Integrating Transcriptomics, Genomics, and Imaging in Alzheimer's Disease: A Federated Model.
Alzheimer's disease
GWAS
brain imaging
chow test
federated learning
transcriptomics
Journal
Frontiers in radiology
ISSN: 2673-8740
Titre abrégé: Front Radiol
Pays: Switzerland
ID NLM: 9918367586306676
Informations de publication
Date de publication:
2021
2021
Historique:
received:
14
09
2021
accepted:
21
12
2021
medline:
21
1
2022
pubmed:
21
1
2022
entrez:
26
7
2023
Statut:
epublish
Résumé
Alzheimer's disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. In clinical practice, structural magnetic resonance imaging (sMRI) is the most accessible and widely used diagnostic imaging modality. Additionally, genome-wide association studies (GWAS) and transcriptomics-the study of gene expression-also play an important role in understanding AD etiology and progression. Sophisticated imaging genetics systems have been developed to discover genetic factors that consistently affect brain function and structure. However, most studies to date focused on the relationships between brain sMRI and GWAS or brain sMRI and transcriptomics. To our knowledge, few methods have been developed to discover and infer multimodal relationships among sMRI, GWAS, and transcriptomics. To address this, we propose a novel federated model, Genotype-Expression-Imaging Data Integration (GEIDI), to identify genetic and transcriptomic influences on brain sMRI measures. The relationships between brain imaging measures and gene expression are allowed to depend on a person's genotype at the single-nucleotide polymorphism (SNP) level, making the inferences adaptive and personalized. We performed extensive experiments on publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrated our proposed method outperformed state-of-the-art expression quantitative trait loci (eQTL) methods for detecting genetic and transcriptomic factors related to AD and has stable performance when data are integrated from multiple sites. Our GEIDI approach may offer novel insights into the relationship among image biomarkers, genotypes, and gene expression and help discover novel genetic targets for potential AD drug treatments.
Identifiants
pubmed: 37492173
doi: 10.3389/fradi.2021.777030
pmc: PMC10365097
doi:
Types de publication
Journal Article
Langues
eng
Pagination
777030Informations de copyright
Copyright © 2022 Wu, Chen, Wang, Caselli, Thompson, Wang and Wang.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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