Behavior Genetics

Background: Across phenotypic and genotypic studies, executive functioning (EF) has been shown to be associated with externalizing behaviors, including attention-deficit/hyperactivity disorder (ADHD), aggression, and substance use, and is a proposed underlying mechanism of these behaviors. However, there is limited research on the shared biological mechanisms underpinning EF and externalizing behaviors, including their overlap at the level of genomic regions. Methods: The current study used genome-wide association study summary statistics of EF, ADHD, aggressive behavior, alcohol use, and alcohol use disorder to examine whether the heritability of these traits or the co-heritability between them is enriched for genes expressed in particular tissues or stratified by other annotation types. Additionally, the study examined the genomic regions contributing to the covariation between EF and the externalizing traits to identify genetic variants and genes that may be underlying their overlap. Results: First, across all phenotypes, there was evidence for enrichment of genetic signals in DNase hypersensitive sites and H3K4me1 regions, and significant enrichment among genes expressed in the anterior cingulate cortex and frontal cortex. Second, when combining results across regional methods, there was evidence for associations between EF and each of the externalizing behaviors in regions on chromosomes 1, 11, and 15. Conclusions: These findings highlight biological pathways that may link the relations between EF and externalizing behaviors and provides additional evidence of EF as an underlying mechanism of externalizing behavior.

The phenotype of a focal individual can be affected not only by the individuals own genes (direct genetic effects, DGE) but also by genes of other individuals it interacts with (indirect genetic effects, IGE). Better understanding IGE can help better understand the impact and mechanisms of social effects. Here, we investigated, in laboratory mice, the modulation of IGE by genetic background of the focal individual (social epistasis) and by sex. We focused on IGE arising from Epha4, a gene that has been associated with the individuals own stress-related and social behaviours (i.e. DGE) and the stress-coping behaviour and wound healing of the other mice in the cage (i.e. IGE). Among four different genetic backgrounds for focal mice, significant IGE from Epha4 were detected in CBA females only. A formal model selection analysis was consistent with the modulation of IGE by the similarity of the genetic backgrounds of the focal mouse and its Epha4 cage mate, as well as by sex. Observations of fighting in cages of males with dissimilar genetic backgrounds suggested aggression might underpin these modulatory effects, which prompted to us to expand the conceptual framework describing the mechanisms of social epistasis. Thus, our results strengthen the emerging view that IGE are not fixed properties of social genotypes but can depend on the genetic and sex context of interacting individuals, revealing an underexplored layer of their genetic architecture.

Intelligence is often considered to be fundamentally different from other personality traits. Twin studies show that intelligence and the Big Five personality traits are partly influenced by the same genetic factors, but it remains unclear to what degree shared genes, SNPs, and biological pathways influence them. To address these questions, we analyzed the genetic associations of all six traits. First, we calculated their genetic correlations using LD score regression. We then quantified polygenic overlap using a bivariate causal mixture model, finding that approximately 90% of the genetic variants influencing the Big Five personality traits also impact intelligence. Finally, we performed gene prioritization and gene set enrichment analysis on each trait, once again finding substantial overlap. At each level, intelligence was as similar to the Big Five traits as they were to one another. Genetically and biologically, intelligence fits in with the rest of personality.

Polygenic scores (PGS) predict an individual's genetic value for a complex trait of interest by aggregating effect size estimates from genome-wide association studies (GWAS). While powerful, this approach assumes that genetic variants affect the phenotype in an additive way, which might be problematic when dominance is part of the genetic architecture. To address this limitation, we developed PGSa,d, a simple polygenic scoring framework, rooted in quantitative genetics theory, that incorporates both additive (a) and dominance effects (d) directly. We evaluated PGSa,d against the standard GWAS-based PGS (PGS{beta}) in simulations. The results showed that PGSa,d consistently achieved lower root mean squared error (RMSE) and progressively higher R2 as dominance contributed more to the genetic architecture. We then applied the method to 16 blood cell traits, height, and body mass index (BMI) from UK Biobank. PGSa,d showed comparable R2 to PGS{beta} for all traits (including height and BMI whose architecture is predominantly additive) and significantly lower RMSE for four traits after strict Bonferroni correction. Across traits, RMSE improvement from PGSa,d was positively associated with the magnitude of the estimated dominance effects, whereby traits with larger dominance effects displayed lower RMSE from PGSa,d. As a byproduct of our analysis, we also identified 141 variants in 39 genomic regions with significant dominance effects after Bonferroni correction across traits, recapitulating known associations and identifying novel ones. Together, these results show that explicitly modeling additive and dominance effects is a robust strategy that can improve individual-level predictions, especially when dominance contributes to complex trait architecture.

