FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Sci Rep / New genomic insights into the conformation of Lipizzan horse...
Scientific reports2023; 13(1); 8990; doi: 10.1038/s41598-023-36272-4

New genomic insights into the conformation of Lipizzan horses.

Abstract: Conformation traits are important selection criteria in equine breeding, as they describe the exterior aspects of the horse (height, joint angles, shape). However, the genetic architecture of conformation is not well understood, as data of these traits mainly consist of subjective evaluation scores. Here, we performed genome-wide association studies on two-dimensional shape data of Lipizzan horses. Based on this data, we identified significant quantitative trait loci (QTL) associated with cresty neck on equine chromosome (ECA)16 within the MAGI1 gene, and with type, hereby differentiating heavy from light horses on ECA5 within the POU2F1 gene. Both genes were previously described to affect growth, muscling and fatty deposits in sheep, cattle and pigs. Furthermore, we pin-pointed another suggestive QTL on ECA21, near the PTGER4 gene, associated with human ankylosing spondylitis, for shape differences in the back and pelvis (roach back vs sway back). Further differences in the shape of the back and abdomen were suggestively associated with the RYR1 gene, involved in core muscle weakness in humans. Therefore, we demonstrated that horse shape space data enhance the genomic investigations of horse conformation.
© 2023. The Author(s).
Get Full Text
Publication Date: 2023-06-02 PubMed ID: 37268682PubMed Central: PMC10238546DOI: 10.1038/s41598-023-36272-4Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • 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.

This research looks at the genetic factors affecting the physical traits of Lipizzan horses, providing insights into which genes play major roles.

Introduction

  • In this study, the authors investigate the genetic basis for the conformation, or the physical traits and shape of Lipizzan horses. These traits, such as height, joint angles, and shape, are crucial in equine breeding.

Research Method

  • To better understand the genetic factors involved, they carried out a Genome-Wide Association Study (GWAS), making use of two-dimensional shape data from these horses.

Findings

  • A key finding was the identification of significant Quantitative Trait Loci (QTL) related with cresty neck – a condition where an excessive accumulation fat is present on a horse’s neck – in the MAGI1 gene located on the equine chromosome ECA16.
  • Additionally, the QTL responsible for differentiating heavier from lighter horses was found in the POU2F1 gene on ECA5.
  • Both the MAGI1 and POU2F1 genes have been previously reported to influence growth, muscle development and fat deposits in other species like sheep, cattle and pigs.
  • Another interesting QTL identified was near the PTGER4 gene on ECA21, a gene associated with a form of chronic arthritis in humans called ankylosing spondylitis. In Lipizzan horses, this QTL seems to affect the shape of the back and pelvis.
  • Differences in the shape of both the back and abdomen were potentially linked to a gene called RYR1. This gene is involved in causing core muscle weakness in humans.

Conclusion

  • This research underscores the usefulness of horse shape data in enhancing the genomic investigations of equine conformation.
  • The findings also pave the way for further research into how these genes might influence the physical traits and diseases in other horse breeds or even other species.

Cite This Article

APA
Gmel AI, Brem G, Neuditschko M. (2023). New genomic insights into the conformation of Lipizzan horses. Sci Rep, 13(1), 8990. https://doi.org/10.1038/s41598-023-36272-4

Publication

Scientific reports
ISSN: 2045-2322
NlmUniqueID: 101563288
Country: England
Language: English
Volume: 13
Issue: 1
Pages: 8990
PII: 8990

Researcher Affiliations

Gmel, A I
  • Equine Department, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 260, 8057, Zurich, Switzerland.
  • Animal GenoPhenomics, Agroscope, Rte de La Tioleyre 4, 1725, Posieux, Switzerland.
Brem, G
  • Institute of Animal Breeding and Genetics, Veterinary University Vienna, Veterinärplatz 1, 1220, Vienna, Austria.
Neuditschko, M
  • Animal GenoPhenomics, Agroscope, Rte de La Tioleyre 4, 1725, Posieux, Switzerland. markus.neuditschko@agroscope.admin.ch.
  • Institute of Animal Breeding and Genetics, Veterinary University Vienna, Veterinärplatz 1, 1220, Vienna, Austria. markus.neuditschko@agroscope.admin.ch.

