Bayesian Inference of Genetic Parameters for Reproductive and Performance Traits in White Leghorn Hens

Abstract

This study estimated the genetic parameters for reproductive and performance traits and determined which ones can be used as selection criteria for egg production in laying hens using the Bayesian inference. The data of 1894 animals from three generations of White Leghorn laying hens were analyzed for fertility (FERT), hatchability (HATC), and birth rate measurements at 60 weeks of age (BIRTH), body weight at 16 and 60 weeks of age (BW16 and BW60), age at sexual maturity (ASM), egg height/width ratio, weight, and density at 28, 36, and 40 weeks of age (RHW28, RHW36, RHW40, WEGG28, WEGG36, WEGG40, DENS28, DENS36, and DENS40, respectively) traits. The genetic parameters were estimated by the Bayesian inference method of multi-trait animal model. The model included the additive and residual genetic random effects and the fixed effects of generation. The a posteriori mean distributions of the heritability estimates for reproductive traits ranged from 0.14 +/- 0.003 (HATC) to 0.22 +/- 0.005 (FERT) and performance from 0.07 +/- 0.001 (RHW28) to 0.42 +/- 0.001 (WEGG40). The a posteriori mean distributions of the genetic correlation between reproductive traits ranged from 0.18 +/- 0.026 (FERT and HACT) to 0.79 +/- 0.007 (FERT and BIRTH) and those related to performance ranged from -0.49 +/- 0.001 (WEGG36 and DENS36) to 0.75 +/- 0.003 (DENS28 and DENS36). Reproductive and performance traits showed enough additive genetic variability to respond to selection, except for RHW28. This trait alone would have little impact on the genetic gain because environmental factors would have a higher impact compared to those from the additive genetic factors. Based on the results of this study, the selection applied on the BIRTH trait can be indicated to improve FERT and HATC of eggs. Furthermore, the use of the WEGG40 could improve egg quality in this population.