Robust inference and correlates from genetic associations with personality

Personality is defined by relatively stable patterns of thinking, feeling and behaving that vary across individuals^1,2. Decades of research have demonstrated that human personality traits can be meaningfully and efficiently summarized by five relatively independent broad factors known as the Big Five: extraversion (which encompasses traits such as sociability, assertiveness and energy level), agreeableness (compassion, respectfulness and trust), conscientiousness (organization, productiveness and responsibility), neuroticism (anxiety, depression and volatility) and openness to experience (imagination, curiosity and aesthetic sensitivity)^6,7. Given personality’s relevance to labour market outcomes^8,9,10, physical health^10,11,12, mental health^13,14 and human demography^15,16,17, understanding the molecular genetics of personality is important for developing biopsychosocial models of human behaviour and life outcomes.

Here we report meta-analytic GWAS results from the Revived Genomics of Personality Consortium (ReGPC), a collaboration across 46 cohorts incorporating between 611,037 and 1.14 million participants per Big Five trait. We also provide a frequently asked questions document that explains the rationale and results of this study in everyday language (https://osf.io/crh9d). This effort increases the number of significant loci relative to most recent work¹⁸ from 3 to 131 for conscientiousness, from 4 to 39 for agreeableness, from 8 to 126 for openness to experience, from 14 to 258 for extraversion and from 224 to 706 for neuroticism. The additive effects tagged by the common genetic variants (single-nucleotide polymorphism (SNP) heritability, h²_SNP) account for an estimated 4.8–16.2% of total personality variation across traits and models. This is consistent with previous GWAS of personality^{3,19,20,21,22} and less than estimates of h² from classic twin and pedigree studies and molecular genetic methods that index effects of rare variants and genetic interactions, which range from around 40% to 60%^18,23,24,25. Critically, all methods find that personality is substantially influenced by a person’s environment.

Our analyses reveal that genetic associations with personality traits are generalizable and minimally confounded, with widespread external correlates. Although personality traits often vary on average across groupings of people^26,27, we find that genetic associations with each trait are highly similar across four Western country clusters, reporter perspective (self versus close other), age and measurement instrument. Similarly, polygenic indices (PGIs) constructed from each primary GWAS predict personality consistently across five independent cohorts. Biological characterization implicates trait-differentiated molecular and cellular systems of greater complexity than previously theorized^28,29. Within-family analyses indicate that, in contrast to GWAS of other behavioural traits^4,5,30, genetic associations with personality traits are only minimally confounded by population stratification, dynastic effects and assortative mating. Finally, we characterize personality’s wide-ranging genetic correlates and consequences across socially relevant behaviours and life outcomes ranging from psychopathology and physical health to research participation and residential mobility, highlighting a central role of personality in understanding the human experience.

We performed population-level GWAS meta-analysis of each Big Five trait in genetic similarity-stratified and genetic similarity-combined cohorts of participants with European-like (EUR-like) (k_cohorts = 46) and African-like (AFR-like) (k_cohorts = 10) genomes, as measured by genetic similarity to reference panels (Supplementary Tables 1–3 and Supplementary Notes 1 and 2). We jointly analysed results using inverse variance-weighted meta-analysis (IVW), supplemented with multi-ancestry meta-analysis (MAMA)³¹, resulting in pooled associations for around 10 million SNPs for each Big Five trait.

We identified 1,260 approximately independent (r² < 0.10 within a 250 kb range) genome-wide-significant (GWS; P < 5 × 10⁻⁸) SNPs associated with the Big Five traits, an increase of 3 to 43 times over the most recent findings for each trait (Table 1 and Supplementary Tables 4–13). Of the 1,260 lead SNPs, 824 (65%) were found in novel 250 kb loci that had not previously been associated with variation in the respective trait. Manhattan plots of IVW results are displayed in Fig. 1a–e. Demonstrating the independence between the Big Five, 82% of genomic loci containing a GWS SNP were associated with only a single trait (Supplementary Table 14), and the average absolute genetic correlation among Big Five traits among participants with EUR-like genomes was 0.19 (Fig. 1f (below the diagonal)), similar to phenotypic correlations³² (Fig. 1f (above the diagonal)).

Genetic effects were highly congruent when splitting EUR-like GWAS cohorts into two halves, re-estimating the GWAS in each half and estimating genetic correlations (r_g) between the two (mean r_g = 0.94) (Supplementary Table 15). Individual SNP effects were extremely small, reflecting the highly distributed genetic architecture of personality. For example, the median association estimate among extraversion lead SNPs, corrected for the winner’s curse³³, is 0.009 s.d. per effect allele (corresponding to scoring at the 50.35th versus the 50th percentile in extraversion). On average, the estimated effective number of independently associated common SNPs for each trait was 16,180 (Supplementary Table 17). We provide additional GWAS results, including genetic ancestry-stratified analyses and comparisons illustrating similarity between IVW and MAMA estimates, in Extended Data Fig. 1 and Supplementary Tables 16–20.

Among participants with EUR-like genomes, SNP heritability (h²_SNP) estimated for the Big Five traits using linkage disequilibrium score regression (LDSC)³⁴ ranged from 4.8% (s.e. = 0.2%) for agreeableness to 9.3% (s.e. = 0.3%) for extraversion (Table 1, Supplementary Table 4 and Supplementary Note 3). Importantly, these SNP heritability estimates from GWAS meta-analysis index genetic effects that are consistent across contributing cohorts. To allow for variability in genetic effects across cohorts, we conducted a random effects meta-analysis of cohort-specific h²_SNP estimates, which indicated an average h²_SNP of 8.6% (s.e. = 0.6%) across traits (ranging from 7.4% for agreeableness to 10.6% for extraversion; Table 1 and Supplementary Table 21), with significant variability across cohorts (mean τ = 3.6%). Random response error by the participants cannot systematically relate to their genome^35,36. Accordingly, we found that personality measures with greater reliability (lower random response error) tended to be more heritable (b = 6.7%, s.e. = 0.7%; Extended Data Fig. 2). In this analysis, the expected h²_SNP for a measure of typical (median) reliability (α = 0.81) ranged from 9.3% for agreeableness (s.e. = 0.6%) to 13.3% for extraversion (s.e. = 0.6%), and h²_SNP completely disattenuated for measurement error ranged from 10.8% for agreeableness (s.e. = 0.9%) to 15.8% for extraversion (s.e. = 0.9%; Table 1).

