Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

BBJ, BioMe, BioVU, Canadian Partnership for Tomorrow's Health/OHS, China Kadoorie Biobank Collaborative Group, Colorado Center for Personalized Medicine, deCODE Genetics, ESTBB, FinnGen, Generation Scotland, Genes & Health, LifeLines, Mass General Brigham Biobank, Michigan Genomics Initiative, QIMR Berghofer Biobank, Taiwan Biobank, The HUNT Study, UCLA ATLAS Community Health Initiative, UKBB, Ying Wang*Shinichi Namba, Esteban Lopera, Kristin Tsuo, Kristi Läll, Masahiro Kanai, Wei Zhou, Kuan Han H. Wu, Marie Julie Favé, Laxmi Bhatta, Philip Awadalla, Ben Brumpton, Patrick Deelen, Kristian Hveem, Valeria Lo Faro, Reedik Mägi, Yoshinori Murakami, Serena Sanna, Jordan W. Smoller, Jasmina Uzunovic, Brooke N. Wolford, Alicia R. Martin*, Jibril B. Hirbo*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.

Original languageEnglish
Article number100241
Number of pages18
JournalCell Genomics
Issue number1
Publication statusPublished - 11-Jan-2023


  • accuracy heterogeneity
  • Global-Biobank Meta-analysis Initiative
  • multi-ancestry genetic prediction
  • polygenic risk scores

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