Introduction

Antisocial personality disorder (ASPD) is characterized by traits of antagonism and disinhibition. Specific traits within these domains include manipulativeness deceitfulness, recklessness, and lack of empathy. These traits often arise in childhood or early adolescence and continue into adulthood (APA, 2000). ASPD is associated with adverse outcomes not only for the person living with the disorder, but also for their families and friends and for wider society (NICE, Excellence C, 2014). The prevalence estimates of ASPD vary considerably, from 1 to 6.8% in men and from 0.2 to 1% in women (Torgersen et al., 2001; Coid et al., 2006), with the higher prevalence in men being a consistent finding (Glenn et al., 2013). The Diagnostic and Statistical Manual of Mental Disorders (fourth edition, DSM-IV) diagnostic criteria for ASPD include symptoms and signs of conduct disorder in childhood and difficulties with cognition, affectivity, interpersonal functioning and impulse control in adulthood (APA, 2000).

Epidemiological studies indicate that both genetic and environmental factors influence the development of ASPD. Ferguson et al. (Ferguson, 2010), undertook a meta-analytical review of antisocial personality and behaviour that covered a range of personality traits and antisocial behaviour itself. The results showed that 56% of the variance in antisocial personality and behaviour could be explained by genetic influences, while shared and unique environmental factors, including non-genetic biological factors such as trauma and non-family socialization, explained the remainder.

Genome-wide association studies (GWASs) have been used to identify common genetic risk variants in psychiatric disorders (Ripke et al., 2014). To date, three GWASs have been undertaken to identify association with antisocial behaviour including ASPD. Tielbeek et al. reported the first GWAS of adult antisocial behaviour using a community sample of twin pairs registered with the Australian Twin Registry and their families comprising 4816 individuals from 2227 independent families; the definition of adult antisocial behaviour was based largely on DSM-IV criteria (Tielbeek et al., 2012). No genome-wide significance for association with antisocial behaviour was identified. Rautiainen et al. (Abram et al., 2015) conducted a discovery GWAS in 370 Finish criminal offenders who fulfilled DSM-IV criteria for a diagnosis of ASPD and 5850 general population controls (Rautiainen et al., 2016). None of the associations reached genome-wide significance in this analysis; however, eight suggestive variants associated with ASPD originated in the vicinity of HLA-DRA on chromosome 6. These SNPs were genotyped in a replication cohort of 173 offenders and 3766 controls and the results from both cohorts were meta-analyzed. One SNP, rs4714329, on chromosome 6p21.2 close to LINC00951 (Long Intergenic Non-Protein Coding RNA 951) was associated with ASPD at genome-wide significance (P = 1.6 × 10−9). More recently Tielbeek et al. reported a genome-wide significant association with SNPs at the FOXP2 (Forkhead Box Protein P2) locus and a broadly defined antisocial behaviour phenotype in data from 28 discovery samples involving 85 359 participants and five independent replication samples involving 8058 participants (Tielbeek et al., 2022).

ASPD displays considerable comorbidity with other psychiatric diseases (Abram et al., 2015; Tielbeek et al., 2018b). Thus individuals with ASPD have been shown to be at high risk for substance use disorders such as alcohol dependence (Yoshino et al., 2000; Bahlmann et al., 2002); depression (Moody et al., 2016), attention deficit hyperactivity disorder (ADHD) (Anney et al., 2009; Instanes et al., 2016), anxiety disorder (Galbraith et al., 2014; Brook et al., 2016), post-traumatic stress disorder (PTSD) (Goodwin and Hamilton, 2003), and schizophrenia (Schiffer et al., 2017; Sedgwick et al., 2017). The rate of alcohol use disorder in people with ASPD is particularly high at an estimated 76.7% (Guy et al., 2018). Moreover, data from two consecutively collected cohorts of prisoners has shown that alcohol dependence syndrome (ADS) shows high comorbidity with ASPD, suggesting that there may be common biological risk mechanisms (Yoshino et al., 2000; Bahlmann et al., 2002). Malone et al. (2004) found a significant but moderate genetic influence on adult antisocial behaviour and ADS at ages 17, 20, and 24 years in a cross-sectional twin study. Moreover, Malone et al. (Malone et al., 2004) showed, using cross-twin cross-trait correlations, that the covariation of antisocial behaviour and ADS was due to genetic factors, and that both disorders have a common genetic vulnerability, suggesting they might share susceptibility genes. However, Tielbeek et al. (2018a) assessed the relationships between antisocial behaviour risk and substance use disorder risk and identified significant genetic correlations with cannabis use and smoking but not with alcohol consumption. However, the nature of any common genetic susceptibility has yet to be discovered.

