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Osteocyte transcriptome mapping identifies a molecular landscape controlling skeletal homeostasis and susceptibility to skeletal disease

Scott E. Youlten, John P. Kemp, John G. Logan and 34 more (opens in a new tab)

Nature Communications · 2021 · CC BY

doi:10.1101/2020.04.20.051409 (opens in a new tab)

Abstract

Osteocytes are master regulators of the skeleton. We mapped the transcriptome of osteocytes from different skeletal sites, across age and sexes in mice to reveal genes and molecular programs that control this complex cellular-network. We define an osteocyte transcriptome signature of 1239 genes that distinguishes osteocytes from other cells. 77% have no previously known role in the skeleton and are enriched for genes regulating neuronal network formation, suggesting this programme is important in osteocyte communication. We evaluated 19 skeletal parameters in 733 knockout mouse lines and reveal 26 osteocyte transcriptome signature genes that control bone structure and function. We showed osteocyte transcriptome signature genes are enriched for human orthologs that cause monogenic skeletal disorders ( P = 2.4 × 10 −22 ) and are associated with the polygenic diseases osteoporosis ( P = 1.8 × 10 −13 ) and osteoarthritis ( P = 1.6 × 10 −7 ). Thus, we reveal the molecular landscape that regulates osteocyte network formation and function and establish the importance of osteocytes in human skeletal disease.

References 119

  1. 1.

    Dallas SL, Prideaux M, Bonewald LF. The osteocyte: an endocrine cell… and more. Endocr. Rev. 2013;34:658-690.

    Not in our library10.1210/er.2012-1026 (opens in a new tab)

  2. 2.

    Kollmannsberger, P. et al. The small world of osteocytes: connectomics of the lacuno-canalicular network in bone. New J. Phys. 19, 073019 (2017).

    Not in our library

  3. 3.

    Buenzli PR, Sims NA. Quantifying the osteocyte network in the human skeleton. Bone. 2015;75:144-150.

    Not in our library10.1016/j.bone.2015.02.016 (opens in a new tab)

  4. 4.

    Schaffler MB, Cheung WY, Majeska R, Kennedy O. Osteocytes: master orchestrators of bone. Calcif. Tissue Int. 2014;94:5-24.

    Not in our library10.1007/s00223-013-9790-y (opens in a new tab)

  5. 5.

    Bergwitz C, Jüppner H. Regulation of phosphate homeostasis by PTH, vitamin D, and FGF23. Annu. Rev. Med. 2010;61:91-104.

    Not in our library10.1146/annurev.med.051308.111339 (opens in a new tab)

  6. 6.

    Sato M, et al. Osteocytes regulate primary lymphoid organs and fat metabolism. Cell Metab. 2013;18:749-758.

    Not in our library10.1016/j.cmet.2013.09.014 (opens in a new tab)

  7. 7.

    Asada N, et al. Matrix-embedded osteocytes regulate mobilization of hematopoietic stem/progenitor cells. Cell Stem Cell. 2013;12:737-747.

    Not in our library10.1016/j.stem.2013.05.001 (opens in a new tab)

  8. 8.

    Shen H, Grimston S, Civitelli R, Thomopoulos S. Deletion of connexin43 in osteoblasts / osteocytes leads to impaired muscle formation in mice. J. Bone Miner. Res. 2015;30:596-605.

    Not in our library10.1002/jbmr.2389 (opens in a new tab)

  9. 9.

    Forrest ARRR, et al. A promoter-level mammalian expression atlas. Nature. 2014;507:462-470.

    Not in our library10.1038/nature13182 (opens in a new tab)

  10. 10.

    Lonsdale J, et al. The genotype-tissue expression (GTEx) project. Nat. Genet. 2013;45:580-585.

    Not in our library10.1038/ng.2653 (opens in a new tab)

  11. 11.
  12. 12.

    The Tabula Muris Consortium et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature. 2018.

    Not in our library10.1038/s41586-018-0590-4 (opens in a new tab)

  13. 13.

    Ayturk UM, et al. An RNA-seq protocol to identify mRNA expression changes in mouse diaphyseal bone: applications in mice with bone property altering Lrp5 mutations. J. Bone Miner. Res. 2013;28:2081-2093.

    Not in our library10.1002/jbmr.1946 (opens in a new tab)

  14. 14.

    Kelly NH, Schimenti JC, Ross FP, van der Meulen MCH. Transcriptional profiling of cortical versus cancellous bone from mechanically-loaded murine tibiae reveals differential gene expression. Bone. 2016;86:22-29.

