Antisocial behaviors, serotonergic genes, polygenic index, family violence, candidate genes, gene-environment interplay (GxE).
Violence taking place within the family has long been regarded as a risk factor for antisocial behaviors (Holt et al., 2008; Jaffee et al., 2004; Steketee et al., 2019; Widom, 1989). Considering the relentless efforts devoted to understanding the origins and consequences of family violence for children’s well-being and development, there is a surprising lack of consistency regarding the definition and the measurement of family violence. Different forms of violence, including child-directed parental violence and child-witnessed intimate partner violence (IPV) between the parents, are often aggregated under the same umbrella construct of “child maltreatment”. The use of all-encompassing indexes of familial violence may nevertheless obscure the detection of distinct patterns of associations between various forms of family violence and children’s outcomes such as antisocial behaviors (Holt et al., 2008; McLaughlin et al., 2014). Even scarcer are the studies that have investigated these specific associations while accounting for well-known co-occurrence between various forms of intra-familial violence (Edleson, 2001).
Previous research has given more attention to the link between child-directed parental violence and children’s later display of behavioral problems, mainly antisocial behaviors (Braga et al., 2017; Huizinga et al., 2006; Maneta et al., 2017; Ogloff et al., 2012; Park et al., 2012; Sternberg et al., 2006; Sunday et al., 2011). However, nearly one in five men report using IPV (Walsh et al., 2020) and epidemiological investigations report that between 40% and a staggering 80% of children who live in such households witness it (Artz et al., 2014). Not only children witnessing intra-parental violence are at higher risk of adopting antisocial behaviors (Artz et al., 2014; Carpenter & Stacks, 2009; Forke et al., 2018; Holmes et al., 2022), but they are likely to pass it on to the next generation. Social learning processes have been hypothesized to be a driving force behind the intergenerational transmission of violence and crime by offering models reinforcing the belief that coercion is an acceptable or efficient strategy to get what one wants (Akers & Jennings, 2016; Holt et al., 2008; Kitzmann et al., 2003; Widom, 1989; Wood & Sommers, 2011).
Complementarily to the social learning theory, genetically-informed studies point to biosocial mechanisms by which family violence may influence future risk of antisocial behaviors, either through inherited characteristics confounded within these family environments (gene-environment correlations), or by moderating the individual’s sensitivity to these criminogenic environments (Gene-environment interaction, GXE; Byrd & Manuck, 2014; Cicchetti et al., 2012; Ouellet-Morin et al., 2016; Thibodeau et al., 2015; Widom & Brzustowicz, 2006). Several researchers have hypothesized that the interplay between genes and environments may take the form of a Diathesis-stress model, for which the pathogenic environmental triggers a genetic susceptibility, other types of GxE may arise (Bronfenbrenner & Ceci, 1994; Caspi et al., 2002; Kendler, 2011). Among them, the Social push model posits that highly adverse or criminogenic environments may mask inherited risk factors that would have been otherwise been expressed and more readily detected in the absence of these environments (Raine, 2002). The higher heritability estimate of antisocial behaviors reported among individuals with lower levels of family dysfunction, compared to those exposed to higher dysfunction, would fit this model (Button et al., 2005). Others, still, reported no GxE interaction at all (Haberstick et al., 2014; Young et al., 2006; for a review of models relevant to antisocial outcomes, see Barnes et al., 2022).
Despite overwhelming evidence pointing to the highly polygenic nature of complex traits such as antisocial behaviors (Plomin et al., 2016), prior GxE studies mostly focused on a single candidate gene. Polygenic indexes offer the possibility to test the presence of GxE simultaneously according to several genes known to be implicated in a given neurophysiological system (e.g., serotonergic system) instead of considering one candidate polymorphism or gene at the time (Plomin, 2013). Genes involved in the synthesis and regulation of serotonin production have, by far, been the main focus of prior investigations aiming to document the genetic etiology of antisocial behaviors (Ficks & Waldman, 2014; Runions et al., 2019; Tielbeek et al., 2016). Serotonin is a key neurotransmitter that contributes to regulate a broad range of psychological functions and behaviors, such as learning, mood, aggression, and the modulation of cognitions (Banlaki et al., 2015; Burt & Mikolajewski, 2008; Coccaro et al., 2015; Crockett et al., 2013; Duke et al., 2013; Gibson, 2018; Hariri & Holmes, 2006; Lesch & Merschdorf, 2000; Muller & Jacobs, 2009; Summers et al., 2005). Lower serotonin levels have repeatedly been associated with increased aggression in animal (Bochis et al., 2022) and human models (da Cunha-Bang & Knudsen, 2021). Notably, our team has previously examined an association between cumulative indices of serotonergic haplotype blocks –a combination of nearby single nucleotide polymorphisms (SNPs) based on linkage disequilibrium– and antisocial behaviors (Langevin et al., 2019). Because haplotypes maximize information gathered from multiple variants and thus contribute to clarifying the genetic etiology of antisocial behaviors, they should be more strongly associated with the targeted phenotypes and thus contribute to further clarifying the genetic etiology of antisocial behaviors (Clark, 2004; Morris & Kaplan, 2002). From a statistical perspective, haplotypes reduce the burden of multiple testing, thereby also potentially reducing the risk of false-positive findings. In this previous investigation conducted in the same subsample of the current study, we showed that individuals who carried a higher number of haplotypic alleles independently conferring risk for antisocial outcomes, also had higher levels of antisocial behaviors in adolescence (conduct disorder symptoms) and in early adulthood (antisocial personality disorder symptoms, property/violent crimes, intimate partner violence)(Langevin et al., 2019). However, we did not test whether these associations could be explained by the experiences of family violence, nor did we investigate whether these genetic risk indices moderated the strength of the association between family violence and antisocial outcomes. These haplotype-based serotonergic polygenic scores allow to capture more information than single variants as they arise from patterns of linkage disequilibrium present within candidate genes. It may thus represent a more powerful strategy to examine the interaction between the candidate serotonergic genes and between family violence on antisocial outcomes (Khoury, 2017; Kraft & Aschard, 2015; McAllister et al., 2017).
There is growing evidence from cross-sectional and longitudinal studies of children, adolescents, and adults suggesting that the magnitude of the association between family violence and antisocial behaviors may be contingent upon individual differences within the serotonergic genes (Caspi et al., 2002; Liu et al., 2015; Maglione et al., 2018; Taylor & Kim-Cohen, 2007; Tielbeek et al., 2016; Tuvblad et al., 2011; Veroude et al., 2016). However, others have failed to confirm this gene-environment interplay (Haberstick et al., 2014; Sadeh et al., 2013). These inconsistencies may arise, in part, from differences in the operationalisation of family violence. Indeed, an extensive review of 39 studies highlighted the extent to which inconsistencies arise in detecting the interplay between the SLC6A4 gene and family violence for anxiety and depression as a function of methods used to measure family violence (Uher & McGuffin, 2010). Despite the plead for a nosology of family violence (Cicchetti & Barnett, 1991; McLaughlin et al., 2014), as well as growing evidence for adversity-specific pathways underlying a range of partly co-occurring long-lasting vulnerabilities to psychopathology (Lacey & Minnis, 2020; Lacey et al., 2020; McLaughlin et al., 2014; Pinto Pereira et al., 2019), the investigation of GxE for antisocial behaviors relies, on the most part, on indexes encompassing multiple forms of psychosocial and socioeconomic indicators of adversity (Lacey & Minnis, 2020). Investigations that untangle specific forms of family influences may help to identify with greater accuracy for whom, according to their genetic background, the association between specific adverse experiences and antisocial behaviors is magnified (or buffered). This could eventually help to uncover mechanisms by which these social environments translate into antisocial risk.
In sum, previous research offers strong support that child-directed and child-witnessed parental violence are associated with antisocial behaviors. Additional evidence, albeit mixed, further suggests that individual differences in serotonergic genes may exacerbate (or buffer) this association. To date, however, existing studies have mainly focused on single genes rather than on cumulative index considering multiple variants located across several candidate genes within the serotonergic system. Moreover, few studies have tested whether distinct patterns of GxE emerge as a function of child-directed or child-witnessed parental violence and across developmental periods (e.g., adolescence, adulthood). Accordingly, the aim of this study was to test whether haplotype-based serotonergic polygenic risk scores previously documented (Langevin et al., 2019) modified the strength of the association between family violence and antisocial behaviors, and whether the same patterns of GxE findings would emerge for child-directed vs. child-witnessed parental violence, as well as in adolescence vs. early adulthood. Based on prior investigation of adversity, multigenic (or polygenic) scores and antisocial behaviors (e.g., Barnes et al., 2019), we hypothesized that we would find significant GxE taking the form of a Diathesis-stress model.
The Quebec Longitudinal Study of Kindergarten Children (QLSKC) is a representative sample of 3,017 children attending kindergarten in French-speaking state schools of the province of Quebec, Canada (Rouquette et al., 2014). Children aged 6 years at baseline were followed until adulthood, thus allowing for data collections to take place during three important developmental stages for antisocial behaviors: childhood, adolescence, and early adulthood. During the first years of follow-up, information on environmental factors (e.g., socioeconomic factors, parenting practices) and child and family member characteristics (e.g., temperament, physical and mental health, antisocial behaviors) were collected to explore their role to antisocial behaviors. Health, lifestyle, and social adjustment outcomes were assessed during adolescence and early adulthood. To date, 12 data collections have been conducted over 24 years and three generations of participants have been involved in the study (i.e., the study child, their parents and the study child’s first child). Informed consent was obtained from the parents and/or the participants at each time of data collection (for more information, see Rouquette et al., 2004).
