Composite genotypes of progestogen-associated endometrial protein gene and their association with composition and quality of dairy cattle milk

Genetic variants of CSN1S1, CSN2, CSN3, and BLG genes and their association with dairy production traits in Sahiwal cattle and Nili-Ravi buffaloes

Milk protein genes are associated with milk yield and composition in dairy animals. The present study aimed to identify milk protein genes (CSN1S1, CSN2, CSN3, and BLG) genetic variants and their association with milk yield in Sahiwal cattle and Nili-Ravi buffaloes. One hundred animals from each species were selected to collect blood samples and milk production records. Primers were designed for these milk protein genes for PCR amplification. Sequencing of resultant PCR products revealed a higher number of SNPs (13 vs. 7, 5 vs. 1, and 6 vs. 2) in Sahiwal as compared to Nili-Ravi animals in CSN1S1, CSN2, and CSN3 genes, respectively. However, a single SNP was observed in BLG gene of both species. Association analysis revealed that one SNP in BLG gene of Nili-Ravi was associated (p < 0.05) with 305-day milk yield. Two SNPs at CSN1S1 gene in Sahiwal were associated with dry-period. Similarly, one SNP at CSN1S1 and two SNPs at CSN3 gene showed significant association (p < 0.05) with average calving-interval in Sahiwal while two SNPs in CSN1S1 gene were associated (p < 0.05) with this trait in Nili-Ravi. These SNPs could be helpful as candidate variants for marker-assisted selection in cattle and buffaloes for improvement of lactation performance.

Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population

The objective of our study was to evaluate the predictive ability of a multi-trait genomic prediction model that accounts for interactions between marker effects to estimate heritability and genetic correlations of traits including 305-day milk yield, milk fat percentage, milk protein percentage, milk lactose percentage, and milk dry matter percentage in the Polish Holstein Friesian cow population. For this aim, 14,742 SNP genotype records for 586 Polish Holstein Friesian dairy cows from Poland were used. Single-Trait-ssGBLUP (ST) and Multi-Trait-ssGBLUP (MT) methods were used for estimation. We examined 305-day milk yield (MY, kg), milk fat percentage (MF, %), milk protein percentage (MP, %), milk lactose percentage (ML, %), and milk dry matter percentage (MDM, %). The results showed that the highest marker effect rank correlation was found between milk fat percentage and milk dry matter. The weakest marker effect rank correlation was found between ML and all other traits. Obtained accuracies of this study were between 0.770 and 0.882, and 0.773 and 0.876 for MT and ST, respectively, which were acceptable values. All estimated bias values were positive, which is proof of underestimation. The highest heritability value was obtained for MP (0.3029) and the lowest heritability value was calculated for ML (0.2171). Estimated heritability values were low for milk yield and milk composition as expected. The strongest genetic correlation was estimated between MDM and MF (0.4990) and the weakest genetic correlation was estimated between MY and ML (0.001). The genetic relations with milk yield were negative and can be ignored as they were not significant. In conclusion, multi-trait genomic prediction can be more beneficial than single-trait genomic prediction.

Identification and annotation of milk associated genes from milk somatic cells using expression and RNA-seq data

It is of interest to identify and annotate milk associated genes using expression profiling and RNA-Seq data from milk somatic cells. RNA-Seq data was pre-processed and mapping was done to identify differentially expressed genes (DEG). The functional insights about the up and down regulated genes were gleaned using the protein-protein interaction Network in the STRING database followed by CytoHubba analysis in Cytoscope. Gene ontology, annotation and pathway enrichment was completed using ShinyGO, David tool and QTL analysis. These analysis shows that 21 genes are linked with the secretion of milk.