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Home / Equine Research Bank / Genet Sel Evol / Using high-density SNP data to unravel the origin of the Fra...
Genetics, selection, evolution : GSE2024; 56(1); 53; doi: 10.1186/s12711-024-00922-6

Using high-density SNP data to unravel the origin of the Franches-Montagnes horse breed.

Abstract: The Franches-Montagnes (FM) is the last native horse breed of Switzerland, established at the end of the 19th century by cross-breeding local mares with Anglo-Norman stallions. We collected high-density SNP genotype data (Axiom™ 670 K Equine genotyping array) from 522 FM horses, including 44 old-type horses (OF), 514 European Warmblood horses (WB) from Sweden and Switzerland (including a stallion used for cross-breeding in 1990), 136 purebred Arabians (AR), 32 Shagya Arabians (SA), and 64 Thoroughbred (TB) horses, as introgressed WB stallions showed TB origin in their pedigrees. The aim of the study was to ascertain fine-scale population structures of the FM breed, including estimation of individual admixture levels and genomic inbreeding (F) by means of Runs of Homozygosity. Results: To assess fine-scale population structures within the FM breed, we applied a three-step approach, which combined admixture, genetic contribution, and F of individuals into a high-resolution network visualization. Based on this approach, we were able to demonstrate that population substructures, as detected by model-based clustering, can be either associated with a different genetic origin or with the progeny of most influential sires. Within the FM breed, admixed horses explained most of the genetic variance of the current breeding population, while OF horses only accounted for a small proportion of the variance. Furthermore, we illustrated that FM horses showed high TB admixture levels and we identified inconsistencies in the origin of FM horses descending from the Arabian stallion Doktryner. With the exception of WB, FM horses were less inbred compared to the other breeds. However, the relatively few but long ROH segments suggested diversity loss in both FM subpopulations. Genes located in FM- and OF-specific ROH islands had known functions involved in conformation and behaviour, two traits that are highly valued by breeders. Conclusions: The FM remains the last native Swiss breed, clearly distinguishable from other historically introgressed breeds, but it suffered bottlenecks due to intensive selection of stallions, restrictive mating choices based on arbitrary definitions of pure breeding, and selection of rare coat colours. To preserve the genetic diversity of FM horses, future conservation managements strategies should involve a well-balanced selection of stallions (e.g., by integrating OF stallions in the FM breeding population) and avoid selection for rare coat colours.
© 2024. The Author(s).
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Publication Date: 2024-07-10 PubMed ID: 38987703PubMed Central: PMC11238448DOI: 10.1186/s12711-024-00922-6Google Scholar: Lookup
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  • Journal Article

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 study explores the origins of the Franches-Montagnes, Switzerland’s last native horse breed, using a high volume of single nucleotide polymorphism data. Through studying various horse breeds, the research provides comprehensive insight into genetic diversity and potential protective management strategies for the breed.

Details of the Study

Researchers examined a high volume of genotype data—namely from Single Nucleotide Polymorphisms (SNPs)—gathered from 522 Franches-Montagnes (FM) horses. Other breeds examined included 514 horses of the European Warmblood (WB) breed, 136 purebred Arabians (AR), 32 Shagya Arabians (SA), and 64 Thoroughbred (TB) horses. These breeds were selected due to their historical linkage as seen from pedigree data which showed their influence in the development of FM.

  • The goal of the research was to classify the population structure of the FM breed by estimating individual admixture levels and assessing genomic inbreeding through Runs of Homozygosity.
  • A three-step process was applied to evaluate the fine-scale population structures within the FM breed. This combined: admixture, genetic contribution, and F of individuals.

Results of the Research

The results demonstrated that population substructures were either linked with variances in genetic origin or the offspring of the most influential stallions.

  • The study found that admixed horses mainly accounted for the genetic variance in the current breeding population.
  • Old-type horses (OF), on the other hand, contributed to only a minor portion of the genetic variance.
  • The FM horses demonstrated high TB admixture levels.
  • The study identified discrepancies in the descent of FM horses from the Arabian stallion Doktryner.
  • Compared to the other breeds, with the exception of WB, FM horses were found to be less inbred. However, infrequent yet long ROH (Runs of Homozygosity) segments suggested a loss of diversity in both FM subpopulations.

