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Article

Genetic Differentiation among Livestock Breeds—Values for Fst

by
Stephen J. G. Hall
Department of Environmental Protection and Landscape, Estonian University of Life Sciences, Kreutzwaldi 5, 51014 Tartu, Estonia
Animals 2022, 12(9), 1115; https://doi.org/10.3390/ani12091115
Submission received: 1 April 2022 / Revised: 19 April 2022 / Accepted: 25 April 2022 / Published: 26 April 2022
(This article belongs to the Section Animal Genetics and Genomics)
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Abstract

:

Simple Summary

The degree of relationship among livestock breeds can be quantified by the Fst statistic, which measures the extent of genetic differentiation between them. An Fst value of 0.1 has often been taken as indicating that two breeds are indeed genetically distinct, but this concept has not been evaluated critically. Here, Fst values have been collated for the six major livestock species: cattle, sheep, goats, pigs, horses, and chickens. These values are remarkably variable both within and between species, demonstrating that Fst > 0.1 is not a reliable criterion for breed distinctiveness. However, the large body of Fst data accumulated in the last 20–30 years represents an untapped database that could contribute to the development of interdisciplinary research involving livestock breeds.

Abstract

(1) Background: The Fst statistic is widely used to characterize between-breed relationships. Fst = 0.1 has frequently been taken as indicating genetic distinctiveness between breeds. This study investigates whether this is justified. (2) Methods: A database was created of 35,080 breed pairs and their corresponding Fst values, deduced from microsatellite and SNP studies covering cattle, sheep, goats, pigs, horses, and chickens. Overall, 6560 (19%) of breed pairs were between breeds located in the same country, 7395 (21%) between breeds of different countries within the same region, 20,563 (59%) between breeds located far apart, and 562 (1%) between a breed and the supposed wild ancestor of the species. (3) Results: General values for between-breed Fst were as follows, cattle: microsatellite 0.06–0.12, SNP 0.08–0.15; sheep: microsatellite 0.06–0.10, SNP 0.06–0.17; horses: microsatellite 0.04–0.11, SNP 0.08–0.12; goats: microsatellite 0.04–0.14, SNP 0.08–0.16; pigs: microsatellite 0.06–0.27, SNP 0.15–0.22; chickens: microsatellite 0.05–0.28, SNP 0.08–0.26. (4) Conclusions: (1) Large amounts of Fst data are available for a substantial proportion of the world’s livestock breeds, (2) the value for between-breed Fst of 0.1 is not appropriate owing to its considerable variability, and (3) accumulated Fst data may have value for interdisciplinary research.

1. Introduction

Much research effort over the last 30 years has been applied to the characterization of livestock breeds by molecular genetics, primarily by microsatellite (MS) and single-nucleotide polymorphism (SNP) technologies. This research has usually aimed to support the conservation and sustainable utilization of livestock biodiversity, and also to elucidate the processes of domestication and the evolution and differentiation of breeds.
One of the outputs has been the calculation of the extents to which breeds have diverged from each other, and a very widely used measure for this genetic differentiation has been the Fst statistic. As originally described [1], Fst values from 0.05 to 0.15 were taken to indicate moderate differentiation between populations, from 0.15 to 0.25 is high differentiation, and greater than 0.25 is very high differentiation. In principle, Fst could therefore be used to inform discussion relating to particular breeds, for example, that they are sufficiently different from each other to justify support for their conservation, or, conversely, that they are sufficiently similar for them to merge. In practice, Fst measurements are not often used as the main genetic justification for policy decisions regarding breed conservation, but the large number of Fst measurements available represents a data resource that could yield insights into overall patterns of breed differentiation. Indeed, genetic differentiation of breeds has often been placed in a spatial context by investigating how it is paralleled by geographic distance [2,3]. Further work has shown correlations with human [4,5] and ecological [6] diversity.
For many years, the literature has included such statements as “… the level often found between related breeds (e.g., Fst > 0.1) …” [7]; “a close relationship between [two breeds] (Fst = 0.019)…” [8]; “a threshold value …” [9]; “… strongly indicated that the two … are sufficiently different to be considered separate breeds” [10]; “… the overall differentiation assessed in the entire dataset was higher than most other studies carried out on European cattle …..” [11]; “… pairwise comparison … showed Fst < 0.1 and suggested clearly differentiated populations …” [12]. The present study aimed to provide an extensive review of the literature, and is therefore a test of the informal hypothesis embodied in the foregoing statements; namely, that differentiation of breeds can be signalled by Fst > 0.1.

2. Materials and Methods

Published data on Fst calculations were obtained from MS or SNP studies on cattle, sheep, goats, horses, pigs, sheep and chickens. A keyword search was not made because Fst is seldom used as a keyword or included in the title of a paper. The search proceeded initially by studying reference lists and citations of key papers such as [13,14,15,16,17] and, for cattle, an extensive bibliography assembled for a compilation on world cattle breeds [18]. Data presented solely as heatmaps or as Nei’s genetic distance were not used. Attempts were made to obtain unpublished information directly from authors. Only data that clearly used Wright’s Fst [19] were used, and Reynolds genetic distance measures DR were transformed to Fst [20]. Fst calculations among herds or flocks were not used except when they related to differentiation between these entities and other, distinct breeds. Breed names and country affiliations were according to [18] when these were available, otherwise the usage considered most widespread and valid was adopted, or a breed name was assigned for the purposes of the study. Technical details such as sample sizes, numbers of alleles, and details of SNP technology were not considered. The references cited are listed in Table 1.
Preliminary analysis when 30,000 breed pairs had been obtained showed interpretable patterns of distribution of Fst for each of the twelve combinations of species and methodology (MS and SNP). Attention was then focused on recent publications, and a further 5080 breed pairs were added from a final total of 166 papers. No claim is made that this is a complete literature survey.
Fst calculations were classified according to whether the two breeds involved were affiliated to the same country, or to different countries. Those of different countries were coded according to the spatial relationships of the two countries, as defined by their borders. Pairs that included a wild ancestor (as defined in the respective studies) were also considered (Table 2).
For some analyses, to achieve an overview of coverage of different spatial relationships, Fst values relating to wild ancestor were excluded, and those for breed pairs classified as 2-Land-adj, 3-Marine-adj, 4-Nbut1, 5-Nbut1marine were merged into a combined geographical class designated Regional.
Some breeds, occurring internationally and often with national prefixes, were identified here as global breeds (Table 3) regardless of their country affiliation. No sheep breeds were thus designated. Although Merino sheep, for example, are very widely distributed, these populations represent well-established distinct breeds [178] and there is no equivalent to the global trades in germplasm seen, for example, in Holstein cattle, Large White (Yorkshire) pigs, and Angora goats. All breed pairs that included a global breed were classified as 6-Remote.
Owing to the large number of breeds considered, direct assessment of which breeds had been characterized by which methodology was not practicable, but preliminary analysis suggested that global breeds were more frequently included in studies using SNP approaches than in those using MS. This was investigated by comparing–for each methodology × species combination–the frequencies of occurrence of breed pairs, which included a global breed.
Statistical comparisons used non-parametric tests; specifically, the Kruskal-Wallis test and, for comparison of rank orders, Kendall’s coefficient of concordance [179,180,181].

3. Results

The literature search concluded in August 2021, with 35,080 Fst calculations having been assembled (Table 4). Numbers of breed pairs ranged very widely between studies, from 1 to 10,296.
The breakdown of the dataset according to spatial relationship is in Table 5, and according to specific breeds in Supplementary Information File S1. The complete dataset is in Supplementary Information File S2. In order to characterize the range of Fst values within each species × methodology group, the largest and smallest of the medians calculated for each spatial relationship were identified. The medians relating to wild ancestors were excluded for this purpose.
Species varied in the degree to which different spatial relationships were covered in the literature. Reflecting the relatively small numbers of breed pairs in the four regional classes (Table 2), in Table 6 these were condensed into geographical classes 1-Same, 6-Remote and Regional. Of the Fst calculations, 562 included a wild ancestor, and of the remaining 34,518, 59% (20,563) were of breed pairs classified as 6-Remote, 21% (7395) were Regional, and 19% (6560) were 1-Same. The proportion of breed pairs defined as 6-Remote was, for most species, higher in studies conducted with SNP methodologies than in MS studies.
Fst values for breed pairs also varied according to the spatial relationships of breeds. In all twelve (species × methodology) cases from cattle MS through to chicken SNP, differences in Fst between spatial relationships were highly significant (p < 0.001; Kruskal-Wallis statistic, d.f. in brackets, respectively, 201.59 (5), 1165.5 (5), 186.71 (6), 1117.9 (6), 914.75 (6), 19.39 (5), 249.67 (5), 1605.03 (6), 207.23 (6), 120.45 (6), 196.11 (6), 107.11 (6)).
The rank orders of the median Fst values for each of the spatial relationship categories (excluding 7-Wild_ancestor) were significantly correlated (Kendall concordance test; for MS, W = 0.52, for SNP, W = 0.66, both p < 0.01).
These differences are illustrated in Figure 1 and Figure 2, for MS and SNP data, respectively.

4. Discussion

It is reported [182] that there are 5517 livestock breeds in the world (1047 cattle, 1164 sheep, 580 goat, 720 horse, 569 pig, and 1437 chicken). It is evident that about one-fifth of the world’s breeds are represented in the dataset assembled in this report; at least 1040 different breeds have been studied by MS, and 797 by SNP (both methodologies have been applied to some breeds, almost always in separate studies), respectively. Principal reasons for this work have included characterization and conservation of this livestock biodiversity. Much of it has been on establishing the extent of differentiation of national breeds from those of remote countries (many of which are global breeds), with an emphasis on breed pairs of which one member was a national breed and the other was from a remote country (59% of breed pairs). Only 19% of breed pairs comprised breeds that were both of the same country. Thus, an unexpected result of this study has been to suggest that so far, as conservation is concerned, genetic studies have been more interested in introgression of breeds from abroad, than in maintaining the genetic distinctiveness of the diverse breeds of a country. This tendency is evident in both MS and SNP studies, particularly the latter. However, as 21% of breed pairs related to breeds of different–but nearby–countries, there has been a degree of interest in regional patterns of breed differentiation.
The original aim of this study was, however, to test the general prediction that a realistic threshold value for between-breed Fst is 0.1. The ranges of Fst values between pairs of breeds are shown to be so wide that this prediction appears obsolete for practical purposes. It is now very evident that the Fst approach is only one method of visualizing the findings of genomic studies of breeds [183], and reports are now typically accompanied by genetic distance calculations, STRUCTURE plots, plots generated by multivariate statistics, and heatmaps, often within a framework of landscape genomics [12]. Nevertheless, there may still be a requirement for benchmark values of Fst as indicating breed differentiation, for example for interdisciplinary studies or to provide a context for conservation genetics of wild populations. For these purposes, the following benchmarks could be adopted, based on the median values obtained in the present study, cattle: MS 0.06–0.12, SNP 0.08–0.15; sheep: MS 0.06–0.10, SNP 0.06–0.17; horse: MS 0.04–0.11, SNP 0.08–0.12; goat: MS 0.04–0.14, SNP 0.08–0.16; pig: MS 0.06–0.27, SNP 0.15–0.22; chicken: MS 0.05–0.28, SNP 0.08–0.26. However, use of these values as benchmarks must be conditional on acknowledging their stochastic nature and probable dependence on geographical factors.
The finding that different spatial relationships of breed pairs may influence Fst values is novel but not surprising. Fst statistics are well known [10,17,184] to lead to insights into patterns of migration and gene flow when placed in a geographical framework. In principle, a formal statistical analysis of the dataset assembled for this paper might enable quantification of the relative contributions of the different variates (to include species, methodology, and spatial relationship) but with the public availability of genotype data, the extensive meta-analysis of published Fst values from earlier studies may itself be an obsolete approach, as raw genotypes from multiple sources could be combined and Fst values reliably calculated from the pooled data.
The considerable amount of Fst data accumulated over the last few decades is still likely to represent a valuable resource. It could be used to audit breed conservation activities, although it will not be a definitive determinant of whether a breed is truly distinctive [185,186]. At the level of original research, these data may help in the formation of hypotheses for future work on breed differentiation and, as awareness increases of their existence and accessibility, they could provide stimulus for new interdisciplinary research.

