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Microsatellite based genetic diversity and population structure of the endangered Spanish Guadarrama goat breed



Assessing genetic biodiversity and population structure of minor breeds through the information provided by neutral molecular markers, allows determination of their extinction risk and to design strategies for their management and conservation. Analysis of microsatellite loci is known to be highly informative in the reconstruction of the historical processes underlying the evolution and differentiation of animal populations. Guadarrama goat is a threatened Spanish breed which actual census (2008) consists of 3057 females and 203 males distributed in 22 populations more or less isolated. The aim of this work is to study the genetic status of this breed through the analysis of molecular data from 10 microsatellites typed in historic and actual live animals.


The mean expected heterozygosity across loci within populations ranged from 0.62 to 0.77. Genetic differentiation measures were moderate, with a mean FST of 0.074, GST of 0.081 and RST of 0.085. Percentages of variation among and within populations were 7.5 and 92.5, respectively. Bayesian clustering analyses pointed out a population subdivision in 16 clusters, however, no correlation between geographical distances and genetic differences was found. Management factors such as the limited exchange of animals between farmers (estimated gene flow Nm = 3.08) mostly due to sanitary and social constraints could be the major causes affecting Guadarrama goat population subdivision.


Genetic diversity measures revealed a good status of biodiversity in the Guadarrama goat breed. Since diseases are the first cause affecting the census in this breed, population subdivision would be an advantage for its conservation. However, to maintain private alleles present at low frequencies in such small populations minimizing the inbreeding rate, it would necessitate some mating designs of animals carrying such alleles among populations. The systematic use of molecular markers will facilitate the comprehensive management of these populations, which in combination with the actual breeding program to increase milk yield, will constitute a good strategy to preserve the breed.


Before the intensification and industrialisation process of the last decades, European livestock farming was generally extensive and closely linked to the use of farmland. This is still the case for small ruminants and especially for goats, where not only local breeds do not benefit from modern breeding techniques but they also are about to disappear. Thus, the decline of local breeds and their production systems are raising concern about the importance of European agro-ecosystems and cultural landscapes maintenance. The goat is among the earliest species to be domesticated [1]. Goats are distributed over all types of eco-niches, including tropical areas, dry zones and mountain regions. With such a wide distribution and adaptability [2], the goat is expected to have high genetic diversity as a result of both, natural selection for fitness under varied environmental conditions and the artificial selection for milk, meat, fibre and other purposes.

However, in contrast to high productive foreign goat breeds, most local breeds are not subject to breeding programs to improve production traits, which would increase their genetic ability for productivity and consequently their profitability. Due to the extensive conditions of animal management, existing breeding strategies applied to local breeds are constrained by poor pedigree recording. In this way, the lack of pedigree records can lead to both a limited genetic progress for the selected trait and a suboptimal inbreeding control. The use of highly variable molecular genetic markers, such as microsatellites, is one of the most powerful means for studying genetic diversity and pedigree reconstruction because of their high degree of polymorphism, random distribution across the genome and neutrality with respect to selection [35].

Guadarrama goat, a rustic breed which has been exploited in mountain areas in the centre of Spain since XVIII century, constitutes a good illustrative example. Guadarrama goat is a threatened breed whose actual population consists of 3057 females and 203 males distributed in 22 herds, which correspond to an effective population size of 763 individuals considering only the unequal number of males and females [6]. Other effects not considered here, as unequal parental contributions, overlapping generations, genetic drift, selection etc, can also influence the magnitude of the effective size. Common diseases such as tuberculosis and paratuberculosis are the main causes of the high culling rates (near 25% per year) existing in this breed. Generally, herd's size ranged from 300 to 500 animals.

This breed is mainly used for milk production, although it is also exploited in a local meat industry. Since 1998 Guadarrama breeders association has established a breeding program to increase milk yield. There are a high degree of disconnectedness among the herds of this breed and scarce pedigree information due to the low spread of the artificial insemination. Therefore, genetic evaluations of animals for milk yield are only comparable at the intra-herd level. For this reason, a pilot project for pedigree reconstruction based on molecular markers information (10 microsatellite) has been established in 2003 as an alternative to pedigree recording.