Conclusions and Recommendations

Despite having experienced bottlenecks from intensive stallion selection, restrictive mating choices, and the selection of rare coat colours, the FM remains uniquely distinct from historically introgressed breeds, holding its status as the last native Swiss breed.

  • The researchers suggested that to maintain genetic diversity in FM horses, there needs to be a well-balanced selection of stallions, possibly by integrating OF stallions into the FM breeding population.
  • It was recommended to steer away from selecting rare coat colours, to prevent further reduction in genetic diversity.

Cite This Article

APA
Gmel AI, Mikko S, Ricard A, Velie BD, Gerber V, Hamilton NA, Neuditschko M. (2024). Using high-density SNP data to unravel the origin of the Franches-Montagnes horse breed. Genet Sel Evol, 56(1), 53. https://doi.org/10.1186/s12711-024-00922-6

Publication

Genetics, selection, evolution : GSE
ISSN: 1297-9686
NlmUniqueID: 9114088
Country: France
Language: English
Volume: 56
Issue: 1
Pages: 53
PII: 53

Researcher Affiliations

Gmel, Annik Imogen
  • Animal GenoPhenomics, Agroscope, Route de la Tioleyre 4, 1725, Posieux, Switzerland.
  • Equine Department, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 260, 8053, Zurich, Switzerland.
Mikko, Sofia
  • Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 750 07, Uppsala, Sweden.
Ricard, Anne
  • Institut National de la Recherche Agronomique, Domaine de Vilvert, 78350, Jouy-en-Josas, France.
Velie, Brandon D
  • Equine Genetics and Genomics Group, School of Life and Environmental Sciences, University of Sydney, RMC Gunn B19-603, Sydney, NSW, 2006, Australia.
Gerber, Vinzenz
  • Institut Suisse de Médecine Equine ISME, Vetsuisse Faculty, University of Bern, Länggassstrasse 124, 3012, Bern, Switzerland.
Hamilton, Natasha Anne
  • Sydney School of Veterinary Science, University of Sydney, Sydney, NSW, 2006, Australia.
Neuditschko, Markus
  • Animal GenoPhenomics, Agroscope, Route de la Tioleyre 4, 1725, Posieux, Switzerland. markus.neuditschko@agroscope.admin.ch.

MeSH Terms

  • Horses / genetics
  • Animals
  • Polymorphism, Single Nucleotide
  • Inbreeding
  • Pedigree
  • Male
  • Breeding / methods
  • Female
  • Switzerland
  • Genotype
  • Homozygote

Grant Funding

  • 625000469 / Bundesamt fu00fcr Landwirtschaft

Conflict of Interest Statement

The authors declare that they have no competing interests.

