Integrating Transcriptomics, Genomics, and Imaging in Alzheimer's Disease: A Federated Model
Year: 2022
Type: article
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.
Source: Frontiers in Radiology
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,900
Funders: U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Biogen, University of Southern California, F. Hoffmann-La Roche, Medpace, Meso Scale Diagnostics, Synarc, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, Arizona Alzheimer’s Consortium, Eisai, Servier, IXICO, National Institutes of Health, Genentech, University of California, San Diego, Canadian Institutes of Health Research, National Institute on Aging, National Eye Institute, National Institute of Biomedical Imaging and Bioengineering, U.S. National Library of Medicine
Awards: G1012222616, G1554142629, G1593117279, Predicting the early childhood outcomes of preterm brain shape abnormalities, G1650819308, G2100670906, G2152643862, G2189010064, G2340207221, G2409989180, G2423387114, G2522063444, G2835601961, G29951154, G3131374986, G3216574877, G3553156159, G3710456697, G3752451908, G3870774570, G3908963977, G3966393351, G4119534169, G43180419, Discover and Analyze Germline-Somatic Interactions in Cancer, G4475667458, G4501223628, G4800177743, G5207404921, G5209990037, G528601746, G5324093878, G5365730499, G5496739235, G5654639750, G5740349702, G5850682332, Hierarchical Bayesian Analysis of Retinotopic Maps of the Human Visual Cortex with Conformal Geometry, G613619302, G6173816828, G6210868510, G6371410416, G6412110892, G6447063303, G6719828067, G6792506723, G6899163935, G6948409098, Developing a Univariate Neurodegeneration Imaging Biomarker with Optimal Transportation, G72415811, G7298697282, G764699806, G7799614282, G781725486, G8228018387, G824491993, G8281378126, G839208290, G8636843673, Multi-Source Sparse Learning to Identify MCI and Predict Decline, G8852128998, G8895907669, G8897063270, G8957659042, Arizona Alzheimer's Disease Research Center, APOE in the Predisposition to, Protection from and Prevention of Alzheimer's Disease, Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
OpenAlex ID: W4207068566