Machine learning in Alzheimer’s disease genetics

Bracher-Smith, Matthew, Melograna, Federico, Ulm, Brittany, Bellenguez, Céline, Grenier-Boley, Benjamin, Duroux, Diane, Nevado, Alejo J., Holmans, Peter ORCID: https://orcid.org/0000-0003-0870-9412, Tijms, Betty M., Hulsman, Marc, de Rojas, Itziar, Campos-Martin, Rafael, der Lee, Sven van, Castillo, Atahualpa, Küçükali, Fahri, Peters, Oliver, Schneider, Anja, Dichgans, Martin, Rujescu, Dan, Scherbaum, Norbert, Deckert, Jürgen, Riedel-Heller, Steffi, Hausner, Lucrezia, Molina-Porcel, Laura, Düzel, Emrah, Grimmer, Timo, Wiltfang, Jens, Heilmann-Heimbach, Stefanie, Moebus, Susanne, Tegos, Thomas, Scarmeas, Nikolaos, Dols-Icardo, Oriol, Moreno, Fermin, Pérez-Tur, Jordi, Bullido, María J., Pastor, Pau, Sánchez-Valle, Raquel, Álvarez, Victoria, Boada, Mercè, García-González, Pablo, Puerta, Raquel, Mir, Pablo, Real, Luis M., Piñol-Ripoll, Gerard, García-Alberca, Jose María, Rodriguez-Rodriguez, Eloy, Soininen, Hilkka, Heikkinen, Sami, de Mendonça, Alexandre, Mehrabian, Shima, Traykov, Latchezar, Hort, Jakub, Vyhnalek, Martin, Sandau, Nicolai, Thomassen, Jesper Qvist, Pijnenburg, Yolande A. L., Holstege, Henne, van Swieten, John, Ramakers, Inez, Verhey, Frans, Scheltens, Philip, Graff, Caroline, Papenberg, Goran, Giedraitis, Vilmantas, Williams, Julie ORCID: https://orcid.org/0000-0002-4069-0259, Amouyel, Philippe, Boland, Anne, Deleuze, Jean-François, Nicolas, Gael, Dufouil, Carole, Pasquier, Florence, Hanon, Olivier, Debette, Stéphanie, Grünblatt, Edna, Popp, Julius, Ghidoni, Roberta, Galimberti, Daniela, Arosio, Beatrice, Mecocci, Patrizia, Solfrizzi, Vincenzo, Parnetti, Lucilla, Squassina, Alessio, Tremolizzo, Lucio, Borroni, Barbara, Wagner, Michael, Nacmias, Benedetta, Spallazzi, Marco, Seripa, Davide, Rainero, Innocenzo, Daniele, Antonio, Piras, Fabrizio, Masullo, Carlo, Rossi, Giacomina, Jessen, Frank, Kehoe, Patrick, Magda, Tsolaki, Sánchez-Juan, Pascual, Sleegers, Kristel, Ingelsson, Martin, Hiltunen, Mikko, Sims, Rebecca ORCID: https://orcid.org/0000-0002-3885-1199, van der Flier, Wiesje, Andreassen, Ole A., Ruiz, Agustín, Ramirez, Alfredo, Frikke-Schmidt, Ruth, Amin, Najaf, Roshchupkin, Gennady, Lambert, Jean-Charles, Van Steen, Kristel, van Duijn, Cornelia and Escott-Price, Valentina ORCID: https://orcid.org/0000-0003-1784-5483 2025. Machine learning in Alzheimer’s disease genetics. Nature Communications 16 (1) , 6726. 10.1038/s41467-025-61650-z

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Abstract

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.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Medicine
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	Nature Research
Date of First Compliant Deposit:	28 July 2025
Date of Acceptance:	24 June 2025
Last Modified:	28 Jul 2025 15:46
URI:	https://orca.cardiff.ac.uk/id/eprint/180083

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