A total of 1,241 participants took part in the DNA collection (n=422 males). This study focused on males because of well-known sex differences in antisocial behaviors displayed in adolescence and adulthood, and because the 5-HTR2C, MAOA, and MAOB genes are located on the X chromosome, which role may be harder to ascertain because of X-inactivation processes in females (Kukurba et al., 2016). The data of 12 participants were excluded from the sample due to population stratification, leading to a final sample of 410 genotyped Caucasian males for whom antisocial behaviors were assessed during adolescence and early adulthood. Non-random attrition was noted. On average, male participants for whom DNA had not been collected had higher levels of disruptive behavior in kindergarten (t(1, 528.25)=-3.70, p=.001), were from lower socioeconomic family background (t(1, 469.76)=-6.40, p=.001) and were more likely to be born to a younger mother (Z = -3.20, p=.001). All statistical analyses were thus weighted for this selective attrition by including inverse probability weights in the analyses. Written informed consent was obtained for each data collection. The study was approved by the research ethics board of Sainte-Justine Hospital and the University of Montréal.
Conduct disorder symptoms were assessed at age 15 using a semi-structured interview based on the Diagnostic Interview Schedule (DIS) for Children (Shaffer et al., 2000). The test-retest reliability and internal consistency of the French version of the DISC were satisfactory (Breton et al., 1998). A total score was created by summing up the symptoms indicated as present (range=0-6; M=0.77; SD=1.20). In this population-based sample, the internal consistency was low (ordinal α=.62), likely due to low variability and base rates of some (more severe) items in this relatively small, population-based sample for whom males participated to the study from age 6 years to early adulthood (22 years), when the DNA data collection took place. Antisocial personality disorder symptoms were measured at age 21 years using the DIS for adults, a semi-structured interview based on DSM-III-R criteria (Robins et al., 1989). We derived a total score by summing up each symptom indicated as present (range=0-6; M=1.07; SD=1.38). The ordinal α was good (α=.81). Physical intimate partner violence (IPV) perpetration was self-reported by participants at age 21 years using 15 items drawn from the French version of the Conflict Tactics Scale (e.g., pushed, grabbed, shoved; Cyr et al., 1997). The internal consistency of this instrument was satisfactory. A total of 40 participants (10.1%) reported at least one instance of physical violence against their partner (Fortin et al., 2000). Finally, property crimes (e.g., stealing, fraud, burglary) and violent crimes (e.g., assault, possession of a weapon) were reported at 21 years of age using the Life History Calendar (Freedman et al., 1988), the DIS and the Dimensional Assessment of Personality Pathology–Basic Questionnaire, respectively (α=0.89-0.91; Livesley & Jackson, 1986). A total of 78 participants (20.7%) reported having committed at least one property/violent crime in early adulthood.
Child-directed parental violence that occurred before age 18 years was assessed using the Life History Calendar (Caspi et al., 1996; Freedman et al., 1988) according to five indicators: maternal and paternal psychological and physical violence, and sexual abuse. Psychological violence was assessed using an adapted version of the Revised Conflict Tactics Scale (Straus et al., 1998), for which good internal consistency was noted for the two informants (mothers: α=0.85; fathers: α=0.83). Physical abuse was measured using eight items drawn from the Parent–Child Conflict Tactics Scale (α=0.64; Straus et al., 1998). Sexual abuse was assessed using five items (α=0.65) adapted from the Adverse Childhood Experiences Study (Felitti et al., 1998) and the Sexually Victimized Children Questionnaire (Hébert, 2000). A confirmatory factor analysis supported the grouping of the five measures of child-directed violence into a single, general factor distributed continuously in this sample (range=-.69-2.52; mean [M]=-.004; standard deviation [SD]=.52; root mean square error of approximation [RMSEA]=0.045; comparative fit index [CFI]=0.918; Tucker–Lewis Index [TLI]=0.901). The scale used in the analyses was created by summing up the experiences of child-directed parental violence. The total score varied from 0 to 92 (M=17.90, SD=15.84) and was winsorized according to a cut-off score of 3 SD from the mean prior to statistical analyses to minimize the disproportionate influence of elevated score in this sample (range=0-65, M=17.62, SD=14.80).
Experiences of witnessed intimate partner violence between the parents, taking the forms of physical or psychological violence, perpetrated against the mother by the father (and vice versa), occurring before the child was 18 years of age, were reported by the participants at age 21 years using an adapted version of the Revised Conflict Tactics Scales (Straus et al., 1998). Good internal consistency was noted (α=0.70). Physical (e.g., push, pull hair, threaten with a weapon) and psychological violence (insults, controls the comings and goings) perpetrated against the mother and father were reported. A confirmatory factor analysis confirmed that the independent measures of witnessed IPV could be grouped into a single, general factor distributed continuously in this sample (range=-1.47-2.52; M=-.004; SD=.52; RMSEA=0.001; CFI=0.965; TLI=0.895). The total score was derived by summing up experiences of witnessed IPV. The total score varied from 0 to 71 (M=5.03, SD=7.93) and was winsorized according to a cut-off score of 3 SD from the mean prior to statistical analyses (range=0-28, M=4.74, SD=6.35).
This study builds on previous work from the authors (Langevin et al., 2019), in which eleven serotonergic candidate genes (5-HTR1A, 5-HTR2A, 5-HTR2C, 5-HTR5A, 5-HTR6, 5-HTR7, SLC6A4, MAOA, MAOB, TPH-1, TPH-2) were selected on the basis of previous evidence suggesting their relevance to antisocial behaviors, and according to availability of informative genetic markers in this cohort (Supplementary materials lists the haplotype-superalles included in each cumulative genetic risk indexes). A high-throughput, 768-SNP Illumina platform and GoldenGate panel based on BeadArray technology was used (Oliphant et al., 2002). Common SNPs (minor allele frequency >5%) located 5kbp upstream of the transcription sites were selected. Additionally, 44 anonymous markers spread across the genome and located outside of gene-coding regions were genotyped to detect population stratification. The initial genotyping success rate for the SNPs was 95.4%. SNPs fewer than 60 base pairs apart were eliminated, and 33 SNPs were eliminated because of low call rate (<.90). A genotype call rate of 100% was achieved in the remaining sample. Hardy-Weinberg equilibrium (HWE) analyses were completed using Haploview 4.0 (Barrett et al., 2005), which resulted in the rejection of an additional 10 SNPs. Linkage disequilibrium (LD) between the SNPs located within each gene was assessed and haplotype blocks with a frequency of at least 10% were identified using the Haploview software (Barrett et al., 2005). The associations between the haplotype-based superalleles and each antisocial outcome in adolescence (conduct disorder symptoms) and adulthood (antisocial personality disorder symptoms, IPV perpetration, property/violent crimes) were tested using PLINK 1.7 software (Purcell et al., 2007). Haplotype-based superalleles conferring risk at an empirical p˂.10 after 10,000 permutations were summed to create a haplotype-based serotonergic polygenic score specific to each antisocial outcome (Langevin et al., 2019).
Unaccounted gene-environment correlations (rGE) can falsely pose as GxE (Jaffee & Price, 2007; Rutter et al., 2006). Indeed, rGE arises when the inherited factors associated with the expression of antisocial behaviors overlaps with, evokes or co-occur with "environments" pertaining the manifestation of antisocial behaviors (D'Onofrio et al., 2011; Jaffee et al., 2004; Jaffee & Price, 2008; Kendler, 2011). Notably, mothers’ antisocial behavior is a known risk factor for their children’s antisocial behavior and also relates to adverse parenting behavior and family environment, of which the genetic transmission is thought to partly occur through passive gene-environment correlation (rGE; Jaffee et al., 2004, 2005). To rule out the possibility of rGE confounding GxE results, we statistically controlled for the mother’s symptoms of antisocial personality disorder, assessed using the Diagnostic Interview Schedule (DIS) for adults, a semi-structured interview based on the DSM-III-R criteria (range=0-2; M=.53; SD=.65) and investigated for possible rGE. The reliability of the French version of the DIS was satisfactory (Lepage et al., 1996). We also tested the possibility that the family SES, another documented risk factor for antisocial behavior, which is partly under genetic influence (Marees et al., 2021), could bias our GxE analyses. Family SES was measured using family income, parental educational attainment, and professional prestige. A confirmatory factor analysis confirmed that these indicators of SES belonged to a single factor (range=-1.32-1.98; M=.12; SD=.62; RMSEA=.45; CFI=.92; TLI=.90).
Analyses were conducted in two steps. First, preliminary analyses were first conducted to rule out rGE between each haplotype-based serotonergic polygenic score, maternal antisociality, SES and both types of parental violence. Maternal antisociality and familial SES were included as confounders in subsequent analyses, if indicated. Second, main analyses were conducted in two sequences using negative binomial regressions with robust estimators or logistic regression (for continuous and dichotomous variables, respectively) and were weighted to control for non-random attrition. In the first sequence, child-directed parental violence was investigated, followed by witnessed IPV (second sequence). Four models are presented in each sequence. Regression analyses included the main effect of the haplotype-based polygenic score (Model 1) and parental violence (i.e., either child-directed violence or witnessed IPV; Model 2). Model 3 tested the unique contributions of the polygenic score and parental violence, while Model 4 also included their interaction term. Significant interactions were illustrated using a simple slope approach, which depicts the association between parental violence and antisocial outcomes according to the mean and ± 1 SD of the continuously distributed serotonergic polygenic score. These analyses, reported in the online supplementary material document, simultaneously consider the two forms of familial violence to specify their unique contributions.
