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 have limited predictive ability on their own.
“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]
The Introduction says polygenic risk scores typically explain only a small proportion of genotypic variance, supporting the claim that genetic data have limited predictive ability on their own. The paper does not state that genetic data are becoming more cost-effective or require only one ascertainment over a lifetime. (LLM, conf=0.85)
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“There may be no benefit for relaxing threshold beyond GWA significance (63), and there may be no good reason to go beyond the genomewide significant SNPs for PRSs that are to be used in healthcare.”
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“Associations would however be expected as earlier studies on PRSs that included smaller numbers of selected SNPs did report associations with clinical risk factors (28,29), since PRSs are investigated for these intermediate phenotypes themselves, such as for obesity (30) and hypertension (31).”
ResultDiscriminative accuracy of genomic profiling comparing multiplicative and additive risk models
Moonesinghe, R., Khoury, M. J., Liu, T., & Janssens, A. C. J. W. (2010) · European Journal of Human Genetics · doi:10.1038/ejhg.2010.165 · Cited by 15 · cited 1× · see citing works
“Two studies that compared the additive model with alternatives showed that not only the weighted sum of risk alleles was compatible with empirical data but that several other mathematical models fitted as well when effects at single loci are small (17,18).”
ResultSelf-citationRevisiting the Polygenic Additive Liability Model through the Example of Diabetes Mellitus
Génin, E., & Clerget‐Darpoux, F. (2015) · Human Heredity · doi:10.1159/000447683 · Cited by 14 · cited 2× · see citing works
“‘Residual’ effects may be observed based on measurement error in the clinical variables and in the genetic data if SNPs do not accurately capture the underlying risk-increasing associations (16).”
BackgroundAdditional value of a combined genetic risk score to standard cardiovascular stratification
Pereira, A., Mendonça, M. I., Borges, S., Sousa, A. C., Freitas, S., Henriques, E., Rodrigues, M. F., Freitas, A. I., Guerra, G., Freitas, C., et al. (2018) · Genetics and Molecular Biology · doi:10.1590/1678-4685-gmb-2017-0173 · Cited by 9 · cited 1× · see citing works
“Independent effects have been reported for PRSs in breast cancer (23), coronary heart disease (24,25) and coronary artery disease (26,27) and were based on a formal mediation analysis (25), a statistically significant effect size for PRS after combining with clinical risk factors (26), and the absence of correlations…”
BackgroundCalvin, C. M., Hagenaars, S. P., Gallacher, J., Harris, S. E., Davies, G., Liewald, D. C. M., Galé, C. R., & Deary, I. J. (2019) · Scientific Reports · doi:10.1038/s41598-018-36629-0 · Cited by 9 · see citing works
Estimating and testing direct genetic effects in directed acyclic graphs using estimating equations
Konigorski, S., Wang, Y., Çiğşar, C., & Yilmaz, Y. E. (2017) · Genetic Epidemiology · doi:10.1002/gepi.22107 · Cited by 1 · see citing works
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Tutorial: a guide to performing polygenic risk score analyses
Choi, S. W., Mak, T. S. H., & O’Reilly, P. F. (2020) · Nature Protocols · doi:10.1038/s41596-020-0353-1 · Cited by 1,913 · see citing works
Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365 · see citing works
Polygenic risk scores: from research tools to clinical instruments
Lewis, C. M., & Vassos, E. (2020) · Genome Medicine · doi:10.1186/s13073-020-00742-5 · Cited by 1,360 · see citing works
“Despite methodological concerns about construct, content, and criterion validity of PRS [1], many studies have shown that PRSs can predict disease status in researchbased case-control studies [2–4].”
BackgroundImproving reporting standards for polygenic scores in risk prediction studies
Wand, H., Lambert, S. A., Tamburro, C. P., Iacocca, M. A., O’Sullivan, J. W., Sillari, C. H., Kullo, I. J., Rowley, R. K., Dron, J. S., Brockman, D., et al. (2021) · Nature · doi:10.1038/s41586-021-03243-6 · Cited by 470 · see citing works
Self-citationAdult Hippocampal Neurogenesis in Major Depressive Disorder and Alzheimer's Disease.
