Polygenic risk score prediction accuracy convergence
Summary: Polygenic risk scores (PRSs) models trained from genome-wide association study (GWAS) results are set to play a pivotal role in biomedical research addressing multifactorial human diseases. The prospect of using these risk scores in clinical care and public health is generating both enthusi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | HGG Advances |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666247725000600 |
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| author | Léo Henches Jihye Kim Zhiyu Yang Simone Rubinacci Gabriel Pires Clara Albiñana Christophe Boetto Hanna Julienne Arthur Frouin Antoine Auvergne Yuka Suzuki Sarah Djebali Olivier Delaneau Andrea Ganna Bjarni Vilhjálmsson Florian Privé Hugues Aschard |
| author_facet | Léo Henches Jihye Kim Zhiyu Yang Simone Rubinacci Gabriel Pires Clara Albiñana Christophe Boetto Hanna Julienne Arthur Frouin Antoine Auvergne Yuka Suzuki Sarah Djebali Olivier Delaneau Andrea Ganna Bjarni Vilhjálmsson Florian Privé Hugues Aschard |
| author_sort | Léo Henches |
| collection | DOAJ |
| description | Summary: Polygenic risk scores (PRSs) models trained from genome-wide association study (GWAS) results are set to play a pivotal role in biomedical research addressing multifactorial human diseases. The prospect of using these risk scores in clinical care and public health is generating both enthusiasm and controversy, with varying opinions among experts about their strengths and limitations. The performance of existing polygenic scores is still limited but is expected to improve with increasing GWAS sample sizes and the development of new, more powerful methods. Theoretically, the variance explained by PRS can be as high as the total additive genetic variance, but it is unclear how much of that variance has already been captured by PRS. Here, we conducted a retrospective analysis to assess progress in PRS prediction accuracy since the publication of the first large-scale GWASs, using data from six common human diseases with sufficient GWAS information. We show that although PRS accuracy has grown rapidly over the years, the pace of improvement from recent GWAS has decreased substantially, suggesting that merely increasing GWAS sample sizes may lead to only modest improvements in risk discrimination. We next investigated the factors influencing the maximum achievable prediction using whole-genome sequencing data from 125,000 UK Biobank participants and state-of-the-art modeling of polygenic outcomes. Our analyses suggest that increasing the variant coverage of PRS, using either more imputed variants or sequencing data, is a key component for future improvements in prediction accuracy. |
| format | Article |
| id | doaj-art-293258ce375a4fb380a212bfb0fdd4eb |
| institution | OA Journals |
| issn | 2666-2477 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | HGG Advances |
| spelling | doaj-art-293258ce375a4fb380a212bfb0fdd4eb2025-08-20T02:02:25ZengElsevierHGG Advances2666-24772025-07-016310045710.1016/j.xhgg.2025.100457Polygenic risk score prediction accuracy convergenceLéo Henches0Jihye Kim1Zhiyu Yang2Simone Rubinacci3Gabriel Pires4Clara Albiñana5Christophe Boetto6Hanna Julienne7Arthur Frouin8Antoine Auvergne9Yuka Suzuki10Sarah Djebali11Olivier Delaneau12Andrea Ganna13Bjarni Vilhjálmsson14Florian Privé15Hugues Aschard16Institut Pasteur, Université de Paris, Department of Computational Biology, 75015 Paris, FranceDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USAInstitute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, FinlandInstitute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, FinlandInstitut Pasteur, Université de Paris, Department of Computational Biology, 75015 Paris, FranceNational Centre for Register-Based Research, Aarhus University, 8210 Aarhus, DenmarkInstitut Pasteur, Université de Paris, Department of Computational Biology, 75015 Paris, FranceInstitut Pasteur, Université de Paris, Department of Computational Biology, 75015 Paris, FranceInstitut Pasteur, Université de Paris, Department of Computational Biology, 75015 Paris, FranceInstitut Pasteur, Université de Paris, Department of Computational Biology, 75015 Paris, FranceInstitut Pasteur, Université de Paris, Department of Computational Biology, 75015 Paris, FranceIRSD, Université de Toulouse, INSERM, INRAE, ENVT, University Toulouse III - Paul Sabatier (UPS), Toulouse, FranceDepartment of Computational Biology, University of Lausanne, Lausanne, SwitzerlandInstitute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, FinlandNational Centre for Register-Based Research, Aarhus University, 8210 Aarhus, Denmark; Bioinformatics Research Centre, Aarhus University, 8000 Aarhus, DenmarkNational Centre for Register-Based Research, Aarhus University, 8210 Aarhus, DenmarkInstitut Pasteur, Université de Paris, Department of Computational Biology, 75015 Paris, France; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Corresponding authorSummary: Polygenic risk scores (PRSs) models trained from genome-wide association study (GWAS) results are set to play a pivotal role in biomedical research addressing multifactorial human diseases. The prospect of using these risk scores in clinical care and public health is generating both enthusiasm and controversy, with varying opinions among experts about their strengths and limitations. The performance of existing polygenic scores is still limited but is expected to improve with increasing GWAS sample sizes and the development of new, more powerful methods. Theoretically, the variance explained by PRS can be as high as the total additive genetic variance, but it is unclear how much of that variance has already been captured by PRS. Here, we conducted a retrospective analysis to assess progress in PRS prediction accuracy since the publication of the first large-scale GWASs, using data from six common human diseases with sufficient GWAS information. We show that although PRS accuracy has grown rapidly over the years, the pace of improvement from recent GWAS has decreased substantially, suggesting that merely increasing GWAS sample sizes may lead to only modest improvements in risk discrimination. We next investigated the factors influencing the maximum achievable prediction using whole-genome sequencing data from 125,000 UK Biobank participants and state-of-the-art modeling of polygenic outcomes. Our analyses suggest that increasing the variant coverage of PRS, using either more imputed variants or sequencing data, is a key component for future improvements in prediction accuracy.http://www.sciencedirect.com/science/article/pii/S2666247725000600polygenic risk predictionGWASsample sizepolygenicity |
| spellingShingle | Léo Henches Jihye Kim Zhiyu Yang Simone Rubinacci Gabriel Pires Clara Albiñana Christophe Boetto Hanna Julienne Arthur Frouin Antoine Auvergne Yuka Suzuki Sarah Djebali Olivier Delaneau Andrea Ganna Bjarni Vilhjálmsson Florian Privé Hugues Aschard Polygenic risk score prediction accuracy convergence HGG Advances polygenic risk prediction GWAS sample size polygenicity |
| title | Polygenic risk score prediction accuracy convergence |
| title_full | Polygenic risk score prediction accuracy convergence |
| title_fullStr | Polygenic risk score prediction accuracy convergence |
| title_full_unstemmed | Polygenic risk score prediction accuracy convergence |
| title_short | Polygenic risk score prediction accuracy convergence |
| title_sort | polygenic risk score prediction accuracy convergence |
| topic | polygenic risk prediction GWAS sample size polygenicity |
| url | http://www.sciencedirect.com/science/article/pii/S2666247725000600 |
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