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PloS one2018; 13(6); e0199376; doi: 10.1371/journal.pone.0199376

Genetic diversity and population structure of three traditional horse breeds of Bhutan based on 29 DNA microsatellite markers.

Abstract: The genetic variability and population structure of three Bhutanese traditional horse breeds were assessed through genotyping of 74 horses (Boeta 25, Sharta 14 and Yuta 35) for 29 microsatellite DNA loci. Altogether, 282 alleles were detected across 29 polymorphic loci. The allelic diversity (NE) (Boeta 4.94; Sharta 4.65; Yuta 5.30) and gene diversities (HE) (Boeta 0.78; Sharta 0.77; Yuta 0.79) were high. None of the breeds deviated significantly from the Hardy-Weinberg equilibrium. There was no sign of significant population bottleneck for all the breeds. The inbreeding estimates (FIS) of the breeds were low (Boeta 0.023; Sharta 0.001; Yuta 0.021). Analysis of molecular variance showed 0.6% of the total genetic variation among breeds, 1.9% among individuals and 97.5% within individuals. The global FIT, FST, and FIS estimates for the population were 0.025, 0.006 and 0.019 respectively. The analysis of population structure failed to distinguish subpopulations in traditional horses and this was supported by a high genetic exchange among the breeds. Overall, the results of this study suggest a rich genetic diversity in the traditional horse despite a very low genetic differentiation among the breeds in Bhutan.
Publication Date: 2018-06-27 PubMed ID: 29949614PubMed Central: PMC6021118DOI: 10.1371/journal.pone.0199376Google Scholar: Lookup
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  • Journal Article
  • Research Support
  • Non-U.S. Gov't

Summary

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This study investigates the genetic variation and the population structure of three traditional horse breeds in Bhutan through DNA microsatellite analysis.

Objective of the Study

  • The main goal of this research was to understand the genetic diversity and population structure of three traditional Bhutanese horse breeds namely Boeta, Sharta, and Yuta.

Methodology

  • A total of 74 horses from the three breeds were genotyped using 29 microsatellite DNA markers.
  • Altogether, 282 alleles were detected across the polymorphic loci assessed.
  • A series of statistical parameters including allelic diversity, gene diversities, and inbreeding estimates were calculated for each breed.
  • Techniques such as Analysis of Molecular Variance were employed to get a deeper understanding of the genetic variations among and within the horse breeds.

Key Findings

  • The allelic diversity (NE) and gene diversities (HE) were significantly high in all breeds indicating a rich genetic variability.
  • All studied breeds aligned with the Hardy-Weinberg equilibrium, which suggests that the populations are genetically stable with no significant evolutionary change.
  • No significant population bottleneck effect was noted. This is crucial as a bottleneck effect can lead to a rapid decrease in genetic diversity.
  • Inbreeding estimates, which are indicative of genetic mixing through continuous breeding within the same line, were found to be low for all studied breeds.
  • The majority of genetic variation observed was within individuals (97.5%), with a minimal variation among breeds (0.6%) and among individuals (1.9%).
  • The low global FIT, FST, and FIS estimates for the population indicate negligible inbreeding or population subdivisions within these horse breeds.
  • There was no distinct subpopulation identification among the traditional horse breeds, implying a high level of genetic exchange between them.

Conclusion

  • The study concludes that despite minimal genetic differentiation among the different horse breeds in Bhutan, there is a rich inherent genetic diversity within the population as a whole.

Cite This Article

APA
Dorji J, Tamang S, Tshewang T, Dorji T, Dorji TY. (2018). Genetic diversity and population structure of three traditional horse breeds of Bhutan based on 29 DNA microsatellite markers. PLoS One, 13(6), e0199376. https://doi.org/10.1371/journal.pone.0199376

Publication

ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 13
Issue: 6
Pages: e0199376
PII: e0199376

Researcher Affiliations

Dorji, Jigme
  • National Biodiversity Centre, Serbithang, Ministry of Agriculture and Forests, Thimphu, Bhutan.
Tamang, Sonam
  • National Biodiversity Centre, Serbithang, Ministry of Agriculture and Forests, Thimphu, Bhutan.
Tshewang, Tshewang
  • National Biodiversity Centre, Serbithang, Ministry of Agriculture and Forests, Thimphu, Bhutan.
Dorji, Tshering
  • National Biodiversity Centre, Serbithang, Ministry of Agriculture and Forests, Thimphu, Bhutan.
Dorji, Tashi Yangzome
  • National Biodiversity Centre, Serbithang, Ministry of Agriculture and Forests, Thimphu, Bhutan.

