Asian-Australasian Journal of Animal Sciences

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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Assessment of genetic diversity and phylogenetic relationship of Limousin herds in Hungary using microsatellite markers

  • Szucs, Marton (Association of Hungarian Limousin and Blonde d'Aquitaine Breeders) ;
  • Szabo, Ferenc (Department of Animal Sciences, Szechenyi Istvan University) ;
  • Ban, Beata (National Foodchain Safety Office) ;
  • Jozsa, Csilla (National Foodchain Safety Office) ;
  • Rozsa, Laszlo (NARIC-Research Institute for Animal Breeding Nutrition and Meat Science) ;
  • Zsolnai, Attila (NARIC-Research Institute for Animal Breeding Nutrition and Meat Science) ;
  • Anton, Istvan (NARIC-Research Institute for Animal Breeding Nutrition and Meat Science)
  • Received : 2018.02.22
  • Accepted : 2018.06.09
  • Published : 2019.02.01
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Abstract

Objective: This study was conducted to investigate basic information on genetic structure and characteristics of Limousin population in Hungary. Obtained results will be taken into consideration when adopting the new breeding strategy by the Association of Hungarian Limousin and Blonde d'Aquitaine Breeders (AHLBB). Methods: Genetic diversity and phylogenetic relationship of 3,443 Limousin cattle from 16 different herds were investigated by performing genotyping using 18 microsatellite markers. Amplified DNA was genotyped using an automated genetic analyzer. Results: Mean of effective alleles ($n_e$) of the populations was 3.77. Population C had the lowest number of effective alleles (3.01) and the lowest inbreeding coefficient ($F_{IS}$) value (-0.15). Principal component analysis of estimated genetic distance ($F_{ST}$) values (p<0.000) revealed two herds (C and E) distinct from the majority of other Limousin herds. The pairwise $F_{ST}$ values of population C compared to the others (0.066 to 0.120) fell into the range of moderate genetic distance: 0.050 to 0.150, while population E displayed also moderate genetic distance ($F_{ST}$ values in range 0.052 to 0.064) but only to six populations (G, H, J, L, N, and P). $F_{ST(C-E)}$ was 0.148, all other pairs -excluding C and E herds- displayed low genetic distance ($F_{ST}$<0.049). Population D, F, I, J, K, L, N, O, and P carried private alleles, which alleles belonged to 1.1% of the individuals. Most probable number of clusters (K) were 2 and 7 determined by Structure and BAPS software. Conclusion: This study showed useful genetic diversity and phylogenetic relationship data that can be utilized for the development of a new breeding strategy by AHLBB. The results presented could also contribute to the proper selection of animals for further whole genome scan studies of Limousins.

