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Polygenic risk score (PRS) serves as a valuable tool for predicting the genetic risk of complex human diseases for individuals, 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. We present PRS-Net, an interpretable deep learning-based framework designed to effectively model 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 it encapsulates gene-gene interactions for genetic risk prediction leveraging a graph neural network, thereby enabling the characterization of biological nonlinearity underlying complex diseases. An attentive readout module is specifically introduced into the framework to facilitate model …