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UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits

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Thompson, Deborah J., Wells, Daniel, Selzam, Saskia, Peneva, Iliana, Moore, Rachel, Sharp, Kevin, Tarran, William A., Beard, Edward J., Riveros-Mckay, Fernando, Giner-Delgado, Carla, Palmer, Duncan, Seth, Priyanka, Harrison, James, Futema, Marta, Genomics England Research Consortium, McVean, Gil, Plagnol, Vincent, Donnelly, Peter, Weale, Michael E.·2022·medRxiv

DOI 10.1101/2022.06.16.22276246

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Abstract

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 UK Biobank resource with deep phenotyping and genomic data
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Method
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10.1136/bmj.k1687 · DOI
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Liang, K. Y. H., Zheng, T. M., Tan, D., Sasako, T., Ilboudo, Y., Chen, Y., Butler‐Laporte, G., Yoshiji, S., & Richards, J. B. (2026) · PLoS Medicine · doi:10.1371/journal.pmed.1004802 · Cited by 1
“For the association analysis, we used T2D and T1D PRS released by Thompson and colleagues [45].”
Association of baseline brain care score with the incidence of age-related eye diseases
Yu, J., Zhang, Y.-Z., Gao, Y.-L., Ho, M., Kam, K.-W., Gong, B., Young, A. L., Pang, C., Tham, C. C., Yam, J. C. S., et al. (2026) · Eye · doi:10.1038/s41433-026-04701-z · Cited by 1
“Baseline BCS was analysed as a continuous variable (per 5-unit increase) to estimate the overall association, and as a categorical variable (quintiles) to assess potential non-linear associations.”
Air pollution, road traffic noise, residential green space and hypertension, type 2 diabetes, and cardiovascular disease trajectories
Liu, W., Song, J.-H., Shi, W.-D., Wan, S.-H., Shi, D., Lai, X.-F., Wang, H., Huo, Z.-Y., Hong, L., Liu, Q., et al. (2026) · npj Clean Air · doi:10.1038/s44407-026-00087-0 · Cited by 1
“The UK Biobank polygenetic risk score (PRS), including Standard PRS for hypertension, T2D, and CVD, were provided by Thompson et al. based on the external genome-wide association study 56 .”
Association of dietary copper intake with Alzheimer’s disease, brain structure and cognitive function
Wu, X., Chen, Y., Liu, H., Sun, Z., Sun, P., Liu, Y., Shuai, P., Yang, H., Yang, L., & Wan, Z. (2026) · The journal of nutrition health & aging · doi:10.1016/j.jnha.2026.100868 · Cited by 1
“The AD PRS was constructed based on a previously described method from a UKB study [15], with higher scores indicating greater genetic susceptibility to AD.”
Joint Associations of Sleep Patterns and Genetic Susceptibility With Dynamic Transitions of Chronic Obstructive Pulmonary Disease and Coronary Heart Disease and the Mediating Role of Inflammatory Biomarkers and Metabolites
Zhang, Y., Zhu, P., Gao, D., Ji, M., Pan, Y., Dai, Y., Liu, Y., Zheng, F., & Xie, W. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.125.046469 · Cited by 1
Metabolic vulnerability index and MetaboHealth score as risk factors for age-related macular degeneration in a large-scale prospective cohort
Yu, J., Zhang, Y.-Z., Gao, Y.-L., Xu, J.-M., Chen, H.-Y., Ho, M., Kam, K.-W., Young, A. L., Pang, C., Tham, C. C., et al. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-07863-2 · Cited by 1
“The PRS for AMD (data field 26,204 in UKB), using summary statistics from an external GWAS meta-analysis, was variance-standardized [30] and has shown good performance for AMD prediction [31, 32].”
Region-specific brain structural modulation and amyloid-β pathology associated with dietary biotin: insights into dementia neuropathology
Zhang, J.-H., Yang, C.-L., Tan, Y., Na, M., Shakya, E., Zhang, D., Shi, X., Na, X., Li, Z., Ji, J. S., et al. (2026) · EBioMedicine · doi:10.1016/j.ebiom.2026.106155 · Cited by 1
“24 In current study, PRS was categorised as low and high level by its median.”
