Purebred : For the draught horse breeds only purebred and unrelated animals….

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N1 e 413 300 113 34 184 89 F (%)2 2.79 1.53 2.13 2.61 5.75 4.68 Anglo-Arabian, Trakehner and different warmblood breeds with the regional Hanoverian horses.

The main objective of this study was to show the levels of genetic variability among the German draught horse breeds and to estimate genetic distances between them using a highly polymorphic set of microsatellites representing all autosomes.

Furthermore, it should be clarified whether the Rhenish German Draught Horse and its East German subpopulations are distinct enough from each other to justify defining separate breeds. European Association for Animal Production – Animal Genetic Data Bank (EAAP-AGDB), http://www.tiho-hannover.de/einricht/zucht/ eaap/index.htm. 2 Averaged from the data on the horses from each population analysed here in consideration of 11 generations of ancestors. 3 Subpopulations of the Rhenish German Draught Horse; a further subpopulation of the Rhenish German Draught Horse, the Altmaerkish Coldblood could not be included in the present study because of the low numbers of samples available. Materials and methods Sampling and DNA extraction A total of 403 animals were analysed from six German heavy draught horse breeds and from a total of six riding, wild and primitive horse populations.

Blood or hair root samples were collected from South German Coldblood (N ¼ 45), Rhenish German Draught Horses (N ¼ 45), Saxon Thuringa Coldblood (N ¼ 23), Mecklenburg Coldblood (N ¼ 22), Black Forest (N ¼ 45) and Schleswig Draught Horses (N ¼ 45).

To place the results in context, DNA samples were also analysed from Hanoverian Warmblood (N ¼ 47), Arabian (N ¼ 25), Sorraia Horses (N ¼ 23), Icelandic Horses (N ¼ 45), Exmoor Ponies (N ¼ 20) and Przewalski’s Horses (N ¼ 18).

Because of the very low population size and missing readiness of most breeders to cooperate, there were only a few samples of Altmaerkisch draught horses available and thus we did not include this breed in our analysis.

In the German Democratic Republic, the Saxon Thuringa, Mecklenburg and Altmaerkisch Coldblood horses were not bred as different breeds and as early as 1990 the East German breeding organizations of coldblood horses were founded, the forementioned three breeds were distinguished according to the location of the breeding organization of the federal country.

For the draught horse breeds, only purebred and unrelated animals were sampled according to pedigree information in order to make their inbreeding coefficients comparable with those of the entire breed.

Inbreeding coefficients for the draught horses under study were calculated under consideration of 11 generations of ancestors with the methods described by Aberle et al. (2003a,b).

The samples were also representative for the present sire lines of each draught horse population.

Genomic DNA was extracted from whole blood using the QIAampÒ 96 DNA Blood Kit (Qiagen, Hilden, Germany), and from hair root samples using the DNeasyÒ Tissue Kit (Qiagen), following the manufacturer’s protocol. and Altmaerkisch Coldblood are East German subpopulations of the Rhenish German Draught Horse.

This breed was founded by breeding primarily with Belgian Draught Horses.

Today the largest heavy horse population is the South German Coldblood, a member of the so-called Noric horse group, which also includes the Black Forest Horse, which is bred in Baden-Wuerttemberg.

Molecular techniques have been widely used to analyse phylogenetic relationships among various animal groups and different breeds.

Microsatellite loci comprise an attractive potential source of information about population histories and evolutionary processes, as these loci permit simple and accurate typing in combination with high levels of polymorphism and widespread distribution in the genome.

The usefulness of microsatellite markers has been documented in many previous equine population genetic ´ ndez et al.

Studies (eg Can ˜ on et al. 2000; Aranguren-Me 2001; Bjørnstad & Røed 2001; Cunningham et al. 2001).

We compared German draught horse breeds with several endangered and very old populations as well as with breeds, which have been isolated for a long time, and with common riding horses of different histories and origins.

For this comparison, the Sorraia Horse and the Exmoor Pony were chosen as representatives of primitive horse breeds, the Przewalski’s Horse as representative of a wild horse and the Icelandic Horse represented for almost 1000 years isolated breed and was included because of origin.

However, the captive population of 11 Przewalski’s horses was interbred with a domestic horse and a domestic/Przewalski hybrid.

The Arabian was included because of its influence on most of all European riding horses and one of the oldest known breeds of riding horses.

The Hanoverian Warmblood is the largest riding horse population of Germany and has evolved since 1735 by interbreeding Holsteiner stallions, English Thoroughbreds and other horse breeds such as Arabian, Microsatellite amplifications and analysis The 31 microsatellite markers were chosen from the linkage map generated by Swinburne et al. (2000a), from the HORSEMAP database on the INRA Biotechnology Ó 2004 International Society for Animal Genetics, Animal Genetics, 35, 270–277 272 Aberle et al.

Laboratories Home Page (http://locus.jouy.inra.fr), and from earlier publications on genetic diversity in horses.

One microsatellite marker was selected per autosome to avoid linkage between the loci.