To further characterize the generalizability of genetic associations with personality, we examined the concordance of genetic signal across geography, age, veteran status, measurement instrument and reporter perspective (Table 1 and Extended Data Fig. 3). Genetic effects were similar but not identical across four western country clusters (USA, continental Europe, Nordic and UK–Australia, mean r_g = 0.86, mean s.e. = 0.15), three age groups (young (≤25 years), middle (25–64 years) and older (65 years and older), mean r_g = 0.80, mean s.e. = 0.18), between the Million Veteran Program and other, primarily non-veteran cohorts (mean r_g = 0.82, mean s.e. = 0.04), and across five personality measurement instruments (mean r_g = 0.85, mean s.e. = 0.07). Additional characterization of genetic architecture across measurement instruments using genomic structural equation modelling³⁷ confirmed that genetic effects plausibly operate at the level of broad cross-instrument latent factors, with only one locus showing significantly heterogenous effects across measurement instruments (Supplementary Tables 22–24 and Extended Data Fig. 4). Notably, genetic associations with agreeableness were less consistent across cohorts (Table 1), explaining in part why agreeableness exhibited lower heritability than other traits in the meta-analytic GWAS. In the Estonian Biobank, in which the personality of the participants was assessed both by their self-report (n = 73,983) and by reports by close others (n = 20,269), we found strong genetic overlap between rater perspectives (mean r_g = 0.84, mean s.e. = 0.12), indicating that the genetic architecture of personality is not an epiphenomenon of self-perception. In sex-stratified analyses of neuroticism in the UK Biobank cohort, X-chromosome-linked h²_SNP did not differ between male individuals (n = 168,989; h²_SNP,X = 0.23%; s.e. = 0.04%) and female individuals (n = 198,139; h²_SNP,X = 0.18%; s.e. = 0.03%; P_difference = 0.33). The dosage compensation ratio (\(\hat{{\rm{\gamma }}}\) = 1.26, s.e. = 0.30) was intermediate between no compensation (0.5) and full compensation (2.0) but was estimated relatively imprecisely. Genetic effects were correlated near-unity across sex (r_g = 0.96; 95% confidence interval (CI) = 0.81–1.10).

Biological follow-up of GWAS signals indicated that enriched gene sets intersected across the Big Five (mean enrichment rank-order ρ = 0.72; Extended Data Fig. 5), providing evidence for trait-overlapping molecular and cellular systems in personality neurobiology despite only modest genetic correlations (Fig. 1f). Consistent with theories of personality development that emphasize the prefrontal cortex^38,39, genetic associations for each Big Five trait, except for agreeableness, were enriched in genes expressed in the prefrontal cortex (among these, top lead SNPs implicate RCE1, FOXP2 and SEMA6D, indicated in Fig. 1; Supplementary Tables 25–34). All traits demonstrated strong enrichment in protein-truncating variant-intolerant gene sets specifically expressed in neurons (such as ARNTL, TCF4 and NEGR1; Fig. 1). This pattern suggests personality-relevant variants are under negative selection, which appears inconsistent with evolutionary theories that posit balancing selection mechanisms as responsible for maintaining genetic variation in personality^40,41. Neurobiological theories of the Big Five attribute dopaminergic aetiology to variation in openness to experience and extraversion, and serotonergic aetiology to variation in conscientiousness, neuroticism and agreeableness^28,29. Based on sets of genes differentially expressed in specific brain cell types, both in mouse and human post mortem tissues, we found little support for these hypotheses across trait-stratified tests: neither the effect size nor P value placed serotonin or dopamine among the most enriched neuron types (Supplementary Tables 25–29), suggesting that their prominence in the literature relative to other types of neurons is not warranted.

We predicted personality traits in five independent cohorts using PGIs. PGI weights were constructed using our EUR-like discovery GWAS with SBayesR⁴². In all Big Five traits in all five cohorts, PGIs significantly predicted their respective phenotypic personality trait among participants with EUR-like genomes (meta-analytic β ranged from 0.097 (0.005) for agreeableness to 0.189 (0.005) for extraversion; Fig. 2a, Supplementary Tables 35 and 36 and Supplementary Note 4), representing an improvement in prediction over past research for extraversion, conscientiousness and openness to experience^3,43. Highly consistent estimates across cohorts indicates a limited role of birth year, nationality and ascertainment method in prediction accuracy.

We also predicted personality traits among participants with AFR-like genomes in the Add Health and Health and Retirement Study cohorts, again using weights constructed from the EUR-like GWAS. PGI prediction is expected to substantially decrease when there are differences in genetic ancestry between discovery and target samples⁴⁴. Nevertheless, PGIs significantly predicted extraversion (meta-analytic β = 0.099 (0.016)), neuroticism (β = 0.071 (0.017)) and openness to experience (β = 0.047 (0.017)) at P < 0.01; Fig. 2a and Supplementary Tables 35 and 36), providing the first evidence for significant PGI prediction of personality traits beyond individuals with EUR-like genomes.

We quantified the relevance of personality genetics to relevant behaviours and life outcomes across tests of genetic correlation, PGI prediction and Mendelian randomization (MR) applied to EUR-like GWAS data (Supplementary Note 5). Genetic correlation and PGI prediction results applied to AFR-like GWAS data are reported in Supplementary Note 5 and Extended Data Fig. 6.

Genetic correlations

Genetic correlations estimated using LDSC indicate widespread genetic sharing between personality traits and health-relevant daily behaviours. Conscientiousness in particular was genetically correlated with reduced substance use (across substances, mean r_g = −0.21), greater sports frequency (r_g = 0.35) and greater preference for low-calorie foods (r_g = 0.17), as well as the likely downstream consequences of these behaviours: fewer spells in the hospital (r_g = −0.21), healthier ageing (r_g = 0.14) and lower body mass index (BMI; r_g = −0.16) (Supplementary Tables 37 and 38). Personality was also genetically correlated with fluctuations in accelerometer-measured behaviour across the day, with openness to experience linked to increased night-time activity (r_g ≈ 0.25) and conscientiousness to increased activity during the day (r_g ≈ 0.30; Fig. 2d).

We found substantial genetic correlations between the Big Five and each of ten psychiatric disorders and four transdiagnostic psychiatric disorder factors (Fig. 3). Genetic profiles associated with neuroticism were associated with increased risk for all forms of psychopathology (mean cross-disorder r_g = 0.43), particularly internalizing disorders (factor r_g = 0.73), whereas genetic profiles associated with agreeableness displayed cross-cutting protective associations (mean r_g = −0.20). We also identified broad patterns of positive genetic associations between openness and psychopathology (mean r_g = 0.20), which are notably discrepant from the small and inconsistent associations commonly reported in phenotypic research^13,14. Many other personality–psychopathology associations were specific and differentiated: extraversion genetics were associated with lower risk for internalizing disorders (factor r_g = −0.08) but greater risk for neurodevelopmental disorders (factor r_g = 0.18), and conscientiousness genetics were associated with lower risk for neurodevelopmental disorders (factor r_g = −0.24) but greater risk for compulsive disorders (factor r_g = 0.13). These findings provide strong support for close connections between personality traits and psychiatric disorders posited in psychiatric nosologies^13,14.

Genetic correlations between personality traits and survey behaviour in the UK Biobank indicated links between personality and research participation (Fig. 3). In particular, genetic variants associated with openness to experience and agreeableness were also associated with completing optional questionnaires (mean r_g = 0.22 and 0.11, respectively), whereas genetic variants associated with neuroticism were associated with fewer questionnaire responses (r_g = −0.23) and more ‘I don’t know’ and ‘prefer no response’ answers (mean r_g = 0.32) after beginning a questionnaire. The genetics of personality may therefore have a widespread, underacknowledged influence on sample composition and responses in published research that relies on survey data. In Supplementary Note 5.1, we further describe genetic correlation results from each of these nine outcome clusters.

PGI associations with outcomes

Genetic variants inherited at conception are expected to partially predict outcomes later in life through nondeterministic transactions with the environment^45,46. We examined PGI prediction of interviewer–rater qualities and delinquency in Add Health—a representative sample of young US adults (n = 5,110). We found that personality PGIs predicted how participants were perceived by their in-person interviewer during data collection (Fig. 2b and Supplementary Table 39). Those with PGIs reflecting lower neuroticism and higher extraversion, agreeableness and openness to experience were more often perceived to have a more attractive personality, and those with PGIs reflecting higher agreeableness and conscientiousness were perceived to be more well groomed. In a subsample of at-risk Add Health participants, personality PGIs were associated with a greater likelihood of delinquent behaviour and intergenerational contact with the prison system (Fig. 2b and Supplementary Table 39): high polygenic propensity for neuroticism and low polygenic propensity for agreeableness had small-to-moderate associations with school suspension, running away from home and ever pulling a knife or gun on another person.

Our sociodemographic analysis in the UK Biobank (n = 419,261) linked personality PGIs to the characteristics of one’s residence in later adulthood, controlling for place of birth to reflect residential mobility. Genetic profiles indicative of higher openness to experience predicted greater likelihood of moving into cosmopolitan professional areas and away from suburbia and challenged neighbourhoods by middle–older adulthood (age 50 years), whereas genetic profiles indicative of higher conscientiousness predicted greater likelihood of moving away from cosmopolitan areas and into affluent suburban communities (Fig. 2e). We present additional residential mobility analyses focused on urban/rural migration in Extended Data Fig. 7. These analyses substantiate theoretical associations between personality and life outcomes that have yet to be submitted to GWAS in large samples. Importantly, PGI prediction of behaviours and outcomes from personality-relevant genetic variants was small in magnitude. These PGIs cannot predict an individual’s future behaviour.

We also quantified personality similarity between spouses over recent generations. Inferred genetic correlations between parents, estimated using error-corrected PGI similarity³⁶ meta-analysed across spouses, sibling pairs and cousin pairs from UK Biobank (n_pairs = 187,703) and deCODE (n_dyads = 133,358), indicated minimal assortative mating for each of the Big Five traits (estimated parental r_g = 0.01–0.04; Fig. 2c and Supplementary Table 40). Over past generations, parents were only minimally more genetically similar in their personality traits, on average, than would be expected by chance, and far less genetically similar than in educational attainment (r_g = 0.37)^47,48. Phenotypic assortative mating estimates between deCODE parents (n_pairs = 5,317) were stronger in magnitude but remained small for all traits besides openness to experience (r_s = 0.07–0.12; openness r = 0.18; Extended Data Fig. 8 and Supplementary Table 41), consistent with previous estimates⁴⁸.

Mendelian randomization

To test causality and directionality in associations between personality traits and biobehavioural outcomes, we applied MR tests to a subset of 13 outcomes most likely to comport with core assumptions of this method (Supplementary Tables 42 and 43). We found plausible causal effects for 33 exposure–outcome pairings, where effects directionally replicated across MR estimators and multiple 95% CIs excluded 0 (Extended Data Fig. 9). Twenty-four analyses indicated effects of personality on biobehavioural outcomes, providing evidence that personality traits affect physical health—for example, extraversion increased COVID infection risk, conscientiousness decreased BMI and reduced likelihood of smoking initiation and frequency of spells in hospital, and neuroticism increased the likelihood of scoring lower on a healthy ageing/longevity factor. Furthermore, consistent with theoretical perspectives that life experiences affect personality development^1,26, we found nine plausibly causal effects of biobehavioural outcomes on personality, encompassing broad multi-trait effects of educational attainment, increased BMI and smoking initiation on average levels of personality. Reassuringly, we found no evidence for MR associations between personality and three negative controls (birthweight, number of sisters and number of brothers) that would only be associated with one’s own personality through intergenerational confounding (residual gene–environment correlation). These tests provide valuable information on the potential causes and consequences of personality traits, which to date have been especially challenging to obtain using other methods⁴⁹. Nevertheless, future work should further triangulate the MR-based causal inferences reported here with additional methods and data that permit strong causal inference⁵⁰.

Polygenic prediction within families

We used data from the Netherlands Twin Register (NTR) (n_families = 2,956) and Twins Early Development Study (TEDS) (n_families = 4,751) to predict personality traits within dizygotic twin pairs. These models predict personality from twin differences in PGIs constructed from the primary EUR-like population GWAS. By holding family environment and parent genotype constant, and leveraging the randomness of intergenerational genetic transmission, these within-family comparisons substantially reduce genetic confounding that may be present in population-level GWAS^4,5,51,52 (Supplementary Note 6).

For all Big Five traits in both cohorts, within-twin pair PGI differences predicted the respective personality trait, with indistinguishable magnitude from population-level PGI prediction. The average within-family effect (β), meta-analysed across the NTR and TEDS cohorts and averaged across the Big Five, was β = 0.159, compared with β = 0.165 for the population-level effect (a prediction ratio of 96%; Fig. 4b and Supplementary Tables 35 and 36). This similarity suggests negligible familial confounding in population-level genetic associations with personality. This result contrasts starkly with the notable within-family attenuation found for other behavioural traits, such as educational attainment (mean within/population predictive ratio of around 60%)^4,5,53, cognitive ability (around 80%)^4,5 and externalizing psychopathology (about 75%)²².

We further tested for familial confounding using a complementary method that is expected to be less biased by potential effects that siblings may have on one another⁵². We used parent–offspring data from deCODE (n = 34,506) to decompose population-level PGI prediction into direct effects (genetic variants passed down from parent to offspring) and familial confounds (parental genetic variants not transmitted to the offspring, population stratification and assortative mating). Across the Big Five, direct effects accounted for an average of 96.2% of variance in population-level PGI prediction (Fig. 4 and Supplementary Table 44), indicating that genetic associations with personality operated nearly exclusively through direct transmission from parent to offspring. The identicality of these results to those of within-twin PGI analyses indicates negligible confounding from effects that siblings have on one another. Only for conscientiousness, non-transmitted genetic confounds were significantly predictive of offspring personality (explaining 8.1% of the total predicted effect, indirect effect B = 0.015, 95% CI = 0.003–0.027, P = 0.01). Non-transmitted confounds did not differ in magnitude across mothers and fathers (Supplementary Table 45). By contrast, for educational attainment, only 75.1% of variance was explained by direct effects (indirect effect B = 0.069, 95% CI = 0.055–0.083, P = 3.3 × 10⁻²¹; Supplementary Table 44). After controlling for familial confounds, PGI prediction of extraversion by the extraversion PGI (B = 0.207) was similar in magnitude to PGI prediction of educational attainment by the educational attainment PGI⁵³ (B = 0.208). Overall, these tests rule out large or moderate effects of familial confounding in genetic associations with personality; they indicate that such effects are negligible to small in magnitude.

Within-family genome-wide analyses

As a test of familial confounding at the level of individual SNPs^5,52, we conducted within-family GWAS for each Big Five trait. We assembled data across 12 contributing EUR-like cohorts (n = 31,544–50,725 across traits) that ascertained genetic data among family members, using best-practice quality control⁵. For neuroticism, we combined these data with published within-family GWAS estimates (Supplementary Table 46 and Extended Data Fig. 10).

