Global metadata catalogue of youth cohorts with genetic and aggression-related data

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Abstract Aggressive behavior in youth is partially heritable and clinically and socially important. Despite its importance, research is fragmented across disciplines and cohorts, limiting large-scale genetically informed analyses. Therefore, we systematically mapped existing cohorts that include measures of aggression and genetic data from participants under 18 years of age. We conducted a systematic search of PubMed using 5,400 automatically generated queries combining five controlled vocabularies (age, mental health, aggression, data type, study type). From 5,254 unique records (retrieved January 20, 2025), a large language model pipeline extracted explicitly named cohorts, followed by semi-automated alias deduplication. Human reviewers then screened 188 studies using standardized inclusion criteria: documented accessibility, available genetic data, sample size > 1,000, inclusion of minors < 18 years old, and validated aggression phenotypes. Forty-four cohorts met all criteria, representing ∼ 890,000 individuals worldwide. Technical validation assessed the reproducibility of the automated name extraction and inter-rater agreement. The catalog supports discovery of cohorts and facilitates future cross-cohort studies of aggression-related outcomes in youth.
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Global metadata catalogue of youth cohorts with genetic and aggression-related data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF data-descriptor Global metadata catalogue of youth cohorts with genetic and aggression-related data Moritz Sturm, Lisa M. Berg, Inken Berg, Harishny Sarma, Jasmin Hartmann, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8595091/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aggressive behavior in youth is partially heritable and clinically and socially important. Despite its importance, research is fragmented across disciplines and cohorts, limiting large-scale genetically informed analyses. Therefore, we systematically mapped existing cohorts that include measures of aggression and genetic data from participants under 18 years of age. We conducted a systematic search of PubMed using 5,400 automatically generated queries combining five controlled vocabularies (age, mental health, aggression, data type, study type). From 5,254 unique records (retrieved January 20, 2025), a large language model pipeline extracted explicitly named cohorts, followed by semi-automated alias deduplication. Human reviewers then screened 188 studies using standardized inclusion criteria: documented accessibility, available genetic data, sample size > 1,000, inclusion of minors < 18 years old, and validated aggression phenotypes. Forty-four cohorts met all criteria, representing ∼ 890,000 individuals worldwide. Technical validation assessed the reproducibility of the automated name extraction and inter-rater agreement. The catalog supports discovery of cohorts and facilitates future cross-cohort studies of aggression-related outcomes in youth. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8595091","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"data-descriptor","associatedPublications":[],"authors":[{"id":619473866,"identity":"27443846-7a58-443c-bde2-d0542a339510","order_by":0,"name":"Moritz Sturm","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBADOQYGxgaGCiBLgvkASIAZn2qgUgYGYzB9BqSFLYGB4QARWhJBBHFadNvbnz/m3bMtfcPt5gaGg3vs8iTbeMw+f2CwlsOlxezMGcNmnme3czfcOdjAcOBZcrE0G4/xjAMM6cY4tdzIYWzmOQDUciOx/feHA8yJ8+R7jIEOOwxxKlYt6Q9BWtINbgDVHDhQnzgPaAtISz1uLQmGIC0JUC2HE2dDtSTg88vMOQduG86EaDmeOLONrZjhjEG6IU5bjrc/+PDmwG15vhvpD4BaqhNnHGPezFBRYS2PyxZcwIBUDaNgFIyCUTAKkAEAGGti8zBxZp8AAAAASUVORK5CYII=","orcid":"","institution":"Johann Wolfgang Goethe-Universität Frankfurt am Main","correspondingAuthor":true,"prefix":"","firstName":"Moritz","middleName":"","lastName":"Sturm","suffix":""},{"id":619473867,"identity":"472b4734-f987-4a20-8463-981658470216","order_by":1,"name":"Lisa M. 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