MeSH Terms

  • Humans
  • Horses / genetics
  • Animals
  • Cattle
  • Swine
  • Sheep / genetics
  • Genome-Wide Association Study
  • Phenotype
  • Quantitative Trait Loci
  • Genomics
  • Genes, Homeobox
  • Polymorphism, Single Nucleotide
  • Cell Adhesion Molecules / genetics
  • Adaptor Proteins, Signal Transducing / genetics
  • Guanylate Kinases / genetics

Conflict of Interest Statement

The authors declare no competing interests.

References

This article includes 72 references
  1. Szmatoła T, Gurgul A, Jasielczuk I, Oclon E, Ropka-Molik K, Stefaniuk-Szmukier M, Polak G, Tomczyk-Wrona I, Bugno-Poniewierska M. Assessment and Distribution of Runs of Homozygosity in Horse Breeds Representing Different Utility Types.. Animals (Basel) 2022 Nov 25;12(23).
    doi: 10.3390/ani12233293pmc: PMC9736150pubmed: 36496815google scholar: lookup
  2. Grilz-Seger G, Neuditschko M, Ricard A, Velie B, Lindgren G, Mesarič M, Cotman M, Horna M, Dobretsberger M, Brem G, Druml T. Genome-Wide Homozygosity Patterns and Evidence for Selection in a Set of European and Near Eastern Horse Breeds.. Genes (Basel) 2019 Jun 28;10(7).
    doi: 10.3390/genes10070491pmc: PMC6679042pubmed: 31261764google scholar: lookup
  3. Grilz-Seger G, Druml T, Neuditschko M, Mesarič M, Cotman M, Brem G. Analysis of ROH patterns in the Noriker horse breed reveals signatures of selection for coat color and body size.. Anim Genet 2019 Aug;50(4):334-346.
    doi: 10.1111/age.12797pmc: PMC6617995pubmed: 31199540google scholar: lookup
  4. Holmström M, Back W. The effects of conformation.. .
  5. Gmel AI, Druml T, Portele K, von Niederhäusern R, Neuditschko M. Repeatability, reproducibility and consistency of horse shape data and its association with linearly described conformation traits in Franches-Montagnes stallions.. PLoS One 2018;13(8):e0202931.
    doi: 10.1371/journal.pone.0202931pmc: PMC6110498pubmed: 30148872google scholar: lookup
  6. Druml T, Dobretsberger M, Brem G. Ratings of equine conformation–new insights provided by shape analysis using the example of Lipizzan stallions.. Arch. Anim. Breed. 2016;59:309–317.
    doi: 10.5194/aab-59-309-2016google scholar: lookup
  7. Schroderus E, Ojala M. Estimates of genetic parameters for conformation measures and scores in Finnhorse and Standardbred foals.. J Anim Breed Genet 2010 Oct;127(5):395-403.
    doi: 10.1111/j.1439-0388.2010.00856.xpubmed: 20831564google scholar: lookup
  8. Samoré A, Pagnacco G, Miglior F. Genetic parameters and breeding values for linear type traits in the Haflinger horse.. Livest. Prod. Sci. 1997;52:105–111.
    doi: 10.1016/S0301-6226(97)00143-7google scholar: lookup
  9. Rosengren MK, Sigurðardóttir H, Eriksson S, Naboulsi R, Jouni A, Novoa-Bravo M, Albertsdóttir E, Kristjánsson Þ, Rhodin M, Viklund Å, Velie BD, Negro JJ, Solé M, Lindgren G. A QTL for conformation of back and croup influences lateral gait quality in Icelandic horses.. BMC Genomics 2021 Apr 14;22(1):267.
    doi: 10.1186/s12864-021-07454-zpmc: PMC8048352pubmed: 33853519google scholar: lookup
  10. Druml T, Dobretsberger M, Brem G. The use of novel phenotyping methods for validation of equine conformation scoring results.. Animal 2015 Jun;9(6):928-37.
