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Basic Science and Pathogenesis.

Emily Greenfest-Allen1, Yuk Yee Leung1, Wan-Ping Lee1

  • 1Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 23, 2025
PubMed
Summary
This summary is machine-generated.

The Alzheimer's Disease Sequencing Project (ADSP) offers AI/ML researchers vast genomic data for Alzheimer's disease (AD) research. This data supports predictive modeling, biomarker discovery, and therapeutic target identification for precision medicine.

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Area of Science:

  • Genomics
  • Artificial Intelligence
  • Machine Learning
  • Precision Medicine

Background:

  • Alzheimer's disease (AD) research is exploring genomic frontiers.
  • The Alzheimer's Disease Sequencing Project (ADSP) provides significant opportunities for AI/ML researchers.
  • ADSP data can drive discoveries in predictive modeling, biomarker identification, and therapeutic target development.

Purpose of the Study:

  • To introduce the genomic resources available through the ADSP for AI/ML researchers.
  • To highlight the potential of ADSP data for advancing Alzheimer's research and precision medicine.
  • To explore the vast possibilities for data-driven discoveries using ADSP datasets.

Main Methods:

  • The ADSP integrates numerous projects to analyze up to 150,000 complete genomes and associated clinical/functional data.
  • Focus on unraveling the genetic landscape of AD to advance precision medicine.
  • Utilizing diverse population data for a comprehensive understanding of genetic underpinnings.

Main Results:

  • ADSP emphasizes diversity, with whole genomes from 57,302 participants (including underrepresented groups) in release 5.
  • Phenotype harmonization across cohorts facilitates comparative analyses.
  • Open access to summary statistics, genetic associations, and functional annotations enables knowledge base generation.
  • Integration of functional genomics to understand biological mechanisms beyond genetic variations.

Conclusions:

  • AI/ML potential within ADSP underscores the need for collaboration between AI/ML researchers and the Alzheimer's research community.
  • Synergy between AI/ML and Alzheimer's research can lead to breakthroughs.
  • Genomic insights from ADSP can translate into clinical advancements for Alzheimer's disease.