Bivariate correlations were estimated between each covariate (mother’s antisocial personality, family SES) and the serotonergic polygenic scores to examine the likelihood of rGE affecting the GxE (Table 1). Results show a significant correlation between the mother’s antisocial personality disorder and the participants’ serotonergic polygenic score for IPV perpetration. Maternal antisocial personality disorder was thus included as a confounder in the subsequent IPV analyses. No other bivariate correlations were significant, thus minimizing the possibility that unaccounted rGE may bias our subsequent tests of GxE.
Do serotonergic polygenic scores predict and moderate the association between child-directed parental violence and antisocial outcomes?
Consistent with a prior use of the data (Langevin et al., 2019), each serotonergic polygenic score was significantly associated with its antisocial outcome, explaining from 2.0% to 8.0% of the variance in antisocial behavior (Table 2, Model 1). Table 2 further shows that child-directed parental violence was associated with a higher number of conduct disorder symptoms in adolescence [B(SE)=.02(.00), p.001, R2=4.9%], increased risk of IPV perpetration [B(SE)=.05(.00), p.001, R2=7.8%] and of property/violent crimes in early adulthood [B(SE)=.02(.00), p.001, R2=2.8%; Table 2, Model 2]. Both child-directed parental violence and serotonergic polygenic scores remained significant when re-examined concurrently, together explaining between 6.1% and 18.6% of the phenotypes’ variance (Table 2, Model 3).
Notably, the association between child-directed parental violence and the perpetration of property/violent crimes in adulthood was moderated by the participants’ polygenic score [B(SE)=.02(.00), p.001, ΔR2=2.1%; Table 2, Model 4]. To illustrate this effect, Figure 1 depicts the conditional association between child-directed parental violence and the reported perpetration of property/violent crimes plotted at lower (-1 SD), moderate (M) and higher (+1 SD) serotonergic polygenic scores. The report of parental violence was associated with property/violent crimes only for those who carried lower [B(SE)=.12(.05), p=.016] or average [B(SE)=.02(.01), p=.038] serotonergic genetic risk. Contrastingly, child-directed parental violence was not associated with property/violent crimes among individuals with higher genetic risk [B(SE)=.01(.01), p=.53]. In fact, the serotonergic genetic load for property/violent crime perpetration was most apparent in absence of child-directed parental violence. In that context, participants carrying the highest genetic load reported approximately three times the prevalence of property and violent crimes in early adulthood compared to those with the lowest genetic load. Alternatively, the prevalence of property/violent crimes did not differ according to the serotonergic polygenetic scores for the participants who reported having been the victims of higher levels of violence as children [F(2, 390)=.36, p=.698]. These results should be interpreted with caution, however, due to the large standard errors depicted in Figure 1. Supplementary analyses revealed that the main and interaction effects remained significant when statistically controlling for witnessed IPV (Supplementary Table 1, Models 3 and 4).
Do serotonergic polygenic scores predict and moderate the association between child-witnessed IPV and antisocial outcomes?
Table 3 shows that having witnessed IPV is associated with a higher number of antisocial personality disorder symptoms [B(SE)=.01(.00), p.01, R2=0.7%], the reported occurrence of IPV perpetration [B(SE)=.05(.01), p.001, R2=1.5%], and of property/violent crimes [B(SE)=.02(.01), p.05, R2=1.3%] in early adulthood. The association between witnessed IPV and antisocial personality disorder symptoms was reduced to a non-significant trend for association when the haplotype-based serotonergic polygenic score was included in the model. The association between witnessed IPV and IPV perpetration, and between witnessed IPV and property/violent crimes remained significant when re-examined concurrently with the serotonergic polygenic score, together explaining from 7.8% to 8.5% of the variance of these antisocial outcomes (Table 3, Model 3).
Table 3, Model 4 shows a significant interaction between the serotonergic polygenic scores and witnessed IPV in association with conduct disorder symptoms in adolescence [B(SE)=.05(.01), p.001, ΔR2=1.5%] and antisocial personality disorder symptoms in early adulthood [B(SE)=.01(.00), p=.037, ΔR2=1.5%]. Figure 2 (Panel A) shows that, among individuals with higher serotonergic genetic risk (+1 SD of the sample’s mean), having witnessed IPV was marginally associated with a higher number of conduct disorder symptoms [B(SE)=.07(.04), p=.08], whereas this association was nonsignificant for those at the sample’s mean [B(SE)=.00(.02), p=.94] and lower genetic risk [-1 SD of the sample’s mean: B(SE)=-.02(.04), p=.18]. Having witnessed IPV was also marginally associated with antisocial personality disorder symptoms among individuals with the higher serotonergic genetic risk [B(SE)=.07(.03), p=.06], although this was not the case at the sample’s mean [B(SE)=.01(.01), p=.58] nor at lower genetic risk [B(SE)=-.01(.02), p=.56; Figure 2, Panel B]. At higher levels of witnessed IPV, the difference in antisocial personality disorder symptoms between individuals carrying a lower vs. higher genetic risk became apparent [B(SE)=.33(.08), p.001]. Among individuals who did not witness IPV, these antisocial outcomes did not differ according to the serotonergic polygenic score groups [B(SE)=.05(.05), p=.35]. The large standard error depicted in Figure 2 (Panels A and B), however, calls for caution in the interpretation of these gene-environment interactions.
Of note, sensitivity analyses show that these interaction effects remained significant when we included child-directed parental violence in the model, for a total explained variance of 7.6% and 7.1% for conduct disorder and antisocial personality disorder symptoms, respectively (Supplementary Table 1, Model 5). This suggests that the interplay between witnessed IPV and the serotonergic polygenic scores is unlikely to be due to partially overlapping, unaccounted effects of child-directed parental violence.
Despite overwhelming evidence for the polygenicity of antisocial behaviors, studies have mostly examined the interplay between candidate genes and family violence through the lens of a single genetic polymorphism at a time. Moreover, existing research has mainly relied on the construct of family violence (or “child maltreatment” or “child adversity”) encompassing multiple psychosocial and socioeconomic experiences of distinct nature, from parental harshness, emotional neglect and physical abuse to housing problems and lower family SES.
The use of this umbrella construct may be useful to examine the cumulative load of many adverse experiences on antisocial behaviors. Nevertheless, building on wide-ranging indices of adversity to test the presence of GxE assumes prior knowledge or a clear assumption that each factor embedded within this index is similarly exacerbated (or concealed) by the targeted polymorphisms, taking the same form of GxE (e.g., Diathesis-stress or Social push models), and according to the same level of intensity across their continous distribution. Using a haplotype-based serotonergic polygenic scores encompassing 11 serotonergic genes (116 SNPs), we expanded on our previous study that evidenced associations between cumulative haplotype-based serotonergic scores and antisocial outcomes, to test the moderating role of these candidate gene in the association between child-directed and child-witnessed parental violence and antisocial behaviors measured in adolescence and in early adulthood. The present findings contribute to prior research in four ways.
First, child-directed parental violence increased the risk of manifesting conduct disorder symptoms in adolescence, as well as intimate partner violence perpetration, property, and violent crimes in early adulthood. This association is consistent with the substantial literature linking child maltreatment (as an umbrella construct) to antisocial behaviors (Braga et al., 2018; Braga et al., 2017; Widom, 2017), as well as investigations that have specifically targeted connections between child-directed parental violence and antisocial outcomes (Wilson et al., 2009). Notably, our findings showed that this association remained significant over and above witnessed IPV, as well as differences present at the level of the targeted serotonergic genes.
Second, findings from this cohort study provide additional support for gene-environment interplay; child-directed parental violence was associated with higher risk of perpetrating property/violent crimes in early adulthood, but only among individuals carrying the lowest and the sample’s mean serotonergic genetic risk for this antisocial outcome. This pattern of GxE is consistent with the Social push model (Raine, 2002), which proposes that biological risk factors are more salient in absence (or at lower levels) of adversity rather than in its presence (or at higher levels). Accordingly, adverse environments –in this case child-directed parental violence– may conceal inherited risk for property or violent crimes by providing a “social push”, that is, a social-based drive toward the manifestation of antisocial outcomes over and beyond biological, in this study genetic, risk (Raine, 2002; Raine & Venables, 1981). This pattern of GxE, whereby genetic differences are noted in absence rather than in the presence of criminogenic environments, echoes previous reports of biosocial interplay targeting neurobiological systems (e.g., resting heart rate, cortisol response to stress), as well as of quantitative and molecular genetic studies proposed to be involved in antisocial behaviors and psychopathy (Beaver et al., 2007; Lu & Menard, 2017; McCrory et al., 2010; Ouellet-Morin et al., 2008; Raine, 2013; Schoorl et al., 2017; Tuvblad et al., 2006). For example, in an investigation of 1133 adolescent twin pairs, a stronger heritability estimate of antisocial behaviors was detected among twins who grew up in wealthier neighborhoods (37% of the variance explained by genetic factors) in comparison to those were raised in more socioeconomically deprived neighborhoods (1% of the variance explained by genetic factors; Tuvblad et al., 2006). Similarly, the associations between the MAOA and DRD2 genes and serious offending appeared to be concealed in the context of higher exposure to deviant peers, but not otherwise (Beaver et al., 2007; Lu & Menard, 2017).
In the case of child-directed parental violence, the identification of a GxE consistent with the Social push model departs from previous reports of GxE taking the form of the Diathesis-stress model (e.g., Caspi et al., 2002). While the jury is still out as to what might drive these opposite patterns of findings, some have argued that the GxE may vary according to the relative severity of environmental pathogens (Ouellet-Morin et al., 2016; van Hazebroek et al., 2019). In their systematic review, van Hazebroek and colleagues (2019) suggested that the studies supporting the Social push model had mostly been conducted in low-risk samples, whereas those that were in line with the Diathesis-stress model were more often drawn from samples comprising participants exposed to higher environmental adversity (van Hazebroek et al., 2019).
While our study focused on child-directed parental violence, including moderate-to-severe behaviors such as physical and sexual abuse, the prevalence of these behaviors was rather low (e.g., sexual abuse, 3.1%), suggesting that our population-based sample would fall into van Hazebroek and colleagues’ category of low-risk studies. This is also consistent with the sample’s reported moderately high family income and parents’ education attainment (Rouquette et al., 2014). Our sample may thus not have been optimal to capture the higher end of the continuum of adversity (Moffitt et al., 2006; Ouellet-Morin et al., 2016). Furthermore, other factors pertaining to parental violence, such as the timing at first occurrence, the chronicity, as well as the delay since these experiences occurred at the time of the measurement may have further complicated the test of its joint contribution with the serotonergic polygenic scores on antisocial behaviors in adolescence and early adulthood. The large standard errors in our analyses may thus signal that other factors may be at play and, more generally, call for caution in the interpretation of the results. Future studies should examine more systematically whether gene-environment interactions for antisocial behaviors vary in shapes and magnitude in larger samples, according to parental violence measured prospectively and repeatedly to circumscribe the additional role timing of onset, chronicity and severity may play in these associations.
Third, our results suggest that having witnessed parental IPV before the age of 18 years was associated with antisocial personality disorder symptoms and property or violent crimes in early adulthood. Previous neuroimaging studies have also lent support for the distinct role of witnessed IPV on antisocial behaviors, beyond that of child-directed parental violence (Carr et al., 2013; Maneta et al., 2017). Having witnessed IPV has also been associated with neuropsychological deficits (e.g., reduced gray and white matter density in the temporal gyrus, limbic irritability) after controlling for the effect of the child-directed parental violence (Choi et al., 2012; Choi et al, 2009; Dackis et al., 2012; Jouriles et al., 2008; Tomoda et al., 2012; Tomoda et al., 2011). Altogether, these findings suggest that witnessing parental violence may also be detrimental to the child’s well-being beyond child-directed violence and, as such, deserves to be further investigated in the etiology of antisocial behaviors and according to genetically sensitive research designs.
Fourth, our study offers preliminary evidence that a serotonergic genetic risk load moderates, to some extent, the association between witnessed IPV and behavioral (i.e., conduct disorder symptoms) and personality (i.e., antisocial) disorders, which was consistent with the Diathesis-stress model. Specifically, while having witnessed IPV was marginally associated with these antisocial outcomes among the males carrying a higher serotonergic polygenic risk, this association was not significant among those with the sample’s average and lower genetic risk. These findings are somewhat consistent with a wide literature supporting the Diathesis-stress model for these phenotypes (Caspi et al., 2002; Kim-Cohen et al., 2006). In these studies, however, the experiences of having witnessed IPV were combined with child-directed parental violence and, in some cases, neglect. The present study is the first, to the best of our knowledge, to show that a GxE may also emerge in the specific context of WIPV and in regard to antisocial behaviors.
The detection of gene-environment interplay between the haplotype-based serotonergic polygenic score and witnessed IPV, taking the form of a Diathesis-stress, contrasts with the previously discussed interaction found in relation to child-directed parental violence, which was consistent with a ‘Social push’ model. In line with the hypothesis that different patterns of GxE might emerge according to the severity and chronicity of the exposure to criminogenic environments (van Hazebroek et al., 2019), it is possible that the exposure to IPV was more severe in our sample in comparison to that of child-directed parental violence. Exposure to IPV may have a triple whammy effect, whereby: a) it simultaneously fosters the belief that controlling others through coercion is an acceptable or efficient strategy to achieve their desired goal (Akers & Jennings, 2016; Holt et al., 2008; Kitzmann et al., 2003; Widom, 1989), b) exposes the child to a deterioration of the parents’ mental health (e.g., chronic stress, depressive symptoms, alcohol or drug abuse, poor coping strategies), c) which in turn may trigger harsher parenting practices (Carpenter & Stacks, 2009; Ehrensaft et al., 2017; McLaughlin et al., 2014). These cumulative stressful experiences may induce a cascade of neuroendocrine responses more likely to be repeated over time, jeopardizing key emotional and behavioral regulatory processes, leading to higher risk of antisocial outcomes in later life (McCrory et al., 2017; Susman, 2006).
Our findings should be considered in light of several limitations. First, the measures of child-directed and child-witnessed parental violence relied on retrospective assessments completed at 21 years of age for events that occurred before 18 years of age, raising the possibility that recall biases may have affected the reliability of this measure. Moreover, the retrospective assessments were not conducted according to the specific timing of exposure to parental violence (e.g., childhood vs. adolescence). As such, we could not assess whether distinct patterns of GxE might emerge according to the timing of the first occurrence of parental violence. Future studies should investigate these interaction effects across multiple developmental periods, especially considering violence occurring in early childhood has been shown to exert more potent and long-lasting effects on antisocial behaviors (Mueller & Tronick, 2020). Second, participants’ IPV perpetration and property/violent crimes were measured using self-reports. Bias related to shared informant and methods could have inflated the magnitude of the associations between these constructs, whereas this possibility is reduced for the other antisocial outcomes, which were assessed using semi-structured interviews. Third, the sample size is relatively small to investigate GxE and the analyses were conducted in a subsample of males who participated to this longitudinal study from the ages of six to 22 years. While we statistically controlled for longitudinal attrition using inversed probability weights, the relatively low prevalence of participants with high levels of antisocial outcomes and parental violence combined with the large standard errors in our analyses calls for caution in the interpretation of the results. Replication in larger independent studies is required. Fourth, this study cannot entirely rule out the possibility that rGEs have confounded our tests of GxE for polymorphisms other than those considered in the present study, beyond the potential genetic confounding of the targeted 11 serotonergic genes (116 SNPs) included in the study. Fifth, our study was conducted in a primarily white and middle-class cohort of males. Future studies should thus investigate whether the same patterns of findings could be generalized to females and more diverse samples. Notwithstanding these limitations, our analysis is strengthened on two methodological accounts. First, relying on a population-based cohort is advantageous to investigate GxE because of the expected representativeness of the population variation in genotype and environmental risk exposure (Moffitt, 2005). Second, the use of haplotype-based serotonergic polygenic scores is expected to increase the power to detect GxE as they capture a more comprehensive signal of genetic influences thought to cumulatively affect more a given neurophysiological system, such as the serotonergic system (Plomin et al., 2009).
This study offers additional, albeit partial, evidence that differences in our genome may affect vulnerability to environmental conditions, giving rise to a heterogeneous display of antisocial behavior among people similarly exposed to criminogenic environments. Specifically, our findings provide support to the implication of serotonergic genes in the etiology of antisocial behavior, which may trigger distinct patterns of associations between parental violence and antisocial outcomes as function of the type of violence experienced, and across the various forms of antisocial behavior measured during adolescence and early adulthood. Our findings extend prior studies of serotonergic genes by using a haplotype-based serotonergic polygenic score capturing the pattern of covariance of multiple SNPs within several genes which are part of a functionally relevant neurophysiological system for antisocial behavior, indicating their cumulative effect. Our findings also indicate partly distinct GxE findings for child-directed and witnessed IPV, suggesting that it may be premature to use aggregated indicators of family violence as it may obscure dysmorphic hidden patterns of findings (McLaughlin et al., 2014).
Akers, R. L., & Jennings, W. G. (2016). Social learning theory. In A. R. Piquero (Ed.), Wiley Handbooks in Criminology and Criminal Justice (pp. 230-240): Wiley Blackwell.
Artz, S., Jackson, M. A., Rossiter, K. R., Nijdam-Jones, A., Géczy, I., & Porteous, S. (2014). A comprehensive review of the literature on the impact of exposure to intimate partner violence for children and youth. International Journal of Child, Youth and Family Studies, 5(4), 493-587. doi:10.18357/ijcyfs54201413274
Banlaki, Z., Elek, Z., Nanasi, T., Szekely, A., Nemoda, Z., Sasvari-Szekely, M., & Ronai, Z. (2015). Polymorphism in the Serotonin Receptor 2a (HTR2A) Gene as Possible Predisposal Factor for Aggressive Traits. PloS One, 10(2), e0117792. doi:10.1371/journal.pone.0117792
Barnes, J. C., Raine, A., & Farrington, D. P. (2022). The interaction of biopsychological and socio-environmental influences on criminological outcomes. Justice Quarterly, 39(1), 26-50. doi:10.1080/07418825.2020.1730425
Barrett, J. C., Fry, B., Maller, J., & Daly, M. J. (2005). Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics, 21(2), 263-265. doi:10.1093/bioinformatics/bth457
Beaver, K. M., Wright, J. P., DeLisi, M., Daigle, L. E., Swatt, M. L., & Gibson, C. L. (2007). Evidence of a Gene X Environment Interaction in the Creation of Victimization: Results From a Longitudinal Sample of Adolescents. International Journal of Offender Therapy and Comparative Criminology, 51(6), 620-645. doi:10.1177/0306624X07304157
Belsky, J., & Beaver, K. M. (2011). Cumulative-genetic plasticity, parenting and adolescent self-regulation. Journal of Child Psychology and Psychiatry, 52(5), 619-626. doi:https://doi.org/10.1111/j.1469-7610.2010.02327.x
Bochis, T. A., Imre, K., Marc, S., Vaduva, C., Florea, T., Dégi, J., ... & Ţibru, I. (2022). The Variation of Serotonin Values in Dogs in Different Environmental Conditions. Veterinary Sciences, 9(10), 523.
Braga, T., Cunha, O., & Maia, Â. (2018). The enduring effect of maltreatment on antisocial behavior: A meta-analysis of longitudinal studies. Aggression and Violent Behavior, 40, 91-100. doi:https://doi.org/10.1016/j.avb.2018.04.003
Braga, T., Gonçalves, L. C., Basto-Pereira, M., & Maia, Â. (2017). Unraveling the link between maltreatment and juvenile antisocial behavior: A meta-analysis of prospective longitudinal studies. Aggression and Violent Behavior, 33, 37-50. doi:https://doi.org/10.1016/j.avb.2017.01.006
Breton, J.-J., Bergeron, L., Valla, J.-P., Berthiaume, C., & St-Georges, M. (1998). Diagnostic Interview Schedule for Children (DISC-2.25) in Quebec: Reliability Findings in Light of the MECA Study. Journal of the American Academy of Child and Adolescent Psychiatry, 37(11), 1167-1174. doi:https://doi.org/10.1097/00004583-199811000-00016
Bronfenbrenner, U., & Ceci, S. J. (1994). Nature-nuture reconceptualized in developmental perspective: A bioecological model. Psychological Review, 101(4), 568-586. doi:10.1037/0033-295X.101.4.568
Burt, S. A., & Mikolajewski, A. J. (2008). Preliminary evidence that specific candidate genes are associated with adolescent-onset antisocial behavior. Aggressive Behavior, 34(4), 437-445. doi:https://doi.org/10.1002/ab.20251
Button, T. M. M., Scourfield, J., Martin, N., Purcell, S., & McGuffin, P. (2005). Family Dysfunction Interacts with Genes in the Causation of Antisocial Symptoms. Behavior Genetics, 35(2), 115-120. doi:10.1007/s10519-004-0826-y
Byrd, A. L., & Manuck, S. B. (2014). MAOA, childhood maltreatment, and antisocial behavior: meta-analysis of a gene-environment interaction. Biological Psychiatry, 75(1), 9-17. doi:10.1016/j.biopsych.2013.05.004
Carpenter, G. L., & Stacks, A. M. (2009). Developmental effects of exposure to Intimate Partner Violence in early childhood: A review of the literature. Children and Youth Services Review, 31(8), 831-839. doi:https://doi.org/10.1016/j.childyouth.2009.03.005
Carr, C. P., Martins, C. M. S., Stingel, A. M., Lemgruber, V. B., & Juruena, M. F. (2013). The Role of Early Life Stress in Adult Psychiatric Disorders: A Systematic Review According to Childhood Trauma Subtypes. The Journal of Nervous and Mental Disease, 201(12). https://journals.lww.com/jonmd/Fulltext/2013/12000/The_Role_of_Early_Life_Stress_in_Adult_Psychiatric.1.aspx
Caspi, A., McClay, J., Moffitt Terrie, E., Mill, J., Martin, J., Craig Ian, W., . . . Poulton, R. (2002). Role of Genotype in the Cycle of Violence in Maltreated Children. Science, 297(5582), 851-854. doi:10.1126/science.1072290
Caspi, A., Moffitt, T. E., Thornton, A., Freedman, D., Amell, J. W., Harrington, H., . . . Silva, P. A. (1996). The life history calendar: A research and clinical assessment method for collecting retrospective event-history data. International Journal of Methods in Psychiatric Research, 6(2), 101-114. doi:10.1002/(SICI)1234-988X(199607)6:2<101::AID-MPR156>3.3.CO;2-E
Choi, J., Jeong, B., Polcari, A., Rohan, M. L., & Teicher, M. H. (2012). Reduced fractional anisotropy in the visual limbic pathway of young adults witnessing domestic violence in childhood. Neuroimage, 59(2), 1071-1079. doi:https://doi.org/10.1016/j.neuroimage.2011.09.033
Choi, J., Jeong, B., Rohan, M. L., Polcari, A. M., & Teicher, M. H. (2009). Preliminary evidence for white matter tract abnormalities in young adults exposed to parental verbal abuse. Biological Psychiatry, 65(3), 227-234. doi:10.1016/j.biopsych.2008.06.022
Cicchetti, D., & Barnett, D. (1991). Toward the development of a scientific nosology of child maltreatment. In Thinking clearly about psychology: Essays in honor of Paul E. Meehl, Vol. 1: Matters of public interest; Vol. 2: Personality and psychopathology. (pp. 346-377). Minneapolis, MN, US: University of Minnesota Press.
Cicchetti, D., Rogosch, F. A., & Thibodeau, E. L. (2012). The effects of child maltreatment on early signs of antisocial behavior: genetic moderation by tryptophan hydroxylase, serotonin transporter, and monoamine oxidase A genes. Development and Psychopathology, 24(3), 907-928. doi:10.1017/s0954579412000442
Coccaro, E. F., Fanning, J. R., Phan, K. L., & Lee, R. (2015). Serotonin and impulsive aggression. CNS Spectrums, 20(3), 295-302. doi:10.1017/S1092852915000310
Crockett, M. J., Apergis-Schoute, A., Herrmann, B., Lieberman, M. D., Müller, U., Robbins, T. W., & Clark, L. (2013). Serotonin Modulates Striatal Responses to Fairness and Retaliation in Humans. The Journal of Neuroscience, 33(8), 3505. doi:10.1523/JNEUROSCI.2761-12.2013
Cyr, M., Fortin, A., & Chénier, N. (1997). Questionnaire sur la résolution de conflits conjugaux (traduction française de Strauss, MA, Hamby, SL, Boney-McCoy, S., & Sugarman, DB (1996), Conflict Tactics Scale-II). Montréal: Université de Montréal.
da Cunha-Bang, S., & Knudsen, G. M. (2021). The modulatory role of serotonin on human impulsice aggression. Biological Psychiatry, 90(7), 44-457.
D'Onofrio, B. M., Rathouz, P. J., & Lahey, B. B. (2011). The importance of understanding gene-environment correlations in the development of antisocial behavior. In S. R. Jaffee, K. S. Kendler, & D. Romer (Eds.), The dynamic genome and mental health: the role of genes and environments in youth development. : Oxford University Press.
Dackis, M. N., Rogosch, F. A., Oshri, A., & Cicchetti, D. (2012). The role of limbic system irritability in linking history of childhood maltreatment and psychiatric outcomes in low-income, high-risk women: Moderation by FK506 binding protein 5 haplotype. Development and Psychopathology, 24(4), 1237-1252. doi:10.1017/S0954579412000673
Duke, A. A., Bègue, L., Bell, R., & Eisenlohr-Moul, T. (2013). Revisiting the serotonin–aggression relation in humans: A meta-analysis. Psychological Bulletin, 139(5), 1148-1172. doi:10.1037/a0031544
Edleson, J. L. (2001). Studying the co-occurence of child maltreatment and domestic violence in families. In Domestic violence in the lives of children: The future of research, intervention, and social policy. (pp. 91-110). Washington, DC, US: American Psychological Association.
Ehrensaft, M. K., Knous-Westfall, H., & Cohen, P. (2017). Long-term influence of intimate partner violence and parenting practices on offspring trauma symptoms. Psychology of Violence, 7(2), 296-305. doi:10.1037/a0040168
Farrington, D. P. (2005). Integrated developmental and life-course theories of offending: Advances in Criminological Theory (1 ed., vol. 14). Routledge.
Felitti, V. J., Anda, R. F., Nordenberg, D., Williamson, D. F., Spitz, A. M., Edwards, V., . . . Marks, J. S. (1998). Relationship of Childhood Abuse and Household Dysfunction to Many of the Leading Causes of Death in Adults: The Adverse Childhood Experiences (ACE) Study. American Journal of Preventive Medicine, 14(4), 245-258. doi:https://doi.org/10.1016/S0749-3797(98)00017-8
Fernàndez-Castillo, N., & Cormand, B. (2016). Aggressive behavior in humans: Genes and pathways identified through association studies. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 171(5), 676-696. doi:https://doi.org/10.1002/ajmg.b.32419
Ficks, C. A., & Waldman, I. D. (2014). Candidate Genes for Aggression and Antisocial Behavior: A Meta-analysis of Association Studies of the 5HTTLPR and MAOA-uVNTR. Behavior Genetics, 44(5), 427-444. doi:10.1007/s10519-014-9661-y
Forke, C. M., Myers, R. K., Fein, J. A., Catallozzi, M., Localio, A. R., Wiebe, D. J., & Grisso, J. A. (2018). Witnessing intimate partner violence as a child: How boys and girls model their parents’ behaviors in adolescence. Child Abuse and Neglect, 84, 241-252. doi:https://doi.org/10.1016/j.chiabu.2018.07.031
Fortin, A., Chamberland, C., & Lachance, L. (2000). La justification de la violence envers l'enfant: un facteur de risque de violences. La Revue internationale de l'éducation familiale, 4(2), 5-34.
