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

作者：Jianfeng Wu, Yanxi Chen, Panwen Wang, Richard J. Caselli, Paul M. Thompson, Junwen Wang, Yalin Wang · 发表于：Frontiers in Radiology · 年份：2022 · DOI：10.3389/fradi.2021.777030 · 被引用次数：15 · 研究领域：Genetic Associations and Epidemiology、Bioinformatics and Genomic Networks、Gene expression and cancer classification

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) me...