Behavior Genetics

Wellbeing is commonly defined as the combination of feeling good and functioning well and typically conceptualized as two related but distinct components. Hedonic wellbeing emphasizes pleasure, happiness, and life satisfaction, while eudaimonic wellbeing focuses on meaning, personal growth, flourishing, and the realization of ones potential. The Mental Health Continuum-Short Form was developed as a comprehensive measure of wellbeing and includes three subscales assessing emotional, social, and psychological wellbeing. Although the Mental Health Continuum total score is often interpreted as an indicator of overall wellbeing, the underlying genetic structure of its three subscales and its genetic overlap with other commonly used wellbeing measures remains unclear. Using data from 5,212 individuals from the Netherlands Twin Register (72% female, mean age 36.4), we fitted multivariate twin models to examine the genetic architecture of the Mental Health Continuum and its associations with other wellbeing measures (quality of life, life satisfaction, subjective happiness, and flourishing). Results indicate that, at the genetic level, the Mental Health Continuum is best explained by its three distinct subscales rather than by a latent factor. When considering the Mental Health Continuum together with the other wellbeing measures, we found moderate to high genetic correlations (r = 0.52 - 0.83), indicating substantial overlap in the genetics underlying the wellbeing constructs. However, we did not find evidence for a single common genetic factor underlying all constructs. These findings highlight the multidimensional structure of wellbeing, but the moderate to high genetic correlations across measures suggest that it is important to align the level of measurement (phenotypic vs genetic) with the research question.

BackgroundSubstance use initiation in adolescence is influenced by both genetic and environmental factors; however, large-scale genetic studies often treat initiation as a binary outcome and underuse longitudinal timing information. MethodsWe conducted time-to-event (survival) genome-wide association analyses (GWAS) of initiation for four outcomes--alcohol, nicotine, cannabis, and any substance use--using longitudinal follow-up data from the Adolescent Brain Cognitive Development (ABCD) Study. We performed ancestry-stratified GWAS within European (EUR), African (AFR), and Hispanic (HISP) groups, applying consistent quality control and covariate adjustment. Summary statistics were harmonized across ancestries and meta-analyzed using inverse-variance weighted fixed-effects and DerSimonian-Laird random-effects models. We evaluated genomic inflation and heterogeneity (Cochrans Q and I2), identified independent lead variants at genome-wide and suggestive significance thresholds, and assessed cross-trait overlap of associated loci. ResultsIn the multi-ancestry meta-analysis, we observed suggestive association signals across traits (minimum p-values: alcohol [~] 1 x 10-7, any [~] 1 x 10-7, cannabis [~] 5 x 10-8, nicotine [~] 1 x 10-8). Nicotine initiation showed one genome-wide significant variant in both fixed- and random-effects meta-analyses (p < 5 x 10-8). Across traits, suggestive loci demonstrated limited overlap, with the strongest concordance between alcohol and any substance use, consistent with shared liability. Heterogeneity statistics indicated that some loci exhibited cross-ancestry variation in effect estimates. ConclusionsSurvival GWAS leveraging initiation timing can identify genetic signals that may be missed by binary designs and enables principled multi-ancestry synthesis. Our results highlight both shared and trait-specific genetic contributions to early substance initiation and provide a foundation for downstream functional annotation and integrative modeling with environmental risk factors. These findings demonstrate the value of incorporating developmental timing into genetic discovery and provide a framework for integrating longitudinal risk modeling with genomic analyses.

Background: Attention problems are common transdiagnostic symptoms of psychiatric illness. Although environmental exposures and experiences influence attention during adolescent development, the underlying neural pathways by which they do so is unclear. Methods: We measured attention problems, attention-related brain networks, and multidimensional environmental experiences (the exposome) using data from the ABCD Study (N = 11,878). We tested whether the exposome is associated with 9-10-year-olds attention-related brain network strength and current and future attention problems. We further examined cross-sectional indirect pathways linking the exposome, brain network strength, and attention problems. Results: The exposome predicted youths current and future self-, caregiver-, and teacher-reported attention problems as well as their current attention-related brain network strength. This brain network signature of sustained attention also predicted attention problems from all three reporters. Indirect effects models revealed that the exposome was associated with current reported attention problems both directly and indirectly though this brain signature. Conversely, predictive brain network strength was related to attention problems both directly and indirectly through the exposome. Conclusion: Interactions between environmental exposures, experiences, and brain network organization are associated with attention problems in early adolescence. These findings support a bidirectional framework linking the environment and functional brain networks in the development of attention problems.

