University of Groningen Gene-environment interactions in disruptive behaviors Ruisch, Hyun

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University of Groningen

Gene-environment interactions in disruptive behaviors

Ruisch, Hyun

DOI:

10.33612/diss.136546089

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Ruisch, H. (2020). Gene-environment interactions in disruptive behaviors. University of Groningen. https://doi.org/10.33612/diss.136546089

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Chapter

6

Aggression based genome-wide,

glutamatergic, dopaminergic and

neuroendocrine polygenic risk scores

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6 | Aggression based genome-wide, glutamatergic, dopaminergic and neuroendocrine polygenic risk scores predict callous-unemotional traits

Acknowledgements, funding and declarations

We are very grateful to all families who participated in the NeuroIMAGE-study, and the whole NeuroIMAGE-team, including interviewers, technicians, scientists, clinicians, volunteers and managers of all involved organizations and facilities, for recruitment, and collection and preprocessing of the data used in this study. We are very grateful to the Donders Institute for Brain, Cognition and Behavior of the Radboud University Nijmegen for granting us access to the high-performance computing environment required to conduct this study. This publication is the work of the authors and this research is supported by the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 603016 (MATRICS) and no. 602805 (Aggressotype). The NeuroIMAGE-project was supported by NIH Grant R01MH62873 (to Stephen V. Faraone), NWO Large Investment Grant 1750102007010 and ZonMW Grant 60-60600-97-193 (to Jan K. Buitelaar), and grants from Radboud University Nijmegen Medical Center, University Medical Center Groningen and Accare, and VU University Amsterdam. I. Hyun Ruisch, Andrea Dietrich, Marieke Klein, Jaap Oosterlaan and Pieter J. Hoekstra reported no financial interests or potential conflicts of interest. Jan K. Buitelaar has been a consultant to/advisory board member of/and/or a speaker for Janssen Cilag BV, Eli Lilly, Shire, Lundbeck, Roche, and Servier. He is not an employee of any of these companies and not a stock shareholder of any of these companies. He has no other financial or material support, including expert testimony, patents, or royalties. Dr. S. Faraone received income, potential income, travel expenses, continuing education support and/or research support from Tris, Otsuka, Arbor, Ironshore, Shire, Akili Interactive Labs, VAYA, Ironshore, Sunovion, Supernus and Genomind. With his institution, he has US patent US20130217707 A1 for the use of sodium-hydrogen exchange inhibitors in the treatment of ADHD. I. Hyun Ruisch, Andrea Dietrich and Pieter J. Hoekstra designed the study. I. Hyun Ruisch wrote a manuscript draft and conducted the statistical analyses. Marieke Klein assisted with the data analysis and interpretation of results. Andrea Dietrich, Marieke Klein, Stephen V. Faraone, Jaap Oosterlaan, Jan K. Buitelaar and Pieter J. Hoekstra provided both general feedback and discussed specific methodological, clinical or theoretical issues related to the study. All authors discussed the results and implications as well as contributed to and approved of the final version of the manuscript.

Abstract

Aggression and callous, uncaring, and unemotional (CU) traits are clinically related behavioral constructs caused by genetic and environmental factors. We performed polygenic risk score (PRS) analyses to investigate shared genetic etiology between aggression and these three CU-traits. Furthermore, we studied interactions of PRS with smoking during pregnancy and childhood life events in relation to CU-traits. Summary statistics for the base phenotype were derived from the EAGLE-consortium genome-wide association study of children’s aggressive behavior and were used to calculate individual-level genome-wide and gene-set PRS in the NeuroIMAGE target-sample. Target phenotypes were ‘callousness’, ‘uncaring’, and ‘unemotional’ sumscores of the Inventory of Callous-Unemotional traits. A total of 779 subjects and 1,192,414 single nucleotide polymorphisms were available for PRS-analyses. Gene-sets comprised serotonergic, dopaminergic, glutamatergic, and neuroendocrine signaling pathways. Genome-wide PRS showed evidence of association with uncaring scores (explaining up to 1.59% of variance; self-contained Q=0.0306, competitive-P=0.0015). Dopaminergic, glutamatergic, and neuroendocrine PRS showed evidence of association with unemotional scores (explaining up to 1.33%, 2.00% and 1.20% of variance respectively; self-contained Q-values 0.037, 0.0115 and 0.0473 respectively, competitive-P-values 0.0029, 0.0002 and 0.0045 respectively). Smoking during pregnancy related to callousness scores while childhood life events related to both callousness and unemotionality. Moreover, dopaminergic PRS appeared to interact with childhood life events in relation to unemotional scores. Our study provides evidence suggesting shared genetic etiology between aggressive behavior and uncaring and unemotional CU-traits in children. Gene-set PRS confirmed involvement of shared glutamatergic, dopaminergic and neuroendocrine genetic variation in aggression and CU-traits. Replication of current findings is needed.

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Introduction

Aggressive behaviors in children are common, multifactorial, and continuous traits (1–3). A clinically important subgroup of aggressive children displays high levels of callous-unemotional (CU) traits. CU-traits describe a lack of guilt, limited empathy, and shallow affect (1,4). Although high levels of CU-traits are considered a subphenotype within youth diagnosed with conduct disorder (CD), CU-traits also occur in frequently comorbid disorders such as oppositional-defiant disorder (ODD) and attention-deficit/hyperactivity disorder (ADHD) (1), and adversely affect quality of life in these children (1,3,5). In addition to the aforementioned clinical diagnoses, a distinction can be made between reactive and proactive aggression. Reactive aggression occurs in reaction to frustration or perceived threat, whereas proactive aggression is considered an instrumental behavior driven by reward anticipation and this subtype of aggression is conceptually also more closely related to CU-traits (6).

From twin studies and a recent genome-wide association study (GWAS) it is known that up to approximately half of the variance in aggression can be explained by genetic factors (7,8). GWASs of aggression-related phenotypes have implicated some susceptibility loci, yet genome-wide significant findings are still few (8–10). To detect more variants with smaller effects, larger samples are needed (11). The effects of multiple variants can, however, be aggregated into a polygenic risk score (PRS). Based on GWAS summary statistics, PRS can be calculated in an independent target sample (12). PRS can also be restricted to gene-sets to specifically investigate pathways of interest (13). A monoaminergic and neuroendocrine signaling gene-set was recently linked to reactive aggression in females (and nominally significant to proactive aggression in males) (14) and a glutamatergic gene-set was implicated in hyperactivity/impulsivity (15).

Some well-studied environmental risk factors that have been linked to aggression-related phenotypes (i.e. CD, CU-traits, antisocial behavior, ODD, ADHD) include maternal smoking during pregnancy and adverse childhood experiences (1,16–19). Other factors such as poor parental monitoring or poverty have also been implicated in CD and CU-traits (1). In addition to independent contributions from genes and environment, gene-environment

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traits (using the NeuroIMAGE-sample (24); enriched for ADHD). When such genetic sharing exists, it indicates that children displaying CU-traits share etiology with other aggressive phenotypes. In addition to PRS as a general measure of genetic liability, we also investigated evidence for shared genetic etiology restricted to previously implicated pathways (gene-sets) mentioned above. Moreover, we were interested in potential interactions of PRS with smoking during pregnancy and/or childhood traumatic experiences in relation to CU-traits. Because CU-traits can be assessed as a three-factor construct, with three meaningful dimensions, namely ‘callousness’ (describing a callous attitude towards others), ‘uncaring’ (describing a lack of caring about performance), and ‘unemotional’ traits (describing a lack of emotional expression), we investigated these three dimensions separately (25). All of these three CU-dimensions have been related to (sub)scales of antisocial behavior (25). Only few genetic association studies to date have specifically investigated CU-traits; in the available studies CU-traits were assessed as a single dimension and only suggestive hits were identified (26,27). We used NeuroIMAGE as our target sample, because of the availability of extensive phenotypic and environmental data, in addition to individual-level genome-wide genotyping data. Moreover, the relatively high number of ADHD-cases allowed for more robust control of ADHD as a comorbid condition, which genetic association studies of aggression often lack.

