https://orcid.org/0000-0001-7160-4512
Figures
Abstract
Background
The abuse of psychogenic drugs can lead to multiple health-related problems. Genetic and environmental vulnerabilities are factors in the emergence of substance use disorders. Empirical evidence regarding the gene–environment interaction in substance use is mixed. Summaries of the latest findings from a candidate gene approach will be useful for revealing the significance of particular gene contributions. Thus, we aim to identify different gene–environment interactions in patterns of substance use and investigate whether any effects trend notably across different genders and races.
Methods
We reviewed published studies, until March 1, 2022, on substance use for candidate gene–environment interaction. Basic demographics of the included studies, target genes, environmental factors, main findings, patterns of gene–environment interaction, and other relevant information were collected and summarized.
Results
Among a total of 44 studies, 38 demonstrated at least one significant interaction effect. About 61.5% of studies on the 5-HTTLPR gene, 100% on the MAOA gene, 42.9% on the DRD2 gene, 50% on the DRD4 gene, 50% on the DAT gene, 80% on the CRHR1 gene, 100% on the OPRM1 gene, 100% on the GABRA1 gene, and 50% on the CHRNA gene had a significant gene–environment interaction effect. The diathesis–stress model represents a dominant interaction pattern (89.5%) in the studies with a significant interaction effect; the remaining significant effect on substance use is found in the differential susceptibility model. The social push and swing model were not reported in the included studies.
Conclusion
The gene–environment interaction research on substance use behavior is methodologically multidimensional, which causes difficulty in conducting pooled analysis, or stated differently–making it hard to identify single sources of significant influence over maladaptive patterns of drug taking. In decreasing the heterogeneity and facilitating future pooled analysis, researchers must (1) replicate the existing studies with consistent study designs and measures, (2) conduct power calculations to report gene–environment correlations, (3) control for covariates, and (4) generate theory-based hypotheses with factorial based experiments when designing future studies.
Citation: Jiang Z, Chen Z, Chen X (2023) Candidate gene-environment interactions in substance abuse: A systematic review. PLoS ONE 18(10): e0287446. https://doi.org/10.1371/journal.pone.0287446
Editor: Herb Covington, University of Wisconsin-Green Bay, UNITED STATES
Received: July 28, 2022; Accepted: June 6, 2023; Published: October 31, 2023
Copyright: © 2023 Jiang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
According to the United Nations Office on Drugs and Crime, 275 million people used licit or illicit drugs in 2020 worldwide: 22.3% of them used tobacco [1], and nearly 4% of all deaths are related to alcohol misuse [2]. Meanwhile, 36 million people are suffering from substance use disorders [3]. Taking the United States as an example, 11.7% of persons aged over 12 used illicit drugs in 2018 [4]. Substance use imposes a great burden on individuals and countries as it leads to various health problems, including substance dependence, cardiovascular diseases, and and other psychological illnesses. Thus, prevention and treatment of substance use remain as a public health priority.
It is well-established that both genetic and environmental factors contribute to substance use behavior [5]. Twin studies used to investigate the heritability of substance use behavior [6] revealed that heritability only partially explains (about 30%–75%) substance use, abuse, and dependence [5], when considering the possibility that heritability scores are vulnerable to exaggeration, there remains considerable room for environmental factors, including an interaction between genetic and environmental factors. Environmental factors refer to the physical and social environment in which people live and conduct their daily activities [5, 7]. In this review, environmental factors include both individual- and societal-level variables, such as stressful life events and familial relationships. In most cases, analog scales are used to to evaluate, quantify, and operationally define the level or intensity of an environment variable.
Like substance abuse, genetic and environmental factors also contribute to psychiatric outcomes with varying effects in adulthood. For instance, although 5-HTTLPR (serotonin transporter gene-linked polymorphic region) variation was found to be significantly associated with anxiety and depression, it only accounted for 7%–9% of inherited variance in anxiety-related personality traits [7]. Social factors, such as stressful life events, may also play an important role in mental health and interact with genetic factors. A same-sex twin longitudinal study [8] revealed a causal relationship between the experience of stressful life events and the onset of major depression. In addition, there may be a significant interaction between genetic and environmental factors on the expression of psychiatric disorders. A study on depression showed that s/s homozygotes of 5-HTTLPR increased depressive symptoms only when early traumatic life events occurred [9]. Similarly, people who experienced childhood maltreatment with a genotype of high levels of monoamine oxidase A enzyme (MAOA) expression had a weaker association with antisocial problems than people with low MAOA activity [10].
Exposure to negative psychosocial factors may also increase the risk of developing substance use or substance abuse behavior [11–13], although some studies provide no significant effects here, because people exposed to similar difficult environments, do not necessarily develop substance use behaviors[14]. More clarity on the interaction between environment and biology in the ontogeny of substance use disorders is therefore required, and more specificity to this question can be obtained by asking: “Are there specific factors that relate to the genetic effect or the psychosocial effect in the onset and maintenance of the problems associated with substance use? And equally important is the question of how robustly and precisely does any one factor contribute?” [15]. Prior research has revealed four major types of the gene–environment interaction (G×E) effect: the diathesis–stress model, the differential susceptibility model, the “social push” model, and the “swing” model.
The diathesis–stress model (Fig 1) [16–19] suggests that individuals with high genetic risk are more likely to experience adverse outcomes when exposed to high-risk environments. This model is commonly reported in G×E effects on patterns of substance use behavior [17–19]. The differential susceptibility model (Fig 2) [20] indicates that though individuals with a certain genetic predisposition have greater vulnerability to high-risk environments, and they can also obtain more benefits from positive environments. This model was also observed in many substance use studies [21, 22].
The other two models are less commonly observed in substance use behavior studies. The “social push” model (Fig 3) [23] implies that genetic factors have a strong effect at low or medium levels of environmental risk, whereas genetic risk has a lesser impact in high-risk environments perhaps because social influences dominate in extreme environments. The “swing” model (Fig 4)[24] suggests that individuals with medium genetic risk are most likely to be influenced by environmental factors to develop health risk behavior than individuals at the extremes with a low and high propensity.
Two systematic reviews have carefully considered G×E interactions in substance use and misuse [5, 14]. Pasman et al. [5] reviewed genome-wide association studies (GWAS), candidate gene score (CGS), and haplotype methods, showing that under some analytical frameworks a G×E effect can be viewed as weak. In a separate analysis approach, Do and Maes [14] found that 13 of 16 studies (81.2%) using either “candidate genes” or twin study, providing more evidence for significant G×E effects in patterns of maladaptive substance use. Specifically, Do and Maes highlighted the examination of nicotine use, through smoking studies. The analyses provided did not include an interpretative focus on the role of certain genetic or environmental factors [14]. Pasman et al. [5] focused on polygenic G×E studies, which may cause difficulty in investigating the exact genetic pathway or mechanism of the development of substance use behavior. For instance, GWAS, which can test thousands of genetic variants of many individuals to identify novel variant–trait association, may lead to discovery of new biological mechanisms. However, it carries the statistical burden of multiple testing, and sometimes only explains a modest fraction of heritability [25]. In addition, GWAS cannot directly determine causality when the mapping of most association signals occurs in non-coding regions of DNA, which makes biological interpretations of causality somewhat challenging, since these regions of DNA are directly engaged by forces determined by the cellular ‘environment’ [25].
In contrast, the candidate gene approach has been adopted by many studies in studying G×E effects of various unfavorable behavior such as antisocial behavior [26], gambling [27], and drug abuse [28]. The candidate gene method enables us to comprehensively understand and determine the exact neurological pathway of substance use behavior by classifying them into dopaminergic, serotoninergic, or other pathways. Although the candidate gene method is useful and economical to reveal genetic architecture of complex traits, it is restricted to existing knowledge and presumptions [29] and inconsistent findings in previous studies make it challenging to create a consistent picture of the G×E effect in certain behavior.
This study aims to provide an additional explanation or overview of potential GxE interaction effects on maladaptive patterns of substance use behaviors using, specifically, a candidate gene approach. We fit the studies into different theoretical G×E models and examine whether distinct G×E patterns exist for different races, genders, and types of substances. Our approach extends the previous work by further exploring the role of specific genetic and environmental factors in the development of substance use behavior.
Methods
This review was in accordance with the PRISMA guideline [30].
