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Genes2019; 10(12); doi: 10.3390/genes10120976

Genomic Divergence in Swedish Warmblood Horses Selected for Equestrian Disciplines.

Abstract: The equestrian sport horse Swedish Warmblood (SWB) originates from versatile cavalry horses. Most modern SWB breeders have specialized their breeding either towards show jumping or dressage disciplines. The aim of this study was to explore the genomic structure of SWB horses to evaluate the presence of genomic subpopulations, and to search for signatures of selection in subgroups of SWB with high or low breeding values (EBVs) for show jumping. We analyzed high density genotype information from 380 SWB horses born in the period 2010-2011, and used Principal Coordinates Analysis and Discriminant Analysis of Principal Components to detect population stratification. Fixation index and Cross Population Extended Haplotype Homozygosity scores were used to scan the genome for potential signatures of selection. In accordance with current breeding practice, this study highlights the development of two separate breed subpopulations with putative signatures of selection in eleven chromosomes. These regions involve genes with known function in, e.g., mentality, endogenous reward system, development of connective tissues and muscles, motor control, body growth and development. This study shows genetic divergence, due to specialization towards different disciplines in SWB horses. This latter evidence can be of interest for SWB and other horse studbooks encountering specialized breeding.
Publication Date: 2019-11-27 PubMed ID: 31783652PubMed Central: PMC6947233DOI: 10.3390/genes10120976Google Scholar: Lookup
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  • Journal Article
  • Research Support
  • Non-U.S. Gov't

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research article delves into the genomic differences found in the specialized Swedish Warmblood horse breed, specifically investigating the subpopulations bred for show jumping versus dressage disciplines. In essence, it reveals there exists a discernible genetic distinction between the two, highlighting implications for future breeding practices.

Objective of the Study

  • The study sought to investigate the genomic structure of Swedish Warmblood (SWB) horses, particularly spotlighting the presence of subpopulations bred for different equestrian disciplines: dressage and show jumping.
  • Furthermore, it aimed to identify any potential selection signatures in these subgroups, which may be indicative of specialized breeding.

Methodology

  • A sample of 380 SWB horses born between 2010 and 2011 were selected for this study.
  • High density genotype information from these horses was analyzed using a Principal Coordinates Analysis and Discriminant Analysis of Principal Components. This was to detect the existence of population stratification.
  • The team used the Fixation index and Cross Population Extended Haplotype Homozygosity scores to scan the genome for potential selection signatures.

Findings of the Study

  • The study found that there were distinct subpopulations within the SWB breed, bred specifically for different equestrian competitions. This aligns with current breeding practices.
  • Signatures of selection were identified in eleven chromosomes. These are associated with traits such as mentality, reward system, development of muscles and connective tissues, motor control, and body growth and development.

Implication of the Study

  • This discovery validates the existence of genetic variance between specialized SWB subpopulations, attributed to the divergence in breeding for different equestrian disciplines.
  • It provides a valuable insight that could be useful to SWB and other horse studbooks, where specialized breeding is commonplace. Better understanding of these genetic distinctions could influence future breeding practices and the overall development of the breed.

Cite This Article

APA
Ablondi M, Eriksson S, Tetu S, Sabbioni A, Viklund Å, Mikko S. (2019). Genomic Divergence in Swedish Warmblood Horses Selected for Equestrian Disciplines. Genes (Basel), 10(12). https://doi.org/10.3390/genes10120976

Publication

ISSN: 2073-4425
NlmUniqueID: 101551097
Country: Switzerland
Language: English
Volume: 10
Issue: 12

Researcher Affiliations

Ablondi, Michela
  • Department of Veterinary Science, University of Parma, 43126 Parma, Italy.
Eriksson, Susanne
  • Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, PO Box 7023,S-75007 Uppsala, Sweden.
Tetu, Sasha
  • Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, PO Box 7023,S-75007 Uppsala, Sweden.
Sabbioni, Alberto
  • Department of Veterinary Science, University of Parma, 43126 Parma, Italy.
Viklund, Åsa
  • Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, PO Box 7023,S-75007 Uppsala, Sweden.
Mikko, Sofia
  • Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, PO Box 7023,S-75007 Uppsala, Sweden.

