大多数常见疾病在患病率、严重程度和遗传易感性方面存在性别二态性。然而，大多数遗传和临床结果研究都是在性别组合框架中设计的，将性别视为协变量。很少有针对性别的研究分别分析男性和女性，未能确定基因与性别之间的相互作用。在这里，我们提出了一种新颖的统一的生物可解释的基于深度学习的框架（名为 SPIN），用于性别二态性分析。我们证明，SPIN 在 TCGA 癌症数据集中将 C 指数显着提高了 23.6%，并使用哮喘数据集进一步验证了这一点。此外，SPIN 还可以识别性别特异性和共享风险位点，而这些位点在之前的性别组合/单独分析中经常被遗漏。我们还表明，SPIN 可以解释生物途径如何导致性别二态性并改善个体水平的风险预测，这可以导致针对特定个体特征的精准医学的发展。© 作者 2024。已发表牛津大学出版社。

Sexual dimorphism in prevalence, severity and genetic susceptibility exists for most common diseases. However, most genetic and clinical outcome studies are designed in sex-combined framework considering sex as a covariate. Few sex-specific studies have analyzed males and females separately, which failed to identify gene-by-sex interaction. Here, we propose a novel unified biologically interpretable deep learning-based framework (named SPIN) for sexual dimorphism analysis. We demonstrate that SPIN significantly improved the C-index up to 23.6% in TCGA cancer datasets, and it was further validated using asthma datasets. In addition, SPIN identifies sex-specific and -shared risk loci that are often missed in previous sex-combined/-separate analysis. We also show that SPIN is interpretable for explaining how biological pathways contribute to sexual dimorphism and improve risk prediction in an individual level, which can result in the development of precision medicine tailored to a specific individual's characteristics.© The Author(s) 2024. Published by Oxford University Press.