Freedman, D., Thornton, A., Camburn, D., Alwin, D., & Young-DeMarco, L. (1988). The Life History Calendar: A Technique for Collecting Retrospective Data. Sociological Methodology, 18, 37-68. doi:10.2307/271044
Gibson, E. L. (2018). Tryptophan supplementation and serotonin function: genetic variations in behavioural effects. Proceedings of the Nutrition Society, 77(2), 174-188. doi:10.1017/S0029665117004451
Haberstick, B. C., Lessem, J. M., Hewitt, J. K., Smolen, A., Hopfer, C. J., Halpern, C. T., . . . Mullan Harris, K. (2014). MAOA Genotype, Childhood Maltreatment, and Their Interaction in the Etiology of Adult Antisocial Behaviors. Biological Psychiatry, 75(1), 25-30. doi:https://doi.org/10.1016/j.biopsych.2013.03.028
Hariri, A. R., & Holmes, A. (2006). Genetics of emotional regulation: the role of the serotonin transporter in neural function. Trends in Cognitive Sciences, 10(4), 182-191. doi:https://doi.org/10.1016/j.tics.2006.02.011
Hébert, M. (2000). Adaptation Francaise du Adverse Childhood Experience (ACE) Study Questionnaire et du Sexually Victimized Children Questionnaire de Finkelhor (1979) French Adaptation of the Adverse Childhood Experience (ACE) Study Questionnaire and of the Finkelhor’s Sexually Victimised Children Questionnaire. [Document inédit].
Holmes, M. R., Berg, K. A., Bender, A. E., Evans, K. E., O’Donnell, K., & Miller, E. K. (2022). Nearly 50 Years of Child Exposure to Intimate Partner Violence Empirical Research: Evidence Mapping, Overarching Themes, and Future Directions. Journal of Family Violence. doi:10.1007/s10896-021-00349-3
Holt, S., Buckley, H., & Whelan, S. (2008). The impact of exposure to domestic violence on children and young people: A review of the literature. Child Abuse and Neglect, 32(8), 797-810. doi:https://doi.org/10.1016/j.chiabu.2008.02.004
Holz, N., Boecker, R., Buchmann, A. F., Blomeyer, D., Baumeister, S., Hohmann, S., . . . Laucht, M. (2016). Evidence for a Sex-Dependent MAOA× Childhood Stress Interaction in the Neural Circuitry of Aggression. Cerebral Cortex, 26(3), 904-914. doi:10.1093/cercor/bhu249
Huizinga, D., Haberstick, B. C., Smolen, A., Menard, S., Young, S. E., Corley, R. P., . . . Hewitt, J. K. (2006). Childhood Maltreatment, Subsequent Antisocial Behavior, and the Role of Monoamine Oxidase A Genotype. Biological Psychiatry, 60(7), 677-683. doi:https://doi.org/10.1016/j.biopsych.2005.12.022
Jaffee, S. R., Caspi, A., Moffitt, T. E., & Taylor, A. (2004). Physical Maltreatment Victim to Antisocial Child: Evidence of an Environmentally Mediated Process. Journal of Abnormal Psychology, 113(1), 44-55. doi:10.1037/0021-843X.113.1.44
Jaffee, S. R., & Price, T. S. (2007). Gene–environment correlations: a review of the evidence and implications for prevention of mental illness. Molecular Psychiatry, 12(5), 432-442. doi:10.1038/sj.mp.4001950
Jaffee, S. R., & Price, T. S. (2008). Genotype–environment correlations: implications for determining the relationship between environmental exposures and psychiatric illness. Psychiatry, 7(12), 496-499. doi:https://doi.org/10.1016/j.mppsy.2008.10.002
Jouriles, E. N., Brown, A. S., McDonald, R., Rosenfield, D., Leahy, M. M., & Silver, C. (2008). Intimate partner violence and preschoolers' explicit memory functioning. Journal of Family Psychology, 22(3), 420-428. doi:10.1037/0893-322.214.171.1240
Kendler, K. S. (2011). A conceptual overview of gene-environment interaction and correlation in a developmental context. In K. S. Kendler, S. R. Jaffee, & D.Romer (Eds.), The dynamic genome and mental health (pp. 5-28): Oxford University Press.
Khoury, M. J. (2017). Editorial: Emergence of Gene-Environment Interaction Analysis in Epidemiologic Research. American Journal of Epidemiology, 186(7), 751-752. doi:10.1093/aje/kwx226
Kim-Cohen, J., Caspi, A., Taylor, A., Williams, B., Newcombe, R., Craig, I. W., & Moffitt, T. E. (2006). MAOA, maltreatment, and gene–environment interaction predicting children's mental health: new evidence and a meta-analysis. Molecular Psychiatry, 11(10), 903-913. doi:10.1038/sj.mp.4001851
Kitzmann, K. M., Gaylord, N. K., Holt, A. R., & Kenny, E. D. (2003). Child witnesses to domestic violence: A meta-analytic review. Journal of Consulting and Clinical Psychology, 71(2), 339-352. doi:10.1037/0022-006X.71.2.339
Koss, K. J., & Gunnar, M. R. (2018). Annual Research Review: Early adversity, the hypothalamic–pituitary–adrenocortical axis, and child psychopathology. Journal of Child Psychology and Psychiatry, 59(4), 327-346. doi:https://doi.org/10.1111/jcpp.12784
Kraft, P., & Aschard, H. (2015). Finding the missing gene–environment interactions. European Journal of Epidemiology, 30(5), 353-355. doi:10.1007/s10654-015-0046-1
Kukurba, K. R., Parsana, P., Balliu, B., Smith, K. S., Zappala, Z., Knowles, D. A., . . . Montgomery, S. B. (2016). Impact of the X Chromosome and sex on regulatory variation. Genome Research, 26(6), 768-777. doi:10.1101/gr.197897.115
Lacey, R. E., Pinto Pereira, S. M., Li, L., & Danese, A. (2020). Adverse childhood experiences and adult inflammation: Single adversity, cumulative risk and latent class approaches. Brain, Behavior, and Immunity, 87, 820-830. doi:https://doi.org/10.1016/j.bbi.2020.03.017
Langevin, S., Mascheretti, S., Côté, S. M., Vitaro, F., Boivin, M., Turecki, G., . . . Ouellet-Morin, I. (2019). Cumulative risk and protection effect of serotonergic genes on male antisocial behaviour: results from a prospective cohort assessed in adolescence and early adulthood. The British Journal of Psychiatry, 214(3), 137-145. doi:10.1192/bjp.2018.251
Lesch, K. P., & Merschdorf, U. (2000). Impulsivity, aggression, and serotonin: a molecular psychobiological perspective. Behavioral Sciences and the Law, 18(5), 581-604. doi:10.1002/1099-0798(200010)18:5<581::aid-bsl411>3.0.co;2-l
Liu, H., Li, Y., & Guo, G. (2015). Gene by Social-Environment Interaction for Youth Delinquency and Violence: Thirty-Nine Aggression-Related Genes. Social Forces, 93(3), 881-903. doi:10.1093/sf/sou086
Livesley, W. J., & Jackson, D. N. (1986). The internal consistency and factorial structure of behaviors judged to be associated with DSM-III personality disorders. American Journal of Psychiatry, 143(11), 1473-1474. doi:10.1176/ajp.143.11.1473
Lu, Y. F., & Menard, S. (2017). The Interplay of MAOA and Peer Influences in Predicting Adult Criminal Behavior. Psychiatric Quarterly, 88(1), 115-128. doi:10.1007/s11126-016-9441-3
Maglione, D., Caputi, M., Moretti, B., & Scaini, S. (2018). Psychopathological consequences of maltreatment among children and adolescents: A systematic review of the GxE literature. Research in Developmental Disabilities, 82, 53-66. doi:https://doi.org/10.1016/j.ridd.2018.06.005
Maneta, E. K., White, M., & Mezzacappa, E. (2017). Parent-child aggression, adult-partner violence, and child outcomes: A prospective, population-based study. Child Abuse and Neglect, 68, 1-10. doi:https://doi.org/10.1016/j.chiabu.2017.03.017
Marees, A. T., Smit, D. J. A., Abdellaoui, A., Nivard, M. G., van den Brink, W., Denys, D., . . . Derks, E. M. (2021). Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits. Nature human behaviour, 5(8), 1065-1073. doi:10.1038/s41562-021-01053-4
McAllister, K., Mechanic, L. E., Amos, C., Aschard, H., Blair, I. A., Chatterjee, N., . . . on behalf of workshop, p. (2017). Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. American Journal of Epidemiology, 186(7), 753-761. doi:10.1093/aje/kwx227
McCrory, E. J., De Brito, S. A., & Viding, E. (2010). Research review: the neurobiology and genetics of maltreatment and adversity. Journal of Child Psychology and Psychiatry and Allied Disciplines, 51(10), 1079-1095. doi:10.1111/j.1469-7610.2010.02271.x
McCrory, E. J., Gerin, M. I., & Viding, E. (2017). Annual Research Review: Childhood maltreatment, latent vulnerability and the shift to preventative psychiatry – the contribution of functional brain imaging. Journal of Child Psychology and Psychiatry, 58(4), 338-357. doi:https://doi.org/10.1111/jcpp.12713
McLaughlin, K. A., Sheridan, M. A., & Lambert, H. K. (2014). Childhood adversity and neural development: Deprivation and threat as distinct dimensions of early experience. Neuroscience and Biobehavioral Reviews, 47, 578-591. doi:https://doi.org/10.1016/j.neubiorev.2014.10.012
Moffitt, T. E. (2005). The new look of behavioral genetics in developmental psychopathology: gene-environment interplay in antisocial behaviors. Psychological Bulletin, 131(4), 533-554. doi:10.1037/0033-2909.131.4.533
Moffitt, T. E., Caspi, A., & Rutter, M. (2006). Measured Gene-Environment Interactions in Psychopathology: Concepts, Research Strategies, and Implications for Research, Intervention, and Public Understanding of Genetics. Perspectives on Psychological Science, 1(1), 5-27. doi:10.1111/j.1745-6916.2006.00002.x
Mueller, I., & Tronick, E. (2020). The long shadow of violence: The impact of exposure to intimate partner violence in infancy and early childhood. International Journal of Applied Psychoanalytic Studies, 17(3), 232-245. doi:https://doi.org/10.1002/aps.1668
Muller, C. P., & Jacobs, B. (2009). Handbook of the Behavioral Neurobiology of Serotonin (Vol. 21). Elsevier Science.