MeSH Terms

  • Animals
  • Breeding / methods
  • Female
  • Horses / genetics
  • Horses / growth & development
  • Linkage Disequilibrium
  • Male
  • Oligonucleotide Array Sequence Analysis / veterinary
  • Polymorphism, Single Nucleotide
  • Principal Component Analysis
  • Quantitative Trait Loci
  • Selection, Genetic
  • Sports
  • Sweden

Conflict of Interest Statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

This article includes 99 references
  1. Rovere G, Madsen P, Norberg E, van Arendonk JAM, Ducro BJ. Genetic connections between dressage and show-jumping horses in Dutch Warmblood horses.. Acta Agric. Scand. A Anim. Sci. 2014;64:57–66.
  2. SWB SWBs Avelsplan 2015. [(accessed on 28 October 2019)]; Available online: https://swb.org/wp-content/uploads/2016/11/Avelsplan-fi%CC%82r-SWB.pdf.
  3. Viklund Å, Granberg L, Eriksson S. Genetic analysis of data from Swedish stallion performance test. Proceedings of the 69th Annual Meeting of the EAAP Dubrovnik, Croatia. 27–31 August 2018; p. 660.
  4. Viklund Å, Näsholm A, Strandberg E, Philipsson J. Genetic trends for performance of Swedish Warmblood horses.. Livest. Sci. 2011;141:113–122.
  5. Maiorano AM, Lourenco DL, Tsuruta S, Toro Ospina AM, Stafuzza NB, Masuda Y, Filho AEV, Dos Santos Goncalves Cyrillo JN, Curi RA, De Vasconcelos Silva JA. Assessing genetic architecture and signatures of selection of dual purpose Gir cattle populations using genomic information.. PLoS ONE 2018;13:e0200694.
  6. Marchiori CM, Pereira GL, Maiorano AM, Rogatto GM, Assoni AD, Augusto V II, Silva J, Chardulo LAL, Curi RA. Linkage disequilibrium and population structure characterization in the cutting and racing lines of Quarter Horses bred in Brazil.. Livest. Sci. 2019;219:45–51.
  7. Bomba L, Nicolazzi EL, Milanesi M, Negrini R, Mancini G, Biscarini F, Stella A, Valentini A, Ajmone-Marsan P. Relative extended haplotype homozygosity signals across breeds reveal dairy and beef specific signatures of selection.. Genet. Sel. Evol. 2015;47:25.
    doi: 10.1186/s12711-015-0113-9pmc: PMC4383072pubmed: 25888030google scholar: lookup
  8. Petersen JL, Mickelson JR, Cleary KD, McCue ME. The american quarter horse: Population structure and relationship to the thoroughbred.. J. Hered. 2014;105:148–162.
    doi: 10.1093/jhered/est079pmc: PMC3920813pubmed: 24293614google scholar: lookup
  9. Avila F, Mickelson JR, Schaefer RJ, McCue ME. Genome-Wide Signatures of Selection Reveal Genes Associated With Performance in American Quarter Horse Subpopulations.. Front. Genet. 2018;9:249.
    doi: 10.3389/fgene.2018.00249pmc: PMC6060370pubmed: 30105047google scholar: lookup
  10. Lopes MS, Mendonça D, Rojer H, Cabral V, Bettencourt SX, da Câmara Machado A. Morphological and genetic characterization of an emerging Azorean horse breed: The Terceira Pony.. Front. Genet. 2015;6:62.
    doi: 10.3389/fgene.2015.00062pmc: PMC4343030pubmed: 25774165google scholar: lookup
  11. Ovchinnikov IV, Dahms T, Herauf B, McCann B, Juras R, Castaneda C, Cothran EG. Genetic diversity and origin of the feral horses in Theodore Roosevelt National Park.. PLoS ONE 2018;13:e0200795.
  12. Velie BD, Shrestha M, Francois L, Schurink A, Tesfayonas YG, Stinckens A, Blott S, Ducro BJ, Mikko S, Thomas R. Using an Inbred Horse Breed in a High Density Genome-Wide Scan for Genetic Risk Factors of Insect Bite Hypersensitivity (IBH). PLoS ONE 2016;11:e0152966.
  13. Grilz-Seger G, Druml T, Neuditschko M, Dobretsberger M, Horna M, Brem G. High-resolution population structure and runs of homozygosity reveal the genetic architecture of complex traits in the Lipizzan horse.. BMC Genom. 2019;20:1–17.
    doi: 10.1186/s12864-019-5564-xpmc: PMC6402180pubmed: 30836959google scholar: lookup
  14. Metzger J, Karwath M, Tonda R, Beltran S, Águeda L, Gut M, Gut IG, Distl O. Runs of homozygosity reveal signatures of positive selection for reproduction traits in breed and non-breed horses.. BMC Genom. 2015;16:764.
    doi: 10.1186/s12864-015-1977-3pmc: PMC4600213pubmed: 26452642google scholar: lookup
  15. Grilz-seger G, Neuditschko M, Mesaric M, Cotman M, Brem G, Druml T. Changes in breeding objectives of the Haflinger horse breed from a genome—wide perspective.. Züchtungskunde 2019;91:296–311.
  16. Ablondi M, Viklund Å, Lindgren G, Eriksson S, Mikko S. Signatures of selection in the genome of Swedish warmblood horses selected for sport performance.. BMC Genom. 2019;20:717.
    doi: 10.1186/s12864-019-6079-1pmc: PMC6751828pubmed: 31533613google scholar: lookup
  17. Sabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, Cotsapas C, Xie X, Byrne EH, McCarroll SA, Gaudet R. Genome-wide detection and characterization of positive selection in human populations.. Nature 2007;449:913–918.
    doi: 10.1038/nature06250pmc: PMC2687721pubmed: 17943131google scholar: lookup
  18. Frischknecht M, Flury C, Leeb T, Rieder S, Neuditschko M. Selection signatures in Shetland ponies.. Anim. Genet. 2016;47:370–372.
    doi: 10.1111/age.12416pubmed: 26857482google scholar: lookup
  19. Moon S, Lee JW, Shin D, Shin KY, Kim J, Choi IY, Kim J, Kim H. A Genome-wide scan for selective sweeps in racing horses.. Asian-Australas. J. Anim. Sci. 2015;28:1525–1531.
    doi: 10.5713/ajas.14.0696pmc: PMC4647090pubmed: 26333666google scholar: lookup
  20. Metzger J, Philipp U, Lopes MS, da Camara Machado A, Felicetti M, Silvestrelli M, Distl O. Analysis of copy number variants by three detection algorithms and their association with body size in horses.. BMC Genom. 2013;14:487.
    doi: 10.1186/1471-2164-14-487pmc: PMC3720552pubmed: 23865711google scholar: lookup
  21. Wang W, Wang S, Hou C, Xing Y, Cao J, Wu K, Liu C, Zhang D, Zhang L, Zhang Y. Genome-wide detection of copy number variations among diverse horse breeds by array CGH.. PLoS ONE 2014;9:e86860.
  22. Schurink A, da Silva VH, Velie BD, Dibbits BW, Crooijmans RPMA, François L, Janssens S, Stinckens A, Blott S, Buys N. Copy number variations in Friesian horses and genetic risk factors for insect bite hypersensitivity.. BMC Genet. 2018;19:49.
    doi: 10.1186/s12863-018-0657-0pmc: PMC6065148pubmed: 30060732google scholar: lookup
  23. . SAS® 9.4 Fourth Edition.. SAS Institute Inc.; Cary, NC, USA: 2015.
  24. Schaefer RJ, Schubert M, Bailey E, Bannasch DL, Barrey E, Bar-Gal GK, Brem G, Brooks SA, Distl O, Fries R. Developing a 670 k genotyping array to tag~2 M SNPs across 24 horse breeds.. BMC Genom. 2017;18:565.
    doi: 10.1186/s12864-017-3943-8pmc: PMC5530493pubmed: 28750625google scholar: lookup
  25. Kalbfleisch TS, Rice E, DePriest MS, Walenz BP, Hestand MS, Vermeesch JR, O’Connell BL, Fiddes IT, Vershinina AO, Petersen JL. Eq쪳, an Updated Reference Genome for the Domestic Horse.. bioRxiv 2018.
    doi: 10.1101/306928pmc: PMC6240028pubmed: 30456315google scholar: lookup
  26. Beeson SK, Schaefer RJ, Mason VC, McCue ME. Robust remapping of equine SNP array coordinates to Eq쪳.. Anim. Genet. 2019;50:114–115.
    doi: 10.1111/age.12745pmc: PMC6349531pubmed: 30421446google scholar: lookup
  27. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses.. Am. J. Hum. Genet. 2007;81:559–575.
    doi: 10.1086/519795pmc: PMC1950838pubmed: 17701901google scholar: lookup
  28. Wade CM, Giulotto E, Sigurdsson S, Zoli M, Gnerre S, Imsland F, Lear TL, Adelson DL, Bailey E, Bellone RR. Genome Sequence, Comparative Analysis, and Population Genetics of the Domestic Horse.. Science 2009;326:865–867.
