The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Genetic predisposition to mosaic Y chromosome loss in blood
Open accessAbstract
Mosaic loss of chromosome Y (LOY) in circulating white blood cells is the most common form of clonal mosaicism1–5, yet our knowledge of the causes and consequences of this is limited. Here, using a computational approach, we estimate that 20% of the male population represented in the UK Biobank study (n = 205,011) has detectable LOY. We identify 156 autosomal genetic determinants of LOY, which we replicate in 757,114 men of European and Japanese ancestry. These loci highlight genes that are involved in cell-cycle regulation and cancer susceptibility, as well as somatic drivers of tumour growth and targets of cancer therapy. We demonstrate that genetic susceptibility to LOY is associated with non-haematological effects on health in both men and women, which supports the hypothesis that clonal haematopoiesis is a biomarker of genomic instability in other tissues. Single-cell RNA sequencing identifies dysregulated expression of autosomal genes in leukocytes with LOY and provides insights into why clonal expansion of these cells may occur. Collectively, these data highlight the value of studying clonal mosaicism to uncover fundamental mechanisms that underlie cancer and other ageing-related diseases. A genome-wide association study of mosaic loss of chromosome Y (LOY) in UK Biobank participants identifies 156 genetic determinants of LOY, showing that LOY is associated with cancer and non-haematological health outcomes.
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-citationBowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816
Bowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922
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
Szklarczyk, D., Morris, J. H., Cook, H., Kuhn, M., Wyder, S., Simonovic, M., Santos, A., Doncheva, N. T., Roth, A., Bork, P., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw937 · Cited by 6,980
The Bcl2 family: regulators of the cellular life-or-death switch
Cory, S., & Adams, J. M. (2002) · Nature Reviews Cancer · doi:10.1038/nrc883 · Cited by 4,032
Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis
Võsa, U., Claringbould, A., Westra, H.-J., Bonder, M. J., Deelen, P., Zeng, B., Kirsten, H., Saha, A., Kreuzhuber, R., Kasela, S., et al. (2018) · doi:10.1101/447367 · Cited by 2,607
Self-citationPartitioning heritability by functional annotation using genome-wide association summary statistics
ReproGen Consortium, Finucane, H. K., Schizophrenia Working Group of the Psychiatric Genomics Consortium, The RACI Consortium, Bulik-Sullivan, B., Gusev, A., Trynka, G., Reshef, Y., Loh, P.-R., Anttila, V., et al. (2015) · Nature Genetics · doi:10.1038/ng.3404 · Cited by 2,604
Self-citationIntegrative approaches for large-scale transcriptome-wide association studies
Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B. W. J. H., Jansen, R., de Geus, E. J. C., Boomsma, D. I., Wright, F. A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3506 · Cited by 2,479
Machiela, M. J., & Chanock, S. J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv402 · Cited by 2,069
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
Finucane, H. K., Reshef, Y. A., Anttila, V., Slowikowski, K., Gusev, A., Byrnes, A., Gazal, S., Loh, P.-R., Lareau, C., Shoresh, N., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0081-4 · Cited by 1,097
Self-citationThe UK10K project identifies rare variants in health and disease
The UK10K Consortium, Writing group, Walter, K., Min, J. L., Huang, J., Crooks, L., Memari, Y., McCarthy, S., Perry, J. R. B., Xu, C., et al. (2015) · Nature · doi:10.1038/nature14962 · Cited by 1,040
Self-citationAssociation analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci
Study, T. P., Schumacher, F. R., (APCB), A. P. C. B., Study, T. I., Canary PASS Investigators, Breast and Prostate Cancer Cohort Consortium (BPC3), The PRACTICAL (Prostate Cancer Association Group to Investigate Cancer-Associated Alterations in the Genome) Consortium, (CAPS), C. O. T. P. I. S., Prostate Cancer Genome-wide Association Study of Uncommon Susceptibility Loci (PEGASUS), The Genetic Associations and Mechanisms in Oncology (GAME-ON)/Elucidating Loci Involved in Prostate Cancer Susceptibility (ELLIPSE) Consortium, et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0142-8 · Cited by 902