Ogloff, J. R. P., Cutajar, M. C., Mann, E. C., & Mullen, P. E. (2012). Child sexual abuse and subsequent offending and victimisation: A 45 year follow-up study. Trends and issues in crime and criminal justice, 2012(440), 1-6.
Oliphant, A., Barker, D. L., Stuelpnagel, J. R., & Chee, M. S. (2002). BeadArray technology: enabling an accurate, cost-effective approach to high-throughput genotyping. Biotechniques, Suppl, 56-58, 60-51.
Ouellet-Morin, I., Boivin, M., Dionne, G., Lupien, S. J., Arseneault, L., Barr, R. G., . . . Tremblay, R. E. (2008). Variations in heritability of cortisol reactivity to stress as a function of early familial adversity among 19-month-old twins. Archives of General Psychiatry, 65(2), 211-218. doi:10.1001/archgenpsychiatry.2007.27
Ouellet-Morin, I., Côté, S. M., Vitaro, F., Hébert, M., Carbonneau, R., Lacourse, É., . . . Tremblay, R. E. (2016). Effects of the MAOA gene and levels of exposure to violence on antisocial outcomes. British Journal of Psychiatry, 208(1), 42-48. doi:10.1192/bjp.bp.114.162081
Park, A., Smith, C., & Ireland, T. (2012). Equivalent harm? The relative roles of maltreatment and exposure to intimate partner violence in antisocial outcomes for young adults. Children and Youth Services Review, 34(5), 962-972. doi:10.1016/j.childyouth.2012.01.029
Pinto Pereira, S. M., Stein Merkin, S., Seeman, T., & Power, C. (2019). Understanding associations of early-life adversities with mid-life inflammatory profiles: Evidence from the UK and USA. Brain, Behavior, and Immunity, 78, 143-152. doi:https://doi.org/10.1016/j.bbi.2019.01.016
Plomin, R. (2013). Child Development and Molecular Genetics: 14 Years Later. Child Development, 84(1), 104-120. doi:https://doi.org/10.1111/j.1467-8624.2012.01757.x
Plomin, R., DeFries, J. C., Knopik, V. S., & Neiderhiser, J. M. (2016). Top 10 Replicated Findings From Behavioral Genetics. Perspectives on Psychological Science, 11(1), 3-23. doi:10.1177/1745691615617439
Plomin, R., Haworth, C. M. A., & Davis, O. S. P. (2009). Common disorders are quantitative traits. Nature Reviews Genetics, 10(12), 872-878. doi:10.1038/nrg2670
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., . . . Sham, P. C. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81(3), 559-575. doi:10.1086/519795
Raine, A. (2002). Biosocial studies of antisocial and violent behavior in children and adults: A review. Journal of Abnormal Child Psychology, 30(4), 311-326. doi:10.1023/A:1015754122318
Raine, A. (2013). The anatomy of violence: The biological roots of crime. Pantheon/Random House.
Raine, A., & Venables, P. H. (1981). Classical conditioning and socialization—A biosocial interaction. Personality and Individual Differences, 2(4), 273-283. doi:https://doi.org/10.1016/0191-8869(81)90082-9
Robins, L., Helzer, J., Cottler, L., & Golding, E. (1989). National Institute of Mental Health Diagnostic Interview Schedule, Version Three Revised: DSM-III-R. St. Louis, MO: Washington University Press.
Rouquette, A., Côté, S. M., Pryor, L. E., Carbonneau, R., Vitaro, F., & Tremblay, R. E. (2014). Cohort profile: the Quebec Longitudinal Study of Kindergarten Children (QLSKC). International Journal of Epidemiology, 43(1), 23-33. doi:10.1093/ije/dys177
Runions, K. C., Morandini, H. A. E., Rao, P., Wong, J. W. Y., Kolla, N. J., Pace, G., . . . Zepf, F. D. (2019). Serotonin and aggressive behaviour in children and adolescents: a systematic review. Acta Psychiatrica Scandinavica, 139(2), 117-144. doi:https://doi.org/10.1111/acps.12986
Rutter, M., Moffitt, T. E., & Caspi, A. (2006). Gene–environment interplay and psychopathology: multiple varieties but real effects. Journal of Child Psychology and Psychiatry, 47(3-4), 226-261. doi:https://doi.org/10.1111/j.1469-7610.2005.01557.x
Sadeh, N., Javdani, S., & Verona, E. (2013). Analysis of monoaminergic genes, childhood abuse, and dimensions of psychopathy. Journal of Abnormal Psychology, 122(1), 167-179. doi:10.1037/a0029866
Schoorl, J., van Rijn, S., de Wied, M., van Goozen, S. H. M., & Swaab, H. (2017). Neurobiological stress responses predict aggression in boys with oppositional defiant disorder/conduct disorder: a 1-year follow-up intervention study. European Child and Adolescent Psychiatry, 26(7), 805-813. doi:10.1007/s00787-017-0950-x
Shaffer, D., Fisher, P., Lucas, C. P., Dulcan, M. K., & Schwab-Stone, M. E. (2000). NIMH Diagnostic Interview Schedule for Children Version IV (NIMH DISC-IV): Description, Differences From Previous Versions, and Reliability of Some Common Diagnoses. Journal of the American Academy of Child and Adolescent Psychiatry, 39(1), 28-38. doi:https://doi.org/10.1097/00004583-200001000-00014
Steketee, M., Aussems, C., & Marshall, I. H. (2019). Exploring the Impact of Child Maltreatment and Interparental Violence on Violent Delinquency in an International Sample. Journal of Interpersonal Violence, 36(13-14), NP7319-NP7349. doi:10.1177/0886260518823291
Sternberg, K. J., Baradaran, L. P., Abbott, C. B., Lamb, M. E., & Guterman, E. (2006). Type of violence, age, and gender differences in the effects of family violence on children’s behavior problems: A mega-analysis. Developmental Review, 26(1), 89-112. doi:https://doi.org/10.1016/j.dr.2005.12.001
Straus, M. A., Hamby, S. L., Finkelhor, D., Moore, D. W., & Runyan, D. (1998). Identification of Child Maltreatment With the Parent-Child Conflict Tactics Scales: Development and Psychometric Data for a National Sample of American Parents. Child Abuse and Neglect, 22(4), 249-270. doi:https://doi.org/10.1016/S0145-2134(97)00174-9
Summers, Cliff H., Korzan, Wayne J., Lukkes, Jodi L., Watt, Michael J., Forster, Gina L., øverli, ø., . . . Greenberg, N. (2005). Does Serotonin Influence Aggression? Comparing Regional Activity before and during Social Interaction. Physiological and Biochemical Zoology, 78(5), 679-694. doi:10.1086/432139
Sunday, S., Kline, M., Labruna, V., Pelcovitz, D., Salzinger, S., & Kaplan, S. (2011). The Role of Adolescent Physical Abuse in Adult Intimate Partner Violence. Journal of Interpersonal Violence, 26(18), 3773-3789. doi:10.1177/0886260511403760
Susman, E. J. (2006). Psychobiology of persistent antisocial behavior: stress, early vulnerabilities and the attenuation hypothesis. Neuroscience and Biobehavioral Reviews, 30(3), 376-389. doi:10.1016/j.neubiorev.2005.08.002
Taylor, A., & Kim-Cohen, J. (2007). Meta-analysis of gene–environment interactions in developmental psychopathology. Development and Psychopathology, 19(4), 1029-1037. doi:10.1017/S095457940700051X
Thibodeau, E. L., Cicchetti, D., & Rogosch, F. A. (2015). Child maltreatment, impulsivity, and antisocial behavior in African American children: Moderation effects from a cumulative dopaminergic gene index. Development and Psychopathology, 27(4pt2), 1621-1636. doi:10.1017/S095457941500098X
Tielbeek, J. J., Karlsson Linnér, R., Beers, K., Posthuma, D., Popma, A., & Polderman, T. J. (2016). Meta-analysis of the serotonin transporter promoter variant (5-HTTLPR) in relation to adverse environment and antisocial behavior. American Journal of Medical Genetics. Part B: Neuropsychiatric Genetics, 171(5), 748-760. doi:10.1002/ajmg.b.32442
Tomoda, A., Polcari, A., Anderson, C. M., & Teicher, M. H. (2012). Reduced visual cortex gray matter volume and thickness in young adults who witnessed domestic violence during childhood. PloS One, 7(12), e52528. doi:10.1371/journal.pone.0052528
Tomoda, A., Sheu, Y. S., Rabi, K., Suzuki, H., Navalta, C. P., Polcari, A., & Teicher, M. H. (2011). Exposure to parental verbal abuse is associated with increased gray matter volume in superior temporal gyrus. Neuroimage, 54 Suppl 1, S280-286. doi:10.1016/j.neuroimage.2010.05.027
Tuvblad, C., Grann, M., & Lichtenstein, P. (2006). Heritability for adolescent antisocial behavior differs with socioeconomic status: gene–environment interaction. Journal of Child Psychology and Psychiatry, 47(7), 734-743. doi:https://doi.org/10.1111/j.1469-7610.2005.01552.x
