Abstract
Genome-wide association studies (GWAS) have uncovered thousands of risk variants that individually have small effects on the risk of human diseases, including chronic kidney disease, type 2 diabetes, heart diseases and inflammatory disorders, but cumulatively explain a substantial fraction of disease risk, underscoring the complexity and pervasive polygenicity of common disorders. This complexity poses unique challenges to the clinical translation of GWAS findings. Polygenic scores combine small effects of individual GWAS risk variants across the genome to improve personalized risk prediction. Several polygenic scores have now been developed that exhibit sufficiently large effects to be considered clinically actionable. However, their clinical use is limited by their partial transferability across ancestries and a lack of validated models that combine polygenic, monogenic, family history and clinical risk factors. Moreover, prospective studies are still needed to demonstrate the clinical utility and cost-effectiveness of polygenic scores in clinical practice. Here, we discuss evolving methods for developing polygenic scores, best practices for validating and reporting their performance, and the study designs that will empower their clinical implementation. We specifically focus on the polygenic scores relevant to nephrology and other chronic, complex diseases and review their key limitations, necessary refinements and potential clinical applications.
Key points
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A polygenic score is a numerical measure of inherited susceptibility conveyed by multiple genetic risk variants for a particular trait or disease and is computed by summing the effects of thousands (or millions) of risk alleles across the genome.
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Family history and polygenic risk contribute independently to susceptibility for most common complex traits and diseases.
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Additive effects of monogenic and polygenic risk have been demonstrated for breast and colorectal cancer, cardiovascular disease and chronic kidney disease.
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An integrative genomic risk predictor combines monogenic, polygenic, family history, environmental and clinical risk factors into a single risk prediction framework.
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The key limitation of existing polygenic scores is their partial cross-ancestry transferability or diminished predictive performance in various non-European populations currently underrepresented in genome-wide association studies.
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Acknowledgements
The authors’ work is funded by the National Human Genome Research Institute (NHGRI) Electronic Medical Records and Genomics-IV (eMERGE-IV grant 5U01HG008680-07). Additional sources of funding include U01HG013201 (K.K.), R01LM013061 (K.K.), R01DK136765 (K.K.), R01DK105124 (K.K.), RC2DK116690 (K.K.), K25DK128563 (A.K.) and UL1TR001873 (A.K., K.K.).
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All of Us: https://allofus.nih.gov/
eMERGE: https://emerge-network.org/
Michigan Inputation Server: https://imputationserver.sph.umich.edu/index.html
PGS Catalog: https://www.pgscatalog.org/
UK Biobank: https://www.ukbiobank.ac.uk/
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Khan, A., Kiryluk, K. Polygenic scores and their applications in kidney disease. Nat Rev Nephrol (2024). https://doi.org/10.1038/s41581-024-00886-2
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DOI: https://doi.org/10.1038/s41581-024-00886-2
