The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Exome sequencing and analysis of 454,787 UK Biobank participants
Open accessAbstract
A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome. Here we used exome sequencing 1 to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study 2 . We identified 12 million coding variants, including around 1 million loss-of-function and around 1.8 million deleterious missense variants. When these were tested for association with 3,994 health-related traits, we found 564 genes with trait associations at P ≤ 2.18 × 10 −11 . Rare variant associations were enriched in loci from genome-wide association studies (GWAS), but most (91%) were independent of common variant signals. We discovered several risk-increasing associations with traits related to liver disease, eye disease and cancer, among others, as well as risk-lowering associations for hypertension ( SLC9A3R2 ), diabetes ( MAP3K15 , FAM234A ) and asthma ( SLC27A3 ). Six genes were associated with brain imaging phenotypes, including two involved in neural development ( GBE1 , PLD1 ). Of the signals available and powered for replication in an independent cohort, 81% were confirmed; furthermore, association signals were generally consistent across individuals of European, Asian and African ancestry. We illustrate the ability of exome sequencing to identify gene–trait associations, elucidate gene function and pinpoint effector genes that underlie GWAS signals at scale.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247
Self-citationThe 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-citationThe GTEx Consortium atlas of genetic regulatory effects across human tissues
The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., et al. (2020) · Science · doi:10.1126/science.aaz1776 · Cited by 5,547
An atlas of genetic correlations across human diseases and traits
ReproGen Consortium, Bulik-Sullivan, B., Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., Duncan, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3406 · Cited by 4,255
Predicting Functional Effect of Human Missense Mutations Using PolyPhen‐2
Adzhubei, I., Jordan, D. M., & Sunyaev, S. R. (2013) · Current Protocols in Human Genetics · doi:10.1002/0471142905.hg0720s76 · Cited by 3,705
MutationTaster evaluates disease-causing potential of sequence alterations
Schwarz, J. M., Rödelsperger, C., Schuelke, M., & Seelow, D. (2010) · Nature Methods · doi:10.1038/nmeth0810-575 · Cited by 2,975
Clonal hematopoiesis and risk of atherosclerotic cardiovascular disease
Jaiswal, S., Natarajan, P., Silver, A. J., Gibson, C. J., Bick, A. G., Shvartz, E., McConkey, M., Gupta, N., Gabriel, S., Ardissino, D., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1701719 · Cited by 2,683
Fast and accurate genotype imputation in genome-wide association studies through pre-phasing
Howie, B., Fuchsberger, C., Stephens, M., Marchini, J., & Abecasis, G. R. (2012) · Nature Genetics · doi:10.1038/ng.2354 · Cited by 1,737
Self-citationGenetic Investigation of ANthropometric Traits (GIANT) Consortium, Yang, J., DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Ferreira, T., Morris, A. P., Medland, S. E., Madden, P. A. F., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2012) · Nature Genetics · doi:10.1038/ng.2213 · Cited by 1,712
Computationally efficient whole-genome regression for quantitative and binary traits
Mbatchou, J., Barnard, L., Backman, J., Marcketta, A., Kosmicki, J. A., Ziyatdinov, A., Benner, C., O’Dushlaine, C., Barber, M., Boutkov, B., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00870-7 · Cited by 1,502
Self-citation
Cited by
The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource
Sollis, E., Mosaku, A., Abid, A., Buniello, A., Cerezo, M., Gil, L., Groza, T., Güneş, O., Hall, P., Hayhurst, J., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1010 · Cited by 1,641
A saturated map of common genetic variants associated with human height
Yengo, L., Vedantam, S., Marouli, E., Sidorenko, J., Bartell, E., Sakaue, S., Graff, M., Eliasen, A. U., Jiang, Y., Raghavan, S., et al. (2022) · Nature · doi:10.1038/s41586-022-05275-y · Cited by 825