    doi: 10.1017/S1751731114003309pubmed: 25582051google scholar: lookup
  11. Gmel AI, Burren A, Neuditschko M. Estimates of Genetic Parameters for Shape Space Data in Franches-Montagnes Horses.. Animals (Basel) 2022 Aug 25;12(17).
    doi: 10.3390/ani12172186pmc: PMC9454882pubmed: 36077906google scholar: lookup
  12. Gmel AI, Druml T, von Niederhäusern R, Leeb T, Neuditschko M. Genome-Wide Association Studies Based on Equine Joint Angle Measurements Reveal New QTL Affecting the Conformation of Horses.. Genes (Basel) 2019 May 14;10(5).
    doi: 10.3390/genes10050370pmc: PMC6562990pubmed: 31091839google scholar: lookup
  13. Kess T, Boulding EG. Genome-wide association analyses reveal polygenic genomic architecture underlying divergent shell morphology in Spanish Littorina saxatilis ecotypes.. Ecol Evol 2019 Sep;9(17):9427-9441.
    doi: 10.1002/ece3.5378pmc: PMC6745682pubmed: 31534666google scholar: lookup
  14. Sakamoto L, Kajiya-Kanegae H, Noshita K, Takanashi H, Kobayashi M, Kudo T, Yano K, Tokunaga T, Tsutsumi N, Iwata H. Comparison of shape quantification methods for genomic prediction, and genome-wide association study of sorghum seed morphology.. PLoS One 2019;14(11):e0224695.
    doi: 10.1371/journal.pone.0224695pmc: PMC6872133pubmed: 31751371google scholar: lookup
  15. Pitchers W, Nye J, Márquez EJ, Kowalski A, Dworkin I, Houle D. A Multivariate Genome-Wide Association Study of Wing Shape in Drosophila melanogaster.. Genetics 2019 Apr;211(4):1429-1447.
    doi: 10.1534/genetics.118.301342pmc: PMC6456314pubmed: 30792267google scholar: lookup
  16. Claes P, Shriver MD. New Entries in the Lottery of Facial GWAS Discovery.. PLoS Genet 2016 Aug;12(8):e1006250.
    doi: 10.1371/journal.pgen.1006250pmc: PMC4999261pubmed: 27560833google scholar: lookup
  17. Gordon CR, Marchant TW, Lodzinska J, Schoenebeck JJ, Schwarz T. Morphological variation of the caudal fossa of domestic cat skulls assessed with CT and geometric morphometrics analysis.. J Feline Med Surg 2018 Aug;20(8):752-758.
    doi: 10.1177/1098612X17730707pubmed: 28925790google scholar: lookup
  18. Marchant TW, Johnson EJ, McTeir L, Johnson CI, Gow A, Liuti T, Kuehn D, Svenson K, Bermingham ML, Drögemüller M, Nussbaumer M, Davey MG, Argyle DJ, Powell RM, Guilherme S, Lang J, Ter Haar G, Leeb T, Schwarz T, Mellanby RJ, Clements DN, Schoenebeck JJ. Canine Brachycephaly Is Associated with a Retrotransposon-Mediated Missplicing of SMOC2.. Curr Biol 2017 Jun 5;27(11):1573-1584.e6.
    doi: 10.1016/j.cub.2017.04.057pmc: PMC5462623pubmed: 28552356google scholar: lookup
  19. Zechner P. Analysis of diversity and population structure in the Lipizzan horse breed based on pedigree information.. Livest. Prod. Sci. 2002;77:137–146.
    doi: 10.1016/S0301-6226(02)00079-9google scholar: lookup
  20. Druml T, Horna M, Grilz-Seger G, Dobretsberger M, Brem G. Association of body shape with amount of Arabian genetic contribution in the Lipizzan horse.. Arch. Anim. Breed. 2018;61:79–85.
    doi: 10.5194/aab-61-79-2018google scholar: lookup