Background: The Adolescent Brain Cognitive Development (ABCD) Study provides rich longitudinal data on environmental, genetic, and behavioral factors related to substance use initiation. Classical marginal structural models (MSMs) require selecting covariates for propensity models, which is challenging when there are many correlated predictors. Methods: We analyzed longitudinal panel data from 11,868 ABCD participants with repeated observations over time. Interval-level binary outcomes were defined for initiation of alcohol, nicotine, cannabis, and any substance, including only participants at risk before initiation. All predictors were constructed as lagged variables to preserve temporal ordering. We used a two-stage machine learning-based causal framework. First, we performed graph discovery using a Granger-inspired lagged predictive modeling approach with elastic-net logistic regression to identify relationships between past predictors and future outcomes. Stable candidate edges were selected using subject-level bootstrap stability selection. Second, we estimated adjusted effects for stable predictors using double machine learning (DML) with partialling-out and cross-fitting. For each predictor, the lagged variable was treated as the exposure and adjusted for high-dimensional lagged covariates. Cross-fitting with group-based splitting accounted for within-subject dependence. Nuisance functions were estimated using random forests, and cluster-robust standard errors were used for inference. Results: We identified stable predictors across multiple domains, including sleep patterns, family environment, peer relationships, behavioral traits, and genetic risk. Many predictors were shared across substance outcomes, while some were outcome-specific. Effect sizes were modest, typically ranging from -0.01 to 0.02 per standard deviation increase in the predictor. Both risk-increasing and protective associations were observed. Risk factors included sleep disturbance and behavioral risk indicators, while protective factors included parental monitoring and structured environments. Conclusions: This study presents a practical framework for analyzing high-dimensional longitudinal data and identifying time-varying predictors of substance use initiation. The approach combines machine learning for variable selection with causal inference for effect estimation. The results highlight both shared and outcome-specific risk factors and identify modifiable targets, such as family environment and sleep, that may inform prevention strategies.

Childhood weight development, especially overweight and obesity, has been associated with mental health, but their dynamic, causal relationships, and whether these differ by sex, remain unclear. We applied causal discovery to data from the Danish National Birth Cohort (n=67,593) spanning six periods from pregnancy to late adolescence and considering 67 variables related to child and parental weight, mental health, lifestyle, and socio-economic factors. We found no statistically significant difference between the causal graphs for boys and girls (P=0.079). The data-driven models found causal influence of childhood weight on subsequent weight status. Mental health pathways were exclusively within or across adjacent periods and centered on early adolescent stress. We examined the interplay between a subset of mental health variables, containing information on externalizing and internalizing problems, and weight, and found no direct causal pathway between the two processes. These findings suggest that observed links between weight and these mental health measures may be attributable to confounding. Our findings demonstrate the value of data-driven causal discovery in large cohort studies and how to test for differences in causal mechanisms across subgroups. Results are available in an interactive application, enabling future research to further explore the interplay between weight and mental health.

The most highly predictive polygenic scores in the behavioural sciences are for cognitive traits, especially general cognitive ability (g) and educational attainment. We combined polygenic scores derived from genome-wide association studies of adult g and educational attainment to create adult 'polygenic g scores' which we used to chart the course of cognitive development of 10,000 white British children from toddlerhood through early adulthood. We integrated cross-sectional regression, latent growth curve, and confirmatory factor analysis to systematically characterise cognitive development. Polygenic g score showed minimal prediction in toddlerhood, modest prediction in childhood, and substantial prediction by early adulthood accounting for 12% of the variance. Higher polygenic g scores were associated with faster cognitive growth in latent growth models. Prediction was strongest for a cross-time latent cognitive factor (15%) capturing cognitive ability across development. By integrating polygenic prediction directly into a structural equation model framework, we provided a theoretical upper bound of genetic influences on g under minimal measurement error. We also examined the polygenic g score's prediction of educational achievement, behaviour problems, and anthropometric outcomes and found similar developmental increases in prediction for educational achievement. Together, our findings demonstrate that adult polygenic g scores can be a useful tool for charting the development of cognitive traits.

