Bayesian censored models for genetic analysis of age at first kidding in Murciano-Granadina goats

Articles in Press

Document Type : Research Article (Regular Paper)

Authors

1 Department of Animal Science, Faculty of Agriculture, University of Jiroft

2 Department of Animal Science, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

3 Department of Animal Science, Kerman Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education & Extension Organization (AREEO), Kerman, Iran

Abstract

The purpose of the present study was to investigate the effect of censoring in the genetic analysis of age at first kidding (AFK) in the Murciano-Granadina goat breed. The dataset included information on first-kidding records collected from 2016 to 2024 from a private dairy farm of the Murciano-Granadina goat breed, in Ghale-Ganj city, located in the southern area of Kerman province, Iran. Five animal models, including linear model (LM), penalty model (PM), modified penalty model (MPM), linear-threshold model (LTM), and modified linear-threshold model (MLTM), were applied for genetic analysis of AFK. The predictive ability of the models was assessed by cross-validation via mean square error of prediction (MSE) and Pearson's correlation coefficient between observed and predicted values (r(y, )). The lowest MSE and highest r(y,y ̂) were obtained under MLTM, indicating the adequacy of MLTM for genetic analysis of AFK, including censored records. The Spearman's rank correlations between predicted breeding values of animals under LM and other censorship handling investigated models for all and 1% top-ranked animals were low and statistically non-significant (p>0.05). By considering the models that contained censoring scenarios, all Spearman's rank correlations among the models were positive and statistically significant (p<0.01), ranging from 0.56 (LTM-MPM) to 0.96 (LTM-MLTM) when all animals were considered and from 0.75 (LTM-MPM) to 0.96 (LTM-MLTM and PM-MLTM) when 1% top-ranked animals were considered. The averages for accuracy of the predicted breeding values of animals were 0.43, 0.49, 0.47, 0.47, and 0.49 under LM, PM, MPM, LTM, and MLTM, respectively, implying that the accuracy of the model under uncensored data is partly lower than the model accuracies under censoring scenarios. Posterior means for heritability estimates of AFK were 0.20±0.03, 0.23±0.02, 0.19±0.02, 0.18±0.03, and 0.18±0.02 under LM, PM, MPM, LTM, and MLTM, respectively. Due to the superiority of MLTM over LM, according to the predictive ability measures, it may be concluded that ignoring censored records under LM results in re-ranking of animals based on the predicted breeding values. Therefore, including censored records by applying MLTM is of crucial importance for genetic evaluation of AFK in the Murciano-Granadina goat breed.

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References

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Journal of Livestock Science and Technologies

Articles in Press, Accepted Manuscript
Available Online from 13 March 2026

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