  21. Druml T, Dobretsberger M, Brem G. The interplay of performing level and conformation—a characterization study of the Lipizzan riding stallions from the Spanish riding school in Vienna.. J. Equine Vet. Sci. 2018;60:74–82.
    doi: 10.1016/j.jevs.2017.06.003google scholar: lookup
  22. Pasandideh M, Rahimi-Mianji G, Gholizadeh M. A genome scan for quantitative trait loci affecting average daily gain and Kleiber ratio in Baluchi Sheep.. J Genet 2018 Jun;97(2):493-503.
    doi: 10.1007/s12041-018-0941-9pubmed: 29932070google scholar: lookup
  23. Cesarani A. Investigation of genetic diversity and selection signatures between Sarda and Sardinian Ancestral black, two related sheep breeds with evident morphological differences.. Small Rumin. Res. 2019;177:68–75.
    doi: 10.1016/j.smallrumres.2019.06.014google scholar: lookup
  24. Geor RJ. Metabolic predispositions to laminitis in horses and ponies: Obesity, insulin resistance and metabolic syndromes.. J. Equine Vet. 2008;28:753–759.
    doi: 10.1016/j.jevs.2008.10.016google scholar: lookup
  25. Carter RA, Treiber KH, Geor RJ, Douglass L, Harris PA. Prediction of incipient pasture-associated laminitis from hyperinsulinaemia, hyperleptinaemia and generalised and localised obesity in a cohort of ponies.. Equine Vet J 2009 Feb;41(2):171-8.
    doi: 10.2746/042516408X342975pubmed: 19418747google scholar: lookup
  26. Sánchez MJ, Azor PJ, Molina A, Parkin T, Rivero JL, Valera M. Prevalence, risk factors and genetic parameters of cresty neck in Pura Raza Español horses.. Equine Vet J 2017 Mar;49(2):196-200.
    doi: 10.1111/evj.12569pubmed: 26877245google scholar: lookup
  27. Martin-Gimenez T, Aguirre-Pascasio CN, de Blas I. Beyond scoring systems: usefulness of morphometry considering demographic variables, to evaluate neck and overall obesity in Andalusian horses.. Animal 2018 Mar;12(3):597-605.
    doi: 10.1017/S1751731117001628pubmed: 28712370google scholar: lookup
  28. Martin-Gimenez T, de Blas I, Aguilera-Tejero E, Diez de Castro E, Aguirre-Pascasio CN. Endocrine, morphometric, and ultrasonographic characterization of neck adiposity in Andalusian horses.. Domest Anim Endocrinol 2016 Jul;56:57-62.
    doi: 10.1016/j.domaniend.2016.02.003pubmed: 27088603google scholar: lookup
  29. Ellis KL, Zhou Y, Beshansky JR, Ainehsazan E, Selker HP, Cupples LA, Huggins GS, Peter I. Genetic modifiers of response to glucose-insulin-potassium (GIK) infusion in acute coronary syndromes and associations with clinical outcomes in the IMMEDIATE trial.. Pharmacogenomics J 2015 Dec;15(6):488-95.
    doi: 10.1038/tpj.2015.10pmc: PMC4573824pubmed: 25778467google scholar: lookup
  30. Palmer ND, Langefeld CD, Ziegler JT, Hsu F, Haffner SM, Fingerlin T, Norris JM, Chen YI, Rich SS, Haritunians T, Taylor KD, Bergman RN, Rotter JI, Bowden DW. Candidate loci for insulin sensitivity and disposition index from a genome-wide association analysis of Hispanic participants in the Insulin Resistance Atherosclerosis (IRAS) Family Study.. Diabetologia 2010 Feb;53(2):281-9.
    doi: 10.1007/s00125-009-1586-2pmc: PMC2809812pubmed: 19902172google scholar: lookup
  31. Norris JM, Rich SS. Genetics of glucose homeostasis: implications for insulin resistance and metabolic syndrome.. Arterioscler Thromb Vasc Biol 2012 Sep;32(9):2091-6.
