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Animals : an open access journal from MDPI2020; 10(6); 1005; doi: 10.3390/ani10061005

Genetic Diversity and Signatures of Selection in a Native Italian Horse Breed Based on SNP Data.

Abstract: Horses are nowadays mainly used for sport and leisure activities, and several local breeds, traditionally used in agriculture, have been exposed to a dramatic loss in population size and genetic diversity. The loss of genetic diversity negatively impacts individual fitness and reduces the potential long-term survivability of a breed. Recent advances in molecular biology and bioinformatics have allowed researchers to explore biodiversity one step further. This study aimed to evaluate the loss of genetic variability and identify genomic regions under selection pressure in the Bardigiano breed based on GGP Equine70k SNP data. The effective population size based on Linkage Disequilibrium (N) was equal to 39 horses, and it showed a decline over time. The average inbreeding based on runs of homozygosity (ROH) was equal to 0.17 (SD = 0.03). The majority of the ROH were relatively short (91% were ≤ 2Mbp long), highlighting the occurrence of older inbreeding, rather than a more recent occurrence. A total of eight ROH islands, shared among more than 70% of the Bardigiano horses, were found. Four of them mapped to known quantitative trait loci related to morphological traits (e.g., body size and coat color) and disease susceptibility. This study provided the first genome-wide scan of genetic diversity and selection signatures in an Italian native horse breed.
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Publication Date: 2020-06-08 PubMed ID: 32521830PubMed Central: PMC7341496DOI: 10.3390/ani10061005Google Scholar: Lookup
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Summary

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

The research investigated the loss of genetic variability and potential sections of genome under selective pressure in the Bardigiano breed of horses, using the GGP Equine70k SNP dataset. This was the first such comprehensive study of its kind for an Italian native horse breed.

Research Goals and Methods

  • The primary aim of this study was to evaluate the genetic diversity of the Bardigiano horse breed, an Italian native breed, and to identify genomic regions that might be under selective pressure.
  • To accomplish this, the researchers utilized the GGP Equine70k SNP dataset. SNP stands for Single Nucleotide Polymorphism, a type of genetic variation among individuals. This dataset is often used in genetic studies of horses.
  • In addition, the researchers calculated the effective population size (N) of the breed based on Linkage Disequilibrium, a measure of the non-random association of alleles at different loci. They also calculated the average inbreeding based on runs of homozygosity (ROH), which refers to a contiguous sequence of identical genetic markers that are inherited from both parents.

Key Findings

  • The effective population size was found to be 39 horses, indicating a significant decline over time. This small size could lead to a loss of genetic diversity, which can negatively impact individual fitness and the breed’s long-term survival.
  • The average inbreeding was reported to be 0.17 (with a standard deviation of 0.03). High inbreeding can also be harmful as it can increase the chances of offspring being affected by deleterious or recessive traits.
  • Most of the ROH identified were relatively short (91% were less than or equal to 2Mbp long), suggesting a history of older inbreeding rather than recent. Shorter ROH tend to be the result of older, distant inbreeding events, whereas longer ROH suggest more recent inbreeding.
  • Researchers identified eight shared ROH islands (contiguous areas of homozygosity), four of which corresponded to known regions related to morphological traits (like body size and coat color) and susceptibility to diseases. This suggests that these traits have been under selective pressure in the Bardigiano breed.

Contribution to the Field

  • This investigation represents the first genome-wide scan of genetic diversity and selection pressures in the Bardigiano breed, or indeed any Italian native breed of horse.
  • The findings can aid in conservation strategies for the breed by identifying areas where selective breeding has potentially diminished genetic diversity. Also, the identification of traits under selective pressure might assist in making breeding decisions aimed at maintaining breed characteristics while reducing inbreeding.

Cite This Article

APA
Ablondi M, Dadousis C, Vasini M, Eriksson S, Mikko S, Sabbioni A. (2020). Genetic Diversity and Signatures of Selection in a Native Italian Horse Breed Based on SNP Data. Animals (Basel), 10(6), 1005. https://doi.org/10.3390/ani10061005

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 10
Issue: 6
PII: 1005

Researcher Affiliations

Ablondi, Michela
  • Dipartimento di Scienze Medico-Veterinarie, University of Parma Via del Taglio 10, 43126 Parma, Italy.
Dadousis, Christos
  • Dipartimento di Scienze Medico-Veterinarie, University of Parma Via del Taglio 10, 43126 Parma, Italy.
Vasini, Matteo
  • Libro Genealogico Cavallo Bardigiano, Associazione Regionale Allevatori dell'Emilia-Romagna, 43126 Parma, Italy.
Eriksson, Susanne
  • Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, PO Box 7023, S-75007 Uppsala, Sweden.
Mikko, Sofia
  • Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, PO Box 7023, S-75007 Uppsala, Sweden.
Sabbioni, Alberto
  • Dipartimento di Scienze Medico-Veterinarie, University of Parma Via del Taglio 10, 43126 Parma, Italy.

Conflict of Interest Statement

The authors declare no conflict of interest.

