| Title: |
Polygenic risk score prediction accuracy convergence |
| Authors: |
Henches, Léo; Kim, Jihye; Yang, Zhiyu; Rubinacci, Simone; Pires, Gabriel; Albiñana, Clara; Boetto, Christophe; Julienne, Hanna; Frouin, Arthur; Auvergne, Antoine; Suzuki, Yuka; Djebali, Sarah; Delaneau, Olivier; Ganna, Andrea; Vilhjálmsson, Bjarni; Privé, Florian; Aschard, Hugues |
| Contributors: |
Département de Biologie Computationnelle - Department of Computational Biology; Institut Pasteur Paris (IP)-Université Paris Cité (UPCité); Harvard T.H. Chan School of Public Health; Helsingin yliopisto = Helsingfors universitet = University of Helsinki; Université de Lausanne = University of Lausanne (UNIL); Aarhus University Aarhus; Institut de Recherche en Santé Digestive (IRSD ); Université Toulouse III - Paul Sabatier (UT3); Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT); Institut National Polytechnique (Toulouse) (Toulouse INP); Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP); Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE); This work has been conducted as part of tshe INCEPTION program (Investissement d’Avenir grant ANR-16-CONV-0005). This research was supported by the Agence Nationale pour la Recherche (ANR-20-CE36-0009-02 and ANR-20-CE15-0012-01).; ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016); ANR-20-CE36-0009,GenCAST,Clusters Génétiques afin de déterminer le risque d'Asthme et les décisions de Traitement(2020); ANR-20-CE15-0012,MICMAT,Prédicteurs de la variabilité du microbiome et leurs role dans les maladies inflammatoires chroniques de l'intestin(2020) |
| Source: |
https://pasteur.hal.science/pasteur-04368572 ; 2024. |
| Publisher Information: |
HAL CCSD |
| Publication Year: |
2024 |
| Collection: |
Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
| Subject Terms: |
[SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics |
| Description: |
Posted June 29, 2023 on bioRxiv. ; Polygenic risk scores (PRS) 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 about strengths and limitations across experts 1 . The performances of existing polygenic scores are still limited, and although it is expected to improve with increasing sample size of GWAS and the development of new powerful methods, it remains unclear how much prediction can be ultimately achieved. Here, we conducted a retrospective analysis to assess the progress in PRS prediction accuracy since the publication of the first large-scale GWASs using six common human diseases with sufficient GWAS data. We show that while PRS accuracy has grown rapidly for years, the improvement pace from recent GWAS has decreased substantially, suggesting that further increasing GWAS sample size may translate into very modest risk discrimination improvement. We next investigated the factors influencing the maximum achievable prediction using recently released whole genome-sequencing data from 125K UK Biobank participants, and state-of-the-art modeling of polygenic outcomes. Our analyses point toward increasing the variant coverage of PRS, using either more imputed variants or sequencing data, as a key component for future improvement in prediction accuracy. |
| Document Type: |
report |
| Language: |
English |
| Relation: |
pasteur-04368572; https://pasteur.hal.science/pasteur-04368572; https://pasteur.hal.science/pasteur-04368572/document; https://pasteur.hal.science/pasteur-04368572/file/2023.06.27.546518v1.full.pdf; BIORXIV: 2023.06.27.546518 |
| DOI: |
10.1101/2023.06.27.546518 |
| Availability: |
https://pasteur.hal.science/pasteur-04368572; https://pasteur.hal.science/pasteur-04368572/document; https://pasteur.hal.science/pasteur-04368572/file/2023.06.27.546518v1.full.pdf; https://doi.org/10.1101/2023.06.27.546518 |
| Rights: |
http://creativecommons.org/licenses/by-nc/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.C02976C1 |
| Database: |
BASE |