Sel’skokhozyaistvennaya Biologiya [Agricultural Biology], 2017, Vol. 52, № 6, p. 1148-1156
(how to cite this article)

doi: 10.15389/agrobiology.2017.6.1148eng

UDC 636.2:636.082:575.113.1

Acknowledgements:
Equipment of Farm Animal Bioresources and Bioengineering (FSC for Animal Husbandry) was used.
Supported financially by Russian Foundation for Basic Research and Government of Moscow Province (project № 17-44-500324). Bulls’ genomic profiles were constructed under the Russian Science Foundation project № 14-36-00039

 

GENOMIC EVALUATION OF BULLS FOR DAUGHTERS’ MILK TRAITS
IN RUSSIAN BLACK-AND-WHITE AND HOLSTEIN CATTLE
POPULATION THROUGH THE VALIDATION PROCEDURE

A.A. Sermyagin1, A.A. Belous1, A.F. Conte1, A.A. Filipchenkov1,
A.N. Ermilov1, I.N. Yanchukov1, K.V. Plemyashov2,
G. Brem3, N.A. Zinovieva1

1L.K. Ernst Federal Science Center for Animal Husbandry, Federal Agency of Scientific Organizations, 60, pos. Dubrovitsy, Podolsk District, Moscow Province, 142132 Russia,
e-mail alex_sermyagin85@mail.ru (corresponding author), belousa663@gmail.com, alexandrconte@yandex.ru, filipchenko-90@mail.ru, mos-bulls@mail.ru, n_zinovieva@mail.ru (corresponding author),
2All-Russian Research Institute for Farm Animal Genetics and Breeding — branch of L.K. Ernst Federal Science Center for Animal Husbandry,Federal Agency of Scientific Organizations, 55А, Moskovskoe sh., pos. Tyarlevo, St. Petersburg—Pushkin, 196625 Russia, e-mail spbvniigen@mail.ru;
3Institut für Tierzucht und Genetik, University of Veterinary Medicine (VMU), Veterinärplatz, A-1210, Vienna, Austria, e-mail gottfried.brem@vetmeduni.ac.at

ORCID:
Sermyagin A.A. orcid.org/0000-0002-1799-6014
Conte A.F. orcid.org/0000-0003-4877-0883
Ermilov A.N. orcid.org/0000-0003-2046-9917
Plemyashov K.V. orcid.org/0000-0002-5952-0436
Zinovieva N.A. orcid.org/0000-0003-4017-6863
Belous A.A. orcid.org/0000-0001-7533-4281
Filipchenko A.A. orcid.org/0000-0001-7359-9307
Yanchukov I.N. orcid.org/0000-0002-6051-3655
Brem G. orcid.org/0000-0002-7522-0708

Received September 25, 2017

 

