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UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits
Open accessAbstract
We present and assess the UK Biobank (UKB) Polygenic Risk Score (PRS) Release, a set of PRSs for 28 diseases and 25 quantitative traits being made available on the individuals in UKB. We also release a benchmarking software tool to enable like-for-like performance evaluation for different PRSs for the same disease or trait. Extensive benchmarking shows the PRSs in the UKB Release to outperform a broad set of 81 published PRSs. For many of the diseases and traits we also validate the PRS algorithms in other cohorts. The availability of PRSs for 53 traits on the same set of individuals also allows a systematic assessment of their properties, and the increased power of these PRSs increases the evidence for their potential clinical benefit.
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“The PRS was derived using external Genome-wide association study (GWAS) training data and has been validated for its association with Late-Onset Alzheimer’s Disease (LOAD) within the UKB.80”
From TET2-mutant myeloid cells mitigate Alzheimer’s disease progression via CNS infiltration and enhanced phagocytosis in mice (2025) · cites this paper as [80]
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Cites
The UK Biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0579-z · Cited by 8,876
Self-citationFry, A., Littlejohns, T. J., Sudlow, C., Doherty, N., Adamska, L., Sprosen, T., Collins, R., & Allen, N. E. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx246 · Cited by 3,979
Khera, A. V., Chaffin, M., Aragam, K. G., Haas, M. E., Roselli, C., Choi, S. H., Natarajan, P., Lander, E. S., Lubitz, S. A., Ellinor, P. T., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0183-z · Cited by 2,953
Clinical use of current polygenic risk scores may exacerbate health disparities
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019) · Nature Genetics · doi:10.1038/s41588-019-0379-x · Cited by 2,821
The personal and clinical utility of polygenic risk scores
Torkamani, A., Wineinger, N. E., & Topol, E. J. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0018-x · Cited by 1,655 · cited 1×
“Throughout, we use: (1) PRS score, or just PRS, for the score assigned to a particular individual; (2) PRS algorithm for the function which calculates the PRS score from genetic data on an individual; and (3) PRS methodology for the approach used to determine a particular PRS algorithm.”
MethodPolygenic 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 · cited 1×
“Throughout, we use: (1) PRS score, or just PRS, for the score assigned to a particular individual; (2) PRS algorithm for the function which calculates the PRS score from genetic data on an individual; and (3) PRS methodology for the approach used to determine a particular PRS algorithm.”
MethodAnalysis of polygenic risk score usage and performance in diverse human populations
Duncan, L., Shen, H., Gelaye, B., Meijsen, J., Ressler, K., Feldman, M., Peterson, R., & Domingue, B. (2019) · Nature Communications · doi:10.1038/s41467-019-11112-0 · Cited by 1,038
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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 484
Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions
Fahed, A. C., Wang, M., Homburger, J. R., Patel, A. P., Bick, A. G., Neben, C. L., Lai, C., Brockman, D., Philippakis, A., Ellinor, P. T., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17374-3 · Cited by 459
Genetic identification of familial hypercholesterolemia within a single U.S. health care system
Abul-Husn, N. S., Manickam, K., Jones, L. K., Wright, E. A., Hartzel, D. N., Gonzaga-Jauregui, C., O’Dushlaine, C., Leader, J. B., Lester Kirchner, H., Lindbuchler, D. M., et al. (2016) · Science · doi:10.1126/science.aaf7000 · Cited by 425
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Items 201–239 of 239
Xie, Y., Du, J., Jiang, Y., Zhao, Y., Lin, S., Zhu, W., & Cao, D. (2026) · Psychological Medicine · doi:10.1017/s0033291726104334
Yue, Z., Tan, Q., Wu, S., & Xu, X. (2026) · Alzheimer s Research & Therapy · doi:10.1186/s13195-026-02066-9 · Cited by 1
“The PRS for AD was generated using a Bayesian approach applied to meta-analysed summary statistics from external genome-wide association study data [34], and was provided by the UK Biobank in the data field 26,206.”
Zhang, Y.-F., Guo, S., Ma, Y., Chen, W.-Q., Jiang, L.-N., Wang, T.-T., Lin, Y., Cai, X.-Y., Wang, J., Chen, Y.-L., et al. (2026) · HYPERTENSION · doi:10.1161/hypertensionaha.125.26388
Stow, D., Tsang, R. S. M., Katzourou, I., Underwood, J. F. G., Holmans, P. A., Barroso, I., Martin, H., van den Bree, M. V. D., Finer, S., & Timpson, N. (2026) · Human Molecular Genetics · doi:10.1093/hmg/ddag012
“More detailed methods for how the Genomics PLC PRSs were derived may be found in Thompson et al, 2022 [82], and Thompson et al., 2024 [81].”
