Polygenic risk scores (PRS), which are also called (PGS) or (GPS), are tools that aggregate (often large) numbers of genetic variants to estimate risk, e.g., susceptibility to multifactorial diseases. The development of the PRS often follows conducting a genome-wide association study (GWAS) which identifies relevant positions in the genome associated with a condition, trait or outcome. Thus, the development and calculation of PRS increasingly draws on large biobanks that integrate genomic data with phenotypic, environmental and clinical information, such as disease subtypes or diagnoses. This data-intensive approach enables expanded PRS scores as the sample sizes and available data increase, but also has the potential to embed structural biases stemming from especially the earlier steps in the production of data, such as recruitment bias and imbalances, population underrepresentation and variation in healthcare access. The growing attention toward AI-enhanced PRS may leverage AI for countering these issues with the promise of improved prediction, diagnosis and prognosis for the global human population thus bearing hopes and opportunities; however, such approaches also bring their own challenges and risk, as we discussed previously from an ELSI perspective: Fritzsche, M.-C., Akyüz, K., Cano Abadía, M., McLennan, S., Marttinen, P., Mayrhofer, M. T., & Buyx, A. M. (2023). Ethical layering in AI-driven polygenic risk scores—New complexities, new challenges. Frontiers in Genetics, 14. https://doi.org/10.3389/fgene.2023.1098439.
From an ELSI standpoint, PRS development and use intersect directly with debates about fairness, representativity, clinical validity and utility, communication of findings that are probabilistic and relative, and potential for exacerbating health inequities. While laws, regulations and ethical principles specifically on PRS generation is lacking, biobanks are central actors as infrastructures, especially for PRS research, and thus it is important to consider named challenges, especially regarding topics such as recruitment bias, data governance, public trust, consent, return of findings and benefit sharing. Topics such as clinical applications, population-level screening, embryo selection/preimplantation use, commercial direct-to-consumer context, risks of misinterpretation, genetic determinism, genomic privacy as well as potential and current of use AI have been some of the topics that are discussed in the literature (see for more: Fritzsche et al., 2023).
Considering the lack of laws, regulations and specific ethics guidelines regarding PRS generation and use in biobanking, we have compiled below relevant statements and proposals by major professional societies and groups that are relevant for PRS.
RESOURCES
European Society of Human Genetics (ESHG)
Viewpoint: The use of polygenic risk scores in pre-implantation genetic testing: an unproven, unethical practice
Forzano, F., Antonova, O., Clarke, A., de Wert, G., Hentze, S., Jamshidi, Y., Moreau, Y., Perola, M., Prokopenko, I., Read, A., Reymond, A., Stefansdottir, V., van El, C., Genuardi, M., Peterlin, B., Oliveira, C., Writzl, K., Houge, G. D., Cordier, C., . . . Professional Policy Committee of the European Society of Human Genetics (2022). The use of polygenic risk scores in pre-implantation genetic testing: an unproven, unethical practice. European Journal of Human Genetics, 30(5), 493-495. https://doi.org/10.1038/s41431-021-01000-x (see also supporting statement by European Society of Human Reproduction and Embryology (ESHRE): https://www.eshre.eu/Europe/Position-statements/PRS)
American College of Medical Genetics and Genomics (ACMG)
The clinical application of polygenic risk scores: A points to consider statement
Abu-El-Haija, A., Reddi, H. V., Wand, H., Rose, N. C., Mori, M., Qian, E., & Murray, M. F. (2023). The clinical application of polygenic risk scores: A points to consider statement of the American College of Medical Genetics and Genomics (ACMG). Genetics in Medicine, 25(5). https://doi.org/10.1016/j.gim.2023.100803
Human Genetics Society of Australasia
Position Statement: Use of Polygenic Scores in Clinical Practice and Population Health
Young, M.-A., Yanes, T., Cust, A. E., Dunlop, K., Limb, S., Newson, A. J., Purvis, R., Thiyagarajan, L., Scott, R. J., Verma, K., James, P. A., & Steinberg, J. (2023). Human Genetics Society of Australasia Position Statement: Use of Polygenic Scores in Clinical Practice and Population Health. Twin Research and Human Genetics, 26(1), 40-48. https://doi.org/10.1017/thg.2023.10
Polygenic Risk Score Task Force of the International Common Disease Alliance
Perspective: Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps
Polygenic Risk Score Task Force of the International Common Disease Alliance. (2021). Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps. Nature Medicine, 27(11), 1876-1884. https://doi.org/10.1038/s41591-021-01549-6
Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog
Polygenic Risk Score Reporting Standards (PRS-RS)
Wand, H., Lambert, S. A., Tamburro, C., Iacocca, M. A., O’Sullivan, J. W., Sillari, C., Kullo, I. J., Rowley, R., Dron, J. S., Brockman, D., Venner, E., McCarthy, M. I., Antoniou, A. C., Easton, D. F., Hegele, R. A., Khera, A. V., Chatterjee, N., Kooperberg, C., Edwards, K., . . . Wojcik, G. L. (2021). Improving reporting standards for polygenic scores in risk prediction studies. Nature, 591(7849), 211-219. https://doi.org/10.1038/s41586-021-03243-6
National Society of Genetic Counselors
Clinical genetic counseling and translation considerations for polygenic scores in personalized risk assessments: A Practice Resource
Wand, H., Kalia, S. S., Helm, B. M., Suckiel, S. A., Brockman, D., Vriesen, N., Goudar, R. K., Austin, J., & Yanes, T. (2023). Clinical genetic counseling and translation considerations for polygenic scores in personalized risk assessments: A Practice Resource from the National Society of Genetic Counselors. Journal of Genetic Counseling, 32(3), 558-575. https://doi.org/https://doi.org/10.1002/jgc4.1668
Acknowledgements:
This Knowledge Base entry benefited from funding from INTERVENE (INTERnational consortium for integratiVE geNomics prEdiction), a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101016775.
