Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands.
Tinbergen Institute, Amsterdam, the Netherlands.
Nat Hum Behav. 2023 May;7(5):802-811. doi: 10.1038/s41562-023-01544-6. Epub 2023 Mar 13.
Polygenic indices (PGIs) are increasingly used to identify individuals at risk of developing disease and are advocated as screening tools for personalized medicine and education. Here we empirically assess rank concordance between PGIs created with different construction methods and discovery samples, focusing on cardiovascular disease and educational attainment. We find Spearman rank correlations between 0.17 and 0.93 for cardiovascular disease, and 0.40 and 0.83 for educational attainment, indicating highly unstable rankings across different PGIs for the same trait. Potential consequences for personalized medicine and gene-environment (G × E) interplay are illustrated using data from the UK Biobank. Simulations show how rank discordance mainly derives from a limited discovery sample size and reveal a tight link between the explained variance of a PGI and its ranking precision. We conclude that PGI-based ranking is highly dependent on PGI choice, such that current PGIs do not have the desired precision to be used routinely for personalized intervention.
多基因指数 (PGI) 越来越多地被用于识别有患病风险的个体，被倡导作为个性化医学和教育的筛查工具。在这里，我们通过实证评估了不同构建方法和发现样本创建的 PGI 之间的等级一致性，重点关注心血管疾病和教育程度。我们发现心血管疾病的斯皮尔曼等级相关系数在 0.17 到 0.93 之间，教育程度的斯皮尔曼等级相关系数在 0.40 到 0.83 之间，这表明对于同一特征，不同 PGI 的排名非常不稳定。使用英国生物库的数据说明了个性化医学和基因-环境（G×E）相互作用的潜在后果。模拟结果表明，等级不一致主要源于发现样本量有限，并揭示了 PGI 的解释方差与其排名精度之间的紧密联系。我们得出的结论是，基于 PGI 的排名高度依赖于 PGI 的选择，因此当前的 PGI 没有达到用于个性化干预的预期精度。