The aim of the present study was to use genome-wide data to examine the genetic architecture of ASPD symptoms and to identify potential genetic risk factors. By using a cohort of people clinically diagnosed with alcohol dependence syndrome for whom there was also data for ASPD diagnostic criteria, we reduced potential confounding of the genetic risk for alcohol dependence syndrome and environmental exposure to alcohol. We also estimated the genetic correlation of ASPD diagnostic criteria with other complex traits.

Methods Participants

The UCL cohort

A total of 644 participants were recruited from a variety of UK community and hospital-based services providing support and treatment for individuals with alcohol dependence. All participants had received a clinical diagnosis of alcohol dependence according to ICD-10 (F10.2). A clinical diagnosis of alcohol dependence was confirmed by clinicians and trained researchers using the Alcohol Dependence Syndrome section of the Semi-Structured Assessment for the Genetics of Alcoholism II questionnaire (Bucholz et al., 1994). The assessment allowed the diagnosis of ASPD to be made as a DSM-IV binary trait but also allowed for the generation of a quantitative ASPD criterion score (American Psychiatric Association, 2000). All participants were of English, Scottish, Welsh, or Irish descent with a maximum of one grandparent of non-British (but still Western European) ancestry; none of the individuals was related. Approval for the study was obtained from the NHS Metropolitan Multi-centre Research Ethics Committee (now the South Central - Hampshire A Research Ethics Committee) approval number MREC/03/11/090. All participants provided signed informed consent.

The Yale-Penn cohort

Participants were recruited as part of the Yale-Penn study of the genetic bases of drug and alcohol dependence, as described elsewhere (Kember et al., 2023). The subjects were interviewed using the Semi-Structured Assessment for Drug Dependence and Alcoholism (Pierucci-Lagha et al., 2005). Lifetime psychiatric and substance use disorders were diagnosed based on DSM-IV criteria. A European American (EA) subset of subjects from the Yale-Penn dataset were included in the current study. These included 1081 from Yale-Penn Phase 1, 1029 in Yale-Penn Phase 2, and 463 in Yale-Penn Phase 3. The study was approved by the Institutional Review Boards at the sampling sites and written informed consent was obtained from all study participants. Certificates of confidentiality were issued by the National Institute on Drug Abuse and the National Institute on Alcohol Abuse and Alcoholism.

Phenotypes The DSM-IV criteria for ASPD include

A: Evidence of conduct disorder (15 criteria) with onset before age 15; B: evidence of ASPD (9 criteria) that is a pervasive pattern of disregard for, and violation of, the rights of others occurring since age 15 years (see Supplementary Material 1, Supplemental digital content 1, https://links.lww.com/PG/A312 for the ASPD diagnostic criteria). A count of fulfilled ASPD diagnostic criteria rather than ASPD diagnosis was used as the phenotype to maximize the informativeness of the data. For each participant positive criteria were summed. Participants were excluded if their behaviour occurred during schizophrenia/manic episodes.

Genotyping and quality control

Genome-wide genotyping of genomic DNA from the UCL cohort was undertaken using the Illumina PsychArray. Genomic DNA from the Yale-Penn cohort underwent genotyping in three phases using the Illumina HumanOmni1-Quad array (phase 1), the Illumina HumanCore Exome array (phase 2), the Illumina Multi-Ethnic Genotyping array (phase 3), and each phase was analysed separately. Details of the genotyping, pre-imputation quality control and imputation are provided in the Supplement.

