耶鲁大学 Xiang Zhou团队研究出基于多基因评分的表型预测校准预测区间的统计构建。这一研究成果发表在2025年10月13日出版的国际学术期刊《自然—遗传学》上。

研究团队提出了PredInterval，一种用于构造校准良好的预测区间的非参数方法。PredInterval与任何PGS方法兼容，将个人水平的数据或汇总统计数据作为输入，并通过交叉验证依赖于表型残差分位数的信息，从而在不同的遗传结构中实现对真实表型值的校准覆盖。应用PredInterval对实际数据中的17个性状进行了分析，结果表明，PredInterval不仅是实现全性状预测覆盖率校准良好的唯一方法，而且为识别高风险个体主题预测区间提供了一种有原则的方法，与现有方法相比，平均识别率提高了8.7-830.4%。总的来说，PredInterval代表了一个灵活和通用的工具，以提高临床应用的PGS。

据了解，准确量化基于多基因评分（PGS）的应用中预测表型的不确定性对于可靠的PGS临床解释，支持有效的疾病风险评估和知情决策至关重要。

附：英文原文

Title: Statistical construction of calibrated prediction intervals for polygenic score-based phenotype prediction

Author: Xu, Chang, Ganesh, Santhi K., Zhou, Xiang

Issue&Volume: 2025-10-13

Abstract: Accurately quantifying uncertainty in predicted phenotypes from polygenic score (PGS)-based applications is essential for reliable clinical interpretation of PGS, supporting effective disease risk assessment and informed decision-making. Here, we present PredInterval, a nonparametric method for constructing well-calibrated prediction intervals. PredInterval is compatible with any PGS method, takes either individual-level data or summary statistics as input and relies on information from quantiles of phenotypic residuals through cross-validation to achieve well-calibrated coverage of true phenotypic values across diverse genetic architectures. We apply PredInterval to analyze 17 traits in real-data applications, where PredInterval not only represents the sole method achieving well-calibrated prediction coverage across traits, but it also offers a principled approach to identify high-risk individuals using prediction intervals, leading to an average improvement of identification rates by 8.7–830.4% compared with existing approaches. Overall, PredInterval represents a robust and versatile tool for enhancing the clinical utility of PGS.

DOI: 10.1038/s41588-025-02360-6

Source: https://www.nature.com/articles/s41588-025-02360-6