References

This article includes 95 references
  1. Poncet P-A, Wermeille V. Le cheval des Franches-Montagnes à travers l’histoire. Porrentruy: Société jurassienne d'émulation; 2009.
  2. Poncet P-A, Pfister W, Muntwyler J, Glowatzki-Mullis ML, Gaillard C. Analysis of pedigree and conformation data to explain genetic variability of the horse breed Franches-Montagnes. J Anim Breed Genet 2006;123:114–121.
    doi: 10.1111/j.1439-0388.2006.00569.xpubmed: 16533365google scholar: lookup
  3. Hasler H, Flury C, Menet S, Haase B, Leeb T, Simianer H. Genetic diversity in an indigenous horse breed–implications for mating strategies and the control of future inbreeding. J Anim Breed Genet 2011;128:394–406.
    doi: 10.1111/j.1439-0388.2011.00932.xpubmed: 21906185google scholar: lookup
  4. Gmel AI, Burren A, Neuditschko M. Estimates of genetic parameters for shape space data in Franches-Montagnes horses. Animals 2022;12:2186.
    doi: 10.3390/ani12172186pmc: PMC9454882pubmed: 36077906google scholar: lookup
  5. Ceballos FC, Joshi PK, Clark DW, Ramsay M, Wilson JF. Runs of homozygosity: windows into population history and trait architecture. Nat Rev Genet 2018;19:220–234.
    doi: 10.1038/nrg.2017.109pubmed: 29335644google scholar: lookup
  6. McQuillan R, Leutenegger A-L, Abdel-Rahman R, Franklin CS, Pericic M, Barac-Lauc L. Runs of homozygosity in European populations. Am J Hum Genet 2008;83:359–372.
    doi: 10.1016/j.ajhg.2008.08.007pmc: PMC2556426pubmed: 18760389google scholar: lookup
  7. Purfield DC, Berry DP, McParland S, Bradley DG. Runs of homozygosity and population history in cattle. BMC Genet 2012;13:70.
    doi: 10.1186/1471-2156-13-70pmc: PMC3502433pubmed: 22888858google scholar: lookup
  8. Zhang Q, Guldbrandtsen B, Bosse M, Lund MS, Sahana G. Runs of homozygosity and distribution of functional variants in the cattle genome. BMC Genomics 2015;16:542.
    doi: 10.1186/s12864-015-1715-xpmc: PMC4508970pubmed: 26198692google scholar: lookup
  9. 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:e0176780.
    doi: 10.1371/journal.pone.0176780pmc: PMC5413029pubmed: 28463982google scholar: lookup
  10. Signer-Hasler H, Burren A, Ammann P, Drögemüller C, Flury C. Runs of homozygosity and signatures of selection: A comparison among eight local Swiss sheep breeds. Anim Genet 2019;50:512–525.
    doi: 10.1111/age.12828pubmed: 31365135google scholar: lookup
  11. 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
  12. Gmel AI, Guichard M, Dainat B, Williams GR, Eynard S, Vignal A. Identification of runs of homozygosity in Western honey bees (Apis mellifera) using whole-genome sequencing data. Ecol Evol 2023;13:e9723.
    doi: 10.1002/ece3.9723pmc: PMC9843643pubmed: 36694553google scholar: lookup
  13. 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;20:717.
    doi: 10.1186/s12864-019-6079-1pmc: PMC6751828pubmed: 31533613google scholar: lookup
  14. Grilz-Seger G, Neuditschko M, Ricard A, Velie B, Lindgren G, Mesarič M. Genome-wide homozygosity patterns and evidence for selection in a set of European and near eastern horse breeds. Genes 2019;10:491.
    doi: 10.3390/genes10070491pmc: PMC6679042pubmed: 31261764google scholar: lookup
  15. 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;50:334–346.
    doi: 10.1111/age.12797pmc: PMC6617995pubmed: 31199540google scholar: lookup
  16. Fawcett JA, Sato F, Sakamoto T, Iwasaki WM, Tozaki T, Innan H. Genome-wide SNP analysis of Japanese Thoroughbred racehorses. PLoS ONE 2019;14:e0218407.
    doi: 10.1371/journal.pone.0218407pmc: PMC6655603pubmed: 31339891google scholar: lookup
  17. 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 Genomics 2019;20:174.
    doi: 10.1186/s12864-019-5564-xpmc: PMC6402180pubmed: 30836959google scholar: lookup
  18. 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;16:764.