The aim of this work is to evaluate the genetic status of the Guadarrama goat breed making use of the molecular information generated in the breeding program analyzing both, their genetic diversity and its population structure or subdivision using clustering methods.


Biological samples

Fresh blood samples were obtained between years 2004-2007 from 6635 goats pertaining to 20 different herds, which constitutes the whole animals of the Guadarrama breed. Figure 1 illustrates geographical locations of these 20 populations. Total genomic DNA was isolated from blood using the Real Biotech Corporation ADN extraction kit (Durbiz).

Figure 1

Geographic distribution of the 20 Guadarrama goat populations analyzed.

Genetic loci

Ten microsatellite markers were studied: ILSTS005 [7], CSSM31 [8], BM8125 [9], BM1818 [9], ILSTS011 [10], INRA006 [11], CSSM066 [12], RM006 [13], BM6526 [9] and MCM53 [14]. Some of them had been previously recommended in biodiversity studies by FAO and ISAG. Microsatellites amplification was carried out using fluorescent labelled primers. The amplified products were analysed with a DNA capillary sequencer ABI Prism® 310 Genetic Analyzer (Applied Biosystems).

Statistical analysis

The expected heterozygosity corrected for sampling bias [15], the observed heterozygosity, the polymorphic information content and the estimated null allele frequency were calculated for each locus in the whole population using CERVUS version 3.0.3. [16]. GENEPOP 3.4 package [17] was used to perform the exact test for Hardy-Weinberg equilibrium by microsatellite loci (test multi-population) and by population (test multi-locus) using the Markov chain method with 1000 iterations, and considering the heterozygote deficit as the alternative hypothesis. Wright's F-statistics FIS, FST, and FIT [18] jackknifing over populations and loci were calculated by FSTAT version [19]. Gene flow (Nm) was estimated by the approximation of Wright [20] FST ≈ 1/(1+4Nm) assuming genetic markers neutrality and an island model. Heterozygosities, mean number of alleles across populations, FIS within populations and Gst were calculated with GENETIX 4.03 software [21]. Furthermore, FST values for pairwise comparisons of the 20 Guadarrama goat herds and their significance level for genetic differentiation and Rst were tested with FSTAT. Significance levels were set using the sequential Bonferroni correction (initial k = 190). GENCLASS 2.0 package [22] under a Bayesian approach [23] was used in the assessment of animals to the predefined populations in which their respective genotypes were most likely to occur. The Mantel test [24] was performed with GENETIX 4.03 to test the correlation between the FST values and the geographic distances between populations. The population structure was analyzed by cluster techniques with the software STRUCTURE 2.1 [25] and BAPS 4.14 [26, 27]. Due to the high number of missing data for the BM1818 marker, only nine of the ten loci genotyped were used in these analyses. According to Falush et al. [28], STRUCTURE analysis was performed considering both the admixture model and the correlated allele frequencies between populations. The length of the burn-in and MCMC (Monte Carlo Markov chain) were 10,000 and 100,000, respectively. For the whole data set (6635 animals distributed in 20 original populations) 15 runs were carried out for each value of K, being K the number of clusters. The range of possible Ks tested was from 2 to 23 (the real number of herds plus 3). For each value of K the mean of the log probability of data (L(K)) over 15 runs were calculated. FST mean values for each cluster were also estimated. BAPS was run setting the maximum number of cluster at 20. Results were based on 50 simulations from the posterior allele frequencies. Finally, locus by locus AMOVA analysis considering groups and populations as sources of variation was assessed by ARLEQUIN 3.1 software package [29].


