Mathematical modeling for genomic selection in Serbian dairy cattle

Abstract

This manuscript has come as a result of an efficient breeding program in Serbian cattle populations for some economically important traits. Genomic selection in the last two decades has been the main challenge in animal breeding programs and genetics. Many SNP markers are used in statistical analysis in predicting the accuracy of breeding values for young animals without their performance. The new breeding tendency in the selection of young animals allows their genetic progress with reducing cost. In this study, 92 Holstein cows from various regions in Serbia were analyzed based on SNP molecular markers. Within this investigation, an empirical model was developed for the prediction of Yield Traits and Fertility Traits variables, according to Key traits data for dairy cattle. The developed model gave a reasonable fit to the data and successfully predicted Yield Traits (such as Fat and Protein Percent, Cheese Merit, Fluid Merit, and Cow Livability) and Fertility Traits variables (such as Sire Calving Ease, Heifer Conception Rate, Cow Conception Rate, Daughter Stillbirth, Sire Stillbirth, and Gestation Length). A total of 92 dairy cattle data were used to build a prediction model for the prediction of Yield Traits and Fertility Traits variables. The artificial neural network model, based on the Broyden- Fletcher-Goldfarb-Shanno iterative algorithm, showed good prediction capabilities (the r2 values during the training cycle for the before mentioned output variables were in the range between 0.444 and 0.989).