    doi: 10.1186/s12864-015-1977-3pmc: PMC4600213pubmed: 26452642google scholar: lookup
  19. Nolte W, Thaller G, Kuehn C. Selection signatures in four German warmblood horse breeds: tracing breeding history in the modern sport horse. PLoS ONE 2019;14:e0215913.
    doi: 10.1371/journal.pone.0215913pmc: PMC6483353pubmed: 31022261google scholar: lookup
  20. Chen C, Zhu B, Tang X, Chen B, Liu M, Gao N. Genome-wide assessment of runs of homozygosity by whole-genome sequencing in diverse horse breeds worldwide. Genes 2023;14:1211.
    doi: 10.3390/genes14061211pmc: PMC10298080pubmed: 37372391google scholar: lookup
  21. 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 2020;10:1310.
    doi: 10.3390/ani10081310pmc: PMC7460293pubmed: 32751586google scholar: lookup
  22. 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 2020;10:1005.
    doi: 10.3390/ani10061005pmc: PMC7341496pubmed: 32521830google scholar: lookup
  23. 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
  24. Szmatoła T, Gurgul A, Jasielczuk I, Oclon E, Ropka-Molik K, Stefaniuk-Szmukier M. Assessment and distribution of runs of homozygosity in horse breeds representing different utility types. Animals 2022;12:3293.
    doi: 10.3390/ani12233293pmc: PMC9736150pubmed: 36496815google scholar: lookup
  25. Criscione A, Mastrangelo S, D’Alessandro E, Tumino S, Di Gerlando R, Zumbo A. Genome-wide survey on three local horse populations with a focus on runs of homozygosity pattern. J Anim Breed Genet 2022;139:540–555.
    doi: 10.1111/jbg.12680pmc: PMC9541879pubmed: 35445758google scholar: lookup
  26. Drögemüller M, Jagannathan V, Welle MM, Graubner C, Straub R, Gerber V. Congenital hepatic fibrosis in the Franches-Montagnes horse is associated with the polycystic kidney and hepatic disease 1 (PKHD1) gene. PLoS ONE 2014;9:e110125.
    doi: 10.1371/journal.pone.0110125pmc: PMC4190318pubmed: 25295861google scholar: lookup
  27. Schaefer RJ, Schubert M, Bailey E, Bannasch DL, Barrey E, Bar-Gal GK. Developing a 670k genotyping array to tag~ 2M SNPs across 24 horse breeds. BMC Genomics 2017;18:565.
    doi: 10.1186/s12864-017-3943-8pmc: PMC5530493pubmed: 28750625google scholar: lookup
  28. Kalbfleisch TS, Rice ES, DePriest MS, Walenz BP, Hestand MS, Vermeesch JR. 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
  29. Browning BL, Zhou Y, Browning SR. A one-penny imputed genome from next-generation reference panels. Am J Hum Genet 2018;103:338–348.
    doi: 10.1016/j.ajhg.2018.07.015pmc: PMC6128308pubmed: 30100085google scholar: lookup
  30. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 2015;4:7.
    doi: 10.1186/s13742-015-0047-8pmc: PMC4342193pubmed: 25722852google scholar: lookup
  31. R Core Team. R: A language and environment for statistical computing. Version 3.6.1. R Foundation for Statistical Computing; 2013.
  32. Huson DH, Bryant D. Application of phylogenetic networks in evolutionary studies. Mol Biol Evol 2006;23:254–267.
    doi: 10.1093/molbev/msj030pubmed: 16221896google scholar: lookup
  33. Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res 2009;19:1655–1664.
    doi: 10.1101/gr.094052.109pmc: PMC2752134pubmed: 19648217google scholar: lookup
  34. Rosenberg NA. DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes 2004;4:137–138.
    doi: 10.1046/j.1471-8286.2003.00566.xgoogle scholar: lookup
  35. Neuditschko M, Raadsma HW, Khatkar MS, Jonas E, Steinig EJ, Flury C. Identification of key contributors in complex population structures. PLoS ONE 2017;12:e0177638.
    doi: 10.1371/journal.pone.0177638pmc: PMC5433729pubmed: 28520805google scholar: lookup
  36. Dinno A, Dinno MA. Package ‘paran’. Version 1.5.2: CRAN (The Comprehensive R Archive Network); 2018.https://cran.r-project.org/web/packages/paran/index.html. Accessed 24 Apr 2024.