    doi: 10.1126/science.1178158pmc: PMC3785132pubmed: 19892987google scholar: lookup
  29. Mardia KV. Some properties of clasical multi-dimesional scaling.. Commun. Stat. Theory Methods. 1978;7:1233–1241.
    doi: 10.1080/03610927808827707google scholar: lookup
  30. R Development Core Team. R: A Language and Environment for Statistical Computing.. R Foundation for Statistical Computing; Vienna, Austria: 2014.
  31. Jombart T, Devillard S, Balloux F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations.. BMC Genet. 2010;11:94.
    doi: 10.1186/1471-2156-11-94pmc: PMC2973851pubmed: 20950446google scholar: lookup
  32. Jombart T. Adegenet: A R package for the multivariate analysis of genetic markers.. Bioinformatics 2008;24:1403–1405.
    doi: 10.1093/bioinformatics/btn129pubmed: 18397895google scholar: lookup
  33. Nei M. Molecular Evolutionary Genetics.. Columbia University Press; New York, NY, USA: 1987.
  34. Akey JM. Interrogating a High-Density SNP Map for Signatures of Natural Selection.. Genome Res. 2002;12:1805–1814.
    doi: 10.1101/gr.631202pmc: PMC187574pubmed: 12466284google scholar: lookup
  35. Wright S. Evolution and the Genetics of Populations, Variability within and among Natural Populations. Volume 4. University of Chicago Press; Chicago, IL, USA: 1978.
  36. Makina SO, Muchadeyi FC, Van Marle-Köster E, Taylor JF, Makgahlela ML, Maiwashe A. Genome-wide scan for selection signatures in six cattle breeds in South Africa.. Genet. Sel. Evol. 2015;47:1–14.
    doi: 10.1186/s12711-015-0173-xpmc: PMC4662009pubmed: 26612660google scholar: lookup
  37. Zhao F, McParland S, Kearney F, Du L, Berry DP. Detection of selection signatures in dairy and beef cattle using high-density genomic information.. Genet. Sel. Evol. 2015;47:49.
    doi: 10.1186/s12711-015-0127-3pmc: PMC4472243pubmed: 26089079google scholar: lookup
  38. Delaneau O, Coulonges C, Zagury J-F. Shape-IT: New rapid and accurate algorithm for haplotype inference.. BMC Bioinform. 2008;9:540.
    doi: 10.1186/1471-2105-9-540pmc: PMC2647951pubmed: 19087329google scholar: lookup
  39. Gautier M, Vitalis R. Rehh An R package to detect footprints of selection in genome-wide SNP data from haplotype structure.. Bioinformatics 2012;28:1176–1177.
    doi: 10.1093/bioinformatics/bts115pubmed: 22402612google scholar: lookup
  40. Gautier M, Naves M. Footprints of selection in the ancestral admixture of a New World Creole cattle breed.. Mol. Ecol. 2011;20:3128–3143.
  41. Benjamini Y, Hochberg Y. Controlling The False Discovery Rate—A Practical And Powerful Approach To Multiple Testing.. J. R. Stat. Soc. 1995;57:289–300.
  42. Aken BL, Ayling S, Barrell D, Clarke L, Curwen V, Fairley S, Fernandez Banet J, Billis K, García Girón C, Hourlier T. The Ensembl gene annotation system.. Database 2016;2016:93.
    doi: 10.1093/database/baw093pmc: PMC4919035pubmed: 27337980google scholar: lookup
  43. Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B. PANTHER: A Library of Protein Families and Subfamilies Indexed by Function.. Genome Res. 2003;13:2129–2141.
    doi: 10.1101/gr.772403pmc: PMC403709pubmed: 12952881google scholar: lookup
  44. Hu ZL, Park CA, Reecy JM. Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB.. Nucleic Acids Res. 2019;47:D701–D710.
    doi: 10.1093/nar/gky1084pmc: PMC6323967pubmed: 30407520google scholar: lookup
  45. Viklund Å, Braam Å, Näsholm A, Strandberg E, Philipsson J. Genetic variation in competition traits at different ages and time periods and correlations with traits at field tests of 4-year-old Swedish Warmblood horses.. Animal 2010;4:682.
    doi: 10.1017/S1751731110000017pubmed: 22444120google scholar: lookup