5. Conclusions

The use of specific values of Fst as indicating breed differentiation is not justified, but benchmark values can be proposed for use in specified contexts. Fst data, as obtained from published studies, represent a resource for interdisciplinary research.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ani12091115/s1. Animals April 2022 Supplementary Information File S1 List of breeds; Animals April 2022 Supplementary Information File S2 Full dataset (MS Excel files).

Funding

This research received no external funding.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data are available in Supplementary Files S1 and S2.

Acknowledgments

Magdalena Serrano, Gregoire Leroy, Tiago do Prado Paim, Emily Clark and Pam Wiener, Egill Gautason, Filippo Cendron and Masaaki Taniguchi kindly provided unpublished data. I thank Anne Da Silva for helpful discussions. Referees of a previous version of the manuscript are thanked for their valuable comments.

Conflicts of Interest

The author declares no conflict of interest.

References

  1. Wright, S. The Interpretation of Population Structure by F-Statistics with Special Regard to Systems of Mating. Evolution 1965, 19, 395–420. [Google Scholar] [CrossRef]
  2. Gizaw, S.; van Arendonk, J.; Komen, H.; Windig, J.J.; Hanotte, O. Population Structure, Genetic Variation and Morphological Diversity in Indigenous Sheep of Ethiopia. Anim. Genet. 2007, 38, 621–628. [Google Scholar] [CrossRef] [PubMed]
  3. Handley, L.-J.L.; Byrne, K.; Santucci, F.; Townsend, S.; Taylor, M.; Bruford, M.; Hewitt, G.M. Genetic Structure of European Sheep Breeds. Heredity 2007, 99, 620–631. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Colino-Rabanal, V.J.; Rodríguez-Díaz, R.; Blanco-Villegas, M.J.; Peris, S.J.; Lizana, M. Human and Ecological Determinants of the Spatial Structure of Local Breed Diversity. Sci. Rep. 2018, 8, 6452. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Berthouly, C.; Ngoc, D.D.; Thévenon, S.; Bouchel, D.; Van, T.N.; Danes, C.; Grosbois, V.; Thanh, H.H.; Chi, C.V.; Maillard, J.-C. How Does Farmer Connectivity Influence Livestock Genetic Structure? A Case-Study in a Vietnamese Goat Population. Mol. Ecol. 2009, 18, 3980–3991. [Google Scholar] [CrossRef]
  6. Cortellari, M.; Barbato, M.; Talenti, A.; Bionda, A.; Carta, A.; Ciampolini, R.; Ciani, E.; Crisa, A.; Frattini, S.; Lasagna, E.; et al. The Climatic and Genetic Heritage of Italian Goat Breeds with Genomic SNP Data. Sci. Rep. 2021, 11, 10986. [Google Scholar] [CrossRef]
  7. Maudet, C.; Luikart, G.; Taberlet, P. Genetic Diversity and Assignment Tests among Seven French Cattle Breeds Based on Microsatellite DNA Analysis. J. Anim. Sci. 2002, 80, 942–950. [Google Scholar] [CrossRef] [Green Version]
  8. Strillacci, M.G.; Vevey, M.; Blanchet, V.; Mantovani, R.; Sartori, C.; Bagnato, A. The Genomic Variation in the Aosta Cattle Breeds Raised in an Extensive Alpine Farming System. Animals 2020, 10, 2385. [Google Scholar] [CrossRef]
  9. Kukučková, V.; Moravčíková, N.; Ferenčaković, M.; Simčič, M.; Mészáros, G.; Sölkner, J.; Trakovická, A.; Kadlečík, O.; Curik, I.; Kasarda, R. Genomic Characterization of Pinzgau Cattle: Genetic Conservation and Breeding Perspectives. Conserv. Genet. 2017, 18, 893–910. [Google Scholar] [CrossRef]
  10. Kijas, J.W.; Miller, J.E.; Hadfield, T.; McCulloch, R.; García-Gámez, E.; Neto, L.R.P.; Cockett, N. Tracking the Emergence of a New Breed Using 49,034 SNP in Sheep. PLoS ONE 2012, 7, e41508. [Google Scholar] [CrossRef]
  11. Bozzi, R.; Álvarez, I.; Crovetti, A.; Fernández, I.; De Petris, D.; Goyache, F. Assessing Priorities for Conservation in Tuscan Cattle Breeds Using Microsatellites. Animal 2012, 6, 203–211. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Stronen, A.V.; Pertoldi, C.; Iacolina, L.; Kadarmideen, H.; Kristensen, T.N. Genomic Analyses Suggest Adaptive Differentiation of Northern European Native Cattle Breeds. Evol. Appl. 2019, 12, 1096–1113. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Leroy, G.; Callède, L.; Verrier, E.; Mériaux, J.-C.; Ricard, A.; Danchin-Burge, C.; Rognon, X. Genetic Diversity of a Large Set of Horse Breeds Raised in France Assessed by Microsatellite Polymorphism. Genet. Sel. Evol. 2009, 41, 5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Peter, C.; Bruford, M.; Perez, T.; Dalamitra, S.; Hewitt, G.; Erhardt, G.; The ECONOGENE Consortium. Genetic Diversity and Subdivision of 57 European and Middle-Eastern Sheep Breeds. Anim. Genet. 2007, 38, 37–44. [Google Scholar] [CrossRef] [PubMed]
  15. Ajmone-Marsan, P.; Colli, L.; Han, J.; Achilli, A.; Lancioni, H.; Joost, S.; Crepaldi, P.; Pilla, F.; Stella, A.; Taberlet, P.; et al. The Characterization of Goat Genetic Diversity: Towards a Genomic Approach. Small Rumin. Res. 2014, 121, 58–72. [Google Scholar] [CrossRef]
  16. Wilkinson, S.; Wiener, P.; Teverson, D.; Haley, C.S.; Hocking, P.M. Characterization of the Genetic Diversity, Structure and Admixture of British Chicken Breeds. Anim. Genet. 2012, 43, 552–563. [Google Scholar] [CrossRef]
  17. Colli, L.; The AdaptMap Consortium; Milanesi, M.; Talenti, A.; Bertolini, F.; Chen, M.; Crisà, A.; Daly, K.G.; Del Corvo, M.; Guldbrandtsen, B.; et al. Genome-wide SNP Profiling of Worldwide Goat Populations Reveals Strong Partitioning of Diversity and Highlights Post-domestication Migration Routes. Genet. Sel. Evol. 2018, 50, 58. [Google Scholar] [CrossRef] [Green Version]
  18. Porter, V.; Alderson, G.L.H.; Hall, S.J.G.; Sponenberg, D.P. Mason’s World Encyclopedia of Livestock Breeds and Breeding; CABI International: Wallingford, UK, 2016; ISBN 97817 8064 7944. [Google Scholar]
  19. Weir, B.S.; Cockerham, C.C. Estimating F-Statistics for the Analysis of Population Structure. Evolution 1984, 38, 1358–1370. [Google Scholar] [CrossRef]
  20. Cañón, J.; Alexandrino, P.; Bessa, I.; Carleos, C.; Carretero, Y.; Dunner, S.; Ferran, N.; Garcia, D.; Jordana, J.; Laloë, D.; et al. Genetic Diversity Measures of Local European Beef Cattle Breeds for Conservation Purposes. Genet. Sel. Evol. 2001, 33, 311–332. [Google Scholar] [CrossRef] [Green Version]
  21. Zerabruk, M.; Li, M.-H.; Kantanen, J.; Olsaker, I.; Ibeagha-Awemu, E.M.; Erhardt, G.; Vangen, O. Genetic Diversity and Admixture of Indigenous Cattle from North Ethiopia: Implications of Historical Introgressions in the Gateway Region to Africa. Anim. Genet. 2012, 43, 257–266. [Google Scholar] [CrossRef]
  22. Li, M.-H.; Kantanen, J.; Michelson, A.; Saarma, U. Genetic Components of Grey Cattle in Estonia as Revealed by Microsatellite Analysis Using Two Bayesian Clustering Methods. BMC Res. Notes 2011, 4, 37. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Pham, L.D.; Do, D.N.; Binh, N.T.; Nam, L.Q.; Van Ba, N.; Thuy, T.T.T.; Hoan, T.X.; Cuong, V.C.; Kadarmideen, H.N. Assessment of Genetic Diversity and Population Structure of Vietnamese Indigenous Cattle Populations by Microsatellites. Livest. Sci. 2013, 155, 17–22. [Google Scholar] [CrossRef]
  24. Bray, T.C.; Chikhi, L.; Sheppy, A.J.; Bruford, M.W. The Population Genetic Effects of Ancestry and Admixture in a Subdivided Cattle Breed. Anim. Genet. 2009, 40, 393–400. [Google Scholar] [CrossRef] [PubMed]
  25. 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, 201–208. [Google Scholar] [CrossRef] [PubMed]
  26. Ilie, D.E.; Cean, A.; Cziszter, L.T.; Gavojdian, D.; Ivan, A.; Kusza, S. Microsatellite and Mitochondrial DNA Study of Native Eastern European Cattle Populations: The Case of the Romanian Grey. PLoS ONE 2015, 10, e0138736. [Google Scholar] [CrossRef] [Green Version]
  27. Svishcheva, G.; Babayan, O.; Lkhasaranov, B.; Tsendsuren, A.; Abdurasulov, A.; Stolpovsky, Y. Microsatellite Diversity and Phylogenetic Relationships among East Eurasian Bos taurus Breeds with an Emphasis on Rare and Ancient Local Cattle. Animals 2020, 10, 1493. [Google Scholar] [CrossRef]
  28. Demir, E.; Balcioğlu, M.S. Genetic Diversity and Population Structure of Four Cattle Breeds Raised in Turkey Using Microsatellite Markers. Czech J. Anim. Sci. 2019, 64, 411–419. [Google Scholar] [CrossRef] [Green Version]
  29. Laoun, A.; Harkat, S.; Lafri, M.; Gaouar, S.B.S.; Belabdi, I.; Ciani, E.; De Groot, M.; Blanquet, V.; Leroy, G.; Rognon, X.; et al. Inference of Breed Structure in Farm Animals: Empirical Comparison between SNP and Microsatellite Performance. Genes 2020, 11, 57. [Google Scholar] [CrossRef] [Green Version]
  30. Tantia, M.S.; Vijh, R.K.; Mishra, B.; Kumar, S.T.B. Genetic Diversity among Four Short Stature Cattle Populations of India. Anim. Genet. Resour. Inf. 2008, 43, 15–23. [Google Scholar] [CrossRef]
  31. Delgado Bermejo, J.V.; Barba Capote, C.J.; Aguirre Riofrío, E.L.; Cabezas Congo, R.; Cevallos Falquez, O.F.; Rizzo Zamora, L.G.; Vargas Burgos, J.C.; Navas González, F.J.; Álvarez Franco, L.Á.; Biobovis, C.; et al. Molecular Inference in the Colonization of Cattle in Ecuador. Res. Vet. Sci. 2020, 132, 357–368. [Google Scholar] [CrossRef]