Heat Shock Protein 90 (HSP90) functions as an evolutionary capacitor, allowing populations to store cryptic genetic variation that can be released under stress. While former studies have described the release of morphological variation, its behavioural consequences remain unexplored. In the red flour beetle, Tribolium castaneum, HSP90 inhibition released a phenotype with much smaller, less defined eyes that confers fitness benefits in continuous light and was subsequently assimilated. We hypothesized that altered eye morphology affects light perception and thereby changes light-dependent behaviours. To test whether phenotypes released via evolutionary capacitance can beneficially alter behaviour, we examined locomotor activity rhythm entrainment to light-dark cycles as well as individual and group light choice behaviour. Males of the reduced-eye phenotype exhibited a diminished startle response to sudden light exposure in locomotor activity assays. We also found reduced negative phototaxis in groups of beetles with reduced eyes. This modified behaviour, indicating reduced light sensitivity, may stem from impaired light perception caused by altered eye morphology. Lower light sensitivity could be beneficial under stressful environmental conditions by promoting the exploration of alternative niches. Therefore, this study provides the first evidence for potentially beneficial behavioural changes in a HSP90-released phenotype, reinforcing HSP90s role as an evolutionary capacitor.

Adult ADHD is increasingly recognized across the lifespan, yet the psychometric equivalence of the Adult ADHD Self-Report Scale (ASRS) remains unverified for older populations. This study examined age-related Differential Item Functioning (DIF) in 600 adults (n = 100 per decade, ages 20-80) who completed the 18-item ASRS. Using a bi-factor Graded Response Model, we extracted latent ADHD trait scores ({omega}H = .895) and assessed DIF via ordinal logistic regression with adaptive age modeling. Five of 18 items exhibited significant uniform DIF. At equivalent latent severity, older adults were less likely to endorse hyperactivity symptoms in Part A (fidgeting, feeling "driven by a motor") but more likely to endorse specific symptoms in Part B (careless mistakes, misplacing items, interrupting). From ages 20 to 80, expected Part A scores decreased by 1.36 points (~0.27 per decade), while Part B scores increased by 1.15 points (~0.23 per decade). These findings indicate a phenotypic redistribution of ADHD symptoms as individuals age. Because the 6-item Part A screener serves as the primary clinical gatekeeper, its concentration of negative DIF suggests standard screening practice may systematically underestimate ADHD severity in older adults. We recommend using the full 18-item ASRS when screening older populations and suggest that developing age-adjusted norms would improve diagnostic accuracy.

Background: Epigenetic clocks based on DNA methylation (DNAm) have emerged as promising biomarkers of biological aging, yet their associations with cognitive performance remain inconsistent. This study investigates the relationship between epigenetic age acceleration and cognitive performance in older adults using 14 DNAm clocks from five generations of development. Methods: We analyzed data from the Berlin Aging Study II (BASE-II) using genome-wide DNAm profiles and cognitive assessments ascertained at baseline (T0) and two follow-up time points (T1, T2) in up to 1,014 individuals. DNAm-based age and age acceleration estimates were calculated using Biolearn and MethylCIPHER. Analyses focused on cross-sectional and longitudinal associations between DNAm clock estimates and cognitive performance, including sex-specific effects and comparisons with frailty as non-cognitive positive control. Results: Among all tested DNAm clocks, DunedinPACE (a third-generation clock) showed the strongest and most consistent associations with cognitive performance. In addition, the fifth-generation SystemsAge framework also demonstrated robust associations with cross-sectional and longitudinal cognitive outcomes. In contrast, second-generation clocks (GrimAge [v2], PhenoAge) showed occasional nominal associations, while first-generation clocks (Horvath [v1], Hannum) and the causally-informed, fourth-generation clocks (e.g. YingCausAge, YingDamAge) showed no noteworthy signals. Likewise, telomere length estimated from DNAm was not strongly associated with cognitive performance in this dataset. Conclusions: Our findings highlight DunedinPACE as a particularly informative biomarker for various aspects of cognitive aging, while other DNAm aging measures showed no consistent associations. Future work should further refine domain-specific epigenetic biomarkers to improve biological aging assessments and achieve a more reliable early detection of cognitive decline.