  37. Glorfeld LW. An improvement on Horn's parallel analysis methodology for selecting the correct number of factors to retain. Educ Psychol Meas 1995;55:377–393.
    doi: 10.1177/0013164495055003002google scholar: lookup
  38. Neuditschko M, Khatkar MS, Raadsma HW. NetView: a high-definition network-visualization approach to detect fine-scale population structures from genome-wide patterns of variation. PLoS ONE 2012;7:e48375.
    doi: 10.1371/journal.pone.0048375pmc: PMC3485224pubmed: 23152744google scholar: lookup
  39. Steinig EJ, Neuditschko M, Khatkar MS, Raadsma HW, Zenger KR. netview p: a network visualization tool to unravel complex population structure using genome-wide SNPs. Mol Ecol Resour 2016;16:216–227.
    doi: 10.1111/1755-0998.12442pubmed: 26129944google scholar: lookup
  40. Graves S, Piepho H-P, Selzer ML. Package ‘multcompView’. Version 0.1-9: CRAN (The Comprehensive R Archive Network). 2015. https://cran.stat.unipd.it/web/packages/multcompView/multcompView.pdf. Accessed 24 Apr 2024.
  41. Coster A. Pedigree: package to deal with pedigree data. Version 1.4.2: CRAN (The Comprehensive R Archive Network). 2022. https://cran.r-project.org/web/packages/pedigree/index.html Accessed 11 June 2024.
  42. Biscarini F, Cozzi P, Gaspa G, Marras G. detectRUNS: an R package to detect runs of homozygosity and heterozygosity in diploid genomes. Version 0.9.6: CRAN (The Comprehensive R Archive Network). 2019. https://cran.r-project.org/web/packages/detectRUNS/index.html. Accessed 24 Apr 2024.
  43. Makvandi-Nejad S, Hoffman GE, Allen JJ, Chu E, Gu E, Chandler AM. Four loci explain 83% of size variation in the horse. PLoS ONE 2012;7:e39929.
    doi: 10.1371/journal.pone.0039929pmc: PMC3394777pubmed: 22808074google scholar: lookup
  44. Hill EW, Gu J, Eivers SS, Fonseca RG, McGivney BA, Govindarajan P. A sequence polymorphism in MSTN predicts sprinting ability and racing stamina in thoroughbred horses. PLoS ONE 2010;5:e8645.
    doi: 10.1371/journal.pone.0008645pmc: PMC2808334pubmed: 20098749google scholar: lookup
  45. McGivney B, Hernandez B, Katz L, MacHugh D, McGovern S, Parnell A. A genomic prediction model for racecourse starts in the Thoroughbred horse. Anim Genet 2019;50:347–357.
    doi: 10.1111/age.12798pubmed: 31257665google scholar: lookup
  46. Druml T, Neuditschko M, Grilz-Seger G, Horna M, Ricard A, Mesarič M. Population networks associated with runs of homozygosity reveal new insights into the breeding history of the Haflinger horse. J Hered 2018;109:384–392.
    doi: 10.1093/jhered/esx114pubmed: 29294044google scholar: lookup
  47. Bozlak E, Radovic L, Remer V, Rigler D, Allen L, Brem G. Refining the evolutionary tree of the horse Y chromosome. Sci Rep 2023;13:8954.
    doi: 10.1038/s41598-023-35539-0pmc: PMC10238413pubmed: 37268661google scholar: lookup
  48. Felkel S, Vogl C, Rigler D, Dobretsberger V, Chowdhary BP, Distl O. The horse Y chromosome as an informative marker for tracing sire lines. Sci Rep 2019;9:6095.
    doi: 10.1038/s41598-019-42640-wpmc: PMC6465346pubmed: 30988347google scholar: lookup
  49. Remer V, Bozlak E, Felkel S, Radovic L, Rigler D, Grilz-Seger G. Y-Chromosomal insights into breeding history and sire line genealogies of Arabian horses. Genes 2022;13:229.
    doi: 10.3390/genes13020229pmc: PMC8871751pubmed: 35205275google scholar: lookup
  50. Wallner B, Palmieri N, Vogl C, Rigler D, Bozlak E, Druml T. Y chromosome uncovers the recent oriental origin of modern stallions. Curr Biol 2017;27:2029–2035.e5.
    doi: 10.1016/j.cub.2017.05.086pubmed: 28669755google scholar: lookup
  51. Burren A, Signer-Hasler H, Neuditschko M, Leeb T, Rieder S, Flury C. Inzucht beim Freiberger Pferd. Agroscope Sci 2016;32:38–39.
  52. Burren A, Neuditschko M, Signer-Hasler H, Frischknecht M, Reber I, Menzi F. Genetic diversity analyses reveal first insights into breed-specific selection signatures within Swiss goat breeds. Anim Genet 2016;47:727–739.