Self-citationKanai, M., Akiyama, M., Takahashi, A., Matoba, N., Momozawa, Y., Ikeda, M., Iwata, N., Ikegawa, S., Hirata, M., Matsuda, K., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0047-6 · Cited by 801
Self-citationHan, B., & Eskin, E. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.04.014 · Cited by 680
Population dynamics of normal human blood inferred from somatic mutations
Lee-Six, H., Øbro, N. F., Shepherd, M. S., Grossmann, S., Dawson, K., Belmonte, M., Osborne, R. J., Huntly, B. J. P., Martincorena, I., Anderson, E., et al. (2018) · Nature · doi:10.1038/s41586-018-0497-0 · Cited by 604
Segrè, A. V., DIAGRAM Consortium, MAGIC investigators, Groop, L., Mootha, V. K., Daly, M. J., & Altshuler, D. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001058 · Cited by 463
Day, F. R., Hinds, D. A., Tung, J. Y., Stolk, L., Styrkarsdottir, U., Saxena, R., Bjonnes, A., Broer, L., Dunger, D. B., Halldorsson, B. V., et al. (2015) · Nature Communications · doi:10.1038/ncomms9464 · Cited by 459
Self-citationThe PRACTICAL Consortium, Day, F. R., kConFab Investigators, AOCS Investigators, Scotland, G., EPIC-InterAct Consortium, Study, L. C., Ruth, K. S., Thompson, D. J., Lunetta, K. L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3412 · Cited by 402
Self-citationMosaic Loss of Chromosome y in Blood Is Associated with Alzheimer Disease
Dumanski, J. P., Lambert, J.-C., Rasi, C., Giedraitis, V., Davies, H., Grenier-Boley, B., Lindgren, C. M., Campion, D., Dufouil, C., Pasquier, F., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.05.014 · Cited by 266
Self-citationInterrogation of human hematopoiesis at single-cell and single-variant resolution
Ulirsch, J. C., Lareau, C. A., Bao, E. L., Ludwig, L. S., Guo, M. H., Benner, C., Satpathy, A. T., Kartha, V. K., Salem, R. M., Hirschhorn, J. N., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0362-6 · Cited by 208
Self-citationAge- and Tumor Subtype–Specific Breast Cancer Risk Estimates for CH EK 2 *1100delC Carriers
Schmidt, M. K., Hogervorst, F., van Hien, R., Cornelissen, S., Broeks, A., Adank, M. A., Meijers, H., Waisfisz, Q., Hollestelle, A., Schutte, M., et al. (2016) · Journal of Clinical Oncology · doi:10.1200/jco.2016.66.5844 · Cited by 173
Self-citation
Cited by
Kar, S. P., Quiros, P. M., Gu, M., Jiang, T., Mitchell, J., Langdon, R., Iyer, V., Barcena, C., Vijayabaskar, M. S., Fabre, M. A., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01121-z · Cited by 377
Self-citationMechanisms of sex differences in Alzheimer’s disease
Lopez-Lee, C., Torres, E. R. S., Carling, G., & Gan, L. (2024) · Neuron · doi:10.1016/j.neuron.2024.01.024 · Cited by 376
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
Pathogenic mechanisms of somatic mutation and genome mosaicism in aging
Vijg, J., & Dong, X. (2020) · Cell · doi:10.1016/j.cell.2020.06.024 · Cited by 240
Monogenic and polygenic inheritance become instruments for clonal selection
Loh, P.-R., Genovese, G., & McCarroll, S. A. (2020) · Nature · doi:10.1038/s41586-020-2430-6 · Cited by 228
Self-citation
Metadata
- Authors
- International Lung Cancer Consortium (INTEGRAL-ILCCO), Thompson, D. J., The Breast Cancer Association Consortium, Consortium of Investigators of Modifiers of BRCA1/2, The Endometrial Cancer Association Consortium, The Ovarian Cancer Association Consortium, The Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) Consortium, The Kidney Cancer GWAS Meta-Analysis Project, eQTLGen Consortium, Biobank-based Integrative Omics Study (BIOS) Consortium, 23andMe Research Team, Genovese, G., Halvardson, J., Ulirsch, J. C., Wright, D. J., Terao, C., Davidsson, O. B., Day, F. R., Sulem, P., Jiang, Y., Danielsson, M., Davies, H., Dennis, J., Dunlop, M. G., Easton, D. F., Fisher, V. A., Zink, F., Houlston, R. S., Ingelsson, M., Kar, S., Kerrison, N. D., Kinnersley, B., Kristjansson, R. P., Law, P. J., Li, R., Loveday, C., Mattisson, J., McCarroll, S. A., Murakami, Y., Murray, A., Olszewski, P., Rychlicka-Buniowska, E., Scott, R. A., Thorsteinsdottir, U., Tomlinson, I., Moghadam, B. T., Turnbull, C., Wareham, N. J., Gudbjartsson, D. F., Kamatani, Y., Hoffmann, E. R., Jackson, S. P., Stefansson, K., Auton, A., Ong, K. K., Machiela, M. J., Loh, P.-R., Dumanski, J. P., Chanock, S. J., Forsberg, L. A., & Perry, J. R. B.
- Venue
- Nature
- Published
- 2019-11-20
- Volume
- 575
- Issue
- 7784
- Pages
- 652-657
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2990327991
- Cited by
- 337
- References
- 23
- FWCI
- 16.39
- Type
- journal article
- Language
- en
- Open access
- green
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.
