpline service for problematic gambling. Zsolt Demetrovics has also been involved in research on responsible gambling funded by Szerencsejáték Ltd. and the Gambling Supervision Board and provided educational materials for Szerencsejáték Ltd.’s responsible gambling program. The University of Gibraltar receives funding from the Gibraltar Gambling Care Foundation. Zsolt Demetrovics and Marc N. Potenza have been members of a WHO advisory group on the public health consequences of addictive behaviors. In this capacity, they have been eligible for travel support from the WHO or the host center to attend advisory group meetings but have not been remunerated for their work. However, this funding is not related to this study and the funding institution had no role in the study design or the collection, analysis, and interpretation of the data; writing of the manuscript; or the decision to submit the paper for publication. Andrea Vereczkei, Csaba Barta, Anna Magi, Judit Farkas, Andrea Eisinger, Orsolya Király, Andrea Belik, Anna Székely, Mária Sasvári-Székely, Róbert Urbán, Rajendra D. Badgaiyan, and Eszter Kotyuk have no conflict of interest to declare.


  1. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; American Psychiatric Association: Arlington, VA, USA, 2013. [Google Scholar]
  2. World Health Organization. International Statistical Classification of Diseases and Related Health Problems (11th Revision). Available online: (accessed on 10 January 2021).
  3. Potenza, M.N. Should addictive disorders include non-substance-related conditions? Addiction 2006, 101 (Suppl. 1), 142–151. [Google Scholar] [CrossRef] [PubMed]
  4. Rumpf, H.J.; Achab, S.; Billieux, J.; Bowden-Jones, H.; Carragher, N.; Demetrovics, Z.; Higuchi, S.; King, D.L.; Mann, K.; Potenza, M.; et al. Including gaming disorder in the ICD-11: The need to do so from a clinical and public health perspective. J. Behav. Addict. 2018, 7, 556–561. [Google Scholar] [CrossRef] [PubMed]
  5. King, D.L.; Delfabbro, P.H.; Potenza, M.N.; Demetrovics, Z.; Billieux, J.; Brand, M. Internet gaming disorder should qualify as a mental disorder. Aust. N. Z. J. Psychiatry 2018, 52, 615–617. [Google Scholar] [CrossRef] [PubMed]
  6. Király, O.; Demetrovics, Z. Inclusion of Gaming Disorder in ICD has more advantages than disadvantages. J. Behav. Addict. 2017, 6, 280–284. [Google Scholar] [CrossRef] [Green Version]
  7. Kessler, R.C.; Chiu, W.T.; Demler, O.; Walters, E.E. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey replication. Arch. Gen. Psychiatry 2005, 62, 617–627. [Google Scholar] [CrossRef] [Green Version]
  8. Kessler, R.C.; Hwang, I.; LaBrie, R.; Petukhova, M.; Sampson, N.A.; Winters, K.C.; Shaffer, H.J. DSM-IV pathological gambling in the National Comorbidity Survey replication. Psychol. Med. 2008, 38, 1351–1360. [Google Scholar] [CrossRef] [Green Version]
  9. Potenza, M.N.; Balodis, I.M.; Derevensky, J.; Grant, J.E.; Petry, N.M.; Verdejo-Garcia, A.; Yip, S.W. Gambling disorder. Nat. Rev. Dis. Primers 2019, 5, 51. [Google Scholar] [CrossRef]
  10. Christenson, G.A.; Faber, R.J.; de Zwaan, M.; Raymond, N.C.; Specker, S.M.; Ekern, M.D.; Mackenzie, T.B.; Crosby, R.D.; Crow, S.J.; Eckert, E.D. Compulsive buying: Descriptive characteristics and psychiatric comorbidity. J. Clin. Psychiatry 1994, 55, 5–11. [Google Scholar]
  11. Di Nicola, M.; Tedeschi, D.; De Risio, L.; Pettorruso, M.; Martinotti, G.; Ruggeri, F.; Swierkosz-Lenart, K.; Guglielmo, R.; Callea, A.; Ruggeri, G.; et al. Co-occurrence of alcohol use disorder and behavioral addictions: Relevance of impulsivity and craving. Drug Alcohol Depend. 2015, 148, 118–125. [Google Scholar] [CrossRef]
  12. Feigelman, W.; Wallisch, L.S.; Lesieur, H.R. Problem gamblers, problem substance users, and dual-problem individuals: An epidemiological study. Am. J. Public Health 1998, 88, 467–470. [Google Scholar] [CrossRef]
  13. Konkolÿ Thege, B.; Hodgins, D.C.; Wild, T.C. Co-occurring substance-related and behavioral addiction problems: A person-centered, lay epidemiology approach. J. Behav. Addict. 2016, 5, 614–622. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Kotyuk, E.; Magi, A.; Eisinger, A.; Király, O.; Vereczkei, A.; Barta, C.; Griffiths, M.D.; Székely, A.; Kökönyei, G.; Farkas, J.; et al. Co-occurrences of substance use and other potentially addictive behaviors: Epidemiological results from the Psychological and Genetic Factors of the Addictive Behaviors (PGA) Study. J. Behav. Addict. 2020, 9, 272–288. [Google Scholar] [CrossRef]
  15. Müller, A.; Loeber, S.; Söchtig, J.; Te Wildt, B.; De Zwaan, M. Risk for exercise dependence, eating disorder pathology, alcohol use disorder and addictive behaviors among clients of fitness centers. J. Behav. Addict. 2015, 4, 273–280. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Sussman, S.; Arpawong, T.E.; Sun, P.; Tsai, J.; Rohrbach, L.A.; Spruijt-Metz, D. Prevalence and co-occurrence of addictive behaviors among former alternative high school youth. J. Behav. Addict. 2014, 3, 33–40. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Villella, C.; Martinotti, G.; Di Nicola, M.; Cassano, M.; La Torre, G.; Gliubizzi, M.D.; Messeri, I.; Petruccelli, F.; Bria, P.; Janiri, L.; et al. Behavioural addictions in adolescents and young adults: Results from a prevalence study. J. Gambl. Stud. 2011, 27, 203–214. [Google Scholar] [CrossRef] [PubMed]
