Multi-trait linear reaction norm model to describe the pattern of phenotypic expression of some economic traits in beef cattle across a range of environments

Júnior, Mário Luiz Santana; Eler, Joanir Pereira; Bignardi, Annaíza Braga; Menéndez-Buxadera, A.; Cardoso, F. F.; Ferraz, José Bento Sterman

doi:10.1007/s13353-014-0242-9

Cited by 24 publications

(20 citation statements)

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“…The genetic correlations have been used to assess the degree of animal sensitivity to environmental changes (Robertson, ). These estimates are in agreement with other results reported by Santana et al (, ) and Chiaia et al (), in which the magnitude of genetic correlation estimates decreased when differences between EC levels increased, indicating the occurrence of G×E interaction with re‐ranking of EBVs across EC levels.…”

Section: Resultssupporting

confidence: 93%

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

et al. 2020

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Summary Brazilian beef cattle are raised predominantly on pasture in a wide range of environments. In this scenario, genotype by environment (G×E) interaction is an important source of phenotypic variation in the reproductive traits. Hence, the evaluation of G×E interactions for heifer’s early pregnancy (HP) and scrotal circumference (SC) traits in Nellore cattle, belonging to three breeding programs, was carried out to determine the animal’s sensitivity to the environmental conditions (EC). The dataset consisted of 85 874 records for HP and 151 553 records for SC, from which 1800 heifers and 3343 young bulls were genotyped with the BovineHD BeadChip. Genotypic information for 826 sires was also used in the analyses. EC levels were based on the contemporary group solutions for yearling body weight. Linear reaction norm models (RNM), using pedigree information (RNM_A) or pedigree and genomic information (RNM_H), were used to infer G×E interactions. Two validation schemes were used to assess the predictive ability, with the following training populations: (a) forward scheme—dataset was split based on year of birth from 2008 for HP and from 2011 for SC; and (b) environment‐specific scheme—low EC (−3.0 and −1.5) and high EC (1.5 and 3.0). The inclusion of the H matrix in RNM increased the genetic variance of the intercept and slope by 18.55 and 23.00% on average respectively, and provided genetic parameter estimates that were more accurate than those considering pedigree only. The same trend was observed for heritability estimates, which were 0.28–0.56 for SC and 0.26–0.49 for HP, using RNM_H, and 0.26–0.52 for SC and 0.22–0.45 for HP, using RNM_A. The lowest correlation observed between unfavorable (−3.0) and favorable (3.0) EC levels were 0.30 for HP and −0.12 for SC, indicating the presence of G×E interaction. The G×E interaction effect implied differences in animals’ genetic merit and re‐ranking of animals on different environmental conditions. SNP marker–environment interaction was detected for Nellore sexual precocity indicator traits with changes in effect and variance across EC levels. The RNM_H captured G×E interaction effects better than RNM_A and improved the predictive ability by around 14.04% for SC and 21.31% for HP. Using the forward scheme increased the overall predictive ability for SC (20.55%) and HP (11.06%) compared with the environment‐specific scheme. The results suggest that the inclusion of genomic information combined with the pedigree to assess the G×E interaction leads to more accurate variance components and genetic parameter estimates.

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Section: Resultssupporting

confidence: 93%

“…Oliveira et al () observed that this occurs by scale effect in the EBV. The correlation estimate between intercept and slope was similar to that observed by Santana et al () in Nellore cattle for HP (0.65) and higher than those reported by Santana et al (, ) for SC of 0.001 and 0.14 respectively.…”

Section: Resultssupporting

confidence: 89%

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

et al. 2020

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“…This trait therefore typically exhibits strong G × E due to heat stress. This finding is expected as traits such as SCS that are less heritable are usually more plastic (environmentally sensitive) and associated with stronger G × E (Tonsor et al, 2013;Santana et al, 2015).…”

