Bayesian analysis of pig growth curves combining pedigree and genomic information

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.

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“…We assumed that , where was equal to 5, and was determined as outlined below. This model is similar to those used in previous studies [17–19].…”

Section: Methodsmentioning

confidence: 66%

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

Onogi

Ogino

Sato³

et al. 2019

Genet Sel Evol

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“…In dairy cattle, the fact that Interbull currently requires only one EBV or GEBV per trait to perform MACE (Interbull, 2017b) has also been a limiting factor for the countries that participate in international evaluations, as extra calculations would be needed to summarize the predictions. To make inferences about the trajectory of the curves, some studies have proposed estimating GEBV for curve parameters (coefficients) calculated in a first step (e.g., Lázaro et al, 2017). However, this approach still requires extra steps to estimate the EBV or GEBV exchanged by Interbull, and it gives limited inferences about the pattern of the phenotypic curve, which might not have biological interpretation in some cases.…”

Section: An Overview Of Genomic Selectionmentioning

confidence: 99%

Invited review: Advances and applications of random regression models: From quantitative genetics to genomics

Oliveira

Brito

Lourenço

et al. 2019

Journal of Dairy Science

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An important goal in animal breeding is to improve longitudinal traits; that is, traits recorded multiple times during an individual's lifetime or physiological cycle. Longitudinal traits were first genetically evaluated based on accumulated phenotypic expression, phenotypic expression at specific time points, or repeatability models. Until now, the genetic evaluation of longitudinal traits has mainly focused on using random regression models (RRM). Random regression models enable fitting random genetic and environmental effects over time, which results in higher accuracy of estimated breeding values compared with other statistical approaches. In addition, RRM provide insights about temporal variation of biological processes and the physiological implications underlying the studied traits. Despite the fact that genomic information has substantially contributed to increase the rates of genetic progress for a variety of economically important traits in several livestock species, less attention has been given to longitudinal traits in recent years. However, including genomic information to evaluate longitudinal traits using RRM is a feasible alternative to yield more accurate selection and culling decisions, because selection of young animals may be based on the complete pattern of the production curve with higher accuracy compared with the use of traditional parent average (i.e., without genomic information). Moreover, RRM can be used to estimate SNP effects over time in genome-wide association studies. Thus, by analyzing marker associations over time, regions with higher effects at specific points in time are more likely to be identified. Despite the advances in applications of RRM in genetic evaluations, more research is needed to successfully combine RRM and genomic information. Future research should provide a better understanding of the temporal variation of biological processes and their physiological implications underlying the longitudinal traits.

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“…Besides the growth curve parameters that are directly obtained and present biological interpretation (such as mature weight and maturation rate), their genetic parameters (heritabilities and genetic correlations) can be estimated and considered in animal breeding programs (Silva, 2008). Therefore, using the estimates of the non-linear models parameters as phenotypes in genetic models, the animals breeding values (EBVs) for these parameters can be predicted and the breeding goals are achieved by changing the shape of the growth curves (Lázaro et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…In pigs, the Gompertz non-linear function (Gompertz, 1825) has been used to accurately describe the potential growth of animals according to age (Wellock et al, 2004;Freitas, 2005;Koivula et al, 2008;Cai et al, 2012;Coyne et al, 2017;Lázaro et al, 2017). Gompertz function requires the determination of three parameters: mature body weight (a), the inflection point (b) and the maturation rate (k).…”

Section: Introductionmentioning

confidence: 99%

Análise genética de curvas de crescimento de suínos: um estudo de simulação

et al. 2021

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Objetivamos estimar parâmetros genéticos para os parâmetros da curva de crescimento não-linear de Gompertz com base no método em duas etapas e em dados simulados de uma população de suínos. Dados de valores genéticos reais, dos parâmetros da curva de crescimento (a, b e k) e dos pesos corporais aos 1, 90, 210, 360 e 540 dias de idade foram simulados para 1700 indivíduos. Posteriormente, na primeira etapa do método em duas etapas, as curvas de Gompertz foram ajustadas para cada animal e os parâmetros da curva de crescimento foram estimados. Na segunda etapa, considerando os parâmetros estimados como fenótipos, os valores genéticos dos animais foram preditos por análises multicaracterísticas. Os processos de simulação e de duas etapas foram replicados 10 vezes. Para cada réplica, os parâmetros genéticos, acurácias e vieses foram calculados. Herdabilidades médias moderadas a altas para os parâmetros da curva [0,62 (0,04), 0,56 (0,04) e 0,57 (0,04) para a, b e k, respectivamente] mostram a possibilidade de seleção eficaz de animais com base nesses parâmetros. Correlações genéticas médias altas e favoráveis, especialmente entre os parâmetros mais importantes da curva de crescimento [a e k, 0,93 (0,01)], mostram que a seleção para o aumento do peso à maturidade (a) também aumentará a taxa de maturação (k). Acurácias médias altas [0,84 (0,02), 0,82 (0,02) e 0,83 (0,02)] e vieses médios baixos [0,06 (0,06), 0,06 (0,06) e 0,07 (0,07) para a, b e k, respectivamente] indicam que a abordagem em duas etapas é viável para predizer valores genéticos para os parâmetros da curva de crescimento e para trabalhar com medidas longitudinais de peso corporal.

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Bayesian analysis of pig growth curves combining pedigree and genomic information

Abstract: analysis of pig growth curves combining pedigree and genomic information, Livestock Science,

Cited by 8 publications

References 30 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

Invited review: Advances and applications of random regression models: From quantitative genetics to genomics

Análise genética de curvas de crescimento de suínos: um estudo de simulação

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