XMR: A cross-population Mendelian randomization method for causal inference using genome-wide summary statistics

XMR: A cross-population Mendelian randomization method for causal inference using genome-wide summary statistics | 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 XMR: A cross-population Mendelian randomization method for causal inference using genome-wide summary statistics Can Yang, Xinrui Huang, Zitong Chao, Zhiwei Wang, Xianghong Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9080412/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Mendelian randomization (MR) is an important tool for inferring causal relationships between exposures (like lifestyle factors or biomarkers) and health outcomes using genome-wide association study (GWAS) summary data, yet the small sample sizes of non-European populations often result in insufficient instrumental variables (IVs) and unreliable causal effect estimates. In this paper, we consider causal inference in underrepresented populations to improve global health equity. We propose a statistical method for cross-population MR, XMR, to enhance causal inference in these target populations by using auxiliary GWAS summary statistics from global biobanks. By leveraging the shared genetic basis of exposure traits in the target and auxiliary populations, XMR increases the number of IVs while maintaining robust estimates via rigorous evaluation of IV validity and accounting for confounding factors. Through extensive simulations and real-data analyses, we demonstrate that XMR can achieve greater statistical power, better control of false positive rates and more replicable results compared to existing methods. Notably, XMR successfully identifies novel causal relationships in our studies of the East Asian (including Japanese and Taiwanese), Central/South Asian, and African populations. These findings reveal potential heterogeneity in causal patterns across populations, highlighting the importance of causal inference in underrepresented populations. Biological sciences/Computational biology and bioinformatics/Statistical methods Biological sciences/Genetics/Genetic association study/Genome-wide association studies Full Text Additional Declarations There is NO Competing Interest. Supplementary Files supptables0310.xlsx Supplementary Information Tables SupportingInformation0310.pdf Supplementary Information Cite Share Download PDF Status: Under Review 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. 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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-9080412","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":608912849,"identity":"cc069f94-4657-4b5e-b2ab-0a6798642710","order_by":0,"name":"Can 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