Search strategy
Literature searches were conducted in PubMed, psycINFO, and Google Scholar. Relevant peer-reviewed articles were included based on titles and abstracts. Keywords included substance use and gene–environment interaction; the detailed search formula and keywords are shown in S1 Table. In addition, we searched reference lists of published studies, meta-analyses, and review articles for more eligible articles. The last search took place on March 1st, 2022. Article screening was performed by two trained research staff using the same formula.
Study eligibility
Included studies should meet the following criteria: (1) human objects only; (2) the outcome of the study should be any kind of licit and illicit substance use; (3) studies focusing on candidate genes and environmental factors (e.g., psychosocial experiences and demographic characteristics); (4) the study was an original research report; (5) gene–environment interaction was calculated statistically in the study; (6) articles written in English (Fig 5).
Data extraction
All data were extracted for the following variables: (1) article title, (2) 1st author, (3) study design, (4) study population, (5) data set used, (6) target gene, (7) environmental exposure and measure, (8) outcome measure, (9) demographics (e.g., age, gender) of the study population, (10) main findings, (11) statistical outcome, (12) P value on the G×E effect, (13) risk of bias, (14) rGE (genotype–environment correlation) status, (15) study power calculation, and (16) G×E effect pattern classification. Meta-analysis or publication bias assessment could not be conducted because of heterogeneity and statistical reporting differences. Data extraction was performed by two reviewers independently and cross-checked afterward.
Result synthesis
The data were tabulated into two tables: Table 1 consisted of basic demographic data, wherein studies using the same dataset were identified and noted. Table 2 summarizes key information of studies, including target genes, environmental factors, outcomes, main findings, risk alleles, G×E patterns, wherein the main findings were summarized into brief sentences. The patterns were coded into four types: A (diathesis–stress model), B (differential susceptibility model), C (“social push” model), and D (“swing” model). The G×E patterns were directly obtained from the studies if they were explicitly stated in the article. Otherwise, the authors coded the pattern based on the G×E results. For example, if individuals with high genetic risk are more likely to engage in substance abuse when exposed to high-risk environments (e.g., maltreatment by parents), such a G×E result is consistent with the (A) diathesis-stress model.
Results
The study selection process is summarized in the flowchart shown in Fig 1. A total of 44 articles were included in this systematic review. We will discuss the results of the review based on individual gene types, and their key features and G×E findings will be summarized. Moreover, pattern classification will be reported and compared across gene types, genders, and races.
Study description
Key demographic data of each study are summarized in Table 1. The major features and G×E findings are summarized in Table 2.
Gene-specific results
5-HTTLPR.
The serotonin transporter gene-linked polymorphic region (5-HTTLPR) is located in the promoter region of the SLC6A4 gene, which codes for presynaptic serotonin transporters responsible for serotonin reuptake [7]. The 5-HTTLPR starts at 28,521,337 base pairs from the pter and consists of a 20–23 base pair repeat sequence. The short (S) allele of 5-HTTLPR has lower transcriptional efficiency compared with the long (L) allele; thus, people carrying S-allele have a higher and unstable concentration of serotonin in the synaptic cleft [7]. Serotonin as a neurotransmitter is involved in the physiological processes of mood [68]. Studies have found that S-allele is related to an increased risk of mental health disorders [69, 70], including polysubstance use [71].
There are 13 studies on 5-HTTLPR × environment interaction (Table 2), where environment factors included socio-demographic variables, childhood adversities, and negative life events. Eight studies reported a significant G×E effect, whereas the remaining five studies showed no significant interaction. Among studies with significant G×E interactions, seven were consistent with the diathesis-stress model [32, 35, 36, 44, 52, 60, 64], whereas one study was consistent with the differential susceptibility model [53]. Six studies suggested S-allele as the risk allele [35, 36, 52, 53, 60, 64]; one study indicated homozygous L-allele as the risk allele [44], and one advocated LS variant as the risk allele [32].
Other than age, no notable differences were found among the five studies [17, 39, 43, 65, 67] with non-significant results regarding study design, environment factor selection, and gender. Four [17, 43, 65, 67] out of the five studies had a mean age over 35, whereas the mean age of the eight studies with significant G×E results had a mean age below 25 [32, 35, 36, 44, 52, 53, 60, 64]. It may suggest that substance use behavior can be affected by age, possibly due to complicated environmental exposure or different social norms between generations.
MAOA.
MAOA codes are involved in the degradation of brain monoamine transmitters such as serotonin, dopamine, and norepinephrine. The enzyme plays a role in stress response and addiction pathogenesis. The MAOA gene is located on the X-chromosome (Xp11.4–p11.23) [62]. The best characterized genetic variants of MAOA are related to an upstream Variable Number Tandem Repeat (uVNTR), comprising a 30-basepair repeat sequence present in 2, 2.5, 3, 3.5, 4, 5, or 6 copies located in the gene promoter [72, 73]. Alleles harboring two and three repeats are classified as “low-activity variant,” whereas three and four repeats are referred to as “high-activity variant.” The rest of the repeats, including 2.5, five, and six repeats, are not studied because of lack of evidence of their exact functions.
Studies have revealed the association of MAOA and substance use behavior [74, 75], particularly in male subjects [76]. We found seven studies on MAOA × environment interaction (Table 2), with six focusing on childhood adversities [19, 40, 41, 61–63] and one focusing on parenting quality [38]. All the studies reported significant G×E effect and were consistent with the diathesis–stress model However, the risk allele varied among different studies. Three studies reported low-activity allele (2–3 repeats) as the risk allele [19, 41, 61]; three studies reported high-activity allele (3.5–4 repeats) as the risk allele [38, 40, 63].
In male subjects, of the five studies, four reported low-activity allele as the risk allele. In female subjects, three out of four studies advocated high-activity allele as the risk allele in contrast to male subjects. Although the mechanism of the interaction between sex and MAOA remains unclear, MAOA is an X-linked polymorphism, whereas X-inactivation of female might increase the variability of X-linked gene expression [77]. Aside from gene expression, androgen might also explain the sex-dimorphic characteristic because testosterone may interact with MAOA uVNTR variants to predispose aggression and risk-taking behavior [78, 79].
DRD2 DRD4 DAT1.
These three genes are all related to the dopaminergic system in the brain. The DRD2 gene codes for the D2 subtype of the dopamine receptors. It is located on chromosome 11 q22–q23 [80]. The A1-allele for the TaqIA polymorphism (rs1800497) is associated with significantly reduced amount of D2 receptors in the brain compared with the A2-allele [81], which might decrease reactivity toward dopamine signals. A meta-analysis of the relationship between D2 dopamine receptor gene and alcoholism reported that alcoholics had a higher prevalence of the A1 allele than controls [82], whereas another meta-analysis of dopaminergic receptors indicated a weak association between DRD4, DAT gene and smoking initiation [83].
The DRD4 gene codes for the D4 subtype of dopamine receptors. It is located on chromosome 11p15 [84]. Variable number tandem repeat (VNTR) in exon III of the DRD4 gene is particularly noteworthy, which comprises a 48 nucleotide repeat sequence and 2–11 repeat units (2R-11R) [28]. Alleles with 2–5 repeats are commonly identified as short, whereas alleles with 6–10 repeats are referred to as long allele. Long alleles, particularly the 7R allele, has a blunted ability to reduce cAMP levels, thereby suppressing the gene expression compared with short alleles[85], whereas the low amount of dopamine receptors has been related to substance abuse [86].
The DAT1 gene (SLC6A3) mediates the presynaptic reuptake of dopamine. It is located on chromosome 5p15.32, and the polymorphism VNTR comprising a 480 base pair repeat sequence is located on the 3′ untranslated region of the gene starting 1,392,905 base pairs from pter [87]. The 9R allele is associated with the low expression of the gene, which lowers the levels of the transporter protein in the brain [88], leading to higher brain levels of dopamine.
A total of nine studies focused on the interaction between these dopaminergic genes and environmental factors, and seven studies included DRD2 [21, 31, 43, 46, 57, 58, 67]. In addition, two studies included DRD4 [28, 57], and two studies investigated DAT1 [37, 57]. The environmental factors included childhood adversities, psychological factors, family factors, and socio-demographic factors. Five out of nine studies reported at least one significant G×E interaction on substance abuse [21, 28, 31, 37, 46].
Seven studies examined DRD2 × environment interaction (Table 2). Three out of seven studies reported a significant G×E effect of the DRD2 gene [21, 31, 46], three of which supported A1 allele as the risk allele [21, 31, 46]. Among studies with significant associations, two were consistent with the diathesis-stress model [31, 46], and one showed support for the differential susceptibility model [21].