White Matter Microstructural Alterations Mediate the Association between Polygenic Risk for Alzheimer’s Disease and Cognitive Performance in Clinically Asymptomatic Adults
Zou, R., Takabayashi, K., Andica, C., Uchida, W., Kitagawa, T., Mizuta, K., Hagiwara, A., Kikuta, J., Aoki, S., & Kamagata, K. (2026) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.mp.2025-0182
Comprehensive risk factor analysis of sick sinus syndrome: a genetic, sociodemographic, clinical and laboratory investigation using the UK Biobank data
Kim, H.-J., Yang, P.-S., Park, H., Kim, D., Bae, H.-J., Lee, C.-H., Son, J.-W., Kim, U., & Joung, B. (2026) · Heart · doi:10.1136/heartjnl-2025-327272 · Cited by 1
Biological aging predicts mortality in Parkinson’s patients: evidence from UK Biobank
Duan, Q., Su, W., Yin, K., He, S.-Y., Liu, R.-Y., Wen, X.-J., Gu, X., Chen, T., Cao, B., & Chen, Y. (2026) · npj Parkinson s Disease · doi:10.1038/s41531-026-01268-0 · Cited by 1
“The genotype data of the UK Biobank were generated through employing a custom Axiom genotyping array, which assays 825,927 genetic variants 43 .”
Influential
Associations of cardiovascular-kidney-metabolic syndrome with age-related macular degeneration risk.
Wu, F., Xu, K., Zhao, Y.-Q., Wang, W., Yang, H.-H., Li, Y.-Q., Zheng, G., Li, Z.-H., Xia, Y., Liu, Y.-S., et al. (2026) · American journal of ophthalmology-glaucoma · doi:10.1016/j.ajo.2026.01.023 · Cited by 1
Loneliness, Genetic Susceptibility, Brain Structure, and the Risk of Schizophrenia: A Large Prospective Study
Wang, J., & Dai, Z. (2025) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaf179 · Cited by 2
“24 All MRI data were processed to derive image-based phenotypes (IDPs) within the UK Biobank dataset.”
Association between accelerometer‐measured physical activity, genetic risk, and incident type 2 diabetes: A prospective cohort study
Pan, Y., Zhang, W.-Y., Dai, Y.-W., Liu, Y.-L., Gao, D.-Y., Zhang, Y.-Y., Ma, J.-Y., Zhu, M.-H., Diao, X.-Y., Yang, X.-Q., et al. (2025) · Diabetes, obesity and metabolism · doi:10.1111/dom.70166 · Cited by 1
“21,22 The PRS for standard T2D (Data-Field: 26285) is available directly from the UK Biobank database 19 and the Polygenic Score (PGS) Catalog 23 (PGS ID: PGS000036).”
A prospective study of eating disorders and risk of rheumatoid arthritis
Chen, J., Wang, P., Li, H., Yu, Z., Olsen, N. J., Su, J., Zeng, X., & Gao, X. (2025) · Seminars in Arthritis and Rheumatism · doi:10.1016/j.semarthrit.2025.152857 · Cited by 1
Identifying novel modifiable risk factors for ischemic stroke through phenome-wide association study and Mendelian randomization analyses in a large-scale prospective cohort
Wu, S., Li, J., Zhao, X., Li, J., Chen, S., Zhang, C., Suo, C., Wang, Y., Chen, X., Cui, M., et al. (2025) · GeroScience · doi:10.1007/s11357-025-01891-4 · Cited by 1
“The standard PRS for IS was generated using a Bayesian approach applied to meta-analyzed summary statistics from external genome-wide association studies (GWAS) data[21].”
Adverse Childhood Experiences, Genetic Susceptibility, and the Risk of Osteoporosis: A Cohort Study
Shu, Y.-L., Tu, C., Liu, Y.-Y., Song, L.-L., Wang, Y.-J., & Wu, M.-Y. (2025) · Medicina · doi:10.3390/medicina61081387 · Cited by 1
“Per-individual Per-individual PRS values were calculated as the genome-wide sum of the per-variant posterior effect size multiplied by allele dosage [29].”
Influential
Plant-based diets, genetic predisposition, and the risk of adult-onset asthma A prospective study
Zhang, J., Wang, B., Tang, Z., He, Y., He, Y. N., An, R., Chen, L., Zhang, H., & Xia, Y. (2025) · European Journal of Nutrition · doi:10.1007/s00394-025-03757-z · Cited by 1
“We utilized the standard released version of the asthma polygenic risk score (PRS) from the UK Biobank [21].”
Associations of domestic hard water metrics with the risk of gout incidence and recurrence
Wei, S.-T., Zhang, J., Ren, S., Ye, D., & Fang, X. (2025) · PLoS ONE · doi:10.1371/journal.pone.0326052 · Cited by 1
“We accessed the standard GRS for gout released by the UK Biobank [20].”
