Many researchers who have studied the relationships between milk protein polymorphisms and some yield traits in dairy cattle have reported incompatible results. In this study, in order to examine the overall relationships between milk protein genes known as major genes (CSN1S1, CSN2, CSN3, and BLG) and some yield traits (daily milk yield, lactation milk yield, fat yield, fat content, protein yield, and protein content), a meta-analysis was performed using some genetic models reported in the results of previous studies on cattle. The results suggest that the relationships of major milk protein genes with other factors should be studied using the codominant genetic model in general. Relationships among some CSN3 genotypes and fat yield, fat content, and protein content, and relationships between some BLG genotypes and daily milk yield, fat content, protein yield, and protein content were significant (P<0.05). No significant (P>0.05) relationships were found between these genotypes and other milk production traits. In addition, no significant (P>0.05) relationships between the CSN1S1 and CSN2 genotypes with the milk production traits examined were observed.

Topal, M., Yildiz, N., Esenbuğa, N., Aksakal, V., Macit, M. and Özdemir, M. 2003. Determination of best fitted regression model for estimation of body weight in Awassi sheep. J. Appl. Anim. Res., 23: 201–208. In this research, linear regression models were improved for estimation of body weights using various body measurements from Awassi sheep. Simple and multiple regression models were formed when body weight (BW) was dependent variable and body length (BL), heart depth (HD), height at wither (HW), height at shoulder (HS), shin girth (SG), chest width (CW), heart girth (HG), pump width (PW) were independent variables. The best-fitted regression model for estimation of body weight was determined by using mean square error (MSE_p), R² _p and C_p statistics. The model including body length, heart depth, heart girth and pump width was the best fitted model (R² _P=0.851, MSE_P =8.803, C_p=5.972) for estimation of body weight in Awassi sheep in this study.

Progress in genetic selection in livestock can be increased by marker asisted selection. The identification of favorable genetic markers is one of the most important stages in marker-asisted selection. In this study, it was aimed to determine the effects of the bGH/AluI and Pit-1/HinfI polymorphisms on the production traits of organic reared cows. Genotyping was performed on total 245 Holstein cows, n=181 for Pit-1 gene and n=186 for bGH gene. Milk yields and some reproduction traits analyzed by analysis of variance using the general linear model procedure, and 703 production records from cows were used to. The results showed that neither the Pit-1/Hinf I nor bGH/Alu I polymorphisms affect the tested milk traits (p>0.05).

GH gene is one of the important genes that affect the productive and physiological traits in livestock. This study aimed to use a meta-analysis to determine the association of the GH gene with growth traits in cattle. Four different genetic models were used: dominant LL + LV versus VV, recessive LL versus LV + VV, complete over dominant LL+VV vs. LV and co-dominant LL vs. LV, LL vs. VV and LV vs. VV. The random-effect model was used in data analysis based on I2. Meta-analysis showed an effect of GH genotypes on the birth weight (BW) under the recessive (p<0.05) and complete over dominant (p0.05) was found under the dominant model. In terms of co-dominant model, the GH polymorphism showed a significant association with BW (SMD = 0.359, 95% CI = 0.119 to 0.599, p = 0.003) under LL vs. LV genotype combination patterns. No association was found between GH polymorphism and BW under LL vs. VV genotype combination. The results showed that the LL genotype had a significant effect on BW traits from LV and VV genotypes when comparing the models. The results showed an effect of the GH on ADG under four genetic models (p<0.05).