There were two studies on DRD4 × environment interaction [28, 57] (Table 2). One study reported a significant interaction between DRD4 (seven repeats allele) and insecure attachment on cannabis use, which was consistent with the diathesis-stress model [28]. Among the two studies on DAT1 × environment interaction [37, 57] (Table 2), one reported a significant interaction between DAT1_E15þ274—DAT1_VNTR C-9 and traumatic life experience in smoke initiation and nicotine dependence. The G×E pattern showed supported for the diathesis-stress model.
The G×E effect of the DRD2 gene remains controversial, and only some early research found a significant interaction between DRD2 and environmental factors. Two out of the three significant studies used male subjects, whereas four non-significant ones used male and female subjects. Considering that many studies favored the association of DRD2 TaqIA with substance addiction in male subjects [89, 90], differential selection might be involved in this controversial result. Regarding the differences in environmental factors selection, studies using socio-demographic variables generated a different result from those studies that used stress as the environmental factor. This finding could be due to high neural reactivity toward emotional stress in subjects carrying A1 allele [91]. For DRD4 and DAT1 genes, the results were inconclusive because of the limited number of studies. Gender, environmental exposure selection, or outcome selection may also be involved.
CRHR1.
The CRHR1 gene codes for corticotropin-releasing hormone receptor in the pituitary gland, which is located in the chromosome 17q21.31 [92]. The gene is approximately 51.55 kb in length, which is contained within a 900 kb inversion polymorphism [93], resulting in H1 or H2 haplotypes. The homozygous H1 haplotype is more prevalent among African ancestry, whereas the H2 haplotype accounts for 20% in individuals from European ancestry [93]. Treutlein et al. [94] studied the association between 14 haplotypes and alcohol use behavior and revealed that two SNPs, namely, rs242938 and rs1876831, may be directly associated with the alcohol use behavior.
Five studies have investigated CRHR1 × environment interaction [18, 45, 47, 49, 56] The environmental factors included negative life events and childhood adversities. Four out of five studies reported a significant G×E effect on substance use behavior [18, 45, 47, 56]. Two studies showed that homozygous rs1876831 C allele is the risk allele [18, 45], whereas one indicated rs242938 A allele as the risk allele [45]. Nelson et al. [47] reported H2 haplotype as the protective haplotype, whereas Ray et al. [56] reported that H1 in block 1 and H1/H7 in block 2 were the protective haplotypes for alcohol dependence. With regard to the G×E pattern, all four studies with a significant G×E effect showed support for the diathesis-stress pattern.
Several studies found no significant interaction between TAT haplotype (rs7209436, rs242924, rs110402) and environmental factors [47, 49] One study [56] even found that rs110402 and rs242924 SNPs in TAT haplotype had a protective effect on trauma-exposed individuals but increased risk for alcohol dependence in individuals who had not experienced adulthood trauma.
OPRM1.
The OPRM1 gene codes for a protein known as the mu (μ) opioid receptor, which is part of the endogenous opioid system for regulating pain, reward, and addictive behavior. It is located on chromosome 6q24–q25 [95]. The A118G variant (rs1799971) is located in the exon 1 of the OPRM2 gene, which has received the most attention because the variant moderates responses to psychoactive drugs such as alcohol [96]. The G allele has an amino acid change from asparagine to aspartic acid, and the Asp-containing receptor has three times higher affinity to β-endorphin, which can enhance positive feelings and craving following alcohol use [97, 98].
Four studies examined OPRM1 × environment interaction [43, 55, 58, 66], and environmental factors included family factors, socio-demographic factors, and medication adherence. All four studies reported a significant G×E effect, which were consistent with the diathesis-stress model. Besides, three studies implicated G allele as the risk allele [55, 58, 66], whereas one study indicated homogenous A allele as the risk factor for alcohol use [43].
Du et al. [43] indicated that OPRM1 A/A instead of G allele, is a risk factor in subjects with education less than 12 years. This conflicting result may be due to environmental exposure selection. Du et al. [43] used education as the environmental exposure, whereas the other three studies used either family factors or medication adherence [55, 58, 66]. The OPRM1 gene might also be a reason for the conflicting results because no association between G allele and alcohol use behavior was found in some studies [99, 100], whereas A allele was advocated as the risk allele in some other studies [101].
GABRA2.
The GABRA2 gene codes for Gamma-aminobutyric acid (GABA) A receptor, alpha 2 subunit. The GABA-A receptor is a ligand-gated chloride activated by inhibitory neurotransmitter GABA and psychoactive drugs such as benzodiazepines. It is located on chromosome 4p12 [102]. The gene can be suitable for examining genetic influence in the disinhibition pathway because of its role in impulsivity [103], and GABRA2 might be associated with alcohol or illicit drug use behavior [104–106].
We found three studies on GABRA2 × environment interaction [17, 22, 54]. The environmental exposures included marital status, childhood adversity, and daily positive life events. All three studies reported a significant G×E effect, of which two implicated homozygous A allele of rs279871 as the risk allele and one indicated rs11503014 11/12 genotype as the risk factor. Regarding the G×E effect, two studies reported a differential susceptibility pattern [22, 54], and one study reported a diathesis-stress pattern [17].
Enoch et al. [22] found that rs11503014 was associated with heroin addiction, and it had a significant interaction with childhood trauma on cocaine use. Homozygote 11 and heterozygote 12 increased the risk with exposure to higher childhood adversity but decreased the risk when the adversity was low in cocaine dependence. The other two studies reported rs279871 AA genotype as the risk factor for alcohol dependence, the G×E pattern reported by Dick et al. [17] was consistent with the diathesis-stress model while the other two studies showed support for the differential susceptibility pattern [22, 54].
CHRNA3 CHRNA5 CHRNA6.
These CHRN genes code for nicotinic acetylcholine receptor subunits alpha three, five, and six. These genes are located on chromosome 15q25.1 and are associated with smoking behavior in some large GWAS [107–109]. The associations have been found between the rs1051730 A allele of CHRNA3/the rs16969968 A allele of CHRNA5 and nicotine dependence [110–113]. With regard to CHRNA6, the rs2304297 variant was found to be associated with [114] tobacco use behavior [115, 116].
Four studies have examined the interaction between CHRNA gene and various environmental exposure, such as family factors and childhood adversity. Two studies reported the significant G×E effect of the CHRNA5 gene with rs16969968 AA as the risk genotype; one study reported the significant effect of CHRNA3 with rs1051730 A allele as the protective allele, and one study reported the significant effect of the CHRNA6 gene with rs2304297 GG genotype as the risk genotype. The G×E patterns of all four studies were consistent with the diathesis-stress model.
Chen et al. [42] and Ducci et al. [48] studied two similar genes: CHRNA3 and CHRNA5; Chen favored the G×E effect of CHRNA5, and Ducci favored that of CHRNA3. The difference could be due to the differential selection of the environmental and demographic variables. In Ducci et al. [48] study, G×E was significant among 14-year-old Finnish subjects, whereas Chen et al. [42] studied European American subjects with a mean age of 36.4 years.
For the three studies focusing on CHRNA5, all of them studied SNP rs16969968 [42, 48, 51]. However, the G×E effect of this variant remained mixed. While Ducci et al. [48] reported no significant G×E effect, Xie et al. [51] reported a significant interaction between CHRNA5 and childhood adversity on alcohol dependence in male subjects.
Other genes.
Other less commonly studied genes, such as the HTR gene, which codes for the 5-hydroxytryptamine receptor, were also associated with alcohol and drug abuse behavior [117]. According to Lerer et al. [33], the HTR6 gene, which is located on chromosome 1p35–p36, had a significant Interaction with traumatic life experience on smoking initiation, and C267T genotype was indicated as the risk genotype.
TPH genes, coding for tryptophan hydroxylase, were also involved in the serotonin neurotransmission pathway, which might have a potential influence on substance dependence development [39], but the previous study failed to find any association between them [118]. TPH1 and TPH2 gene, are located on chromosome 11p15.3–14 [119] and chromosome 12q21 [120], respectively. According to Gacek et al. [39], there was no interaction between TPH1 or TPH2 gene and negative life events on drinking behavior.
NR3C2 gene codes for mineralocorticoid receptor, and rs5522 is the most studied SNP. The rs5522-Bal allele, located on chromosome 4q31.1 [121], has been associated with altered reward learning [122] and tobacco use [123]. In Rovaris et al. [59], NR3C2 rs5522-Val allele exhibited a significant interaction with physical and emotional neglect on cocaine use.
