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

Genetics Selection Evolution

Research Article

Roel F. Veerkamp¹  Piter Bijma¹  Esther D. Ellen¹  Tessa Brinker¹ 

[1] Animal Breeding and Genomics Centre, Wageningen UR, P.O. Box 338, 6700 AH, Wageningen, The Netherlands;

关键词: Genetic Parameter;    Estimate Breeding Value;    Repeat Measure Model;    Cage Mate;    Monthly Survival;   

DOI  :  10.1186/s12711-015-0152-2

 received in 2015-05-26, accepted in 2015-09-11,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundMinimizing 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.MethodsWe 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.ResultsIncluding 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.ConclusionsOur 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.

【 授权许可】

CC BY   
© Brinker et al. 2015

【 图 表 】

12864_2017_3527_Article_IEq10.gif

12864_2017_4186_Article_IEq3.gif

12864_2017_3527_Article_IEq6.gif

12864_2017_4274_Article_IEq11.gif

12864_2015_2055_Article_IEq84.gif

12864_2017_3487_Article_IEq6.gif

12864_2017_3496_Article_IEq2.gif

12864_2017_3938_Article_IEq1.gif

12864_2016_2880_Article_IEq10.gif

12864_2017_4025_Article_IEq13.gif

12864_2017_3500_Article_IEq19.gif

12864_2017_4020_Article_IEq50.gif

12864_2017_4025_Article_IEq9.gif

12864_2016_3440_Article_IEq30.gif

12864_2016_3440_Article_IEq29.gif

12864_2016_3440_Article_IEq28.gif

12864_2017_4025_Article_IEq5.gif

12864_2017_3500_Article_IEq12.gif

12864_2017_4025_Article_IEq4.gif

12864_2016_2443_Article_IEq15.gif

12864_2015_2055_Article_IEq66.gif

12864_2017_4260_Article_IEq1.gif

12864_2017_4130_Article_IEq8.gif

12864_2016_2798_Article_IEq11.gif

12864_2017_4130_Article_IEq7.gif

12864_2016_3098_Article_IEq43.gif

12864_2015_2198_Article_IEq33.gif

12864_2016_2821_Article_IEq27.gif

12864_2017_3655_Article_IEq25.gif

12864_2017_4020_Article_IEq33.gif

12864_2017_3655_Article_IEq24.gif

12864_2016_2821_Article_IEq7.gif

12864_2016_3440_Article_IEq27.gif

12864_2016_2871_Article_IEq4.gif

12864_2017_4030_Article_IEq1.gif

12864_2016_2443_Article_IEq14.gif

12864_2017_4025_Article_IEq2.gif

12864_2016_2443_Article_IEq13.gif

12864_2016_2443_Article_IEq12.gif

12864_2016_3426_Article_IEq2.gif

12864_2015_1994_Article_IEq17.gif

12864_2017_4130_Article_IEq5.gif

12864_2016_3263_Article_IEq15.gif

12864_2017_3604_Article_IEq3.gif

12711_2017_284_Article_IEq69.gif

12864_2017_3604_Article_IEq2.gif

12864_2016_3098_Article_IEq40.gif

12864_2017_4309_Article_IEq11.gif

12711_2017_365_Article_IEq126.gif

12864_2017_4309_Article_IEq9.gif

12864_2017_4225_Article_IEq1.gif

12864_2017_3655_Article_IEq14.gif

12864_2017_4269_Article_IEq8.gif

12864_2017_3492_Article_IEq24.gif

12864_2017_4269_Article_IEq5.gif

12864_2017_3492_Article_IEq22.gif