Large-scale genomic studies have identified biomarkers of adult cognitive functioning and educational attainment, yet the developmental pathways connecting these biomarkers to adult outcomes remain unclear. Drawing on four cohorts, we examined the developmental correlates of an epigenetic index of adult cognitive function ( Epigenetic-g) alongside polygenic indices of cognition and education. Epigenetic-g and polygenic indices were uncorrelated and captured distinct variation in childrens cognitive and academic performance. Longitudinal analyses revealed that Epigenetic-g is plastic in early childhood, reaching moderate stability by adolescence, and, unlike polygenic indices, is not related to longitudinal cognitive growth. Twin models indicated that Epigenetic-g captures genetic and unique environmental variation relevant to cognitive and academic achievement that is not identified by current polygenic indices. Epigenetic indices relevant to psychological development can be generated from DNA methylation studies of adults, with most variation in these indices emerging early in life.

Population ageing increases the importance of cognitive capacity for making decisions about retirement and living independently beyond it. We tested whether post-war educational expansion and working-life social mobility eliminate the association between social class of origin and cognition in early old age using the 1958 National Child Development Study. Two outcomes were analysed at age 62: standard episodic memory (immediate + delayed word recall) and long-term episodic memory, capturing accurate half-century recall of childhood household facts (rooms and people at age 11 validated against mothers' responses). Social mobility trajectories derived in prior work were classified into predominantly manual versus non-manual class trajectories. Models were estimated separately for women and men across three specifications: (i) social origin and controls, (ii) adding social mobility, and (iii) adding weighting to address healthy survivor bias. Education was consistently associated with both outcomes. For long-term episodic memory, social origin gradients were clearer than for short-term episodic memory, with men from service/professional origins showing a 13 percentage-point higher probability of accurate half-century recall than men from manual origins. These findings indicate that education expansion and working-life social mobility failed to release the grip of social origin on long-term episodic memory.

Background Prenatal exposure to pesticides and psychosocial factors often co-occurs, particularly in low- and middle-income settings, yet their joint effects on epigenetic age acceleration (EAA) in early life remain unknown. We investigated the joint associations of prenatal pesticides metabolites and psychosocial factors on EAA in the first five years of life in the South African Drakenstein Child Health Study. Methods In 643 mothers, we measured 11 urinary pesticide metabolites and seven psychosocial factors during the second trimester of pregnancy. Child DNA methylation was measured in whole blood at ages 1, 3, and 5 years. EAA was estimated using the Horvath, Skin & Blood Horvath (skinHorvath), and Wu epigenetic clocks. Longitudinal associations were estimated using generalized estimating equations, adjusted for confounders. Joint mixture associations were evaluated using weighted quantile sum regression (WQS) and quantile g-computation (QGCOMP). Results The joint prenatal exposure mixture was positively associated with Wu ({beta} per one quintile increase in the mixture [95% CI]: 0.41 years [0.15, 0.80]), skinHorvath (0.11 years [0.06, 0.16]), and Horvath EAA (0.31 years [0.20, 0.46]) over time using WQS. Psychosocial factors, particularly food insecurity, physical interpersonal violence, and stress biomarkers, contributed most to the total mixture effect for all clocks. Pyrethroid metabolites PBA and TDCCA were top pesticide contributors to Wu EAA. Pathway enrichment analyses of clock-specific CpGs revealed distinct biological architectures, with the Wu clock enriched for neurodevelopmental and immune pathways, and metabolic pathways for the Horvath clock. Discussion Joint prenatal exposure to pesticides and psychosocial factors was associated with increased EAA across early childhood, with psychosocial factors contributing the most to the total effect. These findings highlight the importance of assessing chemical and non-chemical stressors jointly and clock-specific biological interpretation in epigenetic aging research.

