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Validity of polygenic risk scores: are we measuring what we think we are?
Open accessAbstract
Polygenic risk scores (PRSs) have become the standard for quantifying genetic liability in the prediction of disease risks. PRSs are generally constructed as weighted sum scores of risk alleles using effect sizes from genome-wide association studies as their weights. The construction of PRSs is being improved with more appropriate selection of independent single-nucleotide polymorphisms (SNPs) and optimized estimation of their weights but is rarely reflected upon from a theoretical perspective, focusing on the validity of the risk score. Borrowing from psychometrics, this paper discusses the validity of PRSs and introduces the three main types of validity that are considered in the evaluation of tests and measurements: construct, content, and criterion validity. This introduction is followed by a discussion of three topics that challenge the validity of PRS, namely, their claimed independence of clinical risk factors, the consequences of relaxing SNP inclusion thresholds and the selection of SNP weights. This discussion of the validity of PRS reminds us that we need to keep questioning if weighted sums of risk alleles are measuring what we think they are in the various scenarios in which PRSs are used and that we need to keep exploring alternative modeling strategies that might better reflect the underlying biological pathways.
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“Genetic data are increasingly cost effective, requiring only a one-in-a-lifetime ascertainment, but they also have limited predictive ability on their own 27 .”
From Multimodal biomedical AI (2022) · cites this paper as [27]
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“The validity of a test or measurement indicates whether it measures what it intends to measure (20,21).”
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“The validity of clinical risk models needs to be demonstrated, and the choices in the model development need to be justified (64).”
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“These PRSs go beyond only including genome-wide significant SNPs from GWASs and beyond setting lower thresholds (higher P-values) than genome-wide significance for the selection of SNPs (7).”
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“weak effects (11,12), (2) there seems to be no evidence for strong gene–gene interaction (12) and (3) these PRSs generally hold their discriminative ability in external validation samples”
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“A recent trend in the construction of PRSs is to use millions of SNPs in the prediction of risks (27,57,58).”
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“disease risk, and should they be included in the PRS? When a PRS is constructed based on 2 million of SNPs of which, say, 100 are able to change predicted risks before the decimal, will we tell a patient that their risk is calculated using 2 million or 100 SNPs? When researchers relax the SNP inclusion thresholds to i…”
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“It may be that researchers are aware and accept that the validity is imperfect as many more genetic associations remain to be identified (2), all models have limitations, and they can be useful even when imperfect.”
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“Thus, it has been argued that the utilization of a ‘residual’ PRS – that is a PRS capturing the genetic liability to the outcome minus the liability for the clinical factor – might improve the validity (Janssens, 2019).”
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“…and criterion validity of PRS, to explore alternative ways of quantifying polygenic risk, and to rigorously compare new and current methods” (Janssens 2019) “There remains a gap in evidence from prospective observational studies or treatment trials regarding the appropriate placement of…”
Your height affects your health: genetic determinants and health-related outcomes in Taiwan
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“PRS is the sum of the weighted risk alleles from a combination of independent SNPs, usually with genome-wide significance, derived from GWAS results [13, 35, 36].”
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“While there is currently little evidence for including SNP-SNP interactions in PRS, there is a recognised need to explore alternative modeling strategies [15].”
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“constructed 30 PRSs using millions of SNPs for five common diseases, obtaining PRSs with lower AUC values than those based on genome-wide significant SNPs only [69, 70].”
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“This bias can then affect predictions negatively, as has been shown for PGS when effect sizes are inflated by winner’s curse (Janssens 2019; Forde et al. 2023).”
Brookes, K. J., Guetta‐Baranes, T., Thomas, A. J., & Morgan, K. (2023) · Frontiers in Dementia · doi:10.3389/frdem.2023.1120206 · Cited by 3
“…this has provided greater discriminability (Escott-Price et al., 2015), the clinical utility of including such SNPs, often with negligible effect sizes, is questionable; if they do not impact the biology of the disease or suggest a druggable target, are they useful to include (Janssens, 2019).”
BackgroundInfluentialExome risk score for predicting susceptibility to and severity of isolated thoracic aortic aneurysm
Li, Y., Song, L., Rong, W., Zhang, Y., Yao, X., Fang, X., Li, Y., & Du, J. (2022) · Human Molecular Genetics · doi:10.1093/hmg/ddac099 · Cited by 3
Dai, Y., Shi, G., Chen, M., Chen, G., & Wu, Q. (2022) · Journal of Fungi · doi:10.3390/jof8090914 · Cited by 3
Do we measure or compute polygenic risk scores? Why language matters
Penders, B., & Janssens, A. C. J. W. (2021) · Human Genetics · doi:10.1007/s00439-021-02262-7 · Cited by 3
“This has led to an ongoing conversation about PRS and questions about the validity, limits, character and characteristics of the method (Janssens 2019).”