Statistical analysis Association tests

GWAS analyses of the data generated in the four cohorts were conducted separately on imputed best-guess genotypes using a linear regression model with a quantitative scale of ASPD diagnostic criteria as the phenotype, and sex, age, and the first 10 principal components as covariates. The analyses in the UCL data were performed in PLINK2 (Chang et al., 2015) while the separate analyses of the three Yale-Penn data sets (which include relatives) were performed in GEMMA-v0.98.1 (Zhou and Stephens, 2012). Analysis of chromosome X data was performed using XWAS (version 3.0) with male genotypes on the X chromosome were coded as 0/2 (Gao et al., 2015).

Meta-analysis of four cohorts

Sample size weighted meta-analysis of the four ASPD GWAS data sets including chromosome X (N = 3217) was performed using METAL (Willer et al., 2010).

Fine mapping

LocusZoom was used to make local association plots by uploading the meta-analysis summary statistics (Teslovich et al., 2010). Hg19/1000 Genomes Nov 2014 EUR was used for the background LD structure.

Gene-based test, pathway, and enrichment analyses

Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) software was used to explore gene prioritization, gene expression, pathway process enrichment with meta-analysis summary statistics as input. FUMA implemented Bonferroni correction (Pbon < 0.05) to correct for multiple testing (Watanabe et al., 2017).

Phenome-wide association analysis

To examine whether any of the top hits and related genes identified in the present study are associated with other complex traits, phenome-wide association analysis (PheWAS) plots were created by exploring the 4756 GWAS summary stats available on the GWAS ATLAS platform (Watanabe et al., 2019). All GWAS SNPs and related genes were used in the analysis. SNPs with P-value <0.05 were used in the PheWAS SNP plot. P-values were adjusted for multiple comparisons using Bonferroni correction.

Polygenic risk scores

Polygenic risk score (PRS) analyses were performed to test whether risk alleles for a variety of psychiatric and behavioural traits correlated with genetic risk variants associated with the ASPD diagnostic criteria scores in the UCL and Yale-Penn samples. PRSice2 was used to estimate the best-fit PRS at a range of P-value thresholds (Choi et al., 2020). The linkage disequilibrium (LD) threshold was set to an R2 of 0.1 and a distance of 250 kb.

The meta-analysis of the PRS results from UCL and Yale-Penn was conducted using metagen in the meta package in R (Balduzzi et al., 2019). The FDR method was used to correct for multiple comparisons.

Summary statistics of complex traits and psychiatric disorders were downloaded from publicly available resources [Psychiatric Genomics Consortium (PGC): https://www.med.unc.edu/pgc/results-and-downloads and the GWAS ATLAS: http://atlas.ctglab.nl]. The summary statistics for a GWAS of coronary artery disease was also included to act as a negative control for the PRS analyses (Nikpay et al., 2015).

Results

A total of 3217 individuals with a lifetime history of alcohol dependence syndrome and an ASPD diagnostic criterion score were included in the study (Table 1). There were no significant differences in the sex distribution of age between the UCL and Yale-Penn cohorts but the UCL cohort were less severely affected (P < 0.01) (Table 1).

Table 1 - Demographics and total ASPD diagnostic criteria counts for the GWAS cohorts Sample (n) Sex (% men) Age, mean (range) ASPD diagnostic criteria score (mean) Men Women Men Women UCL (644) 67 44 (19–74) 45 (22–69) 4 2 Yale-Penn Phase 1 (1081) 63 40 (16–71) 39 (16–69) 7 6 Yale-Penn Phase 2 (1029) 67 40 (18–76) 39 (19–80) 7 6 Yale-Penn Phase 3 (463) 68 40 (17–73) 38 (18–75) 7 5 Total 63 41 40 7 5
GWAS of the four cohorts and meta-analysis Results from the individual cohorts

We found a genome-wide significant locus on chromosome 14q.13.1 (rs142893681, P = 3.19 × 10−8), in the vicinity of the SNX6 (sorting nexin 6) gene in the UCL cohort GWAS. A genome-wide significant locus on chromosome 18q11.2 (rs59381075, 1.98 × 10−8), near the ZNF521 (Zinc Finger Protein 521) gene was found in the Yale-Penn Phase 1 GWAS. No genome-wide significant associations were identified in the Yale-Penn Phases 2, 3 cohorts or on chromosome X (Supplementary Tables 1–4. Supplemental digital content 2, https://links.lww.com/PG/A313).

