A team led by Adam Socrates of King’s College London and the Icahn School of Medicine at Mount Sinai, together with Jessie Baldwin of University College London, veteran psychosis researcher Robin Murray, Paul O’Reilly of Mount Sinai, and Jean-Baptiste Pingault of King’s College London, set out to test a deceptively simple question: if you measure someone’s inherited liability to psychopathology, does it predict their exposure to environmental risk? The answer, drawn from one of the largest and most systematic screenings ever attempted in this field, is a qualified but striking yes.

The researchers harnessed data from the UK Biobank, a vast biomedical database containing genetic and health information from roughly half a million British adults. Crucially, they restricted their sample to 307,493 participants who had no recorded diagnosis of schizophrenia, bipolar disorder, or major depressive disorder, and who were not taking antipsychotic medication. This design choice matters: by excluding people already diagnosed with serious mental illness, the team could ask whether genetic risk predicts environmental exposure before any disorder emerges, rather than simply detecting the downstream consequences of being ill.

On the genetic side, the investigators computed nine polygenic scores — numerical summaries of the small genetic variants scattered across a person’s genome that collectively contribute to a trait or condition. These scores covered attention deficit hyperactivity disorder, schizophrenia, bipolar disorder, major depression, neuroticism, educational attainment, and other genetically influenced characteristics. The scores were generated using PRS-CS, a sophisticated statistical method that leverages large-scale genome-wide association data to sharpen the predictive power of polygenic scores, improving on older approaches that count risk variants more crudely.

On the environmental side, the team assembled an extraordinary catalogue of 49 distinct risk factors, spanning five broad categories: victimisation and adverse life events, socioeconomic circumstances, behavioural and lifestyle factors, cognitive and educational measures, and perception-based or subjective factors such as how people appraise their own lives. Each polygenic score was then tested against each environmental factor in a grid of 441 standardised linear regression models, using HC1 robust standard errors — a statistical safeguard that protects against distortions caused by uneven variability in the data, a common problem in large biobank samples.

Because so many tests were run, the risk of false positives was substantial. To guard against this, the researchers applied false discovery rate correction within each polygenic score, a procedure that adjusts the statistical threshold so that the expected proportion of spurious findings stays controlled. Even under this conservative standard, 212 associations survived. That is a remarkable yield, and it suggests that the overlap between genetic liability and environmental exposure is not a statistical artefact but a pervasive feature of the data.

The breadth of the associations varied by genetic score. Polygenic scores for ADHD, educational attainment, major depression, schizophrenia, and bipolar disorder showed the widest-reaching profiles, correlating with environmental factors across multiple domains. Yet the pattern was not uniform: each score carried its own signature of environmental associations, hinting that different forms of inherited liability travel along different social and behavioural routes. A genetic predisposition toward ADHD, for example, may nudge individuals toward different life circumstances than a predisposition toward depression, even when both ultimately relate to elevated psychiatric risk.

One of the study’s most provocative findings emerged from a secondary analysis. When the team separated environmental factors into those involving perception or subjectivity — how people interpret and report their experiences — and those that are more objective, they found that seven perception-related factors showed stronger pooled associations with polygenic scores for schizophrenia, major depression, ADHD, bipolar disorder, and neuroticism than the 25 objective factors did. In other words, the genetic signal seemed to flow more strongly through the lens of subjective experience than through externally verifiable circumstances. Four conceptually tighter matched comparisons — pairing subjective and objective measures of the same underlying construct — produced a similar pattern, although it was not entirely consistent across all comparisons.

The authors are careful about interpretation, and the caveats deserve emphasis. The effects observed were small, and the study is observational: it establishes correlation, not causation. A polygenic score predicting environmental exposure does not mean genes directly cause adversity. Instead, the findings point to what behavioural geneticists call gene–environment correlation, the process by which inherited tendencies influence the situations people encounter. A person genetically inclined toward impulsivity may, for instance, drift into riskier social settings; a person with inherited cognitive tendencies may attain different levels of education and income; and inherited differences in temperament may colour how people perceive and report the events of their lives.

This mechanism has profound implications for psychiatric research. Many celebrated studies of environmental risk — childhood adversity, urban upbringing, socioeconomic deprivation — implicitly assume that these exposures are independent of genetic liability. If they are not, some portion of the apparent environmental effect may actually reflect inherited confounding, meaning that genetic risk inflates both the exposure and the outcome. The new findings suggest that measured environmental risk is partly correlated with inherited liability through behavioural, social, socioeconomic, cognitive, and perception-related pathways. Future studies of environmental effects on mental health, the work implies, should routinely adjust for or otherwise account for polygenic liability, or risk overstating purely environmental causes.

There is also a subtler lesson about measurement. The stronger links between genetic scores and subjective, perception-based factors raise the possibility that some of what researchers record as environmental exposure is filtered through the same psychological tendencies that genetics influence. Two people may live through objectively similar events yet encode them very differently, and those differences in appraisal are themselves partly heritable. That does not make subjective reports unreliable — how people perceive their lives is genuinely consequential for mental health — but it complicates the tidy division between what happens to us and what we are.

The study’s scale and rigour lend it unusual weight. Running 441 pre-specified models with robust standard errors, correcting for multiple testing, and probing results with matched comparisons reflects a level of methodological discipline that the field has often lacked. The use of a sample free of major psychiatric diagnoses strengthens the argument that genetic risk shapes environmental exposure in the general population, not merely among the ill. And the open-access publication means the full analysis is available for scrutiny and reuse by other researchers.

Still, the work is a beginning rather than an endpoint. The UK Biobank is a predominantly British, largely European-ancestry sample, and polygenic scores derived from European genome-wide studies lose accuracy in other populations, so the findings will need replication in more diverse cohorts. The environmental measures, though numerous, are self-reported and cross-sectional, limiting what can be said about the direction of effects over time. Longitudinal designs — following genetically characterised individuals from childhood — will be essential to disentangle whether genetic liability truly precedes environmental exposure, and through which specific pathways.

What the study delivers now is a conceptual correction with viral potential: the tidy story in which genes and environment are separate contributors to mental illness is wrong, or at least incomplete. Our inherited makeup quietly helps write the circumstances of our lives — the neighbourhoods, the stresses, the perceptions — that in turn feed back into mental health. Understanding that loop, rather than pretending it does not exist, may be the key to designing interventions that genuinely break the cycle of psychiatric risk.

Subject of Research: Gene–environment correlation between polygenic scores for psychopathology and environmental risk factors

Article Title: Genetic risk of psychopathology predicts environmental risk

Article References: Socrates, A., Baldwin, J. R., Murray, R. M., O’Reilly, P. F., & Pingault, J.-B. (2026). Genetic risk of psychopathology predicts environmental risk. Schizophrenia. https://doi.org/10.1038/s41537-026-00805-3

Image Credits: AI Generated

DOI: 10.1038/s41537-026-00805-3

Keywords: polygenic scores, gene-environment correlation, psychiatry, UK Biobank, schizophrenia, major depression, ADHD, bipolar disorder, neuroticism, environmental risk factors, behavioural genetics, psychosis

The central challenge the researchers set out to address is one of fragmentation. Studies of the genetics of social behaviour have accumulated rapidly over the past two decades, but they differ in almost every dimension that matters. Some focus on prosocial traits such as empathy, altruism or trust; others examine social difficulties, including autism-related traits, social anxiety or loneliness. Some rely on self-reports, in which individuals describe their own tendencies, while others depend on parent, teacher or clinician ratings that capture behaviour as seen from the outside. And the developmental window varies enormously: some studies probe children in early childhood, others adolescents in the midst of social reorganisation, and still others adults whose social patterns have long since stabilised. When effect estimates are pooled naively across such heterogeneous sources, the signal can be diluted, distorted or falsely amplified.

Meta-regression offers a statistical remedy. Rather than simply averaging genetic associations across studies, a meta-regression treats the characteristics of each study—its social domain, its type of reporter, its developmental stage and its methodological features—as explanatory variables that can account for between-study heterogeneity. In the context of genome-wide data, this means modelling how the strength and direction of genetic associations with social behaviour change systematically as a function of these study-level moderators. The approach, long used in epidemiology and psychology to make sense of heterogeneous evidence, has now been scaled up to the level of the genome, where millions of genetic variants must be considered simultaneously and where statistical power is a perpetual constraint.

The genome-wide dimension of the work is what elevates it beyond a conventional meta-analysis. Genome-wide association studies, or GWAS, have identified genetic variants linked to an ever-expanding list of traits, but social behaviour has been a notoriously difficult target. Individual variants typically explain vanishingly small fractions of variation, and social traits are shaped by environments—family, culture, peer networks—that interact intimately with genetic predispositions. By aggregating evidence across many studies and explicitly modelling the sources of heterogeneity, the meta-regression approach aims to recover signals that would otherwise be lost in the noise, and to characterise how those signals are conditioned by the context in which behaviour is measured.

One of the most consequential distinctions the study interrogates is that between reporters. A parent rating a child’s sociability, a teacher observing classroom interactions, and an adolescent describing their own friendships are not simply measuring the same underlying quantity with different instruments. Self-reports are shaped by introspective access, social desirability and reference groups; observer reports capture externalised behaviour but may miss internal experiences such as social motivation or discomfort. Genetic studies have shown for other traits that rater effects can be substantial, sometimes rivalling the size of the genetic associations themselves. By formally modelling reporter type as a moderator, the new analysis provides a way to quantify how much of the apparent inconsistency in the social genetics literature reflects genuine biological variation and how much reflects measurement.

Developmental stage presents a parallel and equally important axis of variation. Social behaviour is not static: infants engage in attachment and joint attention, children develop play and friendship skills, adolescents navigate peer hierarchies and identity formation, and adults build partnerships, families and professional networks. Twin and family studies have long suggested that the heritability of behavioural traits can change with age, a phenomenon known as developmental behaviour genetics, and molecular genetic evidence increasingly points to age-specific genetic influences. A genetic variant associated with social withdrawal in childhood may have little bearing on adult sociability, or may even show reversed effects, as the biological and social demands of each life stage reshape the expression of inherited predispositions.

The social domain itself—the specific aspect of behaviour under study—is the third pillar of the framework. Prosociality, social communication, social anxiety, peer relationship quality and autistic traits are related but distinct constructs, each with its own measurement traditions and its own literature. Treating them as interchangeable in a meta-analysis risks conflating findings that should not be pooled. The meta-regression approach allows the researchers to ask whether genetic associations are shared across domains, suggesting a common biological substrate for social functioning, or domain-specific, pointing toward differentiated pathways. This question carries direct implications for how genetic findings are interpreted and applied, including in research on neurodevelopmental conditions where social difficulties are a core feature.

Beyond its specific findings, the study represents a methodological statement about how behavioural genetics should be conducted in an era of data abundance. The bottleneck in social genomics is no longer solely the availability of genetic data but the coherence of the behavioural phenotyping that accompanies it. Thousands of participants can be genotyped with ease, yet if the social measures are inconsistent—different instruments, different raters, different age groups—the resulting associations are difficult to interpret or compare. By building heterogeneity into the analysis itself rather than treating it as a nuisance, the meta-regression framework turns the messiness of the literature into a source of information, extracting patterns that a single well-powered study could never reveal on its own.

The approach also speaks to a broader conversation about transparency and reproducibility in genetics research. Genome-wide meta-analyses have transformed fields from anthropology to psychiatry, but critics have noted that pooled estimates can mask important variation across cohorts, ancestries and measurement contexts. Meta-regression is one of the tools being developed to open up the black box of aggregation, showing not just what the average effect is but under what conditions it holds. For social behaviour—a domain where measurement choices are contested and where cultural context shapes both behaviour and its assessment—this kind of analytical transparency is arguably not optional but essential. The study demonstrates that even in a field as heterogeneous as social genomics, systematic patterns can be recovered when the sources of variation are modelled rather than ignored.

What emerges from this work is a picture of social behaviour genetics as fundamentally context-dependent: the genetic correlates of how we connect with others are not fixed quantities but depend on who is observing the behaviour, which facet of social life is being measured, and when in development the measurement takes place. That conclusion may frustrate anyone hoping for a simple list of genes for sociability, but it offers something more durable—a framework for interpreting the vast and messy literature on the genetics of social life, and a roadmap for designing future studies that are comparable across labs, cohorts and continents. As biobanks grow and behavioural phenotyping becomes increasingly sophisticated, approaches like this one are likely to become the standard against which social genomics research is judged.

Subject of Research: Genome-wide meta-regression analysis of the genetic architecture of social behaviour across social domains, reporters and developmental stages

Article Title: Genome-wide analysis of social behaviour across social domains, reporters and developmental stages: a meta-regression approach

Article References: de Hoyos, L., Schlag, F., Jahagirdar, S., Corfield, E. C., Allegrini, A. G., Admiraal, D., de Zeeuw, E. L., Nolte, I. M., Llonga, N., Neumann, A., Lange, K., van den Bedem, S., Du Rietz, E., Motazedi, E., Eising, E., Ng, N. Y. T., Palviainen, T., Wang, C. A., Thiering, E., … St Pourcain, B. (2026). Genome-wide analysis of social behaviour across social domains, reporters and developmental stages: a meta-regression approach. Nature Human Behaviour. https://doi.org/10.1038/s41562-026-02551-z

Image Credits: AI Generated

DOI: 10.1038/s41562-026-02551-z

Keywords: social behaviour, genetics, genome-wide association study, meta-regression, developmental stages, Nature Human Behaviour, behavioural genetics, reporter effects, social domains, heterogeneity, prosociality, heritability