Berger, T., Lee, H., Young, A., Aarsland, D., & Thuret, S. (2020) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2020.03.010 · Cited by 177 · see citing works
The Molecular Basis of Predicting Atherosclerotic Cardiovascular Disease Risk
Nayor, M. G., Brown, K., & Vasan, R. S. (2021) · Circulation Research · doi:10.1161/circresaha.120.315890 · Cited by 147 · see citing works
Dennis, J. K., Sealock, J., Straub, P., Lee, Y. A., Hucks, D., Actkins, K. V., Faucon, A. B., Feng, Y. A., Ge, T., Goleva, S. B., et al. (2021) · Genome Medicine · doi:10.1186/s13073-020-00820-8 · Cited by 129 · see citing works
“addition to other known sources of technical and experimental artifacts [37, 38].”
BackgroundImplementation and implications for polygenic risk scores in healthcare
Slunecka, J. L., van der Zee, M. D., Beck, J. J., Johnson, B. N., Finnicum, C. T., Pool, R., Hottenga, J., de Geus, E. J. C., & Ehli, E. A. (2021) · Human Genomics · doi:10.1186/s40246-021-00339-y · Cited by 114 · see citing works
The emerging field of polygenic risk scores and perspective for use in clinical care
Yanes, T., McInerney-Leo, A. M., Law, M. H., & Cummings, S. A. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa136 · Cited by 75 · see citing works
Liu, C., Zeinomar, N., Chung, W. K., Kiryluk, K., Gharavi, A. G., Hripcsak, G. M., Crew, K. D., Shang, N., Khan, A., Fasel, D. A., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.19084 · Cited by 74 · see citing works
Page, S. C., Sripathy, S. R., Farinelli, F., Ye, Z., Wang, Y., Hiler, D. J., Pattie, E. A., Nguyen, C. V., Tippani, M., Moses, R. L., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2109395119 · Cited by 62 · see citing works
Evaluation of a Multiethnic Polygenic Risk Score Model for Prostate Cancer
Plym, A., Penney, K. L., Kalia, S. S., Kraft, P., Conti, D. V., Haiman, C. A., Mucci, L. A., & Kibel, A. S. (2021) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djab058 · Cited by 64 · see citing works
“While the association between the PRS from the multiethnic GWAS and prostate cancer was confirmed in the replication samples, it is unrealistic to assume that genetic variants influence the risk of prostate cancer equally in all populations (6).”
BackgroundSteinfeldt, J., Buergel, T., Loock, L., Kittner, P., Ruyoga, G., zu Belzen, J. U., Sasse, S., Strangalies, H., Christmann, L., Hollmann, N., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00249-1 · Cited by 56 · see citing works
Polygenic risk scores for predicting outcomes and treatment response in psychiatry: hope or hype?
Fusar‐Poli, L., Rutten, B. P. F., van Os, J., Aguglia, E., & Gülöksüz, S. (2022) · International Review of Psychiatry · doi:10.1080/09540261.2022.2101352 · Cited by 55 · see citing works
“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).”
BackgroundImproving reporting standards for polygenic scores in risk prediction studiesPreprint
Wand, H., Lambert, S. A., Tamburro, C. P., Iacocca, M. A., O’Sullivan, J. W., Sillari, C. H., Kullo, I. J., Rowley, R. K., Dron, J. S., Brockman, D., et al. (2020) · medRxiv · doi:10.1101/2020.04.23.20077099 · Cited by 55 · see citing works
Self-citationRoberts, R., Chang, C. C., & Hadley, T. D. (2021) · JACC Basic to Translational Science · doi:10.1016/j.jacbts.2020.09.004 · Cited by 50 · see citing works
“Because the anticipated use of a CAD GRS for risk stratification, further work will also be necessary to evaluate the validity of the CAD GRS before it can be brought to the clinic (116).”