  32. Acosta, A.; Uffo, O.; Sanz, A.; Ronda, R.; Osta, R.; Rodellar, C.; Martin-Burriel, I.; Zaragoza, P. Genetic Diversity and Differentiation of Five Cuban Cattle Breeds Using 30 Microsatellite Loci. J. Anim. Breed. Genet. 2013, 130, 79–86. [Google Scholar] [CrossRef] [PubMed]
  33. Gororo, E.; Makuza, S.; Chatiza, F.; Chidzwondo, F.; Sanyika, T. Genetic diversity in Zimbabwean Sanga cattle breeds using microsatellite markers. S. Afr. J. Anim. Sci. 2018, 48, 128. [Google Scholar] [CrossRef] [Green Version]
  34. Van der Westhuizen, L.; MacNeil, M.D.; Scholtz, M.M.; Neser, F.W.; Makgahlela, M.L.; van Wyk, J.B. Genetic Variability and Relationships in Nine South African Cattle Breeds Using Microsatellite Markers. Trop. Anim. Health Prod. 2020, 52, 177–184. [Google Scholar] [CrossRef] [PubMed]
  35. Ocampo, R.J.; Martínez, J.F.; Martínez, R. Assessment of Genetic Diversity and Population Structure of Colombian Creole Cattle Using Microsatellites. Trop. Anim. Health. Prod. 2021, 53, 122. [Google Scholar] [CrossRef]
  36. Li, M.-H.; Tapio, I.; Vilkki, J.; Ivanova, Z.; Kiselyova, T.; Marzanov, N.; Ćinkulov, M.; Stojanovic, S.; Ammosov, I.; Popov, R.; et al. The Genetic Structure of Cattle Populations (Bos Taurus) In Northern Eurasia and the Neighbouring near Eastern Regions: Implications for Breeding Strategies and Conservation. Mol. Ecol. 2007, 16, 3839–3853. [Google Scholar] [CrossRef]
  37. Koudandé, O.; Dossou-Gbété, G.; Mujibi, F.; Kibogo, H.; Mburu, D.; Mensah, G.; Hanotte, O.; van Arendonk, J. Genetic Diversity and Zebu Genes Introgression in Cattle Population along the Coastal Region of the Bight of Benin. Anim. Genet. Resour. Inf. 2009, 44, 45–55. [Google Scholar] [CrossRef] [Green Version]
  38. Gautier, M.; Laloë, D.; Moazami-Goudarzi, K. Insights into the Genetic History of French Cattle from Dense SNP Data on 47 Worldwide Breeds. PLoS ONE 2010, 5, e13038. [Google Scholar] [CrossRef]
  39. McKay, S.D.; Schnabel, R.D.; Murdoch, B.M.; Matukumalli, L.K.; Aerts, J.; Coppieters, W.; Crews, D.; Neto, E.D.; Gill, C.A.; Gao, C.; et al. An Assessment of Population Structure in Eight Breeds of Cattle Using a Whole Genome SNP Panel. BMC Genet. 2008, 9, 37. [Google Scholar] [CrossRef] [Green Version]
  40. Cañas-Alvarez, J.; Rodriguez, A.G.; Munilla, S.; Varona, L.; Díaz, C.; Baro, J.A.; Altarriba, J.; Molina, A.; Piedrafita, J. Genetic Diversity and Divergence among Spanish Beef Cattle Breeds Assessed by a Bovine High-Density SNP Chip. J. Anim. Sci. 2015, 93, 5164–5174. [Google Scholar] [CrossRef]
  41. Edea, Z.; Dadi, H.; Kim, S.-W.; Dessie, T.; Lee, T.; Kim, H.; Kim, J.-J.; Kim, K.-S. Genetic Diversity, Population Structure and Relationships in Indigenous Cattle Populations of Ethiopia and Korean Hanwoo Breeds using SNP Markers. Front. Genet. 2013, 4, 35. [Google Scholar] [CrossRef] [Green Version]
  42. Makina, S.O.; Muchadeyi, F.C.; Van Marle-Köster, E.; MacNeil, M.D.; Maiwashe, A. Genetic Diversity and Population Structure among Six Cattle Breeds in South Africa Using a Whole Genome SNP Panel. Front. Genet. 2014, 5, 333. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Mastrangelo, S.; Ciani, E.; Marsan, P.A.; Bagnato, A.; Battaglini, L.; Bozzi, R.; Carta, A.; Catillo, G.; Cassandro, M.; Casu, S.; et al. Conservation Status and Historical Relatedness of Italian Cattle Breeds. Genet. Sel. Evol. 2018, 50, 35. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Yurchenko, A.; Yudin, N.; Aitnazarov, R.; Plyusnina, A.; Brukhin, V.; Soloshenko, V.; Lhasaranov, B.; Popov, R.; Paronyan, I.A.; Plemyashov, K.V.; et al. Genome-Wide Genotyping Uncovers Genetic Profiles and History of the Russian Cattle Breeds. Heredity 2018, 120, 125–137. [Google Scholar] [CrossRef] [PubMed]
  45. Senczuk, G.; Mastrangelo, S.; Ciani, E.; Battaglini, L.; Cendron, F.; Ciampolini, R.; Crepaldi, P.; Mantovani, R.; Bongioni, G.; Pagnacco, G.; et al. The Genetic Heritage of Alpine Local Cattle Breeds Using Genomic SNP Data. Genet. Sel. Evol. 2020, 52, 40. [Google Scholar] [CrossRef] [PubMed]
  46. Kelleher, M.M.; Berry, D.P.; Kearney, J.F.; McParland, S.; Buckley, F.; Purfield, D. Inference of Population Structure of Purebred Dairy and Beef Cattle Using High-Density Genotype Data. Animal 2017, 11, 15–23. [Google Scholar] [CrossRef] [PubMed]
  47. Ben-Jemaa, S.; Mastrangelo, S.; Lee, S.-H.; Lee, J.H.; Boussaha, M. Genome-Wide Scan for Selection Signatures Reveals Novel Insights into the Adaptive Capacity in Local North African Cattle. Sci. Rep. 2020, 10, 19466. [Google Scholar] [CrossRef]
  48. Gautason, E.; Schönherz, A.A.; Sahana, G.; Guldbrandtsen, B. Relationship of Icelandic Cattle with Northern and Western European Cattle Breeds, Admixture and Population Structure. Acta Agric. Scand. Sect. A—Anim. Sci. 2020, 69, 25–38. [Google Scholar] [CrossRef]
  49. Karimi, K.; Strucken, E.M.; Moghaddar, N.; Ferdosi, M.H.; Esmailizadeh, A.; Gondro, C. Local and Global Patterns of Admixture and Population Structure in Iranian Native Cattle. BMC Genet. 2016, 17, 108. [Google Scholar] [CrossRef] [Green Version]
  50. Schmidtmann, C.; Schönherz, A.; Guldbrandtsen, B.; Marjanovic, J.; Calus, M.; Hinrichs, D.; Thaller, G. Assessing the Genetic Background and Genomic Relatedness of Red Cattle Populations Originating from Northern Europe. Genet. Sel. Evol. 2021, 53, 23. [Google Scholar] [CrossRef]
  51. Al-Qamashoui, B.; Simianer, H.; Kadim, I.; Weigend, S. Assessment of Genetic Diversity and Conservation Priority of Omani Local Chickens Using Microsatellite Markers. Trop. Anim. Health Prod. 2014, 46, 747–752. [Google Scholar] [CrossRef]
  52. Zanetti, E.; De Marchi, M.; Dalvit, C.; Cassandro, M. Genetic Characterization of Local Italian Breeds of Chickens Undergoing in Situ Conservation. Poult. Sci. 2010, 89, 420–427. [Google Scholar] [CrossRef]
  53. Tadano, R.; Nishibori, M.; Nagasaka, N.; Tsudzuki, M. Assessing Genetic Diversity and Population Structure for Commercial Chicken Lines Based on Forty Microsatellite Analyses. Poult. Sci. 2007, 86, 2301–2308. [Google Scholar] [CrossRef] [PubMed]
  54. Ceccobelli, S.; Di Lorenzo, P.; Lancioni, H.; Ibáñez, L.V.M.; Tejedor, M.T.; Castellini, C.; Landi, V.; Martínez, A.M.; Bermejo, J.V.D.; Pla, J.L.V.; et al. Genetic Diversity and Phylogeographic Structure of Sixteen Mediterranean Chicken Breeds Assessed with Microsatellites and Mitochondrial DNA. Livest. Sci. 2015, 175, 27–36. [Google Scholar] [CrossRef]
  55. Yang, X.; Liu, C.-L.; Yang, B.-G.; Hu, H.-Q.; Ying, G.; Yi, G.; Chen, B.-E.; Yuan, Y.; Yong-Meng, H.; Zhang, W.-Y.; et al. Investigating Genetic Diversity and Population Phylogeny of Five Chongqing Local Chicken Populations Autosomal Using Microsatellites. Anim. Biotechnol. 2021, 2021, 1–19. [Google Scholar] [CrossRef] [PubMed]
  56. Mahammi, F.Z.; Gaouar, S.B.S.; Laloë, D.; Faugeras, R.; Aoul, N.T.; Rognon, X.; Tixier-Boichard, M.; Saidi-Mehtar, N. A Molecular Analysis of the Patterns of Genetic Diversity in Local Chickens from Western Algeria in Comparison with Commercial Lines and Wild Jungle Fowls. J. Anim. Breed. Genet. 2016, 133, 59–70. [Google Scholar] [CrossRef]
  57. Osei-Amponsah, R.; Kayang, B.B.; Naazie, A.; Osei, Y.D.; Youssao, I.A.K.; Yapi-Gnaore, V.C.; Tixier-Boichard, M.; Rognon, X. Genetic Diversity of Forest and Savannah Chicken Populations of Ghana as Estimated by Microsatellite Markers. Anim. Sci. J. 2010, 81, 297–303. [Google Scholar] [CrossRef]
  58. Bodzsar, N.; Eding, H.; Revay, T.; Hidas, A.; Weigend, S. Genetic Diversity of Hungarian Indigenous Chicken Breeds Based on Microsatellite Markers. Anim. Genet. 2009, 40, 516–523. [Google Scholar] [CrossRef]
  59. Gärke, C.; Ytournel, F.; Bed’Hom, B.; Gut, I.; Lathrop, M.; Weigend, S.; Simianer, H. Comparison of SNPs and Microsatellites for Assessing the Genetic Structure of Chicken Populations. Anim. Genet. 2012, 43, 419–428. [Google Scholar] [CrossRef]
  60. Pirany, N.; Romanov, M.N.; Ganpule, S.P.; Devegowda, G.; Prasad, D.T. Microsatellite Analysis of Genetic Diversity in Indian Chicken Populations. J. Poult. Sci. 2007, 44, 19–28. [Google Scholar] [CrossRef] [Green Version]
  61. Chen, G.; Bao, W.; Shu, J.; Ji, C.; Wang, M.; Eding, H.; Muchadeyi, F.; Weigend, S. Assessment of Population Structure and Genetic Diversity of 15 Chinese Indigenous Chicken Breeds Using Microsatellite Markers. Asian-Australas. J. Anim. Sci. 2008, 21, 331–339. [Google Scholar] [CrossRef]
  62. Van Marle-Köster, E.; Hefer, C.A.; Nel, L.H.; Groenen, M. Genetic Diversity and Population Structure of Locally Adapted South African Chicken Lines: Implications for Conservation. S. Afr. J. Anim. Sci. 2008, 38, 271–281. [Google Scholar]
  63. Rajkumar, U.; Gupta, B.R.; Reddy, A.R. Genomic Heterogeneity of Chicken Populations in India. Asian-Australas. J. Anim. Sci. 2008, 21, 1710–1720. [Google Scholar] [CrossRef]