Methods

NeuroIMAGE

NeuroIMAGE is the follow-up of the Dutch part of the International Multicenter ADHD Genetics (IMAGE) case-control study, including 331 families with at least one child with ADHD and at least one biological sibling and 153 control families. This resulted in a total of 412 children with ADHD and 227 unaffected siblings, 262 healthy controls, and 81 children with ‘subhthreshold’ levels of ADHD-symptoms. The diagnosis of ADHD was ascertained according to DSM-IV-TR criteria using information obtained through a semi-structured diagnostic interview and rating scales. Inclusion criteria were a European Caucasian descent, IQ ≥70, age <18 years, and no diagnosis of autism, epilepsy, learning disorders, neurological diseases or genetic syndromes. More information can be found elsewhere (24). Target phenotypes: ‘Callousness’, ‘Uncaring’, and ‘Unemotional’ dimensions of the Inventory of Callous-Unemotional traits

Main outcomes, i.e. ‘target phenotypes’, were the three self-reported dimensions measuring ‘callousness’ (describing a callous attitude towards others), ‘uncaring’ (describing a lack of caring about performance), and ‘unemotional’ behavior (describing a lack of emotional expression), that constitute the CU-traits construct as assessed by the Inventory of Callous-Unemotional traits (ICU (25); data collected for NeuroIMAGE between 2009 and 2012) The ICU has shown sufficient reliability (estimates for our current sample are provided in the Supplement) and construct validity regarding CU-traits and consists of a total of 24 items rated on a four-point scale (0-3; all individual items are described in the Supplement). Sum scores for the callousness (33 maximum), uncaring (24 maximum), and unemotional (15

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maximum) dimensions were analyzed separately given the three-factor structure of the ICU (25).

Genotyping

Genotyping was performed at the Radboud University, using the Illumina Psych-Array 24 v1.1A. This genotyping chip assesses approximately 560,000 markers, and has been developed in collaboration with the Psychiatric Genomics Consortium for the (genome-wide) analyses of psychiatric phenotypes (28). Imputation was performed using the RICOPILI-pipeline (29). Only single nucleotide polymorphisms (SNPs) passing quality control filters regarding Impute Information scores (0.8), minor allele frequency (0.01), Hardy-Weinberg equilibrium test (P cut-off 1E-06) and SNP-call rate (0.95) were retained. Individual genome-wide genotype data was available for 4,573,985 SNPs for 779 subjects in NeuroIMAGE.

Environmental factors

Environmental factors that were investigated and included in gene-environment (GxE)-interaction analyses were maternal smoking during pregnancy (dichotomized for any amount of smoking in any trimester) and childhood traumatic life events scores. Childhood life events scores were calculated based on a child self-reported traumatic life events questionnaire, consisting of 11 (potentially) traumatic life events that were scored (0/1) based on whether the child had ever experienced the event (the maximum possible score was 11). The following 11 themes were addressed: (1) physical violence, (2) sexual violence, (3) relationship break-up, (4) friendship break-up, (5) personal failure, (6) problems in family, (7) problems at school, (8) problems in peer group, (9) leaving religious community, (10) death of a loved one, and (11) severe illness or injury (24,30).

Statistical analyses

To reduce excess variance and remove outliers (potentially representing error in the data) regarding our outcomes, we excluded participants scoring >3 standard deviations (SDs) on callousness (N=6 excluded), uncaring (N=2 excluded) or unemotional (no subjects excluded) ICU-dimensions. Furthermore, the childhood traumatic life events scores were

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analyses (SNP-matching across base and target samples was based on SNP rs-numbers). SNPs were clumped based on linkage disequilibrium (LD) using PRSice default settings (i.e. a bidirection 250Kb-window and R2_{-threshold of 0.1), resulting in a total of 66,088}

LD-clumped SNPs. PRS were calculated according to Supplementary Equation S1. According to our best knowledge, no prior studies investigating shared genetic etiology between aggression and CU-traits, which we could use for selecting an a-priori P-value threshold for the PRS, were available. Therefore, to avoid underfitting in the absence of a-priori information, PRS were calculated at multiple P-value thresholds (33,34). We first calculated PRS for at most 14 ‘broad’ P-value thresholds (i.e. 0.0001, 0.0002, 0.0005, 0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.4 and 0.5) using an additive and recessive model since recessive SNP-effects have been reported previously in aggression (35,36). If at least nominal significance was reached for one of the thresholds, the PRS was included in the final analyses and calculated for a small range of further thresholds around the best-fitting of the (at most) 14 broad thresholds. This procedure was performed for genome-wide and gene-set (see below) PRS in relation to our three target phenotypes. Multiple testing correction was applied in two stages: first, we computed ‘empirical P-values’ for the association of each best-fitting PRS. Empirical P-values were obtained by comparing the P-value of the PRS for the actual phenotype with a null-distribution of P-values of the PRS regressed on 11,000 randomly permuted phenotypes, to correct for overfitting due to testing multiple P-value thresholds across two inheritance models (33). Second, we adjusted the empirical P-values using the procedure described by Benjamini and Hochberg (37) to control the false discovery rate (FDR) for the number of gene-sets and phenotypes investigated in the final stage of the PRS-analyses (i.e. ‘FDR Q-values’). A detailed description of all PRS-procedures is provided in the Supplement. In addition to investigating whether the PRS were associated with our traits of interest, we also computed ‘competitive P-values’ to investigate the level of enrichment of the SNP-sets representing the best-fitting PRS. The competitive P-values were obtained by comparing the P-value of the PRS with a null-distribution of P-values of 11,000 random SNP-sets of the same size, drawn from the genome-wide genetic background signal outside of the PRS SNP-set, regressed on the phenotype (33).

Gene-set PRS: In addition to genome-wide PRS, we also computed PRS from four gene-sets that were previously implicated in aggression-related phenotypes. We defined serotonergic, dopaminergic, neuroendocrine and glutamatergic gene-sets according to Donkelaar et al. (2017) and Naaijen et al. (2017), given the careful and comprehensive selection of genes related to the pathways of interest in these studies (14,15). Lists of genes included in each gene-set are provided in Supplementary Table S1. Gene-set PRS may provide additional information to genome-wide PRS, as association signals from these individual sets may be more difficult to detect in a genome-wide signal. Different sets/pathways may also have different directions of association with the target phenotype. We calculated gene-set-PRS also for different P-value thresholds as not all genetic variation in a pathway is necessarily linked with the target phenotype (similar to the genome-wide PRS).

PRS-Environment interactions (GxE): Because gene-environment correlations (rGE) could be confounding GxE-interactions (i.e. the genetic factor could be related to both the environmental factor and outcome in the ‘GxE’) (38), we first investigated rGE between

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significant PRS (identified in the PRS-analyses) and childhood life events and smoking during pregnancy. Subsequently, environmental main effects and GxE-interactions between the best-fitting PRS and environment were analyzed in relation to callousness, uncaring and unemotional ICU-dimensions. GxE-interactions were coded in R, using linear mixed models as implemented in ‘Lme4’ and ‘LmerTest’ packages (39,40). In addition to abovementioned control variables a random intercept for each family was added to adjust for sibling relatedness within our sample (41,42). BH-adjustment was used to correct for testing multiple GxE-interactions.

Sensitivity analyses

Since PRSice currently does not support linear mixed models we were unable to correct for sibling relatedness in our main analyses. Therefore, we coded our significant PRS-models in R (39,40) and corrected for sibling relatedness as described (41,42). Second, to investigate whether comorbid ADHD may be driving our results, we also included ADHD-case/control status as a covariate. Third, because of the clustered family structure in NeuroIMAGE, estimation of PCs could be slightly artefactual (42,43) and since it was not feasible to calculate PCs from unrelated subjects only, we investigated potential inaccuracy by removing the PC-covariates (and reincluding subjects with deviant PC-scores as well) and only keeping adjustment for sibling relatedness. Fourth, because the Major Histocompatibility Complex (MHC) locus shows extended LD-structure and many diseases have been associated with this region (44), we adjusted our significant PRS by excluding the MHC-locus, to investigate to which degree SNPs in this region might be affecting our results. These same aforementioned sensitivity analyses were performed for significant GxE-interactions. Since it was not possible to perform permutation-based analyses with the mixed models in R (and hence, apply comprehensive multiple testing correction) we compared the uncorrected PRS association P-values from each sensitivity analysis with the main analyses. Furthermore, by applying P-value thresholding to gene-set PRS, only a subset of the gene-set is represented by the best-fitting PRS. To investigate whether the whole gene-set is more strongly associated with the target phenotype, we repeated the gene-set PRS-analyses keeping the P-value threshold at 1.

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Results

Descriptive statistics of NeuroIMAGE

Table 1 provides descriptive statistics of the phenotypic and environmental variables in NeuroIMAGE. See Supplementary Figure S1 for global and intra-European PCA-plots of NeuroIMAGE merged with the 1000 Genomes reference populations (32). NeuroIMAGE appeared most proximal to CEPH and British European populations.

Phenotypic variable N total Mean ± SD or N (%) Sex (males) 779 450 (57.8%)

Age (years) 779 17.22 ± 3.77 (range: 5.77 to 30.51) Childhood traumatic life events score 705 2.19 ± 1.59 (range: 0 to 10) Maternal smoking during pregnancy 514 101 (19.65%) ADHD-diagnosis 779 310 (39.79%) ICU callousness score 707 5.59 ± 4.27 (range: 0 to 24) ICU uncaring score 722 8.65 ± 3.97 (range: 0 to 24) ICU unemotional score 725 7.10 ± 2.93 (range: 0 to 15)

Table 1: Descriptive statistics of phenotypic and environmental data in NeuroIMAGE (PRS target sample). PRS:

polygenic risk score. ICU: inventory of callous and unemotional traits.(25) ADHD: attention-deficit/hyperactivity disorder. PRS-analyses

Six PRSs reached at least nominal significance at one of the at most 14 broad P-value thresholds and were included in the final analyses. These were a dopaminergic (additive) and glutamaterig (recessive) PRS in relation to callousness scores, a genome-wide (recessive) PRS in relation to uncaring scores, and a glutamatergic, dopaminergic, and neuroendocrine (recessive) PRS in relation to unemotional scores (Supplementary Table S2 provides an overview of intermediate results). For these six PRS we calculated some additional nearby P-value thresholds. See Figure 1(A-R) and Table 2 for all results including the total number of and PRS model-fit across all thresholds tested. Four PRS showed evidence for association with and enrichment for CU-traits. Genome-wide PRS predicted uncaring scores (best-fitting-R2_{=1.59%, 58 SNPs, empirical-P=1.02E-02,} FDR-Q=3.06E-02, competitive-P=1.55E-03; Supplementary Table S3 provides a list of the

nearest genes to the SNPs in this PRS), whereas glutamatergic PRS (best-fitting-R2_=2.00%,

179 SNPs, empirical-P=1.91E-03, FDR-Q=1.15E-02, competitive-P=1.82E-04), dopaminergic PRS (best-fitting-R2_{=1.33%, 3 SNPs, empirical-P=1.85E-02, FDR-Q=3.70E-02,} competitive-P=2.91E-03) and neuroendocrine PRS (best-fitting-R2_{=1.20%, 41 SNPs,}

empirical-P=3.15E-02, FDR-Q=4.73E-empirical-P=3.15E-02, competitive-P=4.55E-03) predicted unemotional scores. In addition, glutamatergic PRS showed evidence of nominal significant association with callousness scores (best-fitting-R2_{=0.93%, 67 SNPs, empirical-P=6.74E-02, FDR-Q=8.09E-02) but}

showed significant enrichment (competitive-P=1.39E-02). Supplementary Tables S4(A-D) provides the minor allele frequency, Hardy-Weinberg test result, and call rate for the SNPs included in the best-fitting PRS that showed evidence for association with and enrichment for CU-traits.

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Base Phenotype Target Phenotype Set N Best-fit threshold (PT) SNPs at PT B (SE) at PT R2 at PT Empirical Pa FDR Qb Competitive Pc

Aggression(8)

(continuous trait) Callousness (continuous trait) Dopamine (additive model) 649 0.0131 (29 thresholds tested) 8 1.08 (0.55) 0.55% 0.239 0.239 0.0531

Glutamate

(recessive model) 649 0.0979 (95 thresholds tested) 67 8.80 (3.43) 0.93% 0.0674 0.0809 0.0139

Uncaring

(continuous trait) Genome-wide (recessive model) 663 0.000183 (139 thresholds tested) 58 -4.92 (1.47) 1.59% 0.0102 0.0306 0.00155

Unemotional

(continuous trait) Dopamine (recessive model) 663 0.00392 (22 thresholds tested) 3 -1.25 (0.41) 1.33% 0.0185 0.037 0.00291

Glutamate

(recessive model) 663 0.3537 (109 thresholds tested) 179 18.01 (4.80) 2.00% 0.00191 0.0115 0.000182

Neuroendocrine

(recessive model) 663 0.0196 (46 thresholds tested) 41 -3.80 (1.32) 1.20% 0.0315 0.0473 0.00455

Table 2: Results from the PRS-analyses showing the best-fitting PRS in relation to callousness, uncaring and unemotional target phenotypes. FDR: False Discovery Rate. SNP: Single Nucleotide Polymorphism. The analyses were

adjusted for sex, age at outcome assessment and the first 10 principal components (a total of 69 subjects socring >|±2|SDs on any of the principal components were removed from the analyses). The best-fitting P-value threshold for the PRS (PT) is

shown in this table; Figure 1(A-R) shows the PRS model-fit across all the P-value thresholds tested. PRS-analyses were performed in two stages, intermediate results from the first stage are provided in Supplementary Table S2. a_Empirical

P-values indicate the level of association and were adjusted for overfitting due to testing multiple P-value thresholds (based on 11,000 phenotypic permutations). b _{FDR Q-values indicate the level of association and were adjusted for overfitting due to}

testing multiple P-value thresholds and additionally for performing the six PRS-analyses shown. c_{Competitive P-values}

indicate the level of enrichment of the SNP-set representing the best-fitting PRS, with respect to the genome-wide background genetic signal outside the SNP-set (based on 11,000 set permutations).

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Dopamine PRS model-fit across 29 P-value thresholds in relation to callousness scores.

A: 10 broad thresholds (add.). B: 10 broad thresholds (rec.). C: 9 further thresholds between 0.01 and 0.05 (add.).

Glutamate PRS model-fit across 95 P-value thresholds in relation to callousness scores.

D: 12 broad thresholds (add.). E: 12 broad thresholds (rec.). F: 71 further thresholds between 0.05 and 0.2 (rec.).

Genome-wide PRS model-fit across 139 P-value thresholds in relation to uncaring scores.

G: 14 broad thresholds (add.). H: 14 broad thresholds (rec.). I: 111 further thresholds between 0.0001 and 0.0005 (rec.).

Dopamine PRS model-fit across 22 P-value thresholds in relation to unemotional scores.

J: 10 broad thresholds (add.). K: 10 broad thresholds (rec.). L: 2 further thresholds between 0.002 and 0.01

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Glutamate PRS model-fit across 109 P-value thresholds in relation to unemotional scores.

M: 12 broad thresholds (add.). N: 12 broad thresholds (rec.). O: 85 further thresholds between 0.2 and 0.4 (rec.).

Neuroendocrine PRS model-fit across 46 P-value thresholds in relation to unemotional scores.

P: 12 broad thresholds (add.). Q: 12 broad thresholds (rec.). R: 22 further thresholds between 0.005 and 0.02

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Figure 1: Plots of PRS-analyses showing the PRS model-fit in relation to callousness, uncaring and unemotional traits across all tested P-value thresholds. Add.: Additive model. PRS: Polygenic Risk Score. Rec.: Recessive model. SNP: Single

Nucleotide Polymorphism. PRS were calculated first for at most 14 ‘broad’ P-value thresholds using the additive and recessive inheritance model (Figures 1-A, B, D, E, G, H, J, K, M, N, P and Q; note that for the gene-set based PRS, some of the lower thresholds included no SNPs). Subsequently PRS were calculated for some further thresholds around the best-fitting of the at most 14 ‘broad’ thresholds (Figures 1-C, F, I, L, O and R; the additional thresholds represent the unique P-values of the SNPs in the GWAS summary statistics). The number of SNPs is shown on top of the bar plots with sufficient space available. The model-fit for the PRS across all tested P-value thresholds as well as the number of thresholds tested is shown.

Table 2 provides specific details (e.g. regression coefficients, association and enrichment test results) for the best-fitting PRS

in relation to callousness, uncaring and unemotional traits. PRS-Environment interactions (GxE)

No gene-environment correlations (rGE) between PRS and childhood life event scores and/or smoking during pregnancy were observed (See Supplementary Table S5 for all results). Regarding environmental main effects and GxE-interactions, see Table 3 for all results. Childhood life events related to callousness (FDR-Q=3.19E-04) and unemotional scores Q=4.55E-02). Smoking during pregnancy related to callousness scores

(FDR-Q=3.47E-03). The best-fitting dopaminergic PRS interacted with childhood life events in

relation to unemotional scores (FDR-Q=4.55E-02; See Figure 2 and Supplementary Table S6 for environment-stratified PRS-effects).

Target phenotype Environment or GxE N B (SE) P FDR Q Callousness

(continuous trait) Childhood life events 678 1.34 (0.31) 2.28E-05 0.000319

Smoking during pregnancy 469 1.65 (0.47) 0.000496 0.003472 Uncaring

(continuous trait) Childhood life events 687 0.37 (0.30) 0.210 0.39375

Smoking during pregnancy 479 0.73 (0.46) 0.111 0.3108 PRS (58 genome-wide SNPs) x Childhood life events 631 -0.26 (0.31) 0.408 0.5495 PRS (58 genome-wide SNPs) x Smoking during pregnancy 443 0.44 (0.53) 0.402 0.5495 Unemotional

(continuous trait) Childhood life events 692 0.56 (0.23) 0.0128 0.0455

Smoking during pregnancy 480 0.40 (0.33) 0.225 0.3938 PRS (3 dopaminergic SNPs) x Childhood life events 632 -0.60 (0.24) 0.013 0.0455 PRS (3 dopaminergic SNPs) x Smoking during pregnancy 442 -0.29 (0.39) 0.463 0.5495 PRS (179 glutamatergic SNPs) x Childhood life events 632 0.16 (0.23) 0.471 0.5495 PRS (179 glutamatergic SNPs) x Smoking during pregnancy 442 0.02 (0.37) 0.947 0.947 PRS (41 neuroendocrine SNPs) x Childhood life events 632 0.15 (0.23) 0.517 0.5568 PRS (41 neuroendocrine SNPs) x Smoking during pregnancy 442 -0.47 (0.37) 0.200 0.3938

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Figure 2: Environment-stratified effects of PRS in relation to unemotional scores. PRS: Polygenic Risk Score. See Supplementary Table S6 for all stratified results.

