Public health strategies aimed at disease prevention or early detection and intervention have the potential to advance human health worldwide. However, their success depends on the identification of risk factors that underlie disease burden in the general population. Genome-wide association studies (GWAS) have implicated thousands of single-nucleotide polymorphisms (SNPs) in common complex diseases or traits. By calculating a weighted sum of the number of trait-associated alleles harboured by an individual, a polygenic score (PGS), also called a polygenic risk score (PRS), can be constructed that reflects an individual’s estimated genetic predisposition for a given phenotype. Here, we ask six experts to give their opinions on the utility of these probabilistic tools, their strengths and limitations, and the remaining barriers that need to be overcome for their equitable use.
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I.J.K. is funded by NIH grants HG-006379, HG-011710 and HL-70710. A.R.M. is supported by funding from the NIH (R00MH117229).
C.M.L. is a member of the Scientific Advisory Board for Myriad Neuroscience. A.R.M. has consulted for 23andMe and Illumina and received speaker fees from Genentech, Pfizer and Illumina. The other contributors declare no competing interests.
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Clinical Genome Resource (ClinGen) Complex Disease Working Group: https://www.clinicalgenome.org/working-groups/complex-disease/
eMERGE Network: https://emerge-network.org/
Global Biobank Meta-analysis Initiative: www.globalbiobankmeta.org
International Consortium for Integrative Genomics Prediction: www.interveneproject.eu
Polygenic Score Catalogue: https://www.pgscatalog.org/
PRIMED consortium: https://primedconsortium.org/
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Kullo, I.J., Lewis, C.M., Inouye, M. et al. Polygenic scores in biomedical research. Nat Rev Genet (2022). https://doi.org/10.1038/s41576-022-00470-z