  18. Walther, B.; Morgenstern, M.; Hanewinkel, R. Co-occurrence of addictive behaviours: Personality factors related to substance use, gambling and computer gaming. Eur. Addict. Res. 2012, 18, 167–174. [Google Scholar] [CrossRef] [Green Version]
  19. Wise, R.A. Roles for nigrostriatal—Not just mesocorticolimbic—Dopamine in reward and addiction. Trends Neurosci. 2009, 32, 517–524. [Google Scholar] [CrossRef] [Green Version]
  20. Koob, G.F.; Buck, C.L.; Cohen, A.; Edwards, S.; Park, P.E.; Schlosburg, J.E.; Schmeichel, B.; Vendruscolo, L.F.; Wade, C.L.; Whitfield, T.W., Jr.; et al. Addiction as a stress surfeit disorder. Neuropharmacology 2014, 76 Pt B, 370–382. [Google Scholar] [CrossRef] [Green Version]
  21. Koob, G.F.; Le Moal, M. Plasticity of reward neurocircuitry and the ‘dark side’ of drug addiction. Nat. Neurosci. 2005, 8, 1442–1444. [Google Scholar] [CrossRef]
  22. Luscher, C.; Malenka, R.C. Drug-evoked synaptic plasticity in addiction: From molecular changes to circuit remodeling. Neuron 2011, 69, 650–663. [Google Scholar] [CrossRef] [Green Version]
  23. Volkow, N.D.; Fowler, J.S.; Wang, G.J. The addicted human brain: Insights from imaging studies. J. Clin. Investig. 2003, 111, 1444–1451. [Google Scholar] [CrossRef] [PubMed]
  24. Nutt, D.J.; Lingford-Hughes, A.; Erritzoe, D.; Stokes, P.R. The dopamine theory of addiction: 40 years of highs and lows. Nat. Rev. Neurosci. 2015, 16, 305–312. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Gyollai, A.; Griffiths, M.; Barta, C.; Vereczkei, A.; Urban, R.; Kun, B.; Kokonyei, G.; Szekely, A.; Sasvari-Szekely, M.; Blum, K.; et al. The genetics of problem and pathological gambling: A systematic review. Curr. Pharm. Des. 2014, 20, 3993–3999. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Leeman, R.F.; Potenza, M.N. A targeted review of the neurobiology and genetics of behavioural addictions: An emerging area of research. Can. J. Psychiatry 2013, 58, 260–273. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Leeman, R.F.; Potenza, M.N. Similarities and differences between pathological gambling and substance use disorders: A focus on impulsivity and compulsivity. Psychopharmacology 2012, 219, 469–490. [Google Scholar] [CrossRef] [Green Version]
  28. Benowitz, N.L. Nicotine addiction. N. Engl. J. Med. 2010, 362, 2295–2303. [Google Scholar] [CrossRef]
  29. Gorwood, P.; Le Strat, Y.; Ramoz, N.; Dubertret, C.; Moalic, J.M.; Simonneau, M. Genetics of dopamine receptors and drug addiction. Hum. Genet. 2012, 131, 803–822. [Google Scholar] [CrossRef]
  30. Guo, G.; Cai, T.; Guo, R.; Wang, H.; Harris, K.M. The dopamine transporter gene, a spectrum of most common risky behaviors, and the legal status of the behaviors. PLoS ONE 2010, 5, e9352. [Google Scholar] [CrossRef] [Green Version]
  31. Werme, M.; Lindholm, S.; Thoren, P.; Franck, J.; Brene, S. Running increases ethanol preference. Behav. Brain Res. 2002, 133, 301–308. [Google Scholar] [CrossRef]
  32. Werme, M.; Thorén, P.; Olson, L.; Brené, S. Running and cocaine both upregulate dynorphin mRNA in medial caudate putamen. Eur. J. Neurosci. 2000, 12, 2967–2974. [Google Scholar] [CrossRef]
  33. Potenza, M.N.; Sofuoglu, M.; Carroll, K.M.; Rounsaville, B.J. Neuroscience of behavioral and pharmacological treatments for addictions. Neuron 2011, 69, 695–712. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Klugah-Brown, B.; Zhou, X.; Pradhan, B.K.; Zweerings, J.; Mathiak, K.; Biswal, B.; Becker, B. Common neurofunctional dysregulations characterize obsessive-compulsive, substance use, and gaming disorders—An activation likelihood meta-analysis of functional imaging studies. Addict. Biol. 2021, 26, e12997. [Google Scholar] [CrossRef] [PubMed]
  35. Agrawal, A.; Lynskey, M.T. Are there genetic influences on addiction: Evidence from family, adoption and twin studies. Addiction 2008, 103, 1069–1081. [Google Scholar] [CrossRef] [PubMed]
  36. Agrawal, A.; Verweij, K.J.; Gillespie, N.A.; Heath, A.C.; Lessov-Schlaggar, C.N.; Martin, N.G.; Nelson, E.C.; Slutske, W.S.; Whitfield, J.B.; Lynskey, M.T. The genetics of addiction-a translational perspective. Transl. Psychiatry 2012, 2, e140. [Google Scholar] [CrossRef]
  37. Gorwood, P.; Le Strat, Y.; Ramoz, N. Genetics of addictive behavior: The example of nicotine dependence. Dialogues Clin. Neurosci. 2017, 19, 237–245. [Google Scholar] [CrossRef]
  38. Polderman, T.J.C.; Benyamin, B.; de Leeuw, C.A.; Sullivan, P.F.; van Bochoven, A.; Visscher, P.M.; Posthuma, D. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 2015, 47, 702–709. [Google Scholar] [CrossRef] [Green Version]
  39. Xian, H.; Giddens, J.L.; Scherrer, J.F.; Eisen, S.A.; Potenza, M.N. Environmental factors selectively impact co-occurrence of problem/pathological gambling with specific drug-use disorders in male twins. Addiction 2014, 109, 635–644. [Google Scholar] [CrossRef] [Green Version]
