An Atlas of Human and Murine Genetic Influences on Osteoporosis Article Swipe
YOU?
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· 2018
· Open Access
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· DOI: https://doi.org/10.1101/338863
Osteoporosis is a common debilitating chronic disease diagnosed primarily using bone mineral density (BMD). We undertook a comprehensive assessment of human genetic determinants of bone density in 426,824 individuals, identifying a total of 518 genome-wide significant loci, (301 novel), explaining 20% of the total variance in BMD—as estimated by heel quantitative ultrasound (eBMD). Next, meta-analysis identified 13 bone fracture loci in ~1.2M individuals, which were also associated with BMD. We then identified target genes from cell-specific genomic landscape features, including chromatin conformation and accessible chromatin sites, that were strongly enriched for genes known to influence bone density and strength (maximum odds ratio = 58, P = 10 −75 ). We next performed rapid throughput skeletal phenotyping of 126 knockout mice lacking eBMD Target Genes and showed that these mice had an increased frequency of abnormal skeletal phenotypes compared to 526 unselected lines (P < 0.0001). In-depth analysis of one such Target Gene, DAAM2 , showed a disproportionate decrease in bone strength relative to mineralization. This comprehensive human and murine genetic atlas provides empirical evidence testing how to link associated SNPs to causal genes, offers new insights into osteoporosis pathophysiology and highlights opportunities for drug development.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/338863
- https://www.biorxiv.org/content/biorxiv/early/2018/07/27/338863.full.pdf
- OA Status
- green
- Cited By
- 8
- References
- 86
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2807672853
Raw OpenAlex JSON
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https://openalex.org/W2807672853Canonical identifier for this work in OpenAlex
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https://doi.org/10.1101/338863Digital Object Identifier
- Title
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An Atlas of Human and Murine Genetic Influences on OsteoporosisWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-06-11Full publication date if available
- Authors
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John Morris, John P. Kemp, Scott E. Youlten, Lætitia Laurent, John G. Logan, Ryan C. Chai, Nicholas A. Vulpescu, Vincenzo Forgetta, Aaron Kleinman, Sindhu T. Mohanty, C. Marcelo Sergio, Julian M.W. Quinn, Loan Nguyen‐Yamamoto, Aimee Lee Luco, Jinchu Vijay, Marie-Michelle Simon, Albéna Pramatarova, Carolina Medina‐Gómez, Katerina Trajanoska, Elena J. Ghirardello, Natalie C. Butterfield, Katharine F. Curry, Victoria D. Leitch, Penny C. Sparkes, Anne‐Tounsia Adoum, Naila S. Mannan, Davide Komla‐Ebri, Andrea S. Pollard, Hannah F. Dewhurst, Thomas Hassall, Michael-John G. Beltejar, Douglas J. Adams, Suzanne M. Vaillancourt, Stephen Kaptoge, Paul A. Baldock, Cyrus Cooper, J. Reeve, Evangelia Ntzani, Εvangelos Εvangelou, Claes Ohlsson, David Karasik, Fernando Rivadeneira, Douglas P. Kiel, Jonathan H. Tobias, Celia L. Gregson, Nicholas C. Harvey, Elin Grundberg, David Goltzman, David J. Adams, Christopher J. Lelliott, David A. Hinds, Cheryl L. Ackert‐Bicknell, Yi‐Hsiang Hsu, Matthew T. Maurano, Peter I. Croucher, Graham R. Williams, J. H. Duncan Bassett, David M. Evans, J. Brent RichardsList of authors in order
- Landing page
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https://doi.org/10.1101/338863Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2018/07/27/338863.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2018/07/27/338863.full.pdfDirect OA link when available
- Concepts
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Osteoporosis, Bone mineral, Gene, Genome-wide association study, Biology, Human genome, Chromatin, Phenotype, Bone density, Single-nucleotide polymorphism, Genetics, Quantitative trait locus, Genome, Bioinformatics, Computational biology, Endocrinology, GenotypeTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1, 2020: 2, 2019: 4, 2018: 1Per-year citation counts (last 5 years)
