Relevance of genomic evaluation for egg quality traits in layers

Abstract

Background: Genomic evaluation, based on thousands of genetic markers, has become the standard evaluation methodology in dairy cattle breeding programs over the past few years. Despite the many differences between dairy cattle breeding and poultry breeding, genomic selection seems very promising for the avian sector, and studies are currently being conducted to optimize avian selection schemes. In this optimization perspective, one of the key parameters is to properly predict the accuracy of genomic evaluation in pure line layers.Methods: Both genetic evaluation and genomic evaluation were performed on three candidate populations (male and female), using different sizes of phenotypic records on five egg quality traits and at two different ages. The methodologies used were BLUP & ssGBLUP, and variance-covariance matrices were estimated through REML. To estimate evaluation accuracy, the LR method was implemented. Four statistics were used to assess the relative accuracy of the estimated breeding values of candidates, their bias and dispersion, as well as the differences between genetic evaluation and genomic evaluation.Results: It was observed that genomic evaluation, whether performed on males or females, always proved more accurate than genetic evaluation. The gain was higher when phenotypic information was narrowed and an augmentation of the size of the reference population led to an increase in accuracy prediction, for what regards genomic evaluation. By taking into account the increase of selection intensity and the decrease of the generation interval induced by genomic selection, the expected annual genetic gain would be higher with ancestry-based genomic evaluation of male candidates than with genetic evaluation based on collaterals. This advantage of genomic selection over genetic selection requires to be studied in more details for female candidates. Conclusions:In conclusion, in the population studied, genomic evaluation for egg quality traits of breeding birds at birth seems a promising strategy, at least for what regards males selection.Genomic evaluation, based on thousands of genetic markers [1], has become the 4 standard evaluation methodology in dairy cattle breeding programs over the past 5 few years. It has allowed for the improvement of the accuracy of estimated breeding 6 values (EBV) of young animals and for the reduction of the generation interval, as 7 well as for the reduction of the phenotyping costs [2]. More recently, avian breeders 8 have started to implement genomic selection in their selection schemes. Indeed, 9 despite the many differences between dairy cattle breeding and poultry breeding, 10 genomic selection is deemed very promising for the avian sector, especially for 11 layers selection [3, 4, 5]. However, to optimize avian selection schemes, one of the 12 key parameters is to properly predict the accuracy of genomic evaluation. 13 14 One of the most important factors directly affecting evaluation accuracy is the 15 makeup of the reference population. From the very beginning, gen...

Assessment of long-term trends in genetic mean and variance after the introduction of genomic selection in layers: a simulation study

“…In our study, the GTS increased the average accuracy for the selection candidates by more than 20% compared to the PTS. This confirms the now well-established positive effect of genomic selection on the accuracy observed across livestock species, including chicken (Wolc et al, 2011;Sitzenstock et al, 2013;Picard Druet et al, 2020;Hidalgo et al, 2021). Despite the higher accuracy with genomic selection, its persistence in the long-term still has not been fully assessed (for example, over 7 years, see Hidalgo et al (2021)), especially when comparing truncation and optimal contribution selection.…”

“…Picard Druet et al, 2020). For example, a two-to three-fold increase in response to selection was observed for egg production and quality traits with genomic compared to conventional selection (Sitzenstock et al, 2013;Wolc et al, 2015).…”