References

This article includes 81 references
  1. Hendricks LB. International Encyclopedia of Horse Breeds. University of Oklahoma Press; Norman, OK, USA: 1995.
  2. Ablondi M, Summer A, Vasini M, Simoni M, Sabbioni A. Genetic parameters estimation in an Italian horse native breed to support the conversion from agricultural uses to riding purposes.. J Anim Breed Genet 2020 Mar;137(2):200-210.
    doi: 10.1111/jbg.12425pubmed: 31310049google scholar: lookup
  3. Di Stasio L, Perrotta G, Blasi M, Lisa C. Genetic characterization of the Bardigiano horse using microsatellite markers. Ital. J. Anim. Sci. 2008;7:243–250.
    doi: 10.4081/ijas.2008.243google scholar: lookup
  4. Sherf B. World Watch List for Domestic Animal Diversity. 3rd ed. FAO; Rome, Italy: 2000.
  5. Ablondi M, Vasini M, Beretti V, Superchi P, Sabbioni A. Exploring genetic diversity in an Italian horse native breed to develop strategies for preservation and management.. J Anim Breed Genet 2018 Oct;135(6):450-459.
    doi: 10.1111/jbg.12357pubmed: 30136312google scholar: lookup
  6. Kim ES, Cole JB, Huson H, Wiggans GR, Van Tassell CP, Crooker BA, Liu G, Da Y, Sonstegard TS. Effect of artificial selection on runs of homozygosity in u.s. Holstein cattle.. PLoS One 2013;8(11):e80813.
    doi: 10.1371/journal.pone.0080813pmc: PMC3858116pubmed: 24348915google scholar: lookup
  7. Howard JT, Pryce JE, Baes C, Maltecca C. Invited review: Inbreeding in the genomics era: Inbreeding, inbreeding depression, and management of genomic variability.. J Dairy Sci 2017 Aug;100(8):6009-6024.
    doi: 10.3168/jds.2017-12787pubmed: 28601448google scholar: lookup
  8. Purfield DC, Berry DP, McParland S, Bradley DG. Runs of homozygosity and population history in cattle.. BMC Genet 2012 Aug 14;13:70.
    doi: 10.1186/1471-2156-13-70pmc: PMC3502433pubmed: 22888858google scholar: lookup
  9. Muñoz M, Bozzi R, García-Casco J, Núñez Y, Ribani A, Franci O, García F, Škrlep M, Schiavo G, Bovo S, Utzeri VJ, Charneca R, Martins JM, Quintanilla R, Tibau J, Margeta V, Djurkin-Kušec I, Mercat MJ, Riquet J, Estellé J, Zimmer C, Razmaite V, Araujo JP, Radović Č, Savić R, Karolyi D, Gallo M, Čandek-Potokar M, Fernández AI, Fontanesi L, Óvilo C. Genomic diversity, linkage disequilibrium and selection signatures in European local pig breeds assessed with a high density SNP chip.. Sci Rep 2019 Sep 19;9(1):13546.
    doi: 10.1038/s41598-019-49830-6pmc: PMC6753209pubmed: 31537860google scholar: lookup
  10. Talenti A, Bertolini F, Pagnacco G, Pilla F, Ajmone-Marsan P, Rothschild MF, Crepaldi P. Erratum to: The Valdostana goat: a genome-wide investigation of the distinctiveness of its selective sweep regions.. Mamm Genome 2017 Apr;28(3-4):129.
    doi: 10.1007/s00335-017-9685-8pubmed: 28303291google scholar: lookup
  11. Kijas JW, Lenstra JA, Hayes B, Boitard S, Porto Neto LR, San Cristobal M, Servin B, McCulloch R, Whan V, Gietzen K, Paiva S, Barendse W, Ciani E, Raadsma H, McEwan J, Dalrymple B. Genome-wide analysis of the world's sheep breeds reveals high levels of historic mixture and strong recent selection.. PLoS Biol 2012 Feb;10(2):e1001258.
    doi: 10.1371/journal.pbio.1001258pmc: PMC3274507pubmed: 22346734google scholar: lookup
  12. Petersen JL, Mickelson JR, Cothran EG, Andersson LS, Axelsson J, Bailey E, Bannasch D, Binns MM, Borges AS, Brama P, da Câmara Machado A, Distl O, Felicetti M, Fox-Clipsham L, Graves KT, Guérin G, Haase B, Hasegawa T, Hemmann K, Hill EW, Leeb T, Lindgren G, Lohi H, Lopes MS, McGivney BA, Mikko S, Orr N, Penedo MC, Piercy RJ, Raekallio M, Rieder S, Røed KH, Silvestrelli M, Swinburne J, Tozaki T, Vaudin M, M Wade C, McCue ME. Genetic diversity in the modern horse illustrated from genome-wide SNP data.. PLoS One 2013;8(1):e54997.
    doi: 10.1371/journal.pone.0054997pmc: PMC3559798pubmed: 23383025google scholar: lookup
  13. Maiorano AM, Lourenco DL, Tsuruta S, Ospina AMT, Stafuzza NB, Masuda Y, Filho AEV, Cyrillo JNDSG, Curi RA, Silva JAIV. Assessing genetic architecture and signatures of selection of dual purpose Gir cattle populations using genomic information.. PLoS One 2018;13(8):e0200694.
    doi: 10.1371/journal.pone.0200694pmc: PMC6071998pubmed: 30071036google scholar: lookup
