The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis
Open accessAbstract
Osteoporosis is a common disease diagnosed primarily by measurement of bone mineral density (BMD). We undertook a genome-wide association study (GWAS) in 142,487 individuals from the UK Biobank to identify loci associated with BMD as estimated by quantitative ultrasound of the heel. We identified 307 conditionally independent single-nucleotide polymorphisms (SNPs) that attained genome-wide significance at 203 loci, explaining approximately 12% of the phenotypic variance. These included 153 previously unreported loci, and several rare variants with large effect sizes. To investigate the underlying mechanisms, we undertook (1) bioinformatic, functional genomic annotation and human osteoblast expression studies; (2) gene-function prediction; (3) skeletal phenotyping of 120 knockout mice with deletions of genes adjacent to lead independent SNPs; and (4) analysis of gene expression in mouse osteoblasts, osteocytes and osteoclasts. The results implicate GPC6 as a novel determinant of BMD, and also identify abnormal skeletal phenotypes in knockout mice associated with a further 100 prioritized genes.
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
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
Second-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709
Integrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559
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
LocusZoom: regional visualization of genome-wide association scan results
Pruim, R. J., Welch, R. P., Sanna, S., Teslovich, T. M., Chines, P. S., Gliedt, T. P., Boehnke, M., Abecasis, G. R., & Willer, C. J. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq419 · Cited by 2,707
The NHGRI GWAS Catalog, a curated resource of SNP-trait associations
Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., Hindorff, L., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1229 · Cited by 2,707
The accessible chromatin landscape of the human genome
Thurman, R. E., Rynes, E., Humbert, R., Vierstra, J., Maurano, M. T., Haugen, E., Sheffield, N. C., Stergachis, A. B., Wang, H., Vernot, B., et al. (2012) · Nature · doi:10.1038/nature11232 · Cited by 2,679
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
A conditional knockout resource for the genome-wide study of mouse gene function
Skarnes, W. C., Rosen, B., West, A. P., Koutsourakis, M., Bushell, W., Iyer, V., Mujica, A. O., Thomas, M., Harrow, J., Cox, T., et al. (2011) · Nature · doi:10.1038/nature10163 · Cited by 1,708
Cited by
An atlas of genetic influences on osteoporosis in humans and mice
23andMe Research Team, Morris, J. A., Kemp, J. P., Youlten, S. E., Laurent, L., Logan, J. G., Chai, R. C., Vulpescu, N. A., Forgetta, V., Kleinman, A., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0302-x · Cited by 773
Self-citationUsing human genetics to understand the disease impacts of testosterone in men and women
The Endometrial Cancer Association Consortium, Ruth, K. S., Day, F. R., Tyrrell, J., Thompson, D. J., Wood, A. R., Mahajan, A., Beaumont, R. N., Wittemans, L., Martin, S., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0751-5 · Cited by 720
A single-cell atlas of chromatin accessibility in the human genome
Zhang, K., Hocker, J. D., Miller, M., Hou, X., Chiou, J., Poirion, O. B., Qiu, Y., Li, Y. E., Gaulton, K. J., Wang, A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.10.024 · Cited by 502
Genetic architecture: the shape of the genetic contribution to human traits and disease
Timpson, N. J., Greenwood, C. M. T., Soranzo, N., Lawson, D. J., & Richards, J. B. (2018) · Nature Reviews Genetics · doi:10.1038/nrg.2017.101 · Cited by 434
Self-citationOsteoclasts recycle via osteomorphs during RANKL-stimulated bone resorption
McDonald, M. M., Khoo, W. H., Ng, P. Y., Xiao, Y., Zamerli, J., Thatcher, P., Kyaw, W., Pathmanandavel, K., Grootveld, A. K., Moran, I., et al. (2021) · Cell · doi:10.1016/j.cell.2021.02.002 · Cited by 433
Self-citationCorces, M. R., Shcherbina, A., Kundu, S., Gloudemans, M. J., Frésard, L., Granja, J. M., Louie, B. H., Eulalio, T., Shams, S., Bagdatli, S. T., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-00721-x · Cited by 358
A road map for understanding molecular and genetic determinants of osteoporosis
Yang, T.-L., Shen, H., Liu, A., Dong, S.-S., Zhang, L., Deng, F.-Y., Zhao, Q., & Deng, H.-W. (2020) · Nature Reviews Endocrinology · doi:10.1038/s41574-019-0282-7 · Cited by 341
Zhang, M. J., Hou, K., Dey, K. K., Sakaue, S., Jagadeesh, K. A., Weinand, K., Taychameekiatchai, A., Rao, P., Pisco, A. O., Zou, J., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01167-z · Cited by 302
Can deep learning improve genomic prediction of complex human traits?
