Genetic risk in Alzheimer’s disease

npj Systems Biology and Applications 2026 Open access

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Authors: Yining Pan, Hwayoung Cho, Qian Lou, Wei Zhang, Breton M. Asken, Qianqian Song
DOI: 10.1038/s41540-026-00665-8
Cited by: 1
References: 86

Abstract

Alzheimer's disease (AD) has a strong genetic predisposition. Genome-wide association studies have identified multiple risk loci, yet many non-coding variants remain uncharacterized. Machine learning-based polygenic risk scores (PRS) enhance prediction by modeling genetic epistasis and sex-specific risks. This review summarizes AD genetic risk factors, PRS methodologies, and ML-based AD risk prediction. It also highlights challenges such as population bias, functional validation, and integrating multi-omics for precision medicine.

Fields of Study & Subjects

Genetic Associations and Epidemiology Genomics and Rare Diseases Bioinformatics and Genomic Networks

Publication details

Journal / source
npj Systems Biology and Applications
Publisher
Springer Science and Business Media LLC
Volume
12
Issue
1
Publication date
14 February 2026
Language
EN