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Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations

Author

Listed:
  • Ying Wang

    (The University of Queensland)

  • Jing Guo

    (The University of Queensland)

  • Guiyan Ni

    (The University of Queensland)

  • Jian Yang

    (The University of Queensland
    Wenzhou Medical University)

  • Peter M. Visscher

    (The University of Queensland)

  • Loic Yengo

    (The University of Queensland)

Abstract

Polygenic scores (PGS) have been widely used to predict disease risk using variants identified from genome-wide association studies (GWAS). To date, most GWAS have been conducted in populations of European ancestry, which limits the use of GWAS-derived PGS in non-European ancestry populations. Here, we derive a theoretical model of the relative accuracy (RA) of PGS across ancestries. We show through extensive simulations that the RA of PGS based on genome-wide significant SNPs can be predicted accurately from modelling linkage disequilibrium (LD), minor allele frequencies (MAF), cross-population correlations of causal SNP effects and heritability. We find that LD and MAF differences between ancestries can explain between 70 and 80% of the loss of RA of European-based PGS in African ancestry for traits like body mass index and type 2 diabetes. Our results suggest that causal variants underlying common genetic variation identified in European ancestry GWAS are mostly shared across continents.

Suggested Citation

  • Ying Wang & Jing Guo & Guiyan Ni & Jian Yang & Peter M. Visscher & Loic Yengo, 2020. "Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17719-y
    DOI: 10.1038/s41467-020-17719-y
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    Cited by:

    1. Qin Qin Huang & Neneh Sallah & Diana Dunca & Bhavi Trivedi & Karen A. Hunt & Sam Hodgson & Samuel A. Lambert & Elena Arciero & John Wright & Chris Griffiths & Richard C. Trembath & Harry Hemingway & M, 2022. "Transferability of genetic loci and polygenic scores for cardiometabolic traits in British Pakistani and Bangladeshi individuals," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Rita Dias Pereira & Pietro Biroli & Titus Galama & Stephanie von Hinke & Hans van Kippersluis & Cornelius A. Rietveld & Kevin Thom, 2022. "Gene-Environment Interplay in the Social Sciences," Papers 2203.02198, arXiv.org, revised Aug 2022.
    3. Brieuc Lehmann & Maxine Mackintosh & Gil McVean & Chris Holmes, 2023. "Optimal strategies for learning multi-ancestry polygenic scores vary across traits," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    4. Lucia Trastulla & Georgii Dolgalev & Sylvain Moser & Laura T. Jiménez-Barrón & Till F. M. Andlauer & Moritz Scheidt & Monika Budde & Urs Heilbronner & Sergi Papiol & Alexander Teumer & Georg Homuth & , 2024. "Distinct genetic liability profiles define clinically relevant patient strata across common diseases," Nature Communications, Nature, vol. 15(1), pages 1-28, December.
    5. Zhen Qiao & Julia Sidorenko & Joana A. Revez & Angli Xue & Xueling Lu & Katri Pärna & Harold Snieder & Peter M. Visscher & Naomi R. Wray & Loic Yengo, 2023. "Estimation and implications of the genetic architecture of fasting and non-fasting blood glucose," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    6. Carla Márquez-Luna & Steven Gazal & Po-Ru Loh & Samuel S. Kim & Nicholas Furlotte & Adam Auton & Alkes L. Price, 2021. "Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets," Nature Communications, Nature, vol. 12(1), pages 1-11, December.

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