PHB-10506; No of Pages 7 Physiology & Behavior xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

Physiology & Behavior journal homepage: www.elsevier.com/locate/phb

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Chunhui Chen a,f,1, Chang Liu b,1, Chuansheng Chen c, Robert Moyzis d,e, Wen Chen a,f, Qi Dong a,f,⁎

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Genetic variations in the serotoninergic system and environmental factors contribute to aggressive behavior in Chinese adolescents

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Keywords: Serotonin Aggressive behavior Polygenicity Heritability

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viewed as a public health issue. Identifying environmental and genetic risk factors is an important first step in preventing violence and has been the focus of decades of research. Behavior genetic studies estimated the heritability of aggressive behavior to be about 50% [2,3]. Recent molecular genetic studies revealed that many genes are associated with aggression, including: (1) sex steroid-related genes; (2) serotonin- (5-HT) related genes; (3) dopamine-related genes; and (4) a variety of other genes (for reviews, see [4–7]). Of all these genes, serotonin-related genes were the most commonly studied [6,8,9], although the results have been inconsistent. Generally speaking, genotypes associated with lower levels

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Aggressive behavior is a major public health problem worldwide and has been associated with many gene variants, especially those related to the serotonin (5-hydroxytryptamine, 5-HT) system, and environmental factors. However, the overall contribution of serotonin-related genes to aggressive behavior is not well understood. With a sample of 478 healthy Chinese volunteers, this study investigated the relation between aggressive behavior and genetic variations of the serotoninergic system (as characterized by 129 representative polymorphisms) interacting with environmental factors (parental warmth and acceptance; stressful life events). We adopted a system-level approach to identify SNPs and environmental factors associated with aggressive behavior, and estimated their overall contribution to aggressive behavior using multiple regression, which was then verified by permutation analysis. We identified 12 SNPs that made statistically significant contributions to aggressive behavior. Next, main effects, interactions among these SNPs, and interactions between these SNPs and environmental factors were assessed using multiple regression. The final model accounted for approximately 19% of the variance for aggressive behavior. Permutation analysis confirmed that the probability of obtaining these findings by chance was low (p = 0.045, permuted for 1000 times). These results showed that genetic variations in the serotoninergic system, combined with environmental risk factors, made a moderate contribution to individual differences in aggressive behavior among a healthy population sample. © 2014 Published by Elsevier Inc.

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Article history: Received 4 May 2014 Received in revised form 26 June 2014 Accepted 30 September 2014 Available online xxxx

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1. Introduction

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According to the World Health Organization [1], the number of people dying in interpersonal conflicts was almost twice the number of war victims in 2002 and aggressive behavior has been increasingly

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• With 478 healthy Chinese, we found that 12 serotonin-related SNPs were associated with aggression. • HTR2C interacts with HTR5A as well as stressful life event on aggression. • Gene, environment and their interactions together accounted for 1/5 of aggression variation.

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State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China Department of Psychology, The Pennsylvania State University, University Park, PA, USA Department of Psychology and Social Behavior, University of California, Irvine, CA, USA d Department of Biological Chemistry, University of California, Irvine, CA, USA e Institute of Genomics and Bioinformatics, University of California, Irvine, CA, USA f Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China b

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⁎ Corresponding author at: State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China. Tel.: +86 10 5880 7615. E-mail address: [email protected] (Q. Dong). 1 These authors contributed equally to this work.

http://dx.doi.org/10.1016/j.physbeh.2014.09.005 0031-9384/© 2014 Published by Elsevier Inc.

Please cite this article as: Chen C, et al, Genetic variations in the serotoninergic system and environmental factors contribute to aggressive behavior in Chinese adolescents, Physiol Behav (2014), http://dx.doi.org/10.1016/j.physbeh.2014.09.005

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C. Chen et al. / Physiology & Behavior xxx (2014) xxx–xxx

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2.2.1. Aggression Questionnaire Aggression Questionnaire (AQ) [35] is a 34-item scale that measures aggression in five aspects: physical aggression, verbal aggression, anger, hostility, and indirect aggression. Sample items include “at times I feel like a bomb ready to explode” and “I may hit someone if he or she provokes me”. Participants rated each item on a 5-point scale, 1 = “Disagree strongly” to 5 = “Agree strongly”. The total score of all items was used for analysis.