- References (count)
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86Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.sites, | 86 |
| abstract_inverted_index.target | 73 |
| abstract_inverted_index.(eBMD). | 53 |
| abstract_inverted_index.426,824 | 28 |
| abstract_inverted_index.chronic | 6 |
| abstract_inverted_index.density | 13, 26, 97 |
| abstract_inverted_index.disease | 7 |
| abstract_inverted_index.genetic | 22, 170 |
| abstract_inverted_index.genomic | 77 |
| abstract_inverted_index.lacking | 121 |
| abstract_inverted_index.mineral | 12 |
| abstract_inverted_index.novel), | 39 |
| abstract_inverted_index.testing | 175 |
| abstract_inverted_index.(maximum | 100 |
| abstract_inverted_index.0.0001). | 145 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.BMD—as | 47 |
| abstract_inverted_index.In-depth | 146 |
| abstract_inverted_index.abnormal | 135 |
| abstract_inverted_index.analysis | 147 |
| abstract_inverted_index.compared | 138 |
| abstract_inverted_index.decrease | 158 |
| abstract_inverted_index.enriched | 90 |
| abstract_inverted_index.evidence | 174 |
| abstract_inverted_index.fracture | 59 |
| abstract_inverted_index.insights | 186 |
| abstract_inverted_index.knockout | 119 |
| abstract_inverted_index.provides | 172 |
| abstract_inverted_index.relative | 162 |
| abstract_inverted_index.skeletal | 115, 136 |
| abstract_inverted_index.strength | 99, 161 |
| abstract_inverted_index.strongly | 89 |
| abstract_inverted_index.variance | 45 |
| abstract_inverted_index.chromatin | 81, 85 |
| abstract_inverted_index.diagnosed | 8 |
| abstract_inverted_index.empirical | 173 |
| abstract_inverted_index.estimated | 48 |
| abstract_inverted_index.features, | 79 |
| abstract_inverted_index.frequency | 133 |
| abstract_inverted_index.including | 80 |
| abstract_inverted_index.increased | 132 |
| abstract_inverted_index.influence | 95 |
| abstract_inverted_index.landscape | 78 |
| abstract_inverted_index.performed | 112 |
| abstract_inverted_index.primarily | 9 |
| abstract_inverted_index.undertook | 16 |
| abstract_inverted_index.accessible | 84 |
| abstract_inverted_index.assessment | 19 |
| abstract_inverted_index.associated | 67, 179 |
| abstract_inverted_index.explaining | 40 |
| abstract_inverted_index.highlights | 191 |
| abstract_inverted_index.identified | 56, 72 |
| abstract_inverted_index.phenotypes | 137 |
| abstract_inverted_index.throughput | 114 |
| abstract_inverted_index.ultrasound | 52 |
| abstract_inverted_index.unselected | 141 |
| abstract_inverted_index.genome-wide | 35 |
| abstract_inverted_index.identifying | 30 |
| abstract_inverted_index.phenotyping | 116 |
| abstract_inverted_index.significant | 36 |
| abstract_inverted_index.Osteoporosis | 1 |
| abstract_inverted_index.conformation | 82 |
| abstract_inverted_index.debilitating | 5 |
| abstract_inverted_index.determinants | 23 |
| abstract_inverted_index.development. | 195 |
| abstract_inverted_index.individuals, | 29, 63 |
| abstract_inverted_index.osteoporosis | 188 |
| abstract_inverted_index.quantitative | 51 |
| abstract_inverted_index.cell-specific | 76 |
| abstract_inverted_index.comprehensive | 18, 166 |
| abstract_inverted_index.meta-analysis | 55 |
| abstract_inverted_index.opportunities | 192 |
| abstract_inverted_index.mineralization. | 164 |
| abstract_inverted_index.pathophysiology | 189 |
| abstract_inverted_index.disproportionate | 157 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5035461704, https://openalex.org/A5100719776 |
| countries_distinct_count | 7 |
| institutions_distinct_count | 59 |
| corresponding_institution_ids | https://openalex.org/I1308309334, https://openalex.org/I165143802, https://openalex.org/I183935753, https://openalex.org/I2801824472, https://openalex.org/I36234482, https://openalex.org/I4210158597, https://openalex.org/I5023651 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.800000011920929 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.68343603 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |