Genetic modeling of feed intake

David, Ingrid; Ruesche, Julien; Drouilhet, Laurence; Garreau, Hervé; Gilbert, Hélène

doi:10.2527/jas.2014-8507

Cited by 13 publications

(19 citation statements)

References 36 publications

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“…Comparison of AIC values showed that the multiple-trait SAD model provided a better fit to the data than the RR model in both species. The same conclusion has been drawn in previous studies on other traits [8, 9]. Results obtained with RR models were essentially consistent with those of the multiple-trait SAD model, which enables us to be confident in the estimations obtained with the SAD approach.…”

Section: Discussionsupporting

confidence: 89%

“…A single-trait SAD model is then defined by the order of the antedependence (), the degree of the polynomial for each antedependence parameter ( to ), and the degree of the polynomial for the innovation variance () for each random effect. We will refer to single-trait SAD models as SAD [8]. For instance SAD 111 stands for a SAD model with: …”

Section: Methodsmentioning

confidence: 99%

“…The most commonly used approach is the RR model [6], although this approach suffers from various drawbacks, the main one being the well-known “border effect” problem [7]. CP and SAD models have been shown to fit the covariance structures better than RR models [4, 5, 8]. However, they are less often used in genetic analyses due to the lack of user-friendly software for SAD models and to the difficulty in accounting for nonstationary longitudinal data without inflation of the number of parameters or without appropriate software for CP models.…”

Section: Introductionmentioning

confidence: 99%

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Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits

David

Garreau

Balmisse³

et al. 2017

Genet Sel Evol

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BackgroundSome genetic studies need to take into account correlations between traits that are repeatedly measured over time. Multiple-trait random regression models are commonly used to analyze repeated traits but suffer from several major drawbacks. In the present study, we developed a multiple-trait extension of the structured antedependence model (SAD) to overcome this issue and validated its usefulness by modeling the association between litter size (LS) and average birth weight (ABW) over parities in pigs and rabbits.MethodsThe single-trait SAD model assumes that a random effect at time can be explained by the previous values of the random effect (i.e. at previous times). The proposed multiple-trait extension of the SAD model consists in adding a cross-antedependence parameter to the single-trait SAD model. This model can be easily fitted using ASReml and the OWN Fortran program that we have developed. In comparison with the random regression model, we used our multiple-trait SAD model to analyze the LS and ABW of 4345 litters from 1817 Large White sows and 8706 litters from 2286 L-1777 does over a maximum of five successive parities.ResultsFor both species, the multiple-trait SAD fitted the data better than the random regression model. The difference between AIC of the two models (AIC_random regression-AIC_SAD) were equal to 7 and 227 for pigs and rabbits, respectively. A similar pattern of heritability and correlation estimates was obtained for both species. Heritabilities were lower for LS (ranging from 0.09 to 0.29) than for ABW (ranging from 0.23 to 0.39). The general trend was a decrease of the genetic correlation for a given trait between more distant parities. Estimates of genetic correlations between LS and ABW were negative and ranged from −0.03 to −0.52 across parities. No correlation was observed between the permanent environmental effects, except between the permanent environmental effects of LS and ABW of the same parity, for which the estimate of the correlation was strongly negative (ranging from −0.57 to −0.67).ConclusionsWe demonstrated that application of our multiple-trait SAD model is feasible for studying several traits with repeated measurements and showed that it provided a better fit to the data than the random regression model.

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Section: Discussionsupporting

confidence: 89%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits

David

Garreau

Balmisse³

et al. 2017

Genet Sel Evol

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“…In Danish pig breeding, Duroc is a terminal sire line with strong selection pressure for feed efficiency, growth, and lean meat production; however, Landrace and Yorkshire are dam line breeds with the primary selection objective of improvement in maternal traits but still a substantial emphasis on feed efficiency and growth. Figure 1 shows smooth results also towards the borders of the data, suggesting no border effects, which are sometimes seen in random regression models using Legendre polynomials, for example, David et al (2015). Cai et al (2011) reported heritabilities of 0.10 to 0.37 for daily FI during the test period using random regression with second-order Legendre polynomials of age in boars of Yorkshire pigs.…”

Section: Genetic Background Of Feed Intakementioning

confidence: 82%

Joint analysis of longitudinal feed intake and single recorded production traits in pigs using a novel Horizontal model

Shirali¹,

Strathe²,

Mark³

et al. 2017

Journal of Animal Science

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A novel Horizontal model is presented for multitrait analysis of longitudinal traits through random regression analysis combined with single recorded traits. Weekly ADFI on test for Danish Duroc, Landrace, and Yorkshire boars were available from the national test station and were collected from 30 to 100 kg BW. Single recorded production traits of ADG from birth to 30 kg BW (ADG30), ADG from 30 to 100 kg BW (ADG100), and lean meat percentage (LMP) were available from breeding herds or the national test station. The Horizontal model combined random regression analysis of feed intake (FI) with single recorded traits of ADG100, LMP, and ADG30. In the Horizontal model, the FI data were horizontally structured with FI on each week as a "trait." The additive genetic and litter effects were modeled to be common across different FI records by reducing the rank of the covariance matrices using second- and first-order Legendre polynomials of age on test, respectively. The fixed effect and random residual variance were estimated for each weekly FI trait. Residual feed intake (RFI) was derived from the conditional distribution of FI given the breeding values of ADG100 and LMP. The heritability of FI varied by week on test in Duroc (0.12 to 0.19), Landrace (0.13 to 0.22), and Yorkshire (0.21 to 0.23). The heritability of RFI was lowest and highest in wk 6 (0.03) and 10 (0.10), respectively, in Duroc and wk 7 (0.04 and 0.02) and 1 (0.09 and 0.20), respectively, in Landrace and Yorkshire. The proportion of FI genetic variance explained by RFI ranged from 20 to 75% in Duroc, from 19 to 75% in Landrace, and from 11 to 91% in Yorkshire. Average daily gain from 30 to 100 kg BW and ADG30 heritabilities were moderate in Duroc (0.24 and 0.22, respectively), Landrace (0.34 and 0.25, respectively), and Yorkshire (0.34 and 0.22, respectively). Lean meat percentage heritability was moderate in Duroc (0.37) and large in Landrace (0.62) and Yorkshire (0.60). The genetic correlation of FI with ADG100 increased by week on test followed by a 32% decrease from wk 7 in Duroc and a 7% decrease in dam line breeds. Defining RFI as genetically independent of production traits leads to consistent and easy interpretable breeding values. The genetic parameters of traits in the feed efficiency complex and their dynamics over the test period showed breed differences that could be related to the fatness and growth potential of the breeds. The Horizontal model can be used to simultaneously analyze repeated and single recorded traits through proper modeling of the environmental variances and covariances.

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“…Conversely, the direct and indirect genetic effects were correlated , where is the 10 × 10 (co)variance matrix for the genetic effects (5 weeks for the direct genetic effects and 5 weeks for the indirect genetic effects) and is the additive relationship matrix based on pedigree. Possible non-null covariances between random effects at different times were taken into account using a SAD approach [ 16 – 18 ]. It should be noted that this model does not include a residual term to help convergence and to avoid identifiability problems between structured permanent and classical residual covariance matrices [ 19 ], as in previous studies using the SAD approach [ 15 , 17 ].…”

Section: Methodsmentioning

confidence: 99%

Longitudinal analysis of direct and indirect effects on average daily gain in rabbits using a structured antedependence model

David¹,

Sánchez²,

Piles³

2018

Genet Sel Evol

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BackgroundIndirect genetic effects (IGE) are important components of various traits in several species. Although the intensity of social interactions between partners likely vary over time, very few genetic studies have investigated how IGE vary over time for traits under selection in livestock species. To overcome this issue, our aim was: (1) to analyze longitudinal records of average daily gain (ADG) in rabbits subjected to a 5-week period of feed restriction using a structured antedependence (SAD) model that includes IGE and (2) to evaluate, by simulation, the response to selection when IGE are present and genetic evaluation is based on a SAD model that includes IGE or not.ResultsThe direct genetic variance for ADG (g/d) increased from week 1 to 3 [from 8.03 to 13.47 (g/d)2] and then decreased [6.20 (g/d)2 at week 5], while the indirect genetic variance decreased from week 1 to 4 [from 0.43 to 0.22 (g/d)2]. The correlation between the direct genetic effects of different weeks was moderate to high (ranging from 0.46 to 0.86) and tended to decrease with time interval between measurements. The same trend was observed for IGE for weeks 2 to 5 (correlations ranging from 0.62 to 0.91). Estimates of the correlation between IGE of week 1 and IGE of the other weeks did not follow the same pattern and correlations were lower. Estimates of correlations between direct and indirect effects were negative at all times. After seven generations of simulated selection, the increase in ADG from selection on EBV from a SAD model that included IGE was higher (~ 30%) than when those effects were omitted.ConclusionsIndirect genetic effects are larger just after mixing animals at weaning than later in the fattening period, probably because of the establishment of social hierarchy that is generally observed at that time. Accounting for IGE in the selection criterion maximizes genetic progress.Electronic supplementary materialThe online version of this article (10.1186/s12711-018-0395-9) contains supplementary material, which is available to authorized users.

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Genetic modeling of feed intake

Cited by 13 publications

References 36 publications

Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits

Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits

Joint analysis of longitudinal feed intake and single recorded production traits in pigs using a novel Horizontal model

Longitudinal analysis of direct and indirect effects on average daily gain in rabbits using a structured antedependence model

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