%0 Journal Article
%A Bracher-Smith, Matthew
%A Melograna, Federico
%A Ulm, Brittany
%A Bellenguez, Céline
%A Grenier-Boley, Benjamin
%A Duroux, Diane
%A Nevado, Alejo J
%A Holmans, Peter
%A Tijms, Betty M
%A Hulsman, Marc
%A de Rojas, Itziar
%A Campos-Martin, Rafael
%A der Lee, Sven van
%A Castillo, Atahualpa
%A Küçükali, Fahri
%A Peters, Oliver
%A Schneider, Anja
%A Dichgans, Martin
%A Rujescu, Dan
%A Scherbaum, Norbert
%A Deckert, Jürgen
%A Riedel-Heller, Steffi
%A Hausner, Lucrezia
%A Molina-Porcel, Laura
%A Düzel, Emrah
%A Grimmer, Timo
%A Wiltfang, Jens
%A Heilmann-Heimbach, Stefanie
%A Moebus, Susanne
%A Tegos, Thomas
%A Scarmeas, Nikolaos
%A Dols-Icardo, Oriol
%A Moreno, Fermin
%A Pérez-Tur, Jordi
%A Bullido, María J
%A Pastor, Pau
%A Sánchez-Valle, Raquel
%A Álvarez, Victoria
%A Boada, Mercè
%A García-González, Pablo
%A Puerta, Raquel
%A Mir, Pablo
%A Real, Luis M
%A Piñol-Ripoll, Gerard
%A García-Alberca, Jose María
%A Rodriguez-Rodriguez, Eloy
%A Soininen, Hilkka
%A Heikkinen, Sami
%A de Mendonça, Alexandre
%A Mehrabian, Shima
%A Traykov, Latchezar
%A Hort, Jakub
%A Vyhnalek, Martin
%A Sandau, Nicolai
%A Thomassen, Jesper Qvist
%A Pijnenburg, Yolande A L
%A Holstege, Henne
%A van Swieten, John
%A Ramakers, Inez
%A Verhey, Frans
%A Scheltens, Philip
%A Graff, Caroline
%A Papenberg, Goran
%A Giedraitis, Vilmantas
%A Williams, Julie
%A Amouyel, Philippe
%A Boland, Anne
%A Deleuze, Jean-François
%A Nicolas, Gael
%A Dufouil, Carole
%A Pasquier, Florence
%A Hanon, Olivier
%A Debette, Stéphanie
%A Grünblatt, Edna
%A Popp, Julius
%A Ghidoni, Roberta
%A Galimberti, Daniela
%A Arosio, Beatrice
%A Mecocci, Patrizia
%A Solfrizzi, Vincenzo
%A Parnetti, Lucilla
%A Squassina, Alessio
%A Tremolizzo, Lucio
%A Borroni, Barbara
%A Wagner, Michael
%A Nacmias, Benedetta
%A Spallazzi, Marco
%A Seripa, Davide
%A Rainero, Innocenzo
%A Daniele, Antonio
%A Piras, Fabrizio
%A Masullo, Carlo
%A Rossi, Giacomina
%A Jessen, Frank
%A Kehoe, Patrick
%A Magda, Tsolaki
%A Sánchez-Juan, Pascual
%A Sleegers, Kristel
%A Ingelsson, Martin
%A Hiltunen, Mikko
%A Sims, Rebecca
%A van der Flier, Wiesje
%A Andreassen, Ole A
%A Ruiz, Agustín
%A Ramirez, Alfredo
%A Frikke-Schmidt, Ruth
%A Amin, Najaf
%A Roshchupkin, Gennady
%A Lambert, Jean-Charles
%A Van Steen, Kristel
%A van Duijn, Cornelia
%A Escott-Price, Valentina
%T Machine learning in Alzheimer's disease genetics.
%J Nature Communications
%V 16
%N 1
%@ 2041-1723
%C [London]
%I Springer Nature
%M DZNE-2025-00882
%P 6726
%D 2025
%X 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
%K Alzheimer Disease: genetics
%K Humans
%K Machine Learning
%K Genome-Wide Association Study
%K Genetic Predisposition to Disease
%K Polymorphism, Single Nucleotide
%K Algorithms
%K GTPase-Activating Proteins: genetics
%K Neural Networks, Computer
%K GTPase-Activating Proteins (NLM Chemicals)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:40691194
%R 10.1038/s41467-025-61650-z
%U https://pub.dzne.de/record/280043