  46. Hellsten ET, Näsholm A, Jorjani H, Strandberg E, Philipsson J. Influence of foreign stallions on the Swedish Warmblood breed and its genetic evaluation.. Livest. Sci. 2009;121:207–214.
  47. Storz JF. Invited review: Using genome scans of DNA polymorphism to infer adaptive population divergence.. Mol. Ecol. 2005;14:671–688.
  48. Chen M, Pan D, Ren H, Fu J, Li J, Su G, Wang A, Jiang L, Zhang Q, Liu JF. Identification of selective sweeps reveals divergent selection between Chinese Holstein and Simmental cattle populations.. Genet. Sel. Evol. 2016;48:1–12.
    doi: 10.1186/s12711-016-0254-5pmc: PMC5054554pubmed: 27716022google scholar: lookup
  49. Doan R, Cohen N, Harrington J, Veazy K, Juras R, Cothran G, McCue ME, Skow L, Dindot SV. Identification of copy number variants in horses.. Genome Res. 2012;22:899–907.
    doi: 10.1101/gr.128991.111pmc: PMC3337435pubmed: 22383489google scholar: lookup
  50. Zhang W, Han Q, Liu Z, Zheou W, Cao Q, Zhou W. Exome sequencing reveals a de novo PRKG1 mutation in a sporadic patient with aortic dissection.. BMC Med. Genet. 2018;19:1–5.
    doi: 10.1186/s12881-018-0735-1pmc: PMC6303953pubmed: 30577811google scholar: lookup
  51. Lee W, Park KD, Taye M, Lee C, Kim H, Lee HK, Shin D. Analysis of cross-population differentiation between Thoroughbred and Jeju horses.. Asian-Australas. J. Anim. Sci. 2018;31:1110–1118.
    doi: 10.5713/ajas.17.0460pmc: PMC6043458pubmed: 29268585google scholar: lookup
  52. Nekrasova T, Jobes ML, Ting JH, Wagner GC, Minden A. Targeted disruption of the Pak5 and Pak6 genes in mice leads to deficits in learning and locomotion.. Dev. Biol. 2008;322:95–108.
    doi: 10.1016/j.ydbio.2008.07.006pubmed: 18675265google scholar: lookup
  53. Carlisle HJ, Luong TN, Medina-Marino A, Schenker L, Khorosheva E, Indersmitten T, Gunapala KM, Steele AD, O’Dell TJ, Patterson PH. Deletion of Densin-180 Results in Abnormal Behaviors Associated with Mental Illness and Reduces mGluR5 and DISC1 in the Postsynaptic Density Fraction.. J. Neurosci. 2011;31:16194–16207.
  54. Udawela M, Scarr E, Hannan AJ, Thomas EA, Dean B. Phospholipase C beta 1 expression in the dorsolateral prefrontal cortex from patients with schizophrenia at different stages of illness.. Aust. N. Z. J. Psychiatry. 2011;45:140–147.
    doi: 10.3109/00048674.2010.533364pubmed: 21091263google scholar: lookup
  55. Weng YT, Chien T, Kuan II, Chern Y. The TRAX, DISC1, and GSK3 complex in mental disorders and therapeutic interventions 06 Biological Sciences 0604 Genetics 11 Medical and Health Sciences 1103 Clinical Sciences.. J. Biomed. Sci. 2018;25:1–14.
    pmc: PMC6171312pubmed: 30285728
  56. Fernandes JCR, Acuña SM, Aoki JI, Floeter-Winter LM, Muxel SM. Long non-coding RNAs in the regulation of gene expression: Physiology and disease.. Non-Coding RNA 2019;5:17.
    doi: 10.3390/ncrna5010017pmc: PMC6468922pubmed: 30781588google scholar: lookup
  57. Qureshi IA, Mehler MF. Emerging roles of non-coding RNAs in brain evolution, development, plasticity and disease.. Nat. Rev. Neurosci. 2012;13:528–541.
    doi: 10.1038/nrn3234pmc: PMC3478095pubmed: 22814587google scholar: lookup
  58. Carneiro M, Rubin CJ, Di Palma F, Albert FW, Alfoldi J, Barrio AM, Pielberg G, Rafati N, Sayyab S, Turner-Maier J. Rabbit genome analysis reveals a polygenic basis for phenotypic change during domestication.. Scince. 2014;345:1074–1079.
    doi: 10.1126/science.1253714pmc: PMC5421586pubmed: 25170157google scholar: lookup
  59. Schroder W, Klostermann A, Stock KF, Distl O. A genome-wide association study for quantitative trait loci of show-jumping in Hanoverian warmblood horses.. Anim. Genet. 2012;43:92–400.
