美国加州大学Bogdan Pasaniuc小组的论文揭示了不同情况下多基因评分（PGS）的校准预测区间。2024年6月17日出版的《自然-遗传学》杂志发表了这项成果。

研究人员建立了一种方法（CalPred），该方法可对所有情境进行联合建模，以产生因情境而异的预测区间，从而实现PGS校准（包含概率为90%的性状），而现有方法则存在校准误差。在对大型、多样化生物库（All of Us和英国生物库）中的72个性状进行分析时，研究发现对于数量性状，预测区间最多需要调整80%。

对于疾病而言，基于PGS的预测在不同社会经济背景（如家庭年收入水平）下都会出现误判，这进一步凸显了基于PGS的预测需要考虑不同人群的背景信息。

据了解，PGS已成为各领域基因组预测的首选工具。先前研究表明，PGS的性能在不同环境和生物库中有很大差异。年龄、性别和收入等环境会影响PGS的准确性，影响程度与遗传祖先相似。

附：英文原文

Title: Calibrated prediction intervals for polygenic scores across diverse contexts

Author: Hou, Kangcheng, Xu, Ziqi, Ding, Yi, Mandla, Ravi, Shi, Zhuozheng, Boulier, Kristin, Harpak, Arbel, Pasaniuc, Bogdan

Issue&Volume: 2024-06-17

Abstract: Polygenic scores (PGS) have emerged as the tool of choice for genomic prediction in a wide range of fields. We show that PGS performance varies broadly across contexts and biobanks. Contexts such as age, sex and income can impact PGS accuracy with similar magnitudes as genetic ancestry. Here we introduce an approach (CalPred) that models all contexts jointly to produce prediction intervals that vary across contexts to achieve calibration (include the trait with 90% probability), whereas existing methods are miscalibrated. In analyses of 72 traits across large and diverse biobanks (All of Us and UK Biobank), we find that prediction intervals required adjustment by up to 80% for quantitative traits. For disease traits, PGS-based predictions were miscalibrated across socioeconomic contexts such as annual household income levels, further highlighting the need of accounting for context information in PGS-based prediction across diverse populations.

DOI: 10.1038/s41588-024-01792-w

Source: https://www.nature.com/articles/s41588-024-01792-w