Genome-wide association studies (GWAS) and polygenic score (PGS) development are typically constrained by the data available in biobank repositories in which European cohorts are vastly overrepresented. Here, we increase the utility of non-European participant data within the UK Biobank (UKB) by characterizing the genetic affinities of UKB participants who self-identify as Bangladeshi, Indian, Pakistani, "White and Asian" (WA), and "Any Other Asian" (AOA), towards creating a more robust South Asian sample size for future genetic analyses. We assess the relationships between genetic structure and self-selected ethnic identities and use consistent patterns of clustering in the dataset to train a support vector machine (SVM). The SVM was utilized to reassign n = 1,853 AOA and WA participants at the subcontinental level, and increase the sample size of the UKB South Asian group by 1,381 additional participants. We further leverage these samples to assess GWAS performance and PGS development. We include environmental covariates in the height GWAS by implementing a rigorous covariate selection procedure, and compare the outputs of two GWAS models: GWASnull and GWASenv. We show that PGS performance derived from both GWAS models yield comparable prediction to PGS models developed with an order of magnitude larger training, and environmentally-adjusted PGS models reduce the sex-bias in predictive performance. In summary, we demonstrate how GWAS performance can be improved by leveraging ambiguous ethnicity codes, ancestry matched imputation panels, and including environmental covariates.

The Five-Factor model (FFM) personality traits (agreeableness, conscientiousness, extraversion, neuroticism and openness) capture stable individual differences in thinking, feeling and behaviour. It has been shown that the FFM traits share variance through two-higher order 'meta-traits', stability and plasticity. It remains unknown, however, whether these meta-traits can capture the shared genetic architecture of the FFM personality traits. Here we combined recent genome-wide association study (GWAS) summary statistics with data from the 23andMe Research Institute (European ancestry; total N = 279,240-682,707) and applied Genomic Structural Equation Modelling, identifying two latent genetic factors consistent with stability and plasticity. We then performed a multivariate GWAS on these factors to capture genetic loci with shared effects across the FFM traits, identifying 81 and 13 independent genome-wide significant loci for stability and plasticity respectively. Transcriptome-wide and cell-type enrichment analyses prioritised candidate effector genes and indicated broad brain involvement for personality traits. Finally, genetic correlation and pleiotropy-aware Mendelian randomisation analyses provided genetic evidence suggesting bi-directional links between stability and psychopathology: higher genetic propensity for stability was protective against psychopathology, whereas greater genetic liability to psychopathology was associated with reduced stability. These results enhance our understanding of the shared genetic architecture underlying personality traits and their overlap with psychopathology.

Shenhar et al. (2026) report 50% "intrinsic" lifespan heritability after calibrating a one-component correlated-frailty survival model to Scandinavian twin lifespans. Their framework is mathematically coherent, but the intrinsic component is not identified if heritable, mortality-relevant extrinsic susceptibility is omitted at calibration. We show that one-component calibration absorbs omitted familial extrinsic structure into the intrinsic frailty scale parameter{sigma}{theta} , and that this variance absorption is visible through separate diagnostics (1) Variance absorption. Under misspecification,{sigma}{theta} is inflated by +22.1% (95% CI: 21.5-22.7%), corresponding to +49% inflation in [Formula]. Falconer h2 is downstream of calibration and inherits a +9.2 pp bias (95% CI: 8.7-9.7). The{sigma}{theta} inflation is model-general: +22% (GM), +18% (MGG), +14% (SR); any dependence summary that is strictly increasing in{sigma}{theta} inherits this inflation, so Falconer h2 is one affected downstream quantity among many (Corollary B3). (2) Structural fingerprint. In the joint twin survival surface S(t1, t2), misspecification produces systematic dependence errors (ISE 48x that of the recovery model). Conditional twin dependence is inflated at all ages, peaking at age 80 ({Delta}r = 0.048). (3) Specificity. The bias requires an omitted component that is both heritable and mortality-relevant. Three negative controls, a boundary check ({rho} = 0), and a two-component recovery refit ({sigma}{theta} restored to within -3.2%) establish specificity. ACE decomposition yields C {approx} 0 throughout: the omitted extrinsic component loads onto A (because it is shared 1.0/0.5 in MZ/DZ), so switching summary statistics does not restore identification. (4) Sensitivity and falsifiability. Over an empirically anchored regime ({sigma}{gamma} [isin] [0.30, 0.65],{rho} [isin] [0.20, 0.50]), Falconer bias ranges from +2.8 to +18.9 pp (mean 9 pp). If{rho} is sufficiently negative, the bias reverses sign in all three model families (Corollary B4). A full-likelihood robustness check shows that this upward pull is partly structural and partly estimator-specific: in the same misspecified one-component model, ML still inflates{sigma}{theta} (+3%), whereas matching only rMZ inflates it much more (+21%). These results do not resolve true intrinsic heritability but establish that Shenhars 50% estimate carries a structured, model-general upward bias originating in the fitted latent variance{sigma}{theta} .

Genetic counselling outcome measures are increasingly adapted for diverse clinical contexts. While the Genetic Counselling Outcome Scale (GCOS-24) is available in Swedish, no autism-specific version has been developed. Therefore, we adapted the Swedish GCOS-24 using the English version of the modified GCOS-24 (mGCSOS-24) to create a Swedish autism-specific mGCOS-24. Thereafter, we evaluated both the Swedish autism mGCOS-24 and the Swedish general GCOS-24 using Rasch analysis to assess their psychometric properties. Both instruments exhibited structural challenges, including multidimensionality, disordered thresholds, local item dependence, and invariance issues. For the Swedish autism mGCOS-24, we were able to identify subscales with acceptable measurement properties. However, applying the same structure to the Swedish general GCOS-24 did not resolve its broader limitations. This study introduces the first Swedish autism-specific mGCOS-24 and represents the first Rasch-based evaluation of any GCOS-24 or mGCOS-24 in Swedish. Our findings highlight important opportunities for measure refinement but also indicate that new or more substantially adapted tools may be needed to capture outcomes of genetic counselling in autistic populations.

BackgroundLoneliness is a psychosocial stressor associated with elevated risk of severe mental illness (SMI), including major depressive disorder (MDD), schizophrenia (SCZ), and bipolar disorder (BD). Loneliness is theorized to become biologically embedded via inflammation-related mechanisms, yet its causal relationship with SMI and the role of inflammatory signaling remain unclear. AimsTo investigate whether loneliness causally influences SMI risk and whether inflammatory cytokines mediate this relationship. MethodWe applied univariable Mendelian randomization (MR) to estimate the causal effect of loneliness on SMI and multivariable MR (MVMR) to assess mediation by inflammatory signaling. We integrated genome-wide association study (GWAS) summary statistics for loneliness and SMI with genetic instruments for inflammatory cytokines. MVMR models estimated the direct effect of loneliness after accounting for inflammatory signaling using eQTL and pQTLs for interleukin-1 receptor antagonist (IL-1RA), interleukin-6 (IL-6), IL-6 receptor (IL-6R), tumor necrosis factor alpha (TNF-), and TNF receptors (TNF-R1/2). Bidirectional MR examined potential reverse causal pathways between inflammation, SMI, and loneliness. ResultsMR provided evidence consistent with a causal effect of loneliness on SCZ and MDD. Results were also consistent with inflammatory cytokine pathways for IL-1RA, IL-6R, and TNF-R1, partially mediating the loneliness-SCZ and loneliness-MDD causal effect. No significant effects were identified for BD in UVMR or MVMR models. Bidirectional MR suggested evidence of reverse causation between SCZ and loneliness. ConclusionsThe findings support a causal risk-increasing effect of loneliness on SCZ and MDD, partially mediated by systemic inflammatory signaling, implicating pathways as a plausible mechanistic link between psychosocial stress and mental illness risk and highlighting potential opportunities for prevention and targeted intervention through inflammation and social pathways.