  60. Napoli E, Ross-Inta C, Wong S, Hung C, Fujisawa Y, Sakaguchi D, Angelastro J, Omanska-Klusek A, Schoenfeld R, Giulivi C. Mitochondrial dysfunction in Pten Haplo-insufficient mice with social deficits and repetitive behavior: Interplay between Pten and p53.. PLoS ONE 2012;7:e42504.
  61. Kwon CH, Luikart BW, Powell CM, Zhou J, Matheny SA, Zhang W, Li Y, Baker SJ, Parada LF. Pten Regulates Neuronal Arborization and Social Interaction in Mice.. Neuron 2006;50:377–388.
  62. Rankinen T, Roth SM, Bray MS, Loos R, Pérusse L, Wolfarth B, Hagberg JM, Bouchard C. Advances in Exercise, Fitness, and Performance Genomics.. Med. Sci. Sports Exerc. 2010;42:835–846.
    doi: 10.1249/MSS.0b013e3181d86cecpubmed: 20400881google scholar: lookup
  63. Coricor G, Serra R. TGF-β regulates phosphorylation and stabilization of Sox9 protein in chondrocytes through p38 and Smad dependent mechanisms.. Sci. Rep. 2016;6:1–11.
    doi: 10.1038/srep38616pmc: PMC5144132pubmed: 27929080google scholar: lookup
  64. Chavez RD, Coricor G, Perez J, Seo HS, Serra R. SOX9 protein is stabilized by TGF-β and regulates PAPSS2 mRNA expression in chondrocytes.. Osteoarthr. Cart. 2017;25:332–340.
    doi: 10.1016/j.joca.2016.10.007pmc: PMC5258840pubmed: 27746378google scholar: lookup
  65. Loeys BL, Schwarze U, Holm T, Callewaert BL, Thomas GH, Pannu H, De Backer JF, Oswald GL, Symoens S, Manouvrier S. Aneurysm Syndromes Caused by Mutations in the TGF-β Receptor.. N. Engl. J. Med. 2006;355:788–798.
    doi: 10.1056/NEJMoa055695pubmed: 16928994google scholar: lookup
  66. Borza CM, Su Y, Tran TL, Yu L, Steyns N, Temple KJ, Skwark MJ, Meiler J, Lindsley CW, Hicks BR. Discoidin domain receptor 1 kinase activity is required for regulating collagen IV synthesis.. Matrix Biol. 2017;57–58:258–271.
  67. Chiusa M, Hu W, Liao HJ, Su Y, Borza CM, de Caestecker MP, Skrypnyk NI, Fogo AB, Pedchenko V, Li X. The Extracellular Matrix Receptor Discoidin Domain Receptor 1 Regulates Collagen Transcription by Translocating to the Nucleus.. J. Am. Soc. Nephrol. 2019;30:1605–1624.
    doi: 10.1681/ASN.2018111160pmc: PMC6727269pubmed: 31383731google scholar: lookup
  68. Eklund L, Piuhola J, Komulainen J, Sormunen R, Ongvarrasopone C, Fässler R, Muona A, Ilves M, Ruskoaho H, Takala TES. Lack of type XV collagen causes a skeletal myopathy and cardiovascular defects in mice.. Proc. Natl. Acad. Sci. USA. 2001;98:1194–1199.
    doi: 10.1073/pnas.98.3.1194pmc: PMC14731pubmed: 11158616google scholar: lookup
  69. Lim PJ, Lindert U, Opitz L, Hausser I, Rohrbach M, Giunta C. Transcriptome Profiling of Primary Skin Fibroblasts Reveal Distinct Molecular Features Between PLOD1- and FKBP14-Kyphoscoliotic Ehlers–Danlos Syndrome.. Genes 2019;10:517.
    doi: 10.3390/genes10070517pmc: PMC6678841pubmed: 31288483google scholar: lookup
  70. Castori M, Tinkle B, Levy H, Grahame R, Malfait F, Hakim A. A framework for the classification of joint hypermobility and related conditions.. Am. J. Med. Genet. Part C Semin. Med. Genet. 2017;175:148–157.
    doi: 10.1002/ajmg.c.31539pubmed: 28145606google scholar: lookup
  71. Monthoux C, de Brot S, Jackson M, Bleul U, Walter J. Skin malformations in a neonatal foal tested homozygous positive for Warmblood Fragile Foal Syndrome.. BMC Vet. Res. 2015;11:1–8.
    doi: 10.1186/s12917-015-0318-8pmc: PMC4327794pubmed: 25637337google scholar: lookup
  72. Giunta C, Elçioglu NH, Albrecht B, Eich G, Chambaz C, Janecke AR, Yeowell H, Weis MA, Eyre DR, Kraenzlin M. Spondylocheiro Dysplastic Form of the Ehlers-Danlos Syndrome-An Autosomal-Recessive Entity Caused by Mutations in the Zinc Transporter Gene SLC39A13.. Am. J. Hum. Genet. 2008;82:1290–1305.
    doi: 10.1016/j.ajhg.2008.05.001pmc: PMC2427271pubmed: 18513683google scholar: lookup
