Combining xQTL and genome-wide association studies from ethnically diverse populations improves druggable gene discovery

Combining xQTL and genome-wide association studies from ethnically diverse populations improves druggable gene discovery | 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 Combining xQTL and genome-wide association studies from ethnically diverse populations improves druggable gene discovery Feixiong Cheng, Noah Lorincz-Comi, Wenqiang Song, Xin Chen, Isabela Rivera Paz, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6700169/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Feb, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Repurposing existing medicines to target disease-associated genes represents a promising strategy for developing new treatments for complex diseases. However, progress has been hindered by a lack of viable candidate drug targets identified through genome-wide association studies (GWAS). Gene-based association tests provide a more powerful alternative to traditional single nucleotide polymorphism (SNP)-based methods, yet current approaches often fail to leverage shared heritability across populations and to effectively integrate functional genomic data. To address these challenges, we developed GenT and its various extensions, comprising a framework of gene-based tests utilizing summary-level GWAS data. Using GenT, we identified 16, 15, 35, and 83 druggable genes linked to Alzheimer’s disease (AD), amyotrophic lateral sclerosis, major depression, and schizophrenia, respectively. Additionally, our multi-ancestry gene-based test (MuGenT) uncovered 28 druggable genes associated with type 2 diabetes that previous trans-ancestry or ancestry-specific GWAS had missed. By integrating brain expression and protein quantitative trait loci (e/pQTLs) into our analysis, we identified 43 druggable genes (e.g., RIPK2, NTRK1, RIOK1) associated with AD that had supporting xQTL evidence. Notably, experimental assays demonstrated that the NTRK1 protein inhibitor GW441756 significantly reduced tau hyper-phosphorylation (including p-tau181 and p-tau217) in AD patient-derived iPSC neurons, thus providing mechanistic support for our predictions. Overall, our findings underscore the power of gene-based association testing as a strategic tool for informed drug target discovery and validation based on human genetic and genomic data for complex diseases. Biological sciences/Computational biology and bioinformatics/Statistical methods Health sciences/Diseases/Neurological disorders/Dementia/Alzheimer's disease Druggable gene genome-wide association studies (GWAS) gene-based testing neurodegenerative disease and expression quantitative trait loci (eQTL) Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementCheng.pdf Supplementary Information SupplementaryS1S50compressedCheng.pdf Supplementary S1-S50 SIfiguresCheng.zip Supplementary S1-S50 Cite Share Download PDF Status: Published Journal Publication published 14 Feb, 2026 Read the published version in Nature Communications → 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. 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quantitative trait loci (eQTL)","lastPublishedDoi":"10.21203/rs.3.rs-6700169/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6700169/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Repurposing existing medicines to target disease-associated genes represents a promising strategy for developing new treatments for complex diseases. 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By integrating brain expression and protein quantitative trait loci (e/pQTLs) into our analysis, we identified 43 druggable genes (e.g., RIPK2, NTRK1, RIOK1) associated with AD that had supporting xQTL evidence. Notably, experimental assays demonstrated that the NTRK1 protein inhibitor GW441756 significantly reduced tau hyper-phosphorylation (including p-tau181 and p-tau217) in AD patient-derived iPSC neurons, thus providing mechanistic support for our predictions. 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