Results from meta-analyses

The meta-analysis of the four individual GWAS of ASPD criteria scores identified a genome-wide significant association on chromosome 15q.26.1 (rs9806493 P = 3.77 × 10−8) near to SLCO3A1 (Solute Carrier Organic Anion Transporter Family Member 3A1) (Table 2 and Supplementary Figure 1, Supplemental digital content 1, https://links.lww.com/PG/A312). Two additional independent SNPs showed suggestive evidence of association, rs10186418 (P = 2.79 × 10−7) in an intron of in KCNS3 (Potassium Voltage-Gated Channel Modifier Subfamily S Member3) and rs11682196 (P = 3.69 × 10−7) in an intron of CTNNA2 (Catenin Alpha 2). The two SNPs, that were identified at genome-wide significance (rs142893681 and rs59381075) (P < 5 × 10−8) in the UCL and Yale-Penn Phase 1 individual GWASs respectively, were NS in the meta-analysis. No significant results were found from the meta-analysis of the X chromosome data.

Table 2 - Top independent variants associated with ASPD in the meta-analysis of the GWAS data from the UCL and Yale-Penn cohorts CHR SNP IDa Effect allele Other allele Gene Weight Z-score P-value Effect allele frequency (%) Direction 15 rs9806493 C T SLCO3A1 1673 −5.501 3.77 × 10−8 47.1 ––?? 2 rs10186418 A G KCNS3 3197 5.137 2.79 × 10−7 86.8 ++++ 2 rs11682196 C A CTNNA2 3157 −5.084 3.69 × 10−7 86.9 –––– 7 rs967758 C T Y_RNA 2153 −4.868 1.13 × 10−6 20.2 ––?– 20 rs6076184 T C RP5-1100I6.1 2181 4.807 1.53 × 10−6 5.3 ++?+ 1 rs6691165 C A MIR552 3175 −4.789 1.68 × 10−6 45.2 ––––

Gene is the gene located closest to the lead SNP; or where there are multiple genes in the region the gene for which the SNP has the most deleterious annotation in ANNOVAR. Direction: - for negative, + for positive and? for missing in the UCL, Yale-Penn Phases 1, 2, and 3 samples respectively. The SNP marked in bold text reached a genome-wide level of significance in the meta-analysis.

CHR, chromosome; SNP, single nucleotide polymorphism.

aOnly SNPs that were present in both UCL and one or more Yale-Penn samples are shown.

Fine mapping and expression quantitative trait loci

rs9806493 is located 7.5 kb downstream from the Solute Carrier Organic Anion Transporter Family Member 3A1 (SLCO3A1) gene (Fig. 1). All of the available SNPs in linkage disequilibrium with rs9806493 (R2 > 0.2) map to a region towards the 3’ end of SLCO3A1. rs9806493 is identified as a peripheral blood cis-expression quantitative trait loci (cis-eQTL) for SLCO3A1 (Z-score = −9.33, P = 1.09 × 10−20) in the eQTLGen database (Võsa et al., 2021). It is also identified as an eQTL for SLCO3A1 (regression slope = −0.035, P = 1.01 × 10−4) in the QTL maps from the PsychEncode project (Gandal et al., 2018). However, it is not identified as an eQTL for SLCO3A1 in the Genotype-Tissue Expression (GTEx) database (version 8.0) (Consortium, 2020).

Fig. 1:

Regional locus plot of the association findings with rs9806493 close to the SLCO3A1 gene in the meta-analysis of ASPD diagnostic criteria scores.