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References

  1. Meat Consumption [Internet]. OECD data; c2016 [cited 2018 Feb 10]. Available from: https://data.oecd.org/agroutput/meat-consumption.htm
  2. Meat consumption per capita in Hungary [Internet]. Pig-information System; c2004-2015 [cited 2018 Feb 10 ]. Available from: https://sertesinfo.aki.gov.hu/publikaciok/kuldes/a:638/Az+egy+f%C5%91re+jut%C3%B3+h%C3%BAsfogyaszt%C3%A1s+alakul%C3%A1sa+Magyarorsz%C3%A1gon
  3. Limousin [Internet]. Cattle International Series; c2018 [cited 2018 Feb 10]. Available from: https://cattleinternationalseries.weebly.com/limousin.html
  4. Limousin [Internet]. The Beef Site; c2018 [cited 2018 Feb 10]. Available from: http://www.thebeefsite.com/breeds/beef/39/limousin/
  5. Bhargava A, Fuentes FF. Mutational dynamics of microsatellites. Mol Biotechnol 2010;44:250-66. https://doi.org/10.1007/s12033-009-9230-4
  6. Guichoux E, Lagache L, Wagner S, et al. Current trends in microsatellite genotyping. Mol Ecol Resour 2011;11:591-611. https://doi.org/10.1111/j.1755-0998.2011.03014.x
  7. Mao Y, Chang H, Yang Z, et al. Genetic structure and differentiation of three chinese indigenous cattle populations. Biochem Genet 2007;45:195-209. https://doi.org/10.1007/s10528-006-9061-y
  8. Mahgoub O, Babiker HA, Kadim IT, et al. Disclosing the origin and diversity of Omani cattle. Anim Genet 2013;44:336-9. https://doi.org/10.1111/j.1365-2052.2012.02399.x
  9. Seo JH, Lee JH, Kong HS. Assessment of genetic diversity and phylogenetic relationships of Korean native chicken breeds using microsatellite markers. Asian-Australas J Anim Sci 2017; 30:1365-71. https://doi.org/10.5713/ajas.16.0514
  10. Zsolnai A, Kovács A, Anton I, et al. Comparison of different Hungarian grey herds as based on microsatellite analysis. Anim Sci Pap Rep 2014;32:121-30.
  11. Amigues Y, Boitard S, Bertrand C, SanCristobal M, Rocha D. Genetic characterization of the Blonde d’Aquitaine cattle breed using microsatellite markers and relationship with three other French cattle populations. J Anim Breed Genet 2011;128:201-8. https://doi.org/10.1111/j.1439-0388.2010.00890.x
  12. Radko A, Rychlik T, Rubis D. Analysis of microsatellite DNA polymorphism in Limousin cattle. Ann Anim Sci 2008;8:225-32.
  13. ISAG species panel [Internet]. ISAG; c2003 [cited 2018 Feb 10]. Available from: http://www.isag.us/comptest.asp?autotry= true&ULnotkn=true
  14. Rousset F. Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Mol Ecol Resour 2008;8:103-6. https://doi.org/10.1111/j.1471-8286.2007.01931.x
  15. Goudet J. FSTAT (Version 1.2): A computer program to calculate F-statistics. J Hered 1995;86:485-6. https://doi.org/10.1093/oxfordjournals.jhered.a111627
  16. Excoffier L, Laval G, Schneider S. Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evol Bioinform Online 2005;1:47-50.
  17. Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P. Microchecker: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes 2004;4:535-8. https://doi.org/10.1111/j.1471-8286.2004.00684.x
  18. Peakall R, Smouse PE. Genalex 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes 2006;6:288-95. https://doi.org/10.1111/j.1471-8286.2005.01155.x
  19. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol 2005;14:2611-20. https://doi.org/10.1111/j.1365-294X.2005.02553.x
  20. Corander J, Waldmann P, Sillanpaa MJ. Bayesian analysis of genetic differentiation between populations. Genetics 2003; 163:367-74. https://doi.org/10.1093/genetics/163.1.367
  21. Takezaki N, Nei M, Tamura K. Software for constructing population trees from allele frequency data and computing other population statistics with Windows interface. Mol Biol Evol 2010;27:747-52. https://doi.org/10.1093/molbev/msp312
  22. Piry S, Alapetite A, Cornuet JM, et al. GeneClass2: A software for genetic assignment and first-generation migrant detection. J Hered 2004;95:536-9. https://doi.org/10.1093/jhered/esh074
  23. Rannala B, Mountain JL. Detecting immigration by using multilocus genotypes. Proc Natl Acad Sci USA 1997;94:9197-221. https://doi.org/10.1073/pnas.94.17.9197
  24. Paetkau D, Slade R, Burden M, Estoup A. Direct, real-time estimation of migration rate using assignment methods: a simulation-based exploration of accuracy and power. Mol Ecol 2004;13:55-65. https://doi.org/10.1046/j.1365-294X.2004.02008.x
  25. Hartl DL, Clark AG. Principles of population genetics, 3rd edn. Sunderland, MA, USA: Sinauer Associates, Inc; 1997. pp. 118-9.