A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression.

Autor:	Bae J; Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA., Logan PE; Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA., Acri DJ; Department of Medical and Molecular Genetics, School of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA., Bharthur A; Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA., Nho K; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA., Saykin AJ; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA., Risacher SL; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA., Nudelman K; Department of Medical and Molecular Genetics, School of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA., Polsinelli AJ; Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA., Pentchev V; Department of Information Technology, Indiana University Network Science Institute, Bloomington, Indiana, USA., Kim J; Department of Medical and Molecular Genetics, School of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA., Hammers DB; Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA., Apostolova LG; Department of Neurology, School of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.; Department of Medical and Molecular Genetics, School of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Jazyk:	angličtina
Zdroj:	Alzheimer's & dementia : the journal of the Alzheimer's Association [Alzheimers Dement] 2023 Dec; Vol. 19 (12), pp. 5690-5699. Date of Electronic Publication: 2023 Jul 06.
DOI:	10.1002/alz.13319
Abstrakt:	Background: Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre-symptomatic risk assessment but also for building personalized therapeutic strategies. Methods: We implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD-risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed. Results: Rs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD-risk SNPs were significant predictors of AD progression. Discussion: The model successfully estimated the contribution of AD-risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine. (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)
Databáze:	MEDLINE
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