The aim of this study was to evaluate the effect of genotype–environment interaction (GEI) on the yearling weight of Simmental cattle raised in Brazil, including the sex dimorphism in reaction norm models. The environmental gradient (EG) was formed using the average weight at 365 days of the contemporary groups. Two approaches were adopted in this study to evaluate reaction norms for weight at 365 days: a single-trait model and a multitrait model in which the data for males and females were separated and considered different traits for the analysis of sexual dimorphism. The genetic parameters were estimated using the Bayesian inference and Gibbs sampling. Analysis of the trend of the heritability estimates obtained with the single-trait model along the EG revealed a value of about 0.33 (EG: −21) in the worst environments, which decreased in the intermediate environments and reached a value of 0.24 in EG: −8, with a subsequent increase of the estimates up to 0.51 in EG: +23. Using the multitrait model, similar trends were observed for the heritability estimates, which ranged from 0.25 to 0.54 for males and from 0.23 to 0.50 for females. The results show that the weight of Simmental cattle raised in the tropics is influenced by GEI and greater genetic progress could be obtained by selecting better environments. However, no significant differences in the response to most environmental changes were observed between sexes and there is only evidence of genetic heteroscedasticity in environments with lower production levels.

Remarkable increases in the production of dairy animals have negatively impacted their tolerance to heat stress (HS). The evaluation of the effect of HS on milk yield is based on the direct impact of HS on performance. However, in practical terms, HS also exerts its influence during gestation (indirect effect). The main purpose of this study was to identify and characterize the genotype by environment interaction (G × E) due to HS during the last 60 days of gestation (THI_g) and also the HS postpartum (THI_m) over first lactation milk production of Brazilian Holstein cattle. A total of 389 127 test day milk yield (TD) records from 1572 first lactation Holstein cows born in Brazil (daughters of 1248 dams and 70 sires) and the corresponding temperature–humidity index (THI) obtained between December 2007 and January 2013 were analyzed using different random regression models. Cows in the cold environment (THI_g = 64 to 73) during the last 60 days of gestation produced more milk than those cows in a hot environment (THI_g = 74 to 84), particularly during the first 150 days of lactation (DIM). The heritabilities (h2) of TD were similar throughout DIM for cows in THI_g hot (0.11 to 0.20) or (0.10 to 0.22), while the genetic correlations (rg) for TD between these two environments ranged from 0.11 to 0.52 along the first 250 DIM. The h2 estimates for TD across THI_m were similar for cows in THI_g hot (0.07 to 0.25) and THI_g cold (0.08 to 0.19). The rg estimates ranged from 0.17 to 0.42 along THI_m between TD of cows in cold and hot THI_g. The results were consistent in demonstrating the existence of an additional source of G × E for TD due to THI_g and THI_m. The present study is probably the first to provide evidence of this source of G × E; further research is needed because of its importance when the breeding objective is to select animals that are more tolerant to HS.

Extreme weather conditions such as cold stress influence the productivity and survivability of beef cattle raised on pasture. The objective of this study was to identify and evaluate the extent of the impact of genotype by environment interaction due to cold stress on birth weight (BW) and weaning weight (WW) in a composite beef cattle population. The effect of cold stress was modelled as the accumulation of total cold load (TCL) calculated using the Comprehensive Climate Index units, considering three TCL classes defined based on temperature: less than −5°C (TCL5), −15°C (TCL15) and −25°C (TCL25). A total of 4221 and 4217 records for BW and WW, respectively, were used from a composite beef cattle population (50% Red Angus, 25% Charolais and 25% Tarentaise) between 2002 and 2015. For both BW and WW, a univariate model (ignoring cold stress) and a reaction norm model were implemented. As cold load increased, the direct heritability slightly increased in both BW and WW for TCL5 class; however, this heritability remained consistent across the cold load of TCL25 class. In contrast, the maternal heritability of BW was constant with cold load increase in all TCL classes, although a slight increase of maternal heritability was observed for TCL5 and TCL15. The direct and maternal genetic correlation for BW and maternal genetic correlation for WW across different cold loads between all TCL classes were high (r > 0.99), whereas the lowest direct genetic correlations observed for WW were 0.88 for TCL5 and 0.85 for TCL15. The Spearman rank correlation between the estimated breeding value of top bulls (n = 79) using univariate and reaction norm models across TCL classes showed some re-ranking in direct and maternal effects for both BW and WW particularly for TCL5 and TCL15. In general, cold stress did not have a big impact on direct and maternal genetic effects of BW and WW.

This work was part of the EU RobustMilk project. In this work package, we have focused on two aspects of robustness, micro- and macro-environmental sensitivity and applied these to somatic cell count (SCC), one aspect of milk quality. We showed that it is possible to combine both categorical and continuous descriptions of the environment in one analysis of genotype by environment interaction. We also developed a method to estimate genetic variation in residual variance and applied it to both simulated and a large field data set of dairy cattle. We showed that it is possible to estimate genetic variation in both micro- and macro-environmental sensitivity in the same data, but that there is a need for good data structure. In a dairy cattle example, this would mean at least 100 bulls with at least 100 daughters each. We also developed methods for improved genetic evaluation of SCC. We estimated genetic variance for some alternative SCC traits, both in an experimental herd data and in field data. Most of them were highly correlated with subclinical mastitis (>0.9) and clinical mastitis (0.7 to 0.8), and were also highly correlated with each other. We studied whether the fact that animals in different herds are differentially exposed to mastitis pathogens could be a reason for the low heritabilities for mastitis, but did not find strong evidence for that. We also created a new model to estimate breeding values not only for the probability of getting mastitis but also for recovering from it. In a progeny-testing situation, this approach resulted in accuracies of 0.75 and 0.4 for these two traits, respectively, which means that it is possible to also select for cows that recover more quickly if they get mastitis.

A Bayesian procedure was used to estimate linear reaction norms (i.e. individual G × E plots) on 297 518 litter size records of 121 104 sows, daughters of 2040 sires, recorded on 144 farms in North and Latin America, Europe, Asia and Australia. The method allowed for simultaneous estimation of all parameters involved. The analysis was carried out on three subsets, comprising (i) parity 1 records of 33 641 sows of line B, (ii) all parity records of 52 120 sows of line B and (iii) all parity records of 121 104 sows of lines A, B and A × B. Estimated heritabilities ranged from 0.09 to 0.10 (smallest to largest subset) for the intercept of the reaction norms, and were 0.15, 0.08 and 0.02 (ditto) for the slope. Estimated genetic correlations between intercept and slope were −0.09, +0.26 and +0.69 (ditto). The three subsets therefore showed a progressively lower genetic component to environmental sensitivity, and progressively less re-ranking of genotypes across the environmental (herd–year–season) range. In a genetic evaluation that does not include reaction norms in the statistical model, part of the G × E effect remains confounded with the additive genetic effect, which may lead to errors in the estimates of the additive genetic effect; the reaction norms model removes this confounding. The intercept estimates from the largest data subset show correlations with litter size estimated breeding values (EBV) from routine genetic evaluation (without reaction norms included) of 0.78 to 0.85 for sows with one to seven litter records, and 0.75 for sires. Hence, including reaction norms in genetic evaluation would increase the reliability of the EBV of young selection candidates without own performance or progeny data by considerably more than 100 × (1/0.75−1) = 33%. Reaction norm slope estimates turn out to be very demanding statistics; environmental sensitivity must therefore be classified as a ‘hard-to-measure’ trait.