LDSC-estimated h²_SNP values from within-family GWAS ranged from 7.6% (s.e. = 1.6%) for agreeableness to 13.4% (3.2%) for openness to experience, evincing comparable magnitudes to those reported for the complete meta-analyses of population GWAS (Fig. 4 and Supplementary Table 47). To maximize comparability between within-family and population-level GWAS, we also estimated h²_SNP using meta-analysis of population-level GWAS among the matched subset of 12 cohorts who contributed to the within-family GWAS (Fig. 4). For each trait, LDSC within-family intercepts were lower than population-level intercepts, suggesting that these models accounted for residual confounding in population-level models.

h²_SNP comparisons across models, using P < 0.05 as a significance threshold to maximize identification of potential differences, indicated that, for only openness to experience, matched population-level h²_SNP estimates were significantly greater than within-family h²_SNP estimates (19.4%, 95% CI = 15.8–22.9% versus 13.4%, 95% CI = 7.1–19.6%, P = 0.02). This observed difference was driven largely by an inflated population-level h²_SNP estimate for openness to experience in the Estonian Biobank sample (Supplementary Table 46). By contrast, previously reported comparisons for other social and behavioural traits, such as educational attainment, depression and household income, indicate that LDSC h²_SNP is attenuated by up to 50% in within-family data⁵. LDSC-estimated genetic correlations between within-family and population-level GWAS summary data averaged r_g = 0.86 and were significantly less than 1.00 only for neuroticism (r_g = 0.75, 95% CI = 0.61–0.90, P < 0.001) and agreeableness (r_g = 0.79, 95% CI = 0.61–0.97, P = 0.03). These results indicate strong correspondence in genetic effect estimates between population-level and within-family analyses, but we note generally wider CIs, indicating a need for additional within-family GWAS data collection. Further comparisons across non-overlapping cohorts indicated that population-level and within-family effect sizes for significant and suggestively associated SNPs (P < 1 × 10⁻⁵) were highly similar in magnitude, demonstrating that uncontrolled confounding in population-level GWAS is small in magnitude and that genetic associations with personality are replicable (Supplementary Note 6.7 and Supplementary Table 47).

Genetic correlations between these within-family Big Five summary statistics and the 85 population-level external GWAS phenotypes were similar but not identical to fully population-level genetic correlations (mean correlation among r_g estimates = 0.83; Supplementary Table 48 and Extended Data Fig. 10). Fully within-family genetic correlations between these summary data and 18 phenotypes from ref. ⁴ were also highly similar to population-level genetic correlations, despite notably less power (mean r between r_g estimates = 0.87; Supplementary Table 49 and Extended Data Fig. 10).

In a consortium effort assembling data from 46 cohorts covering 611,037 to 1.14 million participants, we conducted highly powered GWAS of each of the Big Five personality traits, establishing a rigorous basis for inference in personality genomics and highlighting the fundamental role of personality in the human experience.

Genetic and environmental factors, along with their correlations and interactions, contribute to variation in personality^23,45,46. We estimated h²_SNP at 4.8–9.3% using population-level data and 7.6–13.4% using within-family data. As expected from psychometric theory, heritability estimates were higher when personality was measured using more reliable instruments, revealing a predictable trade-off between reliability (longer tests) and larger samples that should factor into future data collection. Notably, our h²_SNP estimates, which index only effects of common genetic variants, are markedly lower than the 40–60% heritability estimates produced based on twin and family methods^18,23 and molecular genetic methods that more completely incorporate effects of rare variants and genetic interactions^24,25.

Comparisons of population-level and within-family associations indicated that genetic associations with personality are relatively unconfounded by assortative mating or by environmental factors that are shared across family members, including uncontrolled population stratification and dynastic effects⁵¹. This result contrasts with those obtained for other social science traits, such as educational attainment, income, externalizing behaviour and cognitive performance^4,5,22,51,53, for which genetic associations are substantially attenuated in within-family analyses compared to population-level analyses. However, confounding was not entirely absent, and even small confounding effects can be meaningful. For agreeableness and neuroticism, within-family and population GWAS effect estimates were genetically correlated at less than unity. For openness to experience, h²_SNP from within-family GWAS was significantly lower than from matched-sample population GWAS (but not full-population GWAS), and for conscientiousness, non-transmitted parental alleles had a small but significant contribution to PGI prediction. Finally, highly powered analyses of assortative mating identified effects that were significant but very small in magnitude. Collection of additional within-family data will provide further leverage to accurately and precisely identify loci indexing direct genetic effects. Importantly, even direct genetic effects do not determine an individual’s personality. Rather, genetic associations act probabilistically across large samples of people⁵⁴, and may interact with and operate through environmental experiences⁴⁵.

Genetic associations with personality largely generalized across different regions of Western geography, age groups, self-rated versus other-rated report, military service versus general population samples, and measurement instrument. This suggests that widespread genetic inference is possible, even though the mechanisms linking genetic variation to personality trait differences are numerous, dynamic and complex. One key departure from this overall pattern pertains to agreeableness, which exhibited lower genetic correlations across geography and rater instrument, suggesting that genetic effects relating to this trait may be more varied across groups and/or measurements, and inference must be more contextualized. In the future, analyses stratified by sex, more finely by age, and by more expansive sets of ancestral and cultural backgrounds, will further refine inferences about the differentiation of personality genetics across environments.

Functional genomic analyses and biological annotation indicated that, despite their only modest genetic intercorrelations, genetic associations with each of the Big Five personality traits occur through overlapping molecular and cellular systems. Single-cell gene expression data integrated with our GWAS indicated that there is no straightforward reductive mapping from broad, multifaceted personality traits to specific neuron types or neurotransmitters. These analyses did not fully align with pre-existing theories that specifically link serotonergic and dopaminergic neuronal processes to personality^28,29. However, the availability of these high-powered GWAS combined with ever-expanding access to temporal and spatial brain gene expression data will enable future developmental and system-specific analysis of personality aetiology.

Much of the scientific value of studying personality stems from its applicability to nearly all aspects of human life^{6,7,8,9,10,11,12,13,14,15,16,17}. We extend this tenet by linking heritable variation in personality traits to core mental and physical health outcomes, labour market performance and reproduction, as well as specific behaviours such as survey response, lifespan residential mobility and impressions made on others. Causal inference in these associations is especially valuable, given that personality traits are highly stable over time²⁶ and resistant to simple experimental manipulation⁴⁹. MR results implicate personality traits as both potential causes and consequences of a range of health conditions. Given personality’s pervasive relevance, researchers across scientific fields will benefit from incorporating personality traits when predicting and explaining biopsychosocial outcomes.

Cohorts and measures

We incorporated personality trait data from 46 cohorts participating in the ReGPC. Cohorts were included that provided data on genotyped participants with EUR-like and AFR-like genomes who had completed a validated multi-item personality inventory compatible with the Big Five personality trait taxonomy. Descriptive statistics for each cohort are provided in Supplementary Tables 1 and 2. Descriptions of each cohort are provided in Supplementary Note 7. A PRISMA flowchart for this meta-analysis is available at OSF (https://osf.io/sh8gr). These analyses were not pre-registered. As this meta-analysis used de-identified summary-level data, it was deemed exempt from ethics board approval by the University of Texas at Austin Institutional Review Board (STUDY00001941); ethics approval for individual cohorts that collected data and contributed to the meta-analysis is presented in Supplementary Note 7. All of the participants consented to the research, and this research was performed in accordance with all relevant guidelines and regulations.