MeSH Terms

  • Alleles
  • Animals
  • Bhutan
  • Breeding
  • Genetic Loci
  • Genetic Variation
  • Genetics, Population
  • Genotype
  • Horses
  • Microsatellite Repeats

Conflict of Interest Statement

The authors have declared that no competing interests exist.

References

This article includes 37 references
  1. Eden A. Political missions to Bootan comprising the report of the Hon’ble Ashley Eden-1864, Capt R.B. Pemberton 1837;1838 with Dr. W. Griffiths Journal and account by Baboo Kishan Kant Bose. Bengal Secretariat Office, Calcutta: 1865;68–70.
  2. Sarkar R, Ray I. Trend of Bhutan’s trade during 1907–26: Export. Journal of Bhutan Studies 2012;26(1608-411X):100–22.
  3. Department of Livestock (DoL). Livestock statistics 2016. Ministry of Agriculture and Forests, Thimphu, Bhutan, 2016.
  4. Gurung PB, Gurung RB, Tshering G, Dorji T, Rinzin R. Equine genotype survey in Bhutan: Horse Breeding Program, Department of Livestock, Renewable Natural Resources Research Centre, Jakar, Ministry of Agriculture, Bumthang, Bhutan. 1999.
  5. Luethi NB. Bovine and equine in Bhutan: a review of information available on Bhutan, with special reference to Brown Swiss cattle and Haflinger horse. Renewable Natural Resources Research Centre, Jakar, Bhutan. Research, Extension, Irrigation Division, Bhutan; 1999.
  6. Dorji J, Dorji T, Tshewang, Tamang S, Dorji TY. Morphological diversity of principal local horse breeds of Bhutan. Bhutan Journal of Animal Sciences 2017;1(1):22–6.
  7. Glowatzki-Mullis ML, Muntwyler J, Pfister W, Marti E, Rieder S, Poncet PA, Gaillard C. Genetic diversity among horse populations with a special focus on the Franches-Montagnes breed.. Anim Genet 2006 Feb;37(1):33-9.
  8. Hughes AR, Inouye BD, Johnson MT, Underwood N, Vellend M. Ecological consequences of genetic diversity.. Ecol Lett 2008 Jun;11(6):609-23.
  9. Kakoi H, Hirota K, Gawahara H, Kurosawa M, Kuwajima M. Genetic diagnosis of sex chromosome aberrations in horses based on parentage test by microsatellite DNA and analysis of X- and Y-linked markers.. Equine Vet J 2005 Mar;37(2):143-7.
    doi: 10.2746/0425164054223787pubmed: 15779627google scholar: lookup
  10. International Society for Animal Genetics (ISAG). Available from: http://www.isag.us/.
  11. Kakoi H, Nagata S-i, Kurosawa M. DNA typing with 17 microsatellites for parentage verification of racehorses in Japan. Animal Science Journal 2001;72(6):453–60.
    doi: 10.2508/chikusan.72.453google scholar: lookup
  12. Tozaki T, Kakoi H, Mashima S, Hirota K, Hasegawa T, Ishida N, Miura N, Choi-Miura NH, Tomita M. Population study and validation of paternity testing for Thoroughbred horses by 15 microsatellite loci.. J Vet Med Sci 2001 Nov;63(11):1191-7.
    pubmed: 11767052doi: 10.1292/jvms.63.1191google scholar: lookup
  13. Wang J. COANCESTRY: a program for simulating, estimating and analysing relatedness and inbreeding coefficients.. Mol Ecol Resour 2011 Jan;11(1):141-5.
  14. Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research--an update.. Bioinformatics 2012 Oct 1;28(19):2537-9.
  15. Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research--an update.. Bioinformatics 2012 Oct 1;28(19):2537-9.
  16. Kalinowski ST, Taper ML, Marshall TC. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment.. Mol Ecol 2007 Mar;16(5):1099-106.
  17. Weir BS, Cockerham CC. ESTIMATING F-STATISTICS FOR THE ANALYSIS OF POPULATION STRUCTURE.. Evolution 1984 Nov;38(6):1358-1370.
  18. Goudet J. FSTAT (Version 1.2): A computer program to calculate F-statistics. Journal of Heredity 1995;86(6):485–6.
  19. Raymond M, Rousset F. GENEPOP (Version 1.2): Population genetics software for exact tests and ecumenicism. Journal of Heredity 1995;86(3):248–9.