  14. Khayatzadeh N, Mészáros G, Utsunomiya YT, Garcia JF, Schnyder U, Gredler B, Curik I, Sölkner J. Locus-specific ancestry to detect recent response to selection in admixed Swiss Fleckvieh cattle.. Anim Genet 2016 Dec;47(6):637-646.
    doi: 10.1111/age.12470pubmed: 27435758google scholar: lookup
  15. Msalya G, Kim ES, Laisser EL, Kipanyula MJ, Karimuribo ED, Kusiluka LJ, Chenyambuga SW, Rothschild MF. Determination of Genetic Structure and Signatures of Selection in Three Strains of Tanzania Shorthorn Zebu, Boran and Friesian Cattle by Genome-Wide SNP Analyses.. PLoS One 2017;12(1):e0171088.
    doi: 10.1371/journal.pone.0171088pmc: PMC5271371pubmed: 28129396google scholar: lookup
  16. Peripolli E, Munari DP, Silva MVGB, Lima ALF, Irgang R, Baldi F. Runs of homozygosity: current knowledge and applications in livestock.. Anim Genet 2017 Jun;48(3):255-271.
    doi: 10.1111/age.12526pubmed: 27910110google scholar: lookup
  17. Ceballos FC, Joshi PK, Clark DW, Ramsay M, Wilson JF. Runs of homozygosity: windows into population history and trait architecture.. Nat Rev Genet 2018 Apr;19(4):220-234.
    doi: 10.1038/nrg.2017.109pubmed: 29335644google scholar: lookup
  18. McQuillan R, Leutenegger AL, Abdel-Rahman R, Franklin CS, Pericic M, Barac-Lauc L, Smolej-Narancic N, Janicijevic B, Polasek O, Tenesa A, Macleod AK, Farrington SM, Rudan P, Hayward C, Vitart V, Rudan I, Wild SH, Dunlop MG, Wright AF, Campbell H, Wilson JF. Runs of homozygosity in European populations.. Am J Hum Genet 2008 Sep;83(3):359-72.
    doi: 10.1016/j.ajhg.2008.08.007pmc: PMC2556426pubmed: 18760389google scholar: lookup
  19. Curik I, Ferenčaković M, Sölkner J. Inbreeding and runs of homozygosity: A possible solution to an old problem. Livest. Sci. 2014;166:26–34.
    doi: 10.1016/j.livsci.2014.05.034google scholar: lookup
  20. Ferenčaković M, Hamzić E, Gredler B, Solberg TR, Klemetsdal G, Curik I, Sölkner J. Estimates of autozygosity derived from runs of homozygosity: empirical evidence from selected cattle populations.. J Anim Breed Genet 2013 Aug;130(4):286-93.
    doi: 10.1111/jbg.12012pubmed: 23855630google scholar: lookup
  21. Marras G, Gaspa G, Sorbolini S, Dimauro C, Ajmone-Marsan P, Valentini A, Williams JL, Macciotta NP. Analysis of runs of homozygosity and their relationship with inbreeding in five cattle breeds farmed in Italy.. Anim Genet 2015 Apr;46(2):110-21.
    doi: 10.1111/age.12259pubmed: 25530322google scholar: lookup
  22. Schurink A, Arts DJG, Ducro BJ. Genetic diversity in the Dutch harness horse population using pedigree analysis. Livest. Sci. 2012;143:270–277.
    doi: 10.1016/j.livsci.2011.10.005google scholar: lookup
  23. Sørensen MK, Sørensen AC, Baumung R, Borchersen S, Berg P. Optimal genetic contribution selection in Danish holstein depends on pedigree quality. Livest. Sci. 2008;118:212–222.
    doi: 10.1016/j.livsci.2008.01.027google scholar: lookup
  24. Metzger J, Karwath M, Tonda R, Beltran S, Águeda L, Gut M, Gut IG, Distl O. Runs of homozygosity reveal signatures of positive selection for reproduction traits in breed and non-breed horses.. BMC Genomics 2015 Oct 9;16:764.
    doi: 10.1186/s12864-015-1977-3pmc: PMC4600213pubmed: 26452642google scholar: lookup
  25. 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/s12864-019-6079-1pmc: PMC6751828pubmed: 31533613google scholar: lookup
  26. 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
  27. 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(4):e0215913.
    doi: 10.1371/journal.pone.0215913pmc: PMC6483353pubmed: 31022261google scholar: lookup
  28. 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 Mar 5;20(1):174.
    doi: 10.1186/s12864-019-5564-xpmc: PMC6402180pubmed: 30836959google scholar: lookup
  29. Moravčíková N, Kasarda R, Kadlečík O, Trakovická A, Halo M, Candrák J. Runs of homozygosity as footprints of selection in the norik of muran horse genome. Acta Univ. Agric. Silv. Mendel. Brun. 2019;67:1165–1170.
    doi: 10.11118/actaun201967051165google scholar: lookup
  30. Ablondi M, Eriksson S, Tetu S, Sabbioni A, Viklund Å, Mikko S. Genomic Divergence in Swedish Warmblood Horses Selected for Equestrian Disciplines.. Genes (Basel) 2019 Nov 27;10(12).
    doi: 10.3390/genes10120976pmc: PMC6947233pubmed: 31783652google scholar: lookup
  31. 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
  32. Druml T, Neuditschko M, Grilz-Seger G, Horna M, Ricard A, Mesaric M, Cotman M, Pausch H, Brem G. Population Networks Associated with Runs of Homozygosity Reveal New Insights into the Breeding History of the Haflinger Horse.. J Hered 2018 May 11;109(4):384-392.
    doi: 10.1093/jhered/esx114pubmed: 29294044google scholar: lookup
  33. 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/ani10010053pmc: PMC7023322pubmed: 31888018google scholar: lookup
  34. 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
  35. Maignel L, Boichard D, Verrier E. Genetic variability of French dairy breeds estimated from pedigree information. Interbull Bull. 1996;14:49–53.
  36. 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
  37. Beeson SK, Schaefer RJ, Mason VC, McCue ME. Robust remapping of equine SNP array coordinates to Eq쪳.. Anim Genet 2019 Feb;50(1):114-115.
    doi: 10.1111/age.12745pmc: PMC6349531pubmed: 30421446google scholar: lookup
  38. 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
  39. Barbato M, Orozco-terWengel P, Tapio M, Bruford MW. SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data.. Front Genet 2015;6:109.
    doi: 10.3389/fgene.2015.00109pmc: PMC4367434pubmed: 25852748google scholar: lookup
  40. Sved JA, Feldman MW. Correlation and probability methods for one and two loci.. Theor Popul Biol 1973 Mar;4(1):129-32.
    doi: 10.1016/0040-5809(73)90008-7pubmed: 4726005google scholar: lookup
  41. Beeson SK, Mickelson JR, McCue ME. Exploration of fine-scale recombination rate variation in the domestic horse.. Genome Res 2019 Oct;29(10):1744-1752.
    doi: 10.1101/gr.243311.118pmc: PMC6771410pubmed: 31434677google scholar: lookup
  42. Pitt D, Bruford MW, Barbato M, Orozco-terWengel P, Martínez R, Sevane N. Demography and rapid local adaptation shape Creole cattle genome diversity in the tropics.. Evol Appl 2019 Jan;12(1):105-122.
    doi: 10.1111/eva.12641pmc: PMC6304683pubmed: 30622639google scholar: lookup
  43. Biscarini F, Cozzi P, Gaspa G, Marras G. detectRUNS: An R Package to Detect Runs of Homozygosity and Heterozygosity in Diploid Genomes. University of Sassari; Sassari, Italy: 2019.
  44. R Development Core Team. R: A Language and Environment for Statistical Computing. R Development Core Team; Vienna, Austria: 2011.
  45. De Souza-Fonseca PA, dos Santos FC, Rosse IC, Ventura RV, Brunelli FÂT, Penna VM, da Silva Verneque R, Machado MA, da Silva MVGB, Carvalho MRS. Retelling the recent evolution of genetic diversity for Guzerá: Inferences from LD decay, runs of homozygosity and Ne over the generations. Livest. Sci. 2016;193:110–117.
    doi: 10.1016/j.livsci.2016.10.006google scholar: lookup
  46. Aken BL, Ayling S, Barrell D, Clarke L, Curwen V, Fairley S, Fernandez Banet J, Billis K, García Girón C, Hourlier T, Howe K, Kähäri A, Kokocinski F, Martin FJ, Murphy DN, Nag R, Ruffier M, Schuster M, Tang YA, Vogel JH, White S, Zadissa A, Flicek P, Searle SM. The Ensembl gene annotation system.. Database (Oxford) 2016;2016.
    doi: 10.1093/database/baw093pmc: PMC4919035pubmed: 27337980google scholar: lookup
  47. Karolchik D, Hinrichs AS, Furey TS, Roskin KM, Sugnet CW, Haussler D, Kent WJ. The UCSC Table Browser data retrieval tool.. Nucleic Acids Res 2004 Jan 1;32(Database issue):D493-6.
    doi: 10.1093/nar/gkh103pmc: PMC308837pubmed: 14681465google scholar: lookup
  48. Hu ZL, Park CA, Reecy JM. Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB.. Nucleic Acids Res 2019 Jan 8;47(D1):D701-D710.
    doi: 10.1093/nar/gky1084pmc: PMC6323967pubmed: 30407520google scholar: lookup
  49. Schurink A, Wolc A, Ducro BJ, Frankena K, Garrick DJ, Dekkers JC, van Arendonk JA. Genome-wide association study of insect bite hypersensitivity in two horse populations in the Netherlands.. Genet Sel Evol 2012 Oct 30;44(1):31.
    doi: 10.1186/1297-9686-44-31pmc: PMC3524047pubmed: 23110538google scholar: lookup
  50. Shrestha M, Solé M, Ducro BJ, Sundquist M, Thomas R, Schurink A, Eriksson S, Lindgren G. Genome-wide association study for insect bite hypersensitivity susceptibility in horses revealed novel associated loci on chromosome 1.. J Anim Breed Genet 2020 Mar;137(2):223-233.
    doi: 10.1111/jbg.12436pubmed: 31489730google scholar: lookup
