Effects of misidentification and paternity errors on prediction of breeding values and ranking of animals in dairy cattle

Document Type : Research Paper


1 Assistant Pprofessor, Department of Animal Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

2 Former M. Sc. Student, Department of Animal Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

3 Assistant Professor, Department of Animal and Poultry Science, Campus of Aburaihan, University of Tehran, Tehran, Iran


Genetic evaluations are computed to assess the genetic merit of animals based on mixed model equations. An important assumption for setting up these equations is that all genetic relationships among animals are available and correct. The objectives of the present study were to estimate the effects of incomplete pedigree and paternity errors on genetic evaluation. Data and pedigree of 100 dairy herds were obtained from Animal Breeding Center of Iran. Final data edited included milk yield records from 302860 first lactation Holstein cows. DMU Trace program was used for tracing ancestors and creating the full pedigree of animals. To simulate incomplete and wrong pedigrees, different scenarios including 8, 12, 16, 20, 24 percent of paternal identification numbers were removed or replaced using R program. Breeding values for milk yield was predicted by animal model using DMU program. Spearman's rank correlation was estimated for superior animals in different scenarios using SAS software. Estimates of heritability for full, incomplete and wrong pedigrees were 0.29, 0.26 and 0.27, respectively. The results showed a high variation in ranking of animals and determination of superior animals (P<0.01). As an example, at 12% level scenario, Spearman's rank correlation of BVs predicted from full pedigree with incomplete and wrong pedigrees were 0.65 and 0.60, respectively. Selection effectiveness, defined as the ratio of common superior animals in alternative scenarios, was decreased by increasing the rate of misidentification and errors (P<0.01). Incorrect pedigree and misidentification of animals could reduce accuracy of breeding values and consequently bias in animals ranking.


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