Identification of Alzheimer's disease subtypes and biomarkers from human multi-omics data using subspace merging algorithm.

INTRODUCTION: Alzheimer's disease (AD) is a heterogeneous disease with diverse disease progression trajectories and brain pathology. Identifying AD subtypes is essential for understanding AD etiology, heterogeneity, and developing precise treatment. METHODS: We applied a subspace-merging algorithm to integrate multi-omics data from brain tissues of three large AD cohorts and identify data-driven AD subtypes. Within each cohort, we performed multiple analyses to characterize subtype-specific biology. A Phenome-wide Association Study (PheWAS) of expression quantitative trait loci (eQTLs) targeting differentially expressed genes (DEGs) was conducted to link molecular differences to disease phenotypes. RESULTS: We identified AD subtypes that differed in cognitive and pathological phenotypes in three cohorts. Further analyses highlighted synaptic and neurotransmission pathways, and the PheWAS revealed significant associations with disease phenotypes. DISCUSSION: Our developed integration algorithm successfully merged different data modalities into a common subspace for patient clustering and identified data-driven subtypes. The identified transcriptomic signatures provide valuable insights into the molecular mechanisms underlying AD heterogeneity, paving the way for personalized AD treatment.