Microsatellite markers

A total of 170 alleles were detected at the 10 microsatellite loci assessed in the 6335 goats genotyped. Table 1 shows the genetic variability measures corresponding to these 10 loci. Differences in the number of animals genotyped per microsatellite were due to amplification failures. There were many problems with the amplification of the marker BM1818, which finally was genotyped only in 1371 animals. Except ILSTS005 (0.44), all markers were highly informative (PIC>0.50) which make them useful in genetic diversity studies. The number of alleles per locus ranged from 9 (ILSTS005) to 36 (CSSM66) being 17 the mean number of alleles per locus. Private alleles (UAN in table 1) occurred at very low frequencies (<0.025) for all loci in most populations. The mean observed and expected heterozygosities across loci were 0.70 (SD 0.09) and 0.77 (SD 0.10), respectively. Only the CSSM066 marker was characterized by a fairly high frequency of null alleles (11%).

Table 1 Genetic variability at the microsatellites typed in the Guadarrama goat breed.

Table 2 shows Wright' F-statistics and gene flow (Nm) for each locus across the 20 herds of Guadarrama goat breed. Mean values of FIS and FST across loci were 0.023 and 0.074, respectively.

Table 2 Summary of Wright's F-statistics for each loci in the Guadarrama goat breed

Results of the Fisher's exact test for Hardy-Weinberg (HW) equilibrium across loci and populations, considering the heterozygote deficit as the alternative hypothesis, are shown in Table 3. Highly significant (p < 0.001) multilocus departures from HW proportions were found for most populations and significant (p < 0.05) for populations 2 and 20. Populations 10, 12 and 16, had non significant p-values for the statistical test. Single locus test across populations to asses departure from HW showed no significant p-values for ILSTS005, ILSTS011, BM6526, BM8125 and MCM53 markers.

Table 3 Hardy-Weinberg exact test in the Guadarrama goat populations.

Genetic diversity within populations

The mean number of alleles across loci, the mean observed and expected heterozygosities and the FIS estimates within the 20 Guadarrama goats populations, are shown in Table 4. Mean number of alleles across loci was higher than 9 in half of the populations. Heterozygosity deficit, as measured by Wright's FIS, was positive in most populations when averaged across loci, raging from -0.015 (population 10) to 0.066 (population 18). Average value of FIS across loci and populations was 0.022.

Table 4 Genetic diversity measures in each population of the Guadarrama goat breed.

FST values of pair-wise comparisons among the 20 herds (matrix not shown) of Guadarrama goats, showed an overall genetic differentiation FST of 0.074 (SD 0.011) and pair-wise FST values ranging from 0.027 (pop13 vs. pop19) to 0.165 (pop12 vs. pop20). Significant (α = 0.05) genetic differentiation was found after sequential Bonferroni correction (initial k = 190) in 92 out of 190 population pairs.

Results from GENECLASS assignment test revealed that about 77% of the animals were assigned to the population they were collected from. The higher percentages (91.2% to 97.2%) of individuals assigned to its original population occurred in populations 7, 10, 15, 18 and 20 while the lower correspond to populations 4 (68.2%) and 19 (69.0%). Assignment of individuals was consistent with the extent of genetic divergence reported in the FST analysis. The marker MCM53 showed two private alleles, which were present only in population 15. The marker BM8125 had only one private allele found in population 1. On the other hand, populations 4, 12, 14 and 17 did not show private alleles at any microsatellite analyzed.

The Mantel test including the 20 populations of Guadarrama goats and the relative distances among them (Figure 1), depicted no significant correlation between the FST values and the geographical distances (r = 0.105, p = 0.468).

Population structure

Figure 2 shows the log probability of data (L(K)) for the admixture and correlated frequencies model under exhaustive sampling (averaged over the 15 replicates) of the STRUCTURE package. The highest L(K) averaged over replicates running for each value of K (K from 2 to 23), was observed for K = 16 (-175,201.08). For K varying from 2 to 17 and from 20 to 23 the runs reach equilibrium and converged to similar L(K) values. However for K equal to 18 and 19, the system showed more erratic values across replicates. Therefore for these values of K, 10 new replicates were made setting the length of the burning period in 50,000 and of the MCMC in 500,000. Similar but more stable values of L(K) were found in this case.

