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

作者：Rasika Venkatesh, Katie M. Cardone, Yuki Bradford, Anni Moore, Rachit Kumar, Jason H. Moore, Li Shen, Dokyoon Kim, Marylyn D. Ritchie · 发表于：Alzheimer s & Dementia · 年份：2025 · DOI：10.1002/alz.70886 · 被引用次数：6 · 研究领域：Genetic Associations and Epidemiology、Bioinformatics and Genomic Networks、GDF15 and Related Biomarkers

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/PWAS. Enrichment analyses highlighted cholesterol and immune signaling pathways. The best-performing IRM, random forest with transcriptomic and covariate features, achieved area under the receiver operating characteristic (AUROC) of 0.703 and area under the precision-recall curve (AUPRC) of 0.622, significantly outperforming PGS and baseline models. DISCUSSION: Integrating univariate discovery approaches with multivariate modeling enhances AD risk prediction and offers novel insights into underlying biological processes. HIGHLIGHTS: Identified novel contributions to Alzheimer's disease (AD) from a multi-omics perspective. Integrated genome-wide association studies (GWAS), transcriptome-wide association studies (T...