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An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome
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Abstract
The age of large-scale genome-wide association studies (GWAS) has provided us with an unprecedented opportunity to evaluate the genetic liability of complex disease using polygenic risk scores (PRS). In this study, we have analysed 162 PRS (p<5×10−05) derived from GWAS and 551 heritable traits from the UK Biobank study (N = 334,398). Findings can be investigated using a web application (http://mrcieu.mrsoftware.org/PRS_atlas/), which we envisage will help uncover both known and novel mechanisms which contribute towards disease susceptibility. To demonstrate this, we have investigated the results from a phenome-wide evaluation of schizophrenia genetic liability. Amongst findings were inverse associations with measures of cognitive function which extensive follow-up analyses using Mendelian randomization (MR) provided evidence of a causal relationship. We have also investigated the effect of multiple risk factors on disease using mediation and multivariable MR frameworks. Our atlas provides a resource for future endeavours seeking to unravel the causal determinants of complex disease.
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“we and others argue that associations between patient-level PGSs and outcomes are more robust, potentially capturing causal associations.95”
From Cystatin C is glucocorticoid responsive, directs recruitment of Trem2+ macrophages, and predicts failure of cancer immunotherapy (2023) · cites this paper as [95]
Experimental — accuracy not yet measuredIn the citing paper
“In addition, as inherited sequence variation is a prominent cause of phenotypic variation (but the reverse is not true), risk variants identified by GWAS have value as genetic instruments, mapping causal relationships between traits and inferring contributions made by circulating biomarkers and environmental exposures to disease development66.”
From A brief history of human disease genetics (2020) · cites this paper as [66]
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Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816 · cited 2×
“evaluating the MR-Egger intercept term (Bowden et al., 2015) to discern whether estimates may be biased by directional pleiotropic effects.”
BackgroundMethodBowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922 · cited 2×
“, 2013), weighed median (Bowden et al., 2016) and weighted mode approaches.”
MethodThe 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 · cited 2×
“In this study, we have constructed 162 different PRS (based on p<5 10 ) using findings from large-scale GWAS and evaluated their association with 551 traits in up to 334,398 individuals enrolled in the UK Biobank study (Bycroft et al., 2018; Sudlow et al., 2015).”
BackgroundMethodThe MR-Base platform supports systematic causal inference across the human phenome
Hemani, G., Zheng, J., Elsworth, B., Wade, K. H., Haberland, V., Baird, D., Laurin, C., Burgess, S., Bowden, J., Langdon, R., et al. (2018) · eLife · doi:10.7554/elife.34408 · Cited by 7,959 · cited 1×
“Constructing polygenic risk scores from large-scale genome-wide association studies We have used the MR-Base platform (Hemani et al., 2018) to identify SNPs from large-scale GWAS to include in our PRS.”
MethodSelf-citationAn integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711
Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data
Burgess, S., Butterworth, A., & Thompson, S. G. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21758 · Cited by 6,050 · cited 3×
“In terms of MR methods, we applied the inverse variance weighted (IVW) (Burgess et al., 2013), weighed median (Bowden et al.”
MethodInfluentialDavey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882
Mendelian randomization: genetic anchors for causal inference in epidemiological studies
Davey Smith, G., & Hemani, G. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu328 · Cited by 4,677
Self-citationReading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians
Davies, N. M., Holmes, M. V., & Davey Smith, G. (2018) · BMJ · doi:10.1136/bmj.k601 · Cited by 4,404 · cited 1×
“Along with the emerging utility of PRS to predict disease, they have also been previously used to evaluate putative causal relationships (Davies et al., 2018; Palmer et al., 2012).”
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Items 101–199 of 199
Genetically determined intelligence and coronary artery disease risk
Li, L., Pang, S., Zeng, L., Güldener, U., & Schunkert, H. (2020) · Clinical Research in Cardiology · doi:10.1007/s00392-020-01721-x · Cited by 9
Wendt, F. R., Carvalho, C. M., Pathak, G. A., Gelernter, J. E., & Polimanti, R. A. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1009036 · Cited by 10
Bench Research Informed by GWAS Results
Kondratyev, N. V., Алфимова, М. В., Golov, A. K., & Голимбет, В. Е. (2021) · Cells · doi:10.3390/cells10113184 · Cited by 11
“Since GWAS has become bigger and PRS tended to explain more of the variability in a trait, PRS became more suitable for use as IV, for example, in studies of the relationships between gout, BMI, urate and triglyceride levels [208]; brain structure and depression [215]; telomere length and type II diabetes [216]; AD an…”
BackgroundMethodPolygenic scores for obstructive sleep apnoea reveal pathways contributing to cardiovascular disease
Kurniansyah, N., Strausz, S. J., Chittoor, G., Gupta, S., Justice, A. E., Hrytsenko, Y., Keenan, B. T., Cade, B. E., Spitzer, B., Wang, H., et al. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.105790 · Cited by 9
Yue, T., Su, Q., He, S., Liu, Y., Huang, D., An, Z., & He, Y. (2026) · Food Science & Nutrition · doi:10.1002/fsn3.71453 · Cited by 10
“Data quality control included removing palindromic variants and SNPs with allele frequency inconsistencies between datasets (Richardson et al. 2019; Zheng et al. 2020).”
Angelis, F. D., Wendt, F. R., Pathak, G. A., Tylee, D. S., Goswami, A., Gelernter, J. E., & Polimanti, R. A. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01713-z · Cited by 10
Implications of the genomic revolution for education research and policy
Morris, T., von Hinke, S., Pike, L., Ingram, N., Smith, G. D., Munafo, M. R., & Davies, N. M. (2022) · British Educational Research Journal · doi:10.1002/berj.3784 · Cited by 10
“…analysis and ge-netic variation to, in effect, control for the mismeasured or unmeasured genetic variation contributing to a trait (most notably DiPrete et al., 2018; van Kippersluis et al., 2021) and repositories of polygenic indices (preprint: Becker et al., 2021; Richardson et al., 2019).”
BackgroundCross-fitted instrument: A blueprint for one-sample Mendelian randomization
Denault, W. R. P., Bohlin, J., Page, C. M., Burgess, S., & Jugessur, A. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010268 · Cited by 10
Li, D., Lu, D.-X., Yan, T., Zhang, K.-Y., Zhang, B.-F., & Zhang, Y. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12010353 · Cited by 9
“A Mendelian randomization study showed that a polygenic risk score for hemoglobin concentration was positively associated with venous thromboembolism risk in the general population [27].”
BackgroundA pediatric perspective on genomics and prevention in the twenty-first century
Chaudhari, B. P., Manickam, K., & McBride, K. L. (2019) · Pediatric Research · doi:10.1038/s41390-019-0597-z · Cited by 8
“The first such test, against a phenotype of schizophrenia, was successful in 2009(116) and has subsequently been followed by hundreds of other such analyses.(117) While it is often the case that such polygenic risk scores demonstrate superior test characteristics to other classifiers based on genomic data, their actua…”
ResultTobias, J. H., Duncan, E. L., Kague, É., Hammond, C. L., Gregson, C. L., Bassett, D., Williams, G. R., Min, J. L., Gaunt, T. R., Karasik, D., et al. (2021) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.630875 · Cited by 9
“Examples include MR-Base (31), the polygenic risk score atlas (32), and LD-hub (33).”
BackgroundLi, J., Huang, K., Li, J., Liang, F., Chen, S., Cao, J., Yang, X., Shen, C., Zhao, L., Li, Y., et al. (2025) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwaf239 · Cited by 8
Marcotte, B. V., Allaire, J., Guénard, F., de Toro‐Martín, J., Couture, P., Lamarche, B., & Vohl, M. (2020) · Genes & Nutrition · doi:10.1186/s12263-020-00669-x · Cited by 8
Assessing Genetic Overlap and Causality Between Blood Plasma Proteins and Alzheimer’s Disease
Handy, Á., Initiative, O. B. O. T. A. D. N., Lord, J., Green, R., Xu, J., Aarsland, D., Velayudhan, L., Hye, A., Dobson, R., & Proitsi, P. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-210462 · Cited by 9
“approaches have more flexibility to relax assumptions which can help increase statistical power and enable more robust statistical sensitivity analyses [23].”
Fairhurst-Hunter, Z., Lin, K., Millwood, I. Y., Pozarickij, A., Chen, T., Torres, J., Lun, J.-A., Kartsonaki, C., Gan, W., Mahajan, A., et al. (2022) · medRxiv · doi:10.1101/2022.07.05.22277254 · Cited by 8
Self-citationKeith Hayes' experience-producing drives: An appreciation and extension
Bouchard, T. J., & Johnson, W. (2020) · Personality and Individual Differences · doi:10.1016/j.paid.2020.110082 · Cited by 8
Association Between Human Gut Microbiome and N-Glycan Composition of Total Plasma Proteome
Петров, В. А., Sharapov, S., Shagam, L., Nostaeva, A. V., Pezer, M., Li, D., Hanić, M., McGovern, D. P. B., Louis, E. J., Rahmouni, S., et al. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.811922 · Cited by 7
Don, J., Schork, A. J., Glusman, G., Rappaport, N., Cummings, S., Duggan, D. J., Raju, A., Hellberg, K.-L. G., Gunn, S., Monti, S., et al. (2024) · GeroScience · doi:10.1007/s11357-024-01107-1 · Cited by 8
“These tools allow PGS/PRS to be derived from very large data sets or meta-analyses [20–22], and open-source websites have been developed that provide the information needed to compute PRS for over 3200 traits and diseases [13, 23].”
MethodTriangulating evidence in health sciences with Annotated Semantic Queries
Liu, Y., & Gaunt, T. R. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae519 · Cited by 7
Paquin, V., Pries, L.-K., ten Have, M., Bak, M., Günther, N., de Graaf, R., van Dorsselaer, S., Lin, B. D., van Eijk, K. R., Kenis, G., et al. (2022) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-022-02346-3 · Cited by 7
“Genetic variants conferring risk for schizophrenia have been associated with multiple mental and physical health outcomes [6, 33, 34]: for example, in an electronic health record study of 106,160 adults in the US, higher PRS-SCZ was associated with higher risk of mood and anxiety disorders, neurological conditions, an…”
BackgroundOgbunugafor, C. B., & Edge, M. D. (2022) · Genetics · doi:10.1093/genetics/iyac142 · Cited by 8
“In addition to the uses in agriculture and medicine discussed in the main text, polygenic scores are used for research, for example to explore relationships among traits (Richardson et al. 2019).”
Gu, J., Xu, J., Jiao, A., Cai, N., Gu, T., Wu, P., Cheng, X., Chen, B., Chen, Y., & Liu, X. (2024) · International Immunopharmacology · doi:10.1016/j.intimp.2024.112803 · Cited by 6
Алфимова, М. В., Kondratyev, N. V., Golov, A. K., & Голимбет, В. Е. (2020) · Scientific Reports · doi:10.1038/s41598-020-61671-2 · Cited by 6
Deng, Y., & Pan, W. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009266 · Cited by 6
Rosoff, D. B., Yoo, J., & Lohoff, F. W. (2021) · medRxiv · doi:10.1101/2021.02.11.21251581 · Cited by 6
Cystatin C is a glucocorticoid response gene predictive of cancer immunotherapy failurePreprint
Kleeman, S. O., Ferrer, M., Demestichas, B., Bankier, S., Lee, H., Heywood, T., Ruusalepp, A., Bjorkegren, J. L. M., Walker, B., Meyer, H. V., et al. (2021) · medRxiv · doi:10.1101/2021.08.17.21261668 · Cited by 4
A Bayesian approach to correcting the attenuation bias of regression using polygenic risk score
Zhou, G., Qie, X., & Zhao, H. (2025) · Genetics · doi:10.1093/genetics/iyaf018 · Cited by 5
Thomas, P. E., Jensen, B. W., Sørensen, K. K., Jacobsen, S., Aarestrup, J., & Baker, J. L. (2020) · Seminars in Arthritis and Rheumatism · doi:10.1016/j.semarthrit.2020.01.011 · Cited by 5
