[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119549-en":3,"doc-seo-119549-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119549,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Machine learning in Alzheimer’s disease genetics - Published in Nature Communications","Traditional statistical genetics methods explain parts of Alzheimer’s disease (AD) heritability but rely on linear additive assumptions that limit discovery and prediction. This study applies machine learning to genome-wide data from 41,686 individuals from a major European AD consortium to test multiple algorithms, replicate known associations, identify novel loci, and estimate individual risk. Gradient Boosting Machines, pathway-informed neural networks, and MBMDR capture genome-wide significant variants and additional signals beyond larger meta-analyses, achieving predictive performance comparable to classical approaches.","EUR Research Information Portal  \nMachine learning in Alzheimer's disease genetics  \nPublished in:  \nNature Communications  \nPublication status and date:  \nPublished: 01/12/2025  \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) base","cbCaiojPysHSaJtW","https://ap.wps.com/l/cbCaiojPysHSaJtW","pdf",3747216,1,17,"English","en",105,"# Background and motivation\n## Limitations of GWAS and PRS assumptions\n## Need for scalable, flexible machine learning\n# Methods\n## Study design and dataset\n## Machine learning models compared\n# Results\n## Replication of known genetic variants\n## Identification of novel loci and refined signals\n## Predictive performance and implications","[{\"question\":\"How does the study use machine learning in Alzheimer’s disease genetics?\",\"answer\":\"It applies multiple machine learning algorithms to genome-wide data to replicate established findings, discover new loci, and predict individuals at risk.\"},{\"question\":\"Which machine learning methods were evaluated?\",\"answer\":\"The study evaluates Gradient Boosting Machines, biological pathway-informed neural networks, and model-based multifactor dimensionality reduction (MBMDR).\"},{\"question\":\"What does the paper report about predictive performance compared with classical approaches?\",\"answer\":\"Machine learning achieves predictive performance comparable to classical methods in genetic epidemiology while enabling additional locus discovery not captured by traditional GWAS.\"}]","Machine learning in Alzheimer’s disease genetics - Published in Nature Communications | PDF",1785724895,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-in-alzheimers-disease-genetics-published-in-nature-communications","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-in-alzheimers-disease-genetics-published-in-nature-communications/119549/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the study use machine learning in Alzheimer’s disease genetics?","Question",{"text":75,"@type":76},"It applies multiple machine learning algorithms to genome-wide data to replicate established findings, discover new loci, and predict individuals at risk.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods were evaluated?",{"text":80,"@type":76},"The study evaluates Gradient Boosting Machines, biological pathway-informed neural networks, and model-based multifactor dimensionality reduction (MBMDR).",{"name":82,"@type":73,"acceptedAnswer":83},"What does the paper report about predictive performance compared with classical approaches?",{"text":84,"@type":76},"Machine learning achieves predictive performance comparable to classical methods in genetic epidemiology while enabling additional locus discovery not captured by traditional GWAS.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]