FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Vet Sci / Genetic Diversity of Kazakhstani Equus caballus (Linnaeus, 1...
Veterinary sciences2023; 10(10); 598; doi: 10.3390/vetsci10100598

Genetic Diversity of Kazakhstani Equus caballus (Linnaeus, 1758) Horse Breeds Inferred from Microsatellite Markers.

Abstract: Understanding the genetic diversity and structure of domesticated horse () populations is critical for long-term herd management and breeding programs. This study examines 435 horses from Kazakhstan, covering seven groups in three geographic areas using 11 STR markers. Identified are 136 alleles, with the mean number of alleles per locus ranging from 9 to 19. VHL20 is the most variable locus across groups, while loci HTG4, AHT4, AHT5, HTG7, and HMS3 are variable in most populations. The locus AHT5 in the Emba population shows the highest frequency of rare alleles, while the lowest frequency, 0.005, is observed in the Kulandy population. All loci were highly informative for the Kazakhstani populations of , with PIC values higher than 0.5. Pairwise variations in Wright's F distances show that the examined varieties have little genetic differentiation (0.05%), indicating a high degree of admixture and a continuing lineage sorting process. Phylogenetic and population structure analyses reveal three major clusters of Kazakh horses, representing (I) the Uralsk population of the Kushum breed and the monophyly of two groups: (II) the Kozhamberdy population of the Mugalzhar breed, and (III) the Mugalzhar-Kushum breed populations. Kazakhstani horse populations, while being regionally isolated, were recently in contact with each other.
Get Full Text
Publication Date: 2023-09-30 PubMed ID: 37888550PubMed Central: PMC10611244DOI: 10.3390/vetsci10100598Google 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

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 investigates the genetic diversity of domesticated horse breeds in Kazakhstan using microsatellite markers. The study’s findings identify variations within and among breed groups, highlighting points of interest for long-term management and breeding practices.

Research Methodology

  • The study involved 435 horses from three different geographic areas of Kazakhstan. They are categorized into seven groups.
  • To investigate the genetic diversity, 11 Short Tandem Repeat (STR) markers were used. STR markers are also known as microsatellites and are used in genetics to measure the variation at the DNA level.

Research Findings

  • The research identified a total of 136 different alleles. An allele is a variation of a gene; in this case, each represents a different genetic variation within the horse population. The average number of alleles per locus, a fixed spot on a chromosome where a specific gene or marker is located, ranged from 9 to 19.
  • VHL20 was identified as the most variable locus across the groups, suggesting it may be a valuable marker for distinguishing between these horse populations.
  • A number of loci, including HTG4, AHT4, AHT5, HTG7, and HMS3, showed variability in most populations. This suggests there is significant genetic diversity within these horse populations.
  • The locus AHT5 in the Emba population had the highest frequency of rare alleles, whereas the Kulandy population had the lowest.
  • All loci were found to be highly informative for these Kazakhstani horse populations. This means the markers used in this study can be effectively used in future research for these specific horse populations.

Genetic Differentiation and Population Structure

  • The researchers used Wright’s F-statistics to measure genetic differentiation between the horse populations. The statistic F measures the proportion of genetic variance in a subpopulation (a breed or a spatially separated group) relative to the total genetic variance.
  • The researchers found only 0.05% differentiation between the groups, suggesting a high degree of genetic admixture and ongoing lineage sorting. This means there is ongoing mixing of genetic material among the groups despite their distinct geographic locations.
  • The study found three major clusters of Kazakh horses: the Uralsk population of the Kushum breed, the Kozhamberdy population of the Mugalzhar breed, and the Mugalzhar-Kushum breed populations. However, these populations were in recent contact with each other, which further supports the findings of genetic mixing.

