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PGSFusion简化了生物样本库规模队列中的多基因评分构建和流行病学应用。

PGSFusion streamlines polygenic score construction and epidemiological applications in biobank-scale cohorts.

作者信息

Yang Sheng, Ye Xiangyu, Ji Xiaolong, Li Zhenghui, Tian Min, Huang Peng, Cao Chen

机构信息

Department of Biostatistics, Centre for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.

Department of Epidemiology, Key Laboratory of Public Health Safety and Emergency Prevention and Control Technology of Higher Education Institutions in Jiangsu Province, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.

出版信息

Genome Med. 2025 Jul 14;17(1):77. doi: 10.1186/s13073-025-01505-w.

DOI:10.1186/s13073-025-01505-w
PMID:40653480
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12257662/
Abstract

BACKGROUND

The polygenic score (PGS) is an estimate of an individual's genetic susceptibility to a specific complex trait and has been instrumental to the development of precision medicine. As an increasing number of genome-wide association studies (GWAS) have emerged, numerous sophisticated statistical and computational methods have been developed to facilitate the PGS construction. However, both the complex statistical estimation procedure and the various data formats of summary statistics and reference panel make the PGS calculation challenging and not easily accessible to researchers with limited statistical and computational backgrounds.

RESULTS

Here, we propose PGSFusion, a webserver designed to carry out PGS construction for targeting variety of analytic requirements while requiring minimal prior computational knowledge. Implemented with well-established web development technologies, PGSFusion streamlines the construction of PGS using 17 PGS methods in four categories: 11 single-trait, one multiple-trait, two annotation-based and three cross-ancestry based methods. In addition, PGSFusion also utilizes UK Biobank data to provide two kinds of in-depth analyses for 201 complex traits: i) prediction performance evaluation to display the consistency between PGS and specific traits and the effect size of PGS in different genetic risk groups; ii) joint effect analysis to investigate the interaction between PGS and covariates, as well as the effect size of covariates in different genetic subgroups. PGSFusion benchmarks the prediction performances for different methods in one summary statistics. PGSFusion automatically identifies the required parameters in different data formats of uploaded GWAS summary statistics files, provides a selection of suitable methods, and outputs calculated PGSs and their corresponding epidemiological results. Finally, we showcase three case studies in different application scenarios, highlighting its versatility and values to researchers.

CONCLUSIONS

Overall, PGSFusion presents an easy-to-use, effective, and extensible platform for PGS construction, promoting the accessibility and utility of PGS for researchers in the field of precision medicine. PGSFusion is freely available at http://www.pgsfusion.net/ .

摘要

背景

多基因分数(PGS)是对个体对特定复杂性状的遗传易感性的一种估计,对精准医学的发展起到了重要作用。随着越来越多的全基因组关联研究(GWAS)出现,人们开发了许多复杂的统计和计算方法来促进PGS的构建。然而,复杂的统计估计程序以及汇总统计数据和参考面板的各种数据格式,使得PGS计算具有挑战性,对于统计和计算背景有限的研究人员来说并不容易实现。

结果

在此,我们提出了PGSFusion,这是一个网络服务器,旨在满足各种分析需求来进行PGS构建,同时所需的先验计算知识最少。PGSFusion采用成熟的网页开发技术实现,使用四类17种PGS方法简化了PGS的构建:11种单性状方法、1种多性状方法、2种基于注释的方法和3种基于跨祖先的方法。此外,PGSFusion还利用英国生物银行数据对201种复杂性状进行两种深入分析:i)预测性能评估,以显示PGS与特定性状之间的一致性以及PGS在不同遗传风险组中的效应大小;ii)联合效应分析,以研究PGS与协变量之间的相互作用,以及协变量在不同遗传亚组中的效应大小。PGSFusion在一个汇总统计中对不同方法的预测性能进行基准测试。PGSFusion会自动识别上传的GWAS汇总统计文件不同数据格式中所需的参数,提供合适方法的选择,并输出计算得到的PGS及其相应的流行病学结果。最后,我们展示了在不同应用场景下的三个案例研究,突出了其对研究人员的多功能性和价值。

结论

总体而言,PGSFusion为PGS构建提供了一个易于使用、有效且可扩展的平台,提高了PGS在精准医学领域对研究人员的可及性和实用性。PGSFusion可在http://www.pgsfusion.net/免费获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b3/12257662/9a12dbc5504d/13073_2025_1505_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b3/12257662/971a32492f47/13073_2025_1505_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b3/12257662/0da722e2f308/13073_2025_1505_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b3/12257662/faadc8a82caf/13073_2025_1505_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b3/12257662/9a12dbc5504d/13073_2025_1505_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b3/12257662/971a32492f47/13073_2025_1505_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b3/12257662/0da722e2f308/13073_2025_1505_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b3/12257662/faadc8a82caf/13073_2025_1505_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b3/12257662/9a12dbc5504d/13073_2025_1505_Fig4_HTML.jpg

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