Gene-based tests, pathway, and enrichment analyses

MAGMA gene-based, gene-set tests, and tissue expression analysis of the individual UCL and the Yale-Penn GWAS did not identify any statistically significant findings. MAGMA gene-based and tissue expression analyses of the data from the GWAS meta-analysis showed no evidence for association with ASPD. MAGMA gene-set tests of the meta-analysis data provided nominal evidence for association of several gene sets with ASPD. However, none of these survived Bonferroni correction for multiple testing (Supplementary Tables 5–10. Supplemental digital content 2, https://links.lww.com/PG/A313)

Phenome-wide association analysis

PheWAS analyses using the GWAS Atlas platform were performed to examine secondary phenotypes associated with rs9806493 and the SLCO3A1 gene. rs9806493 was associated with the reproductive (and risk-taking) trait: “number of sexual partners” (P = 0.0003, Bonferroni corrected P = 0.00049; Supplementary Figure 2, Supplemental digital content 1, https://links.lww.com/PG/A312 Supplementary Table 11. Supplemental digital content 2, https://links.lww.com/PG/A313). The SLCO3A1 gene was associated with 43 different traits after Bonferroni correction, including educational attainment (P = 9.28 × 10−8), BMI (P = 5.56 × 10−7), broad depression (P = 6.06 × 10−7), seeing a doctor for nerves, anxiety, tension or depression (P = 2.42 × 10−6), alcohol dependence (P = 1.66 × 10−5), and depression (P = 1.13 × 10−4; Table 3 and Fig. 2, and Supplementary Table 12, Supplemental digital content 2, https://links.lww.com/PG/A313).

Table 3 - Top significant trait associations in the SLCO3A1 phenome-wide association analysis Trait Domain Reference P-values Participants (n) Educational attainment Environment Okbayet al. (2016) 9.28 × 10−8 328 917 Broad depression Psychiatric Howard et al. (2018) 6.06 × 10−7 322 580 Educational attainment Environment Lee et al. (2018) 8.40 × 10−7 766 345 Seen doctor (GP) for nerves, anxiety, tension or depression Psychiatric Watanabe et al. (2019) 2.42 × 10−6 383 771 Alcohol dependence Psychiatric Wang et al. (2013) 1.66 × 10−5 2322 Lifetime number of sexual partners Reproduction Watanabe et al. (2019) 4.52 × 10−5 316 569 Depression Psychiatric Howard et al. (2019) 1.13 × 10−4 500 199

A total of 436 GWAS were included in the PheWas., the Bonferroni corrected P-value threshold is 1.15 × 10−4.

Fig. 2: SLCO3A1 gene PheWAS plot. Phenome-wide association analysis for the SLCO3A1 gene from 436 GWASs. The results were sorted by domain and P-value. The Bonferroni corrected P-value threshold is 1.15 × 10−4 (horizontal black line). The studies and phenotypes examined that report an association that survived Bonferroni correction are shown in Supplementary Table 10. Supplemental digital content 2, https://links.lww.com/PG/A313.Polygenic risk score analysis

PRS analyses were performed to investigate the genetic correlation between major psychiatric disorders and other complex behavioural traits with ASPD diagnostic criterion scores (Figs. 3 and 4 and Supplementary Table 13. Supplemental digital content 2, https://links.lww.com/PG/A313). The analyses were performed using publicly available GWAS summary statistics downloaded from the PGC or the GWAS ATLAS (Supplementary Table 14. Supplemental digital content 2, https://links.lww.com/PG/A313).

Fig. 3:

PRS analysis in the UCL and Yale-Penn ASPD samples. PRS results are shown at different P-value thresholds for each trait along with the percentage of variance explained by the PRS (Nagelkerke’s R2) for ten traits in the UCL and Yale-Penn cohorts. *Comparisons significant after FDR correction in the individual PRS analyses (black *) or in the PRS meta-analysis (red*). ADHD, attention deficit hyperactivity disorder; AF, alcohol frequency; Dep 1, depressive symptoms; Dep 2, seen doctor for nerves, anxiety, tension or depression; Education, age completed full-time education; Ever Smoke, Whether a participant had ever smoked a cigarette; MDD, major depression disorder; PTSD, post-traumatic stress disorder; Rep 1, age at first live birth (female); Rep 2, age at first birth (male).