  40. Slutske, W.S.; Eisen, S.; True, W.R.; Lyons, M.J.; Goldberg, J.; Tsuang, M. Common genetic vulnerability for pathological gambling and alcohol dependence in men. Arch. Gen. Psychiatry 2000, 57, 666–673. [Google Scholar] [CrossRef]
  41. Slutske, W.S.; Zhu, G.; Meier, M.H.; Martin, N.G. Genetic and environmental influences on disordered gambling in men and women. Arch. Gen. Psychiatry 2010, 67, 624–630. [Google Scholar] [CrossRef] [Green Version]
  42. Manning, V.; Dowling, N.A.; Lee, S.; Rodda, S.; Garfield, J.B.B.; Volberg, R.; Kulkarni, J.; Lubman, D.I. Problem gambling and substance use in patients attending community mental health services. J. Behav. Addict. 2017, 6, 678–688. [Google Scholar] [CrossRef]
  43. Bühler, K.M.; Giné, E.; Echeverry-Alzate, V.; Calleja-Conde, J.; de Fonseca, F.R.; López-Moreno, J.A. Common single nucleotide variants underlying drug addiction: More than a decade of research. Addict. Biol. 2015, 20, 845–871. [Google Scholar] [CrossRef] [PubMed]
  44. Grant, J.E.; Potenza, M.N.; Weinstein, A.; Gorelick, D.A. Introduction to behavioral addictions. Am. J. Drug Alcohol Abus. 2010, 36, 233–241. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Karkowski, L.M.; Prescott, C.A.; Kendler, K.S. Multivariate assessment of factors influencing illicit substance use in twins from female-female pairs. Am. J. Med. Genet. 2000, 96, 665–670. [Google Scholar] [CrossRef]
  46. Kendler, K.S.; Jacobson, K.C.; Prescott, C.A.; Neale, M.C. Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. Am. J. Psychiatry 2003, 160, 687–695. [Google Scholar] [CrossRef] [PubMed]
  47. Blum, K.; Chen, A.L.; Oscar-Berman, M.; Chen, T.J.; Lubar, J.; White, N.; Lubar, J.; Bowirrat, A.; Braverman, E.; Schoolfield, J.; et al. Generational association studies of dopaminergic genes in reward deficiency syndrome (RDS) subjects: Selecting appropriate phenotypes for reward dependence behaviors. Int. J. Environ. Res. Public Health 2011, 8, 4425–4459. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Nivard, M.G.; Verweij, K.J.H.; Minica, C.C.; Treur, J.L.; Vink, J.M.; Boomsma, D.I.; Consortium, I.C. Connecting the dots, genome-wide association studies in substance use. Mol. Psychiatr. 2016, 21, 733–735. [Google Scholar] [CrossRef]
  49. Liu, M.; Jiang, Y.; Wedow, R.; Li, Y.; Brazel, D.M.; Chen, F.; Datta, G.; Davila-Velderrain, J.; McGuire, D.; Tian, C.; et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 2019, 51, 237–244. [Google Scholar] [CrossRef]
  50. Johnson, E.C.; Demontis, D.; Thorgeirsson, T.E.; Walters, R.K.; Polimanti, R.; Hatoum, A.S.; Sanchez-Roige, S.; Paul, S.E.; Wendt, F.R.; Clarke, T.K.; et al. A large-scale genome-wide association study meta-analysis of cannabis use disorder. Lancet Psychiatry 2020, 7, 1032–1045. [Google Scholar] [CrossRef]
  51. Jensen, K.P. A review of genome-wide association studies of stimulant and opioid use disorders. Mol. Neuropsychiatry 2016, 2, 37–45. [Google Scholar] [CrossRef] [Green Version]
  52. Li, M.D.; Burmeister, M. New insights into the genetics of addiction. Nat. Rev. Genet. 2009, 10, 225–231. [Google Scholar] [CrossRef] [Green Version]
  53. Mallard, T.T.; Ashenhurst, J.R.; Harden, K.P.; Fromme, K. GABRA2, alcohol, and illicit drug use: An event-level model of genetic risk for polysubstance use. J. Abnorm. Psychol. 2018, 127, 190–201. [Google Scholar] [CrossRef] [PubMed]
  54. Koeneke, A.; Ponce, G.; Troya-Balseca, J.; Palomo, T.; Hoenicka, J. Ankyrin repeat and kinase domain containing 1 gene, and addiction vulnerability. Int. J. Mol. Sci. 2020, 21, 2516. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Lee, P.W.; Wang, T.Y.; Chang, Y.H.; Lee, S.Y.; Chen, S.L.; Wang, Z.C.; Chen, P.S.; Chu, C.H.; Huang, S.Y.; Tzeng, N.S.; et al. ALDH2 gene: Its effects on the neuropsychological functions in patients with opioid use disorder undergoing methadone maintenance treatment. Clin. Psychopharmacol. Neurosci. 2020, 18, 136–144. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Reyes-Gibby, C.C.; Yuan, C.; Wang, J.; Yeung, S.C.J.; Shete, S. Gene network analysis shows immune-signaling and ERK1/2 as novel genetic markers for multiple addiction phenotypes: Alcohol, smoking and opioid addiction. BMC Syst. Biol. 2015, 9, 25. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Grant, J.E.; Chamberlain, S.R. Gambling disorder and its relationship with substance use disorders: Implications for nosological revisions and treatment. Am. J. Addict. 2015, 24, 126–131. [Google Scholar] [CrossRef]
  58. Leino, T.; Torsheim, T.; Griffiths, M.D.; Pallesen, S. The relationship between substance use disorder and gambling disorder: A nationwide longitudinal health registry study. Scand. J. Public Health 2021, 14034948211042249. [Google Scholar] [CrossRef]
  59. Vitaro, F.; Hartl, A.C.; Brendgen, M.; Laursen, B.; Dionne, G.; Boivin, M. Genetic and environmental influences on gambling and substance use in early adolescence. Behav. Genet. 2014, 44, 347–355. [Google Scholar] [CrossRef]
  60. Lang, M.; Lemenager, T.; Streit, F.; Fauth-Buhler, M.; Frank, J.; Juraeva, D.; Witt, S.H.; Degenhardt, F.; Hofmann, A.; Heilmann-Heimbach, S.; et al. Genome-wide association study of pathological gambling. Eur. Psychiatry 2016, 36, 38–46. [Google Scholar] [CrossRef]