  64. Leroy, G.; Kayang, B.B.; Youssao, I.A.; Yapi-Gnaoré, C.V.; Osei-Amponsah, R.; Loukou, N.E.; Fotsa, J.-C.; Benabdeljelil, K.; Bed’Hom, B.; Tixier-Boichard, M.; et al. Gene Diversity, Agroecological Structure and Introgression Patterns among Village Chicken Populations across North, West and Central Africa. BMC Genet. 2012, 13, 34. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Chang, C.S.; Chen, C.F.; Berthouly-Salazar, C.; Chazara, O.; Lee, Y.P.; Chang, K.H.; Bed’Hom, B.; Tixier-Boichard, M. A Global Analysis of Molecular Markers and Phenotypic Traits in Local Chicken Breeds in Taiwan. Anim. Genet. 2012, 43, 172–182. [Google Scholar] [CrossRef]
  66. Azimu, W.; Manatbay, B.; Li, Y.; Kaimaerdan, D.; Wang, H.E.; Reheman, A.; Muhatai, G. Genetic Diversity and Population Structure Analysis of Eight Local Chicken Breeds of Southern Xinjiang. Br. Poult. Sci. 2018, 59, 629–635. [Google Scholar] [CrossRef]
  67. Soltan, M.; Farrag, S.; Enab, A.; Abou-Elewa, E.; ElSafty, S.; Abushady, A. Sinai and Norfa Chicken Diversity Revealed by Microsatellite Markers. S. Afr. J. Anim. Sci. 2018, 48, 307. [Google Scholar] [CrossRef]
  68. Sabry, A.; Ramadan, S.; Hassan, M.M.; Mohamed, A.A.; Mohammedein, A.; Inoue-Murayama, M. Assessment of Genetic Diversity among Egyptian and Saudi Chicken Ecotypes and Local Egyptian Chicken Breeds Using Microsatellite Markers. J. Environ. Biol. 2021, 42, 33–39. [Google Scholar] [CrossRef]
  69. Dementieva, N.V.; Mitrofanova, O.V.; Dysin, A.P.; Kudinov, A.A.; Stanishevskaya, O.I.; Larkina, T.A.; Plemyashov, K.V.; Griffin, D.K.; Romanov, M.N.; Smaragdov, M.G. Assessing the Effects of Rare Alleles and Linkage Disequilibrium on Estimates of Genetic Diversity in the Chicken Populations. Animal 2021, 15, 100171. [Google Scholar] [CrossRef]
  70. Cendron, F.; Mastrangelo, S.; Tolone, M.; Perini, F.; Lasagna, E.; Cassandro, M. Genome-Wide Analysis Reveals the Patterns of Genetic Diversity and Population Structure of 8 Italian Local Chicken Breeds. Poult. Sci. 2021, 100, 441–451. [Google Scholar] [CrossRef]
  71. Brekke, C.; Groeneveld, L.F.; Meuwissen, T.H.E.; Sæther, N.; Weigend, S.; Berg, P. Assessing the Genetic Diversity Conserved in the Norwegian Live Poultry Genebank. Acta Agric. Scand. Sect. A—Anim. Sci. 2020, 69, 68–80. [Google Scholar] [CrossRef] [Green Version]
  72. Zhang, J.; Nie, C.; Li, X.; Ning, Z.; Chen, Y.; Jia, Y.; Han, J.; Wang, L.; Lv, X.; Yang, W.; et al. Genome-Wide Population Genetic Analysis of Commercial, Indigenous, Game, and Wild Chickens Using 600K SNP Microarray Data. Front. Genet. 2020, 11, 543294. [Google Scholar] [CrossRef]
  73. Luo, W.; Luo, C.; Wang, M.; Guo, L.; Chen, X.; Li, Z.; Zheng, M.; Folaniyi, B.S.; Luo, W.; Shu, D.; et al. Genome Diversity of Chinese Indigenous Chicken and the Selective Signatures in Chinese Gamecock Chicken. Sci. Rep. 2020, 10, 14532. [Google Scholar] [CrossRef] [PubMed]
  74. Nie, C.; Almeida, P.; Jia, Y.; Bao, H.; Ning, Z.; Qu, L. Genome-Wide Single-Nucleotide Polymorphism Data Unveil Admixture of Chinese Indigenous Chicken Breeds with Commercial Breeds. Genome Biol. Evol. 2019, 11, 1847–1856. [Google Scholar] [CrossRef] [PubMed]
  75. Mekchay, S.; Supakankul, P.; Assawamakin, A.; Wilantho, A.; Chareanchim, W.; Tongsima, S. Population Structure of Four Thai Indigenous Chicken Breeds. BMC Genet. 2014, 15, 40. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Gebeyehu, S.T. Genetic Mapping on Local Swedish Chicken Breeds; Swedish University of Agricultural Science: Uppsala, Sweden, 2021; Available online: https://stud.epsilon.slu.se/16684/1/Gebeyehy_S_210517.pdf (accessed on 31 March 2022).
  77. Bruno de Sousa, C.; Martinez, A.M.; Ginja, C.; Santos-Silva, F.; Carolino, M.I.; Delgado, J.V.; Gama, L.T. Genetic Diversity and Population Structure in Portuguese Goat Breeds. Livest. Sci. 2011, 135, 131–139. [Google Scholar] [CrossRef]
  78. Al-Araimi, N.A.; Al-Atiyat, R.; Luzuriaga-Neira, A.; Gaafar, O.M.; Kadim, I.T.; Al-Marzooqi, W.; Babiker, H.A.; Al-Kindi, M.N.; Al-Ansari, A.S.; Al-Lawati, A.H.; et al. Genetic Structure of Omani Goats Reveals Admixture among Populations from Geographically Proximal Sites. Small Rumin. Res. 2019, 178, 1–6. [Google Scholar] [CrossRef]
  79. Rout, P.K.; Joshi, M.B.; Mandal, A.; Laloe, D.; Singh, L.; Thangaraj, K. Microsatellite-Based Phylogeny of Indian Domestic Goats. BMC Genet. 2008, 9, 11. [Google Scholar] [CrossRef] [Green Version]
  80. Kim, K.S.; Yeo, J.S.; Lee, J.W.; Kim, J.W.; Choi, C.B. Genetic Diversity of Goats from Korea and China Using Microsatellite Analysis. Asian-Australas. J. Anim. Sci. 2002, 15, 461–465. [Google Scholar] [CrossRef]
  81. Korkmaz Ağaoğlu, Ö.; Ertuğrul, O. Assessment of Genetic Diversity, Genetic Relationship and Bottleneck Using Microsatellites in Some Native Turkish Goat Breeds. Small Rumin. Res. 2012, 105, 53–60. [Google Scholar] [CrossRef]
  82. Alvarez, I.; Traoré, A.; Kaboré, A.; Zaré, Y.; Fernández, I.; Tamboura, H.H.; Goyache, F. Microsatellite Analysis of the Rousse de Maradi (Red Sokoto) Goat of Burkina Faso. Small Rumin. Res. 2012, 105, 83–88. [Google Scholar] [CrossRef] [Green Version]
  83. Nomura, K.; Ishii, K.; Dadi, H.; Takahashi, Y.; Minezawa, M.; Cho, C.Y.; Sutopo; Faruque, M.O.; Nyamsamba, D.; Amano, T. Microsatellite DNA Markers Indicate Three Genetic Lineages in East Asian Indigenous Goat Populations. Anim. Genet. 2012, 43, 760–767. [Google Scholar] [CrossRef]
  84. Di, R.; Vahidi, S.M.F.; Ma, Y.H.; He, X.H.; Zhao, Q.J.; Han, J.L.; Guan, W.J.; Chu, M.X.; Sun, W.; Pu, Y.P. Microsatellite Analysis Revealed Genetic Diversity and Population Structure among Chinese Cashmere Goats. Anim. Genet. 2011, 42, 428–431. [Google Scholar] [CrossRef] [PubMed]
  85. Visser, C.; Hefer, C.A.; van Marle-Koster, E.; Kotze, A. Genetic Variation of Three Commercial and Three Indigenous Goat Populations in South Africa. S. Afr. J. Anim. Sci. 2004, 34, 24–27. [Google Scholar]
  86. Karsli, T.; Demir, E.; Fidan, H.G.; Aslan, M.; Karsli, B.A.; Arik, I.Z.; Semerci, E.S.; Karabag, K.; Balcioglu, M.S. Determination of Genetic Variability, Population Structure and Genetic Differentiation of Indigenous Turkish Goat Breeds Based on SSR loci. Small Rumin. Res. 2020, 190, 106147. [Google Scholar] [CrossRef]
  87. Missohou, A.; Poutya, M.R.; Nenonene, A.; Dayo, G.-K.; Ayssiwede, S.B.; Talaki, E.; Issa, Y.; Fané, A. Genetic Diversity and Differentiation in Nine West African Local Goat Breeds Assessed via Microsatellite Polymorphism. Small Rumin. Res. 2011, 99, 20–24. [Google Scholar] [CrossRef]
  88. Naqvi, A.; Bukhari, J.; Vahidi, S.; Utsunomiya, Y.; Garcia, J.; Babar, M.E.; Han, J.-L.; Pichler, R.; Periasamy, K. Microsatellite Based Genetic Diversity and Mitochondrial DNA D-Loop Variation in Economically Important Goat Breeds of Pakistan. Small Rumin. Res. 2017, 148, 62–71. [Google Scholar] [CrossRef] [Green Version]
  89. Du, X.-Y.; Cao, J.; Han, X.; Hao, H.-Z.; Yu, M.; Zhang, G.-X.; Zhao, S.-H.; Xu, H. Genetic Diversity and Population Structure among Eight Chinese Indigenous Goat Breeds in the Yellow River Valley. Small Rumin. Res. 2017, 148, 87–92. [Google Scholar] [CrossRef]
  90. Silva, P.; Dematawewa, C.; Kurukulasuriya, M.; Utsunomiya, Y.T.; Garcia, J.F.; Pichler, R.; Thiruvenkadan, A.; Ramasamy, S.; Han, J.-L.; Periasamy, K. Genetic Diversity Analysis of Major Sri Lankan Goat Populations Using Microsatellite and Mitochondrial DNA D-loop Variations. Small Rumin. Res. 2017, 148, 51–61. [Google Scholar] [CrossRef] [Green Version]
  91. Criscione, A.; Bordonaro, S.; Moltisanti, V.; Marletta, D. Differentiation of South Italian Goat Breeds in the Focus of Biodiversity Conservation. Small Rumin. Res. 2016, 145, 12–19. [Google Scholar] [CrossRef]
  92. Phyu, P.P.; Pichler, R.; Soe, O.; Aung, P.P.; Than, M.; Shamsuddin, M.; Diallo, A.; Periasamy, K. Genetic Diversity, Population Structure and Phylogeography of Myanmar Goats. Small Rumin. Res. 2017, 148, 33–42. [Google Scholar] [CrossRef]
  93. Beketov, S.V.; Piskunov, A.K.; Voronkova, V.N.; Petrov, S.N.; Kharzinova, V.R.; Dotsev, A.V.; Zinovieva, N.A.; Selionova, M.I.; Stolpovsky, Y.A. Genetic Diversity and Phylogeny of Fleece-Bearing Goats of Central and Middle Asia. Russ. J. Genet. 2021, 57, 816–824. [Google Scholar] [CrossRef]
  94. Carvalho, G.M.C.; Paiva, S.R.; Mariante, A.; Blackburn, H.D.; Araújo, A.M. Genetic Structure of Goat Breeds from Brazil and the United States: Implications for Conservation and Breeding Programs. J. Anim. Sci. 2015, 93, 4629–4636. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Nicoloso, L.; The Italian Goat Consortium; Bomba, L.; Colli, L.; Negrini, R.; Milanesi, M.; Mazza, R.; Sechi, T.; Frattini, S.; Talenti, A.; et al. Genetic Diversity of Italian Goat Breeds Assessed with a Medium-Density SNP Chip. Genet. Sel. Evol. 2015, 47, 62. [Google Scholar] [CrossRef] [PubMed]
