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Gradient Boosting Machines, biological pathway-informed Neural Networks, and model-based multifactor dimensionality reduction capture all genome-wide significant variants from the training set and replicate 22% of associations from larger meta-analyses, yielding six novel loci validated in an external dataset.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-in-alzheimers-disease-genetics-published-22072025/290492/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-in-alzheimers-disease-genetics-published-22072025/290492.png","ImageObject",300,407,{"name":92,"@type":93},"Aria","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What data and cohort size were used for the machine learning analysis?","Question",{"text":112,"@type":113},"The study used genome-wide data from 41,686 individuals from the largest European consortium on Alzheimer’s disease.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which machine learning methods were evaluated in the paper?",{"text":117,"@type":113},"Gradient Boosting Machines, biological pathway-informed Neural Networks, and Model-based Multifactor Dimensionality Reduction models were trained and compared.",{"name":119,"@type":110,"acceptedAnswer":120},"How did the machine learning results compare with traditional findings from GWAS and meta-analyses?",{"text":121,"@type":113},"The ML approaches captured all genome-wide significant variants in the training set and replicated 22% of associations from larger meta-analyses, identifying six novel loci validated in an external dataset.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290492,1789641831,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},2336464648322,"https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488","EUR Research Information Portal  \nMachine learning in Alzheimer's disease genetics  \nPublished in:  \nNature Communications  \nPublication status and date:  \nPublished: 22/07/2025  \nDOI (link to publisher):  \n10.1038/s41467-025-61650-z  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nDocument License/Available under:  \nCC BY  \nCitation for the published version (APA):  \nBracher-Smith, M. , Melograna, F. , Ulm, B. , European Alzheimer & Dementia BioBank (EADB), Bellenguez, C. , GrenierBoley, B. , Duroux, D. , Nevado, A. J. , Holmans, P. , Tijms, B. M. , Hulsman, M. , de Rojas, I. , Campos-Martin, R. , der Lee, S. V. , Castillo, A. , Küçükali, F. , Peters, O. , Schneider, A. , Dichgans, M. , ... Escott-Price, V. (2025) . Machine learning in Alzheimer's disease genetics. Nature Communications, 16(1), Article 6726. [https://doi.org/10.1038/s41467-025-61650-z](https://doi.org/10.1038/s41467-025-61650-z)  \nLink to publication on the EUR Research Information Portal  \nTerms and Conditions of Use  \nExcept as permitted by the applicable copyright law, you may not reproduce or make this material available to any third party without the prior written permission from the copyright holder(s) . Copyright law allows the following uses of this material without prior permission:  \n• you may download, save and print a copy of this material for your personal use only;  \n• you may share the EUR portal link to this material.  \nIn case the material is published with an open access license (e.g. a Creative Commons (CC) license), other uses may be allowed. Please check the terms and conditions of the specific license.  \nTake-down policy  \nIf you believe that this material infringes your copyright and/or any other intellectual property rights, you may request its removal by contacting us at the following email address: [openaccess.library@eur.nl. Please](openaccess.library@eur.nl. Please) provide us with all the relevant information, including the reasons why you believe any of your rights have been infringed. In case of a legitimate complaint, we will make the material inaccessible and/or remove it from the website.  \nArticle [https://doi.org/10.1038/s41467-025-61650-z](https://doi.org/10.1038/s41467-025-61650-z)  \nMachine learning in Alzheimer’s disease genetics  \nReceived: 26 July 2024  \n\n| Accepted: 24 June 2025 |\n| --- |\n| |\n| Check for updates |\n\nA list of authors and their affiliations appears at the end of the paper  \nTraditional statistical approaches have advanced our understanding of the genetics of complex diseases, yet are limited to linear additive models. Here we applied machine learning (ML) to genome-wide data from 41,686 individuals in the largest European consortium on Alzheimer’s disease (AD) to investigate the effectiveness of various ML algorithms in replicating known findings, discovering novel loci, and predicting individuals at risk. We utilised Gradient Boosting Machines (GBMs), biological pathway-informed Neural Networks (NNs), and Model-based Multifactor Dimensionality Reduction (MBMDR) models. ML approaches successfully captured all genome-wide significant genetic variants identified in the training set and 22% of associations from larger meta-analyses. They highlight 6 novel loci which replicate in an external dataset, including variants which map to ARHGAP25, LY6H, COG7, SOD1 and ZNF597. They further identify novel association in AP4E1, refining the genetic landscape of the known SPPL2A locus. Our results demonstrate that machine learning methods can achieve predictive performance comparable to classical approaches in genetic epidemiology and have the potential to uncover novel loci that remain undetected by traditional GWAS. These insights provide a complementary avenue for advancing the understanding of AD genetics.  \nGenome-wide association studies (GWAS) have enabled huge progress in identifying variants associated with the risk of developing Alzheimer’s disease (AD)1. Polygenic risk scores (PRS) based","cbCaiqB0K427EZ85","https://ap.wps.com/l/cbCaiqB0K427EZ85","pdf",3742719,17,"English","# Overview\n## Data and study aim\n## Machine learning models and evaluation\n## Key findings and implications\n# Context from GWAS and PRS\n## Modeling assumptions and limitations\n## Need for alternative approaches","[{\"question\":\"What data and cohort size were used for the machine learning analysis?\",\"answer\":\"The study used genome-wide data from 41,686 individuals from the largest European consortium on Alzheimer’s disease.\"},{\"question\":\"Which machine learning methods were evaluated in the paper?\",\"answer\":\"Gradient Boosting Machines, biological pathway-informed Neural Networks, and Model-based Multifactor Dimensionality Reduction models were trained and compared.\"},{\"question\":\"How did the machine learning results compare with traditional findings from GWAS and meta-analyses?\",\"answer\":\"The ML approaches captured all genome-wide significant variants in the training set and replicated 22% of associations from larger meta-analyses, identifying six novel loci validated in an external dataset.\"}]","Machine learning in Alzheimer's disease genetics - Published 22/07/2025 | PDF",43]