  61. Lind, P.A.; Zhu, G.; Montgomery, G.W.; Madden, P.A.; Heath, A.C.; Martin, N.G.; Slutske, W.S. Genome-wide association study of a quantitative disordered gambling trait. Addict. Biol. 2013, 18, 511–522. [Google Scholar] [CrossRef]
  62. Linner, R.K.; Biroli, P.; Kong, E.; Meddens, F.W.; Wedow, R.; Fontana, M.A.; Lebreton, M.; Tino, S.P.; Abdellaoui, A.; Hammerschlag, A.R.; et al. Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat. Genet. 2019, 51, 245–257. [Google Scholar] [CrossRef]
  63. Grant, J.E.; Kim, S.W.; Hartman, B.K. A double-blind, placebo-controlled study of the opiate antagonist naltrexone in the treatment of pathological gambling urges. J. Clin. Psychiatry 2008, 69, 783–789. [Google Scholar] [CrossRef] [PubMed]
  64. Kim, S.W.; Grant, J.E. An open naltrexone treatment study in pathological gambling disorder. Int. Clin. Psychopharmacol. 2001, 16, 285–289. [Google Scholar] [CrossRef] [PubMed]
  65. Linnet, J. The anticipatory dopamine response in addiction: A common neurobiological underpinning of gambling disorder and substance use disorder? Prog. Neuropsychopharmacol. Biol. Psychiatry 2020, 98, 109802. [Google Scholar] [CrossRef]
  66. Popescu, A.; Marian, M.; Drăgoi, A.M.; Costea, R.-V. Understanding the genetics and neurobiological pathways behind addiction (Review). Exp. Ther. Med. 2021, 21, 544. [Google Scholar] [CrossRef] [PubMed]
  67. Cepik, A.; Arikan, Z.; Boratav, C.; Isik, E. Bulimia in a male alcoholic: A symptom substitution in alcoholism. Int. J. Eat. Disord. 1995, 17, 201–204. [Google Scholar] [CrossRef]
  68. Conner, B.T.; Stein, J.A.; Longshore, D.; Stacy, A.W. Associations between drug abuse treatment and cigarette use: Evidence of substance replacement. Exp. Clin. Psychopharmacol. 1999, 7, 64–71. [Google Scholar] [CrossRef]
  69. Koball, A.M.; Glodosky, N.C.; Ramirez, L.D.; Kallies, K.J.; Gearhardt, A.N. From substances to food: An examination of addiction shift in individuals undergoing residential treatment for substance use. Addict. Res. Theory 2019, 27, 322–327. [Google Scholar] [CrossRef]
  70. Sinclair, D.L.; Sussman, S.; Savahl, S.; Florence, M.; Adams, S.; Vanderplasschen, W. Substitute addictions in persons with substance use disorders: A scoping review. Subst. Use Misuse 2021, 56, 683–696. [Google Scholar] [CrossRef]
  71. Kotyuk, E.; Farkas, J.; Magi, A.; Eisinger, A.; Király, O.; Vereczkei, A.; Barta, C.; Griffiths, M.D.; Kökönyei, G.; Székely, A.; et al. The psychological and genetic factors of the addictive behaviors (PGA) study. Int. J. Methods Psychiatr. Res. 2019, 28, e1748. [Google Scholar] [CrossRef]
  72. Demetrovics, Z.; Szeredi, B.; Rózsa, S. The three-factor model of Internet addiction: The development of the Problematic Internet Use Questionnaire. Behav. Res. Methods 2008, 40, 563–574. [Google Scholar] [CrossRef] [Green Version]
  73. Pápay, O.; Urbán, R.; Griffiths, M.D.; Nagygyörgy, K.; Farkas, J.; Kökönyei, G.; Felvinczi, K.; Oláh, A.; Elekes, Z.; Demetrovics, Z. Psychometric properties of the problematic online gaming questionnaire short-form and prevalence of problematic online gaming in a national sample of adolescents. Cyberpsychol. Behav. Soc. Netw. 2013, 16, 340–348. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Andreassen, C.S.; Torsheim, T.; Brunborg, G.S.; Pallesen, S. Development of a Facebook Addiction Scale. Psychol. Rep. 2012, 110, 501–517. [Google Scholar] [CrossRef] [PubMed]
  75. Banyai, F.; Zsila, A.; Kiraly, O.; Maraz, A.; Elekes, Z.; Griffiths, M.; Andreassen, C.S.; Demetrovics, Z. Problematic social media use: Results from a large-scale nationally representative adolescent sample. PLoS ONE 2017, 12, e0169839. [Google Scholar] [CrossRef] [PubMed]
  76. Terry, A.; Szabo, A.; Griffiths, M. The exercise addiction inventory: A new brief screening tool. Addict. Res. Theory 2004, 12, 489–499. [Google Scholar] [CrossRef]
  77. Griffiths, M.; Szabo, A.; Terry, A. The exercise addiction inventory: A quick and easy screening tool for health practitioners. Br. J. Sports Med. 2005, 39, e30. [Google Scholar] [CrossRef]
  78. Keuthen, N.J.; O’Sullivan, R.L.; Ricciardi, J.N.; Shera, D.; Savage, C.R.; Borgmann, A.S.; Jenike, M.A.; Baer, L. The Massachusetts General Hospital (MGH) Hairpulling Scale: 1. development and factor analyses. Psychother. Psychosom. 1995, 64, 141–145. [Google Scholar] [CrossRef]
  79. Fisher, S. Developing the DSM-IV criteria to identify adolescent problem gambling in non-clinical populations. J. Gambl. Stud. 2000, 16, 253–273. [Google Scholar] [CrossRef]
  80. Morgan, J.F.; Reid, F.; Lacey, J.H. The SCOFF questionnaire: Assessment of a new screening tool for eating disorders. BMJ 1999, 319, 1467–1468. [Google Scholar] [CrossRef] [Green Version]
  81. Agrawal, A.; Lynskey, M.T. Candidate genes for cannabis use disorders: Findings, challenges and directions. Addiction 2009, 104, 518–532. [Google Scholar] [CrossRef] [Green Version]
  82. Bierut, L.J. Nicotine dependence and genetic variation in the nicotinic receptors. Drug Alcohol. Depend. 2009, 104 (Suppl. 1), S64–S69. [Google Scholar] [CrossRef] [Green Version]