Understanding Interactions Between Life Satisfaction and Genetic Predisposition on Risk of Alzheimer's Disease up to 14 Years Later: Findings From the UK Biobank
John, A., Desai, R., Bartrés‐Faz, D., Cadar, D., Gaysina, D., Gonzalez, A. S., Marchant, N. L., Willroth, E. C., Richards, M., Saunders, R., et al. (2025) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.70120 · Cited by 1
“Detailed methodologies for how the PRS was derived are available online [22, 23].”
Healthy Cardiovascular Status Attenuates the Detrimental Association Between Osteosarcopenic Adiposity and Alzheimer's Disease‐Related Dementia: A UK Biobank Cohort Study
Wang, W., Ren, R., Yang, H., Jiang, J., Ye, X., Wang, C., Ji, X., Li, W., Zhao, Y., Xia, Y., et al. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.125.041697 · Cited by 1
Interaction of accelerometer-measured physical activity and genetic risk on cardiovascular diseases: a prospective cohort study from UK Biobank
Xu, C., Zhang, Q., Xu, S., Xiao, Y., Zhao, L., Li, T.-J., Guo, W.-J., Zhong, Y., & Chen, H.-T. (2025) · BMJ Open Sport & Exercise Medicine · doi:10.1136/bmjsem-2025-002547 · Cited by 1
“Outcomes were determined by the algorithmically defined outcomes (IS) or the first occurrences of outcomes (AF, CHD and hypertension) which combined data from linkage to electronic health records and self-reported medical conditions at baseline of UK Biobank.”
Comprehensive Benchmarking of Machine Learning Methods for Risk Prediction Modelling from Large-Scale Survival Data: A UK Biobank Study
Oexner, R., Schmitt, R., Ahn, H., Shah, R., Zoccarato, A., Theofilatos, K., & Shah, A. M. (2025) · arXiv.org · doi:10.48550/arxiv.2503.08870 · Cited by 1
Investigating the impact of sex and reproductive aging on latent signatures of modifiable dementia risk factorsPreprint
Mukora, A., Costantino, M., Parent, O., Devenyi, G., & Chakravarty, M. (2025) · medRxiv · doi:10.1101/2025.02.21.25322638 · Cited by 1
Cancer, genetic susceptibility and risk of coronary artery disease: A prospective study
Wang, Y.-D., Zhong, S., Sun, N., Wu, Y.-F., Lyu, J., Piao, M., Qu, W.-B., Wang, X., Ni, W.-J., Gu, X., et al. (2024) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2024.100926 · Cited by 1
“PRS for CAD information for the genotyping and imputation in the UK Biobank has been described in detail elsewhere [14].”
Associations between the life’s essential 8, genetic risk and breast cancer incidence in premenopausal and postmenopausal women: a prospective study in UK Biobank
Zhao, Z.-L., Chang, T.-M., Liu, X.-J., Bai, H., Li, Z., Zhang, Y., Chen, H., Zhang, T.-C., & Lu, M. (2024) · Medical Oncology · doi:10.1007/s12032-024-02570-8 · Cited by 1
“The scores are generated using a Bayesian approach applied to meta-analyzed summary statistics from Genome-wide Association Study (GWAS) data [20, 21].”
Genetic and vascular risk factors for ischemic stroke and cortical morphometry in individuals without a history of stroke: A UK Biobank observational cohort study
Liu, J.-W., Xie, Y.-Y., Liu, F., Qin, W., & Yu, C.-S. (2024) · NeuroImage: Clinical · doi:10.1016/j.nicl.2024.103683 · Cited by 1
Machine learning for comprehensive interaction modelling improves disease risk prediction in the UK BiobankPreprint
Julkunen, H., & Rousu, J. (2024) · medRxiv · doi:10.1101/2024.08.07.24311604 · Cited by 1
“…an assay to inform on multidisease risk [13, 16]; and ﬁnally, we included a set of polygenic risk scores for both disease and quantitative traits [17], which have gained interest for their potential to enhance risk prediction models by providing complementary information to traditional risk…”
Background
Assessing the genetic contribution of cumulative behavioral factors associated with longitudinal type 2 diabetes risk highlights adiposity and the brain-metabolic axisPreprint
Carvalho, N. R. G., He, Y., Smadbeck, P., Flannick, J. A., Mercader, J. M., Udler, M. S., Manrai, A. K., Moreno, J., & Patel, C. J. (2024) · medRxiv · doi:10.1101/2024.01.30.24302019 · Cited by 1
“Using the PXS-T2D model, we computed the PXS score for each subject in the UK Biobank and performed a Genome-Wide Association Study (GWAS) for PXS-T2D and all its component exposure behaviors (Table 1).”
