Causal effect heterogeneity estimation using summary statistics

Causal effect heterogeneity estimation using 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 Causal effect heterogeneity estimation using summary statistics Xingjie Shi, Yadong Yang, Minxi Bai, Jiacheng Miao, Stephen Dorn, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8589460/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 Mendelian randomization (MR) has swiftly gained popularity as a tool for causal inference in genetic epidemiology. However, existing MR methods focus exclusively on estimating the average causal effect and cannot quantify its heterogeneity, posing a major methodological limitation and impeding context-dependent causal findings. Here, we introduce MEndelian Randomization for Linear INteraction (MERLIN), a unified Bayesian framework that jointly estimates the average and context-dependent causal effects using summary data from genome-wide association and interaction studies. Through extensive simulation analyses, we demonstrate the improved power, robustness, and broad utility of MERLIN versus existing methods. We show MERLIN was able to identify sex-specific causal effects of schizophrenia on brain imaging traits, a male-specific causal effect of testosterone on bipolar disorder, and age-dependent causal effects of metabolic biomarkers on coronary artery disease risk. These results illustrate the transformative potential of summary-data-based inference for causal heterogeneity. Together, MERLIN provides a powerful and practical framework for investigating causal effect heterogeneity using summary-level observational data and greatly enhances our capability to elucidate complex disease etiology. Biological sciences/Genetics/Genetic association study/Genome-wide association studies Biological sciences/Computational biology and bioinformatics/Data mining Biological sciences/Genetics/Population genetics Biological sciences/Biological techniques/Genomic analysis/Genome-wide association studies Full Text Additional Declarations There is NO Competing Interest. Supplementary Files 3SupplementaryTables.xlsx Supplementary Tables for “Causal effect heterogeneity estimation using summary statistics” 2SupplementaryNotesandFigures.pdf Supplementary Notes and Figures for “Causal effect heterogeneity estimation using summary statistics” 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-8589460","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":573767405,"identity":"fc6e2792-1770-4d6b-96d8-a871de7af15b","order_by":0,"name":"Xingjie 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