The present study analyzes the impact of naturalistic stress and emotions on saliva cell-free mitochondrial DNA (cf-mtDNA) in daily life across two independent cohorts with different temporal resolutions. Study 1 examined the interaction between daily stress and major depressive disorder (MDD) on cf-mtDNA in young adults (n= 18, 8 MDD, 10 controls) across four days. For individuals with MDD, stress exposure was associated with a 68% reduction in cf-mtDNA. A higher number or greater severity of stressors also reduced cf-mtDNA by 24 to 27%. Study 2 extended this framework by implementing a finer temporal resolution, measuring saliva and affective states every hour, up to 20 times per day for 2 days (n = 25). Negative emotions, including stress and frustration, were associated with reductions in cf-mtDNA of 15%, whereas positive emotions, such as happiness and calm, predicted increases of up to 28%. The strength and direction of the effects were person- and context-dependent. These findings suggest that cf-mtDNA does not exhibit a uniform stress response in daily life. Instead, it reflects dynamic signaling shaped by timing, emotional context, and diagnostic status. Accordingly, cf-mtDNA should be conceptualized as a dynamic biobehavioral signal rather than a static indicator of between-person differences.

Objective: While ADHD symptoms often decline from childhood into adulthood, the underlying neurobiological mechanisms, such as altered brain maturation or neural reorganization, remain incompletely understood. This study investigated how grey matter development relates to ADHD symptom trajectories into adulthood. Method: We analyzed data of individuals with ADHD and controls from the longitudinal Dutch NeuroIMAGE cohort, utilizing dimensional ADHD symptom scores (Conners Parent Rating Scale) from three waves and T1-weighted structural MRI scans from the final two waves. Using General Linear Models with permutation-based inference, we examined: 1) cross-sectional associations between ADHD symptoms and vertex-wise cortical thickness and surface area, and subcortical volumes at Wave 1 (n = 765, mean age = 16.95 years); and 2) longitudinal associations between symptom progression and brain morphometric changes (Wave 0 to 1: n = 644, mean age = 11.55-17.24 years; Wave 1 to 2: n = 149, mean age = 16.45-20.11 years). Results: Cross-sectionally, at Wave 1, more ADHD symptoms were related to widespread reductions in surface area, most prominently in the frontal cortex, and smaller volumes of the cerebellum, amygdala, and hippocampus. Longitudinally, symptom improvement from Wave 1 to Wave 2 was associated with stronger reductions in surface area, particularly in prefrontal and occipital regions, and with more pronounced cortical thinning across multiple brain regions. Conclusion: These findings suggest an association between symptom trajectories and structural brain changes, indicating that clinical improvement in ADHD behaviors might coincide with ongoing neural refinement during the transition to adulthood.

Polygenic risk scores (PRS) have shown increasing utility for risk stratification across complex diseases, but for psychiatric disorders such as bipolar disorder (BD), current PRS explain only a fraction of disorder liability (~1-9%), with predictive performance further diminished in non-European populations and real-world clinical cohorts. To explore the potential of integrating social and environmental risk factors alongside genetic liability to improve risk prediction, we evaluated the relationship between a PRS for BD (PRSBD) and six social risk measures - perceived stress, discrimination in medical settings, neighborhood social cohesion, perceived neighborhood disorder, cost-related medication nonadherence, and adverse childhood experiences - to BD case status in 115,275 participants (7,000 cases; 108,275 controls) from the All of Us Research Program. PRSBD was associated with BD case status across ancestry groups, though liability-scale variance explained was attenuated relative to what has been reported for curated research cohorts (R2 = 1.86% in European, 0.60% in African, 1.65% in Latino/Admixed American ancestries). Each social risk factor tested exhibited a larger effect size than PRSBD, with perceived stress (OR = 2.05 per SD) and adverse childhood experiences (OR = 2.68 for [&ge;]4 ACEs) demonstrating the strongest associations. Individuals in the lowest genetic risk decile with high social burden exhibited BD prevalence comparable to or exceeding those in the highest genetic risk decile with low social burden. These findings demonstrate the substantial explanatory power of social risk factors and support the development of integrated genetic-social risk frameworks for more accurate and equitable psychiatric risk prediction.