Mapping of Quantitative Trait Loci for parameters of growth curve regression in Ghezel sheep

Background and purpose: One of the disadvantages of estimating genetic parameters based on the absolute weight of sheep at a given age is that some genes affect growth only in a specific period of animal life. There are also pleiotropic effects between weight cross-sectional traits of different ages. Therefore, that selection for one trait causes a correlated response in other traits. So focusing on all the stages of the growth curve and the components that characterize the curve can produce more genetic progress. Research results show that growth curve variables are heritable in different species. Therefore, it is possible to modify the growth curve through selection. Understanding the genetic and environmental relationships between different weights, maturity and growth rates at all stages of development is essential for designing a breeding program to improve production efficiency over the lifetime of the animal. Therefore, identifying and locating QTLs controlling growth curve parameters as heritable traits could be a useful research scenario for investigation. Ultimately, it will increase meat production.
Materials and Methods: Growth curve parameters were estimated using the Brody model and data of 27537 Ghezel sheep. These population pedigree checked by software pedigree viewer and the two families were selected for this project To do present research two half-sib family, 51 offspring of two genera of sheep breeding in the Ghezel Miandoab Breeding station of were taken. The chromosomal positions were located on 2 and 5 of the microsatellite were used to determine the genotype. The marker spacing on the chromosomal map was under 30CM. The DNA extraction was performed using Sammady Shams and colleagues from the whole blood and 4% metaphor agarose gel was used for multiplication of microsatellite sites using Touchdown PCR and. UVDOC software was used to score bands. Descriptive statistics were calculated for molecular indices of each markers locus using POPGENE software. Upon completion of genotyping three files map, genotype and records phenotype were prepared and then evaluated normality raw data and correction for fixed effects (gender, type of birth,) by the procedure GLM in SAS 9.2 software made in 2015 was carried out. The analysis results microsatellites showed that polymorphism fit there, but alleles heterozygous rams similarity high, suggesting consanguinity with alleles of the sheep show. The method used to The QTL analysis was identified the relationship between genetic markers and QTLs by two markers.
Results: The results of microsatellite analysis showed that there is a good polymorphism in the studied population. Finally, the results of the two families indicated no significant QTL identification on chromosomes 2 and 5 for traits of brody growth curves parameters (A, B, C).
Conclusions: No significant differences were found in the QTL analysis. Of course, the plan was criticized because of the low number of markers, the number of families and each family member. These factors may, in turn, be the reason for the lack of QTL identification in this design.