  51. Metzger J, Ohnesorge B, Distl O. Genome-wide linkage and association analysis identifies major gene loci for guttural pouch tympany in Arabian and German warmblood horses.. PLoS One 2012;7(7):e41640.
    doi: 10.1371/journal.pone.0041640pmc: PMC3407181pubmed: 22848553google scholar: lookup
  52. Haase B, Signer-Hasler H, Binns MM, Obexer-Ruff G, Hauswirth R, Bellone RR, Burger D, Rieder S, Wade CM, Leeb T. 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
  53. Tetens J, Widmann P, Kühn C, Thaller G. A genome-wide association study indicates LCORL/NCAPG as a candidate locus for withers height in German Warmblood horses.. Anim Genet 2013 Aug;44(4):467-71.
    doi: 10.1111/age.12031pubmed: 23418885google scholar: lookup
  54. Thomer A, Gottschalk M, Christmann A, Naccache F, Jung K, Hewicker-Trautwein M, Distl O, Metzger J. An epistatic effect of KRT25 on SP6 is involved in curly coat in horses.. Sci Rep 2018 Apr 23;8(1):6374.
    doi: 10.1038/s41598-018-24865-3pmc: PMC5913262pubmed: 29686323google scholar: lookup
  55. Makvandi-Nejad S, Hoffman GE, Allen JJ, Chu E, Gu E, Chandler AM, Loredo AI, Bellone RR, Mezey JG, Brooks SA, Sutter NB. Four loci explain 83% of size variation in the horse.. PLoS One 2012;7(7):e39929.
    doi: 10.1371/journal.pone.0039929pmc: PMC3394777pubmed: 22808074google scholar: lookup
  56. Skujina I, Winton CL, Hegarty MJ, McMahon R, Nash DM, Davies Morel MCG, McEwan NR. Detecting genetic regions associated with height in the native ponies of the British Isles by using high density SNP genotyping.. Genome 2018 Oct;61(10):767-770.
    doi: 10.1139/gen-2018-0006pubmed: 30184439google scholar: lookup
  57. Vostrá-Vydrová H, Vostrý L, Hofmanová B, Krupa E, Zavadilová L. Pedigree analysis of the endangered old kladruber horse population. Livest. Sci. 2016;185:17–23.
    doi: 10.1016/j.livsci.2016.01.001google scholar: lookup
  58. Fabbri MC, Gonçalves de Rezende MP, Dadousis C, Biffani S, Negrini R, Souza Carneiro PL, Bozzi R. Population Structure and Genetic Diversity of Italian Beef Breeds as a Tool for Planning Conservation and Selection Strategies.. Animals (Basel) 2019 Oct 29;9(11).
    doi: 10.3390/ani9110880pmc: PMC6912484pubmed: 31671823google scholar: lookup
  59. Mariani E, Summer A, Ablondi M, Sabbioni A. Genetic Variability and Management in Nero di Parma Swine Breed to Preserve Local Diversity.. Animals (Basel) 2020 Mar 24;10(3).
    doi: 10.3390/ani10030538pmc: PMC7142944pubmed: 32213904google scholar: lookup
  60. Giontella A, Pieramati C, Silvestrelli M, Sarti FM. Analysis of founders and performance test effects on an autochthonous horse population through pedigree analysis: structure, genetic variability and inbreeding.. Animal 2019 Jan;13(1):15-24.
    doi: 10.1017/S1751731118001180pubmed: 29807556google scholar: lookup
  61. Flury C, Tapio M, Sonstegard T, Drögemüller C, Leeb T, Simianer H, Hanotte O, Rieder S. Effective population size of an indigenous Swiss cattle breed estimated from linkage disequilibrium.. J Anim Breed Genet 2010 Oct;127(5):339-47.
    doi: 10.1111/j.1439-0388.2010.00862.xpubmed: 20831557google scholar: lookup
  62. Goyache F, Alvarez I, Fernández I, Pérez-Pardal L, Royo LJ, Lorenzo L. Usefulness of molecular-based methods for estimating effective population size in livestock assessed using data from the endangered black-coated Asturcón pony.. J Anim Sci 2011 May;89(5):1251-9.
    doi: 10.2527/jas.2010-3620pubmed: 21257782google scholar: lookup
  63. Sadeghi R, Moradi-Shahrbabak M, Miraei Ashtiani SR, Schlamp F, Cosgrove EJ, Antczak DF. Genetic Diversity of Persian Arabian Horses and Their Relationship to Other Native Iranian Horse Breeds.. J Hered 2019 Mar 5;110(2):173-182.
    doi: 10.1093/jhered/esy061pubmed: 30590570google scholar: lookup
  64. Druml T, Curik I, Baumung R, Aberle K, Distl O, Sölkner J. Individual-based assessment of population structure and admixture in Austrian, Croatian and German draught horses.. Heredity (Edinb) 2007 Feb;98(2):114-22.
    doi: 10.1038/sj.hdy.6800910pubmed: 17035951google scholar: lookup
  65. Gutiérrez JP, Cervantes I, Molina A, Valera M, Goyache F. Individual increase in inbreeding allows estimating effective sizes from pedigrees.. Genet Sel Evol 2008 Jul-Aug;40(4):359-78.
    doi: 10.1186/1297-9686-40-4-359pmc: PMC2674907pubmed: 18558071google scholar: lookup
  66. Gutiérrez JP, Cervantes I, Goyache F. Improving the estimation of realized effective population sizes in farm animals.. J Anim Breed Genet 2009 Aug;126(4):327-32.
    doi: 10.1111/j.1439-0388.2009.00810.xpubmed: 19630884google scholar: lookup
  67. Kamiński S, Hering DM, Jaworski Z, Zabolewicz T, Ruść A. Assessment of genomic inbreeding in Polish Konik horses.. Pol J Vet Sci 2017 Sep 26;20(3):603-605.
    doi: 10.1515/pjvs-2017-0074pubmed: 29166287google scholar: lookup
  68. Schurink A, Shrestha M, Eriksson S, Bosse M, Bovenhuis H, Back W, Johansson AM, Ducro BJ. The Genomic Makeup of Nine Horse Populations Sampled in the Netherlands.. Genes (Basel) 2019 Jun 25;10(6).
    doi: 10.3390/genes10060480pmc: PMC6627704pubmed: 31242710google scholar: lookup
  69. Velie BD, Solé M, Fegraeus KJ, Rosengren MK, Røed KH, Ihler CF, Strand E, Lindgren G. Genomic measures of inbreeding in the Norwegian-Swedish Coldblooded Trotter and their associations with known QTL for reproduction and health traits.. Genet Sel Evol 2019 May 27;51(1):22.
    doi: 10.1186/s12711-019-0465-7pmc: PMC6537210pubmed: 31132983google scholar: lookup
  70. Solé M, Gori AS, Faux P, Bertrand A, Farnir F, Gautier M, Druet T. Age-based partitioning of individual genomic inbreeding levels in Belgian Blue cattle.. Genet Sel Evol 2017 Dec 22;49(1):92.
    doi: 10.1186/s12711-017-0370-xpmc: PMC5741860pubmed: 29273000google scholar: lookup
  71. Pemberton TJ, Absher D, Feldman MW, Myers RM, Rosenberg NA, Li JZ. Genomic patterns of homozygosity in worldwide human populations.. Am J Hum Genet 2012 Aug 10;91(2):275-92.
    doi: 10.1016/j.ajhg.2012.06.014pmc: PMC3415543pubmed: 22883143google scholar: lookup
  72. Takasuga A. PLAG1 and NCAPG-LCORL in livestock.. Anim Sci J 2016 Feb;87(2):159-67.
    doi: 10.1111/asj.12417pmc: PMC5042058pubmed: 26260584google scholar: lookup
  73. Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, Willer CJ, Jackson AU, Vedantam S, Raychaudhuri S, Ferreira T, Wood AR, Weyant RJ, Segrè AV, Speliotes EK, Wheeler E, Soranzo N, Park JH, Yang J, Gudbjartsson D, Heard-Costa NL, Randall JC, Qi L, Vernon Smith A, Mägi R, Pastinen T, Liang L, Heid IM, Luan J, Thorleifsson G, Winkler TW, Goddard ME, Sin Lo K, Palmer C, Workalemahu T, Aulchenko YS, Johansson A, Zillikens MC, Feitosa MF, Esko T, Johnson T, Ketkar S, Kraft P, Mangino M, Prokopenko I, Absher D, Albrecht E, Ernst F, Glazer NL, Hayward C, Hottenga JJ, Jacobs KB, Knowles JW, Kutalik Z, Monda KL, Polasek O, Preuss M, Rayner NW, Robertson NR, Steinthorsdottir V, Tyrer JP, Voight BF, Wiklund F, Xu J, Zhao JH, Nyholt DR, Pellikka N, Perola M, Perry JR, Surakka I, Tammesoo ML, Altmaier EL, Amin N, Aspelund T, Bhangale T, Boucher G, Chasman DI, Chen C, Coin L, Cooper MN, Dixon AL, Gibson Q, Grundberg E, Hao K, Juhani Junttila M, Kaplan LM, Kettunen J, König IR, Kwan T, Lawrence RW, Levinson DF, Lorentzon M, McKnight B, Morris AP, Müller M, Suh Ngwa J, Purcell S, Rafelt S, Salem RM, Salvi E, Sanna S, Shi J, Sovio U, Thompson JR, Turchin MC, Vandenput L, Verlaan DJ, Vitart V, White CC, Ziegler A, Almgren P, Balmforth AJ, Campbell H, Citterio L, De Grandi A, Dominiczak A, Duan J, Elliott P, Elosua R, Eriksson JG, Freimer NB, Geus EJ, Glorioso N, Haiqing S, Hartikainen AL, Havulinna AS, Hicks AA, Hui J, Igl W, Illig T, Jula A, Kajantie E, Kilpeläinen TO, Koiranen M, Kolcic I, Koskinen S, Kovacs P, Laitinen J, Liu J, Lokki ML, Marusic A, Maschio A, Meitinger T, Mulas A, Paré G, Parker AN, Peden JF, Petersmann A, Pichler I, Pietiläinen KH, Pouta A, Ridderstråle M, Rotter JI, Sambrook JG, Sanders AR, Schmidt CO, Sinisalo J, Smit JH, Stringham HM, Bragi Walters G, Widen E, Wild SH, Willemsen G, Zagato L, Zgaga L, Zitting P, Alavere H, Farrall M, McArdle WL, Nelis M, Peters MJ, Ripatti S, van Meurs JB, Aben KK, Ardlie KG, Beckmann JS, Beilby JP, Bergman RN, Bergmann S, Collins FS, Cusi D, den Heijer M, Eiriksdottir G, Gejman PV, Hall AS, Hamsten A, Huikuri HV, Iribarren C, Kähönen M, Kaprio J, Kathiresan S, Kiemeney L, Kocher T, Launer LJ, Lehtimäki T, Melander O, Mosley TH Jr, Musk AW, Nieminen MS, O'Donnell CJ, Ohlsson C, Oostra B, Palmer LJ, Raitakari O, Ridker PM, Rioux JD, Rissanen A, Rivolta C, Schunkert H, Shuldiner AR, Siscovick DS, Stumvoll M, Tönjes A, Tuomilehto J, van Ommen GJ, Viikari J, Heath AC, Martin NG, Montgomery