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Integrating Transcriptomics, Genomics, and Imaging in Alzheimer’s Disease: A Federated Model

View ORCID ProfileJianfeng Wu, Yanxi Chen, Panwen Wang, Richard J Caselli, Paul M Thompson, Junwen Wang, View ORCID ProfileYalin Wang, for the Alzheimer’s Disease Neuroimaging Initiative
doi: https://doi.org/10.1101/2021.09.14.460367
Jianfeng Wu
1School of Computing and Augmented Intelligence, Arizona State Univ., Tempe, AZ, USA
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Yanxi Chen
1School of Computing and Augmented Intelligence, Arizona State Univ., Tempe, AZ, USA
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Panwen Wang
2Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic Arizona, Scottsdale, AZ, USA
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Richard J Caselli
3Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, USA
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Paul M Thompson
4Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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Junwen Wang
2Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic Arizona, Scottsdale, AZ, USA
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  • For correspondence: wang.junwen@mayo.edu ylwang@asu.edu
Yalin Wang
1School of Computing and Augmented Intelligence, Arizona State Univ., Tempe, AZ, USA
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  • For correspondence: wang.junwen@mayo.edu ylwang@asu.edu
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Abstract

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.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 16, 2021.
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Integrating Transcriptomics, Genomics, and Imaging in Alzheimer’s Disease: A Federated Model
Jianfeng Wu, Yanxi Chen, Panwen Wang, Richard J Caselli, Paul M Thompson, Junwen Wang, Yalin Wang, for the Alzheimer’s Disease Neuroimaging Initiative
bioRxiv 2021.09.14.460367; doi: https://doi.org/10.1101/2021.09.14.460367
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Integrating Transcriptomics, Genomics, and Imaging in Alzheimer’s Disease: A Federated Model
Jianfeng Wu, Yanxi Chen, Panwen Wang, Richard J Caselli, Paul M Thompson, Junwen Wang, Yalin Wang, for the Alzheimer’s Disease Neuroimaging Initiative
bioRxiv 2021.09.14.460367; doi: https://doi.org/10.1101/2021.09.14.460367

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