Figure 2

Log probability of data (L(K)) for K values ranging from 2 to 23 for the admixture and correlated frequencies model, under exhaustive sampling (averaged over 15 replicates) for the Guadarrama goat breed (K = number of clusters). Length of burn-in 10,000. MCMC 100,000 Vertical bars reflect standard deviations.

Estimated α values averaged 0.04 for K varying from 2 to 19, indicating that most individuals were essentially from one population or another. However, for K values ranging from 20 to 23, α values varied from 1.50 to 2.50 indicating that most individuals were admixed.

Using BAPS package the highest likelihood was also obtained with K = 16 (-174,885.14).

Table 5 shows the percentage of membership for each predefined population and the mean value of FST in each of the 16 inferred clusters, for the high estimate of L(K) (-174,478.30) among the 15 replicates ran for K = 16. Clusters 2, 7, 11 and 12, had moderate to high proportions of members from two of the original populations. Clusters 11 and 12 were essentially a mixture of animals from populations 5 and 14 and populations 7 and 19, respectively. Cluster 5 seems to be the most heterogeneous group, containing moderate proportions of animals from populations 1, 9, 2 and 6.

Table 5 Percentages of animals at each pre-defined population (20) of Guadarrama goats and FST mean values in each of the 16 clusters inferred.

Table 6 shows locus by locus AMOVA analysis which was performed considering groups (16 clusters) and populations (20) as sources of variation. Percentages of variation of the number of alleles (FST) and of the allele size (Rst) among groups, among populations within groups and within populations were estimated. In both cases, the highest percentage of variation (92-93%) corresponded to the within population component. Components among groups and among populations within groups showed low and similar magnitudes (3-4%).

Table 6 Locus by locus AMOVA analysis considering groups (16) and populations (20) as sources of variation.


In order to maintain genetic diversity, breeding strategies that increase effective population size minimizing genetic drift effect should be implemented. Microsatellite markers in combination with recent statistical methodologies represent a useful tool for the conservation and management of endangered breeds.

A breeding program focused on improving Guadarrama goats milk yield has been carried out in Spain since 1998. In the present work, the actual situation concerning genetic diversity and population structure of this breed has been evaluated using the molecular information derived from 10 microsatellites loci and the use of clustering methods.

The total number of alleles per locus in the present study ranged from 9 to 36. This fact suggested that all markers used were appropriated to analyze genetic diversity in this breed. A more appropriate measure of genetic variation within a population was gene diversity (average expected heterozygosity). Gene diversity estimated in this breed was 0.70, which was in the range (0.3 to 0.8) to be useful for measuring genetic variation [30]. This value was similar to those previously reported (0.69) in other goat breeds [31] and in 31 animals from Guadarrama breed using 30 microsatellites [32]. The mean number of alleles found here (17) was higher than those, 7.7, estimated by Cañón et al. [32]. This could be due to the higher sample size used in our study. In assessing diversity estimates from different studies, it should be mentioned that the values are not directly comparable, as different microsatellite have been used. There were two common microsatellites with Cañon et al. [32]. Hence the comparison has only suggestive indication.

Although only a seven percent of the total genetic variability could be attributed to differences among subpopulations, evidences of a moderate genetic subdivision (mean FST = 0.074) in the Guadarrama goat population were detected. Similar FST value was found in a large analysis [32] using samples of 45 goat breeds from Europe and Middle Eastern countries. Thus, genetic variability within breeds seems to be as important as genetic variability among them. In the Guadarrama goat breed significant genetic differentiation (p < 0.05) was found in 92 out of the 190 population pairs after sequential Bonferroni correction.

The high genetic diversity observed in a breed could be explained by overlapping generations, mixing of populations from different geographical locations, natural selection favouring heterozygosity or subdivision accompanied by genetic drift [33]. Isolation, founder effects, genetic drift and different selection pressures realized by farmers in each population may have played major role in differentiation of Guadarrama goats.