Self-citationThe next-generation Open Targets Platform: reimagined, redesigned, rebuilt
Ochoa, D., Hercules, A., Carmona, M., Suveges, D., Baker, J., Malangone, C., Lopez, I., Miranda, A., Cruz-Castillo, C., Fumis, L., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1046 · Cited by 432
Common and rare variant associations with clonal haematopoiesis phenotypes
Kessler, M. D., Damask, A., O’Keeffe, S., Banerjee, N., Li, D., Watanabe, K., Marketta, A., Van Meter, M., Semrau, S., Horowitz, J., et al. (2022) · Nature · doi:10.1038/s41586-022-05448-9 · Cited by 313
Self-citationEvolutionary constraint and innovation across hundreds of placental mammals
Christmas, M. J., Kaplow, I. M., Genereux, D. P., Dong, M. X., Hughes, G. M., Li, X., Sullivan, P. F., Hindle, A. G., Andrews, G., Armstrong, J. C., et al. (2023) · Science · doi:10.1126/science.abn3943 · Cited by 292
Karczewski, K. J., Solomonson, M., Chao, K. R., Goodrich, J. K., Tiao, G., Lu, W., Riley-Gillis, B. M., Tsai, E. A., Kim, H. I., Zheng, X., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100168 · Cited by 284
Holstege, H., Hulsman, M., Charbonnier, C., Grenier-Boley, B., Quenez, O., Grozeva, D., van Rooij, J. G. J., Sims, R., Ahmad, S., Amin, N., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01208-7 · Cited by 226
Germline Mutations in CIDEB and Protection against Liver Disease
Verweij, N., Haas, M. E., Nielsen, J. B., Sosina, O. A., Kim, M., Akbari, P., De, T., Hindy, G., Bovijn, J., Persaud, T., et al. (2022) · New England Journal of Medicine · doi:10.1056/nejmoa2117872 · Cited by 127
Self-citationA deep catalogue of protein-coding variation in 983,578 individuals
Sun, K. Y., Bai, X., Chen, S., Bao, S., Zhang, C., Kapoor, M., Backman, J., Joseph, T., Maxwell, E., Mitra, G., et al. (2024) · Nature · doi:10.1038/s41586-024-07556-0 · Cited by 127
Self-citationGenome-wide association study meta-analysis of suicide death and suicidal behavior
Li, Q. S., Shabalin, A. A., DiBlasi, E., Gopal, S., Canuso, C. M., FinnGen, I. S. G. C., Palotie, A., Drevets, W. C., Docherty, A. R., & Coon, H. (2023) · Molecular Psychiatry · doi:10.1038/s41380-022-01828-9 · Cited by 68
Clonal Hematopoiesis and Its Impact on Human Health
Ahmad, H., Jahn, N., & Jaiswal, S. (2023) · Annual Review of Medicine · doi:10.1146/annurev-med-042921-112347 · Cited by 66
Proteogenomic links to human metabolic diseases
Koprulu, M., Carrasco-Zanini, J., Wheeler, E., Lockhart, S., Kerrison, N. D., Wareham, N. J., Pietzner, M., & Langenberg, C. (2023) · Nature Metabolism · doi:10.1038/s42255-023-00753-7 · Cited by 57
High-resolution functional mapping of RAD51C by saturation genome editing
Olvera-León, R., Zhang, F., Offord, V., Zhao, Y., Tan, H. K., Gupta, P., Pal, T., Robles-Espinoza, C. D., Arriaga-González, F. G., Matsuyama, L. S. A. S., et al. (2024) · Cell · doi:10.1016/j.cell.2024.08.039 · Cited by 44
Human genetics uncovers MAP3K15 as an obesity-independent therapeutic target for diabetes
Nag, A., Dhindsa, R. S., Mitchell, J., Vasavda, C., Harper, A. R., Vitsios, D., Ahnmark, A., Bilican, B., Madeyski-Bengtson, K., Zarrouki, B., et al. (2022) · Science Advances · doi:10.1126/sciadv.add5430 · Cited by 36
Advancing diagnosis and management of liver disease in adults through exome sequencing
Zheng, M., Hakim, A., Konkwo, C., Deaton, A. M., Ward, L. D., Silveira, M. G., Assis, D. N., Liapakis, A., Jaffe, A., Jiang, Z. G., et al. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104747 · Cited by 34
Exome-wide analysis implicates rare protein-altering variants in human handedness
Schijven, D., Soheili-Nezhad, S., Fisher, S. E., & Francks, C. (2024) · Nature Communications · doi:10.1038/s41467-024-46277-w · Cited by 27
Kierczak, M., Rafati, N., Höglund, J., Gourlé, H., Lo Faro, V., Schmitz, D., Ek, W. E., Gyllensten, U., Enroth, S., Ekman, D., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30208-8 · Cited by 22
DeForest, N., Kavitha, B., Hu, S., Isaac, R., Krohn, L., Wang, M., Du, X., De Arruda Saldanha, C., Gylys, J., Merli, E., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100339 · Cited by 21
