The polygenic risk scores (PRS) have emerged as an important methodology for quantifying genetic predisposition to complex traits and clinical disease. Significant progress has been made in applying PRS to conditions such as obesity, cancer, and type 2 diabetes (T2DM). Studies have demonstrated that PRS can effectively identify individuals at high risk, thereby enabling early screening, personalized treatment, and targeted interventions for diseases with a genetic predisposition. One current limitation of PRS, however, is the lack of interpretability tools. To address this problem for T2DM, researchers at the Graduate School of Data Science at the Seoul National University introduced eXplainable PRS (XPRS). This visualization tool decomposes PRSs into gene-level and single-nucleotide polymorphism (SNP) contribution scores via Shapley Additive Explanations (SHAP), providing granular insights into the specific genetic factors driving an individual's risk profile. We used a co-design approach to assess XPRS trustworthiness by considering legal, medical, ethical, and technical robustness during early design and potential clinical use. For that, we used Z-inspection, an ethically aligned Trustworthy AI co-design methodology, and piloted the Council of Europe's Human Rights, Democracy, and the Rule of Law Impact Assessment for AI Systems (HUDERIA) (Council of Europe (CAI) 2025). The findings of this use-case comprise a comprehensive set of ethical, legal, and technical lessons learned. These insights, identified by a multidisciplinary team of experts (ethics, legal, human rights, computer science, and medical), serve as a framework for designers to navigate future challenges with this and other AI systems. The findings also provide a useful reference for researchers developing explainability frameworks for PRS in diverse clinical contexts.

翻译：多基因风险分数已发展成为量化复杂性状和临床疾病遗传易感性的重要方法。将该方法应用于肥胖、癌症及2型糖尿病等疾病的研究已取得显著进展。研究表明，多基因风险分数能有效识别高风险个体，从而实现对遗传易感疾病的早期筛查、个性化治疗和靶向干预。然而，当前多基因风险分数的一个局限在于缺乏可解释性工具。为解决2型糖尿病领域的这一问题，首尔国立大学数据科学研究生院的研究人员引入了可解释多基因风险分数这一可视化工具。该方法通过沙普利加性解释将多基因风险分数分解为基因水平和单核苷酸多态性贡献分数，从而提供驱动个体风险状况的特定遗传因素的精细见解。我们采用协同设计方法，在早期设计和潜在临床应用阶段综合考虑法律、医学、伦理和技术鲁棒性，以评估可解释多基因风险分数的可信赖性。为此，我们运用了符合伦理规范的可信人工智能协同设计方法Z-inspection，并试点应用了欧洲委员会《人权、民主与法治人工智能系统影响评估》（HUDERIA）。本案例研究结果包含一套全面的伦理、法律和技术经验教训。这些由多学科专家团队（涵盖伦理学、法学、人权、计算机科学和医学领域）识别出的见解，为设计者应对本系统及其他人工智能系统的未来挑战提供了框架。研究结果也为不同临床情境下开发多基因风险分数可解释性框架的研究人员提供了有价值的参考。