Genome-wide association studies (GWAS) have uncovered thousands of risk variants that individually have small effects on the risk of human diseases, including chronic kidney disease, type 2 diabetes, heart diseases and inflammatory disorders, but cumulatively explain a substantial fraction of disease risk, underscoring the complexity and pervasive polygenicity of common disorders. This complexity poses unique challenges to the clinical translation of GWAS findings. Polygenic scores combine small effects of individual GWAS risk variants across the genome to improve personalized risk prediction. Several polygenic scores have now been developed that exhibit sufficiently large effects to be considered clinically actionable. However, their clinical use is limited by their partial transferability across ancestries and a lack of validated models that combine polygenic, monogenic, family history and clinical risk factors. Moreover, prospective studies are still needed to demonstrate the clinical utility and cost-effectiveness of polygenic scores in clinical practice. Here, we discuss evolving methods for developing polygenic scores, best practices for validating and reporting their performance, and the study designs that will empower their clinical implementation. We specifically focus on the polygenic scores relevant to nephrology and other chronic, complex diseases and review their key limitations, necessary refinements and potential clinical applications.

全基因组关联研究 （GWAS） 发现了数千种风险变异，这些变异单独对人类疾病（包括慢性肾病、2 型糖尿病、心脏病和炎症性疾病）的风险影响很小，但累积起来解释了很大一部分疾病风险，强调了常见疾病的复杂性和普遍的多基因性。这种复杂性对 GWAS 结果的临床转化提出了独特的挑战。多基因评分结合了整个基因组中单个 GWAS 风险变异的小效应，以改进个性化风险预测。现在已经开发了几种多基因评分，它们表现出足够大的影响，可以被认为具有临床可操作性。然而，它们的临床应用受到其跨祖先的部分可转移性以及缺乏结合多基因、单基因、家族史和临床风险因素的经过验证的模型的限制。此外，仍需要前瞻性研究来证明 Polygenic 评分在临床实践中的临床效用和成本效益。在这里，我们讨论了开发多基因评分的不断发展的方法、验证和报告其性能的最佳实践，以及将增强其临床实施能力的研究设计。我们特别关注与肾脏病学和其他慢性复杂疾病相关的多基因评分，并回顾其关键局限性、必要的改进和潜在的临床应用。