Rank concordance of polygenic indices

Dilnoza Muslimova*, Rita Dias Pereira, Stephanie von Hinke, Hans van Kippersluis, Cornelius A. Rietveld, S. Fleur W. Meddens

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

Polygenic indices (PGIs) are increasingly used to identify individuals at risk of developing disease and are advocated as screening tools for personalized medicine and education. Here we empirically assess rank concordance between PGIs created with different construction methods and discovery samples, focusing on cardiovascular disease and educational attainment. We find Spearman rank correlations between 0.17 and 0.93 for cardiovascular disease, and 0.40 and 0.83 for educational attainment, indicating highly unstable rankings across different PGIs for the same trait. Potential consequences for personalized medicine and gene–environment (G × E) interplay are illustrated using data from the UK Biobank. Simulations show how rank discordance mainly derives from a limited discovery sample size and reveal a tight link between the explained variance of a PGI and its ranking precision. We conclude that PGI-based ranking is highly dependent on PGI choice, such that current PGIs do not have the desired precision to be used routinely for personalized intervention.

Original languageEnglish
Pages (from-to)802-811
Number of pages10
JournalNature Human Behaviour
Volume7
Issue number5
Early online date13 Mar 2023
DOIs
Publication statusPublished - May 2023

Bibliographical note

Funding Information:
This research has been conducted using the UK Biobank Resource under application number 41382. The authors gratefully acknowledge funding from NORFACE through the Dynamic of Inequality across the Life Course (DIAL) programme (GEIGHEI 462-16-100). Research reported in this publication was also supported by the National Institute on Aging of the National Institutes of Health under Award R56AG058726. S.F.W.M. gratefully acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement (GENIO 101019584). C.A.R. and S.v.H. gratefully acknowledge funding from the European Research Council (GEPSI 946647; DONNI 851725). We are grateful for A. Okbay and employees and research participants of the 23andMe, Inc. cohort for sharing GWAS summary statistics for EA, and we thank P. Biroli, T. Galama, E. Slob and R. de Vlaming for insightful comments. This work made use of the Dutch national e-infrastructure with the support of the SURF Cooperative using grant EINF-1107.

Publisher Copyright: © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

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