  96. Paim, T.D.P.; Faria, D.A.; Hay, E.H.; McManus, C.; Lanari, M.R.; Esquivel, L.C.; Cascante, M.I.; Alfaro, E.J.; Mendez, A.; Faco, O.; et al. New World Goat Populations Are a Genetically Diverse Reservoir for Future Use. Sci. Rep. 2019, 9, 1476. [Google Scholar] [CrossRef]
  97. Oget, C.; Servin, B.; Palhière, I. Genetic Diversity Analysis of French Goat Populations Reveals Selective Sweeps Involved in Their Differentiation. Anim. Genet. 2019, 50, 54–63. [Google Scholar] [CrossRef] [Green Version]
  98. Berg, P.; Groeneveld, L.F.; Brekke, C.; Våge, D.I.; Sørheim, K.M.; Grøva, L. Genetic Characterization of a Small Closed Island Population of Norwegian Coastal Goat. Acta Agric. Scand. Sect. A—Anim. Sci. 2020, 69, 47–52. [Google Scholar] [CrossRef]
  99. Kumar, C.; Song, S.; Dewani, P.; Kumar, M.; Parkash, O.; Ma, Y.; Malhi, K.; Yang, N.; Mwacharo, J.M.; He, X.; et al. Population Structure, Genetic Diversity and Selection Signatures within Seven Indigenous Pakistani Goat Populations. Anim. Genet. 2018, 49, 592–604. [Google Scholar] [CrossRef]
  100. Cañon, J.; Checa, M.L.; Carleos, C.; Vega-Pla, J.L.; Vallejo, M.; Dunner, S. The Genetic Structure of Spanish Celtic Horse Breeds Inferred from Microsatellite Data. Anim. Genet. 2000, 31, 39–48. [Google Scholar] [CrossRef] [Green Version]
  101. Druml, T.; Curik, I.; Baumung, R.; Aberle, K.; Distl, O.; Solkner, J. Individual Based Assessment of Population Structure and Admixture in Austrian, Croatian and German Draught Horses. Heredity 2007, 98, 114–122. [Google Scholar] [CrossRef]
  102. Achmann, R.; Curik, I.; Dovc, P.; Kavar, T.; Bodò, I.; Habe, F.; Marti, E.; Sölkner, J.; Brem, G. Microsatellite Diversity, Population Subdivision and Gene Flow in the Lipizzan Horse. Anim. Genet. 2004, 35, 285–292. [Google Scholar] [CrossRef]
  103. Sild, E.; Rooni, K.; Värv, S.; Røed, K.; Popov, R.; Kantanen, J.; Viinalass, H. Genetic Diversity of Estonian Horse Breeds and Their Genetic Affinity to Northern European and Some Asian Breeds. Livest. Sci. 2019, 220, 57–66. [Google Scholar] [CrossRef]
  104. Ling, Y.H.; Ma, Y.H.; Guan, W.J.; Cheng, Y.J.; Wang, Y.P.; Han, J.L.; Mang, L.; Zhao, Q.J.; He, X.H.; Pu, Y.B.; et al. Evaluation of the Genetic Diversity and Population Structure of Chinese Indigenous Horse Breeds Using 27 Microsatellite Markers. Anim. Genet. 2011, 42, 56–65. [Google Scholar] [CrossRef] [PubMed]
  105. Baena, M.M.; Diaz, S.; Moura, R.S.; Meirelles, S.L.C. Genetic Characterization of Mangalarga Marchador Breed Horses Based on Microsatellite Molecular Markers. J. Equine Veter. Sci. 2020, 95, 103231. [Google Scholar] [CrossRef] [PubMed]
  106. Funk, S.M.; Guedaoura, S.; Juras, R.; Raziq, A.; Landolsi, F.; Luís, C.; Martínez, A.M.; Mayaki, A.M.; Mujica, F.; Oom, M.D.M.; et al. Major Inconsistencies of Inferred Population Genetic Structure Estimated in a Large Set of Domestic Horse Breeds Using Microsatellites. Ecol. Evol. 2020, 10, 4261–4279. [Google Scholar] [CrossRef] [Green Version]
  107. Gurgul, A.; Jasielczuk, I.; Semik-Gurgul, E.; Pawlina-Tyszko, K.; Szmatoła, T.; Polak, G.; Bugno-Poniewierska, M. Genetic Differentiation of the Two Types of Polish Cold-blooded Horses Included in the National Conservation Program. Animals 2020, 10, 542. [Google Scholar] [CrossRef] [Green Version]
  108. Putnová, L.; Štohl, R.; Vrtková, I. Using Nuclear Microsatellite Data to Trace the Gene Flow and Population Structure in Czech Horses. Czech J. Anim. Sci. 2019, 64, 67–77. [Google Scholar] [CrossRef] [Green Version]
  109. Khanshour, A.; Conant, E.; Juras, R.; Cothran, E.G. Data From: Microsatellite Analysis of Genetic Diversity and Population Structure of Arabian Horse Populations. J. Hered. 2013, 104, 386–398. [Google Scholar] [CrossRef] [Green Version]
  110. Petersen, J.L.; Mickelson, J.R.; Cothran, E.G.; Andersson, L.S.; Axelsson, J.; Bailey, E.; Bannasch, D.; Binns, M.M.; Borges, A.S.; Brama, P.; et al. Genetic Diversity in the Modern Horse Illustrated from Genome-Wide SNP Data. PLoS ONE 2013, 8, e54997. [Google Scholar] [CrossRef] [Green Version]
  111. Schurink, A.; Shrestha, M.; Eriksson, S.; Bosse, M.; Bovenhuis, H.; Back, W.; Johansson, A.M.; Ducro, B.J. The Genomic Makeup of Nine Horse Populations Sampled in the Netherlands. Genes 2019, 10, 480. [Google Scholar] [CrossRef] [Green Version]
  112. Almarzook, S.; Reissmann, M.; Arends, D.; Brockmann, G.A. Genetic Diversity of Syrian Arabian Horses. Anim. Genet. 2017, 48, 486–489. [Google Scholar] [CrossRef]
  113. Yousefi-Mashouf, N.; Mehrabani-Yeganeh, H.; Nejati-Javaremi, A.; Bailey, E.; Petersen, J.L. Genomic Comparisons of Persian Kurdish, Persian Arabian and American Thoroughbred Horse Populations. PLoS ONE 2021, 16, e0247123. [Google Scholar] [CrossRef] [PubMed]
  114. Gurgul, A.; Jasielczuk, I.; Semik-Gurgul, E.; Pawlina-Tyszko, K.; Stefaniuk-Szmukier, M.; Szmatoła, T.; Polak, G.; Tomczyk-Wrona, I.; Bugno-Poniewierska, M. A Genome-Wide Scan for Diversifying Selection Signatures in Selected Horse Breeds. PLoS ONE 2019, 14, e0210751. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  115. Al Abri, M.; Brooks, S.; Al-Saqri, N.; Alkharousi, K.; Johnson, E.; Alqaisi, O.; Al-Rawahi, A.; Al Marzooqi, A. Investigating the Population Structure and Genetic Diversity of Arabian Horses in Oman Using SNP Markers. Anim. Genet. 2021, 52, 304–310. [Google Scholar] [CrossRef] [PubMed]
  116. Vicente, A.A.; Carolino, M.I.; Sousa, M.C.O.; Ginja, C.; Silva, F.S.; Martinez, A.M.; Vega-Pla, J.L.; Carolino, N.; Gama, L.T. Genetic Diversity in Native and Commercial Breeds of Pigs in Portugal Assessed by Microsatellites. J. Anim. Sci. 2008, 86, 2496–2507. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  117. Fabuel, E.; Barragán, C.; Silió, L.; Rodríguez, M.C.; Toro, M.A. Analysis of Genetic Diversity and Conservation Priorities in Iberian Pigs Based on Microsatellite Markers. Heredity 2004, 93, 104–113. [Google Scholar] [CrossRef] [Green Version]
  118. Touma, S.; Arakawa, A.; Oikawa, T. Evaluation of the Genetic Structure of Indigenous Okinawa Agu Pigs Using Microsatellite Markers. Asian-Australas. J. Anim. Sci. 2020, 33, 212–218. [Google Scholar] [CrossRef] [Green Version]
  119. Gama, L.T.; BIOPIG Consortium; Martínez, A.M.; Carolino, I.; Landi, V.; Delgado, J.V.; Vicente, A.A.; Vega-Pla, J.L.; Cortés, O.; Sousa, C.O. Genetic Structure, Relationships and Admixture with Wild Relatives in Native Pig Breeds from Iberia and Its Islands. Genet. Sel. Evol. 2013, 45, 18. [Google Scholar] [CrossRef] [Green Version]
  120. Ba, N.V.; Arakawa, A.; Ishihara, S.; Nam, L.Q.; Thuy, T.T.T.; Dinh, N.C.; Ninh, P.H.; Cuc, N.T.K.; Kikuchi, K.; Pham, L.D.; et al. Evaluation of Genetic Richness among Vietnamese Native Pig Breeds Using Microsatellite Markers. Anim. Sci. J. 2020, 91, e13343. [Google Scholar] [CrossRef]
  121. Manea, M.A.; Georgescu, S.; Kevorkian, S.; Costache, M. Genetic Diversity Analyses of Seven Romanian Pig Populations Based on 10 Microsatellites. Rom. Biotechnol. Lett. 2009, 14, 4827–4834. [Google Scholar]
  122. Menéndez, J.; Goyache, F.; Beja-Pereira, A.; Fernández, I.; Menéndez-Arias, N.A.; Godinho, R.; Alvarez, I. Genetic Characterisation of the Endangered Gochu Asturcelta Pig Breed Using Microsatellite and Mitochondrial Markers: Insights for the Composition of the Iberian Native Pig Stock. Livest. Sci. 2016, 187, 162–167. [Google Scholar] [CrossRef]
  123. Druml, T.; Salajpal, K.; Dikic, M.; Urosevic, M.; Grilz-Seger, G.; Baumung, R. Genetic Diversity, Population Structure and Subdivision of Local Balkan Pig Breeds in Austria, Croatia, Serbia and Bosnia-Herzegovina and Its Practical Value in Conservation Programs. Genet. Sel. Evol. 2012, 44, 5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  124. Wilkinson, S. Genetic Diversity and Structure of Livestock Breeds. Ph.D. Thesis, University of Edinburgh, Edinburgh, UK, 2011. Available online: https://era.ed.ac.uk/handle/1842/6488 (accessed on 31 March 2022).
  125. Laval, G.; Iannuccelli, N.; Legault, C.; Milan, D.; Groenen, M.A.; Giuffra, E.; Andersson, L.; Nissen, P.H.; Jørgensen, C.B.; Beeckmann, P.; et al. Genetic Diversity of Eleven European Pig Breeds. Genet. Sel. Evol. 2000, 32, 187. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  126. Kim, T.H.; Kim, K.S.; Choi, B.H.; Yoon, D.H.; Jang, G.W.; Lee, K.T.; Chung, H.Y.; Lee, H.Y.; Park, H.S.; Lee, J.W. Genetic Structure of Pig Breeds from Korea and China Using Microsatellite Loci Analysis. J. Anim. Sci. 2005, 83, 2255–2263. [Google Scholar] [CrossRef] [PubMed]
  127. Wilkinson, S.; Archibald, A.L.; Haley, C.S.; Megens, H.-J.; Crooijmans, R.P.; Groenen, M.A.; Wiener, P.; Ogden, R. Development of a Genetic Tool for Product Regulation in the Diverse British Pig Breed Market. BMC Genom. 2012, 13, 580. [Google Scholar] [CrossRef] [Green Version]
