Genetic diversity and admixture in three native draught horse breeds assessed using microsatellite markers

 

https://doi.org/10.17221/51/2017-CJASCitation:Vostrá-Vydrová H., Vostrý L., Hofmanová B., Moravčíková N., Veselá Z., Vrtková I., Novotná A., Kasarda R. (2018): Genetic diversity and admixture in three native draught horse breeds assessed using microsatellite markers  . Czech J. Anim. Sci., 63: 85-93.
supplementary materialdownload PDF

In this study, we aimed to estimate and compare genetic diversity of two native draught horse breeds and check the possible influence of Noriker breed population on these native breeds. Genetic analyses of relationships and admixture were performed in two native endangered draught horse populations (Silesian Noriker and Czech-Moravian Belgian horses) and one open breed (Noriker). Totally 104 alleles from 13 microsatellite loci were detected in 1298 horses. The average number of alleles per locus was the highest in the Czech-Moravian Belgian horse (7.62) and the lowest in the Silesian Noriker (7.31), the differences were non-significant, whereas the observed and expected heterozygosities per breed ranged from 0.680 (Czech-Moravian Belgian) to 0.719 (Noriker) and from 0.678 (Silesian Noriker) to 0.714 (Noriker). The estimates of Wright’s FST between each pair of breeds indicated a low level of genetic segregation. At the individual level across the analyzed population, formation of two clusters was observed with respect to historical breed development. Moreover, the membership probability outputs showed that the frequencies of alleles varied across the two main regions represented by the Czech-Moravian Belgian and other analyzed breeds. Our results indicated high genetic variability, low inbreeding, and low genetic differentiation, especially between Silesian Noriker and Noriker, which is caused by the high level of admixture. This high level of admixture was in accordance with geographical location, history, and breeding practices of the analyzed breeds. The Silesian Noriker and Noriker breeds seem to be the most genetically related and the decision to consider them as the same population is thus highly supported. The study provides data and information utilizable in the management of conservation programs planned to reduce inbreeding and to minimize loss of genetic variability.  

References:
Aberle K. S., Hamann H., Drögemüller C., Distl O. (2004): Genetic diversity in German draught horse breeds compared with a group of primitive, riding and wild horses by means of microsatellite DNA markers. Animal Genetics, 35, 270-277  https://doi.org/10.1111/j.1365-2052.2004.01166.x
 
Berber N., Gaouar S., Leroy G., Kdidi S., Tabet Aouel N., Saïdi Mehtar N. (2014): Molecular characterization and differentiation of five horse breeds raised in Algeria using polymorphic microsatellite markers. Journal of Animal Breeding and Genetics, 131, 387-394  https://doi.org/10.1111/jbg.12092
 
Bjornstad G., Gunby E., Roed K. H. (2000): Genetic structure of Norwegian horse breeds. Journal of Animal Breeding and Genetics, 117, 307-317  https://doi.org/10.1046/j.1439-0388.2000.00264.x
 
Botstein D., White R.L., Skolnick M., Davis R.W. (1980): Construction of a genetic linkage map in man using restriction fragment length polymorphisms. American Journal of Human Genetics, 32, 314–331.
 
Delgado J.F., De Andrés N., Valera M., Gutiérrez J.P., Cervantes I. (2014): Assessment of population structure depending on breeding objectives in Spanish Arabian horse by genealogical and molecular information. Livestock Science, 168, 9-16  https://doi.org/10.1016/j.livsci.2014.07.012
 
Druml T, Curik I, Baumung R, Aberle K, Distl O, Sölkner J (2007): Individual-based assessment of population structure and admixture in Austrian, Croatian and German draught horses. Heredity, 98, 114-122  https://doi.org/10.1038/sj.hdy.6800910
 
Earl Dent A., vonHoldt Bridgett M. (2012): STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genetics Resources, 4, 359-361  https://doi.org/10.1007/s12686-011-9548-7
 
EVANNO G., REGNAUT S., GOUDET J. (2005): Detecting the number of clusters of individuals using the software structure: a simulation study. Molecular Ecology, 14, 2611-2620  https://doi.org/10.1111/j.1365-294X.2005.02553.x
 
Goudet J. (2001): FSTAT, a program to estimate and test gene diversities and fixation indices (version 2.9.3). Available at http://www2.unil.ch/popgen/softwares/fstat.htm (accessed Nov 15, 2016)
 
Greenbaum Gili, Templeton Alan R., Zarmi Yair, Bar-David Shirli, Hadany Lilach (2014): Allelic Richness following Population Founding Events – A Stochastic Modeling Framework Incorporating Gene Flow and Genetic Drift. PLoS ONE, 9, e115203-  https://doi.org/10.1371/journal.pone.0115203
 
Iwańczyk Ewa, Juras Rytis, Cholewiński Grzegorz, Cothran E. Gus (2006): Genetic structure and phylogenetic relationships of the Polish Heavy Horse. Journal of Applied Genetics, 47, 353-359  https://doi.org/10.1007/BF03194645
 
Jombart T., Ahmed I. (2011): adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics, 27, 3070–3071.
 