Substance use and psychiatric disorders frequently co-occur, but establishing causality of their relationships is difficult given the multifactorial nature of these traits. To assess these relationships, we applied two genetically informed methods using the largest available European-ancestry genome-wide association studies of six substance use behaviors and twelve psychiatric and personality phenotypes. Using Genomic Structural Equation Modelling, we modelled the latent genetic architecture of the psychiatric and personality traits and examined these factors' correlations with substance use traits. A structure with three factors fit the data best: F1 (PTSD, MDD, GAD, ADHD, agreeableness), F2 (BD, SCZ, OCD, openness), and F3 (OCD, GAD, extraversion, neuroticism), which showed differential correlations with substance use. Smoking correlated significantly which each factor, however, strongest with F1, alcohol use showed weak but significant correlations with F1 and F2, and cannabis use showed moderate correlations with F1 and F2, but strongest with F2. Using Mendelian Randomization, we examined bidirectional causal relationships. We found strong evidence that smoking continuation was causally associated with increased bipolar disorder risk, cigarettes per day with increased schizophrenia, and smoking initiation with increased extraversion and decreased agreeableness. Genetic liability to schizophrenia and PTSD were causally associated with increased risk of cannabis use, and schizophrenia with increased alcohol use. Liability to neuroticism, openness and agreeableness was causally linked to smoking behaviors, and extraversion to cannabis use. Most remaining associations showed weak causal evidence, largely due to heterogeneity and pleiotropy. Together, these findings provide evidence for both shared genetic liability and, for a subset of relationships, direct causal effects between substance use behaviors, psychiatric disorders, and personality traits, which can help inform future intervention and prevention research.

Attention Deficit Hyperactivity Disorder (ADHD) frequently co-occurs with distinct personality profiles, but whether ADHD shapes personality (scar model) or personality influences the development of ADHD (vulnerability model) remains unclear. Leveraging genome-wide association study (GWAS) data, we examined directional associations between ADHD and the Big Five personality traits (Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness). We estimated genetic correlations and conducted bidirectional two-sample Mendelian Randomization (MR) analyses between ADHD and the Big Five. Robustness was evaluated using multiple sensitivity tests, including MR-CAUSE, which accounts for correlated and uncorrelated pleiotropy, and colocalization analyses on fine-mapped variants. Higher genetic liability to Neuroticism and Extraversion, alongside lower liability to Conscientiousness, increased ADHD risk, consistent with the vulnerability model. Conversely, ADHD genetic liability was associated with lower Agreeableness, supporting the scar model. Associations were generally robust across sensitivity analyses, with MR-CAUSE supporting the Neuroticism and Agreeableness associations and colocalization providing variant-level support for associations between ADHD and Neuroticism, Conscientiousness, and Agreeableness. These findings provide early, population-level insight into personality-related vulnerability to ADHD and motivate further work on the role of personality traits in ADHD risk.

Local genetic correlation is estimated as a ratio of a local genetic covariance to the geometric mean of two local heritabilities. LAVA, the standard implementation, returns that ratio only where both traits clear a univariate heritability test at P < 0.05, a rule its documentation presents as a stabiliser. We show that it is a selection event acting on the denominator, and characterise what it does to the estimates that survive. Under the noncentral Wishart model LAVA's own interval routine assumes, the gate-conditional mean of the estimator is affine in the true correlation, with slope and intercept available in closed form from quantities every run already reports, that is, the components retained, the two univariate P-values, and the sample-overlap correlation. Attenuation is therefore a property of the analysis, not of the method. Across six psychiatric trait pairs the measured slope runs from 0.579 to 0.923 and the closed form predicts each to within 0.007 with nothing fitted to the panel, while within a single pair it runs from 0.371 to 0.971, so no single factor can correct it. Overlap and the filter jointly manufacture correlation where the truth is zero, most where power is lowest. We invert the closed form into a gate-aware correction, which reduces mean absolute bias from 0.247 to 0.026 over 48 simulation cells, and pair it with a diagnostic that decides whether the correction is identified. Three pairs fail it, all three sharing one trait whose local heritabilities are indistinguishable from a complete local null.