  128. Gurgul, A.; Jasielczuk, I.; Ropka-Molik, K.; Semik-Gurgul, E.; Pawlina-Tyszko, K.; Szmatoła, T.; Szyndler-Nędza, M.; Bugno-Poniewierska, M.; Blicharski, T.; Szulc, K.; et al. A Genome-Wide Detection of Selection Signatures in Conserved and Commercial Pig Breeds Maintained in Poland. BMC Genet. 2018, 19, 95. [Google Scholar] [CrossRef]
  129. Cai, Z.; Sarup, P.; Ostersen, T.; Nielsen, B.; Fredholm, M.; Karlskov-Mortensen, P.; Sørensen, P.; Jensen, J.; Guldbrandtsen, B.; Lund, M.S.; et al. Genomic Diversity Revealed by Whole-Genome Sequencing in Three Danish Commercial Pig Breeds. J. Anim. Sci. 2020, 98, skaa229. [Google Scholar] [CrossRef]
  130. Ai, H.; Huang, L.; Ren, J. Genetic Diversity, Linkage Disequilibrium and Selection Signatures in Chinese and Western Pigs Revealed by Genome-Wide SNP Markers. PLoS ONE 2013, 8, e56001. [Google Scholar] [CrossRef] [Green Version]
  131. Lukić, B.; Ferenčaković, M.; Šalamon, D.; Čačić, M.; Orehovački, V.; Iacolina, L.; Curik, I.; Cubric-Curik, V. Conservation Genomic Analysis of the Croatian Indigenous Black Slavonian and Turopolje Pig Breeds. Front. Genet. 2020, 11, 261. [Google Scholar] [CrossRef]
  132. Herrero-Medrano, J.M.; Megens, H.-J.; Groenen, M.A.; Ramis, G.; Bosse, M.; Perez-Enciso, M.; Crooijmans, R.P. Conservation Genomic Analysis of Domestic and Wild Pig Populations from the Iberian Peninsula. BMC Genet. 2013, 14, 106. [Google Scholar] [CrossRef] [Green Version]
  133. Kim, Y.-M.; Seong, H.-S.; Lee, J.-J.; Son, D.-H.; Kim, J.-S.; Sa, S.-J.; Kim, Y.-S.; Choi, T.-J.; Cho, K.-H.; Hong, J.-K.; et al. Genome-Wide Investigation of a Korean Synthetic Breed, Woori-Heukdon using the Illumina PorcineSNP60K BeadChip. Genes Genom. 2020, 42, 1443–1453. [Google Scholar] [CrossRef]
  134. Gorssen, W.; Meyermans, R.; Buys, N.; Janssens, S. SNP Genotypes Reveal Breed Substructure, Selection Signatures and Highly Inbred Regions in Piétrain Pigs. Anim. Genet. 2020, 51, 32–42. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  135. Faria, D.; Wilson, C.; Paiva, S.; Blackburn, H.D. Assessing Sus Scrofa Diversity among Continental United States, and Pacific Islands Populations Using Molecular Markers from a Gene Banks Collection. Sci. Rep. 2019, 9, 3173. [Google Scholar] [CrossRef] [PubMed]
  136. Bovo, S.; Ribani, A.; Muñoz, M.; Alves, E.; Araujo, J.P.; Bozzi, R.; Čandek-Potokar, M.; Charneca, R.; Di Palma, F.; Etherington, G.; et al. Whole-Genome Sequencing of European Autochthonous and Commercial Pig Breeds Allows the Detection of Signatures of Selection for Adaptation of Genetic Resources to Different Breeding and Production Systems. Genet. Sel. Evol. 2020, 52, 33. [Google Scholar] [CrossRef] [PubMed]
  137. Xiao, Q.; Zhang, Z.; Sun, H.; Wang, Q.; Pan, Y. Pudong White Pig: A Unique Genetic Resource Disclosed by Sequencing Data. Animal 2017, 11, 1117–1124. [Google Scholar] [CrossRef]
  138. Qin, M.; Li, C.; Li, Z.; Chen, W.; Zeng, Y. Genetic Diversities and Differentially Selected Regions Between Shandong Indigenous Pig Breeds and Western Pig Breeds. Front. Genet. 2020, 10, 1351. [Google Scholar] [CrossRef] [PubMed]
  139. Alonso, I.; Ibáñez-Escriche, N.; Noguera, J.L.; Casellas, J.; De Hijas-Villalba, M.M.; Gracia-Santana, M.J.; Varona, L. Genomic Differentiation among Varieties of Iberian Pig. Span. J. Agric. Res. 2020, 18, e0401. [Google Scholar] [CrossRef]
  140. Wang, Y.; Zhao, X.; Wang, C.; Wang, W.; Zhang, Q.; Wu, Y.; Wang, J. High-Density Single Nucleotide Polymorphism Chip-Based Conservation Genetic Analysis of Indigenous Pig Breeds from Shandong Province, China. Anim. Biosci. 2021, 34, 1123–1133. [Google Scholar] [CrossRef]
  141. Pons, A.; Landi, V.; Martinez, A.; Delgado, J. The Biodiversity and Genetic Structure of Balearic Sheep Breeds. J. Anim. Breed. Genet. 2015, 132, 268–276. [Google Scholar] [CrossRef]
  142. Bowles, D.; Carson, A.; Isaac, P. Genetic Distinctiveness of the Herdwick Sheep Breed and Two Other Locally Adapted Hill Breeds of the UK. PLoS ONE 2014, 9, e87823. [Google Scholar] [CrossRef]
  143. Curkovic, M.; Ramljak, J.; Ivankovic, S.; Mioč, B.; Pavić, V.; Brka, M.; Veit-Kensch, C.; Medugorac, I.; Ivanković, A. The Genetic Diversity and Structure of 18 Sheep Breeds Exposed to Isolation and Selection. J. Anim. Breed. Genet. 2016, 133, 71–80. [Google Scholar] [CrossRef]
  144. Tapio, I.; Tapio, M.; Grislis, Z.; Holm, L.-E.; Jeppsson, S.; Kantanen, J.; Miceikiene, I.; Olsaker, I.; Viinalass, H.; Eythorsdottir, E. Unfolding of Population Structure in Baltic Sheep Breeds Using Microsatellite Analysis. Heredity 2005, 94, 448–456. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  145. Loukovitis, D.; Siasiou, A.; Mitsopoulos, I.; Lymberopoulos, A.G.; Laga, V.; Chatziplis, D. Genetic Diversity of Greek Sheep Breeds and Transhumant Populations Utilizing Microsatellite Markers. Small Rumin. Res. 2016, 136, 238–242. [Google Scholar] [CrossRef]
  146. Ciani, E.; Ciampolini, R.; D’andrea, M.; Castellana, E.; Cecchi, F.; Incoronato, C.; D’angelo, F.; Albenzio, M.; Pilla, F.; Matassino, D.; et al. Analysis of Genetic Variability within and among Italian Sheep Breeds Reveals Population Stratification and Suggests the Presence of a Phylogeographic Gradient. Small Rumin. Res. 2013, 112, 21–27. [Google Scholar] [CrossRef]
  147. Baumung, R.; Cubric-Curik, V.; Schwend, K.; Achmann, R.; Sölkner, J. Genetic Characterisation and Breed Assignment in Austrian Sheep Breeds Using Microsatellite Marker Information. J. Anim. Breed. Genet. 2006, 123, 265–271. [Google Scholar] [CrossRef] [PubMed]
  148. Alvarez, I.; Gutiérrez, J.P.; Royo, L.; Fernández, I.; Gómez, E.; Arranz, J.; Goyache, F. Testing the Usefulness of the Molecular Coancestry Information to Assess Genetic Relationships in Livestock Using a Set of Spanish Sheep Breeds. J. Anim. Sci. 2005, 83, 737–744. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  149. Rendo, F.; Iriondo, M.; Jugo, B.; Mazón, L.; Aguirre, A.; Vicario, A.; Estonba, A. Tracking Diversity and Differentiation in Six Sheep Breeds from the North Iberian Peninsula through DNA Variation. Small Rumin. Res. 2004, 52, 195–202. [Google Scholar] [CrossRef]
  150. Zhong, T.; Han, J.L.; Guo, J.; Zhao, Q.J.; Fu, B.L.; He, X.H.; Jeon, J.T.; Guan, W.J.; Ma, Y.H. Genetic Diversity of Chinese Indigenous Sheep Breeds Inferred from Microsatellite Markers. Small Rumin. Res. 2010, 90, 88–94. [Google Scholar] [CrossRef]
  151. Salamon, D.; Gutierrez-Gil, B.; Arranz, J.; Barreta, J.; Batinic, V.; Dzidic, A. Genetic Diversity and Differentiation of 12 Eastern Adriatic and Western Dinaric Native Sheep Breeds Using Microsatellites. Animal 2014, 8, 200–207. [Google Scholar] [CrossRef] [Green Version]
  152. Dossybayev, K.; Orazymbetova, Z.; Mussayeva, A.; Saitou, N.; Zhapbasov, R.; Makhatov, B.; Bekmanov, B. Genetic Diversity of Different Breeds of Kazakh Sheep Using Microsatellite Analysis. Arch. Anim. Breed. 2019, 62, 305–312. [Google Scholar] [CrossRef]
  153. Xia, Q.; Wang, X.; Pan, Z.; Zhang, R.; Wei, C.; Chu, M.; Di, R. Genetic Diversity and Phylogenetic Relationship of Nine Sheep Populations Based on Microsatellite Markers. Arch. Anim. Breed. 2021, 64, 7–16. [Google Scholar] [CrossRef]
  154. Uzun, M.; Gutiérrez-Gil, B.; Arranz, J.-J.; Primitivo, F.S.; Saatci, M.; Kaya, M.; Bayón, Y. Genetic Relationships among Turkish Sheep. Genet. Sel. Evol. 2006, 38, 513. [Google Scholar] [CrossRef] [PubMed]
  155. Selepe, M.M.; Ceccobelli, S.; Lasagna, E.; Kunene, N.W. Genetic Structure of South African Nguni (Zulu) Sheep Populations Reveals Admixture with Exotic Breeds. PLoS ONE 2018, 13, e0196276. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  156. Pichler, R.; Hussain, T.; Xu, W.; Aftab, A.; Babar, M.E.; Thiruvenkadan, A.K.; Ramasamy, S.; Teneva, A.; Sebastino, K.; Sanou, M.; et al. Short Tandem Repeat (STR) Based Genetic Diversity and Relationship of Domestic Sheep Breeds with Primitive Wild Punjab Urial Sheep (Ovis Vignei Punjabiensis). Small Rumin. Res. 2017, 148, 11–21. [Google Scholar] [CrossRef]
  157. Machová, K.; Hofmanová, B.; Rychtářová, J.; Vostrý, L.; Moravčíková, N.; Kasarda, R. Genetic Variability Analysis of 26 Sheep Breeds in the Czech Republic. Acta Fytotech. Zootech. 2020, 23, 38–45. [Google Scholar] [CrossRef]
  158. Ozmen, O.; Kul, S.; Gok, T. Determination of Genetic Diversity of the Akkaraman Sheep Breed from Turkey. Small Rumin. Res. 2020, 182, 37–45. [Google Scholar] [CrossRef]
  159. Naqvi, A.N.; Mahmood, S.; Vahidi, S.M.F.; Abbas, S.M.; Utsunomiya, Y.T.; Garcia, J.F.; Periasamy, K.; Abbas, M. Assessment of Genetic Diversity and Structure of Major Sheep Breeds from Pakistan. Small Rumin. Res. 2017, 148, 72–79. [Google Scholar] [CrossRef]
  160. Ruiz-Larrañaga, O.; Nanaei, H.A.; Montes, I.; Mehrgardi, A.A.; Abdolmohammadi, A.; Kharrati-Koopaee, H.; Sohrabi, S.S.; Rendo, F.; Manzano, C.; Estonba, A.; et al. Genetic Structure of Iranian Indigenous Sheep Breeds: Insights for Conservation. Trop. Anim. Health Prod. 2020, 52, 2283–2290. [Google Scholar] [CrossRef]
