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Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder driven by complex genetic and molecular interactions. Despite major advances in genomics, current discoveries explain less than 40% of AD heritability, underscoring the need for integrative approaches that capture cross-omic regulation. Here, we propose a multiomics integration framework combining genomic, epigenomic, and transcriptomic data sets to identify convergent molecular signatures underlying AD pathogenesis. An integrated epigenome-wide association study-genome wide association study (EWAS–GWAS) analysis using GeneCards and VarElect identified 42 candidate genes, showing overlap between genetic susceptibility and epigenetic dysregulation. These include canonical AD loci (APOE, CLU, BIN1, PICALM, and TREM2) and novel regulatory genes such as AKT1, DOT1L, SREBF1, and PVT1. Network analysis revealed 32 nodes and 30 edges with an average node degree of 1.88 and a protein–protein interaction (PPI) enrichment p-value of 6.45 × 10–6, indicating significant functional connectivity. Integrative pathway mapping highlighted mitochondrial–nuclear cross-talk, metabolic dysfunction, and noncoding RNA regulation as central pathogenic axes. This multilayered approach bridges static genomic variants with dynamic epigenetic and transcriptomic alterations, offering a systems-level view of disease mechanisms. Methodologically, the framework integrates EWAS–GWAS correlation, functional annotation, and PPI modeling to prioritize biologically relevant targets. Translationally, these findings reveal potential methylation-based biomarkers, polygenic–epigenetic risk models, and targetable molecular pathways for early detection and precision therapeutics. Overall, this integrative strategy enhances mechanistic understanding and supports the development of predictive, multiomic tools for individualized AD management.
Mishra et al. (Sun,) studied this question.