Genetic risk in Alzheimer's disease
Yining Pan1, Hwayoung Cho2, Qian Lou3
1Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.
NPJ Systems Biology and Applications
|February 14, 2026
Summary
Alzheimer's disease (AD) genetics are complex. Machine learning polygenic risk scores (PRS) improve prediction by considering gene interactions and sex differences, advancing personalized Alzheimer's risk assessment.
Area of Science:
- Genetics
- Neuroscience
- Computational Biology
Background:
- Alzheimer's disease (AD) exhibits significant genetic influence.
- Genome-wide association studies (GWAS) have identified numerous AD risk loci.
- Many non-coding genetic variants associated with AD risk remain poorly understood.
Purpose of the Study:
- To review current understanding of genetic risk factors for Alzheimer's disease.
- To summarize methodologies for developing polygenic risk scores (PRS).
- To explore the application of machine learning (ML) in predicting AD risk.
Main Methods:
- Review of existing literature on Alzheimer's disease genetics.
- Analysis of polygenic risk score (PRS) construction and validation techniques.
- Examination of machine learning (ML) algorithms applied to AD genetic data.
Main Results:
- Genetic factors, including non-coding variants, contribute substantially to AD predisposition.
- Polygenic risk scores (PRS) offer enhanced predictive power by modeling complex genetic interactions and sex-specific effects.
- Machine learning approaches show promise for improving the accuracy of AD risk prediction.
Conclusions:
- Integrating diverse genetic data and advanced computational methods is crucial for understanding AD.
- Addressing challenges like population bias and functional validation is essential for clinical utility.
- Multi-omics data integration holds potential for precision medicine in Alzheimer's disease management.
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