Influential
DIFFERENT COMBINATIONS OF THE METABOLIC SYNDROME CRITERIA AND CARDIOVASCULAR DISEASEPreprint
Lind, L. (2023) · medRxiv · doi:10.1101/2023.10.11.23296877 · Cited by 1
“For this aim, we also used derived polygenetic risk scores (PRS) 19 .”
Integrating Polygenic Risk Score, Sex, and Family History to Personalize the Initial Screening Age for Colorectal Cancer.
Sun, W., Liu, D., Wang, D.-X., Liu, J.-Y., Zhang, Y.-J., Liu, F., Wang, X., Zheng, D., & Ji, J.-G. (2026) · International Journal of Cancer · doi:10.1002/ijc.70774
Frailty-Related Metabolic Signature in the Association Between Frailty and Coronary Artery Disease: Modification by Genetic Susceptibility
Liu, J., Du, H., Tang, J., Shi, X., Song, S., Zhang, R., Huang, L., Lai, Z., Li, D., Chen, S., et al. (2026) · The journal of nutrition health & aging · doi:10.1016/j.jnha.2026.100998
“The polygenic risk score (PRS) for CAD was obtained from the UK Biobank Standard PRS release (Data-Field 26227), which provides a precomputed polygenic score based on external genome-wide association study (GWAS) training data [14].”
Sex differences in the associations of multidomain risk factor profiles with incident major adverse cardiovascular events
Yang, Y.-H., Chen, X., Hu, Y.-H., Wang, X.-C., Wang, H.-L., Wei, Y.-Q., Zhang, F.-L., Dong, Z., & Yu, R.-H. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1911138
“Detailed methods have been described previously (31 – 33).”
DeepU-PRS: nonlinear and uncertainty-aware SNP heritability modeling for annotation-informed polygenic risk scoring
Lee, J., Kim, D., & Lee, S. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag563
“To ensure a reliable evaluation, the disease traits were drawn from the UK Biobank systematic PRS evaluation of Thompson et al. [ 15] and were restricted to traits with pseudosummary R 2 values greater than 0.001 in the UK Biobank, thereby excluding traits whose PRS accuracies were essentially…”
Influential
Association of the sarcopenia index based on the serum creatinine to cystatin C ratio and genetic risk with venous thromboembolism: a population-based cohort study
Sun, Y.-Y., Liu, T.-T., Deng, J., Cao, X., Tang, L. V., Hao, X.-J., & Hu, Y. (2026) · Research and Practice in Thrombosis and Haemostasis · doi:10.1016/j.rpth.2026.106973
“Standard PRS values for VTE were provided for each participant and can be accessed via UK Biobank data field 26289 [20].”
A normal-inverse-Wishart (NIW) Bayesian synthesizer for multivariate normal data with application to polygenic risk scores
Jørgensen, R. R. K., Nøhr, A., Sørensen, J. R., Bøgsted, M., & Christensen, H. S. (2026) · doi:10.48550/arxiv.2609.01656
“( Thompson et al. 2022).”
Freely available genomic datasets for atrial fibrillation research: current resources and analytical pipeline
Gjika, M., Sbrollini, A., Caputo, V. L., Paratico, S., Locati, E. T., & Burattini, L. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2026.1816698
“58qt.v1 (Kanai et al., 2018), AF GWAS in UK Biobank (van der Harst, 2017), UK Biobank (Publication 9659) (Thompson et al., 2022) and GWAS summary statistics from a 2025 multi-ancestry AF meta-analysis (Roselli et al., 2025).”
Associations between lifestyle, genetic liability for severe mental illness and risk of incident somatic illnesses
Isayeva, U., Guloksuz, S., Lin, B., de Boer, N., Rutten, B., Linden, D. E. J., Manchia, M., & Luykx, J. (2026) · Translational Psychiatry · doi:10.1038/s41398-026-04332-8
“To evaluate the associations between genetic risk for SMIs and the incidence of somatic illnesses, we leveraged the standard PRS for SCZ and BD provided by the UK Biobank PRS Release [43].”
