Assessing the Genetic Diversity of Five Cattle Breeds Using SNP Markers Associated with Health

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Resumo

Currently genetic evaluation of animals is an important part of the development of the agricultural complex. The improvement of molecular technologies every year makes it possible to carry out genetic research aimed at finding the most valuable animals in a cheaper and faster way. Indigenous breeds of cattle are an attractive object for such research because they have greater adaptive potential and resistance to diseases. However, modern comparative data on the genetic diversity of most local breeds based on SNP markers associated with health are lacking. Genetic association tests using these genetic markers for the Tagil, Sychevskaya, Suksun and Istobenskaya breeds are still to be carried out. The purpose of this work was to compare the genetic diversity of five cattle breeds using SNP markers associated with the development of ketosis, mastitis and productive longevity.

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Sobre autores

M. Bytov

Ural Federal Agrarian Scientific Research Centre, Ural Branch of Russian Academy of Sciences

Email: nauka_sokolova@mail.ru
Rússia, Ekaterinburg, 620142

V. Zubareva

Ural Federal Agrarian Scientific Research Centre, Ural Branch of Russian Academy of Sciences

Email: nauka_sokolova@mail.ru
Rússia, Ekaterinburg, 620142

S. Volskaya

Ural Federal Agrarian Scientific Research Centre, Ural Branch of Russian Academy of Sciences

Email: nauka_sokolova@mail.ru
Rússia, Ekaterinburg, 620142

A. Isaeva

Ural Federal Agrarian Scientific Research Centre, Ural Branch of Russian Academy of Sciences

Email: nauka_sokolova@mail.ru
Rússia, Ekaterinburg, 620142

D. Nokhrin

Ural Federal Agrarian Scientific Research Centre, Ural Branch of Russian Academy of Sciences

Email: nauka_sokolova@mail.ru
Rússia, Ekaterinburg, 620142

Yu. Osipova

Ural Federal Agrarian Scientific Research Centre, Ural Branch of Russian Academy of Sciences

Email: nauka_sokolova@mail.ru
Rússia, Ekaterinburg, 620142

O. Sokolova

Ural Federal Agrarian Scientific Research Centre, Ural Branch of Russian Academy of Sciences

Autor responsável pela correspondência
Email: nauka_sokolova@mail.ru
Rússia, Ekaterinburg, 620142