Jombart T., Collins C. (2015): A tutorial for discriminant analysis of principal components (DAPC) using adegenet 2.0.0. MRC Centre for Outbreak Analysis and Modelling, Imperial College London. Available at http://adegenet.r-forge.r-project.org/files/tutorial-dapc.pdf (accessed Nov 20, 2016)
 
Kasarda R., Vostrý L., Moravčíková N., Vostrá-Vydrová H., Dovč P., Kadlečík O. (2016): Detailed insight into genetic diversity of the Old Kladruber horse substructure in comparison to the Lipizzan breed. Acta Agriculturae Scandinavica, Section A — Animal Science, 66, 67-74  https://doi.org/10.1080/09064702.2016.1249400
 
Liu K., Muse S. V. (2005): PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics, 21, 2128-2129  https://doi.org/10.1093/bioinformatics/bti282
 
Nei Masatoshi, Tajima Fumio, Tateno Yoshio (1983): Accuracy of estimated phylogenetic trees from molecular data. Journal of Molecular Evolution, 19, 153-170  https://doi.org/10.1007/BF02300753
 
Paradis E. (2010): pegas: an R package for population genetics with an integrated-modular approach. Bioinformatics, 26, 419-420  https://doi.org/10.1093/bioinformatics/btp696
 
Petersen Jessica L., Mickelson James R., Cothran E. Gus, Andersson Lisa S., Axelsson Jeanette, Bailey Ernie, Bannasch Danika, Binns Matthew M., Borges Alexandre S., Brama Pieter, da Câmara Machado Artur, Distl Ottmar, Felicetti Michela, Fox-Clipsham Laura, Graves Kathryn T., Guérin Gérard, Haase Bianca, Hasegawa Telhisa, Hemmann Karin, Hill Emmeline W., Leeb Tosso, Lindgren Gabriella, Lohi Hannes, Lopes Maria Susana, McGivney Beatrice A., Mikko Sofia, Orr Nicholas, Penedo M. Cecilia T, Piercy Richard J., Raekallio Marja, Rieder Stefan, Røed Knut H., Silvestrelli Maurizio, Swinburne June, Tozaki Teruaki, Vaudin Mark, M. Wade Claire, McCue Molly E., Ellegren Hans (2013): Genetic Diversity in the Modern Horse Illustrated from Genome-Wide SNP Data. PLoS ONE, 8, e54997-  https://doi.org/10.1371/journal.pone.0054997
 
Pritchard J.K., Stephens M., Donnelly P. (2000): Inference of population structure using multilocus genotype data. Genetics, 155, 945–959.
 
Szwaczkowski Tomasz, Greguła-Kania Monika, Stachurska Anna, Borowska Alicja, Jaworski Zbigniew, Gruszecki Tomasz M., Plaizier J. (2016): Inter- and intra-genetic diversity in the Polish Konik horse: implications for the conservation program. Canadian Journal of Animal Science, 96, 570-580  https://doi.org/10.1139/cjas-2015-0173
 
Vostrá-Vydrová H., Vostrý L., Hofmanová B., Krupa E., Veselá Z., Schmidová J. (2016): Genetic diversity within and gene flow between three draught horse breeds using genealogical information. Czech Journal of Animal Science, 61, 462-472  https://doi.org/10.17221/91/2015-CJAS
 
Vostry L., Kracikova O., Hofmanova B., Czernekova V., Kott T., Pribyl J. (2011): Intra-line and inter-line genetic diversity in sire lines of the Old Kladruber horse based on microsatellite analysis of DNA. Czech Journal of Animal Science, 56, 163–175.
 
Weir B.S. (1996): Genetic Data Analysis II: Methods for Discrete Population Genetic Data. Sinauer Associates, Sunderland, USA.
 
Weir B.S., Cockerham C.C. (1984): Estimating F-statistics for the analysis of population structure. Evolution, 38, 1358–1370.
 
Wilson G.A., Rannala B. (2003): Bayesian inference of recent migration rates using multilocus genotypes. Genetics, 163, 1177–1191.
 
supplementary materialdownload PDF

© 2021 Czech Academy of Agricultural Sciences | Prohlášení o přístupnosti