Nature communications · 2025

Machine learning in Alzheimer's disease genetics.

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Bracher-Smith Matthew, Melograna Federico, Ulm Brittany, Bellenguez Céline, Grenier-Boley Benjamin, Duroux Diane, Nevado Alejo J, Holmans Peter, 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, 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, van der Flier Wiesje, Andreassen Ole A, Ruiz Agustín, Ramirez Alfredo, EADB, Frikke-Schmidt Ruth, Amin Najaf, Roshchupkin Gennady, Lambert Jean-Charles, Van Steen Kristel, van Duijn Cornelia, Escott-Price Valentina

Аннотация

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.

Предметные рубрики MeSH

Alzheimer Disease Humans Machine Learning Genome-Wide Association Study Genetic Predisposition to Disease Polymorphism, Single Nucleotide Algorithms GTPase-Activating Proteins Neural Networks, Computer

Machine learning in Alzheimer's disease genetics.

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Аннотация

Предметные рубрики MeSH