GRPa-PRS: A risk stratification method to identify genetically-regulated pathways in polygenic diseases

Area of Science:

• Genetics
• Neuroscience
• Computational Biology

Background:

• Polygenic risk scores (PRS) predict genetic susceptibility to diseases but don't fully explain individual outcomes.
• High-risk individuals sometimes remain healthy, while low-risk individuals develop diseases, suggesting unknown protective or detrimental factors.

Purpose of the Study:

• To develop a framework for identifying genetically-regulated pathways (GRPas) that may influence disease risk.
• To explore how these pathways relate to resilience and susceptibility in Alzheimer's disease (AD) and schizophrenia (SCZ).
• To uncover potential targets for personalized disease prevention.

Main Methods:

• Developed a novel framework using PRS-based stratification in AD and SCZ cohorts.
• Calculated PRS models and stratified individuals by risk and diagnosis.
• Analyzed differential GRPas, including analyses with and without apolipoprotein E (APOE) effects in AD.

Main Results:

• Identified known AD-related pathways, including amyloid-beta clearance and tau protein binding.
• Highlighted resilience-associated pathways such as calcium signaling and divalent inorganic cation homeostasis in AD.
• Observed fewer significant GRPas in the no-APOE AD model and SCZ, suggesting a more polygenic architecture.

Conclusions:

• The GRPas-PRS framework allows flexible exploration of genetically-regulated pathways in stratified subgroups.
• Calcium signaling and cation homeostasis are highlighted as key functions linked to resilience.
• Findings support personalized prevention by targeting individual resilience factors and can be applied to other complex traits.