gh. r. dashab

The main purpose of a breeding program is to increase the genetic capacity of farm animals for important economic traits. Although the success of genetic selection to increase milk production in dairy cows is quite obvious, the fertility of herds has decreased. The heritability of lactation curve parameters has been reported to be low, indicating that the lactation curve is highly influenced by environmental factors. As a result, direct selection for these traits will not lead to effective genetic improvement, and selection based on correlated traits can be investigated. Studies on the lactation curve of Holstein cows in Iran have often been performed using three-parameter functions. The Rook function has recently been introduced as a suitable function to describe the lactation curve of Iranian Holstein cows. The purpose of this research was to estimate the correlation between the lactation curve parameters of the Rook function (initial production (a), the rate of reaching the production peak (b), maximum production (c), the curve changes after reaching the peak of production (d), the peak time (pt), amount of production at the peak (pm), and persistency (p)) with some production traits (305-d milk, fat and protein production), somatic cell score and age at first calving and also evaluating curve parameters in different phenotypic groups of the economic traits in Holstein cows. A total of 847129 test day records of milk production in the first lactation collected by the Animal Breeding Center and Promotion of Animal Products of Iran from 1983 until 2017, were used. The Rook function was fitted to the test day records and the function parameters were separately calculated for all animals. Estimation of variance components and genetic correlation between the parameters of the lactation curve with the production traits (305-d milk, fat, and protein), somatic cell score, and age at first calving was performed by bivariate analysis via the Gibbs sampling method. To investigate the relationship between economic traits and milk curve parameters, animals were divided into three phenotypic groups including high, medium, and low based on the phenotypes of economic trait, and then the value of curve parameters was evaluated between three groups. The highest genetic correlation was estimated between milk production and peak production (pm) (0.958). The genetic correlation between milk production and persistency was relatively high (0.725). Genetic correlation of somatic cell score with initial production (a), maximum production (c), and amount of production at the peak (pm) was undesirable and its correlation with parameter curve changes after maximum yield (d) was favorable. Therefore, it is expected that selection to reduce initial production and production at the peak and increase curve changes after maximum yield could decrease the somatic cell score. The correlation estimates between persistency with 305-d milk, fat, and protein production were positive, and a negative correlation was estimated between somatic cell score with age at first calving, so it can be expected that selection for persistency will increase the production traits, and udder health could be improved by reducing the number of somatic cell score and age at first calving. In most cases, the values of curve parameters among the three phenotypic groups of traits had significant differences, and in the high production group, age at first calving, the value of initial production, peak production, and persistency were higher compared to other groups; however, an inverse relation was observed for somatic cell score. The estimated correlations showed that the selection of animals for increased initial production (a) and production at the peak (pm) will improve milk production during 305 days. Increasing the somatic cell score reduces the peak time (pt), and persistency (p). A comparison of persistency between different phenotypic groups of economic traits suggested that persistency improves with increasing milk production and its components as well as decreasing somatic cell scores.

The objective of this study was to compare the accuracy of genomic breeding values prediction with different marker densities before and after the imputation in the simulated purebred and crossbred populations based on different scenarios of reference population and methods of marker effects estimation. The simulated populations included two purebred populations (lines A and B) and two crossbred populations (Cross and Backcross). Three different scenarios on selection of animals in the reference set including: (1) A high relationship with validation population, (2) Random, and (3) High inbreeding rate, were evaluated for imputation of validation population with the densities of 5 and 50K to 777K single marker polymorphism. Then, the accuracy of breeding values estimation in the validation population before and after the imputation was calculated by ABLUP, GBLUP, and SSGBLUP methods in two heritability levels of 0.25 and 0.5. The results showed that the highest accuracy of breeding values prediction in the purebred populations was obtained by GBLUP method and in the scenario of related reference population with validation set. However, in the crossbred population for the trait with low heritability (h2= 0.25), the highest accuracy of breeding values prediction in the weighting mechanism was equal to (=0.2). Also, results showed that in the scenario of related reference population selection when 50K panel was used for genotype imputation to 777K SNPs, the prediction accuracy of genomic breeding values increased. But, in most scenarios of random and inbred reference set selection, there was no significant difference in the accuracy of genomic breeding values prediction between 5K and 50K SNPs after genotype imputation to 777K.