    doi: 10.1161/ATVBAHA.112.255463pmc: PMC3988457pubmed: 22895670google scholar: lookup
  32. Dey A, Sen S, Uversky VN, Maulik U. Structural facets of POU2F1 in light of the functional annotations and sequence-structure patterns.. J Biomol Struct Dyn 2021 Feb;39(3):1093-1105.
    doi: 10.1080/07391102.2020.1733092pubmed: 32081083google scholar: lookup
  33. Carreño LOD, da Conceição Pessoa M, Espigolan R, Takada L, Bresolin T, Cavani L, Baldi F, Carvalheiro R, de Albuquerque LG, da Fonseca R. Genome Association Study for Visual Scores in Nellore Cattle Measured at Weaning.. BMC Genomics 2019 Feb 20;20(1):150.
    doi: 10.1186/s12864-019-5520-9pmc: PMC6381746pubmed: 30786866google scholar: lookup
  34. Pérez-Montarelo D, Madsen O, Alves E, Rodríguez MC, Folch JM, Noguera JL, Groenen MA, Fernández AI. Identification of genes regulating growth and fatness traits in pig through hypothalamic transcriptome analysis.. Physiol Genomics 2014 Mar 15;46(6):195-206.
    doi: 10.1152/physiolgenomics.00151.2013pmc: PMC3949103pubmed: 24280257google scholar: lookup
  35. Copola AGL, Dos Santos ÍGD, Coutinho LL, Del-Bem LEV, de Almeida Campos-Junior PH, da Conceição IMCA, Nogueira JM, do Carmo Costa A, Silva GAB, Jorge EC. Transcriptomic characterization of the molecular mechanisms induced by RGMa during skeletal muscle nuclei accretion and hypertrophy.. BMC Genomics 2022 Mar 7;23(1):188.
    doi: 10.1186/s12864-022-08396-wpmc: PMC8902710pubmed: 35255809google scholar: lookup
  36. Ng MC, Lam VK, Tam CH, Chan AW, So WY, Ma RC, Zee BC, Waye MM, Mak WW, Hu C, Wang CR, Tong PC, Jia WP, Chan JC. Association of the POU class 2 homeobox 1 gene (POU2F1) with susceptibility to Type 2 diabetes in Chinese populations.. Diabet Med 2010 Dec;27(12):1443-9.
    doi: 10.1111/j.1464-5491.2010.03124.xpubmed: 21059098google scholar: lookup
  37. Batool A, Jahan N, Sun Y, Hanif A, Xue H. Genetic association of IDE, POU2F1, PON1, IL1α and IL1β with type 2 diabetes in Pakistani population.. Mol Biol Rep 2014 May;41(5):3063-9.
    doi: 10.1007/s11033-014-3165-ypubmed: 24477584google scholar: lookup
  38. Park SW, Goodpaster BH, Lee JS, Kuller LH, Boudreau R, de Rekeneire N, Harris TB, Kritchevsky S, Tylavsky FA, Nevitt M, Cho YW, Newman AB. Excessive loss of skeletal muscle mass in older adults with type 2 diabetes.. Diabetes Care 2009 Nov;32(11):1993-7.
    doi: 10.2337/dc09-0264pmc: PMC2768193pubmed: 19549734google scholar: lookup
  39. Cortes A, Hadler J, Pointon JP, Robinson PC, Karaderi T, Leo P, Cremin K, Pryce K, Harris J, Lee S, Joo KB, Shim SC, Weisman M, Ward M, Zhou X, Garchon HJ, Chiocchia G, Nossent J, Lie BA, Førre Ø, Tuomilehto J, Laiho K, Jiang L, Liu Y, Wu X, Bradbury LA, Elewaut D, Burgos-Vargas R, Stebbings S, Appleton L, Farrah C, Lau J, Kenna TJ, Haroon N, Ferreira MA, Yang J, Mulero J, Fernandez-Sueiro JL, Gonzalez-Gay MA, Lopez-Larrea C, Deloukas P, Donnelly P, Bowness P, Gafney K, Gaston H, Gladman DD, Rahman P, Maksymowych WP, Xu H, Crusius JB, van der Horst-Bruinsma IE, Chou CT, Valle-Oñate R, Romero-Sánchez C, Hansen IM, Pimentel-Santos FM, Inman RD, Videm V, Martin J, Breban M, Reveille JD, Evans DM, Kim TH, Wordsworth BP, Brown MA. Identification of multiple risk variants for ankylosing spondylitis through high-density genotyping of immune-related loci.. Nat Genet 2013 Jul;45(7):730-8.