Cite This Article

APA
Orazymbetova Z, Ualiyeva D, Dossybayev K, Torekhanov A, Sydykov D, Mussayeva A, Baktybayev G. (2023). Genetic Diversity of Kazakhstani Equus caballus (Linnaeus, 1758) Horse Breeds Inferred from Microsatellite Markers. Vet Sci, 10(10), 598. https://doi.org/10.3390/vetsci10100598

Publication

Veterinary sciences
ISSN: 2306-7381
NlmUniqueID: 101680127
Country: Switzerland
Language: English
Volume: 10
Issue: 10
PII: 598

Researcher Affiliations

Orazymbetova, Zarina
  • Kazakh Research Institute of Livestock and Fodder Production, Almaty 050035, Kazakhstan.
  • Institute of Genetics and Physiology, Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan, Almaty 050060, Kazakhstan.
Ualiyeva, Daniya
  • Institute of Genetics and Physiology, Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan, Almaty 050060, Kazakhstan.
  • Institute of Zoology, Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan, Almaty 050060, Kazakhstan.
  • Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China.
Dossybayev, Kairat
  • Kazakh Research Institute of Livestock and Fodder Production, Almaty 050035, Kazakhstan.
  • Institute of Genetics and Physiology, Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan, Almaty 050060, Kazakhstan.
Torekhanov, Aibyn
  • Kazakh Research Institute of Livestock and Fodder Production, Almaty 050035, Kazakhstan.
Sydykov, Dauren
  • Kazakh Research Institute of Livestock and Fodder Production, Almaty 050035, Kazakhstan.
Mussayeva, Aizhan
  • Institute of Genetics and Physiology, Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan, Almaty 050060, Kazakhstan.
Baktybayev, Gabiden
  • Kazakh Research Institute of Livestock and Fodder Production, Almaty 050035, Kazakhstan.

Grant Funding

  • BR10764999 / Ministry of Agriculture of the Republic of Kazakhstan

Conflict of Interest Statement

The authors declare no conflict of interest.