    Not in our library10.1016/j.bone.2016.02.007 (opens in a new tab)

  15. 15.

    Qing H, et al. Demonstration of osteocytic perilacunar/canalicular remodeling in mice during lactation. J. Bone Miner. Res. 2012;27:1018-1029.

    Not in our library10.1002/jbmr.1567 (opens in a new tab)

  16. 16.

    Nioi P, et al. Transcriptional profiling of laser capture microdissected subpopulations of the osteoblast lineage provides insight into the early response to sclerostin antibody in rats. J. Bone Miner. Res. 2015;30:1457-1467.

    Not in our library10.1002/jbmr.2482 (opens in a new tab)

  17. 17.

    Brunkow ME, et al. Bone dysplasia sclerosteosis results from loss of the SOST gene product, a novel cystine knot-containing protein. Am. J. Hum. Genet. 2001;68:577-589.

    Not in our library10.1086/318811 (opens in a new tab)

  18. 18.

    Balemans W, et al. Increased bone density in sclerosteosis is due to the deficiency of a novel secreted protein (SOST). Hum. Mol. Genet. 2001;10:537-543.

    Not in our library10.1093/hmg/10.5.537 (opens in a new tab)

  19. 19.

    Balemans W, et al. Identification of a 52 kb deletion downstream of the SOST gene in patients with van Buchem disease. J. Med. Genet. 2002;39:91-97.

    Not in our library10.1136/jmg.39.2.91 (opens in a new tab)

  20. 20.

    Feng JQ, et al. Loss of DMP1 causes rickets and osteomalacia and identifies a role for osteocytes in mineral metabolism. Nat. Genet. 2006;38:1310-1315.

    Not in our library10.1038/ng1905 (opens in a new tab)

  21. 21.

    Mortier GR, et al. Nosology and classification of genetic skeletal disorders: 2019 revision. Am. J. Med. Genet. A. 2019.

    Not in our library10.1002/ajmg.a.61366 (opens in a new tab)

  22. 22.

    Videman T, et al. Heritability of BMD of femoral neck and lumbar spine: a multivariate twin study of Finnish men. J. Bone Miner. Res. 2007;22:1455-1462.

    Not in our library10.1359/jbmr.070606 (opens in a new tab)

  23. 23.

    Karasik D, et al. Mapping of quantitative ultrasound of the calcaneus bone to chromosome 1 by genome-wide linkage analysis. Osteoporos. Int. 2002;13:796-802.

    Not in our library10.1007/s001980200110 (opens in a new tab)

  24. 24.

    Valdes AM, Spector TD. Genetic epidemiology of hip and knee osteoarthritis. Nat. Rev. Rheumatol. 2011;7:23-32.

    Not in our library10.1038/nrrheum.2010.191 (opens in a new tab)

  25. 25.

    Morris JA, et al. An atlas of genetic influences on osteoporosis in humans and mice. Nat. Genet. 2019;51:258-266.

    Not in our library10.1038/s41588-018-0302-x (opens in a new tab)

  26. 26.

    Zengini E, et al. Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis. Nat. Genet. 2018;50:549-558.

    Not in our library10.1038/s41588-018-0079-y (opens in a new tab)

  27. 27.
  28. 28.

    Zhang R, Lahens NF, Ballance HI, Hughes ME, Hogenesch JB. A circadian gene expression atlas in mammals: Implications for biology and medicine. Proc. Natl Acad. Sci. USA. 2014;111:16219-16224.

    Not in our library10.1073/pnas.1408886111 (opens in a new tab)

  29. 29.

    Yanai I, et al. Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification. Bioinformatics. 2005;21:650-659.

    Not in our library10.1093/bioinformatics/bti042 (opens in a new tab)

  30. 30.

    Agarwal P, et al. Tbx5 is essential for forelimb bud initiation following patterning of the limb field in the mouse embryo. Development. 2003;130:623-633.

    Not in our library10.1242/dev.00191 (opens in a new tab)

  31. 31.

    Takeuchi JK, et al. Tbx5 and Tbx4 genes determine the wing/leg identity of limb buds. Nature. 1999;398:810-814.

    Not in our library10.1038/19762 (opens in a new tab)

  32. 32.

    DeLaurier A, Schweitzer R, Logan M. Pitx1 determines the morphology of muscle, tendon, and bones of the hindlimb. Dev. Biol. 2006;299:22-34.