  73. Mushtaq M, Gaza HV, Kashuba EV. Role of the RB-Interacting Proteins in Stem Cell Biology.. Adv. Cancer Res. 2016;131:133–157.
    pubmed: 27451126
  74. Minocherhomji S, Hansen C, Kim HG, Mang Y, Bak M, Guldberg P, Papadopoulos N, Eiberg H, Doh GD, Møllgård K. Epigenetic remodelling and dysregulation of DLGAP4 is linked with early-onset cerebellar ataxia.. Hum. Mol. Genet. 2014;23:6163–6176.
    doi: 10.1093/hmg/ddu337pmc: PMC4222360pubmed: 24986922google scholar: lookup
  75. Rasmussen AH, Rasmussen HB, Silahtaroglu A. The DLGAP family: Neuronal expression, function and role in brain disorders.. Mol. Brain. 2017;10:1–13.
    doi: 10.1186/s13041-017-0324-9pmc: PMC5583998pubmed: 28870203google scholar: lookup
  76. Banerjee A, Wang HY, Borgmann-Winter KE, MacDonald ML, Kaprielian H, Stucky A, Kvasic J, Egbujo C, Ray R, Talbot K. Src kinase as a mediator of convergent molecular abnormalities leading to NMDAR hypoactivity in schizophrenia.. Mol. Psychiatry. 2015;20:1091–1100.
    doi: 10.1038/mp.2014.115pmc: PMC5156326pubmed: 25330739google scholar: lookup
  77. Schob C, Morellini F, Ohana O, Bakota L, Hrynchak MV, Brandt R, Brockmann MD, Cichon N, Hartung H, Hanganu-Opatz IL. Cognitive impairment and autistic-like behaviour in SAPAP4-deficient mice.. Transl. Psychiatry. 2019;9:7.
    doi: 10.1038/s41398-018-0327-zpmc: PMC6341115pubmed: 30664629google scholar: lookup
  78. Ellenbroek SIJ, Collard JG. Rho GTPases: Functions and association with cancer.. Clin. Exp. Metastasis. 2007;24:657–672.
    doi: 10.1007/s10585-007-9119-1pubmed: 18000759google scholar: lookup
  79. Lowe C, Yoneda T, Boycet BF, Chent H, Mundy GR, Sorianot P. Osteopetrosis in Src-deficient mice is due to an autonomous defect of osteoclasts.. Proc. Natl. Acad. Sci. USA. 1993;90:4485–4489.
    doi: 10.1073/pnas.90.10.4485pmc: PMC46536pubmed: 7685105google scholar: lookup
  80. Skujina I, Winton CL, Hegarty MJ, McMahon R, Nash DM, Davies Morel MCG, McEwan NR. Detecting genetic regions associated with height in the native ponies of the British Isles by using high density SNP genotyping.. Genome. 2018;61:767–770.
    doi: 10.1139/gen-2018-0006pubmed: 30184439google scholar: lookup
  81. Davidson TB, Sanchez-Lara PA, Randolph LM, Krieger MD, Wu SQ, Panigrahy A, Shimada H, Erdreich-Epstein A. Microdeletion del(22)(q12.2) encompassing the facial development-associated gene, MN1 (meningioma 1) in a child with Pierre-Robin sequence (including cleft palate) and neurofibromatosis 2 (NF2): A case report and review of the literature.. BMC Med. Genet. 2012;13:19.
    doi: 10.1186/1471-2350-13-19pmc: PMC3359208pubmed: 22436304google scholar: lookup
  82. Hu PQ, Fertig N, Medsger TA, Wright TM. Molecular Recognition Patterns of Serum Anti-DNA Topoisomerase I Antibody in Systemic Sclerosis.. J. Immunol. 2014;173:2834–2841.
    doi: 10.4049/jimmunol.173.4.2834pubmed: 15295002google scholar: lookup
  83. Nevitt C, Tooley JG, Schaner Tooley CE. N-terminal acetylation and methylation differentially affect the function of MYL9.. Biochem. J. 2018;475:3201–3219.
    doi: 10.1042/BCJ20180638pmc: PMC6442934pubmed: 30242065google scholar: lookup
  84. Rylatt DB, Aitken A, Bilham T, Condon GD, Embi N, Cohen P. Glycogen Synthase from Rabbit Skeletal Muscle.. Eur. J. Biochem. 2005;107:529–537.
  85. Yang S, Li X, Liu X, Ding X, Xin X, Jin C, Zhang S, Li G, Guo H. Parallel comparative proteomics and phosphoproteomics reveal that cattle myostatin regulates phosphorylation of key enzymes in glycogen metabolism and glycolysis pathway.. Oncotarget. 2018;9:11352–11370.
    doi: 10.18632/oncotarget.24250pmc: PMC5834288pubmed: 29541418google scholar: lookup
