PGSFusion streamlines polygenic score construction and epidemiological applications in biobank-scale cohorts
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 sophisticate...
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BMC
2025-07-01
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| Series: | Genome Medicine |
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| Online Access: | https://doi.org/10.1186/s13073-025-01505-w |
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| author | Sheng Yang Xiangyu Ye Xiaolong Ji Zhenghui Li Min Tian Peng Huang Chen Cao |
| author_facet | Sheng Yang Xiangyu Ye Xiaolong Ji Zhenghui Li Min Tian Peng Huang Chen Cao |
| author_sort | Sheng Yang |
| collection | DOAJ |
| description | 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/ . |
| format | Article |
| id | doaj-art-46bdbac9c0e84cd9aa2be044fc38825a |
| institution | Kabale University |
| issn | 1756-994X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Medicine |
| spelling | doaj-art-46bdbac9c0e84cd9aa2be044fc38825a2025-08-20T03:43:25ZengBMCGenome Medicine1756-994X2025-07-0117111610.1186/s13073-025-01505-wPGSFusion streamlines polygenic score construction and epidemiological applications in biobank-scale cohortsSheng Yang0Xiangyu Ye1Xiaolong Ji2Zhenghui Li3Min Tian4Peng Huang5Chen Cao6Department of Biostatistics, Centre for Global Health, School of Public Health, Nanjing Medical UniversityDepartment 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 UniversityDepartment of Biostatistics, Centre for Global Health, School of Public Health, Nanjing Medical UniversityKey Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical UniversityKey Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical UniversityDepartment 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 UniversityKey Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical UniversityAbstract 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/ .https://doi.org/10.1186/s13073-025-01505-wGenome-wide association study (GWAS)Polygenic score (PGS)Web serverEpidemiological applicationBiobank scale cohort |
| spellingShingle | Sheng Yang Xiangyu Ye Xiaolong Ji Zhenghui Li Min Tian Peng Huang Chen Cao PGSFusion streamlines polygenic score construction and epidemiological applications in biobank-scale cohorts Genome Medicine Genome-wide association study (GWAS) Polygenic score (PGS) Web server Epidemiological application Biobank scale cohort |
| title | PGSFusion streamlines polygenic score construction and epidemiological applications in biobank-scale cohorts |
| title_full | PGSFusion streamlines polygenic score construction and epidemiological applications in biobank-scale cohorts |
| title_fullStr | PGSFusion streamlines polygenic score construction and epidemiological applications in biobank-scale cohorts |
| title_full_unstemmed | PGSFusion streamlines polygenic score construction and epidemiological applications in biobank-scale cohorts |
| title_short | PGSFusion streamlines polygenic score construction and epidemiological applications in biobank-scale cohorts |
| title_sort | pgsfusion streamlines polygenic score construction and epidemiological applications in biobank scale cohorts |
| topic | Genome-wide association study (GWAS) Polygenic score (PGS) Web server Epidemiological application Biobank scale cohort |
| url | https://doi.org/10.1186/s13073-025-01505-w |
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