  83. Hinrichs, A.L.; Wang, J.C.; Bufe, B.; Kwon, J.M.; Budde, J.; Allen, R.; Bertelsen, S.; Evans, W.; Dick, D.; Rice, J.; et al. Functional variant in a bitter-taste receptor (hTAS2R16) influences risk of alcohol dependence. Am. J. Hum. Genet. 2006, 78, 103–111. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Husemoen, L.L.; Fenger, M.; Friedrich, N.; Tolstrup, J.S.; Beenfeldt Fredriksen, S.; Linneberg, A. The association of ADH and ALDH gene variants with alcohol drinking habits and cardiovascular disease risk factors. Alcohol Clin. Exp. Res. 2008, 32, 1984–1991. [Google Scholar] [CrossRef] [PubMed]
  85. Bierut, L.J.; Madden, P.A.; Breslau, N.; Johnson, E.O.; Hatsukami, D.; Pomerleau, O.F.; Swan, G.E.; Rutter, J.; Bertelsen, S.; Fox, L.; et al. Novel genes identified in a high-density genome wide association study for nicotine dependence. Hum. Mol. Genet. 2007, 16, 24–35. [Google Scholar] [CrossRef]
  86. Berrettini, W.H.; Doyle, G.A. The CHRNA5-A3-B4 gene cluster in nicotine addiction. Mol. Psychiatry 2012, 17, 856–866. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Bierut, L.J. Convergence of genetic findings for nicotine dependence and smoking related diseases with chromosome 15q24-25. Trends Pharmacol. Sci. 2010, 31, 46–51. [Google Scholar] [CrossRef] [Green Version]
  88. Blum, K.; Oscar-Berman, M.; Demetrovics, Z.; Barh, D.; Gold, M.S. Genetic Addiction Risk Score (GARS): Molecular neurogenetic evidence for predisposition to Reward Deficiency Syndrome (RDS). Mol. Neurobiol. 2014, 50, 765–796. [Google Scholar] [CrossRef] [Green Version]
  89. Boor, K.; Ronai, Z.; Nemoda, Z.; Gaszner, P.; Sasvari-Szekely, M.; Guttman, A.; Kalasz, H. Noninvasive genotyping of dopamine receptor D4 (DRD4) using nanograms of DNA from substance-dependent patients. Curr. Med. Chem. 2002, 9, 793–797. [Google Scholar] [CrossRef]
  90. Benjamini, Y.; Drai, D.; Elmer, G.; Kafkafi, N.; Golani, I. Controlling the false discovery rate in behavior genetics research. Behav. Brain Res. 2001, 125, 279–284. [Google Scholar] [CrossRef] [Green Version]
  91. Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 1995, 57, 289–300. [Google Scholar] [CrossRef]
  92. Busygina, V.; Kottermann, M.C.; Scott, K.L.; Plon, S.E.; Bale, A.E. Multiple endocrine neoplasia type 1 interacts with forkhead transcription factor CHES1 in DNA damage response. Cancer Res. 2006, 66, 8397–8403. [Google Scholar] [CrossRef] [Green Version]
  93. Scott, K.L.; Plon, S.E. CHES1/FOXN3 interacts with Ski-interacting protein and acts as a transcriptional repressor. Gene 2005, 359, 119–126. [Google Scholar] [CrossRef] [PubMed]
  94. Schuff, M.; Rössner, A.; Wacker, S.A.; Donow, C.; Gessert, S.; Knöchel, W. FoxN3 is required for craniofacial and eye development of xenopus laevis. Dev. Dyn. 2007, 236, 226–239. [Google Scholar] [CrossRef] [PubMed]
  95. Scott, K.L.; Plon, S.E. Loss of Sin3/Rpd3 histone deacetylase restores the DNA damage response in checkpoint-deficient strains of saccharomyces cerevisiae. Mol. Cell. Biol. 2003, 23, 4522–4531. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  96. Yu, J.; Liu, Y.J.; Lan, X.; Wu, H.; Wen, Y.; Zhou, Z.M.; Hu, Z.B.; Sha, J.H.; Guo, X.J.; Tong, C. CHES-1-like, the ortholog of a non-obstructive azoospermia-associated gene, blocks germline stem cell differentiation by upregulating Dpp expression in Drosophila testis. Oncotarget 2016, 7, 42303–42313. [Google Scholar] [CrossRef] [Green Version]
  97. Lam, E.W.F.; Brosens, J.J.; Gomes, A.R.; Koo, C.Y. Forkhead box proteins: Tuning forks for transcriptional harmony. Nat. Rev. Cancer 2013, 13, 482–495. [Google Scholar] [CrossRef]
  98. Rogers, J.M.; Waters, C.T.; Seegar, T.C.M.; Jarrett, S.M.; Hallworth, A.N.; Blacklow, S.C.; Bulyk, M.L. Bispecific forkhead transcription factor FoxN3 recognizes two distinct motifs with different DNA shapes. Mol. Cell 2019, 74, 245–253.e246. [Google Scholar] [CrossRef] [Green Version]
  99. Sokolowski, M.; Wasserman, J.; Wasserman, D. Polygenic associations of neurodevelopmental genes in suicide attempt. Mol. Psychiatry 2016, 21, 1381–1390. [Google Scholar] [CrossRef]
  100. Galfalvy, H.; Zalsman, G.; Huang, Y.Y.; Murphy, L.; Rosoklija, G.; Dwork, A.J.; Haghighi, F.; Arango, V.; Mann, J.J. A pilot genome wide association and gene expression array study of suicide with and without major depression. World J. Biol. Psychiatry 2013, 14, 574–582. [Google Scholar] [CrossRef] [Green Version]
  101. Le-Niculescu, H.; Levey, D.F.; Ayalew, M.; Palmer, L.; Gavrin, L.M.; Jain, N.; Winiger, E.; Bhosrekar, S.; Shankar, G.; Radel, M.; et al. Discovery and validation of blood biomarkers for suicidality. Mol. Psychiatry 2013, 18, 1249–1264. [Google Scholar] [CrossRef]
  102. Dick, D.M.; Meyers, J.; Aliev, F.; Nurnberger, J., Jr.; Kramer, J.; Kuperman, S.; Porjesz, B.; Tischfield, J.; Edenberg, H.J.; Foroud, T.; et al. Evidence for genes on chromosome 2 contributing to alcohol dependence with conduct disorder and suicide attempts. Am. J. Med. Genet. Part B Neuropsychiatr. Genet. 2010, 153B, 1179–1188. [Google Scholar] [CrossRef] [Green Version]