GW, Province MA, Kayser M, Arnold AM, Atwood LD, Boerwinkle E, Chanock SJ, Deloukas P, Gieger C, Grönberg H, Hall P, Hattersley AT, Hengstenberg C, Hoffman W, Lathrop GM, Salomaa V, Schreiber S, Uda M, Waterworth D, Wright AF, Assimes TL, Barroso I, Hofman A, Mohlke KL, Boomsma DI, Caulfield MJ, Cupples LA, Erdmann J, Fox CS, Gudnason V, Gyllensten U, Harris TB, Hayes RB, Jarvelin MR, Mooser V, Munroe PB, Ouwehand WH, Penninx BW, Pramstaller PP, Quertermous T, Rudan I, Samani NJ, Spector TD, Völzke H, Watkins H, Wilson JF, Groop LC, Haritunians T, Hu FB, Kaplan RC, Metspalu A, North KE, Schlessinger D, Wareham NJ, Hunter DJ, O'Connell JR, Strachan DP, Wichmann HE, Borecki IB, van Duijn CM, Schadt EE, Thorsteinsdottir U, Peltonen L, Uitterlinden AG, Visscher PM, Chatterjee N, Loos RJ, Boehnke M, McCarthy MI, Ingelsson E, Lindgren CM, Abecasis GR, Stefansson K, Frayling TM, Hirschhorn JN. Hundreds of variants clustered in genomic loci and biological pathways affect human height.. Nature 2010 Oct 14;467(7317):832-8.
    doi: 10.1038/nature09410pmc: PMC2955183pubmed: 20881960google scholar: lookup
  74. Gudbjartsson DF, Walters GB, Thorleifsson G, Stefansson H, Halldorsson BV, Zusmanovich P, Sulem P, Thorlacius S, Gylfason A, Steinberg S, Helgadottir A, Ingason A, Steinthorsdottir V, Olafsdottir EJ, Olafsdottir GH, Jonsson T, Borch-Johnsen K, Hansen T, Andersen G, Jorgensen T, Pedersen O, Aben KK, Witjes JA, Swinkels DW, den Heijer M, Franke B, Verbeek AL, Becker DM, Yanek LR, Becker LC, Tryggvadottir L, Rafnar T, Gulcher J, Kiemeney LA, Kong A, Thorsteinsdottir U, Stefansson K. Many sequence variants affecting diversity of adult human height.. Nat Genet 2008 May;40(5):609-15.
    doi: 10.1038/ng.122pubmed: 18391951google scholar: lookup
  75. Weedon MN, Lango H, Lindgren CM, Wallace C, Evans DM, Mangino M, Freathy RM, Perry JR, Stevens S, Hall AS, Samani NJ, Shields B, Prokopenko I, Farrall M, Dominiczak A, Johnson T, Bergmann S, Beckmann JS, Vollenweider P, Waterworth DM, Mooser V, Palmer CN, Morris AD, Ouwehand WH, Zhao JH, Li S, Loos RJ, Barroso I, Deloukas P, Sandhu MS, Wheeler E, Soranzo N, Inouye M, Wareham NJ, Caulfield M, Munroe PB, Hattersley AT, McCarthy MI, Frayling TM. Genome-wide association analysis identifies 20 loci that influence adult height.. Nat Genet 2008 May;40(5):575-83.
    doi: 10.1038/ng.121pmc: PMC2681221pubmed: 18391952google scholar: lookup
  76. Flori L, Fritz S, Jaffrézic F, Boussaha M, Gut I, Heath S, Foulley JL, Gautier M. The genome response to artificial selection: a case study in dairy cattle.. PLoS One 2009 Aug 12;4(8):e6595.
    doi: 10.1371/journal.pone.0006595pmc: PMC2722727pubmed: 19672461google scholar: lookup
  77. Eberlein A, Takasuga A, Setoguchi K, Pfuhl R, Flisikowski K, Fries R, Klopp N, Fürbass R, Weikard R, Kühn C. Dissection of genetic factors modulating fetal growth in cattle indicates a substantial role of the non-SMC condensin I complex, subunit G (NCAPG) gene.. Genetics 2009 Nov;183(3):951-64.
    doi: 10.1534/genetics.109.106476pmc: PMC2778990pubmed: 19720859google scholar: lookup
  78. Loughran G, Huigsloot M, Kiely PA, Smith LM, Floyd S, Ayllon V, O'Connor R. Gene expression profiles in cells transformed by overexpression of the IGF-I receptor.. Oncogene 2005 Sep 8;24(40):6185-93.
    doi: 10.1038/sj.onc.1208772pubmed: 15940254google scholar: lookup
  79. Peeters LM, Janssens S, Coussé A, Buys N. Insect bite hypersensitivity in Belgian warmblood horses: Prevalence and risk factors. Vlaams Diergeneeskd. Tijdschr. 2014;83:240–249.
  80. Hauswirth R, Haase B, Blatter M, Brooks SA, Burger D, Drögemüller C, Gerber V, Henke D, Janda J, Jude R, Magdesian KG, Matthews JM, Poncet PA, Svansson V, Tozaki T, Wilkinson-White L, Penedo MC, Rieder S, Leeb T. Mutations in MITF and PAX3 cause "splashed white" and other white spotting phenotypes in horses.. PLoS Genet 2012;8(4):e1002653.
    doi: 10.1371/journal.pgen.1002653pmc: PMC3325211pubmed: 22511888google scholar: lookup
  81. Rieder S, Taourit S, Mariat D, Langlois B, Guérin G. Mutations in the agouti (ASIP), the extension (MC1R), and the brown (TYRP1) loci and their association to coat color phenotypes in horses (Equus caballus).. Mamm Genome 2001 Jun;12(6):450-5.
    doi: 10.1007/s003350020017pubmed: 11353392google scholar: lookup