References

This article includes 65 references
  1. Lister A.M., Kadwell M., Kaagan L.M., Jordan W.C., Richards M.B., Stanley H.F.. Ancient and modern DNA in a study of horse domestication.. Anc. Biomol. 1998;2:267–280.
  2. Vilà C., Leonard J.A., Götherström A., Marklund S., Sandberg K., Liden K., Wayne R.K., Ellegren H.. Widespread Origins of Domestic Horse Lineages.. Science 2001;291:474–477.
    doi: 10.1126/science.291.5503.474pubmed: 11161199google scholar: lookup
  3. Achilli A., Olivieri A., Soares P., Lancioni H., Hooshiar Kashani B., Perego U.A., Nergadze S.G., Carossa V., Santagostino M., Capomaccio S.. Mitochondrial genomes from modern horses reveal the major haplogroups that underwent domestication.. Proc. Natl. Acad. Sci. USA 2012;109:2449–2454.
    doi: 10.1073/pnas.1111637109pmc: PMC3289334pubmed: 22308342google scholar: lookup
  4. Warmuth V., Eriksson A., Bower M.A., Barker G., Barrett E., Hanks B.K., Li S., Lomitashvili D., Ochir-Goryaeva M., Sizonov G.V.. Reconstructing the origin and spread of horse domestication in the Eurasian steppe.. Proc. Natl. Acad. Sci. USA 2012;109:8202–8206.
    doi: 10.1073/pnas.1111122109pmc: PMC3361400pubmed: 22566639google scholar: lookup
  5. . The Second Report on the State of the World’s Animal Genetic Resources for Food and Agriculture in Brief.. .
  6. . Horse Milk Has More Health Benefits Than Cow’s Milk—Researchers.. .
  7. . Horse Breeding in Kazakhstan: Development Prospects and Today’s Realities.. .
  8. Dmitriez N., Ernst L.. KUSHUM (Kushumskaya), 330–331.. Animal Genetics Resources of the USSR 1989.
  9. . Kushum Horse.. .
  10. Rzabayev S.S.. Mugalzhar Horse Breed.. 2007;p. 154.
  11. Satybaldin A.A.. Current state of horse breeding and horse sports in Kazakhstan; Proceedings of the First International Conference; Kostanay, Kazakhstan.. 2002.
  12. Moridi M., Masoudi A.A., Vaez Torshizi R., Hill E.W.. Mitochondrial DNA D-loop sequence variation in maternal lineages of Iranian native horses.. Anim. Genet. 2013;44:209–213.
    doi: 10.1111/j.1365-2052.2012.02389.xpubmed: 22732008google scholar: lookup
  13. Bowling A.T., Del Valle A., Bowling M.. A pedigree-based study of mitochondrial D-loop DNA sequence variation among Arabian horses.. Anim. Genet. 2000;31:1–7.
    doi: 10.1046/j.1365-2052.2000.00558.xpubmed: 10690354google scholar: lookup
  14. Csizmár N., Mihók S., Jávor A., Kusza S.. Genetic analysis of the Hungarian draft horse population using partial mitochondrial DNA D-loop sequencing.. PeerJ 2018;6:e4198.
    doi: 10.7717/peerj.4198pmc: PMC5797449pubmed: 29404201google scholar: lookup
  15. Effa K., Rosenbom S., Han J., Dessie T., Beja-Pereira A.. Genetic Diversities and Historical Dynamics of Native Ethiopian Horse Populations (Equus caballus) Inferred from Mitochondrial DNA Polymorphisms.. Genes 2021;12:155.
    doi: 10.3390/genes12020155pmc: PMC7912211pubmed: 33503948google scholar: lookup
  16. Almarzook S., Reissmann M., Brockmann G.A.. Diversity of mitochondrial DNA in three Arabian horse strains.. J. Appl. Genet. 2017;58:273–276.
    doi: 10.1007/s13353-016-0384-zpubmed: 27966062google scholar: lookup
  17. Hong J.H., Oh C.H., Kim S., Kang I.U., Shin D.H.. Genetic analysis of mitochondrial DNA from ancient Equus caballus bones found at archaeological site of Joseon Dynasty period capital area.. Anim. Biosci. 2022;35:1141–1150.
    doi: 10.5713/ab.21.0500pmc: PMC9262724pubmed: 35240033google scholar: lookup
  18. McGahern A.M., Edwards C.J., Bower M.A., Heffernan A., Park S.D.E., Brophy P.O., Hill E.W.. Mitochondrial DNA sequence diversity in extant Irish horse populations and in ancient horses.. Anim. Genet. 2006;37:498–502.
    doi: 10.1111/j.1365-2052.2006.01506.xpubmed: 16978181google scholar: lookup
  19. Yatkın S., Özdil F., Özkan Ünal E., Genç S., Kaplan S., Gürcan E.K., Arat S., Soysal M.İ.. Genetic Characterization of Native Donkey (Equus asinus) Populations of Turkey Using Microsatellite Markers.. Animals 2020;10:1093.
    doi: 10.3390/ani10061093pmc: PMC7341297pubmed: 32599857google scholar: lookup
  20. Kim S.M., Yun S.W., Cho G.J.. Assessment of genetic diversity using microsatellite markers to compare donkeys (Equus asinus) with horses (Equus caballus). Anim. Biosci. 2021;34:1460–1465.
    doi: 10.5713/ab.20.0860pmc: PMC8495341pubmed: 33902168google scholar: lookup
  21. Tozaki T., Kakoi H., Mashima S., Hirota K.-I., Hasegawa T., Ishida N., Miura N., Choi-Miura N.-H., Tomita M.. Population study and validation of paternity testing for Thoroughbred Horses by 15 microsatellite loci.. J. Vet. Med. Sci. 2001;63:1191–1197.
    doi: 10.1292/jvms.63.1191pubmed: 11767052google scholar: lookup
  22. Grilz-Seger G., Druml T., Neuditschko M., Dobretsberger M., Horna M., Brem G.. High-resolution population structure and runs of homozygosity reveal the genetic architecture of complex traits in the Lipizzan horse.. BMC Genom. 2019;20:174.
    doi: 10.1186/s12864-019-5564-xpmc: PMC6402180pubmed: 30836959google scholar: lookup
  23. Zhang C., Ni P., Ahmad H.I., Gemingguli M., Baizilaitibei A., Gulibaheti D., Fang Y., Wang H., Asif A.R., Xiao C.. Detecting the Population Structure and Scanning for Signatures of Selection in Horses (Equus caballus) From Whole-Genome Sequencing Data.. Evol. Bioinform. 2018;14:1176934318775106.
    doi: 10.1177/1176934318775106pmc: PMC5990873pubmed: 29899660google scholar: lookup
  24. Ma H., Wang S., Zeng G., Guo J., Guo M., Dong X., Hua G., Liu Y., Wang M., Ling Y.. The Origin of a Coastal Indigenous Horse Breed in China Revealed by Genome-Wide SNP Data.. Genes 2019;10:241.
    doi: 10.3390/genes10030241pmc: PMC6471023pubmed: 30901931google scholar: lookup
  25. Kargayeva M.T., Baimukhanov D.A., Nurbayev S.D., Baimukhanov A.D., Alikhanov O., Yusupbayev Z.. Identification of kazakh horses by microsatellite DNA using modern analytical methods.. Bull. Natl. Acad. Sci. Repub. Kazakhstan 2020;4:55–61.
    doi: 10.32014/2020.2518-1467.104google scholar: lookup
  26. Gemingguli M., Iskhan K.R., Li Y., Qi W., Wunirifu W., Ding L.Y., Wumaierjiang A.. Genetic diversity and population structure of Kazakh horses (Equus caballus) inferred from mtDNA sequences.. Genet. Mol. Res. 2016;15:15048618.
    doi: 10.4238/gmr.15048618pubmed: 27808359google scholar: lookup
  27. Seleuova L.A., Naimanov D.K., Jarowski Z., Aubakirov M.Z., Mustafin B.M., Safronova O.S., Baktybayev G.T., Turabaev A.T., Domatski V.N.. Population genetic characteristic of horses of Mugalzhar breed by STR-markers.. Biomed. Res. 2018;29:3508–3511.
  28. Nguyen T.B., Paul R.C., Okuda Y., Le T.N.A., Pham P.T.K., Kaissar K.J., Kazhmurat A., Bibigul S., Bakhtin M., Kazymbet P.. Genetic characterization of Kushum horses in Kazakhstan based on haplotypes of mtDNA and Y chromosome, and genes associated with important traits of the horses.. J. Equine. Sci. 2020;31:35–43.
    doi: 10.1294/jes.31.35pmc: PMC7538259pubmed: 33061782google scholar: lookup
  29. Kalashnikov V., Koveshnikov V., Sergienko S.. Problems and Prospects of Horse Breeding in Russia at the Turn of the Century.. 2000;pp. 5–7.
  30. Canon J., Checa M.L., Carleos C., Vega-Pla J.L., Vallejo M., Dunner S.. The genetic structure of Spanish Celtic horse breeds inferred from microsatellite data.. Anim. Genet. 2000;31:39–48.
    doi: 10.1046/j.1365-2052.2000.00591.xpubmed: 10690360google scholar: lookup
  31. Bjornstad G., Gunby E., Roed K.H.. Genetic structure of Norwegian horse breeds.. J. Anim. Breed. Genet. 2000;117:307–317.
    doi: 10.1046/j.1439-0388.2000.00264.xgoogle scholar: lookup
  32. Aberle K.S., Hamann H., Drogemuller C., Distl O.. Genetic diversity in German drought horse breeds compared with a group of primitive, riding and wild horses by means of microsatellite DNA markers.. Anim. Genet. 2004;35:270–277.
    doi: 10.1111/j.1365-2052.2004.01166.xpubmed: 15265065google scholar: lookup