Polyandry, multiple mating by females, both benefits females by increases genetic variation within families and provides an opportunity for sexual selection through sperm competition and cryptic female choice. Estimating polyandry in species with large clutches is challenging because genotyping all progeny individually can be impractical. We present a likelihood-based framework for estimating the number of sires and their relative contributions from the maternal genotype and pooled sequencing of entire progeny pools. The method evaluates the probability of observed pooled offspring read counts under alternative paternal genotype mixtures, accounting for Mendelian segregation and sampling variation from finite sequencing depth. We implement this framework for both biallelic SNPs and short-read mini-haplotype markers constructed from closely linked SNPs, which provide multi-allelic information comparable to traditional microsatellite markers. Simulations show that this approach accurately recovers sire number and sire contributions across a range of conditions. We then applied the method to 275 wild-caught Drosophila melanogaster females and their offspring from a Kansas population using 3,053 biallelic SNPs and 239 four-allele mini-haplotype markers. Estimates from the two marker sets were highly concordant, and combined analyses indicate that 57% of families were sired by a single male. These results demonstrate that pooled progeny sequencing provides an efficient genomic approach for estimating polyandry in species with large families.

Ethanol occurs naturally in sugar-rich foods used by insects, but its effects in bees have been studied primarily in honeybees. We tested whether dietary ethanol affects survival and locomotor performance in workers of the bumblebee Bombus terrestris (L.). In the survival experiment, workers were fed ethanol (0, 1, or 10%) for 21 days; in the locomotor experiment, they were fed the same doses for one or seven days, after which we measured spontaneous movement using an open-field assay. We measured the proportion of time in movement, speed of movement, path meander (curviness), and arena exploration. The 10% ethanol concentration greatly increased the risk of mortality, whereas the estimated effect of 1% ethanol was smaller and statistically inconclusive. In terms of locomotion, the largest differences occurred after seven days of exposure to 10% ethanol. Workers in this treatment showed greater path meander and lower exploration than control workers. Speed was also lowest in this group. Evidence that exposure for seven days was worse than for one day at the same concentration was detected only for exploration. No clear locomotor effect was detected at 1% ethanol exposure. These findings provide comparative data on sustained ethanol exposure in bumblebees and show a strong concentration-dependent contrast, with substantial effects under high-concentration challenge but no clear effects on the measured endpoints at 1%.

Statistical associations between two variables can arise via various causal mechanisms. For example, one variable may influence the other, or they may share common causes. Surprisingly little attention has been devoted to quantifying how such causal pathways contribute to the overall association between variables. Here, I present two methods for partitioning a covariance between variables X and Z into a causal component arising from the effect of X and Z and a spurious component arising via other pathways. Both methods rely on averaging causal decompositions over the distributions of the variables, but the decomposition and averaging are performed in different ways. The 'gradient' definition decomposes causal effects in the neighbourhood of naturally occurring values, making it relatively straightforward to estimate. It assumes that the cause variable X is normally distributed. In contrast, the 'two step' definition decomposes large ('step-wise') changes in values and makes no distributional assumptions. I describe applications of these methods to analysing natural selection and illustrate them using the covariance between body size and the number of eggs carried by pregnant male dusky pipefish, Syngnathus floridae. I partition this total covariance into a causal component due to the effect of body size on egg number and a spurious component arising from variation in body size and egg number among collection sites.

