Integrative multi-omics approaches identify molecular pathways and improve Alzheimer's Disease risk prediction.

ABSTRACT: Alzheimer's Disease (AD) is the most prevalent condition that impacts the aging population, with no effective treatment or singular underlying causal factor identified. As a complex disease, characterizing the genetic risk of developing AD has proven to be difficult; polygenic scores (PGS) exclusively use common variants which fail to fully capture disease heterogeneity. This study used univariate and multivariate approaches to characterize AD risk. Genome-, transcriptome-, and proteome-wide association studies (GWAS, TWAS, and PWAS) were conducted on 15,480 individuals from the Alzheimer's Disease Sequencing Project (ADSP) R4 release to identify AD-associated signals, followed by pathway enrichment analysis. Integrative risk models (IRMs) were developed using genetically-regulated compone

SUBMITTER: Venkatesh R 

PROVIDER: S-EPMC12155013 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature