FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
PloS one2019; 14(4); e0215913; doi: 10.1371/journal.pone.0215913

Selection signatures in four German warmblood horse breeds: Tracing breeding history in the modern sport horse.

Abstract: The study of selection signatures helps to find genomic regions that have been under selective pressure and might host genes or variants that modulate important phenotypes. Such knowledge improves our understanding of how breeding programmes have shaped the genomes of livestock. In this study, 942 stallions were included from four, exemplarily chosen, German warmblood breeds with divergent historical and recent selection focus and different crossbreeding policies: Trakehner (N = 44), Holsteiner (N = 358), Hanoverian (N = 319) and Oldenburger (N = 221). Those breeds are nowadays bred for athletic performance and aptitude for show-jumping, dressage or eventing, with a particular focus of Holsteiner on the first discipline. Blood samples were collected during the health exams of the stallion preselections before licensing and were genotyped with the Illumina EquineSNP50 BeadChip. Autosomal markers were used for a multi-method search for signals of positive selection. Analyses within and across breeds were conducted by using the integrated Haplotype Score (iHS), cross-population Extended Haplotype Homozygosity (xpEHH) and Runs of Homozygosity (ROH). Oldenburger and Hanoverian showed very similar iHS signatures, but breed specificities were detected on multiple chromosomes with the xpEHH. The Trakehner clustered as a distinct group in a principal component analysis and also showed the highest number of ROHs, which reflects their historical bottleneck. Beside breed specific differences, we found shared selection signals in an across breed iHS analysis on chromosomes 1, 4 and 7. After investigation of these iHS signals and shared ROH for potential functional candidate genes and affected pathways including enrichment analyses, we suggest that genes affecting muscle functionality (TPM1, TMOD2-3, MYO5A, MYO5C), energy metabolism and growth (AEBP1, RALGAPA2, IGFBP1, IGFBP3-4), embryonic development (HOXB-complex) and fertility (THEGL, ZPBP1-2, TEX14, ZP1, SUN3 and CFAP61) have been targeted by selection in all breeds. Our findings also indicate selection pressure on KITLG, which is well-documented for influencing pigmentation.
Get Full Text
Publication Date: 2019-04-25 PubMed ID: 31022261PubMed Central: PMC6483353DOI: 10.1371/journal.pone.0215913Google 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.

The article reports a study that investigates how breeding programs have shaped the genomes of four distinct breeds of German warmblood horses: the Trakehner, Holsteiner, Hanoverian, and Oldenburger. Blood samples were analyzed from 942 stallions and compared to detect common ‘selection signatures’, which are regions of the genome that have been specifically targeted by selective breeding.

Selection Signatures

  • The investigation looked for areas of the genome that have historically been under selective pressure during breeding, known as selection signatures. Such areas can host genes that influence key traits.
  • By studying these selection signatures, researchers were able to gain an understanding of the genetic effects of different breeding programmes on the four breeds of German warmblood horses.

Study Methods and Breeds

  • The study included 942 stallions from four German warmblood breeds: Trakehner, Holsteiner, Hanoverian and Oldenburger.
  • These breeds have each been bred with different historical and recent focuses and policies. Each is now predominantly bred for athletic performance, particularly in show-jumping, dressage, or eventing, with the Holsteiner specifically focused on show-jumping.

Data Collection and Analysis

  • Blood samples were obtained during health examinations at the horses’ licensing preselections, and genetic information was extracted.
  • The researchers used several methods to search for signs of positive selection in the horses’ genomes. They used the integrated Haplotype Score (iHS), cross-population Extended Haplotype Homozygosity (xpEHH) and Runs of Homozygosity (ROH).

Findings

  • The Oldenburger and Hanoverian breeds showed similar iHS signatures. However, breed-specific genomic features reflecting divergent selective pressures were detected using xpEHH.
  • The Trakehner breed showed distinct genetic features, clustering separately in a principal component analysis. This breed also exhibited the highest number of ROHs, which echoes its known historical genetic bottleneck.
  • Apart from breed-specific differences, some shared selection signatures were found across breeds, particularly on chromosomes 1, 4 and 7.

Suggested Traits Under Selection

  • Upon analyzing these selection signatures, it is suggested that genes influencing muscle function, energy metabolism and growth, embryonic development, and fertility have been major targets of selective breeding across all the breeds studied.
  • The study also suggests a selection pressure on pigmentation, indicated by the presence of the known pigmentation-related gene KITLG.

Cite This Article

APA
Nolte W, Thaller G, Kuehn C. (2019). Selection signatures in four German warmblood horse breeds: Tracing breeding history in the modern sport horse. PLoS One, 14(4), e0215913. https://doi.org/10.1371/journal.pone.0215913

Publication

PloS one
ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 14
Issue: 4
Pages: e0215913

Researcher Affiliations

Nolte, Wietje
  • Institute of Genome Biology, Leibniz-Institute for Farm Animal Biology, Dummerstorf, Germany.
Thaller, Georg
  • Institute for Animal Breeding and Husbandry, Christian-Albrechts-University Kiel, Kiel, Germany.
Kuehn, Christa
  • Institute of Genome Biology, Leibniz-Institute for Farm Animal Biology, Dummerstorf, Germany.
  • Faculty of Agricultural and Environmental Science, University of Rostock, Rostock, Germany.

MeSH Terms

  • Alleles
  • Animals
  • Breeding
  • Genome
  • Genotype
  • Germany
  • Haplotypes / genetics
  • Homozygote
  • Horses / genetics
  • Molecular Sequence Annotation
  • Principal Component Analysis
  • Selection, Genetic
  • Sports

Conflict of Interest Statement

The authors have declared that no competing interests exist.

References

This article includes 90 references
  1. Arnason T, Dale Van Vleck L. Genetic Improvement of the Horse The Genetics of the Horse. 1 ed New York: CABI Publishing; 2000. p. 473–497.
  2. Deutsche Reiterliche Vereinigung (FN). Jahresbericht 2016. Warendorf: FN-Verlag; 2016. p. 180.
  3. Trakehner Verband e.V. Breeding History Neumünster 2016 [cited 23 April 2018]. Available from: http://www.trakehner-verband.de/verband/geschichte/.
  4. Trakehner Verband. Statutes of the Trakehner Breeding Association. Hamburg; 2017.
  5. Hannoveraner Verband eV. Hanoverian Breeding History. Verden a.d. Aller; [cited 6 Sep 2017]. Available from: http://en.hannoveraner.com/hannoveraner-zucht/.
  6. Schüssler J. Kurze Mittheilungen über die Geschichte der Oldenburger Pferdezucht. Rodenkirchen: Littmann, Ad; 1899. p. 16.
  7. Deutsche Reiterliche Vereinigung (FN). Richtlinien für Reiten und Fahren Band 4: Haltung, Fütterung, Gesundheit und Zucht. 13 ed Warendorf: FN-Verlag; 2006. p. 352.
  8. Hendricks BL, Dent AA. International Encyclopedia of Horse Breeds: University of Oklahoma Press; 2007.
  9. Verband der Züchter des Oldenburger Pferdes. Statutes of the Oldenburger Breeding Association. Vechta; 2015.
  10. Hannoveraner Verband. Statutes of the Hanoverian Breeding Association. Verden; 2016.
  11. Springpferdezuchtverband Oldenburg International. Statutes of the Oldenburg International Breeding Association. Vechta; 2017.
  12. Verband der Züchter des Holsteiner Pferdes. Statutes of the Holsteiner Breeding Association. Elmshorn; 2018.
  13. Roos L, Hinrichs D, Nissen T, Krieter J. Investigations into genetic variability in Holstein horse breed using pedigree data. Livest Sci 2015; 177: 25–32.
    doi: 10.1016/j.livsci.2015.04.013google scholar: lookup
  14. Hamann H, Distl O. Genetic variability in Hanoverian warmblood horses using pedigree analysis. J Anim Sci 2008; 86(7): 1503–1513.
    doi: 10.2527/jas.2007-0382pubmed: 18310493google scholar: lookup
  15. Sorbolini S, Marras G, Gaspa G, Dimauro C, Cellesi M, Valentini A. Detection of selection signatures in Piemontese and Marchigiana cattle, two breeds with similar production aptitudes but different selection histories. Genet Sel Evol 2015; 47: 52.
    doi: 10.1186/s12711-015-0128-2pmc: PMC4476081pubmed: 26100250google scholar: lookup
  16. Qanbari S, Simianer H. Mapping signatures of positive selection in the genome of livestock. Livest Sci 2014; 166: 133–143.
    doi: 10.1016/j.livsci.2014.05.003google scholar: lookup
  17. Biswas S, Akey JM. Genomic insights into positive selection. Trends Genet 2006; 22(8): 437–446.
    doi: 10.1016/j.tig.2006.06.005pubmed: 16808986google scholar: lookup
  18. Sabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, Cotsapas C. Genome-wide detection and characterization of positive selection in human populations. Nature 2007; 449(7164): 913–918.
    doi: 10.1038/nature06250pmc: PMC2687721pubmed: 17943131google scholar: lookup
  19. Voight BF, Kudaravalli S, Wen X, Pritchard JK. A Map of Recent Positive Selection in the Human Genome. PLoS One 2006; 4(3): 1–14.
    doi: 10.1371/journal.pbio.0040072pmc: PMC1382018pubmed: 16494531google scholar: lookup
  20. Gouveia JJD, da Silva M, Paiva SR, de Oliveira SMP. Identification of selection signatures in livestock species. Genet Mol Biol 2014; 37(2): 330–342.
    doi: 10.1590/S1415-47572014000300004pmc: PMC4094609pubmed: 25071397google scholar: lookup
  21. Bosse M, Megens H-j, Madsen O, Paudel Y, Frantz LAF, Schook LB. Regions of Homozygosity in the Porcine Genome: Consequence of Demography and the Recombination Landscape. PLoS One 2012; 8(11): 1–15.
    doi: 10.1371/journal.pgen.1003100pmc: PMC3510040pubmed: 23209444google scholar: lookup
  22. Purfield DC, McParland S, Wall E, Berry DP. The distribution of runs of homozygosity and selection signatures in six commercial meat sheep breeds. PLoS One 2017; 12(5): e0176780.
    doi: 10.1371/journal.pone.0176780pmc: PMC5413029pubmed: 28463982google scholar: lookup
  23. Mastrangelo S, Tolone M, Sardina MT, Sottile G, Sutera AM, Di Gerlando R. Genome-wide scan for runs of homozygosity identifies potential candidate genes associated with local adaptation in Valle del Belice sheep. Genet Sel Evol 2017; 49: 84.
    doi: 10.1186/s12711-017-0360-zpmc: PMC5684758pubmed: 29137622google scholar: lookup
  24. Druml T, Neuditschko M, Grilz-Seger G, Horna M, Ricard A, Mesaric M. Population Networks Associated with Runs of Homozygosity Reveal New Insights into the Breeding History of the Haflinger Horse. J Hered 2018; 109(4): 384–392.
    doi: 10.1093/jhered/esx114pubmed: 29294044google scholar: lookup
  25. Grilz-Seger G, Mesarič M, Cotman M, Neuditschko M, Druml T, Brem G. Runs of Homozygosity and Population History of Three Horse Breeds With Small Population Size. J Equine Vet Sci 2018; 71: 27–34.
    doi: 10.1016/j.jevs.2018.09.004google scholar: lookup
  26. Metzger J, Karwath M, Tonda R, Beltran S, Águeda L, Gut M. Runs of homozygosity reveal signatures of positive selection for reproduction traits in breed and non-breed horses. BMC Genomics 2015; 16(1): 1–14.
    doi: 10.1186/s12864-015-1977-3pmc: PMC4600213pubmed: 26452642google scholar: lookup
  27. Liu XX, Pan JF, Zhao QJ, He XH, Pu YB, Han JL. Detecting selection signatures on the X chromosome of the Chinese Debao pony. J Anim Breed Genet 2018; 135(1): 84–92.
    doi: 10.1111/jbg.12314pubmed: 29345071google scholar: lookup
  28. Frischknecht M, Flury C, Leeb T, Rieder S, Neuditschko M. Selection signatures in Shetland ponies. Anim Genet 2016; 47(3): 370–372.
    doi: 10.1111/age.12416pubmed: 26857482google scholar: lookup
  29. Petersen JL, Mickelson JR, Rendahl AK, Valberg SJ, Andersson LS, Axelsson J. Genome-Wide Analysis Reveals Selection for Important Traits in Domestic Horse Breeds. PLoS Genet 2013; 9(1): e1003211.
    doi: 10.1371/journal.pgen.1003211pmc: PMC3547851pubmed: 23349635google scholar: lookup
  30. Gu JJ, Orr N, Park SD, Katz LM, Sulimova G, MacHugh DE. A Genome Scan for Positive Selection in Thoroughbred Horses. PLoS One 2009; 4(6): e5767.
    doi: 10.1371/journal.pone.0005767pmc: PMC2685479pubmed: 19503617google scholar: lookup
  31. 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): 1–13.
    doi: 10.3389/fgene.2018.00249pmc: PMC6060370pubmed: 30105047google scholar: lookup
  32. Heuer C, Scheel C, Tetens J, Kühn C, Thaller G. Genomic prediction of unordered categorical traits: an application to subpopulation assignment in German Warmblood horses. Genet Sel Evol 2016; 48(1): 1–16.
    doi: 10.1186/s12711-016-0192-2pmc: PMC4751658pubmed: 26867647google scholar: lookup
  33. Browning SR, Browning BL. Rapid and Accurate Haplotype Phasing and Missing-Data Inference for Whole-Genome Association Studies By Use of Localized Haplotype Clustering. Am J Hum Genet 2007; 81(5): 1084–1097.
    doi: 10.1086/521987pmc: PMC2265661pubmed: 17924348google scholar: lookup
  34. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: A tool for genome-wide complex trait analysis. Am J Hum Genet 2011; 88(1): 76–82.
    doi: 10.1016/j.ajhg.2010.11.011pmc: PMC3014363pubmed: 21167468google scholar: lookup
  35. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006; 38(8): 904–909.
    doi: 10.1038/ng1847pubmed: 16862161google scholar: lookup
  36. SNP & Variation Suite (Version 881) [Software]. Bozeman, MT: Golden Helix Inc.
  37. Ferenčaković M, Sölkner J, Curik I. Estimating autozygosity from high-throughput information: effects of SNP density and genotyping errors. Genet Sel Evol 2013; 45(1): 1–9.
    doi: 10.1186/1297-9686-45-42pmc: PMC4176748pubmed: 24168655google scholar: lookup
  38. Sabeti PC, Reich DE, Higgins JM, Levine HZP, Richter DJ, Schaffner SF. Detecting recent positive selection in the human genome from haplotype structure. Nature 2002; 419(6909): 832–837.
    doi: 10.1038/nature01140pubmed: 12397357google scholar: lookup
  39. Wade CM, Giulotto E, Sigurdsson S, Zoli M, Gnerre S, Imsland F. Genome sequence, comparative analysis and population genetics of the domestic horse (Equus caballus). Science 2009; 326(5954): 865–867.
    doi: 10.1126/science.1178158pmc: PMC3785132pubmed: 19892987google scholar: lookup
  40. Randhawa AIS, Khatkar MS, Thomson PC, Raadsma HW. Composite selection signals can localize the trait specific genomic regions in multi-breed populations of cattle and sheep. BMC Genet 2014; 15(34): 1–19.
    doi: 10.1186/1471-2156-15-34pmc: PMC4101850pubmed: 24636660google scholar: lookup
  41. Gautier M, Klassmann A, Vitalis R. rehh 2.0: a reimplementation of the R package rehh to detect positive selection from haplotype structure. Mol Ecol Resour 2017; 17(1): 78–90.
    doi: 10.1111/1755-0998.12634pubmed: 27863062google scholar: lookup
  42. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 2005; 21(2): 263–265.
    doi: 10.1093/bioinformatics/bth457pubmed: 15297300google scholar: lookup
  43. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 2010; 26(6): 841–842.
    doi: 10.1093/bioinformatics/btq033pmc: PMC2832824pubmed: 20110278google scholar: lookup
  44. Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2009; 37(1): 1–13.
    doi: 10.1093/nar/gkn923pmc: PMC2615629pubmed: 19033363google scholar: lookup
  45. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009; 4(1): 44–57.
    doi: 10.1038/nprot.2008.211pubmed: 19131956google scholar: lookup
  46. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J Royal Stat Soc Ser B 1995; 57(1): 289–300.
    doi: 10.2307/2346101google scholar: lookup
  47. Park CA, Reecy JM, Hu Z-L. Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB. Nucleic Acids Res 2018; 47(D1): D701–D710.
    doi: 10.1093/nar/gky1084pmc: PMC6323967pubmed: 30407520google scholar: lookup
  48. Oklahoma State University. Breeds of Livestock—Oldenburg Horse. [updated 17 July 1997; cited 19 February 2019]. Available from: http://afs.okstate.edu/breeds/horses/oldenburg/.
  49. Knobel M, O'Toole EA, Smith FJ. Keratins and skin disease. Cell and tissue research 2015; 360(3): 583–589.
    doi: 10.1007/s00441-014-2105-4pubmed: 25620412google scholar: lookup
  50. Schweizer J, Langbein L, Rogers MA, Winter H. Hair follicle-specific keratins and their diseases. Exp Cell Res 2007; 313(10): 2010–2020.
    doi: 10.1016/j.yexcr.2007.02.032pubmed: 17428470google scholar: lookup
  51. Grosenbaugh DA, Hood DM. Keratin and associated proteins of the equine hoof wall. Am J Vet Res 1992; 53(10): 1859–1863.
    pubmed: 1280927pmc: 1280927
  52. Morgenthaler C, Diribarne M, Capitan A, Legendre R, Saintilan R, Gilles M. A missense variant in the coil1A domain of the keratin 25 gene is associated with the dominant curly hair coat trait (Crd) in horse. Genet Sel Evol 2017; 49(1): 1–10.
    doi: 10.1186/s12711-016-0283-0pmc: PMC5686958pubmed: 29141579google scholar: lookup
  53. Seitz JJ, Schmutz SM, Thue TD, Buchanan FC. A missense mutation in the bovine MGF gene is associated with the roan phenotype in Belgian Blue and Shorthorn cattle. Mamm Genome 1999; 10(7): 710–712.
    doi: 10.1007/s003359901076pubmed: 10384045google scholar: lookup
  54. Wilkinson S, Lu ZH, Megens H-J, Archibald AL, Haley C, Jackson IJ. Signatures of Diversifying Selection in European Pig Breeds. PLoS Genet 2013; 9(4): e1003453.
    doi: 10.1371/journal.pgen.1003453pmc: PMC3636142pubmed: 23637623google scholar: lookup
  55. Haase B, Signer-Hasler H, Binns MM, Obexer-Ruff G, Hauswirth R, Bellone RR. Accumulating mutations in series of haplotypes at the KIT and MITF loci are major determinants of white markings in Franches-Montagnes horses. PLoS One 2013; 8(9): e75071.
    doi: 10.1371/journal.pone.0075071pmc: PMC3787084pubmed: 24098679google scholar: lookup
  56. Mau C, Poncet PA, Bucher B, Stranzinger G, Rieder S. Genetic mapping of dominant white (W), a homozygous lethal condition in the horse (Equus caballus). J Anim Breed Genet 2004; 121(6): 374–383.
    doi: 10.1111/j.1439-0388.2004.00481.xgoogle scholar: lookup
  57. Haase B, Brooks SA, Tozaki T, Burger D, Poncet PA, Rieder S. Seven novel KIT mutations in horses with white coat colour phenotypes. Anim Genet 2009; 40(5): 623–629.
    doi: 10.1111/j.1365-2052.2009.01893.xpubmed: 19456317google scholar: lookup
  58. Wutke S, Benecke N, Sandoval-Castellanos E, Döhle H-J, Friederich S, Gonzalez J. Spotted phenotypes in horses lost attractiveness in the Middle Ages. Sci Rep 2016; 6: 1–9.
    doi: 10.1038/s41598-016-0001-8pmc: PMC5141471pubmed: 27924839google scholar: lookup
  59. Gurgul A, Jasielczuk I, Semik-Gurgul E, Pawlina-Tyszko K, Stefaniuk-Szmukier M, Szmatola T. A genome-wide scan for diversifying selection signatures in selected horse breeds. PLoS One 2019; 14(1): e0210751.
    doi: 10.1371/journal.pone.0210751pmc: PMC6353161pubmed: 30699152google scholar: lookup
  60. Akey JM, Ruhe AL, Akey DT, Wong AK, Connelly CF, Madeoy J. Tracking footprints of artificial selection in the dog genome. Proc Natl Acad Sci USA 2010; 107(3): 1160–1165.
    doi: 10.1073/pnas.0909918107pmc: PMC2824266pubmed: 20080661google scholar: lookup
  61. Signer-Hasler H, Flury C, Haase B, Burger D, Simianer H, Leeb T. A genome-wide association study reveals loci influencing height and other conformation traits in horses. PLoS One 2012; 7(5): 3–8.
    doi: 10.1371/journal.pone.0037282pmc: PMC3353922pubmed: 22615965google scholar: lookup
  62. Skujina I, Winton CL, Hegarty MJ, McMahon R, Nash DM, Davies Morel MCG. Detecting genetic regions associated with height in the native ponies of the British Isles by using high density SNP genotyping. Genome 2018; 61(10): 767–770.
    doi: 10.1139/gen-2018-0006pubmed: 30184439google scholar: lookup
  63. Tozaki T, Kikuchi M, Kakoi H, Hirota KI, Nagata SI. A genome-wide association study for body weight in Japanese Thoroughbred racehorses clarifies candidate regions on chromosomes 3, 9, 15, and 18. J Equine Sci 2017; 28(4): 127–134.
    doi: 10.1294/jes.28.127pmc: PMC5735309pubmed: 29270069google scholar: lookup
  64. Berndt SI, Gustafsson S, Magi R, Ganna A, Wheeler E, Feitosa MF. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat Genet 2013; 45(5): 501–512.
    doi: 10.1038/ng.2606pmc: PMC3973018pubmed: 23563607google scholar: lookup
  65. Wit JM, Camacho-Hubner C. Endocrine regulation of longitudinal bone growth. Endocrine development 2011; 21: 30–41.
    doi: 10.1159/000328119pubmed: 21865752google scholar: lookup
  66. Mark M, Rijli FM, Chambon P. Homeobox genes in embryogenesis and pathogenesis. Pediatr Res 1997; 42(4): 421–429.
    doi: 10.1203/00006450-199710000-00001pubmed: 9380431google scholar: lookup
  67. Kijas JW, Lenstra JA, Hayes B, Boitard S, Porto Neto LR, San Cristobal M. Genome-wide analysis of the world's sheep breeds reveals high levels of historic mixture and strong recent selection. PLoS biology 2012; 10(2): e1001258.
    doi: 10.1371/journal.pbio.1001258pmc: PMC3274507pubmed: 22346734google scholar: lookup
  68. Kim ES, Elbeltagy AR, Aboul-Naga AM, Rischkowsky B, Sayre B, Mwacharo JM. Multiple genomic signatures of selection in goats and sheep indigenous to a hot arid environment. Heredity 2015; 116: 255.
    doi: 10.1038/hdy.2015.94pmc: PMC4806575pubmed: 26555032google scholar: lookup
  69. Hodgson DR, McGowan C. Chapter 1—An overview of performance and sports medicine In: Hodgson DR, McKeever KH, McGowan CM, editors. The Athletic Horse (Second Edition): W.B. Saunders; 2014. p. 1–8.
  70. Hodgson DR, Foreman JH. Chapter 2—Comparative aspects of exercise physiology In: Hodgson DR, McKeever KH, McGowan CM, editors. The Athletic Horse (Second Edition): W.B. Saunders; 2014. p. 9–18.
  71. Clemmons DR. Role of IGF Binding Proteins in Regulating Metabolism. Trends in endocrinology and metabolism: TEM 2016; 27(6): 375–391.
    doi: 10.1016/j.tem.2016.03.019pubmed: 27117513google scholar: lookup
  72. Ro HS, Zhang L, Majdalawieh A, Kim SW, Wu X, Lyons PJ. Adipocyte enhancer-binding protein 1 modulates adiposity and energy homeostasis. Obesity 2007; 15(2): 288–302.
    doi: 10.1038/oby.2007.569pubmed: 17299101google scholar: lookup
  73. Layne MD, Endege WO, Jain MK, Yet SF, Hsieh CM, Chin MT. Aortic carboxypeptidase-like protein, a novel protein with discoidin and carboxypeptidase-like domains, is up-regulated during vascular smooth muscle cell differentiation. J Biol Chem 1998; 273(25): 15654–15660.
    doi: 10.1074/jbc.273.25.15654pubmed: 9624159google scholar: lookup
  74. Zhu W, Shiojima I, Ito Y, Li Z, Ikeda H, Yoshida M. IGFBP-4 is an inhibitor of canonical Wnt signalling required for cardiogenesis. Nature 2008; 454(7202): 345–349.
    doi: 10.1038/nature07027pubmed: 18528331google scholar: lookup
  75. Velie BD, Fegraeus KJ, Sole M, Rosengren MK, Roed KH, Ihler CF. A genome-wide association study for harness racing success in the Norwegian-Swedish coldblooded trotter reveals genes for learning and energy metabolism. BMC Genet 2018; 19(1): 1–13.
    doi: 10.1186/s12863-017-0594-3pmc: PMC6114527pubmed: 30157760google scholar: lookup
  76. Hill EW, McGivney BA, Gu JJ, Whiston R, MacHugh DE. A genome-wide SNP-association study confirms a sequence variant (g.66493737C > T) in the equine myostatin (MSTN) gene as the most powerful predictor of optimum racing distance for Thoroughbred racehorses. BMC Genomics 2010; 11: 1–10.
    doi: 10.1186/1471-2164-11-1pmc: PMC3091701pubmed: 20932346google scholar: lookup
  77. Shin DH, Lee JW, Park JE, Choi IY, Oh HS, Kim HJ. Multiple Genes Related to Muscle Identified through a Joint Analysis of a Two-stage Genome-wide Association Study for Racing Performance of 1,156 Thoroughbreds. Asian-Australas J Anim Sci 2015; 28(6): 771–781.
    doi: 10.5713/ajas.14.0008pmc: PMC4412973pubmed: 25925054google scholar: lookup
  78. Moon S, Lee JW, Shin D, Shin K-y, Kim J, Choi I-Y. A Genome-wide Scan for Selective Sweeps in Racing Horses. Asian Austral J Anim Sci 2015; 28(11): 1525–1531.
    doi: 10.5713/ajas.14.0696pmc: PMC4647090pubmed: 26333666google scholar: lookup
  79. Mcnally EM, Lapidos KA, Wheeler MT. Skeletal Muscle Structure and Function In: Runge MS, Patterson C, editors. Principles of Molecular Medicine. Totowa, NJ: Humana Press; 2006. p. 674–681.
  80. Mudry RE, Perry CN, Richards M, Fowler VM, Gregorio CC. The interaction of tropomodulin with tropomyosin stabilizes thin filaments in cardiac myocytes. J Cell Biol 2003; 162(6): 1057–1068.
    doi: 10.1083/jcb.200305031pmc: PMC2172850pubmed: 12975349google scholar: lookup
  81. Jagatheesan G, Rajan S, Ahmed RPH, Petrashevskaya N, Boivin G, Arteaga GM. Striated muscle tropomyosin isoforms differentially regulate cardiac performance and myofilament calcium sensitivity. J Muscle Res Cell Motil 2010; 31(3): 227–239.
    doi: 10.1007/s10974-010-9228-3pmc: PMC3805252pubmed: 20803058google scholar: lookup
  82. Love CC. Relationship between sperm motility, morphology and the fertility of stallions. Theriogenology 2011; 76(3): 547–557.
    doi: 10.1016/j.theriogenology.2011.03.007pubmed: 21497893google scholar: lookup
  83. Sairanen J, Nivola K, Katila T, Virtala AM, Ojala M. Effects of inbreeding and other genetic components on equine fertility. Animal 2009; 3(12): 1662–1672.
    doi: 10.1017/S1751731109990553pubmed: 22443550google scholar: lookup
  84. Mahon GAT, Cunningham EP. Inbreeding and the inheritance of fertility in the thoroughbred mare. Livest Prod Sci 1982; 9(6): 743–754.
    doi: 10.1016/0301-6226(82)90021-5google scholar: lookup
  85. Gottschalk M, Metzger J, Martinsson G, Sieme H, Distl O. Genome-wide association study for semen quality traits in German Warmblood stallions. Animal reproduction science 2016; 171: 81–86.
    doi: 10.1016/j.anireprosci.2016.06.002pubmed: 27334685google scholar: lookup
  86. Gob E, Schmitt J, Benavente R, Alsheimer M. Mammalian sperm head formation involves different polarization of two novel LINC complexes. PLoS One 2010; 5(8): e12072.
    doi: 10.1371/journal.pone.0012072pmc: PMC2919408pubmed: 20711465google scholar: lookup
  87. Lin YN, Roy A, Yan W, Burns KH, Matzuk MA. Loss of zona pellucida binding proteins in the acrosomal matrix disrupts acrosome biogenesis and sperm morphogenesis. Mol Cell Biol 2007; 27(19): 6794–6805.
    doi: 10.1128/MCB.01029-07pmc: PMC2099232pubmed: 17664285google scholar: lookup
  88. Yatsenko AN, O'Neil DS, Roy A, Arias-Mendoza PA, Chen R, Murthy LJ. Association of mutations in the zona pellucida binding protein 1 (ZPBP1) gene with abnormal sperm head morphology in infertile men. Mol Hum Reprod 2012; 18(1): 14–21.
    doi: 10.1093/molehr/gar057pmc: PMC3244884pubmed: 21911476google scholar: lookup
  89. Blake JA, Eppig JT, Kadin JA, Richardson JE, Smith CL, Bult CJ. Mouse Genome Database (MGD)-2017: community knowledge resource for the laboratory mouse. Nucleic Acids Res 2017; 45(D1): D723–D729.
    doi: 10.1093/nar/gkw1040pmc: PMC5210536pubmed: 27899570google scholar: lookup
  90. Greenbaum MP, Yan W, Wu MH, Lin YN, Agno JE, Sharma M. TEX14 is essential for intercellular bridges and fertility in male mice. Proc Natl Acad Sci U S A 2006; 103(13): 4982–4987.
    doi: 10.1073/pnas.0505123103pmc: PMC1458781pubmed: 16549803google scholar: lookup

Citations

This article has been cited 35 times.
  1. Jafari H, Abebe BK, Cong L, Ahmed Z, Zhaofei W, Sun M, Muhatai G, Chuzhao L, Dang R. Review: Genomic insights into the adaptive traits and stress resistance in modern horses. Stress Biol 2026 Jan 12;6(1):5.
    doi: 10.1007/s44154-025-00274-1pubmed: 41521281google scholar: lookup
  2. Kirmse L, Thieme K, Doherr MG, Eule JC. Evaluation of Laboratory Techniques for the Diagnosis of Leptospira-Associated Equine Recurrent Uveitis (ERU) With Focus on the Goldmann-Witmer Coefficient. Vet Ophthalmol 2026 Jan;29(1):e70132.
    doi: 10.1111/vop.70132pubmed: 41518147google scholar: lookup
  3. Chessari G, Reich P, Criscione A, Falker-Gieske C, Mastrangelo S, Tumino S, Bordonaro S, Marletta D, Tetens J. Comparison between SNP array and imputed data to estimate population structure and ROH hotspots in horse breeds. BMC Genomics 2025 Nov 29;26(1):1086.
    doi: 10.1186/s12864-025-12256-8pubmed: 41318401google scholar: lookup
  4. Luštrek B, Šimon M, Turk K, Bogičević S, Potočnik K. Comparing Genomic and Pedigree Inbreeding Coefficients in the Slovenian Lipizzan Horse as a Case Study for Small Closed Populations. Animals (Basel) 2025 Sep 23;15(19).
    doi: 10.3390/ani15192774pubmed: 41096369google scholar: lookup
  5. Duderstadt S, Distl O. Insights into Genomic Patterns of Homozygosity in the Endangered Dülmen Wild Horse Population. Genes (Basel) 2025 Sep 8;16(9).
    doi: 10.3390/genes16091054pubmed: 41009997google scholar: lookup
  6. Walz KR, McCormick ME, Fedorka CE. The Thoroughbred Theory: Influence of Breed on Performance at the CCI5*-L Level of Eventing. Animals (Basel) 2025 Jun 18;15(12).
    doi: 10.3390/ani15121796pubmed: 40564347google scholar: lookup
  7. Asti V, Summer A, Ablondi M, Sartori C, Giontella A, Pilastro V, Mecocci S, Cappelli K, Mancin E, Oian A, Mantovani R, Capomaccio S, Sabbioni A. Selection signatures and inbreeding: exploring genetic diversity in five native horse breeds. BMC Vet Res 2025 May 16;21(1):346.
    doi: 10.1186/s12917-025-04794-wpubmed: 40380299google scholar: lookup
  8. Sievers J, Distl O. Genomic Patterns of Homozygosity and Genetic Diversity in the Rhenish German Draught Horse. Genes (Basel) 2025 Mar 11;16(3).
    doi: 10.3390/genes16030327pubmed: 40149478google scholar: lookup
  9. Folgmann MS, Stock KF, Feige K, Delling U. Clinical findings of candidate stallions presented for licensing at all German Warmblood horse-breeding associations in 2018-2020. Equine Vet J 2025 Nov;57(6):1584-1591.
    doi: 10.1111/evj.14474pubmed: 39838856google scholar: lookup
  10. Silva EFP, Gaia RC, Mulim HA, Pinto LFB, Iung LHS, Brito LF, Pedrosa VB. Genome-Wide Association Study of Conformation Traits in Brazilian Holstein Cattle. Animals (Basel) 2024 Aug 25;14(17).
    doi: 10.3390/ani14172472pubmed: 39272257google scholar: lookup
  11. Sigurðardóttir H, Ablondi M, Kristjansson T, Lindgren G, Eriksson S. Genetic diversity and signatures of selection in Icelandic horses and Exmoor ponies. BMC Genomics 2024 Aug 8;25(1):772.
    doi: 10.1186/s12864-024-10682-8pubmed: 39118059google scholar: lookup
  12. Gmel AI, Mikko S, Ricard A, Velie BD, Gerber V, Hamilton NA, Neuditschko M. Using high-density SNP data to unravel the origin of the Franches-Montagnes horse breed. Genet Sel Evol 2024 Jul 10;56(1):53.
    doi: 10.1186/s12711-024-00922-6pubmed: 38987703google scholar: lookup
  13. Matthews LB, Sanz M, Sellon DC. Long-term outcome after colic surgery: retrospective study of 106 horses in the USA (2014-2021). Front Vet Sci 2023;10:1235198.
    doi: 10.3389/fvets.2023.1235198pubmed: 37859945google scholar: lookup
  14. Lindsay-McGee V, Sanchez-Molano E, Banos G, Clark EL, Piercy RJ, Psifidi A. Genetic characterisation of the Connemara pony and the Warmblood horse using a within-breed clustering approach. Genet Sel Evol 2023 Aug 17;55(1):60.
    doi: 10.1186/s12711-023-00827-wpubmed: 37592264google scholar: lookup
  15. Dementieva N, Nikitkina E, Shcherbakov Y, Nikolaeva O, Mitrofanova O, Ryabova A, Atroshchenko M, Makhmutova O, Zaitsev A. The Genetic Diversity of Stallions of Different Breeds in Russia. Genes (Basel) 2023 Jul 24;14(7).
    doi: 10.3390/genes14071511pubmed: 37510415google scholar: lookup
  16. Sánchez-Guerrero MJ, Ripollés-Lobo M, Bartolomé E, Perdomo-González DI, Valera M. The Relevance of the Expected Value of the Proportion of Arabian Genes in Genetic Evaluations for Eventing Competitions. Animals (Basel) 2023 Jun 13;13(12).
    doi: 10.3390/ani13121973pubmed: 37370483google scholar: lookup
  17. Han H, McGivney BA, Allen L, Bai D, Corduff LR, Davaakhuu G, Davaasambuu J, Dorjgotov D, Hall TJ, Hemmings AJ, Holtby AR, Jambal T, Jargalsaikhan B, Jargalsaikhan U, Kadri NK, MacHugh DE, Pausch H, Readhead C, Warburton D, Dugarjaviin M, Hill EW. Common protein-coding variants influence the racing phenotype in galloping racehorse breeds. Commun Biol 2022 Dec 13;5(1):1320.
    doi: 10.1038/s42003-022-04206-xpubmed: 36513809google scholar: lookup