  161. E, G.-X.; Ma, Y.-H.; Chu, M.-X.; Hong, Q.-H.; Huang, Y.-F. Current Genetic Diversity in Eight Local Chinese Sheep Populations. Mol. Biol. Rep. 2019, 46, 1307–1311. [Google Scholar] [CrossRef]
  162. Soma, P.; Kotze, A.; Grobler, J.P.; van Wyk, J.B. South African Sheep Breeds: Population Genetic Structure and Conservation Implications. Small Rumin. Res. 2012, 103, 112–119. [Google Scholar] [CrossRef]
  163. McHugo, G.P.; Browett, S.; Randhawa, I.A.S.; Howard, D.J.; Mullen, M.P.; Richardson, I.W.; Park, S.D.E.; Magee, D.A.; Scraggs, E.; Dover, M.J.; et al. A Population Genomics Analysis of the Native Irish Galway Sheep Breed. Front. Genet. 2019, 10, 927. [Google Scholar] [CrossRef] [Green Version]
  164. Zsolnai, A.; Egerszegi, I.; Rózsa, L.; Anton, I. Genetic Status of Lowland-Type Racka Sheep Colour Variants. Animal 2020, 15, 100080. [Google Scholar] [CrossRef]
  165. Deniskova, T.E.; Dotsev, A.V.; Selionova, M.I.; Kunz, E.; Medugorac, I.; Reyer, H.; Wimmers, K.; Barbato, M.; Traspov, A.A.; Brem, G.; et al. Population Structure and Genetic Diversity of 25 Russian Sheep Breeds Based on Whole-Genome Genotyping. Genet. Sel. Evol. 2018, 50, 29. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  166. Belabdi, I.; Ouhrouch, A.; Lafri, M.; Gaouar, S.B.S.; Ciani, E.; Benali, A.R.; Ouelhadj, H.O.; Haddioui, A.; Pompanon, F.; Blanquet, V.; et al. Genetic Homogenization of Indigenous Sheep Breeds in Northwest Africa. Sci. Rep. 2019, 9, 7920. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  167. Eydivandi, S.; Sahana, G.; Momen, M.; Moradi, M.; Schönherz, A. Genetic Diversity in Iranian Indigenous Sheep Vis-à-Vis Selected Exogenous Sheep Breeds and Wild Mouflon. Anim. Genet. 2020, 51, 772–787. [Google Scholar] [CrossRef] [PubMed]
  168. Davenport, K.M.; Hiemke, C.; McKay, S.D.; Thorne, J.W.; Lewis, R.M.; Taylor, T.; Murdoch, B.M. Genetic Structure and Admixture in Sheep from Terminal Breeds in the United States. Anim. Genet. 2020, 51, 284–291. [Google Scholar] [CrossRef] [Green Version]
  169. Wiener, P.; Robert, C.; Ahbara, A.; Salavati, M.; Abebe, A.; Kebede, A.; Wragg, D.; Friedrich, J.; Vasoya, D.; Hume, D.A.; et al. Whole-Genome Sequence Data Suggest Environmental Adaptation of Ethiopian Sheep Populations. Genome Biol. Evol. 2021, 13, evab014. [Google Scholar] [CrossRef]
  170. Mukesh, M.; Sodhi, M.; Bhatia, S. Microsatellite-Based Diversity Analysis and Genetic Relationships of Three Indian Sheep Breeds. J. Anim. Breed. Genet. 2006, 123, 258–264. [Google Scholar] [CrossRef]
  171. Getachew, T.; Haile, A.; Mészáros, G.; Rischkowsky, B.; Huson, H.; Gizaw, S.; Wurzinger, M.; Mwai, A.; Sölkner, J. Genetic Diversity, Population Structure and Runs of Homozygosity in Ethiopian Short Fat-Tailed and Awassi Sheep Breeds Using Genome-Wide 50k SNP Markers. Livest. Sci. 2020, 232, 103899. [Google Scholar] [CrossRef]
  172. Michailidou, S.; Tsangaris, G.; Fthenakis, G.C.; Tzora, A.; Skoufos, I.; Karkabounas, S.C.; Banos, G.; Argiriou, A.; Arsenos, G. Genomic Diversity and Population Structure of Three Autochthonous Greek Sheep Breeds Assessed with Genome-Wide DNA Arrays. Mol. Genet. Genom. 2018, 293, 753–768. [Google Scholar] [CrossRef]
  173. Pariset, L.; Mariotti, M.; Gargani, M.; Joost, S.; Negrini, R.; Perez, T.; Bruford, M.; Marsan, P.A.; Valentini, A. Genetic Diversity of Sheep Breeds from Albania, Greece, and Italy Assessed by Mitochondrial DNA and Nuclear Polymorphisms (SNPs). Sci. World J. 2011, 11, 1641–1659. [Google Scholar] [CrossRef] [Green Version]
  174. Gaouar, S.B.S.; Lafri, M.; Djaout, A.; El-Bouyahiaoui, R.; Bouri, A.; Bouchatal, A.; Maftah, A.; Ciani, E.; Da Silva, A.B. Genome-Wide Analysis Highlights Genetic Dilution in Algerian Sheep. Heredity 2017, 118, 293–301. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  175. Wei, C.; Wang, H.; Liu, G.; Wu, M.; Cao, J.; Liu, Z.; Liu, R.; Zhao, F.; Zhang, L.; Lu, J.; et al. Genome-Wide Analysis Reveals Population Structure and Selection in Chinese Indigenous Sheep Breeds. BMC Genom. 2015, 16, 194. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  176. Gurgul, A.; Jasielczuk, I.; Miksza-Cybulska, A.; Kawęcka, A.; Szmatoła, T.; Krupiński, J. Evaluation of Genetic Differentiation and Genome-Wide Selection Signatures in Polish Local Sheep Breeds. Livest. Sci. 2021, 251, 104635. [Google Scholar] [CrossRef]
  177. Abied, A.; Bagadi, A.; Bordbar, F.; Pu, Y.; Augustino, S.M.; Xue, X.; Xing, F.; Gebreselassie, G.; Han, J.-L.; Mwacharo, J.-L.H.J.M.; et al. Genomic Diversity, Population Structure, and Signature of Selection in Five Chinese Native Sheep Breeds Adapted to Extreme Environments. Genes 2020, 11, 494. [Google Scholar] [CrossRef]
  178. Ciani, E.; The International Sheep Genomics Consortium; Lasagna, E.; D’Andrea, M.; Alloggio, I.; Marroni, F.; Ceccobelli, S.; Bermejo, J.V.D.; Sarti, F.M.; Kijas, J.; et al. Merino and Merino-Derived Sheep Breeds: A Genome-Wide Intercontinental Study. Genet. Sel. Evol. 2015, 47, 64. [Google Scholar] [CrossRef] [Green Version]
  179. Siegel, S.; Castellan, N.J., Jr. Nonparametric Statistics for the Behavioral Sciences, 2nd ed.; Mcgraw-Hill Book Company: New York, NY, USA, 1988; ISBN 0-07-057357-3. [Google Scholar]
  180. Crawley, M. The R Book, 2nd ed.; John Wiley & Sons: New York, NY, USA, 2012; p. 1051. ISBN 978-0-470-97392-9. [Google Scholar]
  181. R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2013. [Google Scholar]
  182. FAO. Status and Trends of Animal Genetic Resources-2018; FAO: Rome, Italy, 2019; Available online: http://www.fao.org/3/my867en/my867en.pdf (accessed on 31 March 2022).
  183. Pearse, D.E.; Crandall, K.A. Beyond FST: Analysis of Population Genetic Data for Conservation. Conserv. Genet. 2004, 5, 585–602. [Google Scholar] [CrossRef]
  184. Legendre, P.; Fortin, M.-J. Comparison of the Mantel Test and Alternative Approaches for Detecting Complex Multivariate Relationships in the Spatial Analysis of Genetic Data. Mol. Ecol. Resour. 2010, 10, 831–844. [Google Scholar] [CrossRef]
  185. Winton, C.L.; Hegarty, M.J.; McMahon, R.; Slavov, G.T.; McEwan, N.R.; Davies-Morel, M.C.G.; Morgan, C.M.; Powell, W.; Nash, D.M. Genetic Diversity and Phylogenetic Analysis of Native Mountain Ponies of Britain and Ireland Reveals a Novel Rare Population. Ecol. Evol. 2013, 3, 934–947. [Google Scholar] [CrossRef] [Green Version]
  186. Hall, S.J.G. Livestock Biodiversity. Genetic Resources for the Farming of the Future; Blackwell Science: Oxford, UK, 2004; ISBN 0-632-05499-9. [Google Scholar]
Animals 12 01115 g001 550
Figure 1. Boxplots summarizing, for each species, values of the Fst statistic for breed pairs according to the spatial relationship of the members of the pair. Data from studies using MS methodologies.
Figure 1. Boxplots summarizing, for each species, values of the Fst statistic for breed pairs according to the spatial relationship of the members of the pair. Data from studies using MS methodologies.
Animals 12 01115 g001
Animals 12 01115 g002 550
Figure 2. Boxplots summarizing, for each species, values of the Fst statistic for breed pairs according to the spatial relationship of the members of the pair. Data from studies using SNP methodologies.
Figure 2. Boxplots summarizing, for each species, values of the Fst statistic for breed pairs according to the spatial relationship of the members of the pair. Data from studies using SNP methodologies.
Animals 12 01115 g002
Table
Table 1. Data sources. MS: microsatellites; SNP: single-nucleotide polymorphisms. The reference codes are internal to the project and enable cross-reference to Supplementary Information Files.
Table 1. Data sources. MS: microsatellites; SNP: single-nucleotide polymorphisms. The reference codes are internal to the project and enable cross-reference to Supplementary Information Files.
SpeciesMethodologyReference Code and CitationNumber of Breed Pair Fst Calculations
CattleMS11974 [2]2
CattleMS12398 [21]21
CattleMS12500 [22]2
CattleMS12919 [23]21
CattleMS13438 [20]153
CattleMS13930 [24]3
CattleMS14407 [25]6
CattleMS18988 [26]10
CattleMS18989 [27]153
CattleMS19230 [28]6
CattleMS19287 [29]21
CattleMS1929 [30]6
CattleMS19335 [31]595
CattleMS19649 [32]10
CattleMS19651 [33]6
CattleMS19652 [34]36
CattleMS19653 [35]21
CattleMS7445 [36]209
CattleMS8012 [37]10
CattleSNP11390 [38]1081
CattleSNP11637 [39]28
CattleSNP16251 [40]21
CattleSNP13194 [41]15
CattleSNP16089 [42]15
CattleSNP18750 [4]105
CattleSNP18783 [43]496
CattleSNP18993 [44]741
CattleSNP19039 [45]378
CattleSNP19104 [46]36
CattleSNP19158 [47]135
CattleSNP19261 [48]435
CattleSNP19277 [49]55
CattleSNP19287 [29]21
CattleSNP19409 [50]210
ChickenMS17999 [51]15
ChickenMS19057 [16]276
ChickenMS19163 [52]21
ChickenMS19166 [53]59
ChickenMS19248 [54]120
ChickenMS19280 [55]10