Section: Discussionmentioning

confidence: 90%

Genetics of heat tolerance for milk yield and quality in Holsteins

Júnior

Bignardi

Pereira

et al. 2017

Animal

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Tropical and sub-tropical climates are characterized by high temperature and humidity, during at least part of the year. Consequently, heat stress is common in Holstein cattle and productive and reproductive losses are frequent. Our objectives were as follows: (1) to quantify losses in production and quality of milk due to heat stress; (2) to estimate genetic correlations within and between milk yield (MY) and milk quality traits; and (3) to evaluate the trends of genetic components of tolerance to heat stress in multiple lactations of Brazilian Holstein cows. Thus, nine analyses using two-trait random regression animal models were carried out to estimate variance components and genetic parameters over temperature-humidity index (THI) values for MY and milk quality traits (three lactations: MY × fat percentage (F%), MY × protein percentage (P%) and MY × somatic cell score (SCS)) of Brazilian Holstein cattle. It was demonstrated that the effects of heat stress can be harmful for traits related to milk production and milk quality of Holstein cattle even though most herds were maintained in a modified environment, for example, with fans and sprinklers. For MY, the effect of heat stress was more detrimental in advanced lactations (−0.22 to −0.52 kg/day per increase of 1 THI unit). In general, the mean heritability estimates were higher for lower THI values and longer days in milk for all traits. In contrast, the heritability estimates for SCS increased with increasing THI values in the second and third lactation. For each trait studied, lower genetic correlations (different from unity) were observed between opposite extremes of THI (THI 47 v. THI 80) and in advanced lactations. The genetic correlations between MY and milk quality trait varied across the THI scale and lactations. The genotype × environment interaction due to heat stress was more important for MY and SCS, particularly in advanced lactations, and can affect the genetic relationship between MY and milk quality traits. Selection for higher MY, F% or P% may result in a poor response of the animals to heat stress, as a genetic antagonism was observed between the general production level and specific ability to respond to heat stress for these traits. Genetic trends confirm the adverse responses in the genetic components of heat stress over the years for milk production and quality. Consequently, the selection of Holstein cattle raised in modified environments in both tropical and sub-tropical regions should take into consideration the genetic variation in heat stress.

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“…For PWG, the magnitude of the heritabilities was higher than that reported by Santana et al . () (0.27–0.30). Pegolo et al .…”

Section: Resultsmentioning

confidence: 99%

Study of the effect of genotype–environment interaction on age at first calving and production traits in Nellore cattle using multi‐trait reaction norms and Bayesian inference

Neto

Pegolo

Aspilcueta-Borquis

et al. 2018

Animal Science Journal

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This study investigated the effects of genotype-environment interaction on yearling weight, age at first calving and post-weaning weight gain in Nellore cattle using multi-trait reaction norm models. The environmental gradient was defined as a function of the mean yearling weight of the contemporary groups. A first-order random regression sire model with four classes of residual variance was used in the analyses and Bayesian methods were applied to estimate the (co)variance components. The heritability estimates ranged from 0.284 to 0.547, 0.222 to 0.316 and 0.256 to 0.522 for yearling weight, age at first calving and post-weaning weight gain, respectively. The lowest genetic correlations between environment groups for each trait were 0.38, 0.02 and 0.04 for yearling weight, age at first calving and post-weaning weight gain, respectively. Differences in the correlation estimates were observed between traits in the same environments, with the magnitude of the estimates tending toward zero as the environment improved. The results highlight the importance of including genotype-environment interactions in genetic evaluation programs considering the differences observed between environmental groups not only in terms of heritability, but also of genetic correlations.

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Multi-trait linear reaction norm model to describe the pattern of phenotypic expression of some economic traits in beef cattle across a range of environments

Cited by 24 publications

References 20 publications

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

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

Genetics of heat tolerance for milk yield and quality in Holsteins

Study of the effect of genotype–environment interaction on age at first calving and production traits in Nellore cattle using multi‐trait reaction norms and Bayesian inference

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