Genotype × environment interaction for long-yearling weight in Canchim cattle quantified by reaction norm analysis1

Mattar, Maira; Silva, L. O. C.; Alencar, M. M. de; Cardoso, F. F.

doi:10.2527/jas.2010-3770

Cited by 52 publications

(71 citation statements)

References 23 publications

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“…The a posteriori mean estimates of PWG heritability from the MAMRNH model for different AN and NE breed groups were all directly proportional to the environmental gradient (Figure 2). Similar results have been found in other studies of post-weaning gain in beef cattle (Corrêa et al, 2009;Cardoso et al, 2011;Mattar et al, 2011;Cardoso and Tempelman, 2012;Santana et al, 2013), and in dairy cattle for production and reproductive traits (Kolmodin et al, 2002;Shariati et al, 2007). These results indicate that heritability increases as environmental conditions improve.…”

Section: Fixed Genetic Effectssupporting

confidence: 88%

“…This specification was based on CGs, as used in genetic evaluation, representing the most elemental characterization of production environments. Thus, CG effects are natural covariate candidates for beef cattle reaction norms Mattar et al, 2011;Cardoso and Tempelman, 2012). Additional regression coefficients β AX , β DX and β AAX , were included in model 2 to evaluate interactions of the genetic coefficient effects (A, D and AA) with the environmental gradient.…”

Section: Models and Methodologymentioning

confidence: 99%

“…Similar substantial correlations between intercept and slope were also observed in previous studies of beef cattle where reaction norms were modeled. Examples from these studies include: Cardoso and Tempelman (2012) for Angus (0.95), Cardoso et al (2011) for Hereford (0.97), Mattar et al (2011) for Canchim (0.90) and Santana et al (2013) for tropical composite (0.94). In contrast, the 95% posterior probability support interval for r abðANÞ included zero, implying a nonsignificant correlation in our AN sample (Table 4).…”

Section: Fixed Genetic Effectsmentioning

confidence: 99%

“…Through covariance functions it is possible to determine prediction of breeding values in response to continuous gradations in the production environment (Kirkpatrick et al, 1990). This approach also allows modeling of heterogeneous genetic and environmental variances and has been used successfully to identify G × E interaction effects on performance of dairy (Kolmodin et al, 2002;Shariati et al, 2007;Strandberg et al, 2009) and beef (Corrêa et al, 2009;Cardoso et al, 2011;Mattar et al, 2011;Cardoso and Tempelman, 2012;Santana et al, 2013) cattle. Hierarchical Bayes models have the flexibility necessary to fit these models (Sorensen and Gianola, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Multiple-breed reaction norm animal model accounting for robustness and heteroskedastic in a Nelore–Angus crossed population

Oliveira¹,

Júnior²,

Cardoso³

2016

Animal

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Our objective was to genetically characterize post-weaning weight gain (PWG), over a 345-day period after weaning, of Brangus-Ibagé (Nelore × Angus) cattle. Records (n = 4016) were from the foundation herd of the Embrapa South Livestock Center. A Bayesian approach was used to assess genotype by environment (G × E) interaction and to identify a suitable model for the estimation of genetic parameters and use in genetic evaluation. A robust and heteroscedastic reaction norm multiple-breed animal model was proposed. The model accounted for heterogeneity of residual variance associated with effects of breed, heterozygosity, sex and contemporary group; and was robust with respect to outliers. Additive genetic effects were modeled for the intercept and slope of a reaction norm to changes in the environmental gradient. Inference was based on Monte Carlo Markov Chain of 110 000 cycles, after 10 000 cycles of burn-in. Bayesian model choice criteria indicated the proposed model was superior to simpler sub-models that did not account for G × E interaction, multiple-breed structure, robustness and heteroscedasticity. We conclude that, for the Brangus-Ibagé population, these factors should be jointly accounted for in genetic evaluation of PWG. Heritability estimates increased proportionally with improvement in the environmental conditions gradient. Therefore, an increased proportion of differences in performance among animals were explained by genetic factors rather than environmental factors as rearing conditions improved. As a consequence response to selection may be increased in favorable environments.