Sensitivity analyses

See Supplementary Table S7 for all results. After adjustment for sibling relatedness the association strength for the best-fitting PRS remained similar (genome-wide PRS in relation to uncaring scores P=5.30E-04; glutamatergic PRS P=1.63E-04, dopaminergic PRS P=2.16E-03 and neuroendocrine PRS P=3.60E-P=2.16E-03 in relation to unemotional scores). After further adjustment for ADHD results again remained similar (genome-wide PRS in relation to uncaring scores P=2.74E-04; glutamatergic PRS P=1.91E-04, dopaminergic PRS P=1.70E-03 and neuroendocrine PRS P=3.10E-03 and GxE-interaction P=1.33E-02 in relation to unemotional scores). When covariates were removed and subjects with deviant PC-scores were reincluded, results changed only slightly (genome-wide PRS in relation to uncaring scores P=3.14E-04; glutamatergic PRS P=4.50E-04, dopaminergic PRS P=4.59E-04 and neuroendocrine PRS P=8.79E-03 and GxE-interaction P=1.13E-02 in relation to unemotional scores). When the MHC-locus was removed, results remained unchanged (no MHC-SNPs in the PRSs) except for the genome-wide PRS in relation to uncaring scores (P=2.38E-03, 56 SNPs; 2 MHC-SNPs removed). Including all SNPs in the gene-set PRS (i.e. a threshold of 1) resulted in a reduced association strength of the PRS compared to the identified best-fitting threshold in the main analyses (full glutamate-set PRS [325 SNPs] R2_{=1.22%, P=0.0037, full dopamine-set PRS [224 SNPs] R}2_{=(1.02E-06)%, P=0.9979 and full}

neuroendocrine-set PRS [1107SNPs] R2_{=0.58%, P=0.1827 in relation to unemotional}

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Discussion

We performed PRS-analyses to investigate evidence for shared genetic etiology between aggressive behavior and callous, uncaring and unemotional traits in children/adolescents. In addition to genome-wide PRS, we also studied PRS based on gene-sets that have been previously implicated in aggression-related phenotypes (14,15). Furthermore, we studied GxE-interaction between PRS and two key environmental adversities. Our results suggest that aggression shares genetic etiology with the ICU-dimensions ‘uncaring’ and ‘unemotional’, and confirmed glutamatergic, dopaminergic, and neuroendocrine signaling as shared biological pathways of interest. Moreover, dopaminergic PRS appeared to interact with childhood life events in relation to unemotional scores.

The only genome-wide polygenic association was observed in relation to uncaring scores. This PRS included 58 SNPs at a relatively low P-value threshold, indicating that SNPs that associated more strongly with aggressive behavior in general also combine into a stronger polygenic signal in relation to uncaring traits. A number of the genes related to the SNPs in this PRS have been linked to aggression (e.g. MECOM, AVPR1A) (45), other related psychiatric disorders such as autism or schizophrenia (e.g. MACROD2, ADD2) (46,47), or have been implicated in neurobiological functions such as synapse remodeling and interneuron maturation (e.g. RAPGEF4, DGKG) (48,49). The observation that gene-set PRS explained relatively large amounts of variance in CU-dimensions when compared to the genome-wide PRS could be related to opposite directions of effects of different gene-sets/pathways, that cancel each other out when added together in the genome-wide PRS. Furthermore, as the genome-wide PRS represents a more general genetic liability, the signal could also be susceptible to more noise from irrelevant SNPs.

Glutamatargic PRS related to unemotional scores and explained the most variance (up to 2% at the best-fitting threshold) in an individual trait. The glutamatergic gene-set used for set-based PRS-analysis in the present study, was linked previously to ADHD (i.e. hyperactivity scores) (15). Since the sample of that study was derived from the IMAGE-project, it partially overlaps with our current study sample. However, the degree of overlap is only limited (NeuroIMAGE is based on a distinct subset of IMAGE and these samples were

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aggression PRS had lower unemotional scores. Reactive aggression is more common than proactive aggression in the population (e.g. (52)), and some studies have reported a negative association between reactive aggression and specifically the ICU-unemotional scale (e.g. (53)). Further, a low degree of stress-reactivity has been observed in children displaying CU-traits, whereas in children with CD but without CU-traits, heightened threat-sensitivity and reactive aggression is seen (1,16). Therefore, we theorize that children displaying a more general tendency towards aggression (i.e. with higher aggression-PRS), might be also more reactive aggressive and stress/threat sensitive and, hence, be less unemotional. One of the PRS-SNPs was located within the PRKAG2-gene. The gene PRKAG2 encodes the non-catalytic Gamma 2 subunit of the AMP-activated protein kinase enzyme and associations of

PRKAG2-variants with temporal lobe volume (54) and cognitive impairment (55) have been

reported, which points to the neurobiological role of this gene. In addition, the effect of dopaminergic PRS appeared to be moderated by childhood life events in relation to unemotional scores, such that the PRS related most strongly to unemotional scores in children with higher life event scores. Dopamine plays an important role in motivation, reward, and decision making and antagonism of dopaminergic (D2) receptors by antipsychotic drugs has been shown to reduce aggressive behaviors, pointing to the involvement of the dopaminergic system in aggression (56). Furthermore, in two recent functional imaging studies, reduced activation patterns in parts of the dopaminergic reward system (such as the ventral striatum, amygdala and prefrontal cortex) were linked to CU-traits and disruptive behavior (57,58). Although a significant link with CU-CU-traits was reported in only one of these studies (57) (which may be related to CU-traits being assessed as a unified construct and/or the use of an ethnically-stratified sample (58)) this could suggest that the currently observed shared dopaminergic genetic liability between aggression and unemotional CU-traits is related to functional neural differences in dopaminergic brain circuits involved in reward processing.

Shared genetic factors between aggressive behavior and unemotional scores was also suggested by neuroendocrine PRS. A recent study showed that cortisol reactivity moderated the link between aggression and CU-traits in a pediatric longitudinal cohort, such that the aggression-CU-traits link was present only in the context of low cortisol reactivity (59). Furthermore, interaction between testosteron and cortisol in relation to aggression in adolescents has also been reported, suggesting that only in subjects with low cortisol levels testosterone levels were linked to aggression (60). Moreover, the recent study that linked the currently investigated neuroendocrine gene-set to aggression, also reported that the sub-set of genes in the set that linked most strongly with aggression were glucocorticoid genes (14). Therefore, our current results could point to a key role for cortisol-related genetic variation in a shared genetic liability among aggressive behavior and CU-traits in children.

Strengths and limitations

A unique feature of the current study includes the simultaneous investigation of genome-wide PRS and recently implicated gene-sets to study potential shared genetic etiology between aggression and callous, uncaring and unemotional traits. Furthermore, we

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investigated interactions between the PRS and two key environmental factors for CU-traits, thereby providing an approach to address GxE-polygenicity (although the number of SNPs was somewhat limited in our best-fit PRS). Nevertheless, some limitations should be discussed. First, as NeuroIMAGE consists of a partially referred sample, findings may not necessarily generalize to CU-traits distributed within the general population. Furthermore, although our base sample was large, our target sample had a relatively modest size. While this may have prevented detection of small effects in individual variants, it proved adequate for polygenic analyses (power for detecting PRS-main effects explaining between 1.20 and 2.00% of variance ranged from 81 to 96%) and mostly sufficient to perform GxE-analyses (power for detecting PRS-by-environment interactions based on PRS-main effects ranged from 80 to 95% for childhood life events and from 65 to 86% for smoking during pregnancy). Although GxE-interactions based on individual SNPs typically require large sample sizes, detection of PRS-based GxE-interactions may require less power due to aggregation of SNP-effects into the PRS. Furthermore, although subsetting genes reduced the absolute number of available SNPs, the resulting PRS may be actually more predictive and therefore power should not necessarily be adversely affected by set-based PRS-analyses. Careful selection of gene-sets is, however, important. In addition, observed effects may be (partially) explained by e.g. mediation effects or coexistent aggression. Last, our present study has been among the first studies investigating genetic sharing between aggression and CU-traits and therefore current results need replication (e.g. to validate the predictive ability of the PRS in an independent sample).