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2.2.2. Parental Warmth and Acceptance Scale Parental Warmth and Acceptance Scale (PWAS) [36] measures perceived parental warmth with 11 items, such as “My parents really understand me” and “My parents like me the way I am; they don't try to ‘make me over’ into someone else”. Participants rated each item on a 6-point scale, 1 = “Disagree strongly” to 6 = “Agree strongly”. The total score of all items was used for analysis.

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2.1. Participants

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Four hundred and eighty healthy Chinese college students (mean age = 19.9 years, SD = 0.9) were recruited from Beijing Normal University, Beijing, China. They had normal or corrected-to-normal vision, and had no history of neurological or psychiatric problems according to self-report. None of them were identified to have alcohol or nicotine dependence according to the Alcohol Use Disorders Identification Test [33] and the Fagerström Test for Nicotine Dependence [34]. Two participants were excluded because of poor genotyping results. A written consent form was obtained from each subject after a full explanation of the study procedure. This study was approved by the IRB of the State Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University, China.

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2.3. Genetic analysis

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2.3.1. Gene selection We selected 129 polymorphisms (127 SNPs and 2 VNTR polymorphisms) in the 24 known major genes distributed across the synthesis, degradation, transporter, and receptor subsystems of the 5-HT system. Tryptophan hydroxylase (TPH) is the main enzyme involved in 5-HT synthesis, so we included two TPH genes (TPH1 and TPH2, with three SNPs each). For the degradation subsystem, released 5-HT is directly broken down at the synapse into inactive metabolites by MAO (including MAOA and MAOB). We included monoamine oxidase genes (MAOA, with 5 SNPs and 1 VNTR, and MAOB with 3 SNPs). The 5-HT transporter

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Table 1 Means, standard deviations, Cronbach's alpha coefficients, and inter-scale correlations.

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2.2.3. Stressful life events This scale was adapted from similar measures used in Wills, Vaccaro, and McNamara [37]. The scale has been used with cross-cultural samples including Chinese [36]. It lists 24 possible stressful events such as the death of a relative, not passing an examination, and parents getting divorced. Participants in this study had to indicate whether they experienced each event or not during early childhood (primary school years), early adolescence (secondary school years), and within the past two years (i.e., college years for this sample of college sophomores). The stressful events were counted separately for the three periods. The total score of the stressful life events in three periods was used for analysis. All scales were translated from English to Chinese by a team consisting of Chinese–English bilinguals and native English and Chinese speakers and double-checked with forward and backward translation. All scales had good reliability (Table 1) in this study. Reliability (or internal consistency) for stressful life events was not calculated because stressful life events are assumed to be relatively independent of one another, with no underlying latent factors, and only its cumulative effects are evident.

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or activity of serotonin or its metabolites were associated with increased levels of aggressive behavior and impulsivity [7,8], but exceptions were 67 also often reported. 68 Tryptophan hydroxylase (TPH) is the rate-limiting enzyme for 69 brain serotonin synthesis. TPH1 and TPH2 genes have been linked 70 to impulsivity, aggressive behavior, and suicide [4,8]. MAOA and 71 MAOB are enzymes that degrade serotonin and other neurotransmit72 ters, and the MAOA gene was once called the “warrior gene”. However, 73 some studies found that low MAOA activity (thus high serotonin level) 74 was associated with low aggression [10], but others reported no associ75 ation [11,12] or reversed association [13]. The serotonin transporter 76 pumps serotonin from the synaptic cleft back to presynaptic neu77 rons. Knocking out the serotonin transporter gene in mice (thus in78 creasing serotonin level in the synaptic cleft) resulted in reduced 79 aggressive behavior [14]. The S allele of the well-studied 5HTTLPR 80 polymorphism (resulting in higher levels of serotonin) was also re81 ported to be associated with decreased aggressive behavior [15], 82 but others reported increased aggressive behavior [16–18]. For sero83 Q13 tonin receptor genes, 5-HT1A/1B receptors were extensively studied, 84 with positive results in some studies [19–21], but negative results in 85 others [22–25]. 86 There may be several reasons for these inconsistencies. One most 87 likely reason is polygenicity. All these genes mentioned above, as well 88 as other genes, may contribute to aggressive behavior, each with a 89 small individual effect. As early as 1918, Fisher proposed this polygenic 90 model that combined many genes of small effects to yield the continu91 ous variation for most quantitative traits [26]. Recently, some studies 92 have successfully applied the polygenic model by combining effects 93 from alleles throughout the whole genome [27] or effects of genes with94 in a biochemical pathway [28,29]. Since the serotonin-related genes 95 exert their effects at different stages of the serotonin pathway, their 96 effects may be cumulative and/or interactive [30]. Another possible rea97 son is that environmental factors are likely to make direct contributions 98 to behavioral phenotypes, or interact with genetic factors [5,31]. Finally, 99 other differences between studies such as subject characteristics (age, 100 gender, health, ethnicity, sample size) and behavioral measures (verbal 101 Q14 or physical aggression, suicide, antisocial behavior, etc.) may also have 102 contributed to the inconsistencies in the literature on gene-aggression 103 association. 104 The current study adopted a system-level approach to examine 105 the role of the serotoninergic system and environmental factors as 106 well as their interaction in aggressive behavior in a relatively ho107 mogenous sample (in terms of age, health status, and ethnicity). To 108 do this, we enrolled a sample of young healthy Han Chinese college 109 students, genotyped 129 polymorphic loci within the serotonin 110 system, and assessed relevant environmental factors and aggressive 111 behavior. Because gender has been found to be a potential confound112 ing factor [32], we included it as a covariate.

