Estimation of genetic (co)variances of Gompertz growth function parameters in pigs

Coyne, Jessica M.; Matilainen, Kaarina; Berry, D.P.; Sevón-Aimonen, Marja-Liisa; Mäntysaari, Esa; Juga, Jarmo; Serenius, Timo; McHugh, N.

doi:10.1111/jbg.12237

Cited by 6 publications

(9 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, when fitting a curve to a measured value of mature weight, should decrease as increases, and vice versa, resulting in a negative correlation between the two. This may explain why a negative correlation was consistently observed between these parameters in previous studies [8, 9] and the present study. Thus, the biological interpretation of the genetic correlation between growth curve parameters may be controversial.…”

Section: Resultssupporting

confidence: 88%

“…Because parameter shifts the growth curve back and forth, setting the entry day as the initial day would affect the estimates of and its correlation with the other parameters. Similar contrasting results in genetic correlations were also found in two independent pig studies: Koivula et al [8] reported strong negative genetic correlations between and (− 0.80) and between and (−0.80) but a positive correlation between and (0.88), whereas Coyne et al [9] reported negative correlations between and (− 0.69) and between and (− 0.78) but a positive correlation between and (0.76). Although the estimates in the latter study [9] differed depending on the method used for estimation, a negative correlation between and was consistently observed.…”

Section: Resultssupporting

confidence: 76%

See 1 more Smart Citation

Development of a structural growth curve model that considers the causal effect of initial phenotypes

Onogi

Ogino

Sato³

et al. 2019

Genet Sel Evol

View full text Add to dashboard Cite

Background Growth curves have been widely used in genetic analyses to gain insights into the growth characteristics of both animals and plants. However, several questions remain unanswered, including how the initial phenotypes affect growth and what is the duration of any such impact. For beef cattle production in Japan, calves are procured from farms that specialize in reproduction and then moved to other farms where they are fattened to achieve their market/purchase value. However, the causal effect of growth, while calves are on the reproductive farms, on their growth during fattening remains unclear. To investigate this, we developed a model that combines a structural equation with a growth curve model. The causal effect was modeled with B-splines, which allows inference of the effect as a curve. We fitted the proposed structural growth curve model to repeated measures of body weight from a Japanese beef cattle population (n = 3831) to estimate the curve of the causal effect of the calves’ initial weight on their trajectory of growth when they are on fattening farms. Results Maternal and reproduction farm effects explained 26% of the phenotypic variance of initial weight at fattening farms. The structural growth curve model was fitted to remove the effects of these factors in growth curve analysis at fattening farms. The estimated curve of causal effects remained at approximately 0.8 for 200 d after the calves entered the fattening farms, which means that 64% of the phenotypic variance was explained by the initial weight. Then, the effect decreased linearly and disappeared approximately 620 d after entering the fattening farms, which corresponded to an average age of 871.5 d. Conclusions The proposed model is expected to provide more accurate estimates of genetic values for growth patterns because the confounding causal factors such as maternal and reproduction farm effects are removed. Moreover, examination of the inferred curve of the causal effect enabled us to estimate the effect of a calf’s initial weight at arbitrary times during growth, which could provide suitable information for decision-making when shifting the time of slaughter, building models for genetic evaluation, and selecting calves for market. Electronic supplementary material The online version of this article (10.1186/s12711-019-0461-y) contains supplementary material, which is available to authorized users.

show abstract

Section: Resultssupporting

confidence: 88%

Section: Resultssupporting

confidence: 76%

Development of a structural growth curve model that considers the causal effect of initial phenotypes

Onogi

Ogino

Sato³

et al. 2019

Genet Sel Evol

View full text Add to dashboard Cite

show abstract

“…Recently , Forni et al (2009) estimated the growth curve parameters and genetic parameters simultaneously by one-step approach in beef cattle. In addition, Coyne et al (2017) reported the different results of genetic parameters between one-step and two-step approaches in pig population. The detailed studies on the genetic relationship between growth curve parameters and other economical traits by estimating in one-step approach have not been reported, and further study is necessary to characterize the methods in detail.…”

Section: Genetic Relationships Between Feed Efficiency Traits and Gromentioning

confidence: 99%

Evaluation of feed efficiency traits for genetic improvement in Japanese Black cattle