    Not in our library10.1016/j.ydbio.2006.06.055 (opens in a new tab)

  33. 33.

    Spielmann M, et al. Homeotic arm-to-leg transformation associated with genomic rearrangements at the PITX1 locus. Am. J. Hum. Genet. 2012;91:629-635.

    Not in our library10.1016/j.ajhg.2012.08.014 (opens in a new tab)

  34. 34.

    Richman C, et al. Postnatal and pubertal skeletal changes contribute predominantly to the differences in peak bone density between C3H/HeJ and C57BL/6J mice. J. Bone Miner. Res. 2001;16:386-397.

    Not in our library10.1359/jbmr.2001.16.2.386 (opens in a new tab)

  35. 35.

    Cole TJ, et al. Ethnic and sex differences in skeletal maturation among the Birth to Twenty cohort in South Africa. Arch. Dis. Child. 2015;100:138-143.

    Not in our library10.1136/archdischild-2014-306399 (opens in a new tab)

  36. 36.

    Baxter-Jones ADG, Faulkner RA, Forwood MR, Mirwald RL, Bailey DA. Bone mineral accrual from 8 to 30 years of age: An estimation of peak bone mass. J. Bone Miner. Res. 2011;26:1729-1739.

    Not in our library10.1002/jbmr.412 (opens in a new tab)

  37. 37.

    Jähn K, et al. Osteocytes acidify their microenvironment in response to PTHrP in vitro and in lactating mice in vivo. J. Bone Miner. Res. 2017;32:1761-1772.

    Not in our library10.1002/jbmr.3167 (opens in a new tab)

  38. 38.

    Plotkin LI, Bellido T. Osteocytic signalling pathways as therapeutic targets for bone fragility. Nat. Rev. Endocrinol. 2016;12:593-605.

    Not in our library10.1038/nrendo.2016.71 (opens in a new tab)

  39. 39.

    Taylor S, et al. Differential time-dependent transcriptional changes in the osteoblast lineage in cortical bone associated with sclerostin antibody treatment in ovariectomized rats. Bone Rep. 2018;8:95-103.

    Not in our library10.1016/j.bonr.2018.03.002 (opens in a new tab)

  40. 40.

    Ashburner M, et al. Gene ontology: tool for the unification of biology. Nat. Genet. 2000;25:25-29.

    Not in our library10.1038/75556 (opens in a new tab)

  41. 41.
  42. 42.

    Joeng KS, et al. Osteocyte-specific WNT1 regulates osteoblast function during bone homeostasis. J. Clin. Invest. 2017;127:2678-2688.

    Not in our library10.1172/jci92617 (opens in a new tab)

  43. 43.

    Wang L, et al. Osteoblast-derived FGF9 regulates skeletal homeostasis. Bone. 2017;98:18-25.

    Not in our library10.1016/j.bone.2016.12.005 (opens in a new tab)

  44. 44.

    Bai, M. et al. Targeted genetic screening in mice through haploid embryonic stem cells identifies critical genes in bone development. PLoS Biol.17, e3000350 (2019).

    Not in our library10.1371/journal.pbio.3000350 (opens in a new tab)

  45. 45.

    Han Q, et al. 4H-Thieno[3,2-c]chromene based inhibitors of Notum Pectinacetylesterase. Bioorg. Med. Chem. Lett. 2016;26:1184-1187.

    Not in our library10.1016/j.bmcl.2016.01.038 (opens in a new tab)

  46. 46.

    Brommage, R. et al. NOTUM inhibition increases endocortical bone formation and bone strength. Bone Res. 7, 2 (2019).

    Not in our library10.1038/s41413-018-0038-3 (opens in a new tab)

  47. 47.

    Movérare-Skrtic S, et al. Osteoblast-derived NOTUM reduces cortical bone mass in mice and the NOTUM locus is associated with bone mineral density in humans. FASEB J. 2019;33:11163-11179.

    Not in our library10.1096/fj.201900707r (opens in a new tab)

  48. 48.

    Goff CLE, et al. ADAMTSL2 mutations in geleophysic dysplasia demonstrate a role for ADAMTS-like proteins in TGF-β bioavailability regulation. Nat. Genet. 2008;40:1119-1123.

    Not in our library10.1038/ng.199 (opens in a new tab)

  49. 49.

    Stoeckli ET. Understanding axon guidance: are we nearly there yet? Development. 2018;145:dev151415.

    Not in our library10.1242/dev.151415 (opens in a new tab)

  50. 50.

    St. John HC, et al. The osteoblast to osteocyte transition: epigenetic changes and response to the vitamin D 3 hormone. Mol. Endocrinol. 2014;28:1150-1165.

    Not in our library10.1210/me.2014-1091 (opens in a new tab)

  51. 51.

    Balasubramanian M, et al. Novel PLS3 variants in X-linked osteoporosis: exploring bone material properties. Am. J. Med. Genet. A. 2018;176:1578-1586.

    Not in our library10.1002/ajmg.a.38830 (opens in a new tab)

  52. 52.

    Hori K, et al. Cytoskeletal regulation by AUTS2 in neuronal migration and neuritogenesis. Cell Rep. 2014;9:2166-2179.

    Not in our library10.1016/j.celrep.2014.11.045 (opens in a new tab)

  53. 53.

    Jiang X, et al. DACT3 is an epigenetic regulator of wnt/β-catenin signaling in colorectal cancer and is a therapeutic target of histone modifications. Cancer Cell. 2008;13:529-541.

    Not in our library10.1016/j.ccr.2008.04.019 (opens in a new tab)

  54. 54.

    Lee HK, Deneen B. Daam2 is required for dorsal patterning via modulation of canonical wnt signaling in the developing spinal cord. Dev. Cell. 2012;22:183-196.

    Not in our library10.1016/j.devcel.2011.10.025 (opens in a new tab)

  55. 55.

    Nakano N, et al. C18 orf1, a novel negative regulator of transforming growth factor-β signaling. J. Biol. Chem. 2014;289:12680-12692.

    Not in our library10.1074/jbc.m114.558981 (opens in a new tab)

  56. 56.

    Chen YK, Hsueh YP. Cortactin-binding protein 2 modulates the mobility of cortactin and regulates dendritic spine formation and maintenance. J. Neurosci. 2012;32:1043-1055.

    Not in our library10.1523/jneurosci.4405-11.2012 (opens in a new tab)

  57. 57.

    Marini JC, et al. Osteogenesis imperfecta. Nat. Rev. Dis. Primers. 2017;3:17052.

    Not in our library10.1038/nrdp.2017.52 (opens in a new tab)

  58. 58.

    Doyard M, et al. FAM46A mutations are responsible for autosomal recessive osteogenesis imperfecta. J. Med. Genet. 2018;55:278-284.

    Not in our library10.1136/jmedgenet-2017-104999 (opens in a new tab)

  59. 59.

    Garbes L, et al. Mutations in SEC24D, encoding a component of the COPII machinery, cause a syndromic form of osteogenesis imperfecta. Am. J. Hum. Genet. 2015;96:432-439.

    Not in our library10.1016/j.ajhg.2015.01.002 (opens in a new tab)

  60. 60.

    Finucane HK, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 2015;47:1228-1235.

    Not in our library10.1038/ng.3404 (opens in a new tab)

  61. 61.

    Bulik-Sullivan B, et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 2015;47:291-295.

    Not in our library10.1038/ng.3211 (opens in a new tab)

  62. 62.
  63. 63.

    Glyn-Jones S, et al. Osteoarthritis. Lancet. 2015;386:376-387.

    Not in our library10.1016/s0140-6736(14)60802-3 (opens in a new tab)

  64. 64.

    Tachmazidou I, et al. Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data. Nat. Genet. 2019;51:230-236.

    Not in our library10.1038/s41588-018-0327-1 (opens in a new tab)

  65. 65.

    Sanchez C, et al. Chondrocyte secretome: a source of novel insights and exploratory biomarkers of osteoarthritis. Osteoarthritis Cartilage. 2017;25:1199-1209.

    Not in our library10.1016/j.joca.2017.02.797 (opens in a new tab)

  66. 66.

    Bassett, J. H. D. et al. Rapid-throughput skeletal phenotyping of 100 knockout mice identifies 9 new genes that determine bone strength. PLoS Genet. 8, e1002858 (2012).

    Not in our library10.1371/journal.pgen.1002858 (opens in a new tab)

  67. 67.

    Leucht P, et al. Embryonic origin and Hox status determine progenitor cell fate during adult bone regeneration. Development. 2008;135:2845-2854.

    Not in our library10.1242/dev.023788 (opens in a new tab)

  68. 68.

