Use of genomic information to exploit genotype-by-environment interactions for body weight of broiler chicken in bio-secure and production environments

Abstract

Background The increase in accuracy of prediction by using genomic information has been well-documented. However, benefits of the use of genomic information and methodology for genetic evaluations are missing when genotype-by-environment interactions (G × E) exist between bio-secure breeding (B) environments and commercial production (C) environments. In this study, we explored (1) G × E interactions for broiler body weight (BW) at weeks 5 and 6, and (2) the benefits of using genomic information for prediction of BW traits when selection candidates were raised and tested in a B environment and close relatives were tested in a C environment. Methods A pedigree-based best linear unbiased prediction (BLUP) multivariate model was used to estimate variance components and predict breeding values (EBV) of BW traits at weeks 5 and 6 measured in B and C environments. A single-step genomic BLUP (ssGBLUP) model that combined pedigree and genomic information was used to predict EBV. Cross-validations were based on correlation, mean difference and regression slope statistics for EBV that were estimated from full and reduced datasets. These statistics are indicators of population accuracy, bias and dispersion of prediction for EBV of traits measured in B and C environments. Validation animals were genotyped and non-genotyped birds in the B environment only. Results Several indications of G × E interactions due to environmental differences were found for BW traits including significant re-ranking, heterogeneous variances and different heritabilities for BW measured in environments B and C. The genetic correlations between BW traits measured in environments B and C ranged from 0.48 to 0.54. The use of combined pedigree and genomic information increased population accuracy of EBV, and reduced bias of EBV prediction for genotyped birds compared to the use of pedigree information only. A slight increase in accuracy of EBV was also observed for non-genotyped birds, but the bias of EBV prediction increased for non-genotyped birds. Conclusions The G × E interaction was strong for BW traits of broilers measured in environments B and C. The use of combined pedigree and genomic information increased population accuracy of EBV substantially for genotyped birds in the B environment compared to the use of pedigree information only.

Phenotypically Selective Genotyping Realizes More Genetic Gains in a Rainbow Trout Breeding Program in the Presence of Genotype-by-Environment Interactions

“…According to Gowane et al (2019), genotyping of phenotypically contrasting animals (selective genotyping of top and bottom animals) for selection candidates is superior to selective genotyping of top animals. The undesirable consequences of selectively genotyping top animals have been addressed extensively (VanRaden et al, 2009;Patry and Ducrocq, 2011;Vitezica et al, 2011;Boligon et al, 2012;Jiménez-Montero et al, 2012;Chu et al, 2019;Gowane et al, 2019;Wang et al, 2020), but the superiority of this selective strategy for breeding programs has been shown only by Howard et al (2018). In addition, some simulation studies (Patry and Ducrocq, 2011;Vitezica et al, 2011;Boligon et al, 2012;Gowane et al, 2019) have used the correlation between true breeding values (TBV) and GEBV to compare different genotyping strategies.…”

“…Under this strong re-ranking situation, a sib-testing scheme is required for breeding to improve the performance of animals in C (Mulder and Bijma, 2005;Chu et al, 2018). In a sib-testing scheme, selection candidates are kept for phenotype testing in B, whereas their sibs are transferred to C for phenotype testing (Chu et al, 2019). The individuals in C are then used as a reference population to predict the GEBV of the candidates.…”

Correction to: Use of genomic information to exploit genotype-by-environment interactions for body weight of broiler chicken in bio-secure and production environments

“…After publication of this work [1], we noticed that there was an error: the formula to calculate the standard error of the estimated correlation () between and in Appendix on page 12 was mistyped. The correct formula for calculation of the standard error should be:…”

Genetics of crossbreeding

“…It should be noted, however, that the PB and CB animals in this study were housed in the same environment, so was lower than one due to GxG interactions only. For PB animals placed in a PB and CB environment, estimated genetic correlations for body weight between environments were around 0.5, suggesting a strong effect of GxE interaction (Chu et al 2019). In summary, it seems that most estimates of across species and traits are lower than one, and usually larger than 0.5.…”