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Mean (SD)

Aggression Questionnaire (AQ) Parental warmth Stressful life events

Cronbach's alpha

53.00 (7.49) 9.72 (5.08)

0.827 –

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Correlations

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Parental warmth

68.57 (15.75) 0.855 ⁎

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−0.17⁎⁎ 0.29⁎⁎ −0.17⁎⁎

⁎ p b 0.05. ⁎⁎ p b 0.01.

Please cite this article as: Chen C, et al, Genetic variations in the serotoninergic system and environmental factors contribute to aggressive behavior in Chinese adolescents, Physiol Behav (2014), http://dx.doi.org/10.1016/j.physbeh.2014.09.005

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The goal of the current study was to understand the relation between individual differences in aggressive behavior, environmental factors, and genetic variations in the 5-HT system in healthy subjects. Moving beyond the single-gene or a small number of haplotype approaches used in typical molecular behavior genetics research, this study used the system-level approach [29] to examine the overall contributions of the serotoninergic system (characterized by the major genes and their associated loci) and environmental factors on aggressive behavior. Briefly, the analysis includes three steps: First, ANOVA was used to screen polymorphic loci that showed nominal significance (p b 0.05) on aggressive behavior; second, these loci, two environmental factors (stressful life events and parental warmth), gender, and their interactions were then entered into a regression model to estimate their overall contribution to aggressive behavior; and lastly the regression model was verified by permutation. In this study, we built three kinds of regression models. In Model 1, we included gender, environmental factors, and the loci with significant main effects based on the ANOVA results and used the forward stepwise method to build the model. To run multiple regression analyses, all SNPs were coded in a linear way, i.e., 1 = major allele homozygotes, 2 = heterozygotes, and 3 = minor allele homozygotes. SNPs on the X chromosome were coded as 1 = major allele and 3 = minor allele for males because they have only one copy of the X chromosome; to be consistent, 1 = major allele homozygotes and 3 = minor allele carriers (both heterozygotes and minor allele homozygotes) for females. The MAOA VNTR on the X chromosome was coded as 1 = 3 repeat and 3 = 4 repeat for males; 1 = 3 repeat homozygotes and 3 = 4 repeat carriers for females. In Model 2, all two-way interactions of these loci in Model 1 were added using a forward stepwise method. In Model 3, two-way interactions of these loci and environmental factors were added to Model 2 using a forward stepwise method. Permutations were done by shuffling aggressive behavior (along with gender and environmental factors) across subjects 1000 times, and recalculating probabilities. The probability of getting a larger R2 in shuffled data than in real data was defined as the p value of the model.

