RT Journal
A1 Li, Han
A1 Zeng, Jianyang
A1 Snyder, Michael P.
A1 Zhang, Sai
T1 Modeling gene interactions in polygenic prediction via geometric deep learning
JF Genome Research 
JO Genome Research 
YR 2025 
FD January 01 
VO 35 
IS 1 
SP 178 
OP 187 
DO 10.1101/gr.279694.124 
UL http://genome.cshlp.org/content/35/1/178.abstract 
AB Polygenic risk score (PRS) is a widely used approach for predicting individuals’ genetic risk of complex diseases, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall short in capturing the intricate relationships between genotype and phenotype. In this study, we present PRS-Net, an interpretable geometric deep learning–based framework that effectively models the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the genome-wide PRS at the single-gene resolution and then explicitly encapsulates gene–gene interactions leveraging a graph neural network (GNN) for genetic risk prediction, enabling a systematic characterization of molecular interplay underpinning diseases. An attentive readout module is introduced to facilitate model interpretation. Extensive tests across multiple complex traits and diseases demonstrate the superior prediction performance of PRS-Net compared with a wide range of conventional PRS methods. The interpretability of PRS-Net further enhances the identification of disease-relevant genes and gene programs. PRS-Net provides a potent tool for concurrent genetic risk prediction and biological discovery for complex diseases.