Significance of the G×E effect.
Of the 44 studies, 38 (86.36%) reported at least one significant G×E effect (see S2 Table). Among 32 studies with alcohol-related outcomes, 25 (78.13%) reported a significant G×E effect. Six out of eight (75%) studies on smoking behavior showed a significant G×E effect. With regard to marijuana, cocaine, and polysubstance use, these outcomes were less studied by researchers with only four studies on marijuana, three on polysubstance, and one on cocaine use, all of which reported a significant G×E effect.
European ancestry is the most studied race, with 23 studies looking into the G×E effect in this population, 18 of which (78.26%) reported a significant G×E effect. Three studies examined the G×E effect in African American population, two of which (66.67%) reported at least one significant G×E effect. Three studies focused on the G×E effect in Latino subjects, all of which (100%) reported at least one significant G×E effect.
In total, 15 studies reported the G×E effect separately by gender. Particularly, 10 studies reported the G×E effect in male subjects [19, 21, 22, 31, 38, 54, 61–64], all of which (100%) exhibited a significant G×E effect. Among 10 studies report female-specific results [33, 37, 40, 41, 51, 54, 59, 62–64], seven (70%) reported a significant interaction effect. The G×E effect of substance use behavior seems to exhibit sex dimorphism and the effect was more prominent in males.
G×E patterns.
The G×E patterns are summarized in the column of “G×E pattern” in Table 2. Among the 38 studies, 34 significant G×E findings followed the diathesis–stress pattern [16], which suggests that individuals with high genetical risk likely experience higher levels of adverse outcome when exposed to a risky environment.
The diathesis-stress pattern was reported in all 18 studies focusing on subjects with European ancestry. In contrast, among two studies focusing on African-American subjects, one supported the diathesis-stress model and the other was consistent with differential susceptibility. For Latino subjects, two diathesis-stress and one differential susceptibility pattern were reported. The differential susceptibility G×E pattern seems to occur more often among African–Americans and Latinos than in European ancestry subjects. However, this finding must be further assessed with caution because of the limited number of studies and data on these two ethnic minorities.
The G×E pattern exhibited sex dimorphism. Of the 10 studies that reported a significant G×E pattern in male subjects, eight (80%) reported a diathesis-stress G×E pattern, and two (20%) reported a differential susceptibility pattern. By contrast, in female subjects, all significant studies (7 out of 7) reported a diathesis-stress G×E pattern.
Discussion
In our systematic review, we reviewed 44 studies on G×E effect in various substance use behaviors with mixed results. The significant G×E effect was reported in 38 studies, 34 of which were consistent with the diathesis-stress pattern and four showed support for the differential susceptibility pattern.
Mechanism and theory interpretation
The mechanisms behind these four G×E models appear to settle within three often studied psychological constructs: emotional reactivity, reward sensitivity, and punishment sensitivity. With regards to emotional reactivity, the amygdala has been, for example, linked to the processing of emotional stimuli [124]. The most studied amygdala reactivity-mediating gene is the 5-HTTLPR of the SCL6A4 gene [125, 126], and people carrying the short allele of 5-HTTLPR are generally more vulnerable when exposed to negative stimuli and thus, under some conditions, maybe more likely to develop affective disorders [127, 128]. The results of our 5-HTTLPR study analyses were consistent with this mechanism, indicating that the short allele carries a vulnerability to substance use disorder in individuals that are exposed to severe negative environmental conditions.
Pertaining to the construct of reward sensitivity, dopamine regulates goal-directed behaviors through mesolimbic dopaminergic circuits, which appear to significantly contribute to levels of motivation for acquiring and consuming drugs [129]. Lowered reward sensitivity may also be a risk factor for thrill-seeking behaviors [129]. In impoverished environments with fewer available rewarding stimuli, direct and powerful rewards can be augmented, thereby resulting in somewhat predictable patterns of substance use and misuse [87].
In compliance with this theory, high-activity alleles of MAOA, which codes for an enzyme that is also involved in the degradation of dopamine, DRD4 seven-repeat allele, DRD2 A1 allele, and DAT 10-repeat allele are hypothesized as additional risk alleles, but results are inconsistent. Most of the included studies advocated MAOA low-activity allele as the risk allele. In DRD studies, the DRD4 seven-repeat and DRD2 A1 allele were indicated as risk alleles, which are consistent with a dopamine-linked theoretical model presented above. It is possible that the MAOA gene effect observed here contradicts any expected patterns of gene outcomes, but discrepancies might be accounted for by heterogeneity in population selections or the range of differential measurements applied.
For punishment sensitivity, serotonin and dopamine appear to coordinate their actions in this behavioral process. Low punishment sensitivity can function asa risk factor for various psychopathies or substance use behavior [130, 131]. Moreover, unpleasant family environments can increase insensitivity to punishment, which may result in being less concerned about behavior consequences and may manifest as in reward-driven behavior [87]. The 5-HTTLPR L-allele and MAOA high-activity allele are indicated to be the risk allele for punishment insensitivity. For 5-HTTLPR, emotional reactivity seems to be the dominant mechanism rather than punishment sensitivity because S-allele is more often supported as the risk allele for substance use. For MAOA, the results were mixed, and further investigations are needed in this field.
General discussion
Among the 44 studies, 38 (86.36%) reported as least one significant G×E effect on substance abuse, and the proportion of significant G×E effect is similar to a previous review focusing on cigarette use only (i.e., 87.5%) [14]. However, the mixed results across studies and the high heterogeneity in study design and measurement make it difficult to draw definitive conclusions about the existence of specific G×E effects for these genes. In addition, some studies showed sex dimorphism [63], whereas others did not. Moreover, publication biases might exist that lend to challenges in concluding G×E effects. Overall, large methodological differences continue to provide obstacles when conducting meta-analyses or even when providing less complex cross-study comparisons.
Limitations of the included studies
rGE (gene–environment correlation).
rGE, which refers to the phenomenon where individuals’ genotype also influences their exposure to the environment, is less commonly seen in the studies included in our review. Reporting rGE is important because the environmental exposure can be shaped by genetical risk, which may be a source of confounder [5]. Fifteen out of 44 studies accounted for rGE, which may be considered as a relatively low proportion. Future studies may benefit from considering whether it is important to take rGE into account rather carefully in an effort to improve validity measurements.
Power calculation.
Calculating the sample size in studies is a critical issue that determines the validity and contribution of the study. For many of the studies included in this review, power calculations are oftentimes not reported. A little less than 25 percent of the current studies report to utilize power calculations, and it is possible that some remaining studies were therefore underpowered when reporting insufficient effect sizes. Underpowered sample sizes directly impact on the strength of hypothesis tests, and the use of insufficient sample sizes may increase the likelihood of type II errors, along with any associated economic losses and lack of scientific gains [132]. We recommend future research to consider the use of careful power calculations beforehand to achieve the maximal validity of the study.
Study design.
The candidate gene method has been criticized for its immature view of the effect of a single gene on a complicated biological pathway. Therefore, at present, several researchers use CGS or GWAS to better understand the role of genes in the development of various complex behaviors. Thus, we also conducted a preliminary search of CGS and GWAS, and such studies are summarized in S3 Table. Among six included CGS studies, four reported a statistically significant interaction effect. Among 21 included GWAS, only nine reported a statistically significant result. A similar issue was also observed by Pasman et al. [5], that is, the polygenic risk methods cannot always generate consistent findings. In addition, GWAS will determine many genes with unknown pathway, thereby causing difficulties in interpreting results and understanding the functional involvement of GWAS contributions. Importantly, it does not appear that GWAS methods extend to the precision of theories concerning the hypothetical constructs of reward sensitivity, punishment sensitivity, or emotional reactivity. By contrast, the CGS method can overcome the aforementioned problems because it selects target genes based on some of the hypotheses and biological pathways, making it possible to interpret and fit into behavioral mechanisms. However, studies regarding the CGS method remain insufficient to draw any conclusion; thus, the selection of genes can only be based on a limited body of knowledge, which might not be accurate enough.
Heterogeneity.
The heterogeneity of included studies limited this review from integrating the findings and generating more solid conclusions. The included studies varied in population selection, environmental exposure selection, environmental exposure measurement, outcome selection, and measurement, as well as in the way they test the G×E effect. These methodological heterogeneities make it hard to pool such studies and synthesize findings.
Conclusion
Future studies that replicate previous studies using similar environmental exposure, outcomes, and measurements to facilitate future pooled analysis may be highly beneficial. Conducting power calculation can determine the ideal sample size before starting research to achieve maximal validity. Analyzing and reporting rGE are important as it can minimize the potential confounders. Moreover, adjusting for various covariates such as age, gender, and ethnicity should be encouraged in future analyses. Researchers may wish to prioritize studies using selective theory-based approaches and to stringently control for multiple comparisons when observing complex statistical analyses.
References
- 1. WHO. WHO global report on trends in prevalence of tobacco use 2000–2025. 2021.
- 2. WHO. Action needed to reduce health impact of harmful alcohol use. 2011. 2019.
- 3. UNODC. World Drug Report 2021. 2021.
- 4. CDC. FastStats—illegal drug use 2022 [Available from: https://www.cdc.gov/nchs/fastats/drug-use-illicit.htm.
- 5. Pasman JA, Verweij KJ, Vink JM. Systematic review of polygenic gene–environment interaction in tobacco, alcohol, and cannabis use. Behavior genetics. 2019;49(4):349–65. pmid:31111357
- 6. Maes HH, Sullivan PF, Bulik CM, Neale MC, Prescott CA, Eaves LJ, et al. A twin study of genetic and environmental influences on tobacco initiation, regular tobacco use and nicotine dependence. Psychological medicine. 2004;34(7):1251–61. pmid:15697051
- 7. Lesch KP, Bengel D, Heils A, Sabol SZ, Greenberg BD, Petri S, et al. Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region. Science. 1996;274(5292):1527–31. pmid:8929413
- 8. Kendler KS, Karkowski LM, Prescott CAJAJoP. Causal relationship between stressful life events and the onset of major depression. 1999;156(6):837–41. pmid:10360120
- 9. Caspi A, Sugden K, Moffitt TE, Taylor A, Craig IW, Harrington H, et al. Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene. 2003;301(5631):386–9. pmid:12869766
- 10. Caspi A, McClay J, Moffitt TE, Mill J, Martin J, Craig IW, et al. Role of genotype in the cycle of violence in maltreated children. 2002;297(5582):851–4. pmid:12161658
- 11. Dawson DA, Grant BF, Ruan WJ. The association between stress and drinking: modifying effects of gender and vulnerability. Alcohol and alcoholism. 2005;40(5):453–60. pmid:15972275
- 12. Fox HC, Bergquist KL, Hong KI, Sinha R. Stress‐induced and alcohol cue‐induced craving in recently abstinent alcohol‐dependent individuals. Alcoholism: Clinical and Experimental Research. 2007;31(3):395–403. pmid:17295723
- 13. Handley ED, Rogosch FA, Cicchetti D. Developmental pathways from child maltreatment to adolescent marijuana dependence: Examining moderation by FK506 binding protein 5 gene (FKBP5). Development and psychopathology. 2015;27(4pt2):1489–502. pmid:26535939
- 14. Do EK, Maes HH. Genotype× environment interaction in smoking behaviors: a systematic review. Nicotine & Tobacco Research. 2017;19(4):387–400.
- 15. Milaniak I, Watson B, Jaffee SR. Gene-environment interplay and substance use: a review of recent findings. Current Addiction Reports. 2015;2(4):364–71.
- 16.
Zuckerman M, Riskind JH. Vulnerability to psychopathology: A biosocial model. Springer; 2000.
- 17. Dick DM, Plunkett J, Hamlin D, Nurnberger J Jr, Kuperman S, Schuckit M, et al. Association analyses of the serotonin transporter gene with lifetime depression and alcohol dependence in the Collaborative Study on the Genetics of Alcoholism (COGA) sample. Psychiatric genetics. 2007;17(1):35–8. pmid:17167343
- 18. Blomeyer D, Treutlein J, Esser G, Schmidt MH, Schumann G, Laucht M. Interaction between CRHR1 gene and stressful life events predicts adolescent heavy alcohol use. Biological psychiatry. 2008;63(2):146–51. pmid:17597588
- 19. Nilsson KW, Sjöberg RL, Wargelius HL, Leppert J, Lindström L, Oreland L. The monoamine oxidase A (MAO‐A) gene, family function and maltreatment as predictors of destructive behaviour during male adolescent alcohol consumption. Addiction. 2007;102(3):389–98. pmid:17298646
- 20. Belsky J, Pluess M. Beyond diathesis stress: differential susceptibility to environmental influences. Psychological bulletin. 2009;135(6):885. pmid:19883141
- 21. Madrid G, MacMurray J, Lee J, Anderson B, Comings D. Stress as a mediating factor in the association between the DRD2 TaqI polymorphism and alcoholism. Alcohol. 2001;23(2):117–22. pmid:11331109
- 22. Enoch M-A, Hodgkinson CA, Yuan Q, Shen P-H, Goldman D, Roy A. The influence of GABRA2, childhood trauma, and their interaction on alcohol, heroin, and cocaine dependence. Biological psychiatry. 2010;67(1):20–7. pmid:19833324
- 23. Raine A. Biosocial studies of antisocial and violent behavior in children and adults: A review. Journal of abnormal child psychology. 2002;30(4):311–26. pmid:12108763
- 24. Guo G, Li Y, Wang H, Cai T, Duncan GJ. Peer influence, genetic propensity, and binge drinking: A natural experiment and a replication. American journal of sociology. 2015;121(3):914–54. pmid:26900620
- 25. Tam V, Patel N, Turcotte M, Bossé Y, Paré G, Meyre DJNRG. Benefits and limitations of genome-wide association studies. 2019;20(8):467–84. pmid:31068683
- 26. Fergusson DM, Boden JM, Horwood LJ, Miller A, Kennedy MA. Moderating role of the MAOA genotype in antisocial behaviour. The British Journal of Psychiatry. 2012;200(2):116–23. pmid:22297589
- 27. Hillemacher T, Frieling H, Buchholz V, Hussein R, Bleich S, Meyer C, et al. Alterations in DNA-methylation of the dopamine-receptor 2 gene are associated with abstinence and health care utilization in individuals with a lifetime history of pathologic gambling. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 2015;63:30–4. pmid:26028496
- 28. Olsson CA, Moyzis RK, Williamson E, Ellis JA, Parkinson‐Bates M, Patton GC, et al. Gene–environment interaction in problematic substance use: Interaction between DRD4 and insecure attachments. Addiction biology. 2013;18(4):717–26. pmid:22126256
- 29. Zhu M, Zhao SJIjobs. Candidate gene identification approach: progress and challenges. 2007;3(7):420.
- 30. Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of internal medicine. 2009;151(4):264–9. pmid:19622511
- 31. Bau CH, Almeida S, Hutz MH. The TaqI A1 allele of the dopamine D2 receptor gene and alcoholism in Brazil: association and interaction with stress and harm avoidance on severity prediction. American Journal of Medical Genetics. 2000;96(3):302–6. pmid:10898904
- 32. Nilsson KW, Sjöberg RL, Damberg M, Alm PO, Öhrvik J, Leppert J, et al. Role of the serotonin transporter gene and family function in adolescent alcohol consumption. Alcoholism: Clinical and Experimental Research. 2005;29(4):564–70. pmid:15834221
- 33. Lerer E, Kanyas K, Karni O, Ebstein R, Lerer B. Why do young women smoke? II. Role of traumatic life experience, psychological characteristics and serotonergic genes. Molecular psychiatry. 2006;11(8):771–81. pmid:16770336
- 34. Dick DM, Agrawal A, Schuckit MA, Bierut L, Hinrichs A, Fox L, et al. Marital status, alcohol dependence, and GABRA2: evidence for gene-environment correlation and interaction. Journal of studies on alcohol. 2006;67(2):185–94. pmid:16562401
- 35. Kaufman J, Yang B-Z, Douglas-Palumberi H, Crouse-Artus M, Lipschitz D, Krystal JH, et al. Genetic and environmental predictors of early alcohol use. Biological psychiatry. 2007;61(11):1228–34. pmid:17123474
- 36. Covault J, Tennen H, Armeli S, Conner TS, Herman AI, Cillessen AH, et al. Interactive effects of the serotonin transporter 5-HTTLPR polymorphism and stressful life events on college student drinking and drug use. Biol Psychiatry. 2007;61(5):609–16. pmid:16920076
- 37. Segman RH, Kanyas K, Karni O, Lerer E, Goltser‐Dubner T, Pavlov V, et al. Why do young women smoke? IV. Role of genetic variation in the dopamine transporter and lifetime traumatic experience. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics. 2007;144(4):533–40.
