Genetic and environmental heterogeneity of residual variance of weight traits in Nellore beef cattle

Neves, Haroldo Henrique de Rezende; Carvalheiro, Roberto; Queiroz, Salete Linhares

doi:10.1186/1297-9686-44-19

Cited by 23 publications

(12 citation statements)

References 25 publications

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“…In the case of unscaled data, the estimates of variance components were very similar for the HOM and the HET models. Neves et al (2012) also found similar estimates of variance components for HOM and HET models for body weight traits in Nellore beef cattle. After scaling data, there was a slight increase of additive genetic variances for model 3, but the increment was much bigger for model 2 (Table 4).…”

Section: Estimation Of Variance Componentssupporting

confidence: 58%

Adjusting for heterogeneity of experimental data in genetic evaluation of dry matter intake in dairy cattle

Uddin

Meuwissen

Veerkamp³

2017

J Animal Breeding Genetics

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SummaryThe objectives of the present study were (i) to find the best fitted model for repeatedly measured daily dry matter intake (DMI) data obtained from different herds and experiments across lactations and (ii) to get better estimates of the genetic parameters and better genetic evaluations. After editing, there were 572,512 daily DMI records of 3,495 animals (Holstein cows) from 11 different herds across 13 lactations and the animals were under 110 different nutritional experiments. The fitted model for this data set was a univariate repeated-measure animal model (called model 1) in which additive genetic and permanent environmental (within and across lactations) effects were fitted as random. Model 1 was fitted as two distinct models (called models 2 and 3) based on alternative fixed effect corrections. For unscaled data, each model (models 2 and 3) was fitted as a homoscedastic (HOM) model first and then as a heteroscedastic (HET) model. Then, data were scaled by multiplying with particular herd-scaling factors, which were calculated by accounting for heterogeneity of phenotypic within-herd variances. Models were selected based on cross-validation and prediction accuracy results. Scaling factors were re-estimated to determine the effectiveness of accounting for herd heterogeneity. Variance components and respective heritability and repeatability were estimated based on a pedigree-based relationship matrix. Results indicated that the model fitted for scaled data showed better fit than the models (HOM or HET) fitted for unscaled data. The heritability estimates of the models 2 and 3 fitted for scaled data were 0.30 and 0.08, respectively. The repeatability estimates of the model fitted for scaled data ranged from 0.51 to 0.63. The re-estimated scaling factor after accounting for heterogeneity of residual variances was close to 1.0, indicating the stabilization of residual variances and herd accounted for most of the heterogeneity. The rank correlation of EBVs between scaled and unscaled data ranged from 0.96 to 0.97. K E Y W O R D Sdairy cattle, dry matter intake, genetic evaluation, heritability, heterogeneity, repeatability

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Section: Estimation Of Variance Componentssupporting

confidence: 58%

Adjusting for heterogeneity of experimental data in genetic evaluation of dry matter intake in dairy cattle

Uddin

Meuwissen

Veerkamp³

2017

J Animal Breeding Genetics

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“…where: is the vector of observations; is the regression coefficients vector of fixed trajectory of each sex; is the vector of random regression coefficients attributed to additive genetic effects; X and Z are incidence matrices of effects in b and a, containing Legendre polynomials relative to the value of the control variable adopted for the tryptophan:lysine ratio; and e is the residue vector. Several studies working with the comparison of random models using residual variance observed similar performance among the main evaluation criteria (NEVES et al, 2012, BIGNARDI et al, 2009, BREWER et al, 2016. Thus, the comparisons of homogeneous and heterogeneous models were performed via Bayesian Information Rev.…”

Section: Methodsmentioning

confidence: 86%

Sensitivity of breeding values of meat quail as a result of tryptophan: lysine ratios in the diet

Rodrigues

Sousa

Cruz

et al. 2018

Rev. bras. saúde prod. anim.

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SUMMARY We aimed to evaluate the random regression models that promote the best fit of residual variance predicting the breeding values of quail body weights and the sensitivity of its breeding values to the variations of different tryptophan:lysine ratios in the diets via reaction norms. A total of 1112 meat quails from LF1 and LF2 lines with 35 days of age were evaluated. During the period of 1 to 21 days of age, birds were fed with different tryptophan:lysine ratios (0.17, 0.20, 0.23, 0.26 and 0.29%) containing 2900 kcal ME/kg and 26.10% crude protein, followed by basal diet provided up to 35 days. The best model fit for residual variance was evaluated comparing heterogeneity (2, 3 and 4 classes) and homogeneity (1 class), including sex as fixed effect and the additive genetic effect as random. The second order Legendre polynomial was used to analyze the genotype x environment interaction using reaction norms. The model considering two classes of residual variance was the one that promoted the best fit of the data, being adopted to predict the breeding values. Thus, we observed changes in the sensitivity of the breeding values, characterized by the rearrangement of the breeding values, according to the different ratios of amino acids, suggesting the genotype x environment interaction.

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“…The log-transformation of the variance [31], the Box-Cox power transformation [32], or the coefficient of variation (CV) [33] are commonly used to make σ 2 independent of μ [34]. The transformed data can be compared with the log-log-test [31], the naive test [34], the likelihood ratio test, Bennett's test, the score test, Miller's test, Doornbos and Dijkstra's test or the Wald test.…”

Section: Transformationsmentioning

confidence: 99%

“…The transformed data can be compared with the log-log-test [31], the naive test [34], the likelihood ratio test, Bennett's test, the score test, Miller's test, Doornbos and Dijkstra's test or the Wald test.…”

Section: Transformationsmentioning

confidence: 99%

A New Fluctuating Asymmetry Index, or the Solution for the Scaling Effect?

Pertoldi

Kristensen

2015

Symmetry

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Two principal methods are commonly employed for the estimation of developmental instability at the population level. Some studies use variances of morphological traits (σ 2 p), while others use fluctuating asymmetry (FA). In both cases, differences in the degree of developmental instability can be tested with an F-test, which is the most common way to compare variances. However, the variance is expected to scale proportionally to the square of the mean as there is a tendency in biological data for σ 2 p to scale proportionally to the square of the mean ( μ ): σ 2 p = Zμ ξ , where ξ is the scaling exponent, which is expected to be two for pure statistical reasons, μ is the mean of the trait and Z is a measure of individual-level variability. Because of this scaling effect, the fluctuating asymmetry will be affected, FA is estimated as the variance between the right and the left sides of a trait (σ 2 r − l = σ 2 r + σ 2 l − 2rσrσl), where σ 2 r and σ 2 l are the variances of the right and the left trait values, respectively. In this paper, we propose a novel method that allows an exact correction of the scaling effect, which will enable a proper comparison of the degree of fluctuating asymmetry for a trait. The problem of the scaling of the FA with the trait size is quite crucial if FA is to be considered an indicator of fitness or an indicator of environmental or genetic stress, as different stresses or fitness levels are typically accompanied by a change of the traits' μ .

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Genetic and environmental heterogeneity of residual variance of weight traits in Nellore beef cattle

Cited by 23 publications

References 25 publications

Adjusting for heterogeneity of experimental data in genetic evaluation of dry matter intake in dairy cattle

Adjusting for heterogeneity of experimental data in genetic evaluation of dry matter intake in dairy cattle

Sensitivity of breeding values of meat quail as a result of tryptophan: lysine ratios in the diet

A New Fluctuating Asymmetry Index, or the Solution for the Scaling Effect?

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