  86. Lewis SS, Nicholson AM, Williams ZJ, Valberg SJ. Clinical characteristics and muscle glycogen concentrations in warmblood horses with polysaccharide storage myopathy.. Am. J. Vet. Res. 2017;78:1305–1312.
    doi: 10.2460/ajvr.78.11.1305pubmed: 29076373google scholar: lookup
  87. Zhou J, Blundell J, Ogawa S, Kwon CH, Zhang W, Sinton C, Powell CM, Parada LF. Pharmacological inhibition of mTORCl suppresses anatomical, cellular, and behavioral abnormalities in neural-specific PTEN knock-out mice.. J. Neurosci. 2009;29:1773–1783.
  88. Hassed S, Li S, Mulvihill J, Aston C, Palmer S. Adams–Oliver syndrome review of the literature: Refining the diagnostic phenotype.. Am. J. Med. Genet. Part A. 2017;173:790–800.
    doi: 10.1002/ajmg.a.37889pubmed: 28160419google scholar: lookup
  89. Schindler RFR, Scotton C, Zhang J, Passarelli C, Ortiz-Bonnin B, Simrick S, Schwerte T, Poon KL, Fang M, Rinné S. POPDC1S201F causes muscular dystrophy and arrhythmia by affecting protein trafficking.. J. Clin. Invest. 2015;126:239–253.
    doi: 10.1172/JCI79562pmc: PMC4701561pubmed: 26642364google scholar: lookup
  90. Swan AH, Gruscheski L, Boland LA, Brand T. The Popeye domain containing gene family encoding a family of cAMP-effector proteins with important functions in striated muscle and beyond.. J. Muscle Res. Cell Motil. 2019;40:169–183.
    doi: 10.1007/s10974-019-09523-zpmc: PMC6726836pubmed: 31197601google scholar: lookup
  91. Hendrickson SL. A genome wide study of genetic adaptation to high altitude in feral Andean Horses of the páramo.. BMC Evol. Biol. 2013;13:273.
    doi: 10.1186/1471-2148-13-273pmc: PMC3878729pubmed: 24344830google scholar: lookup
  92. Shin JH, Adrover MF, Wess J, Alvarez VA. Muscarinic regulation of dopamine and glutamate transmission in the nucleus accumbens.. Proc. Natl. Acad. Sci. USA. 2015;112:8124–8129.
    doi: 10.1073/pnas.1508846112pmc: PMC4491757pubmed: 26080439google scholar: lookup
  93. Kader A, Liu X, Dong K, Song S, Pan J, Yang M, Chen X, He X, Jiang L, Ma Y. Identification of copy number variations in three Chinese horse breeds using 70 K single nucleotide polymorphism BeadChip array.. Anim. Genet. 2016;47:560–569.
    doi: 10.1111/age.12451pubmed: 27440410google scholar: lookup
  94. Ueda H, Sasaki K, Halder SK, Deguchi Y, Takao K, Miyakawa T, Tajima A. Prothymosin alpha-deficiency enhances anxiety-like behaviors and impairs learning/memory functions and neurogenesis.. J. Neurochem. 2017;141:124–136.
    doi: 10.1111/jnc.13963pubmed: 28122138google scholar: lookup
  95. George EM, Brown DT. Prothymosin α is a component of a linker histone chaperone.. FEBS Lett. 2010;584:2833–2836.
  96. McNeill EM, Klöckner-Bormann M, Roesler EC, Talton LE, Moechars D, Clagett-Dame M. Nav2 hypomorphic mutant mice are ataxic and exhibit abnormalities in cerebellar development.. Dev. Biol. 2011;353:331–343.
    doi: 10.1016/j.ydbio.2011.03.008pmc: PMC3250223pubmed: 21419114google scholar: lookup
  97. Lanuza GM, Gosgnach S, Pierani A, Jessell TM, Goulding M. Genetic identification of spinal interneurons that coordinate left-right locomotor activity necessary for walking movements.. Neuron 2004;42:375–386.
    doi: 10.1016/S0896-6273(04)00249-1pubmed: 15134635google scholar: lookup
  98. Griener A, Zhang W, Kao H, Haque F, Gosgnach S. Anatomical and electrophysiological characterization of a population of dI6 interneurons in the neonatal mouse spinal cord.. Neuroscience 2017;362:47–59.
  99. Andersson LS, Larhammar M, Memic F, Wootz H, Schwochow D, Rubin CJ, Patra K, Arnason T, Wellbring L, Hjälm G. Mutations in DMRT3 affect locomotion in horses and spinal circuit function in mice.. Nature 2012;488:642–646.
    doi: 10.1038/nature11399pmc: PMC3523687pubmed: 22932389google scholar: lookup