ChickenMS19281 [56]78
ChickenMS19310 [57]10
ChickenMS19330 [58]36
ChickenMS19331 [59]28
ChickenMS19414 [60]3
ChickenMS19415 [61]105
ChickenMS19416 [62]15
ChickenMS19434 [63]28
ChickenMS19637 [64]288
ChickenMS19642 [65]15
ChickenMS19654 [66]28
ChickenMS19655 [67]1
ChickenMS19656 [68]45
ChickenSNP19192 [69]21
ChickenSNP19283 [70]28
ChickenSNP19302 [71]45
ChickenSNP19316 [72]105
ChickenSNP19331 [59]28
ChickenSNP19431 [73]231
ChickenSNP19432 [74]28
ChickenSNP19433 [75]21
ChickenSNP19533 [76]28
GoatMS16043 [77]15
GoatMS18124 [78]36
GoatMS19107 [79]21
GoatMS19108 [80]3
GoatMS19109 [81]10
GoatMS19110 [82]10
GoatMS19122 [83]155
GoatMS19125 [84]66
GoatMS19215 [85]4
GoatMS19231 [86]6
GoatMS19393 [87]36
GoatMS19601 [88]10
GoatMS19603 [89]36
GoatMS19605 [90]10
GoatMS19606 [91]21
GoatMS19609 [92]3
GoatMS19611 [93]21
GoatMS19646 [94]45
GoatSNP15770 [95]105
GoatSNP18126 [17]10,296
GoatSNP19205 [96]136
GoatSNP19256 [97]28
GoatSNP19305 [98]3
GoatSNP19643 [99]21
HorseMS13437 [100]28
HorseMS15788 [101]55
HorseMS19025 [102]7
HorseMS19162 [13]701
HorseMS19169 [103]136
HorseMS19199 [104]325
HorseMS19211 [105]21
HorseMS19264 [106]4371
HorseMS19300 [107]1
HorseMS19303 [108]902
HorseMS19668 [109]3
HorseSNP16649 [110]665
HorseSNP19260 [111]36
HorseSNP19265 [112]3
HorseSNP19299 [113]3
HorseSNP19300 [107]1
HorseSNP19301 [114]15
HorseSNP19400 [115]20
PigMS1144 [116]21
PigMS12225 [117]15
PigMS19293 [118]28
PigMS19295 [119]136
PigMS19327 [120]351
PigMS19426 [121]21
PigMS19636 [122]3
PigMS19639 [123]28
PigMS554 [124]231
PigMS6025 [125]45
PigMS8242 [126]36
PigSNP19058 [127]91
PigSNP19234 [128]6
PigSNP19262 [129]3
PigSNP19270 [130]136
PigSNP19272 [131]105
PigSNP19276 [132]21
PigSNP19292 [133]21
PigSNP19328 [134]28
PigSNP19410 [135]171
PigSNP19412 [136]253
PigSNP19420 [137]55
PigSNP19422 [138]45
PigSNP19425 [139]3
PigSNP19661 [140]66
SheepMS19244 [141]45
SheepMS14033 [142]3
SheepMS15852 [143]153
SheepMS17623 [144]21
SheepMS19002 [145]117
SheepMS19032 [146]12
SheepMS19221 [147]55
SheepMS19222 [148]21
SheepMS19223 [149]36
SheepMS19239 [8]91
SheepMS19240 [150]55
SheepMS19243 [151]66
SheepMS19246 [152]10
SheepMS19247 [153]36
SheepMS19287 [29]146
SheepMS19381 [154]15
SheepMS19385 [155]55
SheepMS19597 [156]66
SheepMS19598 [157]325
SheepMS19599 [158]15
SheepMS19604 [159]10
SheepMS19625 [160]276
SheepMS19630 [161]15
SheepMS2473 [162]190
SheepMS4401 [9]249
SheepMS4722 [14]1596
SheepSNP18433 [163]55
SheepSNP18991 [164]3403
SheepSNP19094 [165]300
SheepSNP19132 [166]89
SheepSNP19136 [167]105
SheepSNP19154 [168]15
SheepSNP19214 [169]78
SheepSNP19226 [170]3
SheepSNP19296 [171]6
SheepSNP19332 [172]3
SheepSNP19436 [173]153
SheepSNP19447 [174]27
SheepSNP19499 [175]45
SheepSNP19561 [176]21
SheepSNP19648 [177]10
Table
Table 2. Classification of geographical relationships of breed pairs.
Table 2. Classification of geographical relationships of breed pairs.
Spatial Relationship of BreedsCodeGeographical Class
In the same country1-SameSame country
In countries sharing a land border2-Land-adj
In countries sharing a water border (sea or lake)3-Marine-adj
In countries separated by a third country with land borders4-Nbut1Regional
In countries separated by a third country with a water border5-Nbut1marine
In more widely separated countries6-RemoteRemote
One member of breed pair a wild ancestor (1)7-Wild_ancestorWild ancestor
(1) Mouflon, bezoar, wild boar, red jungle fowl, Przewalski horse.
Table
Table 3. Global breeds.
Table 3. Global breeds.
CattleChickenGoatHorsePig
Aberdeen AngusAVIANDIV (1)AlpineThoroughbred/Pur SangLarge White
Brown SwissCommercial (2)Angora Piétrain
CharolaisRhode Island RedNubian/Anglo Nubian
Holstein Saanen
Guernsey Toggenburg
Hereford
Jersey
Limousin
Simmental
(1) Fst calculations from a global panel of commercial chicken breeds [56]. (2) All commercial breeds, varieties, and strains.
Table
Table 4. Summary of studies from which Fst data were extracted, according to species and methodology.
Table 4. Summary of studies from which Fst data were extracted, according to species and methodology.
CattleSheepGoatHorsePigChickenTotals
MS
Number of studies192618111119104
Total number of breeds143302124190981831040
Median (range) breed pairs per study10
(2–595)		55
(3–1596)		18
(3–155)		55
(1–4371)		28
(3–351)		28
(1–288)
Total breed pairs129136795086550915118114,124
SNP
Number of studies15156714966
Total number of breeds1841921766110678797
Median (range) breed pairs per study105
(15–1081)		45
(3–3403)		67
(3–10,296)		15
(1–665)		50
(3–253)		28
(21–231)
Total breed pairs3772431310,589743100453520,906
Table
Table 5. Summary statistics for Fst between breed pairs, classified by method (MS and SNP) and by spatial relationships between breeds. For definitions as abbreviated here, see Table 2.
Table 5. Summary statistics for Fst between breed pairs, classified by method (MS and SNP) and by spatial relationships between breeds. For definitions as abbreviated here, see Table 2.
Species and MethodologyMedianMaxMinMeanBreed Pairs
Cattle MS
1-Same0.06 (1)0.3810.0010.069320
2-Land-Adj0.0690.3310.0070.083260
3-Marine-adj0.0850.1130.0480.07811
4-Nbut10.0740.2730.0090.082144
5-Nbut1marine0.1240.190.0810.1289
6-Remote0.1050.4080.0010.127547
Cattle SNPMedianMaxMinMeanbreed pairs
1-Same0.0770.3940.0020.087976
2-Land-Adj0.080.3350.0020.085604
3-Marine-adj0.1270.2460.0450.13201
4-Nbut10.0920.2790.010.095165
5-Nbut1marine0.1260.2310.0470.127271
6-Remote0.1480.4740.0010.1721555
Sheep MSMedianMaxMinMeanbreed pairs
1-Same0.0630.39500.0841258
2-Land-Adj0.0810.2710.0080.089403
3-Marine-adj0.0790.2040.0340.084181
4-Nbut10.0710.2530.0060.079416
5-Nbut1marine0.1020.2750.0460.112120
6-Remote0.0780.2880.0140.0891290
7-Wild_ancestor0.2060.2910.1710.21311
Sheep SNPMedianMaxMinMeanbreed pairs
1-Same0.0610.28600.072643
2-Land-Adj0.0830.32800.09343
3-Marine-adj0.1460.30.0490.155188
4-Nbut10.0960.2170.0240.102160
5-Nbut1marine0.1710.2880.0540.166132
6-Remote0.1360.39100.1452833
7-Wild_ancestor0.4110.4620.3270.39714
Horse MSMedianMaxMinMeanbreed pairs
1-Same0.0410.29100.058763
2-Land-Adj0.090.33900.103515
3-Marine-adj0.110.3490.0070.119244
4-Nbut10.080.35400.101372
5-Nbut1marine0.1060.3100.109546
6-Remote0.090.450.0030.1053938
7-Wild_ancestor0.210.3890.140.217172
Horse SNPMedianMaxMinMeanbreed pairs
1-Same0.0910.2010.0020.09569
2-Land-Adj0.0940.2270.0060.09950
3-Marine-adj0.1070.1850.0380.11131
4-Nbut10.0840.1830.0510.0919
5-Nbut1marine0.1210.2020.040.11959
6-Remote0.0980.360.0150.107515
Goat MSMedianMaxMinMeanbreed pairs
1-Same0.0420.2830.0050.05208
2-Land-Adj0.0480.2590.010.05540
3-Marine-adj0.1070.3390.0350.13619
4-Nbut10.0850.3310.0270.10540
5-Nbut1marine0.1040.2290.0580.11124
6-Remote0.1410.4390.0260.153177
Goat SNPMedianMaxMinMeanbreed pairs
1-Same0.0760.30300.084670
2-Land-Adj0.0960.2740.0050.103550
3-Marine-adj0.1080.2520.0280.1385
4-Nbut10.0840.3030.0940.096440
5-Nbut1marine0.150.2930.0540.16138
6-Remote0.1610.5560.1660.1768547
7-Wild_ancestor0.1720.4640.1790.179159
Pig MSMedianMaxMinMeanbreed pairs
1-Same0.060.7740.010.134464
2-Land-Adj0.2470.5560.0390.24474
3-Marine-adj0.2210.4130.0360.21428
4-Nbut10.3170.490.0670.30312
5-Nbut1marine0.220.340.1080.22443
6-Remote0.2690.5120.0250.249240
7-Wild_ancestor0.2540.650.0810.26954
Pig SNPMedianMaxMinMeanbreed pairs
1-Same0.150.750.030.184299
2-Land-Adj0.1560.670.040.18572
3-Marine-adj0.190.770.0710.26852
4-Nbut10.1390.310.0670.15270
5-Nbut1marine0.170.350.0430.17921
6-Remote0.220.820.0460.263417
7-Wild_ancestor0.230.560.10.24373
Chicken MSMedianMaxMinMeanbreed pairs
1-Same0.1940.520.0060.198629
2-Land-Adj0.0480.160.0170.06135
3-Marine-adj0.280.390.110.26925
4-Nbut10.0750.40.0360.115139
5-Nbut1marine0.2450.420.140.2516
6-Remote0.240.630.0220.242300
7-Wild_ancestor0.2490.120.0880.25137
Chicken SNPMedianMaxMinMeanbreed pairs
1-Same0.1130.5650.0110.166261
2-Land-Adj0.080.3040.0070.11622
3-Marine-adj0.1320.1320.1320.1321
4-Nbut10.2550.280.130.234
5-Nbut1marine0.2450.2450.2450.2451
6-Remote0.2260.560.060.259204
7-Wild_ancestor0.1250.2610.0490.14142
(1) For each species × methodology combination the greatest and lowest median values are underlined (excluding those relating to wild ancestors).
Table
Table 6. Proportions of breed pairs in each geographical class, compared between methodologies.
Table 6. Proportions of breed pairs in each geographical class, compared between methodologies.
MSSNPMSSNPMSSNPMSSNP
TotalTotal%
1-Same		%
1-Same		% Regional% Regional%
6-Remote		%
6-Remote
Cattle1291377224.825.932.832.942.441.2
Chicken114449355.052.918.85.726.241.4
Goat50810,43040.96.424.211.634.881.9
Horse637874312.09.326.321.461.769.3
Pig86193153.932.118.223.127.944.8
Sheep3668429934.315.030.519.135.265.9
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Hall, S.J.G. Genetic Differentiation among Livestock Breeds—Values for Fst. Animals 2022, 12, 1115. https://doi.org/10.3390/ani12091115

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