Inheritance, genetic correlation and cluster analyses of fecal egg count, packed cell volume and body weight in different ages using random regression model in Santa Ines sheep

Abstract

The objectives of this study were to estimate the genetic parameters for body weight (BW), packed cell volume (PCV) and fecal egg count (FEC) in Santa Ines sheep using random regression models in order to indicate which traits could be used as selection criteria and to explore the additive genetic pattern of the animals using cluster analysis in order to select animals that attend the breeding goals. The dataset had 4608 records. The covariance components for traits were estimated using the restricted maximum likelihood (REML) method, by means of single trait random regression models. The cluster analyzes was performed in the software R. The random regression models using Legendre polynomial with 3 parameters (intercept, linear and quadratic) to model the genetic additive effect and permanent environment presented the best fit for the three traits. Heritability estimates for BW ranged from 0.02 (0.02) to 0.40 (0.03). The selection of these animals for PCV and FEC would result in low efficiency due to the low estimates of heritability (0.01 +/- 0.01-0.18 +/- 0.02). Through of the non-hierarchical cluster analysis, only one group presented a genetic profile indicated for selection. It is recommended the selection of animals based on BW (h(2) = 0.12 +/- 0.05) and PCV (h(2) = 0.18 +/- 0.03) from 180 days of age, because although the low heritability estimate, the obtained gains will be permanent.