    doi: 10.1111/age.12476pubmed: 27436146google scholar: lookup
  53. Solé M, Gori A-S, Faux P, Bertrand A, Farnir F, Gautier M. Age-based partitioning of individual genomic inbreeding levels in Belgian Blue cattle. Genet Sel Evol 2017;49:92.
    doi: 10.1186/s12711-017-0370-xpmc: PMC5741860pubmed: 29273000google scholar: lookup
  54. Ferenčaković M, Hamzić E, Gredler B, Solberg T, Klemetsdal G, Curik I. Estimates of autozygosity derived from runs of homozygosity: empirical evidence from selected cattle populations. J Anim Breed Genet 2013;130:286–293.
    doi: 10.1111/jbg.12012pubmed: 23855630google scholar: lookup
  55. de Santos WB, Pimenta Schettini G, Fonseca MG, Pereira GL, Loyola Chardulo LA, Rodrigues Machado Neto O. Fine-scale estimation of inbreeding rates, runs of homozygosity and genome-wide heterozygosity levels in the Mangalarga Marchador horse breed. J Anim Breed Genet 2021;138:161–73.
    doi: 10.1111/jbg.12508pubmed: 32949478google scholar: lookup
  56. Lavanchy E, Goudet J. Effect of reduced genomic representation on using runs of homozygosity for inbreeding characterization. Mol Ecol Resour 2023;23:787–802.
    doi: 10.1111/1755-0998.13755pubmed: 36626297google scholar: lookup
  57. Todd ET, Ho SY, Thomson PC, Ang RA, Velie BD, Hamilton NA. Founder-specific inbreeding depression affects racing performance in Thoroughbred horses. Sci Rep 2018;8:6167.
    doi: 10.1038/s41598-018-24663-xpmc: PMC5906619pubmed: 29670190google scholar: lookup
  58. Orlando L, Librado P. Origin and evolution of deleterious mutations in horses. Genes 2019;10:649.
    doi: 10.3390/genes10090649pmc: PMC6769756pubmed: 31466279google scholar: lookup
  59. Hill EW, McGivney BA, MacHugh DE. Inbreeding depression and durability in the North American Thoroughbred horse. Anim Genet 2023;54:408–411.
    doi: 10.1111/age.13309pubmed: 36843349google scholar: lookup
  60. Hill EW, Stoffel MA, McGivney BA, MacHugh DE, Pemberton JM. Inbreeding depression and the probability of racing in the Thoroughbred horse. Proc Biol Sci 2022;289:20220487.
    pmc: PMC9240673pubmed: 35765835
  61. McGivney BA, Han H, Corduff LR, Katz LM, Tozaki T, MacHugh DE. Genomic inbreeding trends, influential sire lines and selection in the global Thoroughbred horse population. Sci Rep 2020;10:466.
    doi: 10.1038/s41598-019-57389-5pmc: PMC6965197pubmed: 31949252google scholar: lookup
  62. Beadle R, Horohov D, Gaunt S. Interleukin-4 and interferon-gamma gene expression in summer pasture-associated obstructive pulmonary disease affected horses. Equine Vet J 2002;34:389–394.
    doi: 10.2746/042516402776249119pubmed: 12117112google scholar: lookup
  63. Klukowska-Rötzler J, Swinburne J, Drögemüller C, Dolf G, Janda J, Leeb T. The interleukin 4 receptor gene and its role in recurrent airway obstruction in Swiss Warmblood horses. Anim Genet 2012;43:450–453.
    doi: 10.1111/j.1365-2052.2011.02277.xpubmed: 22497430google scholar: lookup
  64. Korn A, Miller D, Dong L, Buckles EL, Wagner B, Ainsworth DM. Differential gene expression profiles and selected cytokine protein analysis of mediastinal lymph nodes of horses with chronic recurrent airway obstruction (RAO) support an interleukin-17 immune response. PLoS ONE 2015;10:e0142622.
    doi: 10.1371/journal.pone.0142622pmc: PMC4642978pubmed: 26561853google scholar: lookup
  65. Langreder N, Schäckermann D, Meier D, Becker M, Schubert M, Dübel S. Development of an inhibiting antibody against equine interleukin 5 to treat insect bite hypersensitivity of horses. Sci Rep 2023;13:4029.
    doi: 10.1038/s41598-023-31173-ypmc: PMC10000358pubmed: 36899044google scholar: lookup
  66. Jonsdottir S, Fettelschoss V, Olomski F, Talker SC, Mirkovitch J, Rhiner T. Safety profile of a virus-like particle-based vaccine targeting self-protein interleukin-5 in horses. Vaccines 2020;8:213.
    doi: 10.3390/vaccines8020213pmc: PMC7349629pubmed: 32397549google scholar: lookup
  67. Pantelyushin S, Rhiner T, Jebbawi F, Sella F, Waldern N, Lam J. Interleukin 5-dependent inflammatory eosinophil subtype involved in allergic insect bite hypersensitivity of horses. Allergy 2023;78:3020–3023.
    doi: 10.1111/all.15859pubmed: 37605865google scholar: lookup
  68. Han H, Randhawa IA, MacHugh DE, McGivney BA, Katz LM, Dugarjaviin M. Selection signatures for local and regional adaptation in Chinese Mongolian horse breeds reveal candidate genes for hoof health. BMC Genomics 2023;24:35.
    doi: 10.1186/s12864-023-09116-8pmc: PMC9854188pubmed: 36658473google scholar: lookup
  69. Ropka-Molik K, Stefaniuk-Szmukier M, Żukowski K, Piórkowska K, Gurgul A, Bugno-Poniewierska M. Transcriptome profiling of Arabian horse blood during training regimens. BMC Genet 2017;18:31.
    doi: 10.1186/s12863-017-0499-1pmc: PMC5382464pubmed: 28381206google scholar: lookup
  70. Mach N, Plancade S, Pacholewska A, Lecardonnel J, Rivière J, Moroldo M. Integrated mRNA and miRNA expression profiling in blood reveals candidate biomarkers associated with endurance exercise in the horse. Sci Rep 2016;6:22932.
    doi: 10.1038/srep22932pmc: PMC4785432pubmed: 26960911google scholar: lookup
  71. Akhmetov I, Popov D, Missina S, Vinogradova O, Rogozkin V. The analysis of PPARGC1B gene polymorphism in athletes. Ross Fiziol Zh Im IM Sechenova 2009;95:1247–1253.
    pubmed: 20058823
  72. Doliş M, Nagy P, Doliş L, Nistor C. Study on the evolution of the body height sizes in female young horses of the Shagya breed. Lucr Științ Univ Agron Ser Zooteh Med Vet 2011;56:219–223.
  73. Petersen JL, Valberg SJ, Mickelson JR, McCue ME. Haplotype diversity in the equine myostatin gene with focus on variants associated with race distance propensity and muscle fiber type proportions. Anim Genet 2014;45:827–835.
    doi: 10.1111/age.12205pmc: PMC4211974pubmed: 25160752google scholar: lookup
  74. López-Rivero JL, Agüera E, Monterde JG, Rodríguez-Barbudo MV, Miró F. Comparative study of muscle fiber type composition in the middle gluteal muscle of Andalusian, Thoroughbred and Arabian horses. J Equine Vet Sci 1989;9:337–340.
    doi: 10.1016/S0737-0806(89)80072-3google scholar: lookup
  75. Serrano AL, Rivero JLL. Myosin heavy chain profile of equine gluteus medius muscle following prolonged draught-exercise training and detraining. J Muscle Res Cell Mot 2000;21:235–245.
    doi: 10.1023/A:1005642632711pubmed: 10952171google scholar: lookup
  76. Wellik DM. Hox patterning of the vertebrate axial skeleton. Dev Dyn 2007;236:2454–2463.
    doi: 10.1002/dvdy.21286pubmed: 17685480google scholar: lookup
  77. Fages A, Hanghøj K, Khan N, Gaunitz C, Seguin-Orlando A, Leonardi M. Tracking five millennia of horse management with extensive ancient genome time series. Cell 2019;177:1419–35.e31.
    doi: 10.1016/j.cell.2019.03.049pmc: PMC6547883pubmed: 31056281google scholar: lookup
  78. Khan S, Mudassir M, Khan N, Marwat A. Brachdactyly instigated as a result of mutation in GDF5 and NOG genes in Pakistani population. Pak J Med Sci 2018;34:82–87.
    doi: 10.12669/pjms.341.12885pmc: PMC5857035pubmed: 29643884google scholar: lookup
  79. Lehmann K, Seemann P, Silan F, Goecke T, Irgang S, Kjaer K. A new subtype of brachydactyly type B caused by point mutations in the bone morphogenetic protein antagonist NOGGIN. Am J Hum Genet 2007;81:388–396.
    doi: 10.1086/519697pmc: PMC1950796pubmed: 17668388google scholar: lookup
  80. Puglisi-Allegra S, Andolina D. Serotonin and stress coping. Behav Brain Res 2015;277:58–67.
    doi: 10.1016/j.bbr.2014.07.052pubmed: 25108244google scholar: lookup
  81. Zhuang X, Gross C, Santarelli L, Compan V, Trillat A-C, Hen R. Altered emotional states in knockout mice lacking 5-HT1A or 5-HT1B receptors. Neuropsychopharmacology 1999;21:52S–60S.