  18. Nolte W, Alkhoder H, Wobbe M, Stock KF, Kalm E, Vosgerau S, Krattenmacher N, Thaller G, Tetens J, Kühn C. Replacement of microsatellite markers by imputed medium-density SNP arrays for parentage control in German warmblood horses. J Appl Genet 2022 Dec;63(4):783-792.
    doi: 10.1007/s13353-022-00725-9pubmed: 36173533google scholar: lookup
  19. Wobbe M, Reinhardt F, Reents R, Tetens J, Stock KF. Quantifying the effect of Warmblood Fragile Foal Syndrome on foaling rates in the German riding horse population. PLoS One 2022;17(7):e0267975.
    doi: 10.1371/journal.pone.0267975pubmed: 35901076google scholar: lookup
  20. Colpitts J, McLoughlin PD, Poissant J. Runs of homozygosity in Sable Island feral horses reveal the genomic consequences of inbreeding and divergence from domestic breeds. BMC Genomics 2022 Jul 12;23(1):501.
    doi: 10.1186/s12864-022-08729-9pubmed: 35820826google scholar: lookup
  21. Abondio P, Cilli E, Luiselli D. Inferring Signatures of Positive Selection in Whole-Genome Sequencing Data: An Overview of Haplotype-Based Methods. Genes (Basel) 2022 May 22;13(5).
    doi: 10.3390/genes13050926pubmed: 35627311google scholar: lookup
  22. Criscione A, Mastrangelo S, D'Alessandro E, Tumino S, Di Gerlando R, Zumbo A, Marletta D, Bordonaro S. Genome-wide survey on three local horse populations with a focus on runs of homozygosity pattern. J Anim Breed Genet 2022 Sep;139(5):540-555.
    doi: 10.1111/jbg.12680pubmed: 35445758google scholar: lookup
  23. de Faria DA, do Prado Paim T, Dos Santos CA, Paiva SR, Nogueira MB, McManus C. Selection signatures for heat tolerance in Brazilian horse breeds. Mol Genet Genomics 2022 Mar;297(2):449-462.
    doi: 10.1007/s00438-022-01862-wpubmed: 35150300google scholar: lookup
  24. Lee J, Shin D, Kim H. National genomic evaluation of Korean thoroughbreds through indirect racing phenotype. Anim Biosci 2022 May;35(5):659-669.
    doi: 10.5713/ab.21.0409pubmed: 35073661google scholar: lookup
  25. Liu D, Chen Z, Zhao W, Guo L, Sun H, Zhu K, Liu G, Shen X, Zhao X, Wang Q, Ma P, Pan Y. Genome-wide selection signatures detection in Shanghai Holstein cattle population identified genes related to adaption, health and reproduction traits. BMC Genomics 2021 Oct 15;22(1):747.
    doi: 10.1186/s12864-021-08042-xpubmed: 34654366google scholar: lookup
  26. Spoormakers TJP, Veraa S, Graat EAM, van Weeren PR, Brommer H. A comparative study of breed differences in the anatomical configuration of the equine vertebral column. J Anat 2021 Oct;239(4):829-838.
    doi: 10.1111/joa.13456pubmed: 33991425google scholar: lookup
  27. Mancin E, Ablondi M, Mantovani R, Pigozzi G, Sabbioni A, Sartori C. Genetic Variability in the Italian Heavy Draught Horse from Pedigree Data and Genomic Information. Animals (Basel) 2020 Jul 30;10(8).
    doi: 10.3390/ani10081310pubmed: 32751586google scholar: lookup
  28. Asadollahpour Nanaei H, Esmailizadeh A, Ayatollahi Mehrgardi A, Han J, Wu DD, Li Y, Zhang YP. Comparative population genomic analysis uncovers novel genomic footprints and genes associated with small body size in Chinese pony. BMC Genomics 2020 Jul 20;21(1):496.
    doi: 10.1186/s12864-020-06887-2pubmed: 32689947google scholar: lookup
  29. Ablondi M, Dadousis C, Vasini M, Eriksson S, Mikko S, Sabbioni A. Genetic Diversity and Signatures of Selection in a Native Italian Horse Breed Based on SNP Data. Animals (Basel) 2020 Jun 8;10(6).
    doi: 10.3390/ani10061005pubmed: 32521830google scholar: lookup
  30. Merkies K, Paraschou G, McGreevy PD. Morphometric Characteristics of the Skull in Horses and Donkeys-A Pilot Study. Animals (Basel) 2020 Jun 8;10(6).
    doi: 10.3390/ani10061002pubmed: 32521777google scholar: lookup
  31. Tan Y, Chen L, Li S, Hao H, Zhang D. MiR-384 Inhibits Malignant Biological Behavior Such as Proliferation and Invasion of Osteosarcoma by Regulating IGFBP3. Technol Cancer Res Treat 2020 Jan-Dec;19:1533033820909125.
    doi: 10.1177/1533033820909125pubmed: 32129151google scholar: lookup
  32. Salek Ardestani S, Aminafshar M, Zandi Baghche Maryam MB, Banabazi MH, Sargolzaei M, Miar Y. Whole-Genome Signatures of Selection in Sport Horses Revealed Selection Footprints Related to Musculoskeletal System Development Processes. Animals (Basel) 2019 Dec 26;10(1).
    doi: 10.3390/ani10010053pubmed: 31888018google scholar: lookup
  33. Raudsepp T, Finno CJ, Bellone RR, Petersen JL. Ten years of the horse reference genome: insights into equine biology, domestication and population dynamics in the post-genome era. Anim Genet 2019 Dec;50(6):569-597.
    doi: 10.1111/age.12857pubmed: 31568563google scholar: lookup
  34. 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 Genomics 2019 Sep 18;20(1):717.
    doi: 10.1186/s12864-019-6079-1pubmed: 31533613google scholar: lookup
  35. 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/genes10070491pubmed: 31261764google scholar: lookup
Subscribe to our newsletter
Join 99,850 horse owners like you who receive our email updates on horse health and nutrition.