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Twin studies show that genetic influences on AUBs change across development, but this is usually not considered in research identifying and investigating the genes linked to AUBs. Understanding the dynamics of how genes shape AUBs could point to critical periods in which interventions may be most effective and provide insight into the mechanisms behind AUB\u2010related genes. In this project, we estimated how genetic influences on AUBs unfold across development using longitudinal modelling of polygenic scores (PGSs).<\/jats:p>\n <\/jats:sec>\n \n Design<\/jats:title>\n \n Using results from genome\u2010wide association studies (GWASs), we created PGSs to index individual\u2010level genetic risk for multiple AUB\u2010related dimensions:\n Consumption<\/jats:italic>\n ,\n Problems<\/jats:italic>\n , a temporally variable pattern of drinking associated with a preference for beer (\n BeerPref<\/jats:italic>\n ) and externalizing behavior (\n EXT<\/jats:italic>\n ). We created latent growth curve models and tested PGSs as predictors of latent growth factors (intercept, slope, quadratic) underlying trajectories of AUBs.\n <\/jats:p>\n <\/jats:sec>\n \n Setting<\/jats:title>\n PGSs were derived in six longitudinal epidemiological cohorts from the United States, United Kingdom and Finland.<\/jats:p>\n <\/jats:sec>\n \n Participants<\/jats:title>\n \n Participant data were obtained from the longitudinal studies AddHealth, ALSPAC, COGA, FinnTwin12, the older Finnish Twin Cohort and Spit for Science (total\n n<\/jats:italic>\n \u2009=\u200919\u2009194). These cohorts included individuals aged 14 to 67, with repeated measures collected over a span of 4 to 36\u2009years.\n <\/jats:p>\n <\/jats:sec>\n \n Measurements<\/jats:title>\n Primary measures included monthly frequency of typical alcohol consumption (CON) and heavy episodic drinking (HED).<\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \n When drinking behaviors were averaged across time, higher\n Consumption, Problems<\/jats:italic>\n and\n EXT<\/jats:italic>\n PGSs were robustly associated with higher levels of CON and HED (\u03b2s ranged from 0.105 to 0.333,\n P<\/jats:italic>\n \u2009<\u20093.09E\u201004) and higher\n BeerPref<\/jats:italic>\n PGSs with higher HED (\u03b2\u2009=\u20090.064,\n P<\/jats:italic>\n \u2009=\u20093.65E\u201005). However, these PGSs were largely not associated with drinking trajectories in the latent growth curve models. In the meta\u2010analysis, only PGSs for chronic alcohol\n Problems<\/jats:italic>\n consistently predicted a steeper slope (increasing trajectory) of CON across time (B\u2009=\u20090.470,\n P<\/jats:italic>\n \u2009=\u20094.20E\u201006). Other PGSs were associated with latent growth factors in some individual cohorts, but there was a large degree of heterogeneity.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Genetic associations appear to differ not only between alcohol use behaviors, but also across developmental time points and across cohorts, highlighting the need for genetic studies to take such heterogeneity into account. Individual\u2010level genetic profiles may be useful to point to personalized intervention timelines, particularly for individuals with high genetic risk scores for alcohol problems.<\/jats:p>\n <\/jats:sec>","DOI":"10.1111\/add.70292","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T06:08:32Z","timestamp":1766556512000},"page":"1153-1165","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Trajectories of genetic risk across dimensions of alcohol use behaviors"],"prefix":"10.1111","volume":"121","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2034-8341","authenticated-orcid":false,"given":"Jeanne E.","family":"Savage","sequence":"first","affiliation":[{"name":"Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research Vrije Universiteit Amsterdam, Amsterdam Neuroscience Amsterdam The 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