Fig. 4: Polygenic risk score relationships among ASPD and other traits. The direction of correlations between ASPD PRS and the PRS for other traits are shown as the standardized mean difference (SMD). Redpoint estimates indicate a positive correlation; blue point estimates indicate a negative correlation. The size of the point estimates reflects the FDR adjusted -log10 of the P-value. ADHD, attention deficit hyperactivity disorder; PTSD, post-traumatic stress disorder, depression traits include: major depression disorder (MDD), depression symptoms and seen doctor for nerves, anxiety, tension or depression, reproduction traits include: age at first birth (female), age at first birth (male). See Supplementary Figure 3, Supplemental digital content 1, https://links.lww.com/PG/A312 for GWAS ATLAS genetic correlation results.

The meta-analysis of PRSs results for educational attainment, alcohol intake frequency, and the reproductive traits ‘age at first live birth in women and men’ were negatively correlated with higher ASPD criterion scores (PFDR corrected < 0.05). The PRS for whether a subject had ever smoked, had depression including major depressive disorder, and two sub-clinical depressive traits were positively correlated with the number of ASPD diagnostic criteria (PFDR corrected < 0.05).

The PRS for post-traumatic stress disorder did not show a consistent direction of effect in the UCL sample but was positively correlated with ASPD diagnostic criteria in the Yale-Penn cohorts (Table 4). None of the results from the PRS analyses with schizophrenia, anxiety, aggression, or coronary artery disease survived correction for multiple testing in the meta-analysis.

Table 4 - PRS analysis in the UCL and Yale-Penn ASPD cohorts, and the meta-analysis results Directions Traits Best P-value threshold R2 Significance (P) Significance (P meta-analysis) UCL Yale-Penn UCL Yale-Penn UCL Yale-Penn Negative correlation Age at first live birth (female) 0.5 1 × 10−5 0.58% 0.05% 0.038 > 0.05 0.044 Age at first live birth (male) 1 × 10−5 1 0.79% 0.08% 0.015 0.01 0.018 Age at last birth (female) 1 × 10−5 1 0.36% 0.08% > 0.05 0.014 > 0.05 Age of completion of full-time education 1 0.5 1.10% 0.09% 0.004 0.006 9.4 × 10−4 Alcohol intake frequency 0.001 1 0.55% 0.07% 0.043 0.039 0.039 Positive correlation Ever smoked 0.05 0.01 1.28% 0.12% 0.002 5.8 × 10−4 9.4 × 10−4 MDD 1 × 10−4 0.05 1.13% 0.07% 0.004 0.036 0.03 Depressive symptoms 0.05 0.5 0.93% 0.07% 0.009 0.036 0.006 Seen doctor for nerves, anxiety, tension or depression 1 1 × 10−4 1.90% 0.06% 1.7 × 10−4 > 0.05 1.6 × 10−3 PTSD 0.05 1 × 10-5 0.36% 0.31% > 0.05 1.0 × 10−9 9.4 × 10−4

MDD, major depression disorder; PTSD, post-traumatic stress disorder.

P-values for the meta-analysis of PRS were corrected for multiple comparisons using FDR method.

Discussion

The present study investigated the genetic architecture of ASPD criteria in the context of alcohol dependence and is to the best of our knowledge the largest meta-analytic GWAS of comparable clinical measures of ASPD undertaken to date. This GWAS meta-analysis identified a novel genome-wide significant signal with rs9806493 on chromosome 15q26.1 close to SLCO3A1. This marker is supported by additional SNPs in linkage disequilibrium with the main finding that did not reach genome-wide significance. In the PRS analysis, genetic correlations that survived correction for multiple testing were identified with genetic risk variants for many complex behavioural traits and psychiatric disorders including education attainment, smoking, alcohol intake frequency, reproductive behaviours, depression, PTSD, and ADHD.

Two of three eQTL databases indicate that rs9806493 is an eQTL for SLCO3A1. The ASPD risk allele in our study (rs9806493:T, note that the Z-score with allele C is negative; Table 2), is associated with increased expression of SLCO3A1.

SLCO3A1 has also been shown to mediate the transport of Na (+)-independent of organic anions and hormones including thyroxine and vasopressin, the cyclic oligopeptides BQ-123 (endothelin receptor antagonist), and prostaglandins (PG) E1 and E2 (Tamai et al., 2000