- 38. Vanyukov MM, Maher BS, Devlin B, Kirillova GP, Kirisci L, Yu LM, et al. The MAOA promoter polymorphism, disruptive behavior disorders, and early onset substance use disorder: gene-environment interaction. Psychiatr Genet. 2007;17(6):323–32. pmid:18075472
- 39. Gacek P, Conner TS, Tennen H, Kranzler HR, Covault J. Tryptophan hydroxylase 2 gene and alcohol use among college students. Addict Biol. 2008;13(3–4):440–8. pmid:18782386
- 40. Nilsson KW, Wargelius HL, Sjöberg RL, Leppert J, Oreland L. The MAO-A gene, platelet MAO-B activity and psychosocial environment in adolescent female alcohol-related problem behaviour. Drug Alcohol Depend. 2008;93(1–2):51–62. pmid:18029114
- 41. Ducci F, Enoch MA, Hodgkinson C, Xu K, Catena M, Robin RW, et al. Interaction between a functional MAOA locus and childhood sexual abuse predicts alcoholism and antisocial personality disorder in adult women. Mol Psychiatry. 2008;13(3):334–47. pmid:17592478
- 42. Chen LS, Johnson EO, Breslau N, Hatsukami D, Saccone NL, Grucza RA, et al. Interplay of Genetic Risk Factors and Parent Monitoring in Risk for Nicotine Dependence. Addiction. 2009;104(10):1731–40. pmid:20871796
- 43. Du Y, Wan YJ. The interaction of reward genes with environmental factors in contribution to alcoholism in mexican americans. Alcohol Clin Exp Res. 2009;33(12):2103–12. pmid:19764934
- 44. Laucht M, Treutlein J, Schmid B, Blomeyer D, Becker K, Buchmann AF, et al. Impact of psychosocial adversity on alcohol intake in young adults: moderation by the LL genotype of the serotonin transporter polymorphism. Biol Psychiatry. 2009;66(2):102–9. pmid:19358979
- 45. Schmid B, Blomeyer D, Treutlein J, Zimmermann US, Buchmann AF, Schmidt MH, et al. Interacting effects of CRHR1 gene and stressful life events on drinking initiation and progression among 19-year-olds. Int J Neuropsychopharmacol. 2010;13(6):703–14. pmid:19607758
- 46. van der Zwaluw CS, Engels RC, Vermulst AA, Franke B, Buitelaar J, Verkes RJ, et al. Interaction between dopamine D2 receptor genotype and parental rule-setting in adolescent alcohol use: evidence for a gene-parenting interaction. Mol Psychiatry. 2010;15(7):727–35. pmid:19238152
- 47. Nelson EC, Agrawal A, Pergadia ML, Wang JC, Whitfield JB, Saccone FS, et al. H2 haplotype at chromosome 17q21.31 protects against childhood sexual abuse-associated risk for alcohol consumption and dependence. Addict Biol. 2010;15(1):1–11. pmid:19878140
- 48. Ducci F, Kaakinen M, Pouta A, Hartikainen AL, Veijola J, Isohanni M, et al. TTC12-ANKK1-DRD2 and CHRNA5-CHRNA3-CHRNB4 influence different pathways leading to smoking behavior from adolescence to mid-adulthood. Biol Psychiatry. 2011;69(7):650–60. pmid:21168125
- 49. Kranzler HR, Feinn R, Nelson EC, Covault J, Anton RF, Farrer L, et al. A CRHR1 haplotype moderates the effect of adverse childhood experiences on lifetime risk of major depressive episode in African-American women. Am J Med Genet B Neuropsychiatr Genet. 2011;156b(8):960–8. pmid:21998007
- 50. Fletcher JM. Why have tobacco control policies stalled? Using genetic moderation to examine policy impacts. PLoS One. 2012;7(12):e50576. pmid:23227187
- 51. Xie P, Kranzler HR, Zhang H, Oslin D, Anton RF, Farrer LA, et al. Childhood adversity increases risk for nicotine dependence and interacts with α5 nicotinic acetylcholine receptor genotype specifically in males. Neuropsychopharmacology. 2012;37(3):669–76.
- 52. Vaske J, Newsome J, Wright JP. Interaction of serotonin transporter linked polymorphic region and childhood neglect on criminal behavior and substance use for males and females. Dev Psychopathol. 2012;24(1):181–93. pmid:22293003
- 53. Daw J, Shanahan M, Harris KM, Smolen A, Haberstick B, Boardman JD. Genetic sensitivity to peer behaviors: 5HTTLPR, smoking, and alcohol consumption. J Health Soc Behav. 2013;54(1):92–108. pmid:23292504
- 54. Perry BL, Pescosolido BA, Bucholz K, Edenberg H, Kramer J, Kuperman S, et al. Gender-specific gene-environment interaction in alcohol dependence: the impact of daily life events and GABRA2. Behav Genet. 2013;43(5):402–14. pmid:23974430
- 55. Miranda R Jr, Reynolds E, Ray L, Justus A, Knopik VS, McGeary J, et al. Preliminary evidence for a gene-environment interaction in predicting alcohol use disorders in adolescents. Alcohol Clin Exp Res. 2013;37(2):325–31. pmid:23136901
- 56. Ray LA, Sehl M, Bujarski S, Hutchison K, Blaine S, Enoch MA. The CRHR1 gene, trauma exposure, and alcoholism risk: a test of G × E effects. Genes Brain Behav. 2013;12(4):361–9.
- 57. Hiemstra M, Kleinjan M, van Schayck OC, Engels RC, Otten R. Environmental smoking and smoking onset in adolescence: the role of dopamine-related genes. Findings from two longitudinal studies. PLoS One. 2014;9(1):e86497. pmid:24466121
- 58. van der Zwaluw CS, Otten R, Kleinjan M, Engels RC. Different trajectories of adolescent alcohol use: testing gene-environment interactions. Alcohol Clin Exp Res. 2014;38(3):704–12. pmid:24134200
- 59. Rovaris DL, Mota NR, Bertuzzi GP, Aroche AP, Callegari-Jacques SM, Guimarães LS, et al. Corticosteroid receptor genes and childhood neglect influence susceptibility to crack/cocaine addiction and response to detoxification treatment. J Psychiatr Res. 2015;68:83–90. pmid:26228405
- 60. Windle M, Kogan SM, Lee S, Chen YF, Lei KM, Brody GH, et al. Neighborhood × Serotonin Transporter Linked Polymorphic Region (5-HTTLPR) interactions for substance use from ages 10 to 24 years using a harmonized data set of African American children. Dev Psychopathol. 2016;28(2):415–31.
