Environmental Factors Affecting Genetic Variation in Coarse-Wool Sheep

Abstract

Economic activities of the population living in the rural areas of the world is 70% dependent on traditional livestock production systems based on indigenous breeds. Consequently, it is very important to protect and preserve the heritage breeds of animals resistant to any diseases and better adapted to the changing environmental conditions. The effects of the natural environmental factors on the formation of the genetic structure in 24 coarse-wool breeds of sheep reared in nine countries of Europe and Asia have been studied. Genetic surveys of 20 microsatellite loci were carried out. The most significant environmental factors causing the genetic variation in the analyzed sheep breeds turned out to be the geographical latitude and the annual mean temperature. The genetic variation in the coarse-wool sheep breeds was generally higher at low geographical latitudes, which corresponds to the data obtained for the other animal species. Therefore, the protection of animal populations inhabiting the areas at the low geographical latitudes can contribute to a higher intraspecific diversity than the protection of the same number of populations distributed in the high latitude ranges. This fact should be especially considered when planning the programs to preserve the biodiversity of animals since the breeds of sheep reared near the centers of animal domestication have a higher genetic variation. Hence, they can serve as the source of genes contributing to adaptation under the conditions of global climate change.

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REFERENCES

  1. 1

    Alderson, L., Breeds at risk: Definition and measurement of the factors which determine endangerment, Livest. Sci., 2009, vol. 123, pp. 23–27.

    Article  Google Scholar 

  2. 2

    Marcos-Carcavilla, An., Mutikainen, M., González, C., Calvo, J.H., Kantanen, J., Sanz, A., Marzanov, N.S., Pérez-Guzmán, M.D., and Serrano, M., A SNP in the HSP90AA1 gene 5' flanking region is associated with the adaptation to differential thermal conditions in the ovine species, Cell Stress Chaperones, 2010, vol. 15, pp. 95–100.

    Article  Google Scholar 

  3. 3

    McManus, C., Hermuche, P., Paiva, S.R., Moraes, J.C.F., Barros de Melo, C., and Mendes, C., Geographical distribution of sheep breeds in Brazil and their relationship with climatic and environmental factors as risk classification for conservation, Braz. J. Sci. Technol., 2014, vol. 1, no. 3. https://doi.org/10.1186/2196-288X-1-3

  4. 4

    Boettcher, P.J., Hoffmann, I., Baumung, R., Drucker, A.G., McManus, C., Berg, P., Stella, A., Nilsen, L.B., Moran, D., Naves, M., and Thomson, M.C., Genetic resources and genomics for adaptation of livestock to climate change, Front. Genet., 2015, vol. 5.

  5. 5

    Ligda, C., ERFP WG “Documentation and Information,” ERFP Annual Assembly, August 25–26,2012, Bratislava, 2012. https//www.animalgeneticresources.net/wp-content/uploads/2018/05/ERFP-Assembly_Bratislava2012_WGDocu_Ligda.pdf. Accessed September 5, 2019.

  6. 6

    Nevo, E., The evolution of genome-phenome diversity under environmental stress, Proc. Natl. Acad. Sci. U.S.A., 2001, vol. 98, pp. 6233–6240.

    CAS  Article  Google Scholar 

  7. 7

    Joost, S., Colli, L., Baret, P.V., Garcia, J.F., Boettcher, P.J., Tixier-Boichard, M., and Ajmone-Marsan, P., The GLOBALDIV Consortium, 2010. Integrating georeferenced multiscale and multidisciplinary data for the management of biodiversity in livestock genetic resources, Anim. Genet., 2010, vol. 41, no. 1, pp. 47–63.

    Article  Google Scholar 

  8. 8

    Joost, S., Colli, L., Baret, P.V., Garcia, J.F., Boettcher, P.J., Tixier-Boichard, M., and Ajmone-Marsan, P., The GLOBALDIV Consortium, 2010. Integrating georeferenced multiscale and multidisciplinary data for the management of biodiversity in livestock genetic resources, Anim. Genet., 2010, vol. 41, no. 1, pp. 47–63.