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2.3.3. Gene data preprocessing Two subjects with more than 10% null genotypes were excluded. In addition to automatic calling of genotypes, the Illumina genotyping platform supplies a quantitative quality measure known as the GenCall score. It measures how close a genotype is to the center of the cluster of other samples assigned to the same genotypes, compared with the centers of the clusters of the other genotypes. This measure ranges from 0 to 1, with a higher score indicating a more reliable result. The conventional cutoff point is .25 [42]. Of the 60 228 genotypes (126 SNPs by 478 subjects) in the current study, 120 genotypes (0.2%) were excluded because their GenCall scores were lower than .25. Additional data cleaning included the treatment of low-frequency alleles. For SNPs with either heterozygote or minor allele homozygotes found in fewer than 10 (about 2%) participants, these two genotype groups were combined. If the combined group still had fewer than 10 participants, the SNP(s) were excluded in further analysis. Purported SNPs that turned out to be monomorphic were also deleted. The Hardy–Weinberg equilibrium (HWE) index was calculated using the Chi square test and setting df to 1. Since males have only one X chromosome, only females were included in HWE calculation for SNPs located on X chromosome. Five of the SNPs showed significant HW disequilibrium (p b 0.01). However, we still kept them (although none of them found to be associated with aggression) because we determined that they did not seem to have resulted from genotyping error but rather reflected the characters of our college student sample [43]. The inclusion of both tSNPs and additional SNPs in regions detected in selection screens [44,45] resulted in high LD among a number of SNPs. Thirty SNPs included in initial analysis were excluded from multiple regression analysis because of their high LD with other adjacent SNPs (R2 N 0.8, calculated with Plink [46]), yielding a final list of 99 polymorphisms for the main data analyses. It is worth mentioning that the “redundant” SNPs showed the same or almost the same results as the linked SNPs, confirming the association. Online Supplementary Table S1 shows the details of all 129 polymorphic loci (127 SNPs and 2 VNTRs) included in our study: location (rs number, chromosome, position), gene, serotonin subsystem, allele polymorphism and frequency, Hardy–Weinberg equilibrium, linkage disequilibrium, and deleted SNPs. Finally, the genetic relatedness of subjects was checked following the Anderson et al. [47] protocol using Plink. We used all 240 unrelated autosome SNPs (R2 b 0.8) available in the larger project of these subjects and set the threshold at 0.95 (personal communication with Drs. Anderson

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2.3.2. Genotyping techniques 191 The SNPs were genotyped using the standard Illumina Golden Gate 192 Genotyping protocol (see Illumina Golden-Gate Assay Protocol for de193 tails, http://www.southgene.com.cn; Shanghai South Gene Technology 194 Q15 Co., Ltd, Shanghai, China). In addition, 5HTTLPR and MAOA VNTR were 195 ascertained by standard PCR procedures [39–41]. 196

2.4. Statistical analysis

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and Zondervan). We found no pair of subjects showing high relatedness 237 (all PI_HAT smaller than or equal to 0.5). 238

subsystem includes (1) SLC6A4, an integral membrane-spanning protein that pumps the neurotransmitter serotonin from synaptic spaces into presynaptic neurons and (2) VMAT, a transport protein integrated into the membrane of intracellular vesicles of presynaptic neurons, which acts to transport monoamines into the synaptic vesicles. We included SLC6A4 (7 SNPs plus 5HTTLPR), VMAT1 (SLC18A1, 9 SNPs), and VMAT2 (SLC18A2, 5 SNPs). For the receptor subsystem, we included all 17 known genes (with the respective number of SNPs in parentheses): HTR1A (2), HTR1B (2), HTR1D (13), HTR1F (5), HTR2A (21), HTR2B (6), HTR2C (3), HTR3A (1), HTR3B (2), HTR3C (3), HTR3D (4), HTR3E (2), HTR4 (10), HTR5A (4), HTR5B (2), HTR6 (5), and HTR7 (7). These genes represent all major genes involved in these four 5-HT subsystems in humans, and SNPs were selected based on capturing most known polymorphisms through LD (linkage disequilibrium) [38]. Details about these genes and the selected loci can be found in Supporting information Table S1.

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3. Results

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Table 1 shows the mean total score, standard deviations, reliability estimates, and inter-correlations among the self-report measures. Aggressive behavior was negatively correlated with parental warmth, and was positively correlated with the number of stressful life events. Parental warmth was negatively correlated with the number of stressful life events. Of the 99 polymorphisms that survived LD testing, 12 showed main effects with p b 0.05. Individuals who were major allele homozygotes for rs10917509 (HTR6) or rs1328684 (HTR2A), or rs909525 (MAOA), or minor allele homozygotes for rs6658108 (HTR6) or rs9534501 (HTR2A) or rs2770296 (HTR2A) reported higher aggressive behaviors, whereas individuals who were major allele homozygotes for rs1487275 (TPH2) or rs8076005 (SLC6A4), heterozygotes for rs732050 (HTR5A) or rs6651806 (MAOB) or rs2192371 (HTR2C) or rs6644065 (HTR2C) reported fewer aggressive behaviors (Table 2, and online Supplementary Table S2 for detailed results for all 129 loci). These 12 SNPs and environmental factors plus gender were used in a regression analysis to build models. Table 3 shows the results of the multiple regression analysis. In step one, gender and both of the environmental factors were entered into the regression equation, accounting for 11% (10.7% adjusted) of the variance in aggressive behavior. Forward stepwise regression was then used and five of the 12 SNPs