MethodSelf-citationTrochet, H., Pelletier, J., Tadros, R., & Hussin, J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.09.243287 · Cited by 3
“Clearly the scores use markers associated with these risk factors, and it may make sense to build separate PRS for each of them, and then combine them into a risk model, as discussed previously [28].”
BackgroundAtrial Fibrillation Identification Using CNNs Based on Genomic Data
Lee, J., Kwon, O., Ryu, G., Shin, H., & Pak, H. (2024) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-024-01998-2 · Cited by 2
Cuesta, M. J., Papiol, S., Giné-Servén, E., Ballesteros, A., Gil‐Berrozpe, G. J., Peralta, D., Crespo‐Facorro, B., Sánchez-Torres, A. M., Peralta, V., SEGPEPs group, et al. (2025) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaf158 · Cited by 2
Genetic risk of steatotic liver disease: Pathogenesis, prognosis, and implications for treatment
Kozlitina, J., Romeo, S., & Hobbs, H. H. (2026) · Hepatology · doi:10.1097/hep.0000000000001758 · Cited by 2
Stein, R., Ferrari, F., & García-Giustiniani, D. (2024) · Arquivos Brasileiros de Cardiologia · doi:10.36660/abc.20240252 · Cited by 1
Navigating the Future of Polygenic Risk Scores: Insights from Child and Adolescent Psychiatrists
Trotter, P. M., Merner, A. R., Ginn, L. A., Martinez, A. C., Battaglino, A. L., Craig, K. P., Bach, J., Freedberg, K. J., Soda, T., Storch, E. A., et al. (2025) · Child Psychiatry & Human Development · doi:10.1007/s10578-025-01854-y · Cited by 2
van der Heide, F. C. T., Steens, I. L. M., Singh‐Manoux, A., Sabia, S., Zhou, T. L., Köhler, S., Schram, M. T., Kallen, C., Berendschot, T. T. J. M., Webers, C. A. B., et al. (2025) · Cardiovascular Diabetology · doi:10.1186/s12933-025-02888-1 · Cited by 2
Fisch, G. S. (2021) · Genetica · doi:10.1007/s10709-021-00138-2 · Cited by 2
Emerging risk factors for QT interval prolongation and torsades de pointes
Lazzerini, P. E., Pasini, F. L., Capecchi, P. L., & Boutjdir, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-821446-6.00009-2 · Cited by 2
Brookes, K. J. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241612799 · Cited by 2
Cho, S., Yeh, T., Wang, H., Du, J., Gau, Y., Lin, Y., Chuang, B. T.-M., Liu, Y., Hsiao, H., & Moi, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-56583-4 · Cited by 2
“This approach enables the comprehensive assessment of the collective impact of multiple genetic variants associated with a specific condition 30,31 .”
BackgroundHerzig, A. F., Noûs, C., Pierre, A. S., & Perdry, H. (2026) · Theoretical Population Biology · doi:10.1016/j.tpb.2026.06.003 · Cited by 2
Herle, M., Pickles, A. R., Pain, O., Viner, R., Pingault, J., & Stavola, B. L. D. (2023) · European Journal of Epidemiology · doi:10.1007/s10654-023-00980-y · Cited by 1
“Regardless, PGS have been described as promising tools in precision medicine and are commonly used in epidemiological research [24], but at the same time have been criticised for the their lack of theoretical and empirical evaluation [25, 26].”
BackgroundMethodThe implementation of genomics in healthcare: The challenge of justice
Clarke, A. J. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-91799-5.00002-4 · Cited by 1
Genetics for ‘equality’? The politics of knowledge production in educational genomics
Kotouza, D. (2025) · History of the Human Sciences · doi:10.1177/09526951251314314 · Cited by 1
Evaluating genetic-based disease prediction approaches through simulation
Shpak, M., Parfitt, E. J., Mahmoudiandehkordi, S., Maadooliat, M., & Schrodi, S. J. (2026) · Human Genetics · doi:10.1007/s00439-025-02798-y · Cited by 2
Fu, R., Wang, S., Wu, Y., Qin, X., Huang, T., Li, Y., & Xu, M. (2026) · Food & Function · doi:10.1039/d5fo05425g · Cited by 1
Genetic analysis of neurodegenerative diseases
Grassano, M., Schindler, A. B., Traynor, B. J., & Scholz, S. W. (2026) · Journal of Clinical Investigation · doi:10.1172/jci199840 · Cited by 1
“Essentially, TWAS combines eQTL data with GWAS effect sizes to assess whether environmental factors (164, 165).”