    Article  Google Scholar 

  9. 9

    Nevo, E., The evolution of genome-phenome diversity under environmental stress, Proc. Natl. Acad. Sci. U.S.A., 2001, vol. 98, pp. 6233–6240.

    CAS  Article  Google Scholar 

  10. 10

    Manel, S., Schwartz, M.K., Luikart, G., and Taberlet, P., Landscape genetics: Combining landscape ecology and population genetics, Trends Ecol. Evol., 2003, vol. 18, pp. 189–197.

    Article  Google Scholar 

  11. 11

    Guillot, G., Estoup, A., Mortier, F., and Cosson, J.C., A spatial statistical model for landscape genetics, Genetics, 2005, vol. 170, pp. 1261–1280.

    CAS  Article  Google Scholar 

  12. 12

    Tapio, M., Ozerov, M., Tapio, I., Toro, M.A., Marzanov, N., Cinkulov, M., Goncharenko, G., Kiselyova, T., Murawski, M., and Kantanen, J., Microsatellite-based genetic diversity and population structure of domestic sheep in northern Eurasia, BMC Genet., 2010, vol. 11, no. 76, pp. 1–36.

    Article  Google Scholar 

  13. 13

    El Mousadik, A. and Petit, R.J., High level of genetic differentiation for allelic richness among populations of the argan tree [Argania spinosa (L.) Skeels] endemic to Morocco, Theor. App. Genet., 1996, vol. 92, pp. 832–839.

    CAS  Article  Google Scholar 

  14. 14

    Goudet, J., FSTAT (version 1.2): A computer program to calculate F-statistics, J. Hered., 1995, vol. 86, pp. 485–486.

    Article  Google Scholar 

  15. 15

    Weir, B.S., Genetic Data Analysis: Methods for Discrete Population Genetic Data, Sinauer Associates Inc, 1990.

    Google Scholar 

  16. 16

    Weir, B.S. and Cockerham, C.C., Estimating F-statistics for the analysis of population structure, Evolution, 1984, vol. 38, no. 6, pp. 1358–1370.

    CAS  PubMed  Google Scholar 

  17. 17

    Langella, O., POPULATIONS 1.2.28. Population Genetic Software (Individuals or Populations Distances, Phylogenetic Trees). http://bioinformatics.org/~tryphon/populations.

  18. 18

    Nei, M., Tajima, F., and Tateno, Y., Accuracy of estimated phylogenetic trees from molecular data, J. Mol. Evol., 1983, vol. 19, pp. 153–170.

    CAS  Article  Google Scholar 

  19. 19

    Saitou, N. and Nei, M., The neighbor-joining method: A new method for reconstructing phylogenetic trees, Mol. Biol. Evol., 1987, vol. 4, pp. 406–425.

    CAS  PubMed  Google Scholar 

  20. 20

    Foll, M. and Gagiotti, O., Indentifying the environmental factors that determine the genetic structure of populations, Genetics, 2006, vol. 174, pp. 875–891.

    CAS  Article  Google Scholar 

  21. 21

    Adams, R.I. and Hadly, E.A., Genetic diversity within vertebrate species is greater at lower latitudes, Evol. Ecol., 2012, vol. 27, pp. 133–143.

    Article  Google Scholar 

  22. 22

    Sun, W., Chang, H., Musa, H.H., Yang, Z.P., Tsunoda, K., Ren, Z.J., and Geng, R.Q., Influence of environmental factors on the genetic diversity of sheep, J. Anim. Vet. Adv., 2009, vol. 8, pp. 1070–1074.

    Google Scholar 

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Correspondence to N. S. Marzanov.

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Conflict of interest. The authors declare that they have no conflict of interest.

Statement of welfare of animals. All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. The article does not concern any researches using animals as objects.

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Translated by O. Zhiryakova

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Ozerov, M.Y., Tapio, M., Kantanen, J. et al. Environmental Factors Affecting Genetic Variation in Coarse-Wool Sheep. Russ. Agricult. Sci. 46, 65–70 (2020). https://doi.org/10.3103/S1068367420010127

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Keywords:

  • sheep
  • breeds
  • microsatellites
  • genotypes
  • alleles