Genetic evaluation of oocyte and embryo production in dairy Gir cattle using repeatability and random regression models
ABSTRACT The objective of this work is to estimate genetic parameters and breeding values to improve embryo and oocyte production, using repeatability and random regression models (RRM) for Gir dairy cattle. We used 11,398 records of ovum pick-up from 1,747 dairy Gir donors and evaluated sixteen different models: the traditional repeatability model and fifteen RRM, each of which considered a different combination of Legendre polynomial regressors to describe the additive genetic and permanent environment effects. The 4G1P model (four regressors for the genetic effect and one regressor for the permanent environment effect) is the most suitable model to analyze the number of viable and total oocytes, while the 3G1P is the best model to analyze the number of cleaved and viable embryos, according to the values of the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). The heritability estimated with the RRM was higher than that estimated with the repeatability model. The high repeatability reported for oocyte and embryo count traits indicates that donors, which had high oocyte and embryo counts in the first ovum pick-up, should maintain this result in the next ovum pick-up. Genetic correlations between adjacent ages were high and positive, while genetic correlations between extreme ages were weak. We observed a reranking of the top sires and females (heifers and cows) over the period evaluated. The reliability of the estimated breeding values by RRM showed changes across age, and the expected genetic gains by RRM are larger. This shows that RRM is most suitable alternative for the evaluation and selection of oocyte and embryo count traits.