Sun, Y., Deng, J., Cao, X., Tang, L. V., Hao, X., & Hu, Y. (2026) · Thrombosis and Haemostasis · doi:10.1055/a-2969-4582
Zhao, X.-R., Ma, Y., Chen, N., Zheng, L., Xing, M.-Q., Cui, F.-P., Wang, J.-N., Tang, L.-X., Tian, Y.-H., Xiong, P., et al. (2026) · Environmental Pollution · doi:10.1016/j.envpol.2026.128171
Huang, Y.-Z., Ruan, M.-H., Zhang, H., Shen, Y., Zhou, M., Song, H.-Z., Lu, F., Gao, H.-B., Chu, M., & Xu, L. (2026) · International Journal of Cardiology · doi:10.1016/j.ijcard.2026.134292
Private Information Leakage from Polygenic Risk Scores
Nikitin, K., & Gürsoy, G. (2026) · bioRxiv · doi:10.64898/2026.02.16.706191
“As a result, genetic association studies periodically publicly release individual PRSs [14, 15, 16], and customers of direct-to-consumer genetic testing companies share their scores on online forums to seek health advice and recommendations [17, 18, 19].”
Eun, Y., Newman, J. C., Lee, J. H., Kim, S., & Kim, H. (2026) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2026.1768532
Beydoun, M. A., Huang, T., Hooten, N. N., Beydoun, H. A., Bouhrara, M., Weiss, J., Evans, M. K., & Zonderman, A. B. (2026) · GeroScience · doi:10.1007/s11357-026-02098-x
“The Enhanced PRS set integrated genetic risk from internal and external sources with the UK Biobank data, whereas the Standard PRS set was taken from external GWAS data [53, 54].”
He, W., Yu, Z., Ding, J., Jiang, J., Zhang, H., Hu, C., Liu, Q., Gu, H., & Chen, H. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1727636
Exposure-wide approaches identifying modifiable factors for age-related macular degeneration
Jf, Z., He, Y., Wang, J., Wu, H., Huang, X., Cao, J., Tham, Y. C., Zhang, C., Cheng, C. L., & Ye, J. (2026) · British Journal of Ophthalmology · doi:10.1136/bjo-2025-328446
“16”
Wu, X.-R., Lian, Z., Peng, S., Liu, Y., Kuang, N.-Y., Yu, G.-C., Liu, Z.-W., Feng, J.-F., & Zhang, J. (2026) · bioRxiv · doi:10.64898/2026.01.06.697897
“Polygenic risk scores (PRSs) were obtained from the UK Biobank's curated PRS dataset (Category 2405)(Thompson et al., 2022, 2024), developed by Genomics PLC under UK Biobank project 9659.”
Bidirectional Associations Between Substance Abuse and Psoriasis: A Prospective Cohort Study
Che, X., Zhou, J., Wang, A., Guo, J., Zhang, C., & Xiao, Y. (2026) · Psoriasis Targets and Therapy · doi:10.2147/ptt.s557781
Fu, Y., Gao, P., Xu, W., & Tan, L. (2026) · Molecular Neurobiology · doi:10.1007/s12035-026-06032-2 · Cited by 1
“In the present study, we utilized AD-PRS derived from the UK Biobank Polygenic Score Release, a comprehensive resource providing validated genetic risk scores for 53 diseases and quantitative traits [30].”
Wang, L., Zhang, J., Xiao, X., Tang, X.-W., Tang, J., Xu, H. Y., Guo, B., & Zhao, X. (2025) · Journal of Medical Internet Research · doi:10.2196/79301
“…(yes or no), BMI level (normal or underweight [<25 kg/m 2 ], overweight [ ≤ 25-<30 kg/m 2 ], and obesity [ ≥ 30 kg/m 2 ]), and the IHD-PRS [34] (categorized into “low” [lowest quartile], “mid” [second and third quartiles], and “high” [highest quartile]); and (4) lifestyle factors…”
Nair, A. T. N., Witham, M. D., Sayer, A. A., Cordell, H. J., Pearson, E. R., Bartle, V., Cooper, R., Holding, R., Marshall, T. P., Matthews, F. E., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-27839-4
“In addition to self-reported diseases, we utilized Polygenic Risk Scores (PRS) for selected diseases (n = 26), available in the UK Biobank under category 300 14 .”
Modification role of tea in the association of dietary meat intake and obesity risk
Cai, S., Wu, C., Zhang, J., Na, X., Tan, Y., & Zhao, A. (2025) · Nutrition Journal · doi:10.1186/s12937-025-01258-7
Identification of gene-sex hormone interactions associated with type 2 diabetes among men and women
Dabbs-Brown, A., Liu, C., Hui, Q., Wilson, P. W. F., Zhou, J. J., Gwinn, M., & Sun, Y. V. (2025) · PLoS Genetics · doi:10.1371/journal.pgen.1011470
“Standard PRS for type 2 diabetes (PRS T2D ) for the UKB participants (field ID 26285) were generated by the UKB from three GWAS datasets external to the UKB using genetic variants with INFO scores of imputation quality > 0.7 [39].”
InfluentialPaulin, A., Ruel, L., Thériault, S., & Arsenault, B. J. (2025) · medRxiv · doi:10.1016/j.ajpc.2026.101505
“For this study, we used the CAD-PRS and the IS-PRS provided by the UK Biobank [20,21].”
Zhang, L., Liu, W., Xue, Y., Wang, L., Gong, C., She, Z.-G., & Li, H. (2025) · medRxiv · doi:10.1101/2025.08.01.25332722
Guo, J., Jiang, Y.-H., Xu, X.-R., Wang, J., Yao, X., Wang, X., Yang, H.-X., Li, M.-L. J., & Yan, H. (2025) · Tobacco Induced Diseases · doi:10.18332/tid/205419
“Although the UKBB dataset includes the standard PRS developed by Thompson et al. 30 (UKBB Data-Field 26204), the corresponding discovery AMD GWAS datasets were not made available.”
Associations between carotid artery intima-media thickness, traditional risk factors and proteins
Lind, L., & Zheng, R. (2025) · npj Cardiovascular Health · doi:10.1038/s44325-025-00073-7
“Further details on the calculations of the PRSs could be found in the reference 46 .”
Lin, Y., Xu, H., Zheng, J., Lin, T., Wang, M., Huang, T., Lin, F., Ye, Q., & Cai, G. (2025) · npj Parkinson s Disease · doi:10.1038/s41531-025-01000-4
“Inaddition,theGWASdatausedbyUKBiobankto calculatePRSwasuploadedintotheZenododatabase 33 .”
Association of calcium supplement with risk of incident arrhythmia
Chen, R., Liu, D., Yang, C., Guo, T., Liu, S., Guo, Y., Xiong, J., & Deng, S. (2025) · The journal of nutrition health & aging · doi:10.1016/j.jnha.2025.100565 · Cited by 2
“The polygenic risk score (PRS) for atrial fibrillation (AF) was derived from the UK Biobank (PRS, field ID 26212), with its validity supported by external genome-wide association studies [22].”
Liu, D.-Q., Yang, C.-X., Guo, T.-Y., Guo, Y., Xiong, J.-J., Chen, R., & Deng, S. (2025) · Healthcare · doi:10.3390/healthcare13091018
“The PRS for CAD were created for all individuals in the UK biobank by meta-analyzing multiple external genome-wide association study (GWAS) sources [34].”
Zheng, Y., Xiong, X., Bao, J., Liu, J., Wang, J., Zou, F., Chen, Z., Guo, Y., Wang, Q., Qiu, Y., et al. (2025) · Current Research in Toxicology · doi:10.1016/j.crtox.2025.100226 · Cited by 1
“This score, published by Thompson et al. in 2022 (Thompson, 2022), was generated by meta -analyzing multiple external Genome-Wide Association Study (GWAS) sources to produce a standard set of PRS for 28 diseases and 8 quantitative traits for all UKB participants (Category ID 301).”
Shaw, C., Williams, C., Tan, T.-T., Illera, D., Di, N., Shulman, J. M., & Belmont, J. W. (2024) · bioRxiv · doi:10.1016/j.ajhg.2025.07.012
Wang, W., Cheng, Y.-K., Meng, Q., Jia, B., Yao, D., & Cheng, Y.-P. (2024) · BMC Gastroenterology · doi:10.1186/s12876-024-03552-w
“We employed the standard PRS for CRC available in the UKB dataset, as described by Thompson et al [34].”
