Using nuclear microsatellite data to trace the gene flow and population structure in Czech horsesá L., Štohl R., Vrtková I. (2019): Using nuclear microsatellite data to trace the gene flow and population structure in Czech horses. Czech J. Anim. Sci., 64: 67-77.
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Based on a data set comprising 2879 animals and 17 nuclear microsatellite DNA markers, we propose the most comprehensive in-depth study mapping the genetic structure and specifying the assignment success rates in horse breeds at the Czech population scale. The STRUCTURE program was used to perform systematic Bayesian clustering via the Markov chain Monte Carlo estimation, enabling us to explain the population stratification and to identify genetic structure patterns within breeds worldwide. In total, 182 different alleles were found over all the populations and markers, with the mean number of 10.7 alleles per locus. The expected heterozygosity ranged from 0.459 (Friesian) to 0.775 (Welsh Part Bred), and the average level reached 0.721. The average observed heterozygosity corresponded to 0.709, with the highest value detected in the Czech Sport Pony (0.775). The largest number of private alleles was found in Equus przewalskii. The population inbreeding coefficient FIS ranged from –0.08 in the Merens to 0.14 in the Belgian Warmblood. The total within-population inbreeding coefficient was estimated to be moderate. As expected, very large genetic differentiation and small gene flow were established between the Friesian and Equus przewalskii (FST = 0.37, Nm = 0.43). Zero FST values indicated no differences between the Czech Warmblood–Slovak Warmblood and the Czech Warmblood–Bavarian Warmblood. A high level of breeding and connectivity was revealed between the Slovak Warmblood–Bavarian Warmblood, Dutch Warmblood–Oldenburg Horse, Bavarian Warmblood–Dutch Warmblood, and Bavarian Warmblood–Oldenburg Horse. The breeds’ contribution equalled about 6% of the total genetic variability. The overall proportion of individuals correctly assigned to a population corresponded to 82.4%. The posterior Bayesian approach revealed a hierarchical dynamic genetic structure in four clusters (hot-blooded, warm-blooded, cold-blooded, and pony). While most of the populations were genetically distinct from each other and well-arranged with solid breed structures, some of the entire sets showed signs of admixture and/or fragmentation.

Barcaccia Gianni, Felicetti Michela, Galla Giulio, Capomaccio Stefano, Cappelli Katia, Albertini Emidio, Buttazzoni Luca, Pieramati Camillo, Silvestrelli Maurizio, Verini Supplizi Andrea (2013): Molecular analysis of genetic diversity, population structure and inbreeding level of the Italian Lipizzan horse. Livestock Science, 151, 124-133
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
Bower M. A., Campana M. G., Whitten M., Edwards C. J., Jones H., Barrett E., Cassidy R., Nisbet R. E. R., Hill E. W., Howe C. J., Binns M. (2011): The cosmopolitan maternal heritage of the Thoroughbred racehorse breed shows a significant contribution from British and Irish native mares. Biology Letters, 7, 316-320
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
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
Fornal A., Radko A., Piestrzynska-Kajtoch A. (2013): Genetic polymorphism of Hucul horse population based on 17 microsatellite loci. Acta Biochimica Polonica, 60, 761–765.
Goudet J. (2001): FSTAT, a program to estimate and test gene diversities and fixation indices (version 2.9.3). Available at (accessed Dec 24, 2017).
Gupta A.K., Chauhan Mamta, Bhardwaj Anuradha, Gupta Neelam, Gupta S.C., Pal Yash, Tandon S.N., Vijh R.K. (2014): Comparative genetic diversity analysis among six Indian breeds and English Thoroughbred horses. Livestock Science, 163, 1-11
Jakobsson M., Rosenberg N. A. (2007): CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics, 23, 1801-1806
Leroy Grégoire, Callède Lucille, Verrier Etienne, Mériaux Jean-Claude, Ricard Anne, Danchin-Burge Coralie, Rognon Xavier (2009): Genetic diversity of a large set of horse breeds raised in France assessed by microsatellite polymorphism. Genetics Selection Evolution, 41, -
Nordborg Magnus, Hu Tina T, Ishino Yoko, Jhaveri Jinal, Toomajian Christopher, Zheng Honggang, Bakker Erica, Calabrese Peter, Gladstone Jean, Goyal Rana, Jakobsson Mattias, Kim Sung, Morozov Yuri, Padhukasahasram Badri, Plagnol Vincent, Rosenberg Noah A, Shah Chitiksha, Wall Jeffrey D, Wang Jue, Zhao Keyan, Kalbfleisch Theodore, Schulz Vincent, Kreitman Martin, Bergelson Joy, Mitchell-Olds Tom (2005): The Pattern of Polymorphism in Arabidopsis thaliana. PLoS Biology, 3, e196-
Park S.D.E. (2001): The Excel Microsatellite Toolkit. Trypanotolerance in West African cattle and the population genetic effects of selection. University of Dublin: Ph.D. Thesis.
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-
Piry S., Alapetite A., Cornuet J.-M., Paetkau D., Baudouin L., Estoup A. (2004): GENECLASS2: A Software for Genetic Assignment and First-Generation Migrant Detection. Journal of Heredity, 95, 536-539
Pritchard J.K., Stephens M., Donnelly P. (2000): Inference of population structure using multilocus genotype data. Genetics, 155, 945–959.
Putnová L., Štohl R., Vrtková I. (2018): Genetic monitoring of horses in the Czech Republic: A large-scale study with a focus on the Czech autochthonous breeds. Journal of Animal Breeding and Genetics, 135, 73-83
Rannala B., Mountain J. L. (1997): Detecting immigration by using multilocus genotypes. Proceedings of the National Academy of Sciences, 94, 9197-9201
Rosenberg Noah A. (2004): distruct: a program for the graphical display of population structure. Molecular Ecology Notes, 4, 137-138
ROUSSET FRANÇOIS (2008): genepop’007: a complete re-implementation of the genepop software for Windows and Linux. Molecular Ecology Resources, 8, 103-106
Slatkin Montgomery, Barton Nicholas H. (1989): A COMPARISON OF THREE INDIRECT METHODS FOR ESTIMATING AVERAGE LEVELS OF GENE FLOW. Evolution, 43, 1349-1368
van de Goor L. H. P., van Haeringen W. A., Lenstra J. A. (2011): Population studies of 17 equine STR for forensic and phylogenetic analysis. Animal Genetics, 42, 627-633
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
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