自适应多基因风险评分模型改进了汉族人群静脉血栓栓塞的风险预测。

IF 6.2 Q2 GENETICS & HEREDITY
Phenomics (Cham, Switzerland) Pub Date : 2025-03-12 eCollection Date: 2025-08-01 DOI:10.1007/s43657-024-00192-8
Zhaoman Wan, Zhu Zhang, Mingming Su, Haobo Li, Yu Zhang, Xinlei Zhang, Aiping Wu, Taijiao Jiang, Peng Zhang, Zhenguo Zhai
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引用次数: 0

摘要

大多数静脉血栓栓塞(VTE)的全基因组关联研究(GWAS)都使用了欧洲血统个体的数据,然而,在中国人群中,VTE的遗传因素尚未完全确定,这导致现有的多基因风险评分(PRS)在识别VTE高风险亚群以进行预防方面的应用有限。因此,我们旨在收集所有潜在的VTE相关单核苷酸多态性(snp),构建基于自适应方法的改进PRS模型,然后评估其在中国人群VTE风险分层中的实用性和有效性。我们综合分析了中国队列中vte相关snp的突变谱,并以风险比、logistic回归系数和惩罚回归系数作为评价标准,对其个体风险效应进行独立排序。通过整合各种算法并评估其性能,我们训练了具有最少SNP特征的中国人群VTE风险的最优预测模型,建立了具有渐进式SNP覆盖的自适应PRS模型,并在独立的中国人群队列上进行了测试。基于318个snp的自适应多基因风险评分模型或基于44个相关性最强的snp的自适应多基因风险评分模型在中国VTE队列测试数据集上的表现相似(受试者操作特征曲线下面积(AUC)分别为0.739和0.709),并且达到了基于已知遗传风险因素的传统PRS模型的总体最佳AUC水平(0.62 -0.718)。此外,我们观察到自适应PRS模型是一个独立有效的风险分层指标,超越了其他临床特征,包括年龄和吸烟状况。我们的数据显示,只有44个snp衍生的PRS模型可以有效地用于区分静脉血栓栓塞高风险亚群。为了在临床上发挥作用,我们的模型可以从一个切实可行的静脉血栓栓塞筛查项目中受益,以便在中国人群中进行精确预防。补充资料:在线版本提供补充资料,网址为10.1007/s43657-024-00192-8。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Self-Adapting Polygenic Risk Score Model Improves Risk Prediction of Venous Thromboembolism in Han Chinese Cohorts.

Most genome-wide association studies (GWAS) of Venous Thromboembolism (VTE) have used data from individuals of European descent, however, genetic factors for VTE have not been fully identified in Chinese populations, which causes the limited use of existing polygenic risk scores (PRS) to identify subpopulations at high risk of VTE for prevention. We, therefore, aimed to curate all the potential VTE-related single-nucleotide polymorphisms (SNPs) for the construction of a new improved PRS model based on the self-adapting method, and then evaluate its utility and effectiveness in the stratification of VTE risk in Chinese populations. We comprehensively analyzed the mutation spectrum of VTE-associated SNPs in the Chinese cohort, and ranked their individual risk effects independently using risk ratio, logistic regression coefficient, and penalty regression coefficient as evaluation criteria. By integrating various algorithms and evaluating their performance, we trained the optimal prediction model of VTE risk in the Chinese population with the least SNP features, established an adaptive PRS model with progressive SNP overlay, and tested it on an independent Chinese population cohort. Self-adaptive polygenic risk score model based on all 318 SNPs or on the 44 most strongly associated SNPs performed similarly (areas under receiver-operating characteristic curves (AUCs) of 0.739 and 0.709, respectively) on the testing dataset of the Chinese VTE cohort, and that achieve the overall best level of the AUC from a conventional PRS model based on known genetic risk factors (0.620-0.718). In addition, we observed the self-adaptive PRS model was an independent effective risk stratification indicator beyond other clinical characteristics including age and smoking status. Our data revealed that only 44 SNPs-derived PRS model can be effectively used in discriminating subpopulations at high risk of VTE. To become clinically useful, our model could benefit from a practically feasible VTE screening program for precision prevention in Chinese populations.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00192-8.

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