Bibliografia

  1. Модоров М.В., Ткаченко И.В., Грин А.А. и др. Генетическая структура популяции голштинизированного черно-пестрого скота на территории Урала // Генетика. 2021. Т. 57. № 4. С. 437−444. https://doi.org/10.31857/S001667582104010X.
  2. Юдин Н.С., Ларкин Д.М.. Происхождение, селекция и адаптация российских пород крупного рогатого скота по данным полногеномных исследований // Вавиловский журн. генетики и селекции. 2019. Т. 23. № 5. С 559−568. https://doi.org/10.18699/VJ19.525
  3. Породы крупного рогатого скота: справочник / Сост. Иванова Н.В., Максимов А.Г. Перси-ановский: Донской ГАУ, 2019. 143 с. URL: https://e.lanbook.com/book/148559
  4. Фетисова Л.В. Создание и совершенствование сычевской породы крупного рогатого скота. Смоленск: Смоленское кн. изд-во, 1959. 163 с.
  5. The State of the World’s Animal Genetic Resources for Food and Agriculture / Eds Rischkowsky B., Pilling D. FAO, 2007. 524 с.
  6. Информационная система по разнообразию домашних животных (ИС-РДЖ). [Электронный ресурс] // URL: https://www.fao.org/dad-is/browse-by-country-and-species/ru/ (дата обращения: 10.11.2023).
  7. Столповский Ю.А., Бекетов С.В., Солоднева Е.В. и др. Генетическая структура аборигенного тагильского скота по STR- и SNP-маркерам // С.-х. биология. 2021. Т. 56. № 6. С. 1111−1122. https://doi.org/10.15389/agrobiology.2021.6.1123rus.
  8. Nayeri S., Schenkel F., Fleming A. et al. Genome-wide association analysis for β-hydroxybutyrate concentration in Milk in Holstein dairy cattle // BMC Genetics. 2019. V. 20. № 58. P. 1−17. https://doi.org/10.1186/s12863-019-0761-9.
  9. Horst R.L., Goff J.P., Reinhardt T.A. Calcium and vitamin D metabolism during lactation // J. Mammary Gland Biol. Neoplasia. 1997. V. 2. P. 253–263. https://doi.org/10.1023/A:1026384421273.
  10. Nayeri S., Sargolzaei M., Abo-Ismail M.K. et al. Genome-wide association study for lactation persistency, female fertility, longevity, and lifetime profit index traits in Holstein dairy cattle // J. Dairy Science. 2017. V. 100. № 2. P. 1246−1258. https://doi.org/10.3168/jds.2016-11770
  11. Бытов М.В., Соколова О.В., Безбородова Н.А. и др. Методы генотипирования крупного рогатого скота для post-GWAS аннотирования SNPs // Аграрный вестник Урала. 2023. № 06 (235). С. 67‒75. https://doi.org/10.32417/1997-4868-2023-235-06-67-75
  12. Haberman S.J. The analysis of residuals in cross-classified tables // Biometrics. 1973. V. 29. № 1. P. 205−220. https://doi.org/10.2307/2529686
  13. Hammer Ø., Harper D.A.T., Ryan P.D. PAST: Paleontological Statistics software package for education and data analysis // Palaeontologia Electronica. 2001. V. 4. № 1. P. 1−9.
  14. Santos F.A.B., Lemes R.B., Otto P.A. HW_TEST, a program for comprehensive Hardy−Weinberg equilibrium testing // Genet. Mol. Biol. 2020. V. 43. № 2. https://doi.org/10.1590/1678-4685-GMB-2019-0380
  15. Henschke H. De Finetti diagram. [Электронный ресурс] // URL: https://web.archive.org/web/20110719103301/https://finetti.meb.unibonn.de/downloads/finetti_3.0.5_windows.zip (дата обращения: 1.11.2023).
  16. Peakall R., Smouse P.E. Genalex 6: Genetic analysis in Excel. Population genetic software for teaching and research // Mol. Ecol. Notes. 2006. V. 6. № 1. P. 288−295. https://doi.org/10.1111/j.1471-8286.2005.01155.x
  17. Tang D., Chen M., Huang X. et al. SRplot: A free online platform for data visualization and graphing // PLoS One. 2023. V. 18. № 11. P. 1−8. https://doi.org/10.1371/journal.pone.0294236
  18. Chen B., Cole J.W., Grond-Ginsbach C. Departure from Hardy−Weinberg equilibrium and genotyping error // Front. Genet. 2017. V. 8. № 167. P. 1−6. https://doi.org/10.3389/fgene.2017.00167
  19. Abramovs N., Brass A., Tassabehji M. Hardy−Weinberg equilibrium in the large scale genomic sequencing era // Front. Genet. 2020. V. 11. № 210. P. 1−11. https://doi.org/10.3389/fgene.2020.00210
  20. Ye J., Coulouris G., Zaretskaya I. et al. Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction // BMC Bioinformatics. 2012. V. 13. № 134. P. 1−11. https://doi.org/10.1186/1471-2105-13-134
  21. Шевелёва О.М., Бахарев А.А., Терещенко И.Я. Экстерьерные особенности крупного рогатого скота мясных пород в условиях Северного Зауралья // Животноводство и кормопроизводство. 2023. Т. 106. № 3. С. 35-45.
  22. Зиновьева Н.А., Доцев А.В., Сермягин А.А. и др. Изучение генетического разнообразия и популяционной структуры российских пород крупного рогатого скота с использованием полногеномного анализа SNP // С.-х. биология. 2016. Т. 51. № 6. С. 788−800. https://doi.org/10.15389 agrobiology.2016.6.788rus

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2. Fig. 1. Deviations of the observed genotype frequencies from those expected by HardyTo Weinberg for two studied breeds of dairy cattle according to rs109452259. The spread on the histogram indicates 95% CI based on the results of Monte Carlo simulation in the HW_TEST package.

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3. Fig. 2. Results of capillary electrophoresis of amplification products obtained using the developed TaqMan genotyping systems.

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4. Fig. 3. The ratio of the frequencies of the genotypes of the three polymorphisms in the Ural populations of dairy cattle on the de Finetti diagrams. The red parabola is the Hardy-Weinberg equilibrium ratio, the red labels are populations with a deviation from equilibrium.

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5. Fig. 4. The scheme of the model of genetic cleavage of the five studied cattle breeds according to the principle of the main coordinates.

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6. Fig. 5. The results of the evaluation of the nonequilibrium coupling of the studied polymorphisms for the Istobenskaya and Tagil cattle breeds.

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