Effect of genetic polymorphisms in LALBA and Prolactin genes on milk traits in Barki ewes

Abstract

This study investigated the genetic polymorphisms in Alpha-lactalbumin (LALBA) and Prolactin (PRL) genes in the Egyptian Barki sheep. The investigated genes were previously identified to be associated with milk and reproductive traits in several livestock species. Nevertheless, little is known about their contribution to milk traits in Egyptian sheep. Milk traits of daily milk yield (DMY), protein percentage (PP), fat percentage (FP), lactose percentage, and total solid percentage (TSP) were available for 256 and 233 Barki ewes for LALBA and PRL, respectively. The association analysis between the phenotypes of milk traits was carried out using the general linear model implemented in SAS software. The single strand conformation polymorphism identified 3 (A, B and C) and 2 (G and C) distinctive alleles in LALBA and PRL, respectively. Allele frequencies for LALBA variants were 0.73 (A), 0.16 (B) and 0.11 (C), and 0.63 (G), and 0.37 (C) for PRL variants. Results showed that age and parity of the ewe significantly (P < 0.05) influenced the studied milk traits. Additionally, a significant (P < 0.05) effect of LALBA variants was reported on milk composition but not DMY. Likewise, a significant (P < 0.05) effect of PRL variants was observed on DMY and TSP. Interestingly, Animals with BB genotypes of LALBA gene produced milk with high FP (5.65 ± 1.25 %), PP (5.77 ± 1.17 %), LP (7.4 ± 1.22 %) and lower TSP (13.06 ± 5.1 %) compared with animals that carry other genotypes. Regarding the PRL polymorphisms, the heterozygote ewes (GC) tended to significantly produce more milk (368.57 ± 20.39 g/day) with higher TSP (19.81 ± 1.57) compared with the homozygotes. This study reported the LALBA and PRL as candidate genes with significant effects on milk yield and composition, which may be appropriate for a wide range of selection programs with breeding objectives that focus on milk yield and composition in sheep. However, further investigations with larger sample size are recommended to confirm these findings with higher accuracy.