Tuvblad, C., Narusyte, J., Grann, M., Sarnecki, J., & Lichtenstein, P. (2011). The genetic and environmental etiology of antisocial behavior from childhood to emerging adulthood. Behavior Genetics, 41(5), 629-640. doi:10.1007/s10519-011-9463-4
Uher, R., & McGuffin, P. (2010). The moderation by the serotonin transporter gene of environmental adversity in the etiology of depression: 2009 update. Molecular Psychiatry, 15(1), 18-22. doi:10.1038/mp.2009.123
van Hazebroek, B. C. M., Wermink, H., van Domburgh, L., de Keijser, J. W., Hoeve, M., & Popma, A. (2019). Biosocial studies of antisocial behavior: A systematic review of interactions between peri/prenatal complications, psychophysiological parameters, and social risk factors. Aggression and Violent Behavior, 47, 169-188. doi:https://doi.org/10.1016/j.avb.2019.02.016
Vassos, E., Collier, D. A., & Fazel, S. (2014). Systematic meta-analyses and field synopsis of genetic association studies of violence and aggression. Molecular Psychiatry, 19(4), 471-477. doi:10.1038/mp.2013.31
Veroude, K., Zhang-James, Y., Fernàndez-Castillo, N., Bakker, M. J., Cormand, B., & Faraone, S. V. (2016). Genetics of aggressive behavior: An overview. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 171(1), 3-43. doi:https://doi.org/10.1002/ajmg.b.32364
Waltes, R., Chiocchetti, A. G., & Freitag, C. M. (2016). The neurobiological basis of human aggression: A review on genetic and epigenetic mechanisms. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 171(5), 650-675. doi:https://doi.org/10.1002/ajmg.b.32388
Widom, C. S. (1989). The Cycle of Violence. Science, 244(4901), 160-166. doi:doi:10.1126/science.2704995
Widom, C. S. (2017). Long-Term Impact of Childhood Abuse and Neglect on Crime and Violence. Clinical Psychology: Science and Practice, 24(2), 186-202. doi:https://doi.org/10.1111/cpsp.12194
Widom, C. S., & Brzustowicz, L. M. (2006). MAOA and the "cycle of violence:" childhood abuse and neglect, MAOA genotype, and risk for violent and antisocial behavior. Biological Psychiatry, 60(7), 684-689. doi:10.1016/j.biopsych.2006.03.039
Wilson, H. W., Stover, C. S., & Berkowitz, S. J. (2009). Research Review: The relationship between childhood violence exposure and juvenile antisocial behavior: a meta-analytic review. Journal of Child Psychology and Psychiatry, 50(7), 769-779. doi:https://doi.org/10.1111/j.1469-7610.2008.01974.x
Wood, S. L., & Sommers, M. S. (2011). Consequences of Intimate Partner Violence on Child Witnesses: A Systematic Review of the Literature. Journal of Child and Adolescent Psychiatric Nursing, 24(4), 223-236. doi:https://doi.org/10.1111/j.1744-6171.2011.00302.x
Young, S. E., Smolen, A., Hewitt, J. K., Haberstick, B. C., Stallings, M. C., Corley, R. P., & Crowley, T. J. (2006). Interaction Between MAO-A Genotype and Maltreatment in the Risk for Conduct Disorder: Failure to Confirm in Adolescent Patients. American Journal of Psychiatry, 163(6), 1019-1025. doi:10.1176/ajp.2006.163.6.1019
Table 1. Bivariate correlation estimates between potential covariates and each haplotype-based serotonergic polygenic scores (n = 410)
Conduct disorder symptoms polygenic score
Antisocial personality disorder symptoms polygenic score
Property/violent crimes polygenic score
IPV perpetration polygenic score
Mother’s antisocial personality disorder symptoms
Family socioeconomic status
Notes. IPV = intimate partner violence; *p ≤ .05.
Table 2. Main and interaction effects of haplotype-based serotonergic polygenic scores and child-directed parental violence on antisocial outcomes in adolescence and adulthood (n = 410)
Conduct disorder Symptoms
Antisocial personality disorder symptoms
Intimate partner violence
Polygenic score x Child-directed violence
Notes. B = Unstandardized beta estimates; SE = Standard errors; R2 = variance explained by the model (%).
1 Maternal antisocial personality disorder was included as a confounder in the regression analyses because of a prior association detected with the polygenic score derived for intimate partner violence.
*** p≤.001, ** p≤.01.
Table 3. Main and interaction effects of haplotype-based serotonergic polygenic scores and witnessed intimate partner violence (IPV) on antisocial outcomes in adolescence and adulthood (n = 410)
Conduct disorder symptoms
Antisocial personality disorder symptoms
Polygenic score x Witnessed IPV
Notes. B = unstandardized beta estimates; SE = Standard error; IPV = intimate partner violence; R2 = variance explained by the model (%).
1 Maternal antisocial personality disorder was included as a confounder in the regression analyses because of a prior association detected with the MGPRS derived for intimate partner violence.
*** p≤.001, ** p≤.01., * p ≤ .05, ⁺ p=.07.
Figure 1. Association between child-directed parental violence and property/violent crimes in early adulthood according to a serotonergic polygenic score (n=410)
Notes. Figure 1 depicts the conditional effect of child-directed parental violence on the probability of exhibiting property/violent crimes according to the participants’ genetic risk (Mean, ± 1 standard deviation).
*p≤.05. n.s. = nonsignificant.
Figure 2. Association between witnessed intra-parental violence and antisocial behaviors according to serotonergic polygenic scores (n=410)
Panel A. Number of conduct disorder personality disorder symptoms in adolescence
Panel B. Number of antisocial symptoms in early adulthood
Notes. Figure 2 depicts the conditional effect of child-witnessed intra-parental violence on conduct disorder symptoms in adolescence (Panel A) and on antisocial personality disorder symptoms in early adulthood (Panel B) according to the participants’ genetic risk (Mean, ± 1 standard deviation).
⁺p ≤ .08. n.s. = nonsignificant.
Haplotype super-alleles included in the conduct disorder symptoms cumulative genetic index are: HTR2A-GACG (rs9534496, 9526240, rs2224721, rs9316233), HTR7-ACAAGT (rs11599921, rs7904560, rs12261011, rs12259401, rs10785973, rs4520504), TPH1-TGGTCTATG (rs10741734, rs1800532, rs10488683, rs10832876, rs685657, rs10488682, rs623580, rs652458, rs546383).
Haplotype super-alleles included in the antisocial personality disorder symptoms cumulative genetic index are: HTR2A-AA (rs2770293, rs9316235), HTR2A-ACCTCGGA (rs582385, rs666693, 6561336, rs972979, rs2770304, rs985934, rs927544, rs4941573), HTR2A-AT (rs2070040, rs9534511; allelic model), HTR2A-TA (rs4142900, rs9534512), HTR5A-CCTTCCGA (rs2873379, rs1017488, rs1881691, rs6320, rs2241859, rs6597455, rs731107, rs1657268), HTR7-AT (rs12259062, rs1891311).
Haplotype super-alleles included in the property/violent crimes cumulative genetic index are: HTR2A-GATT (rs3125, rs7322347, rs7997012, rs977003), HTR2A-ACCTCGAA (rs582385, rs666693, 6561336, rs972979, rs2770304, rs985934, rs927544, rs4941573), HTR5A-TATACCGA (rs2873379, rs1017488, rs1881691, rs6320, rs2241859, rs6597455, rs731107, rs1657268), MAOA-GGCAGAGGG (rs3027400, rs2235186, rs2235185, rs3027405, rs2072744, rs979606, rs979605, rs2239448, rs3027407), TPH2-TGCA (rs4448731, rs10748185, rs4565946; rs11179000).
Haplotype super-alleles included in the intimate partner violence perpetration cumulative genetic index are: HTR2A-GACT (rs3125, rs7322347, rs7997012, rs977003), HTR2A-GA (rs9567739, rs655888; allelic), HTR2A-GACG (rs9534496, 9526240, rs2224721, rs9316233), HTR2A-AA (rs2770293, rs9316235), HTR2A-ACCTCGGA (rs582385, rs666693, 6561336, rs972979, rs2770304, rs985934, rs927544, rs4941573), HTR2A-AT (rs2070040, rs9534511; allelic), HTR2A-TA (block 8, rs4142900, rs9534512).
Supplementary Table 1. Main and interaction effects of serotonergic polygenic score, child-directed and child-witnessed parental violence (WIPV) on antisocial behaviors in adolescence and early adulthood
Conduct disorder symptoms
Antisocial personality disorder symptoms
Child-directed violence X Polygenic score
Witnessed IPV X
Notes. WIPV = witnessed intra-parental violence; R2 = variance explained by the model (%)
*** p≤.001,** p≤.01,* p≤.05