    doi: 10.1038/ng.2667pmc: PMC3757343pubmed: 23749187google scholar: lookup
  40. Chai W, Lian Z, Chen C, Liu J, Shi LL, Wang Y. JARID1A, JMY, and PTGER4 polymorphisms are related to ankylosing spondylitis in Chinese Han patients: a case-control study.. PLoS One 2013;8(9):e74794.
    doi: 10.1371/journal.pone.0074794pmc: PMC3777963pubmed: 24069348google scholar: lookup
  41. Braun J, Sieper J. Ankylosing spondylitis.. Lancet 2007 Apr 21;369(9570):1379-1390.
    doi: 10.1016/S0140-6736(07)60635-7pubmed: 17448825google scholar: lookup
  42. Dakwar E, Reddy J, Vale FL, Uribe JS. A review of the pathogenesis of ankylosing spondylitis.. Neurosurg Focus 2008;24(1):E2.
    doi: 10.3171/FOC/2008/24/1/E2pubmed: 18290740google scholar: lookup
  43. Vosse D, van der Heijde D, Landewé R, Geusens P, Mielants H, Dougados M, van der Linden S. Determinants of hyperkyphosis in patients with ankylosing spondylitis.. Ann Rheum Dis 2006 Jun;65(6):770-4.
    doi: 10.1136/ard.2005.044081pmc: PMC1798162pubmed: 16219704google scholar: lookup
  44. Vander Cruyssen B, Vastesaeger N, Collantes-Estévez E. Hip disease in ankylosing spondylitis.. Curr Opin Rheumatol 2013 Jul;25(4):448-54.
    doi: 10.1097/BOR.0b013e3283620e04pubmed: 23689637google scholar: lookup
  45. Will R, Kennedy G, Elswood J, Edmunds L, Wachjudi R, Evison G, Calin A. Ankylosing spondylitis and the shoulder: commonly involved but infrequently disabling.. J Rheumatol 2000 Jan;27(1):177-82.
    pubmed: 10648036
  46. Mangone M, Scettri P, Paoloni M, Procaccianti R, Spadaro A, Santilli V. Pelvis-shoulder coordination during level walking in patients with ankylosing spondylitis.. Gait Posture 2011 May;34(1):1-5.
    doi: 10.1016/j.gaitpost.2011.02.002pubmed: 21377366google scholar: lookup
  47. Gmel AI, Haraldsdóttir EH, Serra Bragança FM, Cruz AM, Neuditschko M, Weishaupt MA. Determining Objective Parameters to Assess Gait Quality in Franches-Montagnes Horses for Ground Coverage and Over-Tracking - Part 1: At Walk.. J Equine Vet Sci 2022 Aug;115:104024.
    doi: 10.1016/j.jevs.2022.104024pubmed: 35649491google scholar: lookup
  48. Gmel AI, Haraldsdóttir EH, Bragança FMS, Cruz AM, Neuditschko M, Weishaupt MA. Determining Objective Parameters to Assess Gait Quality in Franches-Montagnes Horses for Ground Coverage and Over-Tracking - Part 2: At Trot.. J Equine Vet Sci 2023 Jan;120:104166.
    doi: 10.1016/j.jevs.2022.104166pubmed: 36417944google scholar: lookup
  49. Cook D, Gallagher PC, Bailey E. Genetics of swayback in American Saddlebred horses.. Anim Genet 2010 Dec;41 Suppl 2:64-71.
    doi: 10.1111/j.1365-2052.2010.02108.xpubmed: 21070278google scholar: lookup
  50. YousefiMashouf N, Graves K, Kalbfleisch T, Bailey E. Genetic basis for juvenile onset lordosis in saddlebred horses.. In 13th International Havemeyer Foundation Horse Genome Workshop (July 24–28, 2022)) (2022).
  51. Fujii J, Otsu K, Zorzato F, de Leon S, Khanna VK, Weiler JE, O'Brien PJ, MacLennan DH. Identification of a mutation in porcine ryanodine receptor associated with malignant hyperthermia.. Science 1991 Jul 26;253(5018):448-51.