  103. Fudalej, S.; Ilgen, M.; Fudalej, M.; Wojnar, M.; Matsumoto, H.; Barry, K.L.; Ploski, R.; Blow, F.C. Clinical and genetic risk factors for suicide under the influence of alcohol in a Polish sample. Alcohol Alcohol. 2009, 44, 437–442. [Google Scholar] [CrossRef] [PubMed]
  104. Ye, Y.; Zhong, X.; Zhang, H. A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking. BMC Genet. 2005, 6 (Suppl. 1), S135. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  105. Agrawal, A.; Hinrichs, A.L.; Dunn, G.; Bertelsen, S.; Dick, D.M.; Saccone, S.F.; Saccone, N.L.; Grucza, R.A.; Wang, J.C.; Cloninger, C.R.; et al. Linkage scan for quantitative traits identifies new regions of interest for substance dependence in the Collaborative Study on the Genetics of Alcoholism (COGA) sample. Drug Alcohol Depend. 2008, 93, 12–20. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  106. Liu, Q.R.; Drgon, T.; Johnson, C.; Walther, D.; Hess, J.; Uhl, G.R. Addiction molecular genetics: 639,401 SNP whole genome association identifies many "cell adhesion" genes. Am. J. Med. Genet. Part B Neuropsychiatr. Genet. 2006, 141B, 918–925. [Google Scholar] [CrossRef]
  107. Chen, Y.J.; Liao, C.T.; Chen, P.J.; Lee, L.Y.; Li, Y.C.; Chen, I.H.; Wang, H.M.; Chang, J.T.; Chen, L.J.; Yen, T.C.; et al. Downregulation of Ches1 and other novel genes in oral cancer cells chronically exposed to areca nut extract. Head Neck 2011, 33, 257–266. [Google Scholar] [CrossRef]
  108. Airaksinen, M.S.; Saarma, M. The GDNF family: Signalling, biological functions and therapeutic value. Nat. Rev. Neurosci. 2002, 3, 383–394. [Google Scholar] [CrossRef]
  109. Bonanomi, D.; Chivatakarn, O.; Bai, G.; Abdesselem, H.; Lettieri, K.; Marquardt, T.; Pierchala, B.A.; Pfaff, S.L. Ret is a multifunctional coreceptor that integrates diffusible- and contact-axon guidance signals. Cell 2012, 148, 568–582. [Google Scholar] [CrossRef] [Green Version]
  110. Barroso-Chinea, P.; Cruz-Muros, I.; Aymerich, M.S.; Rodriguez-Diaz, M.; Afonso-Oramas, D.; Lanciego, J.L.; Gonzalez-Hernandez, T. Striatal expression of GDNF and differential vulnerability of midbrain dopaminergic cells. Eur. J. Neurosci. 2005, 21, 1815–1827. [Google Scholar] [CrossRef] [Green Version]
  111. Tomac, A.; Widenfalk, J.; Lin, L.F.; Kohno, T.; Ebendal, T.; Hoffer, B.J.; Olson, L. Retrograde axonal transport of glial cell line-derived neurotrophic factor in the adult nigrostriatal system suggests a trophic role in the adult. Proc. Natl. Acad. Sci. USA 1995, 92, 8274–8278. [Google Scholar] [CrossRef] [Green Version]
  112. Wang, J.; Carnicella, S.; Ahmadiantehrani, S.; He, D.Y.; Barak, S.; Kharazia, V.; Ben Hamida, S.; Zapata, A.; Shippenberg, T.S.; Ron, D. Nucleus accumbens-derived glial cell line-derived neurotrophic factor is a retrograde enhancer of dopaminergic tone in the mesocorticolimbic system. J. Neurosci. 2010, 30, 14502–14512. [Google Scholar] [CrossRef]
  113. Kumar, A.; Kopra, J.; Varendi, K.; Porokuokka, L.L.; Panhelainen, A.; Kuure, S.; Marshall, P.; Karalija, N.; Härma, M.A.; Vilenius, C.; et al. GDNF overexpression from the native locus reveals its role in the nigrostriatal dopaminergic system function. PLoS Genet. 2015, 11, e1005710. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  114. Ghitza, U.E.; Zhai, H.; Wu, P.; Airavaara, M.; Shaham, Y.; Lu, L. Role of BDNF and GDNF in drug reward and relapse: A review. Neurosci. Biobehav. Rev. 2010, 35, 157–171. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  115. Lu, L.; Wang, X.; Wu, P.; Xu, C.; Zhao, M.; Morales, M.; Harvey, B.K.; Hoffer, B.J.; Shaham, Y. Role of ventral tegmental area glial cell line-derived neurotrophic factor in incubation of cocaine craving. Biol. Psychiatry 2009, 66, 137–145. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  116. Carnicella, S.; Kharazia, V.; Jeanblanc, J.; Janak, P.H.; Ron, D. GDNF is a fast-acting potent inhibitor of alcohol consumption and relapse. Proc. Natl. Acad. Sci. USA 2008, 105, 8114–8119. [Google Scholar] [CrossRef] [Green Version]
  117. He, D.Y.; Ron, D. Autoregulation of glial cell line-derived neurotrophic factor expression: Implications for the long-lasting actions of the anti-addiction drug, Ibogaine. FASEB J. 2006, 20, 2420–2422. [Google Scholar] [CrossRef]
  118. Ahmadiantehrani, S.; Barak, S.; Ron, D. GDNF is a novel ethanol-responsive gene in the VTA: Implications for the development and persistence of excessive drinking. Addict. Biol. 2014, 19, 623–633. [Google Scholar] [CrossRef]
  119. Carnicella, S.; Ron, D. GDNF—A potential target to treat addiction. Pharmacol. Ther. 2009, 122, 9–18. [Google Scholar] [CrossRef] [Green Version]
  120. Yan, Y.; Yamada, K.; Niwa, M.; Nagai, T.; Nitta, A.; Nabeshima, T. Enduring vulnerability to reinstatement of methamphetamine-seeking behavior in glial cell line-derived neurotrophic factor mutant mice. FASEB J. 2007, 21, 1994–2004. [Google Scholar] [CrossRef] [Green Version]
  121. Yoshimura, T.; Usui, H.; Takahashi, N.; Yoshimi, A.; Saito, S.; Aleksic, B.; Ujike, H.; Inada, T.; Yamada, M.; Uchimura, N.; et al. Association analysis of the GDNF gene with methamphetamine use disorder in a Japanese population. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2011, 35, 1268–1272. [Google Scholar] [CrossRef]