Conclusion

Our study provides evidence suggesting shared genetic etiology between children’s aggressive behavior and ‘uncaring’ and ‘unemotional’ ICU-dimensions, thereby supporting the notion of a polygenic architecture underlying CU-traits. In addition to genome-wide PRS, gene-set based PRS pointed to shared genetic variation within glutamatergic, dopaminergic and neuroendocrine signaling pathways as well as GxE-interaction with childhood life events. Furthermore, individual genes derived from the PRS have been linked to aggression and neurodevelopment previously. Although ‘callousness’ was not significantly related to PRS, childhood life events and smoking during pregnancy were most strongly linked to this

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References

1. Blair RJR, Leibenluft E, Pine DS. Conduct Disorder and Callous–Unemotional Traits in Youth. N Engl J Med. 2014;371:2207–16. 2. Waltes R, Chiocchetti AG, Freitag CM. The neurobiological basis of human aggression: A review on genetic and epigenetic

mechanisms. Am J Med Genet Part B Neuropsychiatr Genet. 2016;171(5):650–75.

3. Olsson M. DSM diagnosis of conduct disorder (CD)--a review. Nord J Psychiatry. 2009;63(Cd):102–12.

4. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Washington, DC: American Psychiatric Association; 2013.

5. Herpers PCM, Klip H, Rommelse NNJ, Greven CU, Buitelaar JK. Associations between high callous–unemotional traits and quality of life across youths with non-conduct disorder diagnoses. Eur Child Adolesc Psychiatry. 2016;25(5):547–55.

6. Kempes M, Matthys W, De Vries H, Van Engeland H. Reactive and proactive aggression in children. A review of theory, findings and the relevance for child and adolescent psychiatry. Eur Child Adolesc Psychiatry. 2005;14(1):11–9.

7. Veroude K, Zhang-James Y, Fernàndez-Castillo N, Bakker MJ, Cormand B, Faraone S V. Genetics of aggressive behavior: An overview. Am J Med Genet Part B Neuropsychiatr Genet. 2016;171(1):3–43.

8. Pappa I, St Pourcain B, Benke K, Cavadino A, Hakulinen C, Nivard MG, et al. A genome-wide approach to children’s aggressive behavior: The EAGLE consortium. Am J Med Genet Part B Neuropsychiatr Genet. 2016;171(5):562–72.

9. Tielbeek JJ, Johansson A, Polderman TJC, Rautiainen M-R, Jansen P, Taylor M, et al. Genome-Wide Association Studies of a Broad Spectrum of Antisocial Behavior. JAMA Psychiatry. 2017;74(12):1242–50.

10. Rautiainen M, Paunio T, Repo-Tiihonen E, Virkkunen M, Ollila H, Sulkava S, et al. Genome-wide association study of antisocial personality disorder. Transl Psychiatry. 2016;6(9):e883.

11. Andlauer T, Müller-Myhsok B, Ripke S. Statistical Genetics: Genome-Wide Studies. In: Schulze T, McMahon F, editors. Psychiatric Genetics: A Primer for Clinical and Basic Scientists. 1st ed. Oxford University Press; 2018. p. 57–69.

12. Purcell S, Wray N, Stone J, Visscher P, O’Donovan M, Sullivan P, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460(7256):748–52.

13. Euesden J, Lewis CM, O’Reilly PF. PRSice: Polygenic Risk Score software. Bioinformatics. 2015;31(9):1466–8.

14. van Donkelaar MMJ, Hoogman M, Shumskaya E, Buitelaar JK, Bralten J, Franke B. Monoamine and neuroendocrine gene-sets associate with frustration-based aggression in a gender-specific manner. Eur Neuropsychopharmacol. 2017;(855):In press. 15. Naaijen J, Bralten J, Poelmans G, Glennon JC, Franke B, Buitelaar JK, et al. Glutamatergic and GABAergic gene sets in

attention-deficit/hyperactivity disorder: Association to overlapping traits in ADHD and autism. Transl Psychiatry. 2017;7(1):e999-7. 16. Blair RJ. The neurobiology of psychopathic traits in youths. Nat Rev Neurosci. 2013;14(11):786–99.

17. Byrd AL, Manuck SB. MAOA, childhood maltreatment, and antisocial behavior: Meta-analysis of a gene-environment interaction. Biol Psychiatry. 2014;75(1):9–17.

18. Ruisch IH, Dietrich A, Glennon JC, Buitelaar JK, Hoekstra PJ. Maternal substance use during pregnancy and offspring conduct problems: A meta-analysis. Neurosci Biobehav Rev. 2018;84:325–36.

19. Afifi TO, McMillan KA, Asmundson GJG, Pietrzak RH, Sareen J. An examination of the relation between conduct disorder, childhood and adulthood traumatic events, and posttraumatic stress disorder in a nationally representative sample. J Psychiatr Res. 2011;45(12):1564–72.

20. Holz NE, Zohsel K, Laucht M, Banaschewski T, Hohmann S, Brandeis D. Gene x environment interactions in conduct disorder: Implications for future treatments. Neurosci Biobehav Rev. 2018;91:239–58.

21. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747–53.

22. Dick DM, Agrawal A, Keller MC, Adkins A, Aliev F, Monroe S, et al. Candidate Gene–Environment Interaction Research. Perspect Psychol Sci. 2015;10(1):37–59.

23. Vassos E, Collier DA, Fazel S. Systematic meta-analyses and field synopsis of genetic association studies of violence and aggression. Mol Psychiatry. 2014;19(4):471–7.

24. von Rhein D, Mennes M, van Ewijk H, Groenman AP, Zwiers MP, Oosterlaan J, et al. The NeuroIMAGE study: a prospective phenotypic, cognitive, genetic and MRI study in children with attention-deficit/hyperactivity disorder. Design and descriptives. Eur Child Adolesc Psychiatry. 2015;24(3):265–81.

25. Essau CA, Sasagawa S, Frick PJ. Callous-unemotional traits in a community sample of adolescents. Assessment. 2006;13(4):454–69. 26. Viding E, Hanscombe KB, Curtis CJC, Davis OSP, Meaburn EL, Plomin R. In search of genes associated with risk for psychopathic tendencies in children: A two-stage genome-wide association study of pooled DNA. J Child Psychol Psychiatry Allied Discip. 2010;51(7):780–8.

27. Viding E, Price TS, Jaffee SR, Trzaskowski M, Davis OSP, Meaburn EL, et al. Genetics of Callous-Unemotional Behavior in Children. PLoS One. 2013;8(7).

28. Logue MW, Amstadter AB, Baker DG, Duncan L, Koenen KC, Liberzon I, et al. The Psychiatric Genomics Consortium Posttraumatic Stress Disorder Workgroup : Posttraumatic Stress Disorder Enters the Age of Large-Scale Genomic Collaboration. Neuropsychopharmacology. 2015;40:2287–97.

29. Lam M, Awasthi S, Watson HJ, Goldstein J, Panagiotaropoulou G, Trubetskoy V, et al. RICOPILI: Rapid Imputation for COnsortias PIpeLIne. Bioinformatics. 2019;(August):1–4.

30. Reijneveld SA, Bosch NM, Bakker MP, Verhulst FC, Ormel J, Oldehinkel AJ. Timing matters : Long term effects of adversities from prenatal period up to adolescence on adolescents ’ cortisol stress response . The TRAILS study. Psychoneuroendocrinology. 2012;37:1439–47.

31. Keller M. Gene-by-environment interaction studies have not properly controlled for potential confounders: The problem and the (simple) solution. Biol Psychiatry. 2014;75(1):18–24.

32. Auton A, Abecasis GR, Altshuler DM, Durbin RM, Bentley DR, Chakravarti A, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74.

33. Choi SW, O’Reilly PF. PRSice-2: Polygenic Risk Score software for biobank-scale data. Gigascience. 2019;8(7):1–6. 34. Choi SW, Mak TSH, O’Reilly PF. A guide to performing Polygenic Risk Score analyses. bioRxiv. 2018.

35. Ruisch IH, Dietrich A, Glennon JC, Buitelaar JK, Hoekstra PJ. Interplay between genome-wide implicated genetic variants and environmental factors related to childhood antisocial behavior in the UK ALSPAC cohort. Eur Arch Psychiatry Clin Neurosci. 2019;269:741–52.

36. Zhang Y, Wu C, Chang H, Yan Q, Wu L, Yuan S, et al. Genetic variants in oxytocin receptor gene (OXTR) and childhood physical abuse collaborate to modify the risk of aggression in chinese adolescents. J Affect Disord. 2018;229(November 2017):105–10. 37. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc

Ser B. 1995;57(1):289–300.

38. Rice F, Harold GT, Boivin J, van den B, Hay DF, Thapar A. The links between prenatal stress and offspring development and psychopathology: Disentangling environmental and inherited influences. Psychol Med. 2010;40(2):335–45.

39. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2018.

(18)

40. Bates D, Maechler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software. J Stat Softw. 2015;67(1):1–48.

41. Zhang Z, Ersoz E, Lai C, Todhunter RJ, Tiwari HK, Gore MA, et al. Mixed linear model approach adapted for genome-wide association studies. Nat Genet. 2010;42(4):355–60.

42. Price AL, Zaitlen NA, Reich D, Patterson N. New approaches to population stratification in genome-wide association studies. Nat Rev Genet. 2010;11(7):459–63.

43. Patterson N, Price AL, Reich D. Population Structure and Eigenanalysis. PLoS Genet. 2006;2(12).

44. Kennedy AE, Ozbek U, Dorak MT. What has GWAS done for HLA and disease associations? Int J Immunogenet. 2017;44(5):195–211. 45. van Donkelaar MMJ, Hoogman M, Pappa I, Tiemeier H, Buitelaar JK, Franke B, et al. Pleiotropic Contribution of MECOM and AVPR1A

to Aggression and Subcortical Brain Volumes. Front BehavNeurosci. 2018;12(61).

46. The Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium. Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia. Mol Autism. 2017;8(21).

47. Bosia M, Pigoni A, Zagato L, Merlino L, Casamassima N, Lorenzi C, et al. ADDing a piece to the puzzle of cognition in schizophrenia. Eur J Med Genet. 2016;59(1):26–31.

48. Woolfrey KM, Srivastava DP, Photowala H, Yamashita M, Barbolina M V., Cahill ME, et al. Epac2 induces synapse remodeling and depression and its disease-associated forms alter spines. Nat Neurosci. 2009;12(10):1275–84.

49. Fukumoto K, Tamada K, Toya T, Nishino T, Yanagawa Y, Takumi T. Identification of genes regulating GABAergic interneuron maturation. Neurosci Res. 2017;In press.

50. Purkayastha P, Malapati A, Yogeeswari P, Sriram D. A Review on GABA/Glutamate Pathway for Therapeutic Intervention of ASD and ADHD. Curr Med Chem. 2015;22(15):1850–9.

51. Ripke S, Neale BM, Corvin A, Walters JTR, Farh KH, Holmans PA, et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511(7510):421–7.

52. Cima M, Raine A, Meesters C, Popma A. Validation of the Dutch Reactive Proactive Questionnaire (RPQ): Differential Correlates of Reactive and Proactive Aggression From Childhood to Adulthood. Aggress Behav. 2013;39(2):99–113.

53. Ueno K, Ackermann K, Freitag CM, Schwenck C. Assessing Callous–Unemotional Traits in 6- to 18-Year-Olds: Reliability, Validity, Factor Structure, and Norms of the German Version of the Inventory of Callous–Unemotional Traits. Assessment. 2019;In press. 54. Kohannim O, Hibar DP, Stein JL, Jahansha N, Hua X, Rajagopalan P, et al. Discovery and replication of gene influences on brain

structure using LASSO regression. Front Neurosci. 2012;6(115).

55. Kim E, Lee SH, Lee KS, Cheong HK, Namkoong K, Hong CH, et al. AMPK γ2 subunit gene PRKAG2 polymorphism associated with cognitive impairment as well as diabetes in old age. Psychoneuroendocrinology. 2012;37(3):358–65.

56. McCracken JT, McGough J, Shah B, Cronin P, Hong D, Aman MG, et al. Risperidone in Children with Autism and Serious Behavioral Problems. N Engl J Med. 2002;347(5):314–21.

57. Cohn MD, Veltman DJ, Pape LE, Van Lith K, Vermeiren RRJM, Van Den Brink W, et al. Incentive processing in persistent disruptive behavior and psychopathic traits: A functional magnetic resonance imaging study in adolescents. Biol Psychiatry. 2015;78(9):615– 24.

58. Murray L, Shaw DS, Forbes EE, Hyde LW. Reward-Related Neural Correlates of Antisocial Behavior and Callous–Unemotional Traits in Young Men. Biol Psychiatry Cogn Neurosci Neuroimaging. 2017;2(4):346–54.

59. Wright N, Hill J, Pickles A, Sharp H. Callous-unemotional traits, low cortisol reactivity and physical aggression in children: findings from the Wirral Child Health and Development Study. Transl Psychiatry. 2019;9(1):79.

60. Popma A, Vermeiren R, Geluk CAML, Rinne T, van den Brink W, Knol DL, et al. Cortisol Moderates the

Relationship between Testosterone and Aggression in Delinquent Male Adolescents. Biol Psychiatry. 2007;61(3):405–11.

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Supplementary material chapter 6

- Individual items of the Inventory of Callous-Unemotional traits (ICU). - Detailed description of polygenic risk score analyses.

- Table S1: Gene set definitions.

- Table S2: Intermediate results for the genome-wide and gene-set based PRS-analyses investigating genetic overlap between aggression and callousness, uncaring and unemotional traits.

- Table S3: Genes nearest to SNPs in genome-wide PRS in relation to uncaring scores.

- Table S4 (A-D): Minor allele frequency, Hardy-Weinberg test and call rate for SNPs included in the best-fitting PRS that predicted CU-traits.

- Table S5: PRS-environment correlations (rGE).

- Table S6: Environment-stratified effect of PRS in relation to unemotional scores.

- Table S7: Sensitivity analyses: (1) adjustment for sibling relatedness, (2) adjustment for ADHD, (3) adjustment for sibling relatedness without PCs as fixed covariates, (4) exclusion MHC-locus and (5) including the full gene-set in the PRS.

- Equation S1: Calculation of the PRS.

- Figure S1: PCA-plots of NeuroIMAGE versus (A) 1000 Genomes global super populations and (B) 1000 Genomes European populations.

Individual items and reliability of the Inventory of Callous-Unemotional traits (ICU) The ICU consisted of 11 items related to the callousness dimension (‘I do not care who I hurt to get what I want’, ‘I am concerned about the feelings of others’, ‘I do not care if I get into trouble’, ‘I do not feel remorseful when I do something wrong’, ‘I do not care about doing things well’, ‘the feelings of others are unimportant to me’, ‘I do not care about being in time’, ‘I do not like to put the time into doing things well’, ‘What I think is right and wrong is different from what other people think’, ‘I do not let my feelings control me’, ‘I seem very cold and uncaring to others’ – Cronbach’s alpha 0.72 for the current sample), 8 items related to the uncaring dimension (‘I always try my best’, ‘I work hard on everything I do’, ‘I apologize to persons I hurt’, ‘I care about how well I do at school or work’, ‘I try not to hurt others’ feelings’, ‘I do things to make others feel good’, ‘I easily admit to being wrong’, ‘I feel bad or guilty when I do something wrong’ – Cronbach’s alpha 0.76 for the current sample) and 5 items related to the unemotional dimension (‘I express my feelings openly’, ‘I am very expressive and emotional’, ‘I do not show my emotions to others’, ‘I hide my feelings from others’, ‘It is easy for others to tell how I am feeling’ – Cronbach’s alpha 0.69 for the current sample). The reliability for the current sample is slightly better than the values reported in the ICU-paper by Essau et al. (2006) (1). The relatively low value of Cronbach’s alpha for the unemotional scale is most likely related to the low number of items in this scale.

Detailed description of polygenic risk scoring procedures

According to our best knowledge, no prior studies investigating shared genetic etiology between aggression and CU-traits were available, to support the choice for an a-priori P-value threshold for the PRS. Therefore, to avoid underfitting and poor prediction of the PRS in the absence of a-priori information, we used PRSice-software to calculate PRS for multiple

P-value thresholds to study genetic overlap between our base phenotype (‘aggressive

behavior’) and each target phenotype (‘callousness’, ‘uncaring’ and ‘unemotional’ ICU-dimensions) (2–4) (e.g. similar to (5–7)). PRS were first computed for at most 14 ‘broad’ thresholds (i.e. 0.0001, 0.0002, 0.0005, 0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.4

