RT Journal Article
SR Electronic
T1 Integrating Transcriptomics, Genomics, and Imaging in Alzheimer’s Disease: A Federated Model
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.09.14.460367
DO 10.1101/2021.09.14.460367
A1 Jianfeng Wu
A1 Yanxi Chen
A1 Panwen Wang
A1 Richard J Caselli
A1 Paul M Thompson
A1 Junwen Wang
A1 Yalin Wang
A1 for the Alzheimer’s Disease Neuroimaging Initiative
YR 2021
UL http://biorxiv.org/content/early/2021/09/16/2021.09.14.460367.abstract
AB 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 StatementThe authors have declared no competing interest.