  33. Zabek T., Fornal A.. Evaluation of the 17-plex STR kit for parentage testing of Polish coldblood and Hucul horses.. Ann. Anim. Sci. 2009;9:363–372.
  34. Dimsoski P.. Development of a 17-plex microsatellite polymerase chain reaction kit for genotyping horses.. Croat. Med. J. 2003;44:332–335.
    pubmed: 12808728
  35. Marshal T.C., Slate J., Kruuk L.E.B., Pemberton J.M.. Statistical confidence for likelihood-based paternity inference in natural populations.. Mol. Ecol. 1998;7:639–655.
    doi: 10.1046/j.1365-294x.1998.00374.xpubmed: 9633105google scholar: lookup
  36. Kalinowski S.T., Taper M.L., Marshall T.C.. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment.. Mol. Ecol. 2003;16:1099–1106.
    doi: 10.1111/j.1365-294X.2007.03089.xpubmed: 17305863google scholar: lookup
  37. Peakall R., Smouse P.E.. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research.. Mol. Ecol. Notes. 2006;6:288–295.
    doi: 10.1111/j.1471-8286.2005.01155.xpmc: PMC3463245pubmed: 22820204google scholar: lookup
  38. Dieringer D., Schlötterer C.. Microsatellite analyser (MSA): A platform independent analysis tool for large microsatellite data sets.. Mol. Ecol. Notes. 2003;3:167–169.
    doi: 10.1046/j.1471-8286.2003.00351.xgoogle scholar: lookup
  39. Saitou N., Nei M.. The neighbor-joining method: A new method for reconstructing phylogenetic trees.. Mol. Biol. Evol. 1987;4:406–425.
    pubmed: 3447015
  40. Kumar S., Stecher G., Tamura K.. MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.. Mol. Biol. Evol. 2016;33:1870–1874.
    doi: 10.1093/molbev/msw054pmc: PMC8210823pubmed: 27004904google scholar: lookup
  41. Belkhir K.. GENETIX 4.05, Logiciel Sous Windows TM Pour la Génétique Des Populations.. 2004.
  42. Pritchard J.K., Stephens M., Donnelly P.. Inference of population structure using multilocus genotype data.. Genetics 2000;155:945–959.
    doi: 10.1093/genetics/155.2.945pmc: PMC1461096pubmed: 10835412google scholar: lookup
  43. Evanno G., Regnaut S., Goudet J.. Detecting the numbers of clusters of individuals using the software structure: A simulation study.. Mol. Ecol. 2005;14:2611–2620.
    doi: 10.1111/j.1365-294X.2005.02553.xpubmed: 15969739google scholar: lookup
  44. Earl D.A., von Holdt B.M.. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method.. Conserv. Genet. Resour. 2012;4:359–361.
    doi: 10.1007/s12686-011-9548-7google scholar: lookup
  45. Jakobsson M., Rosenberg Noah A.. CLUMPP: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure.. Bioinformatics 2007;23:1801–1806.
    doi: 10.1093/bioinformatics/btm233pubmed: 17485429google scholar: lookup
  46. Rosenberg N.A.. 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
  47. Yordanov G., Mehandjyiski I., Palova N., Atsenova N., Boyko N., Radoslavov G., Hristov P.. Genetic diversity and structure of the main Danubian horse paternal genealogical lineages based on microsatellite genotyping.. Vet. Sci. 2022;9:333.
    doi: 10.3390/vetsci9070333pmc: PMC9322366pubmed: 35878350google scholar: lookup
  48. Jung J.S., Seong J., Lee G.H., Kim Y., An J.H., Yun J.H., Kong H.S.. Genetic diversity and relationship of Halla horse based on polymorphism in microsatellites.. J. Anim. Reprod. Biotechnol. 2021;36:76–81.
    doi: 10.12750/JARB.36.2.76google scholar: lookup
  49. Wright S.. The genetical structure of populations.. Ann. Eugen. 1951;15:323–354.
    doi: 10.1111/j.1469-1809.1949.tb02451.xpubmed: 24540312google scholar: lookup
  50. Benhamadi M.E.A., Berber N., Benyarou M., Ameur A.A., Haddam H.Y., Piro M., Gaouar S.B.S.. Molecular Characterization of Eight Horse Breeds in Algeria Using Microsatellite Markers.. Biodiversitas 2020;21:d210923.
    doi: 10.13057/biodiv/d210923google scholar: lookup
  51. Dorji J., Tamang S., Tshewang T., Dorji T., Dorji T.Y.. Genetic diversity and population structure of three traditional horse breeds of Bhutan based on 29 DNA microsatellite markers.. PLoS ONE 2018;13:e199376.