Background: ADHD symptoms follow heterogeneous trajectories across childhood and adolescence, with important implications for later functioning. Early-life factors, including perinatal health, family environment, cognition, socioeconomic conditions, and community exposures, may contribute to this variability. The present study examined (1) early-life predictors of ADHD symptom trajectories, and (2) ADHD symptom trajectories as predictors of later substance use, suicidality, and new-onset psychiatric disorders. Methods: Growth models were used to fit longitudinal trajectories of inattention, hyperactivity, and irritability measures in two independent samples: the Oregon ADHD-1000 cohort (N=1,215; 49.17% ADHD; 39.9% female, ages 7-23) and a subsample of the Adolescent Brain Cognitive Development (ABCD) study (N=1,252; 33.3% ADHD; 31.6% female, ages 9-16). Regression analyses examined multiple risk factors as predictors of symptom growth parameters as well as associations between symptom trajectories and later adverse outcomes. Results: In both cohorts, growth models fit well (CFI > .96, RMSEA < .04), with all symptoms decreasing over time (ps < .001) and hyperactivity declining significantly faster than inattention. ADHD polygenic risk, family conflict, parenting, and cognition were associated with symptom intercepts in both cohorts (ps < .001). In Oregon ADHD-1000, poorer baseline working memory and processing speed predicted slower declines in inattention (ps < .001), but these effects did not replicate in ABCD; no other early-life factors predicted symptom trajectories. Increasing irritability trajectories predicted new disorder onset in both cohorts (ORs = 1.73 - 2.09, ps < .001), while more positive inattention slopes predicted new disorder onset in ABCD (OR = 3.07, p < .001) and showed a weaker association in Oregon ADHD-1000 (OR = 1.50, p = .007). Conclusion: While several early risk factors were associated with ADHD symptoms initially, none robustly predicted symptom trajectories over time. However, increasing irritability and inattention were robustly associated with increased risk for new disorder onsets later in adolescence.

Amongst measures of linkage disequilibrium, the correlation measure r2, due originally to Sewall Wright, has several advantages. Although basically a haploid statistic, it can readily be estimated using covariance and correlation from diploid data. Its population expectation can also be found from probability methods. We show that the probability of linked identity by descent, LIBD, directly estimates the expectation for r2 for Wright-Fisher models. The simplest such model for two loci is the haploid model, where each haplotype is chosen independently from haplotypes of the previous generation. This gives an equilibrium between recombination (recombination frequency c) and drift (population size N) where the expected value of r2 is approximately 1/(1+4Nc). For diploid models, the expectation is similar for low values of c, but contains a term in c2 and rises to double the haploid value for the limiting value of c = 0.5. An increase in r2 is also expected for the model of monogamous mating, as opposed to random mating for each offspring. The expectation for r2 is unaffected by the use of diploid rather than haploid data, an exception being the case of monogamous mating. Computer simulation shows close agreement of r2 with probability expectations, with minor discrepancies observed for unlinked loci. Using the probabilistic definition, r2 can be intuitively understood as a parameter shaping the effective sample size in genome-wide association studies.

Objective: To provide national norms, percentiles, and results of factor analyses for the ADHD symptoms and performance items on the parent-report and self-report versions of the Vanderbilt Assessment Scale (VAS) for youth ages 6 to 17. We hypothesized that symptom items would demonstrate separate inattention and hyperactivity/impulsivity factors, performance items would demonstrate interpretable latent dimensions, measurement invariance would be supported across age and sex, and symptom ratings would vary systematically by age and sex. Method: In a nationally stratified survey conducted in July 2025-March 2026, 2,230 parents of youth aged 6-17 years and 996 adolescents aged 12-17 years completed Vanderbilt Assessment Scale items. Both versions asked about the frequency of 18 ADHD symptoms and performance in 8 domains. To evaluate the psychometric properties and age-related normative curves of the VAS across parent- and youth-report forms, we conducted exploratory factor analysis, confirmatory factor analysis, and measurement invariance testing. We also generated age and sex-specific norms. Results: Factor analyses produced the expected two symptom factors and two performance factors. Developmental and sex-specific patterns of ratings of inattention and hyperactivity/impulsivity symptoms indicated that after increasing slightly during the preadolescent years, parent ratings of inattention symptoms declined and remained stable. In contrast, ratings of hyperactivity/impulsivity declined steadily with age. Normative data were generated using GAMLSS estimated centiles from fitted probability distributions. Conclusions: This study provides norms for the parent-report and adolescent self-report VAS. Findings underscore the importance of using sex- and age-based norms to better inform estimates of severity and diagnostic decision making.