  122. Kotyuk, E.; Nemeth, N.; Ronai, Z.; Demetrovics, Z.; Sasvari-Szekely, M.; Szekely, A. Association between smoking behaviour and genetic variants of glial cell line-derived neurotrophic factor. J. Genet. 2016, 95, 811–818. [Google Scholar] [CrossRef] [Green Version]
  123. Das, A.; Pagliaroli, L.; Vereczkei, A.; Kotyuk, E.; Langstieh, B.; Demetrovics, Z.; Barta, C. Association of GDNF and CNTNAP2 gene variants with gambling. J. Behav. Addict. 2019, 1–8. [Google Scholar] [CrossRef]
  124. Parsons, L.H.; Hurd, Y.L. Endocannabinoid signalling in reward and addiction. Nat. Rev. Neurosci. 2015, 16, 579–594. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  125. Glass, M.; Faull, R.L.M.; Dragunow, M. Cannabinoid receptors in the human brain: A detailed anatomical and quantitative autoradiographic study in the fetal, neonatal and adult human brain. Neuroscience 1997, 77, 299–318. [Google Scholar] [CrossRef]
  126. Agrawal, A.; Wetherill, L.; Dick, D.M.; Xuei, X.; Hinrichs, A.; Hesselbrock, V.; Kramer, J.; Nurnberger, J.I.; Schuckit, M.; Bierut, L.J.; et al. Evidence for Association Between Polymorphisms in the Cannabinoid Receptor 1 (CNR1) Gene and Cannabis Dependence. Am. J. Med. Genet. Part B. Neuropsychiatr. Genet. 2009, 150B, 736–740. [Google Scholar] [CrossRef] [Green Version]
  127. Hopfer, C.J.; Young, S.E.; Purcell, S.; Crowley, T.J.; Stallings, M.C.; Corley, R.P.; Rhee, S.H.; Smolen, A.; Krauter, K.; Hewitt, J.K.; et al. Cannabis receptor haplotype associated with fewer cannabis dependence symptoms in adolescents. Am. J. Med. Genet. Part B. Neuropsychiatr. Genet. 2006, 141B, 895–901. [Google Scholar] [CrossRef] [Green Version]
  128. Comings, D.E.; Muhleman, D.; Gade, R.; Johnson, P.; Verde, R.; Saucier, G.; MacMurray, J. Cannabinoid receptor gene (CNR1): Association with i.v. drug use. Mol. Psychiatry 1997, 2, 161–168. [Google Scholar] [CrossRef]
  129. Jonsson, E.G.; Nothen, M.M.; Grunhage, F.; Farde, L.; Nakashima, Y.; Propping, P.; Sedvall, G.C. Polymorphisms in the dopamine D2 receptor gene and their relationships to striatal dopamine receptor density of healthy volunteers. Mol. Psychiatry 1999, 4, 290–296. [Google Scholar] [CrossRef] [Green Version]
  130. Pohjalainen, T.; Rinne, J.O.; Nagren, K.; Lehikoinen, P.; Anttila, K.; Syvalahti, E.K.; Hietala, J. The A1 allele of the human D2 dopamine receptor gene predicts low D2 receptor availability in healthy volunteers. Mol. Psychiatry 1998, 3, 256–260. [Google Scholar] [CrossRef] [PubMed]
  131. Thompson, J.; Thomas, N.; Singleton, A.; Piggot, M.; Lloyd, S.; Perry, E.K.; Morris, C.M.; Perry, R.H.; Ferrier, I.N.; Court, J.A. D2 dopamine receptor gene (DRD2) Taql A polymorphism: Reduced dopamine D2 receptor binding in the human striatum associated with the A1 allele. Pharmacogenet. Genom. 1997, 7, 479–484. [Google Scholar] [CrossRef]
  132. Noble, E.P.; Blum, K.; Ritchie, T.; Montgomery, A.; Sheridan, P.J. Allelic association of the D2 dopamine receptor gene with receptor-binding characteristics in alcoholism. Arch. Gen. Psychiatry 1991, 48, 648–654. [Google Scholar] [CrossRef]
  133. De Ruyck, K.; Nackaerts, K.; Beels, L.; Werbrouck, J.; De Volder, A.; Meysman, M.; Salhi, B.; Van Meerbeeck, J.; Thierens, H. Genetic variation in three candidate genes and nicotine dependence, withdrawal and smoking cessation in hospitalized patients. Pharmacogenomics 2010, 11, 1053–1063. [Google Scholar] [CrossRef] [PubMed]
  134. Voisey, J.; Swagell, C.D.; Hughes, I.P.; van Daal, A.; Noble, E.P.; Lawford, B.R.; Young, R.M.; Morris, C.P. A DRD2 and ANKK1 haplotype is associated with nicotine dependence. Psychiat. Res. 2012, 196, 285–289. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  135. Stapleton, J.A.; Sutherland, G.; O’Gara, C.; Spirling, L.I.; Ball, D. Association between DRD2/ANKK1 Taq1A genotypes, depression and smoking cessation with nicotine replacement therapy. Pharmacogenet. Genom. 2011, 21, 447–453. [Google Scholar] [CrossRef]
  136. Wang, F.; Simen, A.; Arias, A.; Lu, Q.W.; Zhang, H.P. A large-scale meta-analysis of the association between the ANKK1/DRD2 Taq1A polymorphism and alcohol dependence. Hum. Genet. 2013, 132, 347–358. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  137. Jung, Y.; Montel, R.A.; Shen, P.H.; Mash, D.C.; Goldman, D. Assessment of the association of D2 dopamine receptor gene and reported allele frequencies with alcohol use disorders: A systematic review and meta-analysis. JAMA Netw. Open 2019, 2, e1914940. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  138. Hou, Q.F.; Li, S.B. Potential association of DRD2 and DAT1 genetic variation with heroin dependence. Neurosci. Lett. 2009, 464, 127–130. [Google Scholar] [CrossRef]
  139. Vereczkei, A.; Demetrovics, Z.; Szekely, A.; Sarkozy, P.; Antal, P.; Szilagyi, A.; Sasvari-Szekely, M.; Barta, C. Multivariate analysis of dopaminergic gene variants as risk factors of heroin dependence. PLoS ONE 2013, 8, e66592. [Google Scholar] [CrossRef]