Population-level meta-analysis

For each trait, in each cohort, we conducted population-level genome-wide association analyses among participants with EUR-like and AFR-like genomes⁵⁶ across the 22 chromosomes and the X chromosome, following a standard operating procedure (https://osf.io/rsc49). In each cohort, we excluded SNPs with in-sample minor allele frequency (MAF) < 1%, call rate < 95%, deviation from Hardy–Weinberg Equilibrium (P < 1 × 10⁻⁵), poor imputation quality (INFO < 0.40) and those with poor clustering on visual inspection of intensity plots. Participants were excluded if they had low overall call rates (<95%), excess autosomal heterozygosity or homozygosity, were duplicated samples, had gender inconsistent with sex (which is often indicative of mislabelled demographic data⁵⁷) or had chromosomal abnormalities. Association analyses were conducted in each cohort by regressing personality trait score on each biallelic 1000 Genomes 3v5 EUR or AFR SNP⁵⁸ with PLINK2 (ref. ⁵⁹), PLINK⁶⁰, GCTA⁶¹, BOLT-LLM⁶², FastGWA⁶³ or proprietary software, using mixed models to account for relatedness when appropriate. These analyses included controls for age, age², birth cohort, sex and genetic principal components, to address potential lifespan and birth cohort differences in personality traits^1,26. Some groups contributed separate results for potentially overlapping cohorts of participants. In these cases, we meta-analysed estimates while accounting for dependency of their estimation errors. We harmonized and directionally aligned effects using EasyQC⁶⁴ and checked that effects were directionally consistent across cohorts by estimating genetic correlations with LDSC³⁴ (conducted on HapMap3 SNPs with an INFO threshold of ≥0.90) in GenomicSEM³⁷.

For each Big Five trait, we then conducted IVW meta-analysis of GWAS estimates across all of the contributing cohorts, separately for participants with EUR-like and AFR-like genomes, in R (v.4.3.1)⁶⁵. For each meta-analysed SNP, we re-estimated n using observed meta-analytic MAF and standard error. This produced 10 sets of summary statistics (five traits × two ancestral groups). We quantified the number of genome-wide significant lead SNPs at P < 5 × 10⁻⁸ that were linkage disequilibrium (LD)-independent of other lead SNPs nearby on the genome (within a 250 kb window) using FUMA⁶⁶. We also identified lead SNPs that were LD-independent of all other lead SNPs on the chromosome regardless of genomic distance. Ancestry-stratified Manhattan plots of meta-analytic genome-wide associations are presented in Extended Data Fig. 1.

We next conducted transancestry IVW meta-analyses to synthesize data across the k = 10 cohorts that included participants with both EUR-like and AFR-like genomes, and the additional k = 36 cohorts including solely participants with AFR-like genomes. As around 95% of participants were of EUR-like ancestry, and because stronger linkage disequilibrium among EUR-like compared with AFR-like superpopulations⁶⁷ means that using the EUR reference panel probably leads to conservative estimation of the number of lead SNPs, we initially identified candidate lead SNPs using the 1000 Genomes 3v5 EUR reference panel. Using information on SNP LD from the 1000 Genomes 3v5 EUR and AFR reference panels, we estimated transancestry sample size-weighted LD matrices for each chromosome across all candidate transancestry lead SNPs and all lead SNPs from the ancestry-stratified analyses, which we used to identify additional LD-independent lead SNPs. To supplement transancestry IVW results, we also conducted transancestry meta-analyses using MAMA³¹.

We tested the consistency of the genetic signal across cohorts by splitting the 46 EUR-like discovery cohorts into two balanced halves, conducting IVW meta-analyses separately in each half and using LDSC to test the concordance of genetic signal across split halves. We identified novel lead SNPs in the current GWAS relative to past GWAS as those that were outside the same 250 kb locus as or LD-independent of a significant SNP in past GWAS of the Big Five. Finally, we quantified similarity in genetic signal among the Big Five by identifying overlapping 250 kb genetic loci across pairs of traits and by estimating genetic correlations between pairs of Big Five traits using LDSC.

Characterizing SNP heritability

We estimated h²_SNP for each personality trait using LDSC. As LDSC requires ancestrally homogenous data, we applied this method separately to GWAS summary statistics of participants with EUR-like genomes, AFR-like genomes and, for comparison purposes, EUR-like genomes in the subset of 10 cohorts that ascertained participants with both EUR-like and AFR-like genomes. We estimated polygenicity using stratified LD fourth moments regression⁶⁸, and we estimated within-trait transancestry genetic correlations using POPCORN⁶⁹.

We additionally applied LDSC to GWAS data on each individual cohort and then meta-analysed h²_SNP estimates across cohorts using fixed-effects and random-effects meta-analyses, with the R package metafor⁷⁰. To quantify the effect of measurement error of the personality measure on h²_SNP, we conducted meta-regressions, where h²_SNP in a cohort was regressed on the Cronbach’s α reliability of its personality measure (reliabilities are shown in Supplementary Table 1). For each trait, we also used LDSC to correlate genetic effects across each pair of cohorts with positive h²_SNP estimates (1,781 total pairings) and used fixed-effects meta-analysis to obtain an average estimate for this cross-cohort genetic correlation.

To quantify the consistency of genetic signal in each of the Big Five across groups, we sorted EUR-like cohorts by five grouping variables with substantial variation and n > 10,000 per group: geographical region (US, UK–Australia, continental Europe and Nordic), age group (most participants <25 years, 25–64 years and 65 years and older), rater perspective (self-report and other-report, with data for these comparisons contributed exclusively from the Estonian Biobank cohort), military veteran status (MVP cohort participants and participants from all 45 other cohorts) and measurement instrument family (Big Five Inventory^71,72,73,74, NEO Personality Inventory^{75,76,77,78,79}, International Personality Item Pool inventories^80,81, 100 Nuances of Personality Inventory⁸² and Eysenck Personality Inventory^83,84). We next estimated genetic correlations across groups using bivariate LDSC. Finally, we compared X-chromosomal associations with neuroticism across male and female individuals in the UK Biobank cohort, estimating h²_SNP, cross-sex r_g and the dosage compensation ratio^85,86 on sex-stratified summary data of unrelated male (n = 168,856) and female (n = 197,927) individuals with EUR-like genomes.

We applied GenomicSEM to formally model the genetic factor structure of the different measurement instruments and to compute the Q_SNP statistic, which quantifies the extent to which SNP associations with each instrument varied beyond what would be expected under the factor model⁸⁷. We specified a five-factor model in which each instrument served as an indicator of its respective Big Five trait and cross-loadings were added based on an iterative data-driven approach. For each SNP, we computed the Q_SNP statistic by comparing a model in which each factor was regressed on the individual SNP to one which all specific measurement instruments were regressed on the individual SNP.

To identify annotations enriched for h²_SNP, we submitted each of the five EUR-like GWAS summary statistics to MAGMA⁸⁸, including SNPs with INFO ≥ 0.80. The gene sets included in this analysis came from previous work⁸⁹. We then indexed the similarity of enrichment across the Big Five using pairwise rank correlations of enrichment effect sizes from these analyses.

Polygenic prediction of personality

We generated PGI weights for each Big Five trait by applying SBayesR⁴² to EUR-like GWAS estimates for the set of 1.3 million HapMap3 SNPs. PGI weights for each of the Big Five were then used to construct PGIs in five cohorts: the National Longitudinal Study of Adolescent to Adult Health (Add Health), German Socioeconomic Panel (GSOEP), Health and Retirement Study (HRS), Netherlands Twin Register (NTR) and Twins Early Development Study (TEDS). Each cohort ascertained participants with EUR-like genomes, and Add Health and HRS also ascertained participants with AFR-like genomes. As HRS, NTR and TEDS also contributed to population-level GWAS, we held data from each respective cohort out when estimating PGI weights in that cohort, to prevent overlap in training and prediction sets.

We used each of these PGIs to predict the corresponding Big Five trait in each cohort. We estimated population-level effects using ordinary least squares regression for cohorts of unrelated participants (Add Health, GSOEP and HRS) and linear regression with cluster-robust standard errors across families to account for relatedness in cohorts of twins (NTR and TEDS). These regressions controlled for age, age², sex, 10 ancestral principal components and genotyping batch effects, with significance set at P < 0.01. We also generated PGI weights both among a broader set of 2.9 million SNPs and, using PRS-CSx⁹⁰, we compared this prediction to prior estimates. As we describe further in Supplementary Note 4, these changes did not improve prediction, so we focus on analyses using SBayesR and the set of 1.3 million HapMap SNPs.

Associations with behaviours and outcomes

We quantified genetic correlations between the Big Five and 85 external phenotypes that had previously been submitted to GWAS, among participants with EUR-like genomes, using LDSC. For 13 of these phenotypes, GWAS data were available to also estimate these genetic correlations among participants with AFR-like genomes. We set the significance threshold at P < 0.01 for these analyses. Outcomes and their source GWAS are presented in Supplementary Table 37.

We also used PGIs to estimate associations between the Big Five and 19 phenotypic behaviours and outcomes that have not been submitted to GWAS and could therefore not be analysed with LDSC. To do this, we adapted the method described in the ‘Polygenic prediction of personality’ section above to predict each behaviour or outcome from each of the Big Five PGIs across participants. In residential mobility PGI analyses, we included statistical controls for place of birth (so that effects reflect residential movement), median home prices (to control for affordability), age, sex and five principal components. We further filtered results to only those that directionally replicated within family to reduce potential confounding (see the ‘Evaluating familial confounding’ section below) and applied the false-discovery rate correction⁹¹.

We quantified assortative mating in personality over recent generations using a method that disattenuates PGIs for measurement error, enabling the estimation of genetic correlations across family members. Specifically, we estimated two independent sets of PGI weights for each participant, one from each split-half GWAS (see the ‘Population-level meta-analysis’ section above). We then estimated a structural equation model in the R package lavaan⁹² that used the two PGIs as indicators of a latent, measurement error-free PGI for each participant³⁶. We correlated these PGIs across pairs of spouses, siblings and cousins in the UK Biobank and deCODE cohorts (a path diagram of this latent correlation model is provided in Extended Data Fig. 8b). PGI correlations between members of spousal dyads in this model directly estimate genetic assortative mating. For siblings and cousin dyads, we transformed PGI correlations to be on the same scale as spouses. We then used IVW meta-analysis to synthesize these estimates across relationship types and the two cohorts.

We conducted MR analyses to examine putatively causal bidirectional effects between personality traits and external phenotypes, using the R package TwoSampleMR⁹³. For these tests, we selected 13 external behaviours and outcomes from the broader pool of 85 on the basis of previous personality theory (Supplementary Note 5.5). We also selected three phenotypes to serve as negative controls (birthweight, number of sisters and number of brothers), as these cannot be directly caused by an individual’s personality. For each phenotype, we conducted MR tests using three estimators chosen for their complementarity and robustness to common violations of MR assumptions⁹⁴: weighted mode with Steiger filtering⁹⁵ weighted median with Steiger filtering⁹⁶ and MR-CAUSE⁹⁷. To identify replicable effects in the context of multiple tests, we considered an effect to be significant only if estimates were directionally consistent across all three MR methods with unadjusted 95% confidence or credibility intervals that excluded 0 across at least two methods.

Evaluating familial confounding

To evaluate familial confounding in population-level PGIs, we compared population-level PGI prediction to within-family PGI prediction: the prediction of phenotypic variation in personality traits from variation in genotype, controlling for the genotypes of one’s family members. For both the NTR and TEDS cohorts, we constructed PGI weights from the primary EUR-like population GWAS meta-analysis, excluding the respective twin cohort used for PGI prediction. We then estimated within-family PGI associations among dizygotic twin pairs, controlling for family level fixed effects, as well as sex, age and age². We pooled estimates across the two cohorts and compared them to pooled population-level PGI estimates to obtain an index of residual confounding in population-level PGIs.

We then estimated within-family PGI prediction in the deCODE cohort, in which parent–offspring units were each genotyped. In this cohort, we predict an offspring’s personality trait from their PGI, controlling for their mother’s PGI, their father’s PGI, sex, age, age² and their county of birth. For each trait, we obtained estimates of the population-level effect (the total prediction) and the indirect genetic confounding (the contribution to prediction made by parental PGI scores, both together and separately by mother and father), and we estimated the direct genetic effect by subtracting the indirect genetic confounding from the population-level effect. To provide a point of comparison, we repeated this within-family PGI prediction using the phenotype of educational attainment. Educational attainment PGI weights were estimated in SBayesR according to the procedure used for the Big Five, using GWAS summary data from a previous study⁵³, excluding the 23&Me and deCODE cohorts.

We also conducted within-family GWAS analyses on EUR-like participant cohorts (k = 12) that ascertained genomic and personality data among family members. In each cohort, analysts estimated population-level and within-family effects for each SNP on each Big Five trait using either snipar³⁰ or a pipeline developed previously⁴. For these analyses, we conducted stringent quality control, including only SNPs with imputation INFO ≥ 0.99 and correcting downwardly biased h²_SNP estimates in small samples (Supplementary Note 6.3), and we pooled effects across cohorts with IVW meta-analysis. For neuroticism, we incorporated additional previously published summary data⁴. For each SNP, sample size was re-estimated using the meta-analytic standard error and 1000 Genomes 3v5 MAF, capped at the 75th percentile of the summed observed sample size across contributing cohorts. This method produced within-family summary statistics for each of the Big Five.

To enable direct comparison, we re-estimated population-level GWAS across these 12 within-family cohorts using the same method as in the ‘Population-level meta-analysis’ section above, restricted to SNPs with INFO ≥ 0.99 and with sample size estimated using 1000 Genomes 3v5 panel MAF rather than meta-analytic sample MAF. We then submitted these within-family GWAS summary statistics and matched population GWAS summary statistics to LDSC.

To confirm that within-family analyses reduced confounding, we compared population-level LDSC intercepts to within-family LDSC intercepts³⁴. We used nested model comparison tests in genomic structural equation modelling to test the equivalence of h²_SNP across matched population-level and within-family GWAS, and to test whether genetic correlations between population-level and within-family GWAS were less than unity⁹⁸. We also compared the average SNP effect sizes between population-level and within-family GWAS, among SNPs with relatively strong statistical signal (P < 1 × 10⁻⁵) that were >250 kb apart on the genome.

Using LDSC, we estimated genetic correlations between the Big Five within-family GWAS summary data and population-level GWAS summary data from the 85 external behaviours or outcomes. We compared these correlations to the corresponding estimates fully based on population-level GWAS. We also estimated genetic correlations entirely based on within-family GWAS (for which summary data were available for 18 external behaviours or outcomes)⁴; these estimates were compared with the corresponding estimates based completely on population-level GWAS.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Code for all analyses performed this study is available at OSF⁹⁹ (https://doi.org/10.17605/OSF.IO/HGNSM).

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Author notes

1. These authors contributed equally: Michel G. Nivard, Elliot M. Tucker-Drob

2. Lists of members and their affiliations appear in the Supplementary Information

Authors and Affiliations

1. Department of Psychology, Michigan State University, East Lansing, MI, USA

   Ted Schwaba, Lindsay S. Ackerman & Josephine C. M. Fealy

2. Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, United States

   Margaret L. Clapp Sullivan

3. Department of Biological Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit, Amsterdam, The Netherlands

   Wonuola A. Akingbuwa, Eco J. C. de Geus, Jouke-Jan Hottenga & Ian J. Deary

4. Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Amsterdam, The Netherlands

   Wonuola A. Akingbuwa & Eco J. C. de Geus

5. Institute of Genomics, University of Tartu, Tartu, Estonia

   Kerli Ilves, Urmo Võsa, Tõnu Esko, Mariliis Vaht, Tõnu Esko & Uku Vainik

6. ALERRT Center, Texas State University, San Marcos, TX, USA

   Peter T. Tanksley

7. Laboratoire de Sciences Cognitives et Psycholinguistique (ENS, EHESS, CNRS), École Normale Supérieure, PSL University, Paris, France

   Camille M. Williams

8. Department of Psychology, University of Texas at Austin, Austin, TX, USA

   Yavor Dragostinov, Javier de la Fuente, K. Paige Harden & Elliot M. Tucker-Drob

9. Population Research Center, University of Texas at Austin, Austin, TX, USA

   Yavor Dragostinov & Elliot M. Tucker-Drob

10. Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

    Travis T. Mallard & Justin D. Tubbs

11. Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Travis T. Mallard & Justin D. Tubbs

12. Department of Psychiatry, Harvard Medical School, Boston, MA, USA

    Travis T. Mallard & Justin D. Tubbs

13. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Travis T. Mallard, Justin D. Tubbs & Margherita Malanchini

14. School of Biological and Behavioral Sciences, Queen Mary University of London, London, UK

    Wangjingyi Liao & Agnieszka Musial

15. Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK

    Gibran Hemani, George Davey Smith, Elizabeth C. Corfield & Michel G. Nivard

16. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

    Gibran Hemani, George Davey Smith & Michel G. Nivard

17. Yale University, Veterans Affairs Connecticut Healthcare System, New Haven, CT, USA

    Priya Gupta, Joel Gelernter & Daniel F. Levey

18. University of California San Diego, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA

    Murray B. Stein

19. Institute of Psychology, University of Tartu, Tartu, Estonia

    Liisi Ausmees, Anu Realo & René Mõttus

20. Department of Psychology, University of Warwick, Coventry, UK

    Anu Realo, Jüri Allik & Uku Vainik

21. School of Educational Sciences, Tallinn University, Tallinn, Estonia

    Anu Realo

22. Department of Psychology, University of Edinburgh, Edinburgh, UK

    René Mõttus, Catharina A. Hartman, Charley Xia, Michelle Luciano, Ian J. Deary & W. David Hill

23. Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada

    Uku Vainik

24. deCODE genetics/Amgen, Reykjavik, Iceland

    Gudrun A. Jonsdottir, Gudmar Thorleifsson, Árni Freyr Gunnarsson, Gyda Bjornsdottir, Thorgeir E. Thorgeirsson, Hreinn Stefansson & Kari Stefansson

25. PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway

    Rosa Cheesman, Qi Qin, Helga Ask, Fartein Ask Torvik & Eivind Ystrom

26. School of Psychology and Neuroscience, University of Bristol, Bristol, UK

    Rosa Cheesman

27. PsychGen Centre for Genetic Epidemiology and Mental Health, Division of Public Health and Prevention, Norwegian Institute of Public Health, Oslo, Norway

    Elizabeth C. Corfield, Helga Ask & Eivind Ystrom

28. Psychiatric Genetic Epidemiology Group, Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway

    Elizabeth C. Corfield

29. Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway

    Fartein Ask Torvik

30. Centre for Research on Equality in Education, Faculty of Educational Sciences, University of Oslo, Oslo, Norway

    Eivind Ystrom

31. Department of Mental Health, Norwegian Institute of Public Health, Oslo, Norway

    Martin Tesli

32. Department of Psychiatry, Østfold Hospital, Sarpsborg, Norway

    Martin Tesli

33. Department of Complex Trait Genetics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Dorret I. Boomsma

34. Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    Dener Cardoso Melo, Catharina A. Hartman & Albertine J. Oldehinkel

35. Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    Harold Snieder & Peter J. van der Most

36. Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh, Edinburgh, UK

    Charley Xia, W. David Hill, Gail Davies, Paul Redmond, Adele Taylor & Janie Corley

37. Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK

    Archie Campbell

38. Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Seon-Kyeong Jang

39. Department of Psychology, University of Minnesota, Minneapolis, MN, USA

    Scott I. Vrieze, Tobias Edwards, Alexandros Giannelis, Emily A. Willoughby, James J. Lee, Matt McGue, Robert F. Krueger & Colin D. Freilich

40. Minnesota Center for Twin and Family Research, Minneapolis, MN, USA

    Scott I. Vrieze

41. Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA

    Gonçalo Abecasis

42. QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Michelle K. Lupton, Brittany L. Mitchell, Petra V. Viher, Lucía Colodro-Conde, Nicholas G. Martin, Sarah E. Medland, Eske M. Derks & Briar Wormington

43. Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia

    Michelle K. Lupton & Brittany L. Mitchell

44. School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia

    Michelle K. Lupton & Brittany L. Mitchell

45. School of Psychology, The University of Queensland, Brisbane, Queensland, Australia

    Lucía Colodro-Conde & Sarah E. Medland

46. Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland

    Jaakko Kaprio & Teemu Palviainen

47. Population Research Unit, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland

    Karri Silventoinen

48. Department of Psychology, Royal Holloway University of London, London, UK

    Kaili Rimfeld

49. Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK

    Kaili Rimfeld & Robert Plomin

50. Rutgers Addiction Research Center, Brain Health Institute, Rutgers University, Piscataway, NJ, USA

    Danielle M. Dick

51. Department of Psychiatry, Rutgers Robert Wood Johnson Medical School, Piscataway, NJ, USA

    Danielle M. Dick & Fazil Aliev

52. Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden

    Laura W. Wesseldijk & Fredrik Ullén

53. Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany

    Laura W. Wesseldijk, Fredrik Ullén & Miriam A. Mosing

54. Department of Psychiatry, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands

    Laura W. Wesseldijk & Abdel Abdellaoui

55. Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Miriam A. Mosing

56. Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia

    Miriam A. Mosing

57. Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Henry R. Kranzler

58. Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA

    Yaira Nunez, Sarah Beck & Renato Polimanti

59. Department of Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands

    Alexandros Giannelis

60. Department of Geriatrics, College of Medicine, Florida State University, Tallahassee, FL, USA

    Antonio Terracciano

61. Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, ML, USA

    Antonio Terracciano

62. Institute for Genetic and Biomedical Research, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy

    Michele Marongiu & Edoardo Fiorillo

63. Department of Biomedical Sciences, University Study of Sassari, Sassari, Italy

    Francesco Cucca

64. Department of Behavioral Sciences and Social Medicine, College of Medicine, Florida State University, Tallahassee, FL, USA

    Angelina R. Sutin

65. Pedagogical Sciences, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands

    Tina Kretschmer

66. Huntsman Mental Health Institute, Department of Psychiatry, University of Utah, Salt Lake City, UT, USA

    Andrey A. Shabalin

67. Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland

    Anna R. Docherty, Binisha H. Mishra & Terho Lehtimäki

68. Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland

    Binisha H. Mishra, Terho Lehtimäki & Mika Kähönen

69. Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland

    Binisha H. Mishra & Terho Lehtimäki

70. Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland

    Olli T. Raitakari

71. Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland

    Olli T. Raitakari

72. Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland

    Olli T. Raitakari

73. InFLAMES Research Flagship, University of Turku, Turku, Finland

    Olli T. Raitakari

74. Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland

    Mika Kähönen

75. Faculty of Medicine, Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland

    Aino Saarinen & Liisa Keltikangas-Järvinen

76. Finnish Institute for Health and Welfare, Helsinki, Finland

    Henrik Dobewall

77. Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany

    Klaus Berger & Marisol Herrera-Rivero

78. Department of Genetic Epidemiology, Institute of Human Genetics, Göttingen, Germany

    Marisol Herrera-Rivero

79. Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Fabian Streit

80. Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Fabian Streit

81. German Center for Mental Health (DZPG), Partner Site Mannheim-Heidelberg-Ulm, Ulm, Germany

    Fabian Streit

82. Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany

    Swapnil Awasthi & Alisha S. M. Hall

83. Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim/Heidelberg University, Central Institute of Mental Health, Mannheim, Germany

    Stephanie H. Witt, Lea Zillich & Lea Sirignano

84. Department of Psychology, University of Helsinki, Helsinki, Finland

    Johanna Tuhkanen & Jari Lahti

85. Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland

    Katri Räikkönen & Johan G. Eriksson

86. Folkhälsan Research Centre, Helsinki, Finland

    Johan G. Eriksson & Jari Lahti

87. Department of Obstetrics and Gynecology, National University Singapore Yong Loo Lin School of Medicine, Singapore, Singapore

    Johan G. Eriksson

88. Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK

    Tom C. Russ

89. Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK

    Tom C. Russ

90. Institute of Genetics and Biophysics “Adriano Buzzati-Traverso”, CNR, Naples, Italy

    Marina Ciullo & Teresa Nutile

91. Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA

    Jun Ding, Yong Qian, Toshiko Tanaka & Luigi Ferrucci

92. Department of Psychology and Neurosciences, Neuroimaging and Interindividual Differences, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany

    Erhan Genç & Christoph Fraenz

93. Department of Ergonomics, Networking Group Aging, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany

    Patrick D. Gajewski & Stephan Getzmann

94. Department of Psychology, Neuropsychology, Ruhr University Bochum, Bochum, Germany

    Javier E. Schneider Peñate

95. Department of Psychosomatic Medicine and Psychotherapy, Medical Faculty Mannheim/Heidelberg University, Central Institute of Mental Health, Mannheim, Germany

    Stefanie Lis & Christian Schmahl

96. Department of Clinical Psychology, Medical Faculty Mannheim/Heidelberg University, Central Institute of Mental Health, Mannheim, Germany

    Stefanie Lis

97. Department of Affective Disorders, Aarhus University Hospital, Aarhus, Denmark

    Alisha S. M. Hall

98. Department of Clinical Medicine, Aarhus University, Aarhus, Denmark

    Alisha S. M. Hall

99. Department of Psychosomatic General Psychiatry, Center for Psychosocial Medicine, Medical Faculty Mannheim/Heidelberg University, Heidelberg, Germany

    Sabine C. Herpertz

100. Amsterdam Neuroscience, Amsterdam, The Netherlands

     Abdel Abdellaoui

Consortia

Contributions

T.S., M.L.C.S., A.A., M.G.N. and E.M.T.-D. conceived and designed the study and drafted and revised the manuscript. T.S., M.L.C.S., W.A.A., K.I., P.T.T., C.M.W., Y.D., T.T.M., J.D.T., W.L., L.S.A., J.C.M.F. and Á.F.G. conducted analyses of meta-analytic data. T.S., K.I., T.T.M., J.D.T., W.L., P.G., M.B.S., J.G., D.F.L., U. Võsa, L.A., A.R., M.V., J.A., T. Esko, R.M., U. Vainik, G.A.J., G.T., Á.F.G., G.B., T.E.T., H. Stefansson, K. Stefansson, R.C., Q.Q., E.C.C., H.A., F.A.T., E.Y., M.T., D.I.B., E.J.C.d.G., J.-J.H., D.C.M., H. Snieder, C.A.H., C.X., A.C., M.L., I.J.D., W.D.H., S.-K.J., S.I.V., G.A., M.K.L., B.L.M., P.V.V., L.C.-C., N.G.M., S.E.M., E.M.D., B.W., J.K., K. Silventoinen, T.P., A.M., K. Rimfeld, R. Plomin, M. Malanchini, D.M.D., F.A., L.W.W., F.U., M.A.M., H.R.K., Y.N., S.B., R. Polimanti, T. Edwards, A.G., E.A.W., J.J.L., M. McGue., A. Terracciano, M. Marongiu, E.F., F.C., A.R.S., P.J.v.d.M., A.J.O., T.K., A.A.S., A.R.D., R.F.K., C.D.F., B.H.M., T.L., O.T.R., M.K., A.S., H.D., L.K.-J., K.B., M.H.-R., F.S., S.A., S.H.W., J.T., K. Räikkönen, J.G.E., J.L., G.D., P.R., A. Taylor, J.C., T.C.R., M.C., T.N., J.D., Y.Q., T.T., L.F., L.Z., L.S., K.P.H., E.G., P.D.G., S.G., C.F., J.E.S.P., S.L., A.S.M.H., C.S., S.C.H., A.A., M.G.N. and E.M.T.-D. collected and conducted analysis of cohort-level data. G.H. and J.d.l.F. developed software used in the study. W.A.A., P.T.T., G.D.S., R.M., U. Vainik, G.A.J., T.E.T., R.C., E.C.C., H.A., F.A.T., N.G.M., E.M.D., J.K., L.W.W., R. Polimanti, A.T., P.J.v.d.M., C.X., M.L., I.J.D., W.D.H., H.D., F.S., L.F. and K.P.H. provided iterative feedback on the manuscript and made substantial contributions to the interpretation of data. M.G.N and E.M.T.-D. oversaw the study. All of the authors approved the final version of the manuscript.

Corresponding authors

Correspondence to Ted Schwaba, Michel G. Nivard or Elliot M. Tucker-Drob.

Competing interests

The authors declare no competing interests.

Peer review information

Nature thanks Matthew Keller, Xuanyu Lyu, Aysu Okbay and Brent Roberts for their contribution to the peer review of this work. Peer reviewer reports are available.

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