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Section: Fixed Genetic Effectssupporting

confidence: 88%

Section: Models and Methodologymentioning

confidence: 99%

Section: Fixed Genetic Effectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multiple-breed reaction norm animal model accounting for robustness and heteroskedastic in a Nelore–Angus crossed population

Oliveira¹,

Júnior²,

Cardoso³

2016

Animal

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“…According to Mattar et al (2011), the CG combines important environmental information that affects animal performance. Therefore, the CG can be naturally defined as environmental descriptor.…”

Section: Resultsmentioning

confidence: 99%

Phenotypic plasticity of composite beef cattle performance using reaction norms model with unknown covariate

Júnior

Eler

Cardoso

et al. 2013

Animal

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The objective of the present study was to determine the presence of genotype by environment interaction (G 3 E) and to characterize the phenotypic plasticity of birth weight (BW), weaning weight (WW), postweaning weight gain (PWG) and yearling scrotal circumference (SC) in composite beef cattle using the reaction norms model with unknown covariate. The animals were born between 1995 and 2008 on 33 farms located throughout all Brazilian biomes between latitude 278 and 2318, longitude 2408 and 2638. The contemporary group was chosen as the environmental descriptor, that is, the environmental covariate of the reaction norms. In general, higher estimates of direct heritability were observed in extreme favorable environments. The mean of direct heritability across the environmental gradient ranged from 0.05 to 0.51, 0.09 to 0.43, 0.01 to 0.43 and from 0.12 to 0.26 for BW, WW, PWG and SC, respectively. The variation in direct heritability observed indicates a different response to selection according to the environment in which the animals of the population are evaluated. The correlation between the level and slope of the reaction norm for BW and PWG was high, indicating that animals with higher average breeding values responded better to improvement in environmental conditions, a fact characterizing a scale of G 3 E. Low correlation between the intercept and slope was obtained for WW and SC, implying re-ranking of animals in different environments. Genetic variation exists in the sensitivity of animals to the environment, a fact that permits the selection of more plastic or robust genotypes in the population studied. Thus, the G 3 E is an important factor that should be considered in the genetic evaluation of the present population of composite beef cattle.

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Reaction norm model to describe environmental sensitivity across first lactation in dairy cattle under tropical conditions

Bignardi

Faro

Pereira

et al. 2015

Trop Anim Health Prod

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Reaction norm models have been widely used to study genotype by environment interaction (G × E) in animal breeding. The objective of this study was to describe environmental sensitivity across first lactation in Brazilian Holstein cows using a reaction norm approach. A total of 50,168 individual monthly test day (TD) milk yields (10 test days) from 7476 complete first lactations of Holstein cattle were analyzed. The statistical models for all traits (10 TDs and for 305-day milk yield) included the fixed effects of contemporary group, age of cow (linear and quadratic effects), and days in milk (linear effect), except for 305-day milk yield. A hierarchical reaction norm model (HRNM) based on the unknown covariate was used. The present study showed the presence of G × E in milk yield across first lactation of Holstein cows. The variation in the heritability estimates implies differences in the response to selection depending on the environment where the animals of this population are evaluated. In the average environment, the heritabilities for all traits were rather similar, in range from 0.02 to 0.63. The scaling effect of G × E predominated throughout most of lactation. Particularly during the first 2 months of lactation, G × E caused reranking of breeding values. It is therefore important to include the environmental sensitivity of animals according to the phase of lactation in the genetic evaluations of Holstein cattle in tropical environments.

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Genotype × environment interaction for long-yearling weight in Canchim cattle quantified by reaction norm analysis1

Cited by 52 publications

References 23 publications

Multiple-breed reaction norm animal model accounting for robustness and heteroskedastic in a Nelore–Angus crossed population

Multiple-breed reaction norm animal model accounting for robustness and heteroskedastic in a Nelore–Angus crossed population

Phenotypic plasticity of composite beef cattle performance using reaction norms model with unknown covariate

Reaction norm model to describe environmental sensitivity across first lactation in dairy cattle under tropical conditions

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