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Please cite this article as: Chen C, et al, Genetic variations in the serotoninergic system and environmental factors contribute to aggressive behavior in Chinese adolescents, Physiol Behav (2014), http://dx.doi.org/10.1016/j.physbeh.2014.09.005

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Table 2 Means and standard deviations of the AQ score, and main effects and post hoc comparisons of SNPs that showed significant main effects and used in subsequent multiple regression analysis. SNP

Subsystem

Gene

Maj

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SD

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SD

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Post hoc (p b 0.05)

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rs1487275 rs909525 rs6651806 rs8076005 rs10917509 rs6658108 rs732050 rs9534501 rs2770296 rs1328684 rs2192371 rs6644065

Synthesis Degradation Degradation Transport Receptor Receptor Receptor Receptor Receptor Receptor Receptor Receptor

TPH2 MAOA MAOB SLC6A4 HTR6 HTR6 HTR5A HTR2A HTR2A HTR2A HTR2C HTR2C

AA GG AA AA AA GG AA GG AA AA AA AA

66.72 70.23 69.36 67.48 69.66 67.54 70.31 68.13 68.61 69.26 70.37 69.42

15.00 15.95 16.03 15.11 16.42 15.35 16.23 15.50 15.95 16.07 17.50 16.27

191 217 381 355 309 280 302 303 265 406 242 373

AC AG AC AG AG AG AG AG AG AG AG AG

68.95 67.16 65.48 71.41 66.59 69.03 65.32 68.20 67.27 64.71 66.72 65.57

15.42 15.50 14.26 16.37 14.28 15.68 14.04 14.53 13.84 13.27 13.52 13.39

223 260 97 111 169 168 157 153 181 72 236 105

CC AA CC GG GG AA GG AA GG GG GG GG

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74.58

23.98

12

75.60 67.68 77.27 75.63

18.28 17.76 23.86 21.88

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3.69 4.53 4.71 3.57 4.18 3.70 5.31 3.56 3.88 5.14 6.48 4.92

0.03 0.03 0.03 0.03 0.04 0.03 0.00 0.03 0.02 0.02 0.01 0.03

AAbCC GGNAG AANAC AAbAG AANAG GG, AGbAA AANAG GG, AGbAA AA, AGbGG AANAG AANAG AANAG

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Only by summing up their overall effects can we understand the genetic basis of a complex trait such as aggressive behavior. TPH2 is expressed in the brain of humans and is responsible for the central nervous system effects of 5-HT. Previous studies found that the C1473G polymorphism in the TPH2 gene was involved in the determination of TPH activity and was linked to inter-male aggression in inbred mouse strains [48,49]. The current study identified that the A allele of the rs1487275 polymorphism of the TPH2 gene was associated with lower aggression. This result is consistent with previous results that the A allele was associated with higher openness and lower agreeableness measured by the NEO-Five Factor Inventory [50] and less social phobia [51], suggesting that people carrying the A allele may be better at handling interpersonal relationship and are thus less aggressive. Previous research has shown a consistent association between the level of MAOA and aggression [10,12,13,52]. In terms of the MAOA gene, the most often studied polymorphism involves variable-number tandem repeats (VNTR) in the upstream region of the MAOA gene. Its association with aggressive behavior, however, has not always been consistent. Among the replicated results was the MAOA VNTR by childhood abuse interaction on adult aggression [13]. We did not find a significant association between this VNTR polymorphism and aggressive behavior, but we did find an effect of MAOA rs909525, which was previously reported to be associated with higher levels of anger expressed outwards in male suicidal patients and associated with suicidality in males [53], openness in NEO-FFI, and problem gambling [50]. Our results and those of Antypa et al.'s both suggest that the G allele carriers are more aggressive. We also found a main effect of MAOB rs6651806, which was associated with negative emotionality, stress reaction, alienation, and aggression in an earlier study [54]. The A allele was associated with higher aggression scores in both studies. Consistent with previous studies, SLC6A4 was associated with aggressive behavior. The polymorphism in SNP rs8076005 that showed