Deep Cross Networks for Intelligent Risk Prediction of Prostate Cancer
Xu, H., Zhang, G., Lin, H., Grosser, M., & Jie Lu, B. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7394-3_24 · Cited by 1
Fu, R., Wang, S., Wu, Y., Qin, X. Y., Huang, T., Li, Y., & Xu, M. (2026) · Aging Cell · doi:10.1111/acel.70581 · Cited by 1
“To capture overall genetic susceptibility, polygenic risk scores (PRS) have been established as composite measures integrating multiple SNP effects (Janssens 2019); for instance, PRS derived from obesity-associated loci have been reported to influence metabolic responses to dietary interventions…”
Kelly, C. M., Onuorah, O., & Gilbert, E. (2026) · Human Genetics · doi:10.1007/s00439-026-02852-3 · Cited by 1
“…by the fact that PRS models are often constructed with SNPs which fall well below the canonical genome-wide significance threshold, plausibly increasing the risk that genetic background and environmental confounding effects will be exploited during prediction (Choi et al. 2020; Janssens 2019).”
Development and Implementation of Polygenic Risk Score in Vietnamese Population
Hùng, N. T. T., & Le, D. (2019) · Research and Development on Information and Communication Technology · doi:10.32913/mic-ict-research.v2019.n2.893 · Cited by 1
“Therefore, there are numerous considerations and thresholds related to developing and validating the formula of PRS that are still controversial [5].”
BackgroundCholesterol associated genetic risk score and acute coronary syndrome in Czech males
Hubacek, J. A., Adámková, V., Lánská, V., Staněk, V., Mrázková, J., Gebauerova, M., Kettner, J., Kautzner, J., & Pitha, J. V. (2024) · Molecular Biology Reports · doi:10.1007/s11033-023-09128-3 · Cited by 1
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Will Genetic Data Significantly Change Cardiovascular Risk Prediction in Daily Practice?
Young, W. J., Ramírez, J., van Duijvenboden, S., Tinker, A., Lambiase, P. D., Munroe, P. B., & Orini, M. (2020) · Computing in cardiology · doi:10.22489/cinc.2020.481 · Cited by 1
“Different P-value thresholds, quality control metrics such as imputation quality, and the effect of underlying population stratification, can significantly influence the resulting PRS [7].”
BackgroundGenetics of Schizophrenia and Bipolar Disorder: Potential Clinical Applications
Gupta, R., Bigdeli, T. B., Buckley, P. F., & Fanous, A. H. (2021) · Psychiatric Annals · doi:10.3928/00485713-20210310-01 · Cited by 1
Dai, Y. L., Shi, G., Chen, M., Chen, G., & Wu, Q. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.07.499257 · Cited by 1
Vercellini, P., Artoni, A., Abbattista, M., Martinelli, I., Pillinini, C., & Forzano, F. (2024) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2024.04.038 · Cited by 1
Usage of biobank data for psychiatric genomics and promotion of precision psychiatry
Davis, L. K., & Dennis, J. K. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-819602-1.00018-8
Metadata
- Authors
- Janssens, A. C. J. W.
- Affiliations
- Emory University (US)
- Corresponding author
- Janssens, A Cecile J W
- Venue
- Human Molecular Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-08-28
- Volume
- 28
- Issue
- R2
- Pages
- R143-R150
- ISSN
- 0964-6906 (ISSN-L)
- 1460-2083
- PMC
- PMC7013150
- OpenAlex
- W2970478489
- Cited by
- 129
- References
- 64
- Citations by year
- 2026: 13 · 2025: 14 · 2024: 24 · 2023: 16 · 2022: 31 · 2021: 22 · 2020: 7 · 2019: 1
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Human Molecular Genetics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- polygenic risk score, validity, construct validity, content validity, criterion validity, psychometrics, single nucleotide polymorphisms, SNP selection, genetic susceptibility, disease risk prediction, clinical risk factors
- MeSH terms
- Alleles, Diabetes Mellitus, Type 2, Genotype, Humans, Models, Genetic, Psychometrics, Risk Factors, Reproducibility of Results, Genetic Predisposition to Disease, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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