Citations

This article has been cited 29 times.
  1. Mousavi SF, Razmkabir M, Rostamzadeh J, Seyedabadi HR, Naboulsi R, Petersen JL, Lindgren G. Genetic diversity and signatures of selection in four indigenous horse breeds of Iran. Heredity (Edinb) 2023 Aug;131(2):96-108.
    doi: 10.1038/s41437-023-00624-7pubmed: 37308718google scholar: lookup
  2. Kang Z, Shi J, Liu T, Zhang Y, Zhang Q, Liu Z, Wang J, Cheng S. Genome-wide single-nucleotide polymorphism data and mitochondrial hypervariable region 1 nucleotide sequence reveal the origin of the Akhal-Teke horse. Anim Biosci 2023 Oct;36(10):1499-1507.
    doi: 10.5713/ab.23.0044pubmed: 37170508google scholar: lookup
  3. Gao C, Du W, Tian K, Wang K, Wang C, Sun G, Kang X, Li W. Analysis of Conservation Priorities and Runs of Homozygosity Patterns for Chinese Indigenous Chicken Breeds. Animals (Basel) 2023 Feb 8;13(4).
    doi: 10.3390/ani13040599pubmed: 36830386google scholar: lookup
  4. Capomaccio S, Ablondi M, Colombi D, Sartori C, Giontella A, Cappelli K, Mancin E, Asti V, Mantovani R, Sabbioni A, Silvestrelli M. Exploring the Italian equine gene pool via high-throughput genotyping. Front Genet 2023;14:1099896.
    doi: 10.3389/fgene.2023.1099896pubmed: 36755577google scholar: lookup
  5. Cardinali I, Giontella A, Tommasi A, Silvestrelli M, Lancioni H. Unlocking Horse Y Chromosome Diversity. Genes (Basel) 2022 Dec 2;13(12).
    doi: 10.3390/genes13122272pubmed: 36553539google scholar: lookup
  6. Perdomo-González DI, Laseca N, Demyda-Peyrás S, Valera M, Cervantes I, Molina A. Fine-tuning genomic and pedigree inbreeding rates in equine population with a deep and reliable stud book: the case of the Pura Raza Española horse. J Anim Sci Biotechnol 2022 Nov 7;13(1):127.
    doi: 10.1186/s40104-022-00781-5pubmed: 36336696google scholar: lookup
  7. Lin R, Li J, Yang Y, Yang Y, Chen J, Zhao F, Xiao T. Genome-Wide Population Structure Analysis and Genetic Diversity Detection of Four Chinese Indigenous Duck Breeds from Fujian Province. Animals (Basel) 2022 Sep 5;12(17).
    doi: 10.3390/ani12172302pubmed: 36078022google scholar: lookup
  8. Momen M, Brounts SH, Binversie EE, Sample SJ, Rosa GJM, Davis BW, Muir P. Selection signature analyses and genome-wide association reveal genomic hotspot regions that reflect differences between breeds of horse with contrasting risk of degenerative suspensory ligament desmitis. G3 (Bethesda) 2022 Sep 30;12(10).
    doi: 10.1093/g3journal/jkac179pubmed: 35866615google scholar: lookup
  9. 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
  10. Li G, Tang J, Huang J, Jiang Y, Fan Y, Wang X, Ren J. Genome-Wide Estimates of Runs of Homozygosity, Heterozygosity, and Genetic Load in Two Chinese Indigenous Goat Breeds. Front Genet 2022;13:774196.
    doi: 10.3389/fgene.2022.774196pubmed: 35559012google scholar: lookup
  11. Ablondi M, Sabbioni A, Stocco G, Cipolat-Gotet C, Dadousis C, van Kaam JT, Finocchiaro R, Summer A. Genetic Diversity in the Italian Holstein Dairy Cattle Based on Pedigree and SNP Data Prior and After Genomic Selection. Front Vet Sci 2021;8:773985.
    doi: 10.3389/fvets.2021.773985pubmed: 35097040google scholar: lookup
  12. Polak G, Gurgul A, Jasielczuk I, Szmatoła T, Krupiński J, Bugno-Poniewierska M. Suitability of Pedigree Information and Genomic Methods for Analyzing Inbreeding of Polish Cold-Blooded Horses Covered by Conservation Programs. Genes (Basel) 2021 Mar 17;12(3).
    doi: 10.3390/genes12030429pubmed: 33802830google scholar: lookup
  13. Dadousis C, Cecchi F, Ablondi M, Fabbri MC, Stella A, Bozzi R. Keep Garfagnina alive. An integrated study on patterns of homozygosity, genomic inbreeding, admixture and breed traceability of the Italian Garfagnina goat breed. PLoS One 2021;16(1):e0232436.
    doi: 10.1371/journal.pone.0232436pubmed: 33449925google scholar: lookup
  14. Kvist L, Honka J, Niskanen M, Liedes O, Aspi J. Selection in the Finnhorse, a native all-around horse breed. J Anim Breed Genet 2021 Mar;138(2):188-203.
    doi: 10.1111/jbg.12524pubmed: 33226152google scholar: lookup
  15. 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
  16. Salek Ardestani S, Mohandesan E. GENOVIS: a Python package for the visualization of population genetic analyses. BMC Genomics 2026 Feb 10;27(1):190.
    doi: 10.1186/s12864-026-12598-xpubmed: 41667945google scholar: lookup
  17. 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
  18. 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
  19. 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
  20. Kassymbekova SN, Bimenova ZZ, Iskhan KZ, Sobiech P, Jastrzebski JP, Brym P, Babis W, Kalykova AS, Otebayev ZM, Kabylbekova DI, Baneh H, Romanov MN. Uncovering Genetic Diversity and Adaptive Candidate Genes in the Mugalzhar Horse Breed Using Whole-Genome Sequencing Data. Animals (Basel) 2025 Sep 11;15(18).
    doi: 10.3390/ani15182667pubmed: 41007912google scholar: lookup
  21. 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
  22. 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
  23. Qi L, Li X, Jiang J, Zhang W, Lu X, Yuan H, Zhang W. Evaluation of the genetic diversity and population structure of 5 pheasant breeds in Shanghai. Poult Sci 2025 Feb;104(2):104819.
    doi: 10.1016/j.psj.2025.104819pubmed: 39842314google scholar: lookup
  24. Guyo M, Tareke M, Tonamo A, Bediye D, Defar G. Evaluations of Morphometric Traits and Body Conformation Indices of Horse Ecotypes Reared in the Highlands of Bale Eco-Region, Ethiopia. Vet Med Sci 2024 Nov;10(6):e70114.
    doi: 10.1002/vms3.70114pubmed: 39501603google scholar: lookup
  25. Asti V, Ablondi M, Molle A, Zanotti A, Vasini M, Sabbioni A. Inertial measurement unit technology for gait detection: a comprehensive evaluation of gait traits in two Italian horse breeds. Front Vet Sci 2024;11:1459553.
    doi: 10.3389/fvets.2024.1459553pubmed: 39479203google scholar: lookup
  26. 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
  27. Ramljak J, Špehar M, Ceranac D, Držaić V, Pocrnić I, Barać D, Mioč B, Širić I, Barać Z, Ivanković A, Kasap A. Genomic Characterization of Local Croatian Sheep Breeds-Effective Population Size, Inbreeding & Signatures of Selection. Animals (Basel) 2024 Jun 29;14(13).
    doi: 10.3390/ani14131928pubmed: 38998043google scholar: lookup
  28. 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
  29. Carrillo Heredero AM, Sabbioni A, Asti V, Ablondi M, Summer A, Bertini S. Fecal microbiota characterization of an Italian local horse breed. Front Vet Sci 2024;11:1236476.
    doi: 10.3389/fvets.2024.1236476pubmed: 38425839google scholar: lookup
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