Robert Plomin

Social behaviour is a heritable trait linked to well-being and mental health, yet it varies across social context, including social domains, informants and development, which may shape its underlying genomic architecture. Here, we conducted a genome-wide meta-regression meta-analysis of social behaviour from infancy to early adulthood, studying ~500,000 repeated measures in European-ancestry cohorts ( N = 73,321). We modelled heterogeneity in single-variant associations across social domains (low prosocial behaviour and peer/social difficulties), multiple informants (parents, teachers and self-reports) and ages (2–29 years), identifying 6 associated loci, including variation within CADM2 ( P = 2.51 × 10 ⁻⁹ ). Single nucleotide polymorphism-based heritability ranged from 2% to 7%, and the genetic architecture comprised 4 factors reflecting differences in either social domains or informants. Polygenic scores ( N = 16,305) accurately predicted contextual variation in independent cohorts, with some cross-ancestry transferability. Context-specific patterns of genetic correlations, particularly when estimating the developmental onset of associations, offer opportunities to disentangle shared trajectories linking social behaviour with later-life and mental health outcomes.

A combination of genetic and environmental factors working in interplay is thought to underlie differences in symptoms of psychopathology between adolescents. Yet, studies that have investigated gene-environment interaction in isolated aspects of developmental psychopathology lack robust effects, highlighting the need for a broader approach. We adopted a multivariable framework to investigate gene-environment interaction in internalising and externalising symptoms of psychopathology in a sample of 3337 16-year-olds from the Twins Early Development Study. We used penalised regression models to examine the main effects of genetic factors (G), indexed by combining 13 polygenic scores for psychopathology, and environmental factors (E), measured by combining multiple environmental exposures during childhood and adolescence, on symptoms of psychopathology. We also examined their additive effects (G+E) and their interaction (G×E). Polygenic scores accounted for, on average, 2.7% of the variance in symptoms of psychopathology, with stronger predictions for externalising symptoms, while environmental measures alone accounted for an average of 7.1% of the variance. G+E accounted for an average of 9.1% of differences between adolescents in symptoms of psychopathology. We observed small G×E effects for internalising symptoms, accounting for an average of 1.1% of the variance. Children with a higher genetic risk showed higher levels of internalising symptoms, especially when exposed to more chaos at home and harsher parenting. Our findings indicate that genetic and environmental influences contribute additively, underscoring the importance of jointly considering both factors to enhance our understanding of youth psychopathology. At the same time, our results highlight the persistent challenges involved in identifying robust G×E effects.

The role of environmental, developmental, and psychological processes in translating genetic dispositions into observed academic achievement remains under-investigated. Here, we examine whether non-cognitive skills—including motivation, attitudes, and emotional and behavioural functioning—mediate the genetic prediction of academic achievement across development. We analyse data from 5,016 children enrolled in the Twins Early Development Study at ages 7, 9, 12, and 16, as well as their parents and teachers. We find that non-cognitive skills mediate between less than 5 and up to 64% of the genetic prediction of academic achievement. Mediation effects are larger and more robust for motivation and attitudes (β ≈ 0.13) than for emotional and behavioural functioning (β ≈ 0.01–0.03). This pattern holds longitudinally and is replicated in within-family analyses, where non-cognitive skills account for up to 83% of the total mediation effects. These findings highlight the contribution of non-cognitive skills beyond shared familial factors, likely reflecting how children evoke and select experiences that align with their genetic propensity and lead to differences in academic development.

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.

Background: Evidence indicates substantial genetic overlap between psychiatric diagnoses. Accounting for these transdiagnostic effects can sharpen research on disorder‐specific genetic architecture and patterns of comorbidity. Methods: We applied genomic structural equation modeling to genome‐wide association study summary statistics from 11 major psychiatric disorders to isolate genetic effects shared across disorders (the genomic p factor) from residual genetic effects associated with each disorder ( non ‐ p ). Using these non‐p summary statistics, we examined SNP heritability, genetic correlations among psychiatric disorders, and genetic correlations with external biobehavioural traits spanning socio‐demographic, anthropometric, health‐related, and psychological domains. Results: After accounting for transdiagnostic effects, genetic associations between psychiatric disorders changed substantially, with many correlations attenuated and some showing marked shifts in magnitude and direction. Genetic correlations between psychiatric disorders and external biobehavioural traits showed greater specificity. Conclusion: Removing transdiagnostic effects provides a more nuanced view of the genetic architecture underlying psychiatric disorders and sharpens inference about genetic relationships between disorders and other comorbid traits. This may inform future work on psychiatric classification, prediction, and treatment research.

Background and Objectives: Cognitive dispersion, reflecting intraindividual variability across tasks, may signal dementia risk in older adults, but its origins and relevance before midlife are unclear. We examined dispersion as a potential marker of cognitive reserve in established adulthood and distinguished it from general ability using heritability analyses. Research Design and Methods: 1,245 primarily non-Hispanic White twins and siblings were assessed at 33.3 years (SD = 5.0; 53% female) using 2 cognitive batteries. Bayesian mixed effects location scale models estimated mean performance and dispersion across cognitive tasks, with tasks standardized against current sample-based norms (age 33) and adolescent norms (age 16). The mean of task scores standardized to current norms reflects current ability, while the mean of the same scores standardized to adolescent norms reflects growth in ability. The respective dispersion estimates represent task-specific deviations from general ability and task-specific gains. Results: Across 4 models fitted by battery and standardization, greater cognitive dispersion was associated with higher general ability (r = 0.23–0.56) and developmental gains (r = 0.62–0.93). Verbal tasks showed lower dispersion relative to performance tasks (β = 0.83–0.87). Dispersion was strongly heritable overall (43%–84%), and moderately heritable independent of general ability (36%–37%) and growth (2%–16%). Dispersion did not predict education or occupational outcomes beyond general ability and growth. Discussion and Implications: Before midlife, cognitive dispersion is distinct from general ability and moderately heritable. Dispersion is associated with higher cognitive ability and growth, but not directly with educational or occupational attainment, suggesting dispersion may relate to cognitive reserve through other pathways.

Children spend a significant part of their lives at school. However, the long-term effects of the school environment on mental health are still not well understood, especially using genetically sensitive designs. Here, we examine the associations between perceived school environment and mental health from childhood to emerging adulthood, and the genetic and environmental factors that underlie these relationships. Using data from over 6,500 participants aged 7–21 from the Twins Early Development Study (TEDS), we found consistent, moderate associations between perceived school environment and mental health (average r ≈ .19). School environment cumulatively explained mental health problems, explaining 26–56% of the variance both contemporaneously and over time. These associations remained substantial after adjusting for genetic predisposition using psychiatric polygenic scores, family socioeconomic status and earlier mental health problems, although the effect sizes were smaller (6–30% of variance explained). Twin analyses showed that not only was psychopathology highly heritable (~ 61%), but also how children experience school was partly due to genetics (~ 46%). The association between perceived school environment and mental health was largely accounted for by shared genetic influences (~ 70%), supporting the role of gene–environment correlation in mental health outcomes. We show that perceived school environments are significantly associated with mental health across development, even after accounting for genetic predisposition, SES and earlier mental health problems. Using a genetically sensitive, longitudinal approach, this research provides a conservative yet clearer estimate of how school environments might influence mental health outcomes over time, because participants completed their schooling more than a decade ago, when reported youth mental health problems were lower, and school environments were likely less pressured. Our findings emphasise the importance of understanding the school environment as a potential setting for prevention and support and underscore the need for further research to identify modifiable factors that could improve children’s wellbeing.

The origins of individual differences in socioeconomic outcomes, including educational attainment and occupational status, reflect a combination of genetic and environmental factors whose relative contributions may shift across historical and societal contexts. Previous research suggested a doubling of genetic influence on these traits in independent Estonia compared to the Soviet era. Using the Estonian Biobank, now tenfold larger compared to the original study, we aimed to replicate and extend these findings. We found only limited evidence for the increase in genetic influence on social outcomes in independent Estonia with results differing between analytical setups. Most importantly, we show that genetic contribution to socioeconomic outcomes, as well as to height and body mass index, varies within each historical cohort depending on birth year and recruitment strategy. This highlights the limitations of generalising from the study cohort due to various participation biases and potential heterogeneity of social environment in both historical periods.

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.