Takeda

Uemoto

Inoue

et al. 2018

Journal of Animal Science

View full text Add to dashboard Cite

We evaluated the genetic relationships (1) among feed efficiency traits with different fattening periods, (2) between feed efficiency traits and growth traits, and (3) between feed efficiency traits and carcass traits, to determine the influence of genetic factors on feed efficiency traits. In total, 4,578 Japanese Black cattle from a progeny testing program were used. Residual feed intake (RFI), residual BW gain (RG), and residual intake and BW gain (RIG) were defined as feed efficiency traits, and were measured for the first half (approximately 9 to 15 months of age), latter half (approximately 15 to 21 months of age), and total period of fattening (approximately 9 to 21 months of age). A single-trait animal model for estimating heritability and a two-trait animal model for estimating genetic and phenotypic correlations were used. The heritability estimates for RFI, RG, and RIG were different in each fattening period, ranging from 0.36 to 0.46, 0.19 to 0.28, and 0.28 to 0.34, respectively, and the heritability estimates for the total fattening period were greater than those for the first and latter halves separately. RIG showed the greatest preferred genetic correlation, with a greater feed conversion ratio than the other feed efficiency traits (ranging from -0.84 to -0.96). RG in the first and latter halves of the fattening period had different genetic correlations with the growth starting point (0.82 and -0.06, respectively) and maturity rate (0.49 and -0.51, respectively) of the Gompertz growth curve parameters, and is strongly dependent on the different fattening periods. Feed efficiency traits in different fattening periods had low genetic correlations with the carcass traits (from -0.05 to 0.19 for RFI; from 0.02 to 0.31 for RG; and from -0.11 to 0.20 for RIG). This study indicated the possibility for genetic improvement through the selection of high-RIG animals to decrease feed intake and increase BW gain without any unfavorable correlated responses affecting mature (asymptotic) weight and carcass grade.

show abstract

“…It is common, for example, in animal genetics (Emmans and Kyriazakis, 1997; Coyne et al, 2017), to use a parametric curve to describe observed growth trajectories of individual organisms. The interpretation of the curve is that the information contained in its parameters offers a low-dimension summary of serial and noisy data difficult to characterise per se.…”

Section: Methodsmentioning

confidence: 99%

“…Mathematical models are a common tool to characterise phenotype expression. In ontogenesis, for example, models are used to summarise or predict responses to environmental or dietary change via reaction norms (Stearns and Koella, 1986; Nussey et al, 2007; Dingemanse et al, 2010) or to describe growth traits, such as size, body composition, and metabolism via nonlinear relationships (Emmans and Kyriazakis, 2001; Coyne et al, 2017; Filipe et al, 2018). In this paper, we introduce a statistical approach to fit models to individual observations and to estimate individual- and population-level trait distributions.…”

Section: Introductionmentioning

confidence: 99%

Bayesian, Likelihood-Free Modelling of Phenotypic Plasticity and Variability in Individuals and Populations

Filipe

Kyriazakis

2019

Front. Genet.

View full text Add to dashboard Cite

There is a paradigm shift from the traditional focus on the “average” individual towards the definition and analysis of trait variation within individual life-history and among individuals in populations. This is a result of increasing availability of individual phenotypic data. The shift allows the use of genetic and environment-driven variations to assess robustness to challenge, gain greater understanding of organismal biological processes, or deliver individual-targeted treatments or genetic selection. These consequences apply, in particular, to variation in ontogenetic growth. We propose an approach to parameterise mathematical models of individual traits (e.g., reaction norms, growth curves) that address two challenges: 1) Estimation of individual traits while making minimal assumptions about data distribution and correlation, addressed via Approximate Bayesian Computation (a form of nonparametric inference). We are motivated by the fact that available information on distribution of biological data is often less precise than assumed by conventional likelihood functions. 2) Scaling-up to population phenotype distributions while facilitating unbiased use of individual data; this is addressed via a probabilistic framework where population distributions build on separately-inferred individual distributions and individual-trait interpretability is preserved. The approach is tested against Bayesian likelihood-based inference, by fitting weight and energy intake growth models to animal data and normal- and skewed-distributed simulated data. i) Individual inferences were accurate and robust to changes in data distribution and sample size; in particular, median-based predictions were more robust than maximum- likelihood-based curves. These results suggest that the approach gives reliable inferences using few observations and monitoring resources. ii) At the population level, each individual contributed via a specific data distribution, and population phenotype estimates were not disproportionally influenced by outlier individuals. Indices measuring population phenotype variation can be derived for study comparisons. The approach offers an alternative for estimating trait variability in biological systems that may be reliable for various applications, for example, in genetics, health, and individualised nutrition, while using fewer assumptions and fewer empirical observations. In livestock breeding, the potentially greater accuracy of trait estimation (without specification of multitrait variance-covariance parameters) could lead to improved selection and to more decisive estimates of trait heritability.

show abstract

Estimation of genetic (co)variances of Gompertz growth function parameters in pigs

Cited by 6 publications

References 21 publications

Development of a structural growth curve model that considers the causal effect of initial phenotypes

Development of a structural growth curve model that considers the causal effect of initial phenotypes

Evaluation of feed efficiency traits for genetic improvement in Japanese Black cattle

Bayesian, Likelihood-Free Modelling of Phenotypic Plasticity and Variability in Individuals and Populations

Contact Info

Product

Resources

About