MethodIncluding diverse and admixed populations in genetic epidemiology research
Caliebe, A., Ayele, F. T., Darst, B. F., Wang, X., Song, Y. E., Gui, J., Sebro, R., Balding, D. J., Saad, M., & Dubé, M. (2022) · Genetic Epidemiology · doi:10.1002/gepi.22492 · Cited by 45 · see citing works
“This variant is rare in European or Asian ancestry individuals, and was only discovered because the GWAS was performed in African ancestry individuals.”
BackgroundInfluentialValidity and Prognostic Value of a Polygenic Risk Score for Parkinson’s Disease
Sendel, S., Laabs, B., Kasten, M., Vollstedt, E., Becktepe, J. S., Brüggemann, N., Franke, A., Krämer, U. M., Kuhlenbäumer, G., Lieb, W., et al. (2021) · Genes · doi:10.3390/genes12121859 · Cited by 37 · see citing works
Chapman, C. R. (2022) · Journal of Community Genetics · doi:10.1007/s12687-022-00625-9 · Cited by 36 · see citing works
“…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
Chiou, J., Cheng, C., Liang, W., Chou, C.-H., Wang, C., Lin, W., Chiu, M.-L., Cheng, W., Lin, C., Lin, T., et al. (2022) · BMC Medicine · doi:10.1186/s12916-022-02450-w · Cited by 33 · see citing works
“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].”
BackgroundFranco, N. R., Massi, M. C., Ieva, F., Manzoni, A., Paganoni, A. M., Zunino, P., Veldeman, L., Ost, P., Fonteyne, V., Talbot, C. J., et al. (2021) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2021.03.024 · Cited by 31 · see citing works
“While there is currently little evidence for including SNP-SNP interactions in PRS, there is a recognised need to explore alternative modeling strategies [15].”
BackgroundPolygenic risk prediction models for colorectal cancer: a systematic review
Sassano, M., Mariani, M., Quaranta, G., Pastorino, R., & Boccia, S. (2022) · BMC Cancer · doi:10.1186/s12885-021-09143-2 · Cited by 33 · see citing works
“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].”
MethodEthical layering in AI-driven polygenic risk scores—New complexities, new challenges
Fritzsche, M.-C., Akyüz, K., Abadía, M. C., McLennan, S., Marttinen, P., Mayrhofer, M. T., & Buyx, A. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1098439 · Cited by 35 · see citing works
“Scientific and clinical validity are challenges on multiple levels (Janssens, 2019; Lewis and Vassos, 2020; Knoppers et al., 2021), which touch ethical as well as epistemic concerns.”
BackgroundTaha, B. A., Abdulrahm, Z. M., Addie, A. J., Haider, A. J., Alkawaz, A. N., Yaqoob, I. A. M., & Arsad, N. (2025) · Talanta · doi:10.1016/j.talanta.2025.127693 · Cited by 34 · see citing works
Newson, A. J. (2021) · Human Genetics · doi:10.1007/s00439-021-02269-0 · Cited by 31 · see citing works
“The use of tools like polygenic risk scores is also not yet ready for population implementation, including on equity grounds (Hunter and Drazen 2019; Janssens 2019; Martin et al.”
MethodSaad, M., Mokrab, Y., Halabi, N. M., Shan, J., Razali, R., Kunji, K. B., Syed, N., Temanni, R., Subramanian, M., Ceccarelli, M., et al. (2022) · The Lancet Oncology · doi:10.1016/s1470-2045(21)00752-x · Cited by 29 · see citing works
Bowman‐Smart, H., Gyngell, C., Mand, C., Amor, D. J., Delatycki, M. B., & Savulescu, J. (2021) · The American Journal of Bioethics · doi:10.1080/15265161.2021.1996659 · Cited by 28 · see citing works
Matthews, L. J., Lebowitz, M. S., Ottman, R., & Appelbaum, P. S. (2021) · Social Psychology of Education · doi:10.1007/s11218-021-09632-z · Cited by 26 · see citing works
“2019; Janssens, 2019; Krapohl et al.”
