Interpretable machine-learning prediction of PSEN1 missense variant pathogenicity based on multi-omics enrichment in six core Alzheimer’s disease genes

This integrative, multi-omics framework links disease-specific enrichment patterns with interpretable gene-level machine learning for AD missense variants. The results highlight the importance of expression level, structural context, etc. for PSEN1 variant pathogenicity and may help prioritize variants for functional studies. Further validation in additional genes and independent cohorts is warranted prior to any clinical application.