[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119609-en":3,"doc-seo-119609-105":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":20,"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},119609,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Machine learning in Alzheimer’s disease genetics","Machine learning models are applied to genome-wide data from 41,686 individuals in the largest European Alzheimer’s disease consortium to replicate established findings, discover novel genetic loci, and predict individual risk. The study evaluates Gradient Boosting Machines, biological pathway-informed Neural Networks, and Model-based Multifactor Dimensionality Reduction. Results recover all genome-wide significant variants in the training set and capture 22% of associations from larger meta-analyses. Six novel loci replicate externally, including ARHGAP25, LY6H, COG7, SOD1 and ZNF597, while AP4E1 refines the SPPL2A locus. Predictive performance is comparable to classical methods and may reveal loci missed by traditional GWAS.","Edinburgh Research Explorer  \nMachine learning in Alzheimer’s disease genetics  \nCitation for published version:  \nEADB 2025, 'Machine learning in Alzheimer’s disease genetics', Nature Communications , vol. 16, no. 1, 6726. [https://doi.org/10.1038/s41467-025-61650-z](https://doi.org/10.1038/s41467-025-61650-z)  \nDigital Object Identifier (DOI):  \n10.1038/s41467-025-61650-z  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nPublished In:  \nNature Communications  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 08. Jan. 2026  \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 on these variants have greatly improved prediction ofdisease status2. However, inherent to GWAS and PRS are the assumptions that variants are independent predictors, linearly associated with the outcome, and therefore combine additively within and between loci3, with no interactions occurring between variants, or between genes and other risk factors. While such simplifying genetic assumptions have proved fruitful across a range of diseases and disorders4,5, they are at odds with biological evidence in AD that disease heterogeneity and responses from cells such as microglia are dependent on APOE status6–9. Further, there is genetic evidence suggesting that different variants are associated with the disease depending on APOE status10–13 and age at diagnosis or assessment14–16. As GWAS sample size increases and PRS approach limits on predictive perfor","cbCaihFql9Ccvkxs","https://ap.wps.com/l/cbCaihFql9Ccvkxs","pdf",3710212,1,17,"English","en",105,"# Abstract\n## Study dataset and aims\n## Machine learning models evaluated\n## Replication results and novel loci\n## Implications for genetic epidemiology","[{\"question\":\"Which machine learning algorithms are evaluated for Alzheimer’s disease genetics in this study?\",\"answer\":\"The study evaluates Gradient Boosting Machines, biological pathway-informed Neural Networks, and Model-based Multifactor Dimensionality Reduction models.\"},{\"question\":\"What does the study use machine learning to accomplish beyond replicating known findings?\",\"answer\":\"It aims to discover novel loci and predict individuals at risk, while comparing model performance to classical approaches.\"},{\"question\":\"What are the key outcomes regarding novel genetic loci and replication?\",\"answer\":\"The results highlight six novel loci that replicate in an external dataset, and identify a novel association in AP4E1 that refines the SPPL2A locus.\"}]","Machine learning in Alzheimer’s disease genetics | 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machine learning algorithms are evaluated for Alzheimer’s disease genetics in this study?","Question",{"text":75,"@type":76},"The study evaluates Gradient Boosting Machines, biological pathway-informed Neural Networks, and Model-based Multifactor Dimensionality Reduction models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the study use machine learning to accomplish beyond replicating known findings?",{"text":80,"@type":76},"It aims to discover novel loci and predict individuals at risk, while comparing model performance to classical approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the key outcomes regarding novel genetic loci and replication?",{"text":84,"@type":76},"The results highlight six novel loci that replicate in an external dataset, and identify a novel association in AP4E1 that refines the SPPL2A 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