Jointly modelling multiple ancestral populations using GWAS summary data improves causal inference

Jointly modelling multiple ancestral populations using GWAS summary data improves causal inference | 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 Article Jointly modelling multiple ancestral populations using GWAS summary data improves causal inference Gibran Hemani, Yoonsu Cho, Amanda Chong, Tom Palmer, Amy Mason, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6091701/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 Population diversity contributing towards genome wide association studies is increasing, which has invited the expansion of causal inference through Mendelian randomization (MR) to multiple populations. Here we show that multi-ancestry MR can easily lead to causal effect estimates being inconsistent between ancestral groups if not performed appropriately. We introduce an analytical framework to manage these issues. It also handles disparities in statistical power for instrument identification between ancestries, and leverages instances of population-specific instrument effects to model horizontal pleiotropy, helping to examine a core MR assumption. We apply our framework to a series of cardiometabolic and behavioural exposures and outcome trait pairs combining European and East Asian samples, which indicate that SNP-exposure, exposure-outcome and horizontal pleiotropic effects tend to be relatively consistent across these ancestral groups, though with some notable apparent discrepancies which are likely driven by gene by gene or environment interactions. We also examine the relationship between LDL cholesterol and stroke across a broader set of ancestral groups which again, after accounting for biasing mechanisms, illustrates overall consistency of effects across ancestral groups. Our results suggest differential health outcomes are more likely driven by differential distributions of risk factors than mechanisms that would change susceptibility to risk factors. Health sciences/Medical research/Epidemiology Health sciences/Medical research/Genetics research Health sciences/Health care/Public health/Epidemiology Biological sciences/Genetics/Population genetics Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementarydocument.docx Supplementary materials 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-6091701","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":434287544,"identity":"c2dbd2a9-14a2-4c28-bc90-198b73d24105","order_by":0,"name":"Gibran 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