    Rux DR, et al. Regionally restricted hox function in adult bone marrow multipotent mesenchymal stem/stromal cells. Dev. Cell. 2016;39:653-666.

    Not in our library10.1016/j.devcel.2016.11.008 (opens in a new tab)

  69. 69.

    Karam N, et al. Bone-specific overexpression of PITX1 induces senile osteoporosis in mice through deficient self-renewal of mesenchymal progenitors and wnt pathway inhibition. Sci. Rep. 2019;9:1-15.

    Not in our library10.1038/s41598-019-40274-6 (opens in a new tab)

  70. 70.

    Fowler TW, et al. Glucocorticoid suppression of osteocyte perilacunar remodeling is associated with subchondral bone degeneration in osteonecrosis. Sci. Rep. 2017;7:1-13.

    Not in our library10.1038/srep44618 (opens in a new tab)

  71. 71.

    Goldring SR, Goldring MB. Changes in the osteochondral unit during osteoarthritis: Structure, function and cartilage bone crosstalk. Nat. Rev. Rheumatol. 2016;12:632-644.

    Not in our library10.1038/nrrheum.2016.148 (opens in a new tab)

  72. 72.

    Blouin S, et al. Hypermineralization and high osteocyte lacunar density in osteogenesis imperfecta type V bone indicate exuberant primary bone formation. J. Bone Miner. Res. 2017;32:1884-1892.

    Not in our library10.1002/jbmr.3180 (opens in a new tab)

  73. 73.

    Zimmerman SM, Dimori M, Heard-Lipsmeyer ME, Morello R. The osteocyte transcriptome is extensively dysregulated in mouse models of osteogenesis imperfecta. JBMR Plus. 2019;3:e10171.

    Not in our library10.1002/jbm4.10171 (opens in a new tab)

  74. 74.

    Storheim K, Zwart JA. Musculoskeletal disorders and the Global Burden of Disease study. Ann. Rheum. Dis. 2014.

    Not in our library10.1136/annrheumdis-2014-205327 (opens in a new tab)

  75. 75.

    Limaye, A. Drishti: A Volume Exploration And Presentation Tool. Vol. 8506 (SPIE Proceedings, 2012).

    Not in our library

  76. 76.

    Schindelin J, et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods. 2012;9:676-682.

    Not in our library10.1038/nmeth.2019 (opens in a new tab)

  77. 77.

    Adiconis X, et al. Comparative analysis of RNA sequencing methods for degraded or low-input samples. Nat. Methods. 2013;10:623-629.

    Not in our library10.1038/nmeth.2483 (opens in a new tab)

  78. 78.

    Haas BJ, et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 2013;8:1494-1512.

    Not in our library10.1038/nprot.2013.084 (opens in a new tab)

  79. 79.

    Pertea M, et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 2015;33:290-295.

    Not in our library10.1038/nbt.3122 (opens in a new tab)

  80. 80.

    Trapnell C, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 2012;7:562-578.

    Not in our library10.1038/nprot.2012.016 (opens in a new tab)

  81. 81.

    Pruitt KD, et al. RefSeq: an update on mammalian reference sequences. Nucleic Acids Res. 2014;42:756-763.

    Not in our library10.1093/nar/gkt1114 (opens in a new tab)

  82. 82.
  83. 83.

    Wang L, et al. CPAT: coding-potential assessment tool using an alignment-free logistic regression model. Nucleic Acids Res. 2013;41:1-7.

    Not in our library10.1093/nar/gkt006 (opens in a new tab)

  84. 84.

    Dobin A, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15-21.

    Not in our library10.1093/bioinformatics/bts635 (opens in a new tab)

  85. 85.
  86. 86.

    Woo SM, Rosser J, Dusevich V, Kalajzic I, Bonewald LF. Cell line IDG-SW3 replicates osteoblast-to-late-osteocyte differentiation in vitro and accelerates bone formation in vivo. J. Bone Miner. Res. 2011;26:2634-2646.

    Not in our library10.1002/jbmr.465 (opens in a new tab)

  87. 87.

    Taylor S, et al. Time-dependent cellular and transcriptional changes in the osteoblast lineage associated with sclerostin antibody treatment in ovariectomized rats. Bone. 2016;84:148-159.

    Not in our library10.1016/j.bone.2015.12.013 (opens in a new tab)

  88. 88.

    Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the R/ Bioconductor package biomaRt. Nat. Protoc. 2009;4:1184-1191.

    Not in our library10.1038/nprot.2009.97 (opens in a new tab)

  89. 89.

