Integration of Genetic and Imaging Data for Alzheimer's Disease Diagnosis and Interpretation

Authors

Yanfei Wang
Qing Wang
Minghao Zhou
Jialu Liang
Lei You
Breton Asken
Xiaobo Zhou
Qianqian Song

Language

English

Publication Date

11-1-2025

Journal

Advanced Science

DOI

10.1002/advs.202507629

PMID

40788127

PMCID

PMC12591116

PubMedCentral® Posted Date

8-11-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by complex interactions between genetic risk factors and structural brain changes. Traditional diagnostic approaches that rely on single-modality data, such as imaging or genomics alone, often fall short in both predictive accuracy and biological interpretability. To address these limitations, AlzCLIP, a novel contrastive learning framework that integrates single nucleotide polymorphism (SNP) profiles and MRI-derived imaging features into a unified embedding space is introduced. This joint representation captures disease-relevant interactions between genetic variation and brain structure, enabling both accurate diagnosis and mechanistic insight into AD. AlzCLIP is trained and evaluated on two large-scale cohorts, the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the UK Biobank (UKB), and demonstrated robust diagnostic performance, outperforming state-of-the-art baselines by up to 19%. More importantly, the model yields interpretable outputs through feature importance and interaction analyses, identifying key contributors to AD risk, including rs1135173, rs7575209, and rs66763080, as well as structural markers such as hippocampal volume and precuneus surface area. Notably, AlzCLIP uncovered genotype-specific effects on imaging phenotypes. Specifically, rs11077054 is associated with increased white matter hyperintensity burden and amygdala atrophy, suggesting a potential link between this variant and AD-related structural brain changes. Together, the results highlight AlzCLIP's potential to enhance AD risk prediction and provide biologically grounded insights by integrating multi-modal genomic and imaging data.

Keywords

Alzheimer Disease, Humans, Magnetic Resonance Imaging, Polymorphism, Single Nucleotide, Neuroimaging, Male, Brain, Female, Aged

Published Open-Access

yes

Recommended Citation

Wang, Yanfei; Wang, Qing; Zhou, Minghao; et al., "Integration of Genetic and Imaging Data for Alzheimer's Disease Diagnosis and Interpretation" (2025). Faculty, Staff and Student Publications. 794.
https://digitalcommons.library.tmc.edu/uthshis_docs/794