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<p>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.</p>

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Genetic risk in Alzheimer’s disease

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月14日

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  • Yining Pan,
  • Hwayoung Cho,
  • Qian Lou,
  • Wei Zhang,
  • Breton Asken,
  • Qianqian Song

摘要

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.