STRUCTURE and BAPS clustering software have the ability of inferring the correct number of subpopulations and assigning individuals appropriately even when genetic differentiation among groups is low (0.02 to 0.05) [34] and using a relative small number of loci (7 microsatellites) [25]. In this case, results derived from both programs provide a strong support of a 16 cluster subdivision. This subdivision seems to be reasonable, since few farmers exchange animals and therefore these populations show more genetic homogeneity. The high average percentage of assignment (77%) of individuals to the population they were collected from, pointed out the existence of clear genetic differences between populations. In addition, AMOVA indicated that 7.5% of the total genetic variation is between populations of this breed while the remaining 92.5% corresponded to differences among individuals.

Genetic differences were not correlated with geographic distances among populations (Mantel test) therefore management factors such as the limited exchange of animals between farmers mostly due to sanitary, social and cultural reasons could constitute the major causes affecting Guadarrama goat population subdivision. In this breed tuberculosis and paratuberculosis are the main causes of mortality and culling. These kinds of diseases have high prevalence in the affected herds. Thus, subdivision would be an advantage preserving the breed from the dissemination of such diseases. Reproductive isolation, consequence of the local use and management of the breed, reduces the effective population size and contributes to the genetic subdivision. Considering Wright' F-statistics results, subdivision processes more than inbreeding (average FIS across loci was 0.022 ± 0.017), could be the cause of the observed genetic differences between populations. Furthermore, populations analyzed were not in HW equilibrium, as it is revealed by the smaller observed than expected heterozygosity. The heterozygote deficiency is probably reflecting a subdivided population structure (Wahlund effect) rather than selection against heterozygotes.


In this work we have demonstrated that Guadarrama goat genetic diversity is still conserved. Management factors such as the limited exchange of animals between farmers (estimated gene flow Nm = 3.08) could be the major causes affecting Guadarrama goat population subdivision. Since diseases are the first cause affecting Guadarrama goat census, population subdivision would be an advantage for the conservation of the breed. In such cases, additional constraints, such as the minimum levels of contribution of each population should be included in the conservation strategy [35]. To maintain private alleles present at low frequencies in such small populations avoiding an increase of the inbreeding rate, it would be necessary to develop some strategies to spread such alleles across populations. Since molecular markers allow inferring genealogical relationships, it would be possible to take measures on the mating scheme to minimize co-ancestry or kinship in the subdivided population. The systematic use of molecular markers can facilitate the comprehensive management of endangered populations and should be combined with breeding schemes to improve economic traits avoiding the deterioration of the breeds.


  1. 1.

    Mason IL: Classification and distribution of goat breeds. Genetic resources of Pig, Sheep and Goats. Edited by: Maijala K. 1981, World Animal Science B8. Elsevier, Amsterdam, 405-411.

    Google Scholar 

  2. 2.

    Galal S: Biodiversity in goats. Small Rum Res. 2005, 60 (1-2): 75-81. 10.1016/j.smallrumres.2005.06.021.

    Article  Google Scholar 

  3. 3.

    Kemp SJ, Hishida O, Wambugu J, Rink A, Longeri ML, Ma RZ, Da Y, Lewin HA, Barendse W, Teale AJ: A panel of polymorphic bovine, ovine and caprine microsatellite markers. Animal Genetics. 1995, 26: 299-306.

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Vankan DM, Faddy MJ: Estimations of the efficacy and reliability of paternity assignments from DNA microsatellite analysis of multiple-sire matings. Animal Genetics. 1999, 30: 355-361. 10.1046/j.1365-2052.1999.00511.x.

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Villanueva B, Verspoor E, Visscher PM: Parental assignment in fish using microsatellite genetic markers with finite numbers of parents and offspring. Animal Genetics. 2002, 33: 33-41. 10.1046/j.1365-2052.2002.00804.x.

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Falconer DS: Introducción a la genética cuantitativa. 1981, C.E.C.S.A. Mexico

    Google Scholar 

  7. 7.