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and 0.5; note that for gene-set-based PRS some of the lower thresholds included no SNPs) using an additive and recessive model since recessive SNP-effects have been reported previously in aggression (8,9). If at least nominal significance was reached for one of the thresholds, the PRS was included in the final analyses and calculated for a small range of further thresholds around the best-fitting of the (at most) 14 thresholds (e.g. the PRS was further investigated between 0.01 and 0.05 when the best-fit from the 14 broad thresholds was at 0.02, etc.; the additional thresholds were defined according to the SNP P-values in the base-phenotype-GWAS (10), e.g. when there were only SNPs with P-values 0.015, 0.0195, 0.026, 0.03 and 0.048 between 0.01 and 0.05, these were chosen as the thresholds). This procedure was performed for genome-wide and gene-set PRS (See Supplementary Table S2 for intermediate results). Of note, PRSice tests by default 10,000 thresholds between 0 and 0.5 with an increment of 0.00005. However, if there remain for example only 500 SNPs after LD-clumping in the gene-set it would be sensible to test (in the absence of a-priori information, at most) only the different GWAS P-values of the SNPs in the clumped set. In addition, first screening across a few broad thresholds and only further investigating a small ‘region’ of thresholds may help in limiting overfitting of the PRS compared to testing thousands of thresholds across a large range. Multiple testing correction was applied in two stages: first, we computed ‘empirical P-values’ for the association of each best-fitting PRS in the final analyses, to correct for overfitting due to testing multiple correlated P-value PRS-thresholds. Empirical P-values were obtained by comparing the P-value of the best-fitting PRS for the actual phenotype with a null-distribution of P-values from the best-fitting PRS for 11,000 randomly permuted phenotypes (computational procedures are provided in the PRSice2 methodology paper(2) and can be found in the online PRSice manual at

http://www.prsice.info/). Subsequently, the empirical P-values were adjusted using the

procedure described by Benjamini and Hochberg (11) to control the false discovery rate (FDR) for the gene-sets and phenotypes investigated in the final stage of the PRS-analyses (i.e. ‘FDR Q-values’; the Benjamini-Hochberg procedure was applied only once to correct all empirical P-values). According to the procedure described by Benjamini and Hochberg, the (potentially positive) non-independency of the empirical P-values for the best-fitting PRSs, should not result in too liberal adjustment (11,12). In addition to investigating whether the PRS were associated with our traits of interest, we also computed ‘competitive P-values’ to

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6 | Aggression based genome-wide, glutamatergic, dopaminergic and neuroendocrine polygenic risk scores predict callous-unemotional traits Gene-set N _genes Genes (gene symbols)

Serotonin 43 HTR1A HTR1B HTR1D HTR1E HTR4 HTR6 HTR7 PCBD1 SLC6A4 ADCY1 ADCY2 ADCY3 ADCY5 ADCY6 ADCY7 ADCY8 ADCY9 TPH2 DDC HTR3C ADCY4 HTR3D IL4I1 GCH1 GNAS HTR3E HTR2A HTR2B HTR2C HTR3A HTR5A MAOA MAOB SMOX ADCY10 PTS QDPR SLC18A1 SLC18A2 SLC18A3 SPR TPH1 HTR3B

Dopamine 77

SLC6A3 PCBD1 PTPA ADCY1 ADCY2 ADCY3 ADCY5 ADCY6 ADCY7 ADCY8 ADCY9 COMT PPM1L DDC DRD1 DRD2 DRD3 DRD4 DRD5 ADCY4 NCS1 IL4I1 GCH1 PPP1R14B GNAS PPP2R3B PPM1J MAOA MAOB PPP1R12A CALY PRKAG2 SMOX PPP1R14D PPP1CA PPP1CB PPP1CC PPP1R3A PPP1R3C PPP1R3D PPP1R7 PPP1R10 PPP2CA PPP2CB PPP2R1A PPP2R1B PPP2R2A PPP2R2B PPP2R2C PPP2R3A PPP2R5A PPP2R5B PPP2R5C PPP2R5D PPP2R5E PRKACA PRKACB PRKACG PRKAG1 PRKAR1A PRKAR1B PRKAR2A PRKAR2B ADCY10 PRL PTH PTS QDPR SLC18A1 SLC18A2 SLC18A3 SPR PPP1R11 TH PPP1R14C PPP1R1B PPP1R14A

Glutamate 48

SLC17A2 GRIN3A GRIN3B GRIP1 SLC17A8 GLS GRIA1 GRIA2 GRIA3 GRIA4 GRID1 GRID2 GRIK1 GRIK2 GRIK3 GRIK4 GRIK5 GRIN1 GRIN2A GRIN2B GRIN2C GRIN2D GRINA GRM1 GRM2 GRM3 GRM4 GRM5 GRM6 GRM7 GRM8 CALML5 SLC17A7 SLC17A6 SLC1A1 SLC1A2 SLC1A3 SLC1A4 SLC1A6 SLC1A7 SLC17A1 CALM1 CAMK4 SLC38A1 HOMER3 HOMER2 HOMER1 PICK1

Neuroendocrine 426

CNR1 DNAJB1 MAP3K1 CREBBP ESR1 DUSP1 SCGB1A1 ESR2 NR3C2 MAP3K7 FCGR1A PTGES3 FKBP5 GTF2B FKBP4 NR3C1 FOXO3 CREBZF TSC22D3 NPPA NCOA1 TGFB1I1 NCOA4 CEBPA IL10 MAP3K14 CEBPB UBE2I IL1RN CREB1 PHB2 NRIP1 CXCL8 SERPINE1 KAT7 PKNOX1 SLC4A1 KAT2B NR0B1 SPEN MED14 RACK1 HMGB1 PPARGC1A MED1 SHC1 AKT3 MED6 SRA1 MED16 MEF2B CDK7 CDK8 CDKN1A CDKN1C TAB1 CARM1 NCOA2 TAF6L GNA13 GNB5 ADCY1 NFAT5 ADCY2 ADCY3 ADCY5 ADCY6 CHP1 ADCY7 ADCY8 CHUK ADCY9 MED12L HIST2H3C TAF1L ATF2 CRH CRHR1 CRHR2 MAPK14 CSF2 CSN2 PIK3R6 CTBP1 CTBP2 ADRB2 DDX5 AGT ARID2 A2M ELK1 EP300 ERCC2 AKT1 ERCC3 AKT2 FGG RRAS2 MRAS MED13L FOS PIK3R5 PRKD3 POLR2J2 G6PC PELP1 TAF5L GLI1 GLI2 GLI3 GNA11 GNA12 GNA15 GNAI1 GNAI2 GNAI3 GNAL GNAO1 GNAQ GNAS GNAT1 GNAT2 GNAZ GNB1 GNB2 GNB3 GNG3 GNG4 GNG5 GNG7 GNG10 GNG11 GRB2 MED4 BRD7 CXCL2 GTF2A1 GTF2A2 GTF2E1 GTF2E2 GTF2F1 GTF2F2 GTF2H1 GTF2H2 GTF2H3 GTF2H4 GUCY1A2 GUCY1A3 GUCY1B3 GUCY2C GUCY2F GUCY2D ANXA1 H3F3A H3F3B PIK3R4 NR4A1 HNRNPD HRAS HSPA1A HSPA1B HSPA1L HSPA2 HSPA4 HSPA5 HSPA6 HSPA8 HSPA9 HSP90AA1 HSP90AB1 ICAM1 IFNG IGFBP1 IKBKB IL1B IL2 FASLG IL3 IL4 IL5 IL6 IL13 AR ITPR1 ITPR2 ITPR3 IVL JAK1 JAK2 JAK3 JUN JUND KRAS KRT1 KRT32 KRT35 GTF2H5 SMAD2 SMAD3 SMAD4 MEF2A MEF2C MEF2D MMP1 MNAT1 COX2 ATF4 ATM NFATC1 NFATC2 NFATC3 NFATC4 NFKB1 NFKB2 NFKBIA NFKBIB NFKBIE NOS1 NOS2 NOS3 NPR1 NPR2 NRAS MED31 PCK1 PCK2 HSPA14 ACTL6B PRKAG2 MED15 TAF9B GNG13 CALML5 PGR PIK3C2A PIK3C2B PIK3C2G PIK3C3 PIK3CA PIK3CB PIK3CD PIK3CG PIK3R1 PIK3R2 PLAU PLCG1 PLCG2 POLR2A POLR2B POLR2C POLR2D GNG2 POLR2E POLR2F POLR2G POLR2H POLR2I POLR2J POLR2K POLR2L POMC TAF7L POU2F1 POU2F2 GNB1L MED18 POLR2J3 PBRM1 PHF10 PPP3CA PPP3CB PPP3CC PPP3R1 PPP3R2 PRKAA1 PRKAA2 PRKAB1 PRKAB2 PRKACA PRKACB PRKACG PRKAG1 PRKAR1A PRKAR1B PRKAR2A PRKAR2B PRKCA PRKCB PRKCD PRKCE PRKCG PRKCH PRKCI PRKD1 PRKCQ PRKCZ PRKDC MAPK1 MAPK3 GNG12 MAPK8 MAPK11 MAPK9 MAPK10 MAPK13 MAP2K1 MAP2K2 MAP2K7 PRL BAG1 G6PC2 RAC1 RAF1 GNB4 CCND1 BCL2 RELA BCL2L1 ACTB OPN1SW RRAS BDNF MAPK12 BGLAP CCL2 CCL3 CCL5 CCL11 CCL13 SELE MAP2K4 SGK1 SHBG SLPI SMARCA2 HLTF SMARCA4 SMARCB1 SMARCC1 SMARCC2 SMARCD1 SMARCD2 SMARCD3 SMARCE1 SOS1 SOS2 SRC BRAF SRY STAT1 STAT3 STAT5A STAT5B TAF1 TAF2 TAF4 TAF4B TAF5 TAF6 TAF7 TAF9 TAF10 TAF11 TAF12 TAF13 TAT TBP TRA TRB TGFB1 TGFB2 TGFB3 TGFBR1 TGFBR2 TNF HSP90B1 TRAF2 TRAF6 TSG101 SUMO1 VCAM1 VIPR1 YWHAH IL1R2 CALM1 CALM2 CALM3 CALR CAMK4 TAF15 DPF1 NCOA3 ARID1A HIST3H3 TRRAP HIST1H3C TAF3 MED10 NR0B2 PIK3R3 IKBKG RUNX2 KRT36 HDAC3 CCNC CCNH MED30 CD3D CD3E CD3G CD247 G6PC3 CD163 MED21 MED23 MED17 MED27 MED20 NCOR1 NCOR2 GNA14 IKBKE MED24 THRAP3 MED12 MED13

