Reaction norm for yearling weight in beef cattle using single-step genomic evaluation1

Abstract

When the environment on which the animals are raised is very diverse, selecting the best sires for different environments may require the use of models that account for genotype by environment interaction (G × E). The main objective of this study was to evaluate the existence of G × E for yearling weight (YW) in Nellore cattle using reaction norm models with only pedigree and pedigree combined with genomic relationships. Additionally, genomic regions associated with each environment gradient were identified. A total of 67,996 YW records were used in reaction norm models to calculate EBV and genomic EBV. The method of choice for genomic evaluations was single-step genomic BLUP (ssGBLUP). Traditional and genomic models were tested on the ability to predict future animal performance. Genetic parameters for YW were obtained with the average information restricted maximum likelihood method, with and without adding genomic information for 5,091 animals. Additive genetic variances explained by windows of 200 adjacent SNP were used to identify genomic regions associated with the environmental gradient. Estimated variance components for the intercept and the slope in traditional and genomic models were similar. In both models, the observed changes in heritabilities and genetic correlations for YW across environments indicate the occurrence of genotype by environment interactions. Both traditional and genomic models were capable of identifying the genotype by environment interaction; however, the inclusion of genomic information in reaction norm models improved the ability to predict animals' future performance by 7.9% on average. The proportion of genetic variance explained by the top SNP window was 0.77% for the regression intercept (BTA5) and 0.82% for the slope (BTA14). Single-step GBLUP seems to be a suitable model to predict genetic values for YW in different production environments.

Genomic reaction norm models exploiting genotype × environment interaction on sexual precocity indicator traits in Nellore cattle

“…The (G)EBV predictive abilities obtained using RNM_H were moderate (from 0.32 to 0.51 for SC and from 0.28 to 0.43 for HP). Our results are similar to those found by Oliveira et al () using a model including G×E interaction for Nellore body weight. The authors reported an increase in the predictive ability of 7.9% using RNM_H instead of RNM_A.…”

“…For SC, the genetic correlations estimated using the H or A matrix were similar (Fig. ), as also found by Mota et al () and Oliveira et al () using genomic information in the reaction norm for beef cattle. However, for HP, larger differences were observed in genetic correlation estimates when using the H matrix, with values ranging from 0.30 to 0.98, instead of 0.60–0.98 with the A matrix.…”

“…The residual variance estimates decreased around 1.10–10.50% using RNM_H instead of RNM_A (Table ). The same trend was reported using the H matrix in RNM for body weight and visual scores in Nellore (Gordo et al ; Oliveira et al ) and for total number born in pigs using the G matrix (Silva et al ). In the present study, the addition of genomic information in RNM (RNM_H) also resulted in a decrease in the genetic parameter estimation standard deviations (SD) of 35.42% for SC and 37.31% for HP, on average (Table ).…”

Clusters De Robustez Como Critério De Seleção No Melhoramento Genético Para Mitigação De Impactos Das Mudanças Climáticas

“…Nas análises, foram obtidas correlações genéticas estimadas entre ambientes extremos de 0,33 em fêmeas e 0,40 em machos. Tais valores confirmam a significância da IGA, mostrando a importância do uso de modelos de norma de reação na avaliação de rebanhos bovinos da raça Nelore, em concordância com outros resultados da literatura (PEGOLO et al, 2011;DE ARAÚJO NETO et al, 2018). …”

“…Na bovinocultura de corte, vários trabalhos já foram apresentados, discutindo a existência de interação genótipo-ambiente (IGA) nas populações em seleção das diferentes raças. O Brasil, sendo um país com grande extensão territorial, possui uma gama de sistemas de produção muito diversificada, tornando-se um local de interesse para estudos de IGA por meio dos modelos de NRAs, conforme descrito por Ferreira et al (2014), Lemos et al (2015); Hay & Roberts (2018), Chiaia et al (2015), Oliveira et al (2018) (2011) consideraram a possibilidade da seleção nas características de crescimento utilizando como critério de seleção os coeficientes preditos das NRAs, que tendem a ser transmitidos para as proles, permitindo a seleção de indivíduos com robustez ambiental, ou seja, com baixa sensibilidade às mudanças ambientais. O maior desafio associado a tal seleção consiste na padronização dos coeficientes das NRAs num índice que permita a diferenciação dos animais robustos e de baixa produção dos animais robustos e com alta produção, equilibrando o progresso de seleção para ganho de peso com a estabilidade genética.…”