The selection criteria were defined characteristics such as high heterozygosity level, high number of alleles and ease of amplification.

The 31 loci were AHT34 (Swinburne et al. 2000b), ASB17 (Breen et al. 1997), COR007, COR017, COR018 (Hopman et al. 1999), COR022, COR024 (Murphie et al. 1999), COR045, COR056, COR058 (Ruth et al. 1999), COR069, COR070, COR071, COR082 (Tallmadge et al. 1999), HMS03, HMS07 (Guerin et al. 1994), HTG03, HTG06 (Ellegren et al. 1992), LEX07 (Coogle et al. 1996a), LEX33 (Coogle et al. 1996b), LEX34 (Coogle et al. 1997), LEX63 (Coogle & Bailey 1997), LEX68 (Coogle & Bailey 1999), LEX73 (Bailey et al. 2000), SGCV16, SGCV28 (Godard et al. 1997), TKY19 (Kakoi et al. 1999), UCDEQ425 (Eggleston-Stott et al. 1997), UM011 (Meyer et al. 1997), VHL20 (van Haeringen et al. 1994), and VHL209 (van Haeringen et al. 1998).

The 31 microsatellites were amplified alone or in multiplexes (two to five co-amplified loci) in 11 independent PCR reactions.

Each PCR reaction tube with a final volume of 12 lL contained 40 ng genomic DNA, 1.2 lL 10x PCR buffer, 15 mM MgCl2, 0.5% DMSO, 100 lM each dNTP, 0.75 U Taq-Polymerase (Qbiogene, Heidelberg, Germany), 5Õ IRD700 or IRD800 (IRD: Infra Red Dye) 1–10 pmol labelled forward primer, and unlabelled reverse primer.

The amplification was carried out in PTC-100TM or PTC-200TM thermocyclers (MJ Research, Inc., Watertown, MA, USA) under the following conditions: an initial denaturation step at 94 °C for 4 min followed by 35 cycles at 94 °C for 30 s, maximum annealing temperatures for 60 s, and a final extension of 30 s at 72 °C.

The dilution of PCR products with formamide loading dye in ratios from 1:6 to 1:30 was determined empirically and carried out prior to size fractionating on 6% denaturing polyacrylamide (RotiphoreseÒ Gel 40; Carl Roth, Karlsruhe, Germany) sequencing gels.

Gelelectrophoresis was performed on an LI-COR 4200S-2 automated sequencer.

Allele size was scored against known samples used as standards on every gel.

Raw data were genotyped by visual examination and manual input.

Multiple test level.

First a correction was performed within each population over all 31 loci, after which the HWE was tested over all population loci combinations (Baumung & So ¨ lkner 2002).

If more than one population locus combination deviated from HWE, this microsatellite marker was not used for calculating genetic distances in order to obtain stable phylogenies with a great number of informative loci, without distorting genetic distances because of the significant deviation from the HWE.

In addition, the hypothesis was tested that all 12 horse breeds are significantly distinguishable on the basis of genic and genotypic differentiation using GENEPOP.

Afterwards differentiation tests were performed between the breeds for each locus to evaluate the significance of genetic differentiation among the populations.

Genetic diversity within populations was measured as the mean number of alleles (NA) per locus, the number of private alleles (PA, alleles found in only one breed), the observed heterozygosity (HO), and the expected heterozygosity (HE) under HWE.

The subpopulation heterozygosity (ie average heterozygosity among subpopulations, HS), the probability for a locus that two gametes chosen at random will carry different alleles (HT) and the coefficient of gene differentiation GST (Nei 1973) were estimated separately for the draught horse populations and the other horse populations included here using the computer programme FSTAT version 2.9.3 (Goudet 1995).

The individual observed heterozygosities were regressed on the individual inbreeding coefficients of the draught horse breeds using the Pearson correlation coefficient.

The chord distance constructed by Cavalli-Sforza & Edwards (1967) (DC) is one of the best qualified for use with populations of intermediate divergence time as represented by breeds worldwide and in the breeds under study (Eding & Laval 1999).

However, standard genetic distance of Nei (1972) (DS) is the more frequently used distance, and this was calculated to obtain the possibility of comparing our results with those of other studies.

The neighbour-joining tree topology was obtained with the PHYLIP software version 3.5 (Felsenstein 1989) using the Cavalli-Sforza distance.

Bootstrap values were computed over 1000 replicates, and a consensus tree was drawn. Statistical analysis Allele frequencies, unbiased estimates for expected (HE) and observed (HO) heterozygosity, and the number of alleles were computed using MSA (Microsatellite Analyzer, Dieringer & Schlo ¨ tterer 2003).

Hardy–Weinberg equilibrium (HWE) tests were conducted with the GENEPOP package version 3.3 (Raymond & Rousset 1995).

Exact P-values were calculated along with their standard deviations using Guo & Thompson (1992) Markov-Chain algorithm with 1000 de-memorization steps for 100 batches and 1000 iterations per batch.

A Bonferroni-Holm correction (Holm 1979) was applied to the exact P-values to maintain a

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