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Base Phenotype Target Phenotype Set N Best-fit threshold (PT) SNPs at PT Direction R2 at PT P

Aggression(10)

(continuous trait) Callousness (continuous trait) Genome-wide (additive model) 649 0.0005 (28 thresholds tested) 141 Negative 0.41% 0.0892

Uncaring

(continuous trait) Genome-wide (recessive model) 663 0.0002 (28 thresholds tested) 60 Negative 1.35% 0.0021

Unemotional

(continuous trait) Genome-wide (additive model) 663 0.002 (28 thresholds tested) 519 Negative 0.40% 0.0966

Gene-set based PRS Aggression(10)

(continuous trait) Callousness (continuous trait) Serotonin (additive model) 649 0.02 (18 thresholds tested) 4 Positive 0.37% 0.1082

Dopamine

(additive model) 649 0.02 (20 thresholds tested) 8 Positive 0.55% 0.0487

Glutamate

(recessive model) 649 0.1 (24 thresholds tested) 69 Positive 0.74% 0.0218

Neuroendocrine

(recessive model) 649 0.3 (24 thresholds tested) 510 Negative 0.51% 0.0577

Uncaring

(continuous trait) Serotonin (recessive model) 663 0.1 (18 thresholds tested) 20 Negative 0.50% 0.0613

Dopamine

(additive model) 663 0.005 (20 thresholds tested) 3 Positive 0.14% 0.3176

Glutamate

(additive model) 663 0.2 (24 thresholds tested) 111 Negative 0.49% 0.0651

Neuroendocrine

Unemotional

(continuous trait) Serotonin (recessive model) 663 0.1 (18 thresholds tested) 20 Negative 0.31% 0.1467

Dopamine

Glutamate

Neuroendocrine

Table S2: Intermediate results for the genome-wide and gene-set based PRS-analyses investigating genetic overlap between aggression and callousness, uncaring and unemotional traits. SNP: Single Nucleotide Polymorphism. The

analyses were adjusted for sex, age at outcome assessment and the first 10 principal components (a total of 69 subjects scoring >|±2|SDs on any of the principal components were removed from the analyses). The best-fitting P-value threshold for the PRS (PT) from 18 to 28 thresholds (i.e. 9 to 14 thresholds tested as an additive and recessive model) is shown. If at

least nominal significance (i.e. uncorrected P-value < 0.05) was reached at PT, the PRS was further investigated and included

in the final analyses (see Main Manuscript Table 2)

SNP A1/A2 Chr. BP Nearest Gene Distance Direction GWAS P-value(10) rs6696025 C/T 1 26617774 UBXN11 0 Within gene 9.79E-05 rs12033744 G/A 1 150960928 ANXA9 0 Within gene 1.12E-04 rs967072 C/T 1 156972485 ARHGEF11 0 Within gene 1.47E-04 rs1393822 C/T 2 68086999 AC010987.6 34305 Upstream 1.26E-04

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6 | Aggression based genome-wide, glutamatergic, dopaminergic and neuroendocrine polygenic risk scores predict callous-unemotional traits rs4547755 C/T 4 162327265 FSTL5 0 Within gene 3.97E-05

rs7716417 C/T 5 29467664 CTD-2010I22.2 71576 Upstream 9.47E-05 rs10514880 C/T 5 59112854 PDE4D 0 Within gene 7.13E-05 rs10067711 T/C 5 128113882 SLC27A6 0 Within gene 2.63E-05 rs12153160 A/C 5 152951200 GRIA1 0 Within gene 1.32E-04 rs7749480 G/A 6 20000048 RP1-130G2.1 42638 Downstream 1.58E-04 rs6912843 T/C 6 28904162 C6orf100 7492 Downstream 1.61E-06 rs9257657 G/A 6 29231172 XXbac-BPG308J9.3, OR2U1P 0 Within gene 2.01E-05 rs314268 G/A 6 105417978 LIN28B 0 Within gene 2.04E-05 rs13202332 G/T 6 107466630 PDSS2 7131 Downstream 6.63E-06 rs12661361 C/T 6 115147966 RNU6-475P 40071 Downstream 1.01E-04 rs10505229 C/T 8 115839385 RP11-192P9.1 35227 Upstream 1.83E-04 rs1412184 T/C 9 2395968 RP11-125B21.2 26734 Downstream 1.01E-04 rs10756720 C/T 9 15919177 CCDC171 0 Within gene 1.41E-04 rs3793815 T/C 10 71707687 COL13A1 0 Within gene 1.14E-04 rs4597005 C/T 10 112712477 SHOC2 0 Within gene 6.89E-05 rs11017177 C/T 10 132070231 GLRX3 87446 Upstream 1.23E-05 rs1225138 T/C 11 76553487 ACER3 18424 Downstream 1.76E-04 rs10842896 A/C 12 27434998 STK38L 0 Within gene 1.39E-04 rs7972829 A/G 12 63530607 AVPR1A 8407 Downstream 2.59E-05 rs7134682 G/T 12 66168151 RPSAP52 0 Within gene 1.44E-04 rs10861171 G/A 12 104617778 TXNRD1 0 Within gene 1.29E-04 rs1394801 T/C 12 116975847 LINC00173 1524 Upstream 7.29E-05 rs4760574 A/C 12 129246968 SLC15A4 30771 Downstream 1.23E-04 rs1483688 A/G 13 61892772 PCDH20 91046 Downstream 1.04E-04 rs9572385 T/G 13 70721292 ATXN8OS 7731 Upstream 1.01E-04 rs11620473 C/T 13 96086948 CLDN10 0 Within gene 4.32E-05 rs572288 G/A 15 54661020 UNC13C 0 Within gene 1.82E-04 rs1431242 C/T 15 86953068 AGBL1 0 Within gene 1.81E-04 rs3924397 A/G 15 92080907 RP11-661P17.1 0 Within gene 4.06E-05 rs2214197 G/T 16 20529746 ACSM2B 17802 Downstream 1.17E-04 rs27790 C/T 16 49364144 RP11-491F9.6 6450 Downstream 1.18E-04 rs10514386 A/G 16 74926805 WDR59 0 Within gene 1.83E-04 rs2079514 C/A 17 68913464 RP11-1003J3.1 94674 Downstream 5.73E-06 rs749818 A/C 19 46496793 CCDC61 1546 Downstream 1.61E-04 rs1225893 G/A 20 16037325 MACROD2 3483 Upstream 1.70E-04

Table S3: Genes overlapping with or nearest to SNPs in genome-wide PRS in relation to Uncaring scores (SNP annotation was carried out using SNP-nexus (15–17)). A1/A2: minor/major allele. Chr.: chromosome. BP: base position

according to GRCh37.

Tables S4A-D: Minor allele frequency (MAF), Hardy-Weinberg equilibrium (HWE) test and call rate for SNPs included in the best-fitting PRS that predicted CU-traits. Statistics are provided for the NeuroIMAGE (i.e. ‘target’) sample.

SNP MAF HWE P call rate rs6696025 0.3045 0.7338 0.98718 rs12033744 0.1423 0.6593 0.991026 rs967072 0.1173 0.8634 1 rs1393822 0.1737 0.6178 1 rs10496166 0.1391 0.6535 1 rs1030043 0.3479 0.6929 0.998718 rs10169036 0.489 0.4717 0.98718 rs16837930 0.06274 1 0.991026 rs12988044 0.0557 1 0.98974 rs16861227 0.05985 0.5143 0.996154 rs2618141 0.4672 0.5174 0.996154 rs1427283 0.1087 1 0.991026 rs13031713 0.184 0.2827 1 rs4687607 0.1273 0.02113 0.98205 rs1656370 0.4712 1 1 rs6774494 0.3109 0.1325 1 rs7625357 0.3216 0.9347 0.998718 rs1524517 0.249 0.5013 0.9859