    Brezinsky L, Kemp SJ, Teale AJ: ILSTS005 - a polymorphic bovine microsatellite. Animal Genetics. 1993, 24: 73-

    Google Scholar 

  8. 8.

    Moore SS, Byrne K, Berger KT, Barendse W, McCarthy F, Womack JE, Hetzel DJ: Characterization of 65 bovine microsatellites. Mammalian Genome. 1994, 5: 84-90. 10.1007/BF00292333.

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Bishop MD, Kappes SM, Keele JW, Stone RT, Sunden SLF, Hawkins GA, Solinas Toldo S, Fries R, Grosz MD, Yoo J, Beattie CW: A genetic linkage map for cattle. Genetics. 1994, 136: 619-639.

    PubMed Central  CAS  PubMed  Google Scholar 

  10. 10.

    Brezinsky L, Kemp SJ, Teale AJ: 5 polymorphic bovine microsatellites (ILSTS010-014). Animal Genetics. 1993, 24: 75-76.

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Vaiman D, Mercier D, Moazami-Goudarzi K, Eggen A, Ciampolini R, Lepingle A, Velmala R, Kaukinen J, Varvio SL, Martin P, Leveziel H, Guerin G: Conservation of a syntenic group of microsatellite loci between cattle and sheep. Mammalian Genome. 1994, 5: 310-314. 10.1007/BF00389547.

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Moore SS, Byrne K, Malcolm N: Three cDNA-derived bovine dinucleotide repeat polymorphisms: CSSME069, CSSME070 and CSSME076. Animal Genetics. 1997, 28 (5): 376-377.

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Kossarek LM, Grosse WM, Finlay O, McGraw RA: Bovine dinucleotide repeat polymorphism RM006. J Anim Sci. 1993, 71: 3176-

    CAS  PubMed  Google Scholar 

  14. 14.

    Smith AJ, Hulme DJ, Silk JP, Redwin JM, Beh KJ: Thirteen polymorphic ovine microsatellites. Animal Genetics. 1995, 26: 277-278.

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Nei M: Molecular evolutionary genetics. 1987, Columbia University Press, New York

    Google Scholar 

  16. 16.

    Kalinowski ST, Taper ML, Marshall TC: Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Molecular Ecology. 2007, 16: 1099-1006. 10.1111/j.1365-294X.2007.03089.x.

    Article  PubMed  Google Scholar 

  17. 17.

    Raymond M, Rousset F: GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. Journal of Heredity. 1995, 86 (3): 248-249.

    Google Scholar 

  18. 18.

    Weir BS, Cockerham CC: Estimating F-statistics for the analysis of population structure. Evolution. 1984, 38: 1358-1370. 10.2307/2408641.

    Article  Google Scholar 

  19. 19.

    Goudet J: FSTAT, a program to estimate and test gene diversities and fixation indices. 2001, []

    Google Scholar 

  20. 20.

    Wrigh S: Evolution in Mendelian populations. Genetics. 1931, 16: 97-159.

    Google Scholar 

  21. 21.

    Belkhir K, Borsa P: GENETIX, logiciel sous WindowsTM pour la génétique des populations. 1998, Laboratoire Génome, Populations, Interactions CNRS UMR 5000, Université de Montpellier II, Montpellier (France), []

    Google Scholar 

  22. 22.

    Piry S, Alapetite A, Cornuet JM, Paetkau D, Baudouin L, Estoup A: GeneClass2: A Software for Genetic Assignment and First-Generation Migrant Detection. Journal of Heredity. 2004, 95: 536-539. 10.1093/jhered/esh074.

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    Rannala B, Mountain JL: Detecting immigration by using multilocus genotypes. Proc Nac Acad Sci. 1997, 94 (17): 9197-9201. 10.1073/pnas.94.17.9197.

    CAS  Article  Google Scholar 

  24. 24.

    Mantel N: The detection of disease clustering and generalized regression approach. Cancer Research. 1967, 27: 209-220.

    CAS  PubMed  Google Scholar 

  25. 25.