The rapid development of molecular genetic methods in animal breeding over the past ten years has given rise to an increase of the selection intensity at the population level. Expansion of the economically useful traits spectrum of dairy cattle allowed increasing the opportunities for breeding to improve the cows’ health and for studying the essence of the metabolic synthesis of milk components. The purpose of this study was to verify the effectiveness of genomic forecasting in the development of the concept of dairy cattle genetic assessment in the regional and national aspects. The study for bulls’ estimations in Russian Black-and-White improved by Holstein and Holstein breeds by simulation of breeding process using 124 herds of the Moscow and Leningrad regions was carried out. The effectiveness of genomic prediction as compared to the parent averages (PA) and the estimated breeding values (EBV) of sires have been shown. The selection of testing bulls based on the genomic information corrects PA and is to refine EBV that is further obtained by using progeny. Repeatability of genomic EBV was obtained through validation of parentage and genomic information for 100 sires with data for at least 300 daughters. This dataset lay down the core of the newly created Russian regional reference group of dairy cattle. For calculating the additive relationship matrix 1050 ancestors was used. For genomic relationship matrix, 39818 nucleotide polymorphisms were taken into analysis. Based on the REML, BLUP SM, GBLUP methods the procedures to assess of animals were carried out. The average annual genetic trend for milk production traits in the studied populations from 1987 to 2006 was +60 kg, +2.5 kg, +1.5 kg by milk yield, milk fat and milk protein, respectively. It was found that the repeatability of genomic estimates was ranging from 0.371 to 0.606 for milk production traits, which on average exceeded the PA value by 0.147. The accuracy of the evaluation obtained by progeny tested bulls ranged from 0.879 to 0.900 that was higher than the genomic prediction by 0.405 units. The principles of creating the reference population based on the analysis of multi dimension scaling and genetic distances were studied. The distinguish between two regional populations (Moscow and Leningrad regions) was Fst = 0.0025. The decay of the linkage disequilibrium between the markers at distances up to 1000 kb is shown. In distance from 5 up to 70 kb the linkage level was get the maximum values from 0.20 to 0.54. In the framework of the metabolic pathways study for the milk components synthesis genetic parameters and mean least square estimates were obtained for the extended milk composition: lactose (h2 = 0.18), dry matter (h2 = 0.10), solids-not-fat (h2 = 0.19), milk freezing point (h2 = 0.06), somatic cells score (h2 = 0.10) and milk urea (h2 = 0.04). The values of additive genetic variances have been get indicating the objective possibilities of using them in Russian dairy cattle breeding sector. To obtain reliable whole-genome associations a further replenishment of the database of the cows’ milk component will be carried out using additional spectra. The complex studies have grounded approaches to the use of genomic estimations, the principles of reference population extension and widening list of features for the quantitative and qualitative milk composition assessment.

Keywords: genomic breeding value, milk production, reference population, linkage disequilibrium, heritability, milk components.

 

Full article (Rus)

Full article (Eng)

 