Dogan, M. V., Knight, S., Dogan, T., Knowlton, K. U., & Philibert, R. A. (2021) · Epigenomics · doi:10.2217/epi-2021-0123 · Cited by 23 · see citing works
Segura, À. G., Martínez-Pinteño, A., Gassó, P., Rodríguez, N., Bioque, M., Cuesta, M. J., González‐Peñas, J., García‐Rizo, C., Lobo, A., González-Pinto, A. M., et al. (2022) · Schizophrenia Research · doi:10.1016/j.schres.2022.05.021 · Cited by 21 · see citing works
The genetics of bipolar disorder with obesity and type 2 diabetes
Miola, A., Filippis, E. D., Veldić, M., Ho, A. M., Winham, S. J., Mendoza, M., Romo‐Nava, F., Núñez, N. A., Gardea‐Resendez, M., Prieto, M. L., et al. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.06.084 · Cited by 25 · see citing works
Torres, G. G., Dose, J., Hasenbein, T. P., Nygaard, M., Krause‐Kyora, B., Mengel‐From, J., Christensen, K., Andersen‐Ranberg, K., Kolbe, D., Lieb, W., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms231810949 · Cited by 22 · see citing works
James, J. E., Riddle, L. S., Koenig, B. A., & Joseph, G. (2021) · PLoS ONE · doi:10.1371/journal.pone.0258571 · Cited by 21 · see citing works
Glaucoma Genetic Risk Scores in the Million Veteran Program
Waksmunski, A. R., Kinzy, T. G., Cruz, L. A., Nealon, C. L., Halladay, C. W., Simpson, P., Canania, R. L., Anthony, S. A., Roncone, D. P., Rogers, L. R. S., et al. (2022) · Ophthalmology · doi:10.1016/j.ophtha.2022.06.012 · Cited by 19 · see citing works
The False Dawn of Polygenic Risk Scores for Human Disease Prediction
Herzig, A. F., Clerget‐Darpoux, F., & Génin, E. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12081266 · Cited by 20 · see citing works
Epistemological Pitfalls in the Proxy Theory of Race: The Case of Genomics-Based Medicine
Malinowska, J., & Serpico, D. (2023) · British Journal for the Philosophy of Science · doi:10.1086/727957 · Cited by 9 · see citing works
Hughes, J., Shymka, M., Ng, T., Phulka, J., Safabakhsh, S., & Laksman, Z. (2024) · Genes · doi:10.3390/genes15121581 · Cited by 17 · see citing works
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Heritability: What's the point? What is it not for? A human genetics perspective
Robette, N., Génin, E., & Clerget‐Darpoux, F. (2022) · Genetica · doi:10.1007/s10709-022-00149-7 · Cited by 17 · see citing works
Han, H. Y., Masip, G., Meng, T., & Nielsen, D. E. (2024) · Journal of Nutrition · doi:10.1016/j.tjnut.2024.10.014 · Cited by 20 · see citing works
Hereditary Breast and Ovarian Cancer
Bellcross, C. A. (2022) · Obstetrics and Gynecology Clinics of North America · doi:10.1016/j.ogc.2021.11.005 · Cited by 16 · see citing works
BURT, C. H. (2024) · Sociological Methodology · doi:10.1177/00811750241236482 · Cited by 17 · see citing works
Genetic risk score is associated with T2DM and diabetes complications risks
Hubacek, J. A., Dlouhá, L., Adámková, V., Dlouhá, D., Pácal, L., Kaňková, K., Galuška, D., Lánská, V., Veleba, J., & Pelikánová, T. (2022) · Gene · doi:10.1016/j.gene.2022.146921 · Cited by 16 · see citing works
The role of polygenic risk scores in breast cancer risk perception and decision-making
Riddle, L. S., Joseph, G., Caruncho, M., Koenig, B. A., & James, J. E. (2023) · Journal of Community Genetics · doi:10.1007/s12687-023-00655-x · Cited by 15 · see citing works