Sun, J., Borné, Y., Edsfeldt, A., Wang, Y., Pan, M., Melander, O., Engström, G., Gonçalves, I., Abecasis, G. R., Baras, A., et al. (2023) · Stroke · doi:10.1161/strokeaha.122.041026 · Cited by 5
SNVformer: An Attention-based Deep Neural Network for GWAS DataPreprint
Elmes, K., Benavides‐Prado, D., Tan, N. Ö., Nguyen, T. B., Sumpter, N. A., Leask, M., Witbrock, M., & Gavryushkin, A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.07.499217 · Cited by 5
“We intend to compare our method to polygenic risk-scores (Richardson et al., 2019), which for gout and its related traits have achieved prediction accuracy (AUROC) of be-tween 0.64 and 0.91 (Zhang & Lee, 2020; Tin et al., 2019; Sun et al., 2022) (Tin et al., 2019).”
MethodInfluentialSato, G., Yamamoto, Y., Sonehara, K., Saiki, R., Ojima, T., Kanai, M., Liu, A., Edahiro, R., Shirai, Y., Namba, S., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04213-z · Cited by 6
“Polygenic risk score (PRS) associations generally offer greater statistical power for causal inference, as they capture a larger fraction of trait heritability 32 .”
Lipid trajectories improve risk models for Alzheimer’s disease and mild cognitive impairment
Chase, B. A., Frigerio, R., Yucus, C. J., Patel, S. V., Maraganore, D. M., Sanders, A. R., Duan, J., & Markopoulou, K. (2024) · Journal of Lipid Research · doi:10.1016/j.jlr.2024.100714 · Cited by 5
A common neural signature between genetic and environmental risk for mental illness
Vedechkina, M., Holmes, J., Warrier, V., & Astle, D. E. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03513-1 · Cited by 8
“Furthermore, our study demonstrates signi fi cant correlations between genetic liability for psychopathology and environmental adversity in this sample [31, 74, 75].”
The Quantitative Genetics of Human Disease: 2 Polygenic Risk Scores
Cutler, D. J., Jodeiry, K., Bass, A. J., & Epstein, M. P. (2024) · Human Population Genetics and Genomics · doi:10.47248/hpgg2404030008 · Cited by 5
Investigating the role of behaviour in the genetic risk for schizophreniaPreprint
Maxwell, J. M., Socrates, A., Glanville, K. P., Forti, M. D., Murray, R. M., Vassos, E., & O’Reilly, P. F. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/611079 · Cited by 4
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Richardson, T. G., Zheng, J., & Gaunt, T. R. (2020) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a039248 · Cited by 4
Self-citationSondervorst, K., Nešporová, K., Herdman, M., Steemans, B., Rosseels, J., & Govers, S. K. (2025) · mSystems · doi:10.1128/msystems.00206-25 · Cited by 3
“Month XXXX Volume 0 Issue 0 10.1128/msystems.00206-25 10”
InfluentialMendelian randomization in cancer research: opportunities and challenges
Tang, M., & Chen, L. (2025) · Infectious Agents and Cancer · doi:10.1186/s13027-025-00672-0 · Cited by 3
“To enhance credibility, MR studies involving infectious exposures should clearly articulate the hypothesized biological pathway linking the genetic variant to disease risk[21]. ii.”
Pan, J., Sun, J., Gonçalves, I., Kessler, M. D., Hao, Y., & Engström, G. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1294218 · Cited by 3
Translational bioinformatics approach to combat cardiovascular disease and cancers
Shahjahan, Dey, J. K., & Dey, S. K. (2024) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2023.11.006 · Cited by 3
What can genetics tell us about the schizophrenia construct?
Smoller, J. W. (2022) · Schizophrenia Research · doi:10.1016/j.schres.2021.12.008 · Cited by 3
Yao, S. (2021) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djab103 · Cited by 3
Trochet, H., Pelletier, J., Tadros, R., & Hussin, J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.09.243287 · Cited by 3
“They have value in clinical applications [1] as well as for assessing relationships between traits and discovering causal determinants of complex disease [2, 3].”
BackgroundBody Mass Index-related genetic factors and COPD imaging phenotypes
Zhang, J., Moll, M. R., Debban, C. L., Hobbs, B. D., Rijhwani, H., Washko, G. R., Celli, B. R., Silverman, E. K., Bakke, P., Oelsner, E. C., et al. (2026) · Annals of the American Thoracic Society · doi:10.1093/annalsats/aaoag007 · Cited by 2
PGS Browser: a public platform for personalized polygenic score analysis and interpretation
Kolosov, N., Reeve, M. P., Parolo, P. D. B., Kurki, M., FinnGen, Parolo, P. D. B., Llorens, V., Sipila, T. P., Heřman, A., Молотков, И. И., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-74461-7 · Cited by 2
“Other top-performing scores included Ankylosing spondylitis (0.38, CI:0.36 – 0.40), Coeliac disease (0.33, CI:0.32 – 0.34), Congenital de fi ciency of clotting factors (0.28, CI:0.20 – 0.35) and Type 1 diabetes (0.24,CI:0.23 – 0.25) (Fig.2e,SupplementaryFig.7,andSupplementary Data 4).”
InfluentialGenetic Determinants of the Association between Osteoarthritis and Psychiatric Disorders
Chen, W., Zhu, J., Han, X., Zeng, Y., Hou, C., Qu, Y., Yang, H., Hu, Y., Sun, Y., & Song, H. (2023) · Depression and Anxiety · doi:10.1155/2023/5253920 · Cited by 2
“weighted by the effect size of each variant using standard clumping+thresholding (C+T) approach [35], represents an individual’s overall genetic risk for a given disease [30].”
InfluentialGao, M., Doody, P., Koutoukidis, D. A., Yu, Y., Aveyard, P. N., Stevens, R., & Dixon, P. (2026) · Obesity Reviews · doi:10.1111/obr.70156 · Cited by 2
The Role of Genetics in Managing Peripheral Arterial Disease
Biagetti, G. M., Thompson, E. W., O’Brien, C., & Damrauer, S. M. (2024) · Annals of Vascular Surgery · doi:10.1016/j.avsg.2024.04.022 · Cited by 2
Complexities of Understanding Function from CKD-Associated DNA Variants
Lin, J. J., & Suszták, K. (2020) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.15771219 · Cited by 2
Polygenic scores for low lung function and the future risk of adverse health outcomes
Zaigham, S., Gonçalves, I., Engström, G., & Sun, J. (2022) · Cardiovascular Diabetology · doi:10.1186/s12933-022-01661-y · Cited by 2
“PGS were also used to examine putative causal relationships with good statistical power [9] and were suggested as an approach in complement to Mendelian randomization (MR) [9, 10] though horizontal pleiotropy need be well considered.”
MethodLeyden, G. M., Gaunt, T. R., & Richardson, T. G. (2020) · medRxiv · doi:10.1101/2020.02.16.20023010 · Cited by 2
Self-citationEtiology of Cognitive Deficits in Schizophrenia: a Review of Studies Based on Polygenic Risk Scores
Алфимова, М. В. (2024) · Psikhiatriya · doi:10.30629/2618-6667-2024-22-4-43-60 · Cited by 2
Influence of Genetics and Sex Hormones on Binge Eating
Wu, Y., Breiner, C. E., & Baker, J. H. (2020) · Springer eBooks · doi:10.1007/978-3-030-43562-2_11 · Cited by 1
Cousminer, D. L., & Grant, S. F. A. (2020) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2020.07.007 · Cited by 1
Cancer Diagnosis, Polygenic Risk, and Longevity-Associated VariantsPreprint
Goetz, L. H., Don, J., Schork, A. J., Duggan, D. J., Price, N. D., Evans, D. S., Cummings, S., Perls, T. T., Sebastiani, P., & Schork, N. J. (2020) · medRxiv · doi:10.1101/2020.09.18.20197475 · Cited by 1
“, Mendelian Randomization tests.(21-23) This work suggests that being susceptible to one type of cancer or phenotype can influence susceptibility - positively or negatively - to another type.”