Mitchell, J., Camacho, N., Shea, P., Stopsack, K. H., Joseph, V., Burren, O. S., Dhindsa, R. S., Nag, A., Berchuck, J. E., O’Neill, A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56944-1 · Cited by 19
Nonlinear transcriptional responses to gradual modulation of transcription factor dosage
Domingo, J., Minaeva, M., Morris, J. A., Ghatan, S., Ziosi, M., Sanjana, N. E., & Lappalainen, T. (2024) · doi:10.1101/2024.03.01.582837 · Cited by 18
Systematic elucidation of genetic mechanisms underlying cholesterol uptake
Hamilton, M. C., Fife, J. D., Akinci, E., Yu, T., Khowpinitchai, B., Cha, M., Barkal, S., Thi, T. T., Yeo, G. H. T., Ramos Barroso, J. P., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100304 · Cited by 16
Pozarickij, A., Said, S., Lin, K., Morris, S., Edris, A., Iona, A., Kartsonaki, C., Wright, N., Yao, P., Fry, H., et al. (2023) · doi:10.1101/2023.11.13.23298365 · Cited by 16
Self-citationFan, X., Pan, H., Tian, A., Chung, W. K., & Shen, Y. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbac584 · Cited by 11
Analysis-ready VCF at Biobank scale using Zarr
(2025) · doi:10.1093/gigascience/giaf049 · Cited by 8
Schiabor Barrett, K. M., Telis, N., McEwen, L. M., Burrows, E. K., Khuder, B., Judge, D. P., Pawloski, P. A., Grzymski, J. J., Washington, N. L., Bolze, A., et al. (2024) · Communications Medicine · doi:10.1038/s43856-024-00663-z · Cited by 8
Functional interrogation of cellular Lp(a) uptake by genome-scale CRISPR screening
Khan, T. G., Bragazzi Cunha, J., Raut, C., Burroughs, M., Vyas, H. S., Leix, K., Goonewardena, S. N., Smrcka, A. V., Speliotes, E. K., & Emmer, B. T. (2025) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2025.119174 · Cited by 6
Rowlands, C. F., Allen, S., Garrett, A., Durkie, M., Burghel, G. J., Robinson, R., Callaway, A., Field, J., Frugtniet, B., Palmer-Smith, S., et al. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.08.014 · Cited by 6
Non-linear transcriptional responses to gradual modulation of transcription factor dosage
Domingo, J., Minaeva, M., Morris, J. A., Ghatan, S., Ziosi, M., Sanjana, N. E., & Lappalainen, T. (2024) · doi:10.7554/elife.100555.1 · Cited by 6
(2023) · doi:10.1101/2023.08.04.551959 · Cited by 6
Pietzner, M., Williamson, A., Hunt, K. A., Koprulu, M., Kohleick, L., Demircan, K., Genes & Health Research Team, Finer, S., Zanini, J. C., van Heel, D. A., et al. (2025) · doi:10.1101/2025.08.12.25333522 · Cited by 4
Yuan, H., Liu, Z., Chen, M., Xu, Q., Jiang, Y., Zhang, T., Suo, C., & Chen, X. (2024) · BMC Medicine · doi:10.1186/s12916-024-03466-0 · Cited by 3
Cysteine-Altering NOTCH3 Variants Are Associated with an Increased Risk of Autoimmune Diseases
Rieder, E., Li, J., Rodriguez-Flores, J. L., Taimur Malik, M., Abedi, V., & Zand, R. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12196278 · Cited by 2
Cardiovascular health and outcomes in carriers with genetic variants of hypertrophic cardiomyopathy
Patel, N., Gaonkar, M., Pampana, A., Dhaliwal, J. S., Vekariya, N., Shetty, N. S., Li, P., Kalra, R., Arora, G., & Arora, P. (2026) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2024.12.018 · Cited by 2
Li, C., & Mei, B. (2026) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720026500125
Metadata
- Authors
- Backman, J. D., Li, A. H., Marcketta, A., Sun, D., Mbatchou, J., Kessler, M. D., Benner, C., Liu, D., Locke, A. E., Balasubramanian, S., Yadav, A., Banerjee, N., Gillies, C. E., Damask, A., Liu, S., Bai, X., Hawes, A., Maxwell, E., Gurski, L., Watanabe, K., Kosmicki, J. A., Rajagopal, V., Mighty, J., Regeneron Genetics Center, DiscovEHR, Jones, M., Mitnaul, L., Stahl, E., Coppola, G., Jorgenson, E., Habegger, L., Salerno, W. J., Shuldiner, A. R., Lotta, L. A., Overton, J. D., Cantor, M. N., Reid, J. G., Yancopoulos, G., Kang, H. M., Marchini, J., Baras, A., Abecasis, G. R., & Ferreira, M. A. R.
- Venue
- Nature
- Published
- 2021-10-18
- Volume
- 599
- Issue
- 7886
- Pages
- 628-634
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3207912190
- Cited by
- 976
- References
- 16
- FWCI
- 75.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.