REFERENCES

  1. Schaeffer L.R. Strategy for applying genome-wide selection in dairy cattle. J. Anim. Breed. Genet., 2006, 123: 218-223.
  2. Smaragdov M.G. Genetika, 2009, 45(1): 725-728 (in Russ.).
  3. García-Ruiz A., Cole J.B., VanRaden P.M., Wiggans G.R., Ruiz-Lópeza F.J., Van Tassell C.P. Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. PNAS, 2016, 113(28): E3995-E4004 CrossRef
  4. Taylor J.F., Taylor K.H., Deckera J.E. Holsteins are the genomic selection poster cows. PNAS, 2016, 113(28): 7690-7692 CrossRef
  5. Interbull Centre. Available http://www.interbull.org/static/web/g_proddoc1708r.pdf. Accessed November 01, 2017.
  6. Vandenplas J., Gengler N. Strategies for comparing and combining different genetic and genomic evaluations: A review. Livest. Sci., 2015: 121-130 CrossRef
  7. Sullivan P.M. Defining a parameter space for GMACE. Interbull Bulletin, 2016, 50: 85-93.
  8. Hayes B.J., Bowman P.J., Chamberlain A.J., Goddard M.E. Invited review: Genomic selection in dairy cattle: Progress and challenges. J. Dairy Sci., 2009, 92: 433-443 CrossRef
  9. Misztal I., Legarra A. Invited review: efficient computation strategies in genomic selection. Animal, 2017, 11(5): 731-736 CrossRef
  10. Masuda Y., Misztal I., Tsuruta S., Legarra A., Aguilar I., Lourenco D.A.L., Fragomeni B., Lawlor T.L. Implementation of genomic recursions in single-step genomic BLUP for US Holsteins with a large number of genotyped animals. J. Dairy Sci., 2016, 99: 1968-1974 CrossRef
  11. Fragomeni B.O., Lourenco D.A.L., Masuda Y., Legarra A., Misztal I. Incorporation of causative quantitative trait nucleotides in single?step GBLUP. Genet. Sel. Evol., 2017, 49: 59 CrossRef
  12. Smaragdov M.G. Genetika, 2008, 44(6): 829-834 (in Russ.). 
  13. Smaragdov M.G. Genetika, 2011, 47(1): 126-132 (in Russ.). 
  14. Smaragdov M.G. Genetika, 2012, 48(9): 1085-1090 (in Russ.). 
  15. Sermyagin A.A., Gladyr’ E.A., Kharitonov S.N., Ermilov A.N., Strekozov N.I., Brem G., Zinovieva N.A. Genome-wide association study for milk production and reproduction traits in Russian Holstein cattle population. Agricultural Biology, 2016, 51(2): 182-193 CrossRef (in Engl.).
  16. Weller J.I., Ezra E., Ron M. Invited review: A perspective on the future of genomic selection in dairy cattle. J. Dairy Sci., 2017, 100: 8633-8644 CrossRef
  17. Nayeri S., Sargolzaei M., Abo-Ismail M.K., May N., Miller S.P., Schenkel F., Moore S.S., Stothard P. Genome-wide association for milk production and female fertility traits in Canadian dairy Holstein cattle. BMC Genet., 2016, 17: 75 CrossRef
  18. Huang W., Peñagaricano F., Ahmad K.R., Lucey J.A., Weigel K.A., Khatib H. Association between milk protein gene variants and protein composition traits in dairy cattle. J. Dairy Sci., 2012, 95: 440-449 CrossRef
  19. Saatchi M., Garrick D.J., Tait Jr. R.G., Mayes M.S., Drewnoski M., Schoon-maker J., Diaz C., Beitz D.C., Reecy J.M. Genome-wide association and prediction of direct genomic breeding values for composition of fatty acids in Angus beef cattle. BMC Genomics, 2013, 14: 730 CrossRef
  20. Filipejová T., Kovácik J., Kirchnerová K., Foltýs V. Changes in milk composition as a result of metabolic disorders of dairy cows. Potravinárstvo, 2011, 5(1): 10-16 (doi: 10.5219/113).
  21. Hanuš O., Gencurová V., Zhang Y., Hering P., Kopecký J., Jedelská R., Dolínková A., Motycka Z. Milk acetone determination by the photometrical method after microdiffusion and via FT infra-red spectroscopy. J. Agrobiol., 2011, 28(1): 33-48 CrossRef
  22. Jonkus D., Paura L. Estimation of genetic parameters for milk urea and milk production traits of Latvian Brown cows. Agriculturae Conspectus Scientificus, 2011, 76(3): 227-230.
  23. Czajkowska A., Sitkowska B., Piwczynski D., Wójcik P., Mroczkowski S. Genetic and environmental determinants of the urea level in cow’s milk. Arch. Anim. Breed., 2015, 58: 65-72 CrossRef
  24. Rzewuska K., Strabel T. The genetic relationship between reproduction traits and milk urea concentration. Anim. Sci. Pap. Rep., 2015, 33(3): 243-256.
  25. Henno M., Ots M., Joudu I., Kaart T., Kart O. Factors affecting the freezing point stability of milk from individual cows. Int. Dairy J., 2008, 18: 210-215 CrossRef
  26. Otwinowska-Mindur A., Ptak E., Grzesiak A. Factors affecting the freezing point of milk from Polish Holstein-Friesian cows. Ann. Anim. Sci., 2017, 17(3): 873-885 CrossRef
  27. VanRaden P.M. Efficient methods to compute genomic predictions. J. Dairy Sci., 2008, 91: 4414-4423 CrossRef
  28. Misztal I., Tsuruta S., Strabel T., Auvray B., Druet T., Lee D.H. BLUPF90 and related programs (BGF90). Proc. 7th World Congress on genetics applied to livestock production. Montpellier, 2002, 28(28-27): 21-22.
  29. Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M., Bender D., Maller J., Sklar P., de Bakker P., Daly M.J., Sham P.C. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am. J. Hum. Genet., 2007, 81(3): 559-575 CrossRef
  30. Pérez O’Brien A.M., Utsunomiya Y.T., Mészáros G., Bickhart D.M., Liu G.E., Van Tassell C.P., Sonstegard T.S., da Silva M.V.B., Garcia J.F., Sölkner J. Assessing signatures of selection through variation in linkage disequilibrium between taurine and indicine cattle. Genet. Sel. Evol., 2014, 46: 19 CrossRef

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