File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

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

TitleIntegrating Transcriptomics, Genomics, and Imaging in Alzheimer's Disease: A Federated Model
Authors
Issue Date21-Jan-2022
PublisherFrontiers Media
Citation
Frontiers in Radiology, 2022, v. 1 How to Cite?
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.


Persistent Identifierhttp://hdl.handle.net/10722/331530
ISSN

 

DC FieldValueLanguage
dc.contributor.authorWu, Jianfeng-
dc.contributor.authorChen, Yanxi-
dc.contributor.authorWang, Panwen-
dc.contributor.authorCaselli, Richard-
dc.contributor.authorThompson, Paul-
dc.contributor.authorWang, Junwen-
dc.contributor.authorWang, Yalin-
dc.date.accessioned2023-09-21T06:56:40Z-
dc.date.available2023-09-21T06:56:40Z-
dc.date.issued2022-01-21-
dc.identifier.citationFrontiers in Radiology, 2022, v. 1-
dc.identifier.issn2673-8740-
dc.identifier.urihttp://hdl.handle.net/10722/331530-
dc.description.abstract<p>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.<br></p>-
dc.languageeng-
dc.publisherFrontiers Media-
dc.relation.ispartofFrontiers in Radiology-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleIntegrating Transcriptomics, Genomics, and Imaging in Alzheimer's Disease: A Federated Model-
dc.typeArticle-
dc.identifier.doi10.3389/fradi.2021.777030-
dc.identifier.volume1-
dc.identifier.eissn2673-8740-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats