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Effects of missing pedigree information on dairy cattle genetic evaluations (short communication)

Mohammad Ali Nilforooshan, A. Khazaeli, M. A. Edriss

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Abstract

Abstract. Estimating genetic merit of livestock closest to their true genetic merit is a preliminary goal in animal breeding. The accuracy of genetic evaluations depends on the recording system and the method of evaluation. Whereas applying more complicated models may improve the accuracy of evaluations inconsiderably, improving data quality is more effective. The data were on pedigree and milk performances (milk yield, fat yield and fat percentage) of 9834 dairy cows in Isfahan, Iran, with both known parents. Genetic parameters were estimated by derivative-free restricted maximum likelihood method, applying an animal model (full relationships), sire model (dam missed), dam model (sire missed) and a half-missed model (half sire / half dam). All the models were compared to animal model. Sire model had the smallest size of pedigree structure, while dam model had an inexistency of between herd relationships. The results showed underestimating additive genetic variance by sire and half-missed models and overestimating it by dam model. An important finding of this study was that there is an unfavorable interaction between missing sire and dam information that caused the lowest goodness of fit for half-missed model. Also, usually, sire missing makes more important problems to the pedigree structure and genetic evaluations than dam missing. The research revealed that, even using an animal model, there are some delicacies in introducing the relationship matrix for sex-limited traits, which requires special attention to the pedigree of sires.

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Abstract. Estimating genetic merit of livestock closest to their true genetic merit is a preliminary goal in animal breeding. The accuracy of genetic evaluations depends on the recording system and the method of evaluation. Whereas applying more complicated models may improve the accuracy of evaluations inconsiderably, improving data quality is more effective. The data were on pedigree and milk performances (milk yield, fat yield and fat percentage) of 9834 dairy cows in Isfahan, Iran, with both known parents. Genetic parameters were estimated by derivative-free restricted maximum likelihood method, applying an animal model (full relationships), sire model (dam missed), dam model (sire missed) and a half-missed model (half sire / half dam). All the models were compared to animal model. Sire model had the smallest size of pedigree structure, while dam model had an inexistency of between herd relationships. The results showed underestimating additive genetic variance by sire and half-missed models and overestimating it by dam model. An important finding of this study was that there is an unfavorable interaction between missing sire and dam information that caused the lowest goodness of fit for half-missed model. Also, usually, sire missing makes more important problems to the pedigree structure and genetic evaluations than dam missing. The research revealed that, even using an animal model, there are some delicacies in introducing the relationship matrix for sex-limited traits, which requires special attention to the pedigree of sires.

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Available abstract

Abstract. Estimating genetic merit of livestock closest to their true genetic merit is a preliminary goal in animal breeding. The accuracy of genetic evaluations depends on the recording system and the method of evaluation. Whereas applying more complicated models may improve the accuracy of evaluations inconsiderably, improving data quality is more effective. The data were on pedigree and milk performances (milk yield, fat yield and fat percentage) of 9834 dairy cows in Isfahan, Iran, with both known parents. Genetic parameters were estimated by derivative-free restricted maximum likelihood method, applying an animal model (full relationships), sire model (dam missed), dam model (sire missed) and a half-missed model (half sire / half dam). All the models were compared to animal model. Sire model had the smallest size of pedigree structure, while dam model had an inexistency of between herd relationships. The results showed underestimating additive genetic variance by sire and half-missed models and overestimating it by dam model. An important finding of this study was that there is an unfavorable interaction between missing sire and dam information that caused the lowest goodness of fit for half-missed model. Also, usually, sire missing makes more important problems to the pedigree structure and genetic evaluations than dam missing. The research revealed that, even using an animal model, there are some delicacies in introducing the relationship matrix for sex-limited traits, which requires special attention to the pedigree of sires.

Key concepts: Sire, Statistics, Animal model, Restricted maximum likelihood, Goodness of fit, Herd, Missing data, Mathematics

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