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entered the model, accounting for 17% (15% adjusted) of the variance in aggressive behavior, F (8, 468) = 11.63, p = 3.89 ∗ 10−15. In step two, 301 we added in two-way interaction terms for these SNPs using the for302 ward stepwise procedure. There were 10 potential SNP–SNP interac303 tions and 1 of them entered the model. In Model 2, the R2 increased to 304 0.18 and adjusted R2 to 0.16, F (9, 468) = 11.03, p = 1.22 ∗ 10−15. In 305 Model 3, the two-way interaction terms of SNPs and environmental fac306 tors were added using the forward stepwise procedure. There were 10 307 potential SNP–environment interactions and 1 of them entered the 308 model. The final model accounted for 19% (17% adjusted) of the vari309 ance in aggressive behavior, F (10, 467) = 10.70, p = 2.22 ∗ 10−16. 310 To validate these results, Monte Carlo permutation was conducted. 311 Fig. 1 shows the permutation results of R2. In each subplot, the X axis 312 represents R2, and the Y axis represents the number of occurrences of 313 a given R2 in 1000 permutations. The curve represents the distribution 314 of R2 based on the permutated data, whereas the vertical line indicates 315 actual R2 obtained in this study. Based on these permutations, the prob316 ability of obtaining the R2 or adjusted R2 found in Model 1 was 0.073 and 317 0.088, respectively. The corresponding probabilities for Model 2 were 318 Q22 0.058 and 0.065 and for Model 3 were 0.045 and 0.053.

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Based on the system-level analysis of 5-HT neurotransmitter genes, we identified 12 SNPs of eight 5-HT-related genes (TPH2, MAOA, 322 MAOB, SLC6A4, HTR2A, HTR2C, HTR5A, and HTR6) showing nominal 323 effects on aggressive behavior. Five of these SNPs made significant unique 324 contributions to aggressive behavior. Together with environmental 325 Q23 factors (parental warmth and acceptance and stressful life events), they 326 accounted for up to 19% of individual difference in aggressive behavior, 327 confirming that aggressive behavior was likely to be influenced to some 328 extent by the serotoninergic system as well as environmental factors.

Table 3 Three regression models for AQ with both genetic data and environmental variables. Regressor

Gene 1

Gene 2

t3:4 t3:5 t3:6 t3:7 t3:8 t3:9 t3:10 t3:11 t3:12 t3:13 Q5 t3:14 t3:15

Gender Parental warmth Stress rs6658108 rs732050 rs1487275 rs8076005 rs2192371 rs732050–rs2192371 rs2192371–stress

HTR6 HTR5A TPH2 SLC6A4 HTR2C HTR5A HTR2C

HTR2C

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Model 2

Model 3

B

T

p

B

T

p

B

T

p

−3.90 −0.24 0.78 3.03 −2.75 2.22 3.38 −1.69

−2.88 −2.67 5.83 2.77 −2.32 2.25 2.54 −2.06

0.00 0.01 0.00 0.01 0.02 0.02 0.01 0.04

−3.94 −0.22 0.77 3.04 −2.72 2.29 3.27 −1.61 3.33

−2.93 −2.49 5.76 2.79 −2.32 2.34 2.48 −1.97 2.28

0.00 0.01 0.00 0.01 0.02 0.02 0.01 0.05 0.02

−3.92 −0.21 0.81 3.31 −2.55 2.17 3.20 2.22 3.11 −0.39

−2.94 −2.28 6.06 3.05 −2.18 2.22 2.43 1.30 2.14 −2.56

0.00 0.02 0.00 0.00 0.03 0.03 0.02 0.19 0.03 0.01

Note: ‘Gene 1’ and ‘Gene 2’ are the corresponding genes for each SNP; ‘B’ is the regression coefficient, ‘T’ and ‘p’ are t-test results.