Importance: Preventing substance use disorder (SUD) is a top public health priority, but clinical practice currently lacks validated prognostic tools to identify risk before the onset of heavy use/problems. Objective: To determine whether models can predict SUD using genetic, behavioral, and environmental factors and to evaluate performance across substance types (alcohol, cannabis, tobacco), sex, and genetic similarity groups. Design: This prognostic study developed and validated a multi-domain risk model using data from four longitudinal cohorts (The National Longitudinal Study of Adolescent to Adult Health, Avon Longitudinal Study of Parents and Children, The Collaborative Study on the Genetics of Alcoholism, and FinnTwin12) with follow-up from early adolescence into young adulthood. Setting: Population-based and family-based samples from the United States, United Kingdom, and Finland. Participants: The analysis included participants with available genomic and behavioral data. The cohorts represented a spectrum of risk, ranging from nationally representative community samples to high-risk populations. Exposure(s) (for observational studies): Predictors included polygenic scores for substance use-related phenotypes and 10 established early-life behavioral and environmental risk factors. Main Outcome(s) and Measure(s): The outcomes were a diagnosis of moderate-to-severe SUD based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive value. Results: A total of 16,362 participants were included in the study (54.7% female; mean [SD] age 26.73 [3.36]). Genetic similarity group composition was 70.4% European, 16.8% African, 9.8% admixed American, 2.2% East Asian, and 0.7% South Asian. SUD prevalence was 22.0%. The integrated model achieved an AUC of 0.78 for predicting any SUD, with a sensitivity of 69% and a positive predictive value of 45%. Performance was comparable for specific substances (AUC range 0.74-0.82) and across genetic similarity groups (AUC range 0.61-0.82). The model demonstrated higher AUC in females compared with males. Conclusions and Relevance: Findings highlight the potential utility of an SUD prediction model to inform clinicians and patients about risk for developing a SUD.

Locomotor response to a novel environment is a heritable, addiction-relevant behavioral phenotype in rodents. We characterized its genetic basis in 9,341 male and female heterogeneous stock (HS) rats from eight studies. Locomotor activity was heritable across studies, while pairwise genetic correlations were positive but variable, indicating shared yet nonidentical genetic influences. Given nonuniform genetic architecture, we compared three complementary approaches to locus discovery that analyzed SNP effects 1) on a pooled locomotor phenotype (MEGA), 2) across studies under a fixed-effect model (METAL), or 3) on a shared genetic factor (Genomic SEM). To ensure comparability, all three locus-discovery approaches were applied to a common subset of seven studies. Together, these approaches identified 38 loci, with both convergence and method-specific discovery across analyses. Genomic SEM identified 31 loci, compared to 8 for MEGA and 13 for METAL. To interpret the loci, we integrated brain molecular QTLs, predicted coding and splice consequences, and open-field locomotor phenotypes from knockouts of orthologous mouse genes. Our results highlighted established neurobehavioral candidates spanning locomotor and reward-related biology (Negr1 and Lmo4), serotonergic signaling (Htr1b), and GABAergic signaling (Gabrg2), as well as relatively novel genes including Tcerg1l, Pcdh10, and Tmem132e. Our results illustrate how Genomic SEM can integrate genetically correlated yet heterogeneous behavioral measures and enable discovery of loci underlying their shared genetic architecture, highlighting its utility for complex-trait studies in rodents.

Objective: Parent-adolescent disagreement in ratings of attention-deficit/hyperactivity disorder (ADHD) symptoms can complicate clinical assessment. This study examined parent - adolescent agreement in ADHD symptom ratings and characteristics associated with disagreement. Method: Participants were 1,008 parent-adolescent dyads recruited from a nationally stratified U.S. community sample. Adolescents were 12--18 years old (49.6% female) and along with their parents completed measures of ADHD and depressive symptoms. Agreement on Inattention (IN), Hyperactivity/Impulsivity (HI), and Total ADHD scores was examined. Item-level agreement was assessed using weighted kappa, Gwet 's AC2, percent agreement, and polychoric correlations. Associations between age, sex, and severity of ADHD and depression with levels of agreement were analyzed. Results: Agreement for ADHD factor and total symptom scores was moderate (intraclass correlations = 0.67--0.71), with moderate-to-strong rank-order correspondence. Adolescents reported slightly higher ADHD symptom levels than parents, although the mean difference was small. Item-level agreement was moderate to high (Gwet 's AC2 = 0.79--0.95). Several HI items demonstrated the kappa's paradox, with relatively low kappa values despite high Gwet's AC2 and percent agreement. Q-correlations revealed low agreement on relative strengths and weaknesses. Age and sex were not associated with disagreement and parent-reported ADHD severity and adolescent-reported depressive symptoms were associated with disagreement. Conclusions: Parent and adolescent ratings showed moderate agreement across boys and girls ages 12-- 18; however, disagreement related to relative strengths and weaknesses. Disagreement was associated with ADHD and depressive symptom severity. Findings suggest that ratings from parents and adolescents offer unique perspectives on the assessment of ADHD symptoms.