    doi: 10.1371/journal.pone.0199376pmc: PMC6021118pubmed: 29949614google scholar: lookup
  52. Yüceer B., Erdoğan M., Yaralı C., Özarslan B., Özbeyaz C.. Genetic diversity between Rahvan (pacing) horses in Turkey.. Ankara Üniv. Vet. Fak. Derg. 2016;63:201–210.
  53. Ling Y.H., Ma Y.H., Guan W.J., Cheng Y.J., Wang Y.P., Han J.L., Mang L., Zhao Q.J., He X.H., Pu Y.B.. Evaluation of the genetic diversity and population structure of Chinese indigenous horse breeds using 27 microsatellite markers.. Anim. Genet. 2011;42:56–65.
    doi: 10.1111/j.1365-2052.2010.02067.xpubmed: 20477800google scholar: lookup
  54. Falush D., Stephens M., Pritchard J.K.. Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies.. Genetics 2003;164:1567–1587.
    doi: 10.1093/genetics/164.4.1567pmc: PMC1462648pubmed: 12930761google scholar: lookup
  55. Behl R., Behl J., Gupta N., Gupta S.C.. Genetic relationships of five Indian horse breeds using a microsatellite markers.. Animal 2007;1:483–488.
    doi: 10.1017/S1751731107694178pubmed: 22444405google scholar: lookup
  56. Solis A., Jugo B.M., Meriaux J.C., Iriondo M., Mazon L.I., Aguirre A.I., Vicario A., Estomba A.. Genetic diversity within and among four south European Native Horse breeds based on microsatellite DNA analysis: Implications for conservation.. J. Hered. 2005;96:670–678.
    doi: 10.1093/jhered/esi123pubmed: 16267169google scholar: lookup
  57. Voronkova V.N., Nikolaeva E.A., Piskunov A.K., Babayan O.V., Takasu M., Tozaki T., Svishcheva G.R., Stolpovsky Y.A.. Assessment of Genetic Diversity and Structure of Russian and Mongolian Autochthonous Horse Breeds Using Nuclear and Mitochondrial DNA Markers.. Russ. J. Genet. 2022;58:927–943.
    doi: 10.1134/S1022795422080105google scholar: lookup
  58. Blokhina N.V., Khrabrova L.A., Gavrilicheva I.S.. Application of modern technologies in identifying distinctive features in the subpopulation of Novoaltaiskaya hoses.. IOP Conf. Ser. Earth Environ. Sci. 2021;624:012019.
    doi: 10.1088/1755-1315/624/1/012019google scholar: lookup
  59. Zaitcev A.M., Gavrilicheva I.S., Blohina N.V., Khrabrova L.A., Kokorina N.B.. Assessment of the population structure of horses of the Priobskaya breed based on modern technologies.. IOP Conf. Ser. Earth Environ. Sci. 2021;624:012032.
    doi: 10.1088/1755-1315/624/1/012032google scholar: lookup
  60. Iwańczyk E., Juras R., Cholewiński G., Cothran E.G.. Genetic structure and phylogenetic relationships of the Polish Heavy horse.. J. Appl Genet. 2006;47:353–359.
    doi: 10.1007/BF03194645pubmed: 17132900google scholar: lookup
  61. Juras R., Cothran E.G., Klimas R.. Genetic Analysis of Three Lithuanian Native Horse Breeds.. Acta. Agric. Scand. 2003;53–54:180–185.
    doi: 10.1080/09064700310012971google scholar: lookup
  62. Funk S.M., Guedaoura S., Juras R., Raziq A., Landolsi F., Luís C., Martínez A.M., Mayaki A.M., Mujica F., Oom M.D.M.. Major inconsistencies of inferred population genetic structure estimated in a large set of domestic horse breeds using microsatellites.. Ecol. Evol. 2020;10:4261–4279.
    doi: 10.1002/ece3.6195pmc: PMC7246218pubmed: 32489595google scholar: lookup
  63. Wright S.. Variability Within and among Natural Populations.. 1978;p. 580.
  64. Petersen J.L., Mickelson J.R., Cothran E.G., Andersson L.S., Axelsson J., Bailey E., Bannasch D., Binns M.M., Borges A.S., Brama P.. Genetic diversity in the modern horse illustrated from genome-wide SNP data.. PLoS ONE 2013;8:e54997.
    doi: 10.1371/journal.pone.0054997pmc: PMC3559798pubmed: 23383025google scholar: lookup
  65. Marchiori C.M., Pereira G.L., Maiorano A.M., Rogatto G.M., Assoni A.D., Silva J.A.V., II, Chardulo L.A.L., Curi R.A.. Linkage disequilibrium and population structure characterization in the cutting and racing lines of Quarter Horses bred in Brazil.. Livest. Sci. 2019;219:45–51.
    doi: 10.1016/j.livsci.2018.11.013google scholar: lookup

Citations

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