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

Polygenic risk score (PRS) assesses genetic risk for diseases, yet some high-risk individuals avoid illness while low-risk individuals develop it. We hypothesize that unknown counterfactors may reverse PRS predictions, offering insights into disease mechanisms and interventions.

We developed a novel framework to identify genetically-regulated pathways (GRPas) using PRS-based stratification in Alzheimer's disease (AD) and schizophrenia (SCZ) cohorts. We calculated PRS models, stratified individuals by risk and diagnosis, and analyzed differential GRPas. For AD, analyses were further conducted with and without apolipoprotein E (APOE) effects, and across APOE haplotype.

In AD, we identified several well-known AD-related pathways, including amyloid-beta clearance, tau protein binding, and resilience-related calcium signaling pathway, and divalent inorganic cation homeostasis.

Our method offers flexibility for exploring GRPas among PRS-stratified subgroups using summary statistics or individual-level data. Fewer GRPas identified in the no-APOE AD model and SCZ suggest a more polygenic architecture, necessitating larger samples to detect significant GRPas.

Characterize genetically-regulated expression (GReX) among groups stratified by polygenic risk score (PRS) Leverage GReX and PRS to explore the resilience and susceptibility at the pathway level Highlight calcium signaling and cation homeostasis functions linked to resilience Enable personalized prevention by reinforcing the different resilience factors present or absent in each individual Our genetically-regulated pathway (GRPa) -PRS framework can be further expanded to other complex polygenic traits.