Metabolic Vulnerability Indices and Alzheimer’s Disease Risk: A Prospective UK Biobank Study
Ding, G., Liu, Y., Zhao, Y., Li, Y., He, Y., He, Y., Chen, N., & Cui, J. (2026) · Journal of Nutrition · doi:10.1016/j.tjnut.2026.101777
Long-term exposure to fine particulate matter and its constituents, genetic susceptibility, and incident schizophrenia
Zhao, X.-R., Ma, Y., Chen, N., Zheng, L., Xing, M.-Q., Cui, F.-P., Wang, J.-N., Tang, L.-X., Tian, Y.-H., & Han, L. (2026) · Journal of Hazardous Materials Advances · doi:10.1016/j.hazadv.2026.101523
Sex-stratified polygenic scores for 47 inflammation and vascular stress biomarkers provide a resource for profiling chronic disease risk
Dowsett, J., Kjerulff, B., von Stemann, J. V., Bentsen, M., Schwinn, M., Bruun, M., Aagaard, B., Astrup, M., Banasik, K., Barghetti, A., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-64145-z
Childhood Adversity and Cardiovascular Disease Risk: Unraveling Plasma Proteomic Pathways and Moderating Role of Genetic Predisposition
Wang, J., Liu, C. Y., Jia, X., Wang, J., Jia, C.-X., & Liu, B.-P. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.125.048564
Association of dietary approaches to stop hypertension, genetic predisposition, and the risk of musculoskeletal disorders: a prospective cohort study
Yang, B., Zhang, D., Song, X., Ma, X. F., Zheng, S., Xiang, R., Zhao, X., Qu, Y., Han, T., Yu, T. P., et al. (2026) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-026-10213-4
“For RA, we utilized the precomputed “Standard PRS” from the UK Biobank PRS Release, which has shown satisfactory predictive performance [23].”
The brain structure and immunometabolic mechanisms underlying the association between frailty and dementia
Chen, W.-J., Min, J.-H., Xu, P., Xu, C.-J., Chen, W., & Cao, Z. (2026) · The Journal of Nutrition, Health & Aging · doi:10.1016/j.jnha.2026.100925
“This set, comprising calculations across all individuals in the UK Biobank for 28 diseases and 8 quantitative traits, was developed by a meta-analysis of multiple external genome-wide association study (GWAS) sources by Thompson et al. [17].”
Impact of modifiable lifestyle factors on dementia subtypes and brain structural changes across KDIGO risk categories in the UK biobank
Li, M. M., Yu, L., Yang, W., Song, L., Li, M. M., Bai, X., Yang, Z., Wang, Z., Lv, H., & Wang, H. (2026) · Brain Imaging and Behavior · doi:10.1007/s11682-026-01170-6
“The UK Biobank genetic dataset included polygenic risk score (PRS) for AD, which was calculated using a Bayesian method applied to summary statistics from meta-analyses of external genome-wide association studies (Thompson et al. 2022, Bycroft et al. 2018).”
Integrative Multi-Omics Profiling of Dynamic Body Mass Index–Systolic Blood Pressure Trajectories in Obesity for Precision Risk Stratification of Heart Failure Subtypes
Zhai, Y.-J., Ma, Q., Zhang, X.-J., He, X.-Q., Wang, G., & Liu, J. (2026) · Biomedicines · doi:10.3390/biomedicines14071473
“Additionally, this study calculated the enhanced PRS [14].”
Socioeconomic disparities in modifiable risk burden and dementia prevention: A prospective cohort study
Zhang, X., Si, K., Chen, N., Cao, Z., Wang, X. L., & Xu, C. (2026) · The journal of nutrition health & aging · doi:10.1016/j.jnha.2026.100909
“This set was developed by a meta-analysis of multiple external genome-wide association study sources by Thompson et al. [14].”
Life’s Essential 8, Polygenic Risk for Type 2 Diabetes, and Dementia: Evidence from the UK Biobank
Li, Y.-J., Ekblom, Ö., Bucha, B., Cojocari, A., Wang, H.-X., & Wang, R. (2026) · Nutrients · doi:10.3390/nu18132080
“The raw PRS was further normalized using the principal component-based ancestry centering step [21].”
Machine Learning‐Driven Prediction of Coronary Artery Disease Risk Based on UK Biobank Plasma Proteomics
Huang, Y.-Z., Chen, X.-L., Zhang, H., Bai, Z.-P., Shen, Y.-F., Zheng, D., Fang, L., Song, H.-Z., Gao, H.-B., Lu, F., et al. (2026) · Journal of the American Heart Association : Cardiovascular and Cerebrovascular Disease · doi:10.1161/jaha.125.047248
“20 The score computation was performed independently by Genomics PLC (Oxford, United Kingdom) within the framework of the UKB project to ensure reproducibility and transparency.”

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: Thompson, Deborah J.
Wells, Daniel
Selzam, Saskia
Moore, Rachel
Sharp, Kevin
Tarran, William A.
Beard, Edward J.
Riveros-Mckay, Fernando
Giner-Delgado, Carla
Palmer, Duncan
Seth, Priyanka
Futema, Marta
McVean, Gil
Plagnol, Vincent
Donnelly, Peter
Weale, Michael E.
Venue: medRxiv
Publisher: openRxiv
Published: 2022-06-16
DOI: 10.1101/2022.06.16.22276246
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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UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits

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