Association between Genetic Risk and the Renal Function for Developing Venous Thromboembolism
Gan, Z., Guan, Y., Li, S., Kong, Y., Deng, J., & Hao, X.-J. (2024) · Journal of atherosclerosis and thrombosis · doi:10.5551/jat.65328
“In brief, the weights used in the PRS model were derived from a meta-analysis of 4 external VTE genome-wide association studies (GWAS) datasets, totaling 29,799 cases and 475,303 controls 29) .”
Monogenic Parkinson’s Disease and the Impact of APOE E4: A Case-Control StudyPreprint
Kmiecik, M. J., Holmes, M. V., Fontanillas, P., Riboldi, G. M., Schneider, R. B., Shi, J., Guan, A. S., Tat, S. A., Stagaman, K., Gottesman, J., et al. (2024) · medRxiv · doi:10.1101/2024.11.15.24317402
“To define PD penetrance incident to genetic exposure, we analyzed participants with pre-existing diagnosis of PD. 14,30,31 We used the Kaplan-Meier method to estimate PD-free survival probability as a function of carrier status across four levels: 1) LRRK2 G2019S, 2) GBA N370S, 3) dual carriers,…”
Genetics-driven Risk Predictions with Differentiable Mendelian RandomizationPreprint
Sens, D., Gräf, L., Shilova, L., & Casale, F. P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.06.583727
“Additionally, we included a polygenic risk score (PRS) predictor, computed externally by [24] and made available in UKBB through field 26285, for comparison.”
MethodApplying Machine Learning on UK Biobank biomarker data empowers case-control discovery yieldPreprint
Garg, M., Karpinski, M., Matelska, D., Middleton, L., Mitchell, J., O'Neill, A., Wang, Q.-L., Harper, A. R., Dhindsa, R., Petrovski, S., et al. (2023) · medRxiv · doi:10.1101/2023.10.10.23296832
Wong, J., Blechter, B., Liu, Z., Shi, J.-X., & Roger, V. (2023) · medRxiv · doi:10.1101/2023.08.03.23293621
Topriceanu, C., Chaturvedi, N., Mathur, R., & Garfield, V. (2023) · medRxiv · doi:10.1101/2023.03.20.23287475
Zhang, J., Shan, Y., Liu, B., Dai, L., Du, S., Song, C., Shi, J., Carrero, J. J., Xiong, Z., & Huang, X. (2023) · medRxiv · doi:10.1101/2023.02.27.23286539 · Cited by 1
Donnelly, P. (2023) · American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.02.001
Self-citationXie, J., Feng, Y., Newby, D., Zheng, B., Feng, Q., Prats‐Uribe, A., Li, C., Wareham, N. J., Paredes, R., & Prieto‐Alhambra, D. (2022) · medRxiv · doi:10.1101/2022.10.26.22281547 · Cited by 1
Improving on polygenic scores across complex traits using select and shrink with summary statisticsPreprint
Tyrer, J., Peng, P., Devries, A., Gayther, S., Jones, M., & Pharoah, P. (2022) · medRxiv · doi:10.1101/2022.09.13.22278911
Metadata
- Authors
- Thompson, D. J., Wells, D., Selzam, S., Peneva, I., Moore, R., Sharp, K., Tarran, W. A., Beard, E. J., Riveros-Mckay, F., Giner-Delgado, C., Palmer, D., Seth, P., Harrison, J., Futema, M., Genomics England Research Consortium, McVean, G., Plagnol, V., Donnelly, P., & Weale, M. E.
- Affiliations
- Genomics (United Kingdom) (GB)
- St George's, University of London (GB)
- Corresponding author
- Weale, Michael E.
- ORCID
- Venue
- medRxiv
- Publisher
- openRxiv
- Published
- 2022-06-16
- OpenAlex
- W4283067507
- Cited by
- 280
- References
- 41
- Citations by year
- 2026: 47 · 2025: 84 · 2024: 109 · 2023: 32 · 2022: 8
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by
- Locations
- medRxiv (acceptedVersion, cc-by)
- Funders
- Medical Research Council (MC_PC_17228)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- polygenic risk score, UK Biobank, disease risk prediction, quantitative traits, genetic risk prediction, clinical utility
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