While creativity is widely recognized as multifaceted, genome-wide association studies have largely focused on individual measures. Here, we used genomic structural equation modeling of six creativity-related GWASs and identified two latent genetic factors: a general factor and an artistic factor, with 22 and 13 genome-wide significant loci, respectively, nine of which are novel. The general factor was almost perfectly genetically correlated with educational attainment (rg = 0.96), suggesting that it primarily captured genetic influences shared with cognitive and educational processes rather than creativity-specific variation. In contrast, the artistic factor showed only a weak genetic correlation with educational attainment (rg = 0.11) and consistent positive genetic correlations with psychiatric disorders, whereas associations for the general factor were largely null or negative. Biological annotation analyses further identified distinct enrichment patterns unique to the artistic factor, providing initial evidence that different dimensions of creativity may be characterized by partially distinct neurobiological pathways.

Population-level biobanks are transforming health and genomic research however they are all subject to recruitment and participation biases, the impacts of which are not fully understood. The UK Biobank (UKBB) is one such cohort; it is widely used for health-related research, but its participants are not representative of the UK general population. In addition, those who complete optional components of UKBB differ from those who do not in terms of health-related exposures and outcomes. Participation in UKBB has a partly genetic basis, but the extent to which rare variants influence participation in optional study components, and how this might impact findings of rare variant association studies, is unknown. We analysed exome sequencing and array data from 398,149 UKBB participants to examine rare variant contributions to UKBB participation bias, focusing on cognitive phenotypes. We found that classes of rare variation previously shown to contribute to cognition were enriched among participants who completed fewer cognitive tests. We then compared the effects of rare variants on cognition itself between groups of participants defined by whether they completed all online cognitive tests. Rare variants had significantly weaker effects on cognition in individuals who completed all online cognitive tests than in those who did not. These findings demonstrate that participation is a non-random phenotype that is influenced by rare variants, and that individuals who engage in optional UKBB components are not representative of the broader cohort. We show how participation bias can reduce the power of rare variant association studies, potentially masking true associations. This bias should therefore be carefully considered when interpreting results derived from phenotypes with substantial missingness.

Courtship is an essential component of reproductive success for many species and many courtship behaviors, such as vocalization, are energetically demanding. In order to successfully balance investment in courtship with feeding and foraging, individuals must be aware of their current energy balance, and hormonal signals of energetic state have been found to influence social signal effort across a variety of species. Ghrelin is one hormone that plays an important role in regulating energy balance by signaling hunger and promoting feeding. Previous studies have investigated the role of ghrelin in courtship, aggression, and mating behavior. In this study, we build on prior work by investigating the effects of ghrelin on ultrasonic vocalizations (USV) and other courtship behaviors of male mice. We expand on previous research by observing ghrelin's effects over longer interactions, allowing us to understand how ghrelin impacts the dynamics of USV production over time. Our results show that while males initially vocally court females at a high rate regardless of treatment, ghrelin reduces overall USV production by increasing the rate of decline of vocalization. Surprisingly, despite its effects on vocal courtship, we found no significant differences in the production or timing of other, non-vocal, courtship behaviors. This study deepens our understanding of the role of hormones traditionally associated with controlling hunger and feeding in regulating social behaviors.

Polygenic risk scores (PRS) are rapidly developing, enabling estimates of genetic risk for developing psychiatric disorders. Psychiatric PRS are also increasingly entering consumer and clinical settings. Yet, little is known about how adults with psychiatric disorders perceive their potential use. Here, we conducted semi-structured interviews with 29 adults with psychiatric disorders to examine their perspectives on hypothetically receiving psychiatric PRS for themselves and their current or future children. Key themes emerged around perceived utility, motivations and deterrents to genetic testing, interpretation of PRS, and anticipated responses to results. In particular, participants expressed hope that PRS could support self-understanding, inform better psychiatric care, and enable earlier recognition of difficulties for their children. Participants however raised multiple concerns, including those of privacy, stigma, discrimination, eugenics, and the risk of self-fulling prophecies. Together, these findings offer critical empirical evidence to inform guidance on the responsible use of psychiatric PRS in clinical and consumer settings.