MACHINE: a robust and scalable multi-ancestry fine-mapping method using a continuous global-local shrinkage prior

MACHINE: a robust and scalable multi-ancestry fine-mapping method using a continuous global-local shrinkage prior | 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 MACHINE: a robust and scalable multi-ancestry fine-mapping method using a continuous global-local shrinkage prior Yan Zhang, Pak Sham, Xiang Li, Zewei Xiong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7737326/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 Fine mapping aims to identify causal genetic variants with nonzero phenotypic effects. Leveraging genome-wide association study (GWAS) data from diverse ancestries enhances fine-mapping accuracy and resolution by exploiting differences in linkage disequilibrium (LD) and increasing sample sizes. However, existing multi-ancestry fine-mapping methods rely on discrete priors and assume that all causal variants are shared across ancestries -- an assumption that may not hold in practice. Although MESuSiE accounts for both shared and ancestry-specific causal effects, it requires a priori specification of prior probabilities for causal variant sharing. Moreover, methods based on discrete priors are prone to sub-optimal convergence. To address these limitations, we introduce Multi-AnCestry Heritability INducEd Dirichlet decomposition (MACHINE), a flexible Bayesian framework that employs a continuous prior to model both shared and ancestry-specific effects without restrictive assumptions. Importantly, we propose an approach to control false discovery rate (FDR) for fine mapping with GWAS summary statistics and out-of-sample LD matrices, a challenge not addressed by existing multi-ancestry fine-mapping methods. We further improve fine-mapping performance by incorporating functional annotations of variants using generalized LD score regression (g-LDSC). Simulation studies across diverse genetic architectures demonstrate robustness and superior FDR control of MACHINE + g-LDSC compared to existing methods. In the real data analyses, we applied MACHINE + g-LDSC to four lipid traits and schizophrenia, identifying previously unknown causal variants and depicting their genetic architectures across ancestries. Biological sciences/Genetics/Genetic association study/Genome-wide association studies Biological sciences/Computational biology and bioinformatics/Statistical methods Biological sciences/Computational biology and bioinformatics/Software Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTables.xlsx Supplementary Tables MACHINEsuppv1.3.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. We do this by developing innovative software and high quality services for the global research community. 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