    MacDonald, J. W. affycoretools: functions useful for those doing repetitive analyses with Affymetrix GeneChips. R Packag. version1, (2008).

    Not in our library

  90. 90.

    Carvalho BS, Irizarry RA. A framework for oligonucleotide microarray preprocessing. Bioinformatics. 2010;26:2363-2367.

    Not in our library10.1093/bioinformatics/btq431 (opens in a new tab)

  91. 91.

    Gautier L, Cope L, Bolstad BM, Irizarry RA. Affy - Analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004;20:307-315.

    Not in our library10.1093/bioinformatics/btg405 (opens in a new tab)

  92. 92.

    Wickham, H. ggplot2: elegant graphics for data analysis. (Springer, 2016).

    Not in our library

  93. 93.
  94. 94.
  95. 95.
  96. 96.

    Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: The Dynamic Tree Cut package for R. Bioinformatics. 2008;24:719-720.

    Not in our library10.1093/bioinformatics/btm563 (opens in a new tab)

  97. 97.

    Ogata H, et al. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999;27:29-34.

    Not in our library10.1093/nar/27.1.29 (opens in a new tab)

  98. 98.
  99. 99.

    Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284-287.

    Not in our library10.1089/omi.2011.0118 (opens in a new tab)

  100. 100.

    Yu G, Wang LG, Yan GR, He QY. DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics. 2015;31:608-609.

    Not in our library10.1093/bioinformatics/btu684 (opens in a new tab)

  101. 101.

    Warnes GR, et al. gplots: various R programming tools for plotting data. R. Package version. 2009;2:1.

    Not in our library

  102. 102.
  103. 103.

    Fraley C, Raftery AE. Enhanced model-based clustering, density estimation, and discriminant analysis software: MCLUST. J. Classif. 2003;20:263-286.

    Not in our library

  104. 104.

    Wu D, Smyth GK. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 2012;40:1-12.

    Not in our library10.1093/nar/gks461 (opens in a new tab)

  105. 105.

    Benaglia T, Chauveau D, Hunter D, Young D. mixtools: an R package for analyzing finite mixture models. J. Stat. Softw. 2009;32:1-29.

    Not in our library

  106. 106.

    Youlten SE, Baldock PA. Using mouse genetics to understand human skeletal disease. Bone. 2019;126:27-36.

    Not in our library10.1016/j.bone.2019.02.015 (opens in a new tab)

  107. 107.
  108. 108.

    Wang X, Qiu W, Zamar RH. CLUES: a non-parametric clustering method based on local shrinking. Comput. Stat. Data Analysis. 2007;52:286-298.

    Not in our library

  109. 109.

    Hahne, F. & Ivanek, R. in Statistical Genomics p. 335–351 (Springer, 2016).

    Not in our library

  110. 110.

    Yang H, Wang H, Jaenisch R. Generating genetically modified mice using CRISPR/Cas-mediated genome engineering. Nat. Protoc. 2014;9:1956-1968.

    Not in our library10.1038/nprot.2014.134 (opens in a new tab)

  111. 111.

    International T, Knockout M. A mouse for all reasons. Cell. 2007;128:9-13.

    Not in our library10.1016/j.cell.2006.12.018 (opens in a new tab)

  112. 112.
  113. 113.

    Skarnes WC, et al. A conditional knockout resource for the genome-wide study of mouse gene function. Nature. 2011;474:337-344.

    Not in our library10.1038/nature10163 (opens in a new tab)

  114. 114.

    Boroviak K, Doe B, Banerjee R, Yang F, Bradley A. Chromosome engineering in zygotes with CRISPR/Cas9. Genesis. 2016;54:78-85.

    Not in our library10.1002/dvg.22915 (opens in a new tab)

  115. 115.

    De Angelis MH, et al. Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics. Nat. Genet. 2015;47:969-978.

    Not in our library10.1038/ng.3360 (opens in a new tab)

  116. 116.

    Doube M, et al. BoneJ: free and extensible bone image analysis in ImageJ. Bone. 2010;47:1076-1079.

    Not in our library10.1016/j.bone.2010.08.023 (opens in a new tab)

  117. 117.

    Ollion J, Cochennec J, Loll F, Escudé C, Boudier T. TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization. Bioinformatics. 2013;29:1840-1841.

    Not in our library10.1093/bioinformatics/btt276 (opens in a new tab)

  118. 118.
  119. 119.

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