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Machine learning in Alzheimer's disease genetics
Cardiff Univ, Sch Med, Cardiff, Wales.;Cardiff Univ, Sch Med, Div Psychol Med & Clin Neurosci, Ctr Neuropsychiat Genet & Genom, Cardiff, Wales..
Katholieke Univ Leuven, Dept Human Genet, BIO3 Syst Med, Leuven, Belgium.;Univ Liege, BIO3 Syst Genet, GIGA R Mol & Computat Biol, Liege, Belgium..
Univ Oxford, Nuffield Dept Populat Hlth, Oxford, England.;Univ Oxford, Ctr Artificial Intelligence Precis Med, Oxford, England..
Univ Lille, Inst Pasteur Lille, RIDAGE Facteurs Risque & Determinants Mol Malad L, CHU Lille,LabEx DISTALZ,U1167,Inserm, Lille, France..
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2025 (engelsk)Inngår i: Nature Communications, E-ISSN 2041-1723, Vol. 16, artikkel-id 6726Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Traditional 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 (MB-MDR) 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. 
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Springer Nature, 2025. Vol. 16, artikkel-id 6726
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Identifikatorer
URN: urn:nbn:se:uu:diva-571314DOI: 10.1038/s41467-025-61650-zISI: 001545630600001PubMedID: 40691194Scopus ID: 2-s2.0-105012217480OAI: oai:DiVA.org:uu-571314DiVA, id: diva2:2012887
Forskningsfinansiär
EU, Horizon 2020, 813533EU, Horizon 2020, 860895Tilgjengelig fra: 2025-11-11 Laget: 2025-11-11 Sist oppdatert: 2025-12-01bibliografisk kontrollert

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Giedraitis, VilmantasIngelsson, MartinKilander, LenaLöwenmark, Malin

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Holmans, PeterTijms, Betty M.Pastor, PauAlvarez, VictoriaGiedraitis, VilmantasIngelsson, MartinRamirez, AlfredoKilander, LenaLöwenmark, Malin
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