    doi: 10.1038/sj.npp.1395371pubmed: 10432489google scholar: lookup
  82. Crabbe JC, Phillips TJ, Feller DJ, Hen R, Wenger CD, Lessov CN. Elevated alcohol consumption in null mutant mice lacking 5–HT1B serotonin receptors. Nat Genet 1996;14:98–101.
    doi: 10.1038/ng0996-98pubmed: 8782828google scholar: lookup
  83. Clark MS, Vincow ES, Sexton TJ, Neumaier JF. Increased expression of 5-HT1B receptor in dorsal raphe nucleus decreases fear-potentiated startle in a stress dependent manner. Brain Res 2004;1007:86–97.
    doi: 10.1016/j.brainres.2004.01.070pubmed: 15064139google scholar: lookup
  84. Seaman S, Davidson H, Waran N. How reliable is temperament assessment in the domestic horse (Equus caballus)?. Appl Anim Behav Sci 2002;78:175–191.
    doi: 10.1016/S0168-1591(02)00095-3google scholar: lookup
  85. Briefer Freymond S, Bardou D, Beuret S, Bachmann I, Zuberbühler K, Briefer EF. Elevated sensitivity to tactile stimuli in stereotypic horses. Front Vet Sci 2019;6:162.
    doi: 10.3389/fvets.2019.00162pmc: PMC6593280pubmed: 31275947google scholar: lookup
  86. Saudou F, Amara DA, Dierich A, LeMeur M, Ramboz S, Segu L. Enhanced aggressive behavior in mice lacking 5-HT1B receptor. Science 1994;265:1875–1878.
    doi: 10.1126/science.8091214pubmed: 8091214google scholar: lookup
  87. Hashimoto T, Maekawa S, Miyata S. IgLON cell adhesion molecules regulate synaptogenesis in hippocampal neurons. Cell Biochem Funct 2009;27:496–498.
    doi: 10.1002/cbf.1600pubmed: 19711485google scholar: lookup
  88. Zhang Z, Ye M, Li Q, You Y, Yu H, Ma Y. The schizophrenia susceptibility gene OPCML regulates spine maturation and cognitive behaviors through Eph-Cofilin signaling. Cell Rep 2019;29:49–61.e47.
    doi: 10.1016/j.celrep.2019.08.091pubmed: 31577955google scholar: lookup
  89. Pan Y, Wang K-S, Aragam N. NTM and NR3C2 polymorphisms influencing intelligence: family-based association studies. Prog Neuro-Psychopharmacol Biol Psychiatry 2011;35:154–160.
    doi: 10.1016/j.pnpbp.2010.10.016pubmed: 21036197google scholar: lookup
  90. Brevik EJ, van Donkelaar MM, Weber H, Sánchez-Mora C, Jacob C, Rivero O. Genome-wide analyses of aggressiveness in attention-deficit hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet 2016;171:733–747.
    doi: 10.1002/ajmg.b.32434pmc: PMC5071721pubmed: 27021288google scholar: lookup
  91. Singh K, Lilleväli K, Gilbert SF, Bregin A, Jayaram M, Rahi M. The combined impact of IgLON family proteins Lsamp and Neurotrimin on developing neurons and behavioral profiles in mouse. Brain Res Bull 2018;140:5–18.
    doi: 10.1016/j.brainresbull.2018.03.013pubmed: 29605488google scholar: lookup
  92. Mazitov T, Bregin A, Philips M-A, Innos J, Vasar E. Deficit in emotional learning in neurotrimin knockout mice. Behav Brain Res 2017;317:311–318.
    doi: 10.1016/j.bbr.2016.09.064pubmed: 27693610google scholar: lookup
  93. Schubert M, Jónsson H, Chang D, Der Sarkissian C, Ermini L, Ginolhac A. Prehistoric genomes reveal the genetic foundation and cost of horse domestication. Proc Natl Acad Sci USA 2014;111:E5661–E5669.
    doi: 10.1073/pnas.1416991111pmc: PMC4284583pubmed: 25512547google scholar: lookup
  94. Han H, McGivney BA, Allen L, Bai D, Corduff LR, Davaakhuu G. Common protein-coding variants influence the racing phenotype in galloping racehorse breeds. Commun Biol 2022;5:1320.
    doi: 10.1038/s42003-022-04206-xpmc: PMC9748125pubmed: 36513809google scholar: lookup
  95. Gurgul A, Jasielczuk I, Semik-Gurgul E, Pawlina-Tyszko K, Stefaniuk-Szmukier M, Szmatoła T. A genome-wide scan for diversifying selection signatures in selected horse breeds. PLoS ONE 2019;14:e0210751.
    doi: 10.1371/journal.pone.0210751pmc: PMC6353161pubmed: 30699152google scholar: lookup

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