Effects of sex and age on genotype × environment interaction for beef cattle body weight studied using reaction norm models1

Pegolo, N. T.; Albuquerque, Lúcia Galvão de; Lôbo, Raysildo Barbosa; Oliveira, Henrique Nunes de

doi:10.2527/jas.2010-3520

Cited by 24 publications

(10 citation statements)

References 43 publications

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“…Correlations between intercept and the slope of the additive genetic direct reaction norm were negative and of moderate magnitude (−0.32 for BLUP and −0.31 for ssGBLUP). According to Pegolo et al (2011), low magnitude correlations between the intercept and the slope indicate that the equation coefficients are almost independent, increasing the possibility of re-ranking of bulls according to the EG. The values obtained in our study indicated that the intercept and the slope were not totally independent, but re-ranking for some animals could be observed in a given EG.…”

Section: Resultsmentioning

confidence: 99%

“…Cardoso et al (2011) investigated G × E in a Bayesian hierarchical reaction standard model using a single-step approach, and in two-steps for weight at 345 d in Hereford cattle. In the two steps, the authors reported heritability estimates between 0.08 and 0.23 evidencing G × E. Pegolo et al (2011) studied G × E for weight at 450 d in Nellore cattle and observed moderate heritability estimates of 0.20-0.39 in the intermediate and the most favorable environment groups, respectively. However, Lemos et al (2015) investigated YW in Nellore cattle using a three-trait reaction norm model and reported higher heritability estimates varying from 0.13 to 0.72 in EG.…”

Section: Resultsmentioning

confidence: 99%

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

Oliveira

Lourenço

Tsuruta

et al. 2018

Journal of Animal Science

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

Oliveira

Lourenço

Tsuruta

et al. 2018

Journal of Animal Science

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“…There is an increase in the number of studies that indicate the need to have specific genetic value predictions for each environment (Bryant et al, 2006;Hammami et al, 2008;Corrêa et al, 2010;Pegolo et al, 2011;Cardoso et al, 2012). Thus, it is possible that GEI studies change the modeling traditionally used in the usual genetic evaluations.…”

Section: Resultsmentioning

confidence: 99%

Reaction norms models in the adjusted weight at 550 days of age for Polled Nellore cattle in Northeast Brazil

et al. 2014

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-The objective of this study was to evaluate the genotype-environment interaction (GEI) in the body weight adjusted to 550 days of age (W550) of Polled Nellore cattle raised in Northeastern Brazil using reaction norms (RN) models. Hierarchical RN models included fixed effects for age of cow (linear and quadratic) and random effects for contemporary groups (CG) and additive genetic RN level and slope. Four RN hierarchical models (RNHM) were used. The RNHM 2S uses the solutions of contemporary groups estimated by the standard animal model (AM) and considers them as environmental level for predicting the reaction norms and the RNHM 1S , which jointly estimate these two sets of unknowns. Two versions were considered for both models, one with a homogeneous (Hm) and another with a heterogeneous (He) residual variance. The one-step homogeneous residual variance model (RNHM 1S Hm) offered better adjustment to the data when compared with other models. For the RNHM 1S Hm model, estimates of additive genetic variance and heritability increased with environment improvement (260.75±75.80 kg 2 to 4298.39±356.56 kg 2 and 0.22±0.05 to 0.82±0.01, for low-and high-performance environments, respectively). High correlation (0.97±0.01) between the intercept and the slope of RN shows that animals with higher genetic values respond better to environment improvement. In the evaluation of breeding sires with higher genetic values in the various environments using Spearman's correlation, values between 0 and 0.98 were observed, pointing to high reclassification, especially among genetic values obtained by the animal model in comparison with those obtained via RNHM 1S Hm. The existence of GEI is confirmed, and so is the need for specific evaluations for low, medium and high level production environments.

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“…; Pégolo et al . ). The extreme points observed when the variable of ‘country of origin’ is evaluated for MBW, differences can be due to the fact that this study compared United States to all other countries.…”

Section: Discussionmentioning

confidence: 97%

Cluster and meta‐analyses of genetic parameters for feed intake traits in growing beef cattle

Diaz

Crews

Enns

2013

J Animal Breeding Genetics

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A data set based on 50 studies including feed intake and utilization traits was used to perform a meta-analysis to obtain pooled estimates using the variance between studies of genetic parameters for average daily gain (ADG); residual feed intake (RFI); metabolic body weight (MBW); feed conversion ratio (FCR); and daily dry matter intake (DMI) in beef cattle. The total data set included 128 heritability and 122 genetic correlation estimates published in the literature from 1961 to 2012. The meta-analysis was performed using a random effects model where the restricted maximum likelihood estimator was used to evaluate variances among clusters. Also, a meta-analysis using the method of cluster analysis was used to group the heritability estimates. Two clusters were obtained for each trait by different variables. It was observed, for all traits, that the heterogeneity of variance was significant between clusters and studies for genetic correlation estimates. The pooled estimates, adding the variance between clusters, for direct heritability estimates for ADG, DMI, RFI, MBW and FCR were 0.32 ± 0.04, 0.39 ± 0.03, 0.31 ± 0.02, 0.31 ± 0.03 and 0.26 ± 0.03, respectively. Pooled genetic correlation estimates ranged from -0.15 to 0.67 among ADG, DMI, RFI, MBW and FCR. These pooled estimates of genetic parameters could be used to solve genetic prediction equations in populations where data is insufficient for variance component estimation. Cluster analysis is recommended as a statistical procedure to combine results from different studies to account for heterogeneity.

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Effects of sex and age on genotype × environment interaction for beef cattle body weight studied using reaction norm models1

Cited by 24 publications

References 43 publications

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

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

Reaction norms models in the adjusted weight at 550 days of age for Polled Nellore cattle in Northeast Brazil

Cluster and meta‐analyses of genetic parameters for feed intake traits in growing beef cattle

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