Bellot, P., de los Campos, G., & Pérez-Enciso, M. (2018) · Genetics · doi:10.1534/genetics.118.301298 · Cited by 225
Discovery of target genes and pathways at GWAS loci by pooled single-cell CRISPR screens
Morris, J. A., Caragine, C., Daniloski, Z., Domingo, J., Barry, T., Lu, L., Davis, K., Ziosi, M., Glinos, D. A., Hao, S., et al. (2023) · Science · doi:10.1126/science.adh7699 · Cited by 208
Self-citationKim, S. K. (2018) · PLOS ONE · doi:10.1371/journal.pone.0200785 · Cited by 185
Dnmt3a -mutated clonal hematopoiesis promotes osteoporosis
Kim, P. G., Niroula, A., Shkolnik, V., McConkey, M., Lin, A. E., Słabicki, M., Kemp, J. P., Bick, A., Gibson, C. J., Griffin, G., et al. (2021) · Journal of Experimental Medicine · doi:10.1084/jem.20211872 · Cited by 164
Self-citationChesi, A., Wagley, Y., Johnson, M. E., Manduchi, E., Su, C., Lu, S., Leonard, M. E., Hodge, K. M., Pippin, J. A., Hankenson, K. D., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09302-x · Cited by 158
Biological Role of Unsaturated Fatty Acid Desaturases in Health and Disease
Czumaj, A., & Śledziński, T. (2020) · Nutrients · doi:10.3390/nu12020356 · Cited by 150
Imprint of assortative mating on the human genome
Yengo, L., Robinson, M. R., Keller, M. C., Kemper, K. E., Yang, Y., Trzaskowski, M., Gratten, J., Turley, P., Cesarini, D., Benjamin, D. J., et al. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0476-3 · Cited by 148
Bovijn, J., Krebs, K., Chen, C.-Y., Boxall, R., Censin, J. C., Ferreira, T., Pulit, S. L., Glastonbury, C. A., Laber, S., Millwood, I. Y., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aay6570 · Cited by 119
New Insights into Monogenic Causes of Osteoporosis
Mäkitie, R. E., Costantini, A., Kämpe, A., Alm, J. J., & Mäkitie, O. (2019) · Frontiers in Endocrinology · doi:10.3389/fendo.2019.00070 · Cited by 108
Heritability of Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies
Zhao, B., Ibrahim, J. G., Li, Y., Li, T., Wang, Y., Shan, Y., Zhu, Z., Zhou, F., Zhang, J., Huang, C., et al. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhy157 · Cited by 58
Young, W. J., Lahrouchi, N., Isaacs, A., Duong, T., Foco, L., Ahmed, F., Brody, J. A., Salman, R., Noordam, R., Benjamins, J.-W., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32821-z · Cited by 49
Self-citationZheng, J., Wheeler, E., Pietzner, M., Andlauer, T. F. M., Yau, M. S., Hartley, A. E., Brumpton, B. M., Rasheed, H., Kemp, J. P., Frysz, M., et al. (2023) · Arthritis & Rheumatology · doi:10.1002/art.42538 · Cited by 44
Self-citationGregson, C. L., Bergen, D. J. M., Leo, P., Sessions, R. B., Wheeler, L., Hartley, A., Youlten, S., Croucher, P. I., McInerney-Leo, A. M., Fraser, W., et al. (2020) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.3875 · Cited by 44
Self-citationStein, M., Elefteriou, F., Busse, B., Fiedler, I. A., Kwon, R. Y., Farrell, E., Ahmad, M., Ignatius, A., Grover, L., Geris, L., et al. (2023) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.4868 · Cited by 42
He, D., Liu, H., Wei, W., Zhao, Y., Cai, Q., Shi, S., Chu, X., Qin, X., Zhang, N., Xu, P., et al. (2023) · Osteoporosis International · doi:10.1007/s00198-023-06852-1 · Cited by 30
The protein inputs of an ultra-predictive aging clock represent viable anti-aging drug targets
Johnson, A. A., Shokhirev, M. N., & Lehallier, B. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101404 · Cited by 22
Zhang, X., Deng, H., Shen, H., & Ehrlich, M. (2021) · JBMR Plus · doi:10.1002/jbm4.10481 · Cited by 20
Metadata
- Authors
- Kemp, J. P., Morris, J. A., Medina-Gomez, C., Forgetta, V., Warrington, N. M., Youlten, S. E., Zheng, J., Gregson, C. L., Grundberg, E., Trajanoska, K., Logan, J. G., Pollard, A. S., Sparkes, P. C., Ghirardello, E. J., Allen, R., Leitch, V. D., Butterfield, N. C., Komla-Ebri, D., Adoum, A.-T., Curry, K. F., White, J. K., Kussy, F., Greenlaw, K. M., Xu, C., Harvey, N. C., Cooper, C., Adams, D. J., Greenwood, C. M. T., Maurano, M. T., Kaptoge, S., Rivadeneira, F., Tobias, J. H., Croucher, P. I., Ackert-Bicknell, C. L., Bassett, J. H. D., Williams, G. R., Richards, J. B., & Evans, D. M.
- Venue
- Nature Genetics
- Published
- 2017-09-04
- Volume
- 49
- Issue
- 10
- Pages
- 1468-1475
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- OpenAlex
- W2751430583
- Cited by
- 465
- References
- 17
- FWCI
- 49.77
- 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.