    Pritchard JK, Stephens M, Donnelly P: Inference of population structure using multilocus genotype data. Genetics. 2000, 155: 945-959.

    PubMed Central  CAS  PubMed  Google Scholar 

  26. 26.

    Corander J, Walmann P, Sillampaa MJ: Bayesian analysis of genetic differentiation between populations. Genetics. 2003, 163: 367-374.

    PubMed Central  CAS  PubMed  Google Scholar 

  27. 27.

    Corander J, Walmann P, Marttinen P, Sillampaa MJ: BAPS2: enhanced possibilities for the analysis of genetic population structure. Bioinformatics. 2004, 20: 2363-2369. 10.1093/bioinformatics/bth250.

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Falush D, Stephens M, Pritchard JK: Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics. 2003, 164: 1567-1587.

    PubMed Central  CAS  PubMed  Google Scholar 

  29. 29.

    Excoffier L, Laval G, Schneider S: Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evolutionary Bioinformatics. 2005, 1: 47-50.

    CAS  Google Scholar 

  30. 30.

    Takezaki N, Nei M: Genetic distances and reconstruction of phylogenetic trees from microsatellite DNA. Genetics. 1996, 144: 389-399.

    PubMed Central  CAS  PubMed  Google Scholar 

  31. 31.

    Behl R, Sheoran N, Behl J, Viijh RK, Tantia MS: Analysis of 22 heterologous microsatellite markers for genetic variability in Indian goats. Anim Biotechnology. 2003, 14 (2): 167-175. 10.1081/ABIO-120026486.

    CAS  Article  Google Scholar 

  32. 32.

    Cañón J, García D, García-Atance MA, Obexer-Ruff G, Lenstra JA, Ajmone-Marsan P, Dunner S: ECOGENE Consortium Geographical partitioning of goat diversity in Europe and the Middle East. Animal genetics. 2006, 37: 327-334. 10.1111/j.1365-2052.2006.01461.x.

    Article  PubMed  Google Scholar 

  33. 33.

    Toro M, Mäki-Tanila A: Genomics reveals domestication history and facilitates breed development. Edited by: Oldenbroek K. 2007, Utilization and Conservation of Farm Animal Genetic Resources. Wageningen, The Netherlands, 75-102.

    Google Scholar 

  34. 34.

    Latch EK, Dharmarajan G, Glaubitz JC, Rhodes OE: Relative performance of bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation. Conservation Genetics. 2006, 7 (2): 295-302. 10.1007/s10592-005-9098-1.

    Article  Google Scholar 

  35. 35.

    Caballero A, Toro MA: Analysis of genetic diversity for the management of conserved subdivided populations. Conservation Genetics. 2002, 3: 289-299. 10.1023/A:1019956205473.

    CAS  Article  Google Scholar 

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Silvia Ródriguez Ramilo has helped us in the use of STRUCTURE and BAPS programs. Breeders Association of Guadarrama goat breed has collected blood samples. The Collaboration Agreement CC02-0002 among INIA, Breeders Association and IMIDRA (Madrid Autonomous Community) has provided refunding to genotype animals.

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Correspondence to Magdalena Serrano.

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Authors' contributions

MSN has developed the design of the work, the statistical analyses and wrote the manuscript. JHC has contributed in markers selection and genotyping design, and in manuscript revision. MM has made the animal genotyping and has contributed in the paper discussion. AMC has collaborated in manuscript writing, and results interpretation. FJC has contributed in animal genotyping. CC has collaborated in some technical aspects. JJJ has contributed in the statistical analyses. PDT is the breeder association secretary and has developed the sample collection.

All authors have been read and approved the final manuscript.

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Serrano, M., Calvo, J.H., Martínez, M. et al. Microsatellite based genetic diversity and population structure of the endangered Spanish Guadarrama goat breed. BMC Genet 10, 61 (2009).

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  • Genetic Differentiation
  • Effective Population Size
  • Private Allele
  • Local Breed
  • Population Subdivision