Please cite this article as: Chen C, et al, Genetic variations in the serotoninergic system and environmental factors contribute to aggressive behavior in Chinese adolescents, Physiol Behav (2014), http://dx.doi.org/10.1016/j.physbeh.2014.09.005

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receptors. However, in the present study, we did not find any significant associations between polymorphic variations in the 5-HT1A and 5-HT1B receptor genes and aggressive behavior in a Chinese population. These null results were in accordance with Takahashi et al.'s review [22] that reported no effects of the 5-HT1A gene and inconsistent effects of the 5-HT1B gene on aggression [also see 23–25]. We should hasten to add that this lack of (consistent) results should not automatically rule out a potential role of these genes in aggression because our SNPs (two per gene in this case) as well as those used in previous studies might have missed the causal loci. In contrast to the above two genes, our analysis revealed that polymorphisms in HTR2A, HTR2C, HTR5A and HTR6 were associated with aggressive behavior in Chinese subjects. Several studies have found significant associations between 5-HT2A (T102C/ rs6313, A1438G/rs6311 and His452Tyr/rs6314) and extraversion, aggressive impulsive trait, impaired behavior control, or adolescentonset antisocial behavior in humans [57–61]. The significant polymorphism rs1328684 in the current study was reported to be associated with extraversion scores [50] as well as treatment response in obsessive–compulsive disorder patients to serotonin reuptake inhibitors [62] and attention-deficit/hyperactivity disorder [63]. In addition, we found that the rs2770296 A allele was associated with less aggression, whereas previous studies showed that this allele was protective against bipolar disorder [64] and associated with novelty seeking [65]. There is little previous work on the remaining 5-HT receptors with respect to aggressive behavior, and much work remains in order to clarify the specific roles of the 5-HT2C, 5-HT5A and 5-HT6 receptors in aggressive behavior found in our study. It should be noted that we found significant HTR5A-by-HTR2C and HTR2C-by-stressful life events interactions, suggesting complex relations within receptor-related genes. Epistasis and epigenetic effects have been frequently reported. For example, Reuter reported an interaction between COMT and DRD2 on personality and suggested that a balance between degradation and receptor affinity may result in positive emotionality [66]. Here the HTR5A-by-HTR2C interaction may suggest a competition mechanism between receptors. Also stressful life events have been reported to interact with MAOA [67], COMT [68], and BDNF [69] on aggressive behavior, suggesting that genes may predispose individuals to aggressive behavior but environmental triggers are also needed for individuals to act aggressively. Further research on an HTR2C–environment interaction is needed. Several limitations of the current study need to be mentioned. First, we focused only on healthy Han Chinese college students, so these results may or may not be generalized to other populations (e.g., clinical samples, other ethnic groups). Second, we selected 129 polymorphic loci related to the 5-HT system according to our previous study and HapMap information to cover variations of the major serotoninrelated genes. A more extensive coverage may reveal more variants with significant effects. In addition, other neuronal systems such as the dopamine system may also contribute to aggressive behavior [70–72]. Such a system should be examined separately or in conjunction with the 5-HT system. Last, we used only two environmental factors in our model. A careful selection of other environmental factors or other measures of parenting and social environments would help refine our model. Future research should also incorporate other examples of gene–environment interactions. In conclusion, the current study used a system-level approach to study gene-behavior associations that considered the polygenic nature of behavior and examined gene–gene interactions and incorporated both genetic and environmental factors. Our results suggest that many genes within the serotonin system may contribute to aggression, each with a small effect and potentially interacting with each other as well as environmental factors. By summing up their effects, one fifth of the variance of aggression can be explained. These results also suggest that the effect of genes within serotonin system can be cumulative and the system-level approach may be a promising way to explore the genetic basis of human aggressive behavior.

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Fig. 1. Permutation results for the three models: Model 1 (first row), Model 2 (second row) and Model 3 (third row). The dashed line represents empirical distribution of R2 obtained from the randomized data, and the solid vertical line represents R2 obtained from the actual data.

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a significant effect in the current study is located in the intron region of SLC6A4. A recent study found that this polymorphism was significantly 363 associated with depression measured by the Beck Depression Inventory 364 and plasma interleukin-6 level [55]. In that study, the G allele 365 carriers tended to be more depressed, and in our study they were 366 more aggressive. The G allele was also found to be associated with 367 ADHD in females [56]. 368 Q24 As summarized in the Introduction section, with regard to 5-HT 369 receptors, studies mainly concentrated on the 5-HT1A and 5-HT1B

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This study was supported by the Research Fund for the Doctoral Program of Higher Education (20110003120001), the 111 Project (B07008) of the Ministry of Education of China, the Fundamental Research Funds for the Central Universities (2012LYB05), and the National Natural Science Foundation of China (31100807 and 31221003). We thank all graduate research assistants who helped with data collection.

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