Hippocampal surface morphological variation-based genome-wide association analysis network for biomarker detection of...

Performing genome-wide association analysis (GWAS) between hippocampus and whole-genome data can facilitate disease-related biomarker detection of Alzheimer's disease (AD). However, most existing studies have prioritized hippocampal volume changes and ignored the morphological variations and subfield differences of the hippocampus in AD progression. This disregard restricts the comprehensive understanding of the associations between hippocampus and whole-genome data, which may result in some potentially specific biomarkers of AD being missed. Moreover, the representation of the complex associations between ultra-high-dimensional imaging and whole-genome data remains an unresolved problem in GWAS. To address these issues, we propose an end-to-end hippocampal surface morphological variation-based genome-wide association analysis network (HSM-GWAS) to explore the nonlinear associations between hippocampal surface morphological variations and whole-genome data for AD-related biomarker detection. First, a multi-modality feature extraction module that includes a graph convolution network and an improved diet network is presented to extract imaging and genetic features from non-Euclidean hippocampal surface and whole-genome data, respectively. Second, a dual contrastive learning-based association analysis module is introduced to map and align genetic features to imaging features, thus narrowing the gap between these features and helping explore the complex associations between hippocampal and whole-genome data. Last, a dual cross-attention fusion module is applied to combine imaging and genetic features for disease diagnosis and biomarker detection of AD. Extensive experiments on the real Alzheimer's Disease Neuroimaging Initiative dataset and simulated data demonstrate that HSM-GWAS considerably improves biomarker detection and disease diagnosis. These findings highlight the ability of HSM-GWAS to discover disease-related biomarkers, suggesting its potential to provide new insights into pathological mechanisms and aid in AD diagnosis. The codes are to be made publicly available at https://github.com/Meiyan88/HSM-GWAS.