Examination of relationships between polymorphisms in GH, VIPR-1 and PRL gene regions and some performance traits in japanese quails (Coturnix coturnix Japonica)

In the conducted Ph.D. dissertation, polymorphisms on the GH (MspI), VIPR-1 (BsrDI and HpyCH4IV), and PRL (Csp6I) genes were examined using the PCR-RFLP method in a total of 150 Japanese quails. The effects of the detected polymorphisms on various performance traits and different growth model parameters were investigated. Egg production, average egg weight, internal and external egg quality characteristics, hatching results, two different ages at slaughter weight, carcass weight, and carcass yield, as well as parameter estimates in 3 different growth models, were analyzed. No polymorphism was detected for the VIPR-1 (HpyCH4IV) and PRL (Csp6I) gene regions, and these regions were found to be monomorphic. Since these two regions were monomorphic, a relationship analysis with performance traits could not be conducted. For the GH gene, A (%52) and B (%48) alleles for MspI polymorphism, and AA (%28), AB (%48), and BB (%24) genotypes were determined. For VIPR-1 (BsrDI) polymorphism, G (%68) and T (0.32) alleles, GG (%48), GT (0.39), and TT (0.13) genotypes were identified. It was found that the population was in Hardy-Weinberg equilibrium in terms of the genes with polymorphism. As a result of the relationship analysis conducted between genotype structures of each gene and performance traits, a statistically significant (p<0.05) relationship was determined between the GH (MspI) gene and egg production. It was found that GH (MspI) and VIPR-1 (BsrDI) genes did not have a statistically significant effect on other performance traits. When the performance traits were evaluated, it was determined that slaughter age and gender affected slaughter weight, carcass weight and yield (p<0.05). Flock ages influences hatchability and fertility rate (p<0.05) but it was not statistically significant for hatchability of fertile eggs. Differences between the BNY and BNA parameters in growth curve models were statistically significant (p<0.05).