- 61. Bendre M, Comasco E, Checknita D, Tiihonen J, Hodgins S, Nilsson KW. Associations Between MAOA‐uVNTR Genotype, Maltreatment, MAOA Methylation, and Alcohol Consumption in Young Adult Males. Alcoholism: clinical and experimental research. 2018;42(3):508–19. pmid:29222910
- 62. Fite PJ, Brown S, Hossain W, Manzardo A, Butler MG, Bortolato M. Tobacco and cannabis use in college students are predicted by sex-dimorphic interactions between MAOA genotype and child abuse. CNS Neurosci Ther. 2019;25(1):101–11. pmid:29952131
- 63. Fite PJ, Brown S, Hossain WA, Manzardo A, Butler MG, Bortolato M. Sex-Dimorphic Interactions of MAOA Genotype and Child Maltreatment Predispose College Students to Polysubstance Use. Front Genet. 2019;10:1314. pmid:32010186
- 64. Su J, Supple AJ, Leerkes EM, Kuo SI. Latent trajectories of alcohol use from early adolescence to young adulthood: Interaction effects between 5-HTTLPR and parenting quality and gender differences. Dev Psychopathol. 2019;31(2):457–69. pmid:29895335
- 65. Navarro-Mateu F, Quesada MP, Escámez T, Alcaráz MJ, Seiquer de la Peña C, Salmerón D, et al. Childhood adversities and 5-HTTLPR polymorphism as risk factors of substance use disorders: retrospective case-control study in Murcia (Spain). BMJ Open. 2019;9(9):e030328. pmid:31488488
- 66. Hendershot CS, Dermody SS, Wardell JD, Zaso MJ, Kennedy JL, Stoner SA. OPRM1 Moderates Daily Associations of Naltrexone Adherence With Alcohol Consumption: Preliminary Evidence From a Mobile Health Trial. Alcohol Clin Exp Res. 2020;44(4):983–91. pmid:32020635
- 67. Ossola P, Gerra MC, Gerra ML, Milano G, Zatti M, Zavan V, et al. Alcohol use disorders among adult children of alcoholics (ACOAs): Gene-environment resilience factors. Prog Neuropsychopharmacol Biol Psychiatry. 2021;108:110167. pmid:33166669
- 68. Pezawas L, Meyer-Lindenberg A, Drabant EM, Verchinski BA, Munoz KE, Kolachana BS, et al. 5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: a genetic susceptibility mechanism for depression. Nature Neuroscience. 2005;8(6):828–34. pmid:15880108
- 69. Li D, He L. Meta-analysis supports association between serotonin transporter (5-HTT) and suicidal behavior. Mol Psychiatry. 2007;12(1):47–54. pmid:16969368
- 70. Takano A, Arakawa R, Hayashi M, Takahashi H, Ito H, Suhara T. Relationship between neuroticism personality trait and serotonin transporter binding. Biol Psychiatry. 2007;62(6):588–92. pmid:17336939
- 71. Cao J, Hudziak JJ, Li D. Multi-cultural association of the serotonin transporter gene (SLC6A4) with substance use disorder. Neuropsychopharmacology. 2013;38(9):1737–47. pmid:23518607
- 72. Huang YY, Cate SP, Battistuzzi C, Oquendo MA, Brent D, Mann JJ. An association between a functional polymorphism in the monoamine oxidase a gene promoter, impulsive traits and early abuse experiences. Neuropsychopharmacology. 2004;29(8):1498–505. pmid:15150530
- 73. Sabol SZ, Hu S, Hamer D. A functional polymorphism in the monoamine oxidase A gene promoter. Hum Genet. 1998;103(3):273–9. pmid:9799080
- 74. Tabakoff B, Hoffman PL. Genetics and biological markers of risk for alcoholism. Public Health Rep. 1988;103(6):690–8. pmid:3141966
- 75. Berlin I M. Anthenelli R. Monoamine oxidases and tobacco smoking. International Journal of Neuropsychopharmacology. 2001;4(1):33–42.
- 76. Vanyukov MM, Moss HB, Yu LM, Tarter RE, Deka R. Preliminary evidence for an association of a dinucleotide repeat polymorphism at the MAOA gene with early onset alcoholism/substance abuse. Am J Med Genet. 1995;60(2):122–6. pmid:7485245
- 77. Carrel L, Willard HF. X-inactivation profile reveals extensive variability in X-linked gene expression in females. Nature. 2005;434(7031):400–4. pmid:15772666
- 78. Sjöberg RL, Nilsson KW, Wargelius HL, Leppert J, Lindström L, Oreland L. Adolescent girls and criminal activity: role of MAOA-LPR genotype and psychosocial factors. Am J Med Genet B Neuropsychiatr Genet. 2007;144b(2):159–64. pmid:17034017
- 79. Wagels L, Votinov M, Radke S, Clemens B, Montag C, Jung S, et al. Blunted insula activation reflects increased risk and reward seeking as an interaction of testosterone administration and the MAOA polymorphism. Hum Brain Mapp. 2017;38(9):4574–93. pmid:28603901
- 80. Grevle L, Güzey C, Hadidi H, Brennersted R, Idle J, Aasly JJMDOJotMDS. Allelic association between the DRD2 TaqI A polymorphism and Parkinson’s disease. 2000;15(6):1070–4. pmid:11104188
- 81. Noble EP, Blum K, Ritchie T, Montgomery A, Sheridan PJ. Allelic association of the D2 dopamine receptor gene with receptor-binding characteristics in alcoholism. Arch Gen Psychiatry. 1991;48(7):648–54. pmid:2069496
- 82. Noble EP. The D2 dopamine receptor gene: a review of association studies in alcoholism and phenotypes. Alcohol. 1998;16(1):33–45. pmid:9650634
- 83. Munafò M, Clark T, Johnstone E, Murphy M, Walton R. The genetic basis for smoking behavior: a systematic review and meta-analysis. Nicotine Tob Res. 2004;6(4):583–97. pmid:15370155
- 84. Demiralp T, Herrmann CS, Erdal ME, Ergenoglu T, Keskin YH, Ergen M, et al. DRD4 and DAT1 Polymorphisms Modulate Human Gamma Band Responses. Cerebral Cortex. 2006;17(5):1007–19. pmid:16751296
- 85. Asghari V, Sanyal S, Buchwaldt S, Paterson A, Jovanovic V, Van Tol HH. Modulation of intracellular cyclic AMP levels by different human dopamine D4 receptor variants. Journal of neurochemistry. 1995;65(3):1157–65. pmid:7643093
- 86. Volkow ND, Wang GJ, Fowler JS, Logan J, Gatley SJ, Hitzemann R, et al. Decreased striatal dopaminergic responsiveness in detoxified cocaine-dependent subjects. Nature. 1997;386(6627):830–3. pmid:9126741
- 87. Weeland J, Overbeek G, de Castro BO, Matthys W. Underlying Mechanisms of Gene-Environment Interactions in Externalizing Behavior: A Systematic Review and Search for Theoretical Mechanisms. Clin Child Fam Psychol Rev. 2015;18(4):413–42. pmid:26537239
- 88. Heinz A, Goldman D, Jones DW, Palmour R, Hommer D, Gorey JG, et al. Genotype influences in vivo dopamine transporter availability in human striatum. Neuropsychopharmacology. 2000;22(2):133–9. pmid:10649826
- 89. Tsou CC, Chou HW, Ho PS, Kuo SC, Chen CY, Huang CC, et al. DRD2 and ANKK1 genes associate with late-onset heroin dependence in men. World J Biol Psychiatry. 2019;20(8):605–15. pmid:28854834
- 90. Ruzilawati AB, Islam MA, Muhamed SKS, Ahmad I. Smoking Genes: A Case-Control Study of Dopamine Transporter Gene (SLC6A3) and Dopamine Receptor Genes (DRD1, DRD2 and DRD3) Polymorphisms and Smoking Behaviour in a Malay Male Cohort. Biomolecules. 2020;10(12).
- 91. Eisenberg DT, Mackillop J, Modi M, Beauchemin J, Dang D, Lisman SA, et al. Examining impulsivity as an endophenotype using a behavioral approach: a DRD2 TaqI A and DRD4 48-bp VNTR association study. Behav Brain Funct. 2007;3:2. pmid:17214892
- 92. Pennisi E. Genetics. 17q21.31: not your average genomic address. Science. 2008;322(5903):842–5. pmid:18988819
- 93. Stefansson H, Helgason A, Thorleifsson G, Steinthorsdottir V, Masson G, Barnard J, et al. A common inversion under selection in Europeans. Nat Genet. 2005;37(2):129–37. pmid:15654335
- 94. Treutlein J, Kissling C, Frank J, Wiemann S, Dong L, Depner M, et al. Genetic association of the human corticotropin releasing hormone receptor 1 (CRHR1) with binge drinking and alcohol intake patterns in two independent samples. Molecular Psychiatry. 2006;11(6):594–602. pmid:16550213
- 95. Mura E, Govoni S, Racchi M, Carossa V, Ranzani GN, Allegri M, et al. Consequences of the 118A> G polymorphism in the OPRM1 gene: translation from bench to bedside? 2013;6:331.
- 96. Heilig M, Goldman D, Berrettini W, O’Brien CP. Pharmacogenetic approaches to the treatment of alcohol addiction. Nat Rev Neurosci. 2011;12(11):670–84. pmid:22011682
- 97. Ray LA, Hutchison KE. A polymorphism of the mu-opioid receptor gene (OPRM1) and sensitivity to the effects of alcohol in humans. Alcohol Clin Exp Res. 2004;28(12):1789–95. pmid:15608594
- 98. van den Wildenberg E, Wiers RW, Dessers J, Janssen RG, Lambrichs EH, Smeets HJ, et al. A functional polymorphism of the mu-opioid receptor gene (OPRM1) influences cue-induced craving for alcohol in male heavy drinkers. Alcohol Clin Exp Res. 2007;31(1):1–10. pmid:17207095
- 99. Franke P, Wang T, Nöthen MM, Knapp M, Neidt H, Albrecht S, et al. Nonreplication of association between mu-opioid-receptor gene (OPRM1) A118G polymorphism and substance dependence. Am J Med Genet. 2001;105(1):114–9. pmid:11424981
- 100. Zhang H, Luo X, Kranzler HR, Lappalainen J, Yang BZ, Krupitsky E, et al. Association between two mu-opioid receptor gene (OPRM1) haplotype blocks and drug or alcohol dependence. Hum Mol Genet. 2006;15(6):807–19. pmid:16476706
- 101. Schinka JA, Town T, Abdullah L, Crawford FC, Ordorica PI, Francis E, et al. A functional polymorphism within the mu-opioid receptor gene and risk for abuse of alcohol and other substances. Mol Psychiatry. 2002;7(2):224–8. pmid:11840318
- 102. Li D, Sulovari A, Cheng C, Zhao H, Kranzler HR, Gelernter JJN. Association of gamma-aminobutyric acid A receptor α2 gene (GABRA2) with alcohol use disorder. 2014;39(4):907–18.
