Effectiveness of genetic grouping with different strategies for estimation of genetic parameters in growth traits in Merino lambs

Duru S., Altincekic S. O., Oral H. H.

Small Ruminant Research, vol.216, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 216
  • Publication Date: 2022
  • Doi Number: 10.1016/j.smallrumres.2022.106835
  • Journal Name: Small Ruminant Research
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, Veterinary Science Database
  • Bursa Uludag University Affiliated: Yes


© 2022 Elsevier B.V.The effectiveness of genetic grouping in genetic evaluations for growth traits in Merino sheep was researched in this study. In the study, 17,537 lambs born to 48 rams and 7118 ewes in 30 flocks were evaluated in animal models using REML. A model without a genetic group (GG0) was used in the analysis, as well as a model with three different genetic groups (GG). Maternal genetic and common environmental effects are included in all models. However, the models with and without covariance between direct and maternal effect were used for each trait. The Akaike Information Criterion (AIC) was used to compare models, taking into account the accuracy and Pearson and Spearman correlations between 48 rams for breeding values. Direct heritability (hd2), maternal heritability (m2) and maternal common environmental effects (cm2) were generally found to be less than 0.10. The most important source of variation was found to be flock-year-season (FYS). It is worth bearing in mind that cFYS2 has a very high share of phenotypic variance in all models, ranging from 0.07 ± 0.01–0.43 ± 0.03. The fact that there is such a wide environmental difference between enterprises in the same region is a major issue for the improvement programme. It is necessary to intensify studies on the one hand for the expansion of small businesses and on the other hand for more modern animal husbandry to achieve this. AIC found that models based on GGs were better suited for all traits. Unlike AIC, the GG0 model had the highest accuracy for rams. The accuracy of the model (GG3), in which the mothers of animals whose father was unknown were also deleted and genetically grouped for both, was found to be lower for ADG and higher for WW. Furthermore, while the average accuracy for BW decreased to 0.60 in the other genetic group (GG1: lambs born in the same year in a flock and GG2: lambs born in a flock) models, it was found to be around 0.80 for ADG and above 0.80 for WW. According to research findings, it is possible to perform a genetic evaluation without losing data, especially in pedigrees with unknown fathers.