BackgroundLeinonen, J. T., Mars, N., Lehtonen, L. E., Ahola‐Olli, A., Ruotsalainen, S., Lehtimäki, T. J., Kähönen, M. A. P., Raitakari, O. T., FinnGen, Daly, M. J., et al. (2021) · medRxiv · doi:10.1101/2021.04.23.21255981 · Cited by 1
“Besides this specific example, genetic studies are also prone to confounding by pleiotropy in a more general 442 sense, whereby a gene influences multiple traits via independent biological pathways (30).”
BackgroundAssociations between COVID-19 Risk Perceptions and Mental Health, Wellbeing, and Risk BehavioursPreprint
Dyer, M. L., Sallis, H., Khouja, J. N., Dryhurst, S., & Munafo, M. R. (2021) · doi:10.31234/osf.io/zup86 · Cited by 1
Robinson, E. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.12.018 · Cited by 1
Lee, K.-H., Lee, Y., Hsieh, T.-T., Chang, Y., Wang, S.-S., Fann, G.-Z., Lin, W., Chang, H.-C., Chen, T.-F., Li, P.-H., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.12.536584 · Cited by 1
Alzheimer’s Polygenic Risk Scores Are Not Interchangeable: Evidence from 1,752 ModelsPreprint
Ward, E. L., Nelson, P. T., Katsumata, Y., Fardo, D. W., Jicha, G. A., Initiative, A. D. N., & Miller, J. B. (2026) · medRxiv · doi:10.64898/2026.09.02.26362060 · Cited by 1
Kiiskinen, T., Mars, N., Palviainen, T., Koskela, J. T., Ripatti, P., Rämö, J. T., Ruotsalainen, S., Palotie, A. V., Madden, P. A. F., Rose, R. J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/652396
A genetically tailored education for birds
Tchernichovski, O., & Conley, D. (2019) · Nature · doi:10.1038/d41586-019-03416-4
Zhu, X., Zhu, L., Wang, H., Cooper, R., & Chakravarti, A. (2021) · Research Square · doi:10.21203/rs.3.rs-133000/v2
Competing analytical strategies of combining associated SNPs for estimating genetic risks
Majumdar, A., & Ghosh, S. (2022) · Journal of Genetics · doi:10.1007/s12041-021-01349-4
Khrystoforova, I., Liu, C., & Karasik, D. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-820088-9.00010-x
Spicuglia, S. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.736064143.793564083
Arvanitis, M., Post, W. S., & Battle, A. J. (2021) · Springer eBooks · doi:10.1007/978-3-030-75055-8_3
Zhu, X., Zhu, L., Wang, H., Cooper, R., & Chakravarti, A. (2021) · Research Square · doi:10.21203/rs.3.rs-133000/v1
Walters, R. G., Fairhurst-Hunter, Z., Lin, K., Millwood, I. Y., Pozarickij, A., Chen, T., Torres, J., Luan, J., Kartsonaki, C., Gan, W., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1832470/v1
Self-citationMcCartney, D. L., Min, J. L., Richmond, R. C., Lu, A. T.-H., Sobczyk, M., Davies, G., Broer, L., Guo, X., Jeong, A., Jung, J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14880975
Self-citationPeng, J., Wang, H., Li, Y., & Dong, X. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1368497
“Previous studies demonstrated a signi fi cant correlation between birth weight and adult height (Richardson et al., 2019).”
BackgroundA common neural signature between genetic and environmental riskPreprint
Vedechkina, M., Holmes, J., Warrier, V., & Astle, D. E. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.13.598859
Ieki, H., Ito, K., Zhang, S., Koyama, S., Kjellberg, M., Yoshida, H., Kurosawa, R., Matsunaga, H., Miyazawa, K., Enzan, N., et al. (2026) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.125.005341
BMI-related Genetic Factors and COPD Imaging PhenotypesPreprint
Zhang, J., Moll, M. R., Debban, C. L., Hobbs, B. D., Rijhwani, H., Washko, G. R., Celli, B. R., Silverman, E. K., Bakke, P. S., Oelsner, E. C., et al. (2024) · medRxiv · doi:10.1101/2024.08.16.24312135
Kurniansyah, N., Strausz, S. J., Chittoor, G., Gupta, S., Justice, A. E., Hrytsenko, Y., Keenan, B. T., Cade, B. E., Spitzer, B., Wang, H., et al. (2024) · medRxiv · doi:10.1101/2024.10.20.24315783
Elgaeva, E. E., Trbojević‐Akmačić, I., Ugrina, I., Vilaj, M. Ć., Slana, A., Pribić, T., Tsepilov, Y. A., Karssen, L. C., Aulchenko, Y. S., Dagostino, C., et al. (2024) · medRxiv · doi:10.1101/2024.11.12.24314768
Finn, S., Ajnakina, O., Bu, F., & Fancourt, D. E. (2024) · medRxiv · doi:10.1101/2024.11.14.24317306
Rojo, A. C., Kivisild, T., Valkenborg, D., & Ertaylan, G. (2025) · Research Square · doi:10.21203/rs.3.rs-6169446/v1
Resting Heart Rate and Risk of Incident Venous Thromboembolism: The Tromsø Study
Leknessund, O. G. R., Hansen, J.-B., & Brækkan, S. K. (2025) · Thrombosis and Haemostasis · doi:10.1055/a-2593-1679
“Of interest, data from an atlas of polygenic risk score associations 29 showed no association between genetically determined heart rate and risk of self-reported DVT, suggesting that the relationship may not be directly causal.”
Zhang, J. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i16.102511
“Future research should incorporate longitudinal cohort studies and Mendelian randomization techniques to better understand the causal relationships between genetic variants and disease progression[8].”
PGS Browser: a public platform for personalized polygenic score interpretationPreprint
Kolosov, N., Reeve, M. P., Parolo, P. D. B., Kurki, M., Llorens, V., FinnGen, Sipila, T. P., Herman, A., Молотков, И. И., Aavikko, M., et al. (2025) · medRxiv · doi:10.1101/2025.07.03.25330851
McCartney, D. L., Min, J. L., Richmond, R. C., Lu, A. T., Sobczyk, M. K., Davies, G., Broer, L., Guo, X., Jeong, A., Jung, J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14880993
Self-citationLyon, M., Andrews, S. J., Elsworth, B., Gaunt, T. R., Hemani, G., & Marcora, E. M. (2021) · Figshare · doi:10.6084/m9.figshare.13572203
Self-citationMcCartney, D. L., Min, J. L., Richmond, R. C., Lu, A. T., Sobczyk, M. K., Davies, G., Broer, L., Guo, X., Jeong, A., Jung, J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14880981
Self-citationLyon, M., Andrews, S. J., Elsworth, B., Gaunt, T. R., Hemani, G., & Marcora, E. M. (2021) · Figshare · doi:10.6084/m9.figshare.13572200
Self-citationZhang, C., Ostrom, Q. T., Semmes, E. C., Ramaswamy, V., Hansen, H. M., Morimoto, L., de Smith, A. J., Pekmezci, M., Vaksman, Z., Hakonarson, H. H., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13161431
Collaud, S., Touilloux, B., von Garnier, C., Marques-Vidal, P. M., & Kraege, V. (2024) · Figshare · doi:10.6084/m9.figshare.25566238
McCartney, D. L., Min, J. L., Richmond, R. C., Lu, A. T., Sobczyk, M. K., Davies, G., Broer, L., Guo, X., Jeong, A., Jung, J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14880969