  140. Deng, X.D.; Jiang, H.; Ma, Y.; Gao, Q.; Zhang, B.; Mu, B.; Zhang, L.X.; Zhang, W.; Er, Z.E.; Xie, Y.; et al. Association between DRD2/ANKK1 TaqIA polymorphism and common illicit drug dependence: Evidence from a meta-analysis. Hum. Immunol. 2015, 76, 42–51. [Google Scholar] [CrossRef]
  141. Noble, E.P.; Blum, K.; Khalsa, M.E.; Ritchie, T.; Montgomery, A.; Wood, R.C.; Fitch, R.J.; Ozkaragoz, T.; Sheridan, P.J.; Anglin, M.D.; et al. Allelic association of the D2 dopamine receptor gene with cocaine dependence. Drug Alcohol Depend. 1993, 33, 271–285. [Google Scholar] [CrossRef]
  142. Persico, A.M.; Bird, G.; Gabbay, F.H.; Uhl, G.R. D2 dopamine receptor Gene TaqI A1 and B1 restriction fragment length polymorphisms: Enhanced frequencies in psychostimulant-preferring polysubstance abusers. Biol. Psychiatry 1996, 40, 776–784. [Google Scholar] [CrossRef]
  143. Kereszturi, E.; Kiraly, O.; Barta, C.; Molnar, N.; Sasvari-Szekely, M.; Csapo, Z. No direct effect of the -521 C/T polymorphism in the human dopamine D4 receptor gene promoter on transcriptional activity. BMC Mol. Biol. 2006, 7, 18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  144. Bachner-Melman, R.; Lerer, E.; Zohar, A.H.; Kremer, I.; Elizur, Y.; Nemanov, L.; Golan, M.; Blank, S.; Gritsenko, I.; Ebstein, R.P. Anorexia nervosa, perfectionism, and dopamine D4 receptor (DRD4). Am. J. Med. Genet. Part B. Neuropsychiatr. Genet. 2007, 144B, 748–756. [Google Scholar] [CrossRef] [PubMed]
  145. Lai, J.H.; Zhu, Y.S.; Huo, Z.H.; Sun, R.F.; Yu, B.; Wang, Y.P.; Chai, Z.Q.; Li, S.B. Association study of polymorphisms in the promoter region of DRD4 with schizophrenia, depression, and heroin addiction. Brain Res. 2010, 1359, 227–232. [Google Scholar] [CrossRef] [PubMed]
  146. Munafo, M.R.; Yalcin, B.; Willis-Owen, S.A.; Flint, J. Association of the dopamine D4 receptor (DRD4) gene and approach-related personality traits: Meta-analysis and new data. Biol. Psychiatry 2008, 63, 197–206. [Google Scholar] [CrossRef]
  147. Szilagyi, A.; Boor, K.; Szekely, A.; Gaszner, P.; Kalasz, H.; Sasvari-Szekely, M.; Barta, C. Combined effect of promoter polymorphisms in the dopamine D4 receptor and the serotonin transporter genes in heroin dependence. Neuropsychopharmacol. Hung. 2005, 7, 28–33. [Google Scholar]
  148. Thomson, C.J.; Hanna, C.W.; Carlson, S.R.; Rupert, J.L. The 521 C/T variant in the dopamine-4-receptor gene (DRD4) is associated with skiing and snowboarding behavior. Scand. J. Med. Sci. Spor. 2013, 23, e108–e113. [Google Scholar] [CrossRef]
Table 1. Genetic association analysis results regarding addiction candidate genes and potentially addictive substance use.
Table 1. Genetic association analysis results regarding addiction candidate genes and potentially addictive substance use.
Substance Use
GenedbSNP No.AlleleNicotineAlcoholCannabisOther Drugs
(n = 3588)
(n = 1026)
(n = 3538)
(n = 440)
(n = 4204)
(n = 160)
(n = 4088)
Ever Use
(n = 1246)
FOXN3rs759364A30.8%26.6%0.01630.0%37.5%0.0025 *30.4%33.6%0.42530.1%32.6%0.103
GDNFrs1549250C41.2%45.1%0.03740.8%44.7%0.13941.0%44.0%0.49140.9%46.0%0.0029 *
GDNFrs2973033C27.8%31.4%0.04027.6%32.2%0.05527.7%32.6%0.22127.3%31.5%0.0078 *
CNR1rs806380G33.7%32.8%0.62033.5%31.2%0.34732.9%33.8%0.82332.1%36.4%0.0077 *
ANKK1rs1800497A18.8%19.1%0.82919.5%20.4%0.67118.6%27.1%0.0113 *18.8%20.5%0.213
Notes: Nominally significant associations are labeled in bold. * Significant after correction for multiple testing.
Table 2. Genetic association analysis results regarding addiction candidate genes and potentially addictive behaviors.
Table 2. Genetic association analysis results regarding addiction candidate genes and potentially addictive behaviors.
Potentially Addictive Behaviors
GenedbSNP No.AlleleInternet Use (PIUQ)Videogame Playing (POGQ)Social Network Site Use (BSMAS)Gambling (DSM IV MR J)Exercise (EAI)Trichotillomania (MGH-HPS)Eating Disorders (SCOFF)
(n = 5936)p(n = 5698)p(n = 3452)p(n = 5916)p(n = 5916)p(n = 3250)p(n = 5978)p
FOXN3rs759364A10.530.0003 *16.110.003 *9.710.1840.260.61212.180.003 *1.440.9750.710.529
DRD4rs1800955C10.250.85415.830.3279.530.4900.260.47512.310.0421.530.3640.670.002 *
Notes. Nominally significant associations are labeled in bold. * Significant after correction for multiple testing.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Vereczkei, A.; Barta, C.; Magi, A.; Farkas, J.; Eisinger, A.; Király, O.; Belik, A.; Griffiths, M.D.; Szekely, A.; Sasvári-Székely, M.; et al. FOXN3 and GDNF Polymorphisms as Common Genetic Factors of Substance Use and Addictive Behaviors. J. Pers. Med. 2022, 12, 690.

AMA Style

Vereczkei A, Barta C, Magi A, Farkas J, Eisinger A, Király O, Belik A, Griffiths MD, Szekely A, Sasvári-Székely M, et al. FOXN3 and GDNF Polymorphisms as Common Genetic Factors of Substance Use and Addictive Behaviors. Journal of Personalized Medicine. 2022; 12(5):690.

Chicago/Turabian Style

Vereczkei, Andrea, Csaba Barta, Anna Magi, Judit Farkas, Andrea Eisinger, Orsolya Király, Andrea Belik, Mark D. Griffiths, Anna Szekely, Mária Sasvári-Székely, and et al. 2022. "FOXN3 and GDNF Polymorphisms as Common Genetic Factors of Substance Use and Addictive Behaviors" Journal of Personalized Medicine 12, no. 5: 690.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop