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 to 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.

中文翻译：

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通过几何深度学习在多基因预测中对基因相互作用进行建模


多基因风险评分 （PRS） 是一种广泛使用的方法，用于预测个体患复杂疾病的遗传风险，在推进精准医疗方面发挥着关键作用。传统的 PRS 方法主要遵循线性结构，通常无法捕捉基因型和表型之间的复杂关系。在这项研究中，我们提出了 PRS-Net，这是一个基于可解释的几何深度学习框架，可有效地模拟生物系统的非线性，以增强疾病预测和生物发现。PRS-Net 首先以单基因分辨率对全基因组 PRS 进行去卷积，然后利用图形神经网络 （GNN） 显式封装基因-基因相互作用进行遗传风险预测，从而能够系统地表征支撑疾病的分子相互作用。引入了一个细心的读出模块，以促进模型解释。对多种复杂性状和疾病的广泛测试表明，与传统的 PRS 方法相比，PRS-Net 具有卓越的预测性能。PRS-Net 的可解释性进一步增强了疾病相关基因和基因程序的鉴定。PRS-Net 为复杂疾病的并发遗传风险预测和生物发现提供了有力的工具。