    doi: 10.1126/science.1862346pubmed: 1862346google scholar: lookup
  52. Roberts MC, Mickelson JR, Patterson EE, Nelson TE, Armstrong PJ, Brunson DB, Hogan K. Autosomal dominant canine malignant hyperthermia is caused by a mutation in the gene encoding the skeletal muscle calcium release channel (RYR1).. Anesthesiology 2001 Sep;95(3):716-25.
    doi: 10.1097/00000542-200109000-00026pubmed: 11575546google scholar: lookup
  53. Aleman M, Nieto JE, Magdesian KG. Malignant hyperthermia associated with ryanodine receptor 1 (C7360G) mutation in Quarter Horses.. J Vet Intern Med 2009 Mar-Apr;23(2):329-34.
    doi: 10.1111/j.1939-1676.2009.0274.xpubmed: 19220734google scholar: lookup
  54. Jungbluth H, Sewry CA, Muntoni F. What's new in neuromuscular disorders? The congenital myopathies.. Eur J Paediatr Neurol 2003;7(1):23-30.
    doi: 10.1016/S1090-3798(02)00136-8pubmed: 12615171google scholar: lookup
  55. Zhou H, Jungbluth H, Sewry CA, Feng L, Bertini E, Bushby K, Straub V, Roper H, Rose MR, Brockington M, Kinali M, Manzur A, Robb S, Appleton R, Messina S, D'Amico A, Quinlivan R, Swash M, Müller CR, Brown S, Treves S, Muntoni F. Molecular mechanisms and phenotypic variation in RYR1-related congenital myopathies.. Brain 2007 Aug;130(Pt 8):2024-36.
    doi: 10.1093/brain/awm096pubmed: 17483490google scholar: lookup
  56. Witting N, Andersen LK, Vissing J. Axial myopathy: an overlooked feature of muscle diseases.. Brain 2016 Jan;139(Pt 1):13-22.
    doi: 10.1093/brain/awv332pubmed: 26667281google scholar: lookup
  57. van Weeren R. Kinematics.. .
  58. Ü Basmanav FB, Cau L, Tafazzoli A, Méchin MC, Wolf S, Romano MT, Valentin F, Wiegmann H, Huchenq A, Kandil R, Garcia Bartels N, Kilic A, George S, Ralser DJ, Bergner S, Ferguson DJP, Oprisoreanu AM, Wehner M, Thiele H, Altmüller J, Nürnberg P, Swan D, Houniet D, Büchner A, Weibel L, Wagner N, Grimalt R, Bygum A, Serre G, Blume-Peytavi U, Sprecher E, Schoch S, Oji V, Hamm H, Farrant P, Simon M, Betz RC. Mutations in Three Genes Encoding Proteins Involved in Hair Shaft Formation Cause Uncombable Hair Syndrome.. Am J Hum Genet 2016 Dec 1;99(6):1292-1304.
    doi: 10.1016/j.ajhg.2016.10.004pmc: PMC5142115pubmed: 27866708google scholar: lookup
  59. Brennan BM, Huynh MT, Rabah MA, Shaw HE, Bisaillon JJ, Radden LA 2nd, Nguyen TV, King TR. The mouse wellhaarig (we) mutations result from defects in epidermal-type transglutaminase 3 (Tgm3).. Mol Genet Metab 2015 Nov;116(3):187-91.
    doi: 10.1016/j.ymgme.2015.07.002pmc: PMC4640993pubmed: 26194162google scholar: lookup
  60. Librado P, Der Sarkissian C, Ermini L, Schubert M, Jónsson H, Albrechtsen A, Fumagalli M, Yang MA, Gamba C, Seguin-Orlando A, Mortensen CD, Petersen B, Hoover CA, Lorente-Galdos B, Nedoluzhko A, Boulygina E, Tsygankova S, Neuditschko M, Jagannathan V, Thèves C, Alfarhan AH, Alquraishi SA, Al-Rasheid KA, Sicheritz-Ponten T, Popov R, Grigoriev S, Alekseev AN, Rubin EM, McCue M, Rieder S, Leeb T, Tikhonov A, Crubézy E, Slatkin M, Marques-Bonet T, Nielsen R, Willerslev E, Kantanen J, Prokhortchouk E, Orlando L. Tracking the origins of Yakutian horses and the genetic basis for their fast adaptation to subarctic environments.. Proc Natl Acad Sci U S A 2015 Dec 15;112(50):E6889-97.
