Genomic analysis of the slope of the reaction norm for body weight in Australian sheep

Validation of reaction norm breeding values for robustness in Australian sheep

BACKGROUND

There can be variation between animals in how stable their genetic merit is across different environments due to genotype-by-environment (G×E) interactions. This variation could be used in breeding programs to select robust genotypes that combine high overall performance with stable genetic ranking across environments. There have been few attempts to validate breeding values for robustness in livestock, although this is a necessary step towards their implementation in selection decisions. The objective of this study was to validate breeding values for the robustness of body weight across different growth environments that were estimated using reaction norm models in sheep data.

RESULTS

Using threefold cross-validation for the progeny of 337 sires, the average correlation between single-step breeding values for the reaction norm slope and the realised robustness of progeny across different growth environments was 0.21. The correlation between breeding values for the reaction slope estimated independently in two different datasets linked by common sires was close to the expected correlation based on theory.

CONCLUSIONS

Slope estimated breeding values (EBV) obtained using reaction norm models were predictive of the phenotypic robustness of progeny across different environments and were consistent for sires with progeny in two different datasets. Selection based on reaction norm EBV could be used to increase the robustness of a population to environmental variation.

Partitioning the forms of genotype-by-environment interaction in the reaction norm analysis of stability

KEY MESSAGE

The reaction norm analysis of stability can be enhanced by partitioning the contribution of different types of G × E to the variation in slope. The slope of regression in a reaction norm model, where the performance of a genotype is regressed over an environmental covariable, is often used as a measure of stability of genotype performance. This method could be developed further by partitioning variation in the slope of regression into the two sources of genotype-by-environment interaction (G × E) which cause it: scale-type G × E (heterogeneity of variance) and rank-type G × E (heterogeneity of correlation). Because the two types of G × E have very different properties, separating their effect would enable a clearer understanding of stability. The aim of this paper was to demonstrate two methods which seek to achieve this in reaction norm models. Reaction norm models were fit to yield data from a multi-environment trial in Barley (Hordeum vulgare), with the adjusted mean yield from each environment used as the environmental covariable. Stability estimated from factor-analytic models, which can disentangle the two types of G × E and estimate stability based on rank-type G × E, was used for comparison. Adjusting the reaction norm slope to account for scale-type G × E using a genetic regression more than tripled the correlation with factor-analytic estimates of stability (0.24-0.26 to 0.80-0.85), indicating that it removed variation in the reaction norm slope that originated from scale-type G × E. A standardisation procedure had a more modest increase (055-0.59) but could be useful when curvilinear reaction norms are required. Analyses which use reaction norms to explore the stability of genotypes could gain additional insight into the mechanisms of stability by applying the methods outlined in this study.