  20. Rousset F. genepop'007: a complete re-implementation of the genepop software for Windows and Linux.. Mol Ecol Resour 2008 Jan;8(1):103-6.
  21. Rice WR. ANALYZING TABLES OF STATISTICAL TESTS.. Evolution 1989 Jan;43(1):223-225.
  22. Takezaki N, Nei M, Tamura K. POPTREEW: web version of POPTREE for constructing population trees from allele frequency data and computing some other quantities.. Mol Biol Evol 2014 Jun;31(6):1622-4.
    doi: 10.1093/molbev/msu093pubmed: 24603277google scholar: lookup
  23. Piry S, Luikart G, Cornuet J-M. Computer note. BOTTLENECK: a computer program for detecting recent reductions in the effective size using allele frequency data. Journal of Heredity 1999;90(4):502–3.
    doi: 10.1093/jhered/90.4.502google scholar: lookup
  24. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data.. Genetics 2000 Jun;155(2):945-59.
    pmc: PMC1461096pubmed: 10835412doi: 10.1093/genetics/155.2.945google scholar: lookup
  25. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study.. Mol Ecol 2005 Jul;14(8):2611-20.
  26. Francis RM. pophelper: an R package and web app to analyse and visualize population structure.. Mol Ecol Resour 2017 Jan;17(1):27-32.
    doi: 10.1111/1755-0998.12509pubmed: 26850166google scholar: lookup
  27. Cortés O, Dunner S, Gama LT, Martínez AM, Delgado JV, Ginja C, Jiménez LM, Jordana J, Luis C, Oom MM, Sponenberg DP, Zaragoza P, Vega-Pla JL. The legacy of Columbus in American horse populations assessed by microsatellite markers.. J Anim Breed Genet 2017 Aug;134(4):340-350.
    doi: 10.1111/jbg.12255pubmed: 28194814google scholar: lookup
  28. Behl R, Behl J, Gupta N, Gupta SC. Genetic relationships of five Indian horse breeds using microsatellite markers.. Animal 2007 May;1(4):483-8.
    doi: 10.1017/s1751731107694178pubmed: 22444405google scholar: lookup
  29. Seo JH, Park KD, Lee HK, Kong HS. Genetic diversity of Halla horses using microsatellite markers.. J Anim Sci Technol 2016;58:40.
    doi: 10.1186/s40781-016-0120-6pmc: PMC5114825pubmed: 27891245google scholar: lookup
  30. Senju N, Tozaki T, Kakoi H, Shinjo A, Matsuyama R, Almunia J, Takasu M. Genetic diversity of the Yonaguni horse based on polymorphisms in microsatellites and mitochondrial DNA.. J Vet Med Sci 2017 Feb 28;79(2):425-431.
    doi: 10.1292/jvms.16-0040pmc: PMC5326952pubmed: 28049866google scholar: lookup
  31. Takasu M, Hiramatsu N, Tozaki T, Kakoi H, Nakagawa T, Hasegawa T, Huricha, Maeda M, Murase T, Mukoyama H. Genetic characterization of the endangered Kiso horse using 31 microsatellite DNAs.. J Vet Med Sci 2012 Feb;74(2):161-6.
    doi: 10.1292/jvms.11-0025pubmed: 21963881google scholar: lookup
  32. Senju N, Tozaki T, Kakoi H, Almunia J, Maeda M, Matsuyama R, Takasu M. Genetic characterization of the Miyako horse based on polymorphisms of microsatellites and mitochondrial DNA.. J Vet Med Sci 2017 Jan 24;79(1):218-223.
    doi: 10.1292/jvms.16-0111pmc: PMC5289264pubmed: 27795462google scholar: lookup
  33. Department of Livestock (DoL). Livestock statistics 2007. Ministry of Agriculture and Forests, Thimphu, Bhutan, 2007.
  34. Pires DAF, Coelho EGA, Melo JB, Oliveira DAA, Ribeiro MN, Gus Cothran E. Genetic diversity and population structure in remnant subpopulations of Nordestino horse breed. Archivos de Zootecnia 2014;63:349–58.
  35. 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.
  36. Ling YH, Ma YH, Guan WJ, Cheng YJ, Wang YP, Han JL, Mang L, Zhao QJ, He XH, Pu YB, Fu BL. Evaluation of the genetic diversity and population structure of Chinese indigenous horse breeds using 27 microsatellite markers.. Anim Genet 2011 Feb;42(1):56-65.
  37. Cañon J, Checa ML, Carleos C, Vega-Pla JL, Vallejo M, Dunner S. The genetic structure of Spanish Celtic horse breeds inferred from microsatellite data.. Anim Genet 2000 Feb;31(1):39-48.

Citations

This article has been cited 9 times.
  1. Tenzin J, Chankitisakul V, Boonkum W. Current Status and Conservation Management of Farm Animal Genetic Resources in Bhutan. Vet Sci 2023 Apr 6;10(4).
    doi: 10.3390/vetsci10040281pubmed: 37104436google scholar: lookup
  2. An J, Tseveen K, Oyungerel B, Kong HS. Analysis of genetic diversity and structure of Mongolian horse using microsatellite markers. J Anim Sci Technol 2022 Nov;64(6):1226-1236.
    doi: 10.5187/jast.2022.e82pubmed: 36812018google scholar: lookup
  3. 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
  4. Luttman AM, Komine M, Thaiwong T, Carpenter T, Ewart SL, Kiupel M, Langohr IM, Venta PJ. Development of a 17-Plex of Penta- and Tetra-Nucleotide Microsatellites for DNA Profiling and Paternity Testing in Horses. Front Vet Sci 2022;9:861623.
    doi: 10.3389/fvets.2022.861623pubmed: 35464354google scholar: lookup
  5. Huang CJ, Chu FH, Huang YS, Tu YC, Hung YM, Tseng YH, Pu CE, Hsu CT, Chao CH, Chou YS, Liu SC, You YT, Hsu SY, Hsieh HC, Wang CT, Chen CT. SSR individual identification system construction and population genetics analysis for Chamaecyparis formosensis. Sci Rep 2022 Mar 8;12(1):4126.
    doi: 10.1038/s41598-022-07870-5pubmed: 35260700google scholar: lookup
  6. Yun J, Oyungerel B, Kong HS. Genetic diversity and population structure of Mongolian regional horses with 14 microsatellite markers. Anim Biosci 2022 Aug;35(8):1121-1128.
    doi: 10.5713/ab.21.0497pubmed: 35240022google scholar: lookup
  7. Hou L, Sulayman A, Zeng Y, Zhou L, Aimaier A, Kader A, Shi L. Analysis of Genetic Diversity and Race Genetic Structure of Major Horse Breeds in Xinjiang, China. Animals (Basel) 2025 Sep 14;15(18).
    doi: 10.3390/ani15182690pubmed: 41007935google scholar: lookup
  8. Duderstadt S, Distl O. Influence of Sires on Population Substructure in Dülmen Wild Horses. Animals (Basel) 2024 Oct 9;14(19).
    doi: 10.3390/ani14192904pubmed: 39409853google scholar: lookup
  9. Orazymbetova Z, Ualiyeva D, Dossybayev K, Torekhanov A, Sydykov D, Mussayeva A, Baktybayev G. Genetic Diversity of Kazakhstani Equus caballus (Linnaeus, 1758) Horse Breeds Inferred from Microsatellite Markers. Vet Sci 2023 Sep 30;10(10).
    doi: 10.3390/vetsci10100598pubmed: 37888550google scholar: lookup