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Predicting direct and indirect breeding values for survival time in laying hens using repeated measures.

Brinker T, Ellen ED, Veerkamp RF, Bijma P - Genet. Sel. Evol. (2015)

Bottom Line: Including the timing of IGE expression in the DGE-IGE model reduced EBV accuracy compared to analysing survival time.Our results suggest that prediction of breeding values for survival time in laying hens can be improved using repeated measures models.This is an important result since more accurate EBV contribute to higher rates of genetic gain.

View Article: PubMed Central - PubMed

Affiliation: Animal Breeding and Genomics Centre, Wageningen UR, P.O. Box 338, 6700 AH, Wageningen, The Netherlands. tessa.brinker@wur.nl.

ABSTRACT

Background: Minimizing bird losses is important in the commercial layer industry. Selection against mortality is challenging because heritability is low, censoring is high, and individual survival depends on social interactions among cage members. With cannibalism, mortality depends not only on an individual's own genes (direct genetic effects; DGE) but also on genes of its cage mates (indirect genetic effects; IGE). To date, studies using DGE-IGE models have focussed on survival time but their shortcomings are that censored records were considered as exact lengths of life and models assumed that IGE were continuously expressed by all cage members even after death. However, since dead animals no longer express IGE, IGE should ideally be time-dependent in the model. Neglecting censoring and timing of IGE expression may reduce accuracy of estimated breeding values (EBV). Thus, our aim was to improve prediction of breeding values for survival time in layers that present cannibalism.

Methods: We considered four DGE-IGE models to predict survival time in layers. One model was an analysis of survival time and the three others treated survival in consecutive months as a repeated binomial trait (repeated measures models). We also tested whether EBV were improved by including timing of IGE expression in the analyses. Approximate EBV accuracies were calculated by cross-validation. The models were fitted to survival data on two purebred White Leghorn layer lines W1 and WB, each having monthly survival records over 13 months.

Results: Including the timing of IGE expression in the DGE-IGE model reduced EBV accuracy compared to analysing survival time. EBV accuracy was higher when repeated measures models were used. However, there was no universal best model. Using repeated measures instead of analysing survival time increased EBV accuracy by 10 to 21 and 2 to 12 % for W1 and WB, respectively. We showed how EBV and variance components estimated with repeated measures models can be translated into survival time.

Conclusions: Our results suggest that prediction of breeding values for survival time in laying hens can be improved using repeated measures models. This is an important result since more accurate EBV contribute to higher rates of genetic gain.

No MeSH data available.


Percentage of survival of layer chickens for lines W1 and WB throughout the experiment (max = 13 months)
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Fig1: Percentage of survival of layer chickens for lines W1 and WB throughout the experiment (max = 13 months)

Mentions: Dead hens were removed daily. After death, wing band number, cage number, date of death, and cause of death were recorded. The latter was done subjectively by the employees of ISA without dissection. The study was terminated when hens were on average 75 weeks old. In total, 59 % W1 and 54 % WB laying hens survived (Fig. 1). Survival rates of W1 and WB hens differed most during the first 4 months of the experiment (Fig. 2). Most hens died because of cannibalism; only 37 W1 and 15 WB hens died for other reasons, e.g. some hens were killed by mink. Observations on hens that died for other reasons were removed from the dataset because the objective was to investigate death from cannibalism. However, the identification numbers were retained in their cage mates’ observations for IGE modelling.Fig. 1


Predicting direct and indirect breeding values for survival time in laying hens using repeated measures.

Brinker T, Ellen ED, Veerkamp RF, Bijma P - Genet. Sel. Evol. (2015)

Percentage of survival of layer chickens for lines W1 and WB throughout the experiment (max = 13 months)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4587788&req=5

Fig1: Percentage of survival of layer chickens for lines W1 and WB throughout the experiment (max = 13 months)
Mentions: Dead hens were removed daily. After death, wing band number, cage number, date of death, and cause of death were recorded. The latter was done subjectively by the employees of ISA without dissection. The study was terminated when hens were on average 75 weeks old. In total, 59 % W1 and 54 % WB laying hens survived (Fig. 1). Survival rates of W1 and WB hens differed most during the first 4 months of the experiment (Fig. 2). Most hens died because of cannibalism; only 37 W1 and 15 WB hens died for other reasons, e.g. some hens were killed by mink. Observations on hens that died for other reasons were removed from the dataset because the objective was to investigate death from cannibalism. However, the identification numbers were retained in their cage mates’ observations for IGE modelling.Fig. 1

Bottom Line: Including the timing of IGE expression in the DGE-IGE model reduced EBV accuracy compared to analysing survival time.Our results suggest that prediction of breeding values for survival time in laying hens can be improved using repeated measures models.This is an important result since more accurate EBV contribute to higher rates of genetic gain.

View Article: PubMed Central - PubMed

Affiliation: Animal Breeding and Genomics Centre, Wageningen UR, P.O. Box 338, 6700 AH, Wageningen, The Netherlands. tessa.brinker@wur.nl.

ABSTRACT

Background: Minimizing bird losses is important in the commercial layer industry. Selection against mortality is challenging because heritability is low, censoring is high, and individual survival depends on social interactions among cage members. With cannibalism, mortality depends not only on an individual's own genes (direct genetic effects; DGE) but also on genes of its cage mates (indirect genetic effects; IGE). To date, studies using DGE-IGE models have focussed on survival time but their shortcomings are that censored records were considered as exact lengths of life and models assumed that IGE were continuously expressed by all cage members even after death. However, since dead animals no longer express IGE, IGE should ideally be time-dependent in the model. Neglecting censoring and timing of IGE expression may reduce accuracy of estimated breeding values (EBV). Thus, our aim was to improve prediction of breeding values for survival time in layers that present cannibalism.

Methods: We considered four DGE-IGE models to predict survival time in layers. One model was an analysis of survival time and the three others treated survival in consecutive months as a repeated binomial trait (repeated measures models). We also tested whether EBV were improved by including timing of IGE expression in the analyses. Approximate EBV accuracies were calculated by cross-validation. The models were fitted to survival data on two purebred White Leghorn layer lines W1 and WB, each having monthly survival records over 13 months.

Results: Including the timing of IGE expression in the DGE-IGE model reduced EBV accuracy compared to analysing survival time. EBV accuracy was higher when repeated measures models were used. However, there was no universal best model. Using repeated measures instead of analysing survival time increased EBV accuracy by 10 to 21 and 2 to 12 % for W1 and WB, respectively. We showed how EBV and variance components estimated with repeated measures models can be translated into survival time.

Conclusions: Our results suggest that prediction of breeding values for survival time in laying hens can be improved using repeated measures models. This is an important result since more accurate EBV contribute to higher rates of genetic gain.

No MeSH data available.