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

ABSTRACT:

Introduction

Alzheimer's disease (AD) is a complex neurodegenerative disorder with heterogeneous genetic and molecular underpinnings. Polygenic scores (PGS) capture little of this complexity.

Methods

We conducted genome-, transcriptome-, and proteome-wide association studies (G/T/PWAS) on 15,480 individuals from the Alzheimer's Disease Sequencing Project R4 (ADSP) to identify AD-associated signals, followed by pathway enrichment analysis. Integrative risk models (IRMs) were developed using genetically regulated components of gene and protein expression and clinical covariates. Elastic-net logistic regression and random forest classifiers were evaluated using standard metrics and compared against baseline PGS.

Results

Known and novel signals were identified via G/T/PWA

SUBMITTER: Venkatesh R 

PROVIDER: S-EPMC12614089 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature