{"id":243548,"date":"2026-09-02T21:24:40","date_gmt":"2026-09-03T02:24:40","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2026\/09\/robust-inference-and-correlates-from-genetic-associations-with-personality"},"modified":"2026-09-02T21:24:40","modified_gmt":"2026-09-03T02:24:40","slug":"robust-inference-and-correlates-from-genetic-associations-with-personality","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2026\/09\/robust-inference-and-correlates-from-genetic-associations-with-personality","title":{"rendered":"Robust inference and correlates from genetic associations with personality"},"content":{"rendered":"

<\/a><\/p>\n

Among participants with EUR-like genomes, SNP heritability (h<\/i>2<\/sup>SNP<\/sub>) estimated for the Big Five traits using linkage disequilibrium score regression (LDSC)34<\/a><\/sup> ranged from 4.8% (s.e. = 0.2%) for agreeableness to 9.3% (s.e. = 0.3%) for extraversion (Table 1<\/a>, Supplementary Table 4<\/a> and Supplementary Note 3<\/a>). 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 h<\/i>2<\/sup>SNP<\/sub> estimates, which indicated an average h<\/i>2<\/sup>SNP<\/sub> of 8.6% (s.e. = 0.6%) across traits (ranging from 7.4% for agreeableness to 10.6% for extraversion; Table 1<\/a> and Supplementary Table 21<\/a>), with significant variability across cohorts (mean \u03c4<\/i> = 3.6%). Random response error by the participants cannot systematically relate to their genome35<\/a>,36<\/a><\/sup>. Accordingly, we found that personality measures with greater reliability (lower random response error) tended to be more heritable (b<\/i> = 6.7%, s.e. = 0.7%; Extended Data Fig. 2<\/a>). In this analysis, the expected h<\/i>2<\/sup>SNP<\/sub> for a measure of typical (median) reliability (\u03b1<\/i> = 0.81) ranged from 9.3% for agreeableness (s.e. = 0.6%) to 13.3% for extraversion (s.e. = 0.6%), and h<\/i>2<\/sup>SNP<\/sub> 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<\/a>).<\/p>\n

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<\/a> and Extended Data Fig. 3<\/a>). Genetic effects were similar but not identical across four western country clusters (USA, continental Europe, Nordic and UK\u2013Australia, mean r<\/i>g<\/sub> = 0.86, mean s.e. = 0.15), three age groups (young (\u226425 years), middle (25\u201364 years) and older (65 years and older), mean r<\/i>g<\/sub> = 0.80, mean s.e. = 0.18), between the Million Veteran Program and other, primarily non-veteran cohorts (mean r<\/i>g<\/sub> = 0.82, mean s.e. = 0.04), and across five personality measurement instruments (mean r<\/i>g<\/sub> = 0.85, mean s.e. = 0.07). Additional characterization of genetic architecture across measurement instruments using genomic structural equation modelling37<\/a><\/sup> 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 <\/a>\u2013 24<\/a> and Extended Data Fig. 4<\/a>). Notably, genetic associations with agreeableness were less consistent across cohorts (Table 1<\/a>), 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<\/i> = 73,983) and by reports by close others (n<\/i> = 20,269), we found strong genetic overlap between rater perspectives (mean r<\/i>g<\/sub> = 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 h<\/i>2<\/sup>SNP<\/sub> did not differ between male individuals (n<\/i> = 168,989; h<\/i>2<\/sup>SNP, X<\/sub> = 0.23%; s.e. = 0.04%) and female individuals (n<\/i> = 198,139; h<\/i>2<\/sup>SNP, X<\/sub> = 0.18%; s.e. = 0.03%; P<\/i>difference<\/sub> = 0.33). The dosage compensation ratio (\\(\\hat{{m{\\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<\/i>g<\/sub> = 0.96; 95% confidence interval (CI) = 0.81\u20131.10).<\/p>\n

Biological follow-up of GWAS signals indicated that enriched gene sets intersected across the Big Five (mean enrichment rank-order \u03c1 = 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 cortex38,39<\/sup>, 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<\/i>, indicated in Fig. 1; Supplementary Tables 25\u201334). All traits demonstrated strong enrichment in protein-truncating variant-intolerant gene sets specifically expressed in neurons (such as ARNTL, TCF4 and NEGR1; Fig. 1).<\/p>\n","protected":false},"excerpt":{"rendered":"

Among participants with EUR-like genomes, SNP heritability (h2SNP) 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 [\u2026]<\/p>\n","protected":false},"author":709,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11,412,41,47,1901],"tags":[],"class_list":["post-243548","post","type-post","status-publish","format-standard","hentry","category-biotech-medical","category-genetics","category-information-science","category-neuroscience","category-sex"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/243548","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/709"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=243548"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/243548\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=243548"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=243548"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=243548"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}