Machine learning in Alzheimer's disease genetics
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064203%3A_____%2F25%3A10499815" target="_blank" >RIV/00064203:_____/25:10499815 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11130/25:10499815
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=plUnP2Pd3g" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=plUnP2Pd3g</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41467-025-61650-z" target="_blank" >10.1038/s41467-025-61650-z</a>
Alternative languages
Result language
angličtina
Original language name
Machine learning in Alzheimer's disease genetics
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30103 - Neurosciences (including psychophysiology)
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Nature Communications
ISSN
2041-1723
e-ISSN
2041-1723
Volume of the periodical
16
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
16
Pages from-to
6726
UT code for WoS article
001545630600001
EID of the result in the Scopus database
2-s2.0-105012217480