An Academic Clinician’s Road Map to Hypertension Genomics
Magavern, E. F., Warren, H. R., Ng, F. L., Cabrera, C., Munroe, P. B., & Caulfield, M. J. (2021) · Hypertension · doi:10.1161/hypertensionaha.120.14535 · Cited by 14 · see citing works
Chen, X., Chen, D. G., Zhao, Z., Zhan, J. Z., Ji, C., & Chen, J. (2021) · Patterns · doi:10.1016/j.patter.2021.100303 · Cited by 14 · see citing works
Infrastructuring Educational Genomics: Associations, Architectures, and Apparatuses
Williamson, B., Kotouza, D., Pickersgill, M. D., & Pykett, J. (2024) · Postdigital Science and Education · doi:10.1007/s42438-023-00451-3 · Cited by 14 · see citing works
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Yang, J., Liu, T., Lu, H., Tsai, S., Liao, W., Chiu, Y., Wang, Y., & Tsai, F. (2024) · Molecular Medicine Reports · doi:10.3892/mmr.2024.13239 · Cited by 13 · see citing works
“Through this approach, healthcare practi‑ tioners can prescribe preventive measures that are customized to the genetic profile of each patient (23,27,28).”
BackgroundMethodFolsom, A. R., Tang, W., Hong, C.-P., Rosamond, W. D., Lane, J., Cushman, M., & Pankratz, N. (2023) · PLoS ONE · doi:10.1371/journal.pone.0280657 · Cited by 9 · see citing works
“Secondly, researchers have pointed out limitations to GRS modeling to date and the need for better models [25, 26].”
BackgroundSatny, M., Todorovová, V., Altschmiedová, T., Hubacek, J. A., Dlouhá, L., Lánská, V., Soška, V., Kyselák, O., Freiberger, T., Bobák, M., et al. (2023) · Journal of clinical lipidology · doi:10.1016/j.jacl.2023.11.010 · Cited by 9 · see citing works
Kang, S., Kim, K., Jun, S., Lee, S., Kim, J., Shin, J., Kim, Y., Kim, M., Park, S.-G., & Kang, N. (2021) · Pharmaceutics · doi:10.3390/pharmaceutics13122006 · Cited by 8 · see citing works
Importance of GWAS in finding un-targeted genetic association of sporadic Alzheimer’s disease
Bagaria, J., Nho, K. T., & An, S. S. A. (2021) · Molecular & Cellular Toxicology · doi:10.1007/s13273-021-00130-z · Cited by 7 · see citing works
Learning high-order interactions for polygenic risk prediction
Massi, M. C., Franco, N. R., Manzoni, A., Paganoni, A. M., Park, H. A., Hoffmeister, M., Brenner, H., Chang-Claude, J. C., Ieva, F., & Zunino, P. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281618 · Cited by 7 · see citing works
“covariates when estimating SNPs effect on complex diseases [7], and that (iv) those 56 models incorporate strict assumptions, e.”
BackgroundZhou, J., Indik, C. E., Kuipers, T. B., Li, C., Nivard, M. G., Ryan, C. P., Tucker–Drob, E. M., Taeubert, M. J., Wang, S., Wang, T., et al. (2024) · American Journal of Epidemiology · doi:10.1093/aje/kwae376 · Cited by 6 · see citing works
Bhatt, I. S., Ramadugu, S. K., Goodman, S. S., Bhagavan, S. G., Ingalls, V., Dias, R., & Torkamani, A. (2023) · Journal of the Association for Research in Otolaryngology · doi:10.1007/s10162-023-00911-4 · Cited by 6 · see citing works
“Large-scale studies involving population-based cohorts and electronic medical databases have documented the predictive utility of PRS models e.g., [24–26], despite methodological challenges in their design and verification [27].”