    doi: 10.1073/pnas.1513696112pmc: PMC4687531pubmed: 26598656google scholar: lookup
  61. Wu X, Cao W, Wang X, Zhang J, Lv Z, Qin X, Wu Y, Chen W. TGM3, a candidate tumor suppressor gene, contributes to human head and neck cancer.. Mol Cancer 2013 Dec 1;12(1):151.
    doi: 10.1186/1476-4598-12-151pmc: PMC4176127pubmed: 24289313google scholar: lookup
  62. Schaefer RJ, Schubert M, Bailey E, Bannasch DL, Barrey E, Bar-Gal GK, Brem G, Brooks SA, Distl O, Fries R, Finno CJ, Gerber V, Haase B, Jagannathan V, Kalbfleisch T, Leeb T, Lindgren G, Lopes MS, Mach N, da Câmara Machado A, MacLeod JN, McCoy A, Metzger J, Penedo C, Polani S, Rieder S, Tammen I, Tetens J, Thaller G, Verini-Supplizi A, Wade CM, Wallner B, Orlando L, Mickelson JR, McCue ME. Developing a 670k genotyping array to tag ~2M SNPs across 24 horse breeds.. BMC Genomics 2017 Jul 27;18(1):565.
    doi: 10.1186/s12864-017-3943-8pmc: PMC5530493pubmed: 28750625google scholar: lookup
  63. Rohlf F. tpsDig2 version 1.78.. (2001).
  64. Rohlf F. tpsRelw. Version 1.70.. (2003).
  65. Purcell S. PLINK Version 1.07.. (2017).
  66. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses.. Am J Hum Genet 2007 Sep;81(3):559-75.
    doi: 10.1086/519795pmc: PMC1950838pubmed: 17701901google scholar: lookup
  67. Aulchenko YS, Ripke S, Isaacs A, van Duijn CM. GenABEL: an R library for genome-wide association analysis.. Bioinformatics 2007 May 15;23(10):1294-6.
    doi: 10.1093/bioinformatics/btm108pubmed: 17384015google scholar: lookup
  68. R Core Team. R: A language and environment for statistical computing.. (2013).
  69. Kalbfleisch TS, Rice ES, DePriest MS Jr, Walenz BP, Hestand MS, Vermeesch JR, O Connell BL, Fiddes IT, Vershinina AO, Saremi NF, Petersen JL, Finno CJ, Bellone RR, McCue ME, Brooks SA, Bailey E, Orlando L, Green RE, Miller DC, Antczak DF, MacLeod JN. Improved reference genome for the domestic horse increases assembly contiguity and composition.. Commun Biol 2018;1:197.
    doi: 10.1038/s42003-018-0199-zpmc: PMC6240028pubmed: 30456315google scholar: lookup
  70. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis.. Am J Hum Genet 2011 Jan 7;88(1):76-82.
    doi: 10.1016/j.ajhg.2010.11.011pmc: PMC3014363pubmed: 21167468google scholar: lookup
  71. Norton EM, Schultz NE, Rendahl AK, Mcfarlane D, Geor RJ, Mickelson JR, McCue ME. Heritability of metabolic traits associated with equine metabolic syndrome in Welsh ponies and Morgan horses.. Equine Vet J 2019 Jul;51(4):475-480.
    doi: 10.1111/evj.13053pubmed: 30472742google scholar: lookup
  72. Signer-Hasler H, Flury C, Haase B, Burger D, Simianer H, Leeb T, Rieder S. A genome-wide association study reveals loci influencing height and other conformation traits in horses.. PLoS One 2012;7(5):e37282.
    doi: 10.1371/journal.pone.0037282pmc: PMC3353922pubmed: 22615965google scholar: lookup

Citations

This article has been cited 0 times.
Subscribe to our newsletter
Join 99,344 horse owners like you who receive our email updates on horse health and nutrition.