Self-citationCollaud, S., Touilloux, B., von Garnier, C., Marques-Vidal, P. M., & Kraege, V. (2024) · Figshare · doi:10.6084/m9.figshare.26703823
McCartney, D. L., Min, J. L., Richmond, R. C., Lu, A. T., Sobczyk, M. K., Davies, G., Broer, L., Guo, X., Jeong, A., Jung, J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14880984
Self-citationCollaud, S., Touilloux, B., von Garnier, C., Marques-Vidal, P. M., & Kraege, V. (2024) · Figshare · doi:10.6084/m9.figshare.25566404
McCartney, D. L., Min, J. L., Richmond, R. C., Lu, A. T., Sobczyk, M. K., Davies, G., Broer, L., Guo, X., Jeong, A., Jung, J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14880978
Self-citationAdditional file 1 of Genetic predispositions to psychiatric disorders and the risk of COVID-19
Chen, W., Zeng, Y., Suo, C., Yang, H., Chen, Y., Hou, C., Hu, Y., Ying, Z., Sun, Y., Qu, Y., et al. (2022) · Figshare · doi:10.6084/m9.figshare.20579734
Li, Y., La, J. O., Naj, A. C., & Martin, E. R. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71111
van Zwol‐Janssens, C., Lamballais, S., Loehrer, E. A., Laven, J. S. E., Kleefstra, T., & Louwers, Y. V. (2026) · EBioMedicine · doi:10.1016/j.ebiom.2026.106267
Farré, X., Gasco, M., Blay, N., & de Cid, R. (2026) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqag056
“The PRS Atlas [ 5 ] and SpPGS [ 6 ] are valuable population-level references for linking polygenic scores to traits in UK and Spanish populations, but are inherently static resources tied to specific cohorts and methods, making them unsuitable for deployment on new cohorts or phenotype sets.”
Wang, Y., Miao, X., Withers, M. H., Wong, M. C. S., & Viwattanakulvanid, P. (2026) · Systematic Reviews · doi:10.1186/s13643-026-03298-4
“However, it might be susceptible to potential horizontal pleiotropy and thus bias the effect estimate [58].”
Zhong, J., Lin, M., Yu, M., Liu, J., Zhang, P., Dong, Y., Xu, Y., Sun, W., Liu, X., & Zhu, Y. (2026) · PROTEOMICS - CLINICAL APPLICATIONS · doi:10.1002/prca.70059
Metadata
- Authors
- Richardson, T. G., Harrison, S., Hemani, G., & Davey Smith, G.
- Affiliations
- University of Bristol (GB)
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Corresponding author
- Richardson, Tom G
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2019-03-05
- Volume
- 8
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC6400585
- OpenAlex
- W2899836172
- Cited by
- 198
- References
- 48
- Citations by year
- 2026: 12 · 2025: 10 · 2024: 24 · 2023: 14 · 2022: 37 · 2021: 41 · 2020: 44 · 2019: 16
- FWCI
- 20.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Explore Bristol Research (publishedVersion, cc-by)
- Bristol Research (University of Bristol) (acceptedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Medical Research Council (MC_UU_00011/1)
- Wellcome Trust (208806/Z/17/Z)
- Health Data Research UK (MR/S003886/1)
- 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 › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- polygenic risk score, phenome-wide association study, schizophrenia, genetic susceptibility, cognitive function, causal relationship, Mendelian randomization, mediation analysis, multivariable Mendelian randomization, complex disease susceptibility, heritable traits, UK Biobank, web application
- MeSH terms
- United Kingdom, Humans, Schizophrenia, Risk Assessment, Genetic Predisposition to Disease, Multifactorial Inheritance, Databases, Genetic, Genome-Wide Association Study
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