BackgroundReview on application and development of pharmacogenomics of adverse drug reactions
Bi, H., Zhu, J., Cai, Y., Shangguan, X., Chen, Z., Ahmed, M. S., & Huang, R. (2025) · Journal of Holistic Integrative Pharmacy · doi:10.1016/j.jhip.2025.03.005 · Cited by 6 · see citing works
Ershova, A. I., Meshkov, A. N., Kutsenko, V. A., Vyatkin, Y. V., Kiseleva, A. V., Сотникова, Е. А., Limonova, A. S., Garbuzova, E. V., Аркадьевна, М. Г., Zaicenoka, M., et al. (2024) · CARDIOVASCULAR THERAPY AND PREVENTION · doi:10.15829/1728-8800-2023-3856 · Cited by 6 · see citing works
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Govindaraju, D. R., & Goldstein, A. M. (2025) · Evolution Education and Outreach · doi:10.1186/s12052-025-00218-3 · Cited by 5 · see citing works
A Blueprint for Genetic Determinism
Joseph, J. (2022) · The American Journal of Psychology · doi:10.5406/19398298.135.4.13 · Cited by 5 · see citing works
Robust Bioinformatics Approaches Result in the First Polygenic Risk Score for BMI in Greek Adults
Kafyra, M., Kalafati, I. P., Dimitriou, M., Grigoriou, E., Kokkinos, A. D., Rallidis, L. S., Kolovou, G. D., Trovas, G., Marouli, E., Deloukas, P., et al. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13020327 · Cited by 5 · see citing works
Evaluating the impact of family history and polygenic risk scores on cardiometabolic disease risk
Onyenobi, E., Oyibo, K., Zhong, M., & Adebamowo, S. N. (2026) · BMC Medicine · doi:10.1186/s12916-026-04666-6 · Cited by 3 · see citing works
“These findings indicate that PRS does not fully capture inherited risk [56, 57].”
Polygenic Risk Scores: The Next Step for Improved Risk Stratification in Coronary Artery Disease?
Stein, R., Ferrari, F., & García-Giustiniani, D. A. (2024) · Arquivos Brasileiros de Cardiologia · doi:10.36660/abc.20240252i · Cited by 4 · see citing works
A Wolf in Sheep's Clothing: Idealisations and the aims of polygenic scores
Serpico, D. (2023) · Studies in History and Philosophy of Science Part A · doi:10.1016/j.shpsa.2023.10.006 · Cited by 3 · see citing works
Lee, S., & Shin, D. (2024) · Nutrition Research and Practice · doi:10.4162/nrp.2024.18.5.721 · Cited by 3 · see citing works
Tiezzi, F., Goda, K., & Morgante, F. (2025) · Genetics · doi:10.1093/genetics/iyaf089 · Cited by 3 · see citing works
“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 · see citing works
“…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 · see citing works
Dai, Y., Shi, G., Chen, M., Chen, G., & Wu, Q. (2022) · Journal of Fungi · doi:10.3390/jof8090914 · Cited by 3 · see citing works
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 · see citing works
“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 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
Stein, R., Ferrari, F., & García-Giustiniani, D. (2024) · Arquivos Brasileiros de Cardiologia · doi:10.36660/abc.20240252 · Cited by 1 · see citing works
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 · see citing works
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Emerging risk factors for QT interval prolongation and torsades de pointes
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“This approach enables the comprehensive assessment of the collective impact of multiple genetic variants associated with a specific condition 30,31 .”
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“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].”
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Genetics for ‘equality’? The politics of knowledge production in educational genomics
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Evaluating genetic-based disease prediction approaches through simulation
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Genetic analysis of neurodegenerative diseases
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“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
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“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…”
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“…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
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“Therefore, there are numerous considerations and thresholds related to developing and validating the formula of PRS that are still controversial [5].”
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Will Genetic Data Significantly Change Cardiovascular Risk Prediction in Daily Practice?
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“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].”
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Usage of biobank data for psychiatric genomics and promotion of precision psychiatry
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- 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 · 2018: 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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