- 103. Villafuerte S, Heitzeg MM, Foley S, Yau WY, Majczenko K, Zubieta JK, et al. Impulsiveness and insula activation during reward anticipation are associated with genetic variants in GABRA2 in a family sample enriched for alcoholism. Mol Psychiatry. 2012;17(5):511–9. pmid:21483437
- 104. Edenberg HJ, Dick DM, Xuei X, Tian H, Almasy L, Bauer LO, et al. Variations in GABRA2, encoding the alpha 2 subunit of the GABA(A) receptor, are associated with alcohol dependence and with brain oscillations. Am J Hum Genet. 2004;74(4):705–14. pmid:15024690
- 105. Agrawal A, Edenberg HJ, Foroud T, Bierut LJ, Dunne G, Hinrichs AL, et al. Association of GABRA2 with drug dependence in the collaborative study of the genetics of alcoholism sample. Behavior genetics. 2006;36(5):640–50. pmid:16622805
- 106. Fehr C, Sander T, Tadic A, Lenzen KP, Anghelescu I, Klawe C, et al. Confirmation of association of the GABRA2 gene with alcohol dependence by subtype-specific analysis. Psychiatr Genet. 2006;16(1):9–17. pmid:16395124
- 107. Liu JZ, Tozzi F, Waterworth DM, Pillai SG, Muglia P, Middleton L, et al. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat Genet. 2010;42(5):436–40. pmid:20418889
- 108. Thorgeirsson TE, Gudbjartsson DF, Surakka I, Vink JM, Amin N, Geller F, et al. Sequence variants at CHRNB3-CHRNA6 and CYP2A6 affect smoking behavior. Nat Genet. 2010;42(5):448–53. pmid:20418888
- 109. Furberg H, Ostroff J, Lerman C, Sullivan PF. The public health utility of genome-wide association study results for smoking behavior. Genome Med. 2010;2(4):26. pmid:20423533
- 110. Thorgeirsson TE, Geller F, Sulem P, Rafnar T, Wiste A, Magnusson KP, et al. A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature. 2008;452(7187):638–42. pmid:18385739
- 111. Bierut LJ, Madden PA, Breslau N, Johnson EO, Hatsukami D, Pomerleau OF, et al. Novel genes identified in a high-density genome wide association study for nicotine dependence. Hum Mol Genet. 2007;16(1):24–35. pmid:17158188
- 112. Saccone SF, Hinrichs AL, Saccone NL, Chase GA, Konvicka K, Madden PA, et al. Cholinergic nicotinic receptor genes implicated in a nicotine dependence association study targeting 348 candidate genes with 3713 SNPs. Hum Mol Genet. 2007;16(1):36–49. pmid:17135278
- 113. Chen X, Chen J, Williamson VS, An SS, Hettema JM, Aggen SH, et al. Variants in nicotinic acetylcholine receptors alpha5 and alpha3 increase risks to nicotine dependence. Am J Med Genet B Neuropsychiatr Genet. 2009;150b(7):926–33. pmid:19132693
- 114. Selya AS, Cannon DS, Weiss RB, Wakschlag LS, Rose JS, Dierker L, et al. The role of nicotinic receptor genes (CHRN) in the pathways of prenatal tobacco exposure on smoking behavior among young adult light smokers. Addict Behav. 2018;84:231–7. pmid:29751336
- 115. Greenbaum L, Lerer B. Differential contribution of genetic variation in multiple brain nicotinic cholinergic receptors to nicotine dependence: recent progress and emerging open questions. Mol Psychiatry. 2009;14(10):912–45. pmid:19564872
- 116. Hoft NR, Corley RP, McQueen MB, Schlaepfer IR, Huizinga D, Ehringer MA. Genetic association of the CHRNA6 and CHRNB3 genes with tobacco dependence in a nationally representative sample. Neuropsychopharmacology. 2009;34(3):698–706. pmid:18704094
- 117. Cao J, Liu X, Han S, Zhang CK, Liu Z, Li D. Association of the HTR2A gene with alcohol and heroin abuse. Hum Genet. 2014;133(3):357–65. pmid:24178752
- 118. Zill P, Preuss UW, Koller G, Bondy B, Soyka M. SNP- and Haplotype Analysis of the Tryptophan Hydroxylase 2 Gene in Alcohol-Dependent Patients and Alcohol-Related Suicide. Neuropsychopharmacology. 2007;32(8):1687–94. pmid:17251907
- 119. Wigner P, Czarny P, Synowiec E, Bijak M, Białek K, Talarowska M, et al. Association between single nucleotide polymorphisms of TPH1 and TPH2 genes, and depressive disorders. 2018;22(3):1778–91. pmid:29314569
- 120. De Luca V, Mueller D, Tharmalingam S, King N, Kennedy JLJMp. Analysis of the novel TPH2 gene in bipolar disorder and suicidality. 2004;9(10):896–7. pmid:15197398
- 121. Plieger T, Felten A, Splittgerber H, Duke É, Reuter MJP. The role of genetic variation in the glucocorticoid receptor (NR3C1) and mineralocorticoid receptor (NR3C2) in the association between cortisol response and cognition under acute stress. 2018;87:173–80. pmid:29100174
- 122. Bogdan R, Perlis RH, Fagerness J, Pizzagalli DA. The impact of mineralocorticoid receptor ISO/VAL genotype (rs5522) and stress on reward learning. Genes Brain Behav. 2010;9(6):658–67. pmid:20528958
- 123. Berrettini W, Yuan X, Tozzi F, Song K, Francks C, Chilcoat H, et al. Alpha-5/alpha-3 nicotinic receptor subunit alleles increase risk for heavy smoking. Mol Psychiatry. 2008;13(4):368–73. pmid:18227835
- 124. Adolphs R, Gosselin F, Buchanan TW, Tranel D, Schyns P, Damasio AR. A mechanism for impaired fear recognition after amygdala damage. Nature. 2005;433(7021):68–72. pmid:15635411
- 125. Brown SM, Hariri AR. Neuroimaging studies of serotonin gene polymorphisms: exploring the interplay of genes, brain, and behavior. Cogn Affect Behav Neurosci. 2006;6(1):44–52. pmid:16869228
- 126. Canli T, Omura K, Haas BW, Fallgatter A, Constable RT, Lesch KP. Beyond affect: a role for genetic variation of the serotonin transporter in neural activation during a cognitive attention task. Proc Natl Acad Sci U S A. 2005;102(34):12224–9. pmid:16093315
- 127. Munafò MR, Brown SM, Hariri AR. Serotonin transporter (5-HTTLPR) genotype and amygdala activation: a meta-analysis. Biol Psychiatry. 2008;63(9):852–7. pmid:17949693
- 128. Murphy SE, Norbury R, Godlewska BR, Cowen PJ, Mannie ZM, Harmer CJ, et al. The effect of the serotonin transporter polymorphism (5-HTTLPR) on amygdala function: a meta-analysis. Mol Psychiatry. 2013;18(4):512–20. pmid:22488255
- 129. Volkow ND, Morales M. The Brain on Drugs: From Reward to Addiction. Cell. 2015;162(4):712–25. pmid:26276628
- 130. Gaher RM, Hahn AM, Shishido H, Simons JS, Gaster S. Associations between sensitivity to punishment, sensitivity to reward, and gambling. Addict Behav. 2015;42:180–4. pmid:25481451
- 131. Frick PJ, Ellis M. Callous-unemotional traits and subtypes of conduct disorder. Clin Child Fam Psychol Rev. 1999;2(3):149–68. pmid:11227072
- 132. Serdar CC, Cihan M, Yücel D, Serdar MA. Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies. Biochem Med (Zagreb). 2021;31(1):010502. pmid:33380887