{"paper_id":"60cbcf34-e141-4704-b07e-55143c90dbb5","body_text":"Multi-ancestry analysis of plasma protein levels influencing and responding to major depression liability | 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 Research Article Multi-ancestry analysis of plasma protein levels influencing and responding to major depression liability Lillian Linda, Anthony B. Mutema, Sandra R. Babirye, Catherine Nabbumba, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5828682/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 The development of novel-acting antidepressant medications with fewer side effects and sustained efficacy requires an in-depth understanding of the aetiology of major depressive disorder (MDD) across diverse populations. Here we used a Mendelian randomization (MR) framework to identify protein levels that influence MDD risk, and that respond to MDD liability in the general population. We use summary-level data from four major ancestral groups to evaluate the consistency of genetic associations and MR estimates across populations. We identified 17 proteins that are putatively causal for MDD, with evidence of differential effects across ancestries for five proteins, which we replicate in independent individual level data. We also identified widespread protein level changes in response to disease liability in the general population. We showed that such associations can appear ancestry-specific until differential power is accounted for, after which the vast majority of associations appear consistent across ancestral groups. The protein response to disease liability can be used to generate a proteomic risk score that is strongly predictive of prospective MDD incidence. Our results indicate that multi-ancestry Mendelian randomization improves power for ancestral groups with smaller sample sizes and will inform our understanding of disease aetiology if differential marginal effects across populations arising due to gene-environment interactions can be studied. Full Text Additional Declarations No competing interests reported. Supplementary Files Supplemenatrytables.xlsx Supplementaryfigures.docx 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-5828682\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":402445263,\"identity\":\"3db1904a-cab8-4128-a888-e4e8800aeb0f\",\"order_by\":0,\"name\":\"Lillian Linda\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Lillian\",\"middleName\":\"\",\"lastName\":\"Linda\",\"suffix\":\"\"},{\"id\":402445264,\"identity\":\"210335ef-1b37-46a1-898f-062cfafcf488\",\"order_by\":1,\"name\":\"Anthony B. 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