Genetic variation in the OPN gene affects milk composition in Chinese Holstein cows

Genetic polymorphism of Β-casein gene and its association with milk production and composition in Azi-Kheli buffalo

The aim of this study was to find out the genetic polymorphism in β-casein gene CSN2 in Azi-Kheli buffaloes found in district Swat. Blood samples from 250 buffaloes were collected and processed in lab for sequencing to see the genetic polymorphism in CSN2 gene on 67 position of exon7. The β-casein is a milk second abundant protein having some variants, wherein A1 and A2 are the most common. After performing sequence analysis, it was found that Azi-Kheli buffaloes were homozygous for only A2 type variant. The amino acid change (proline to histadine) on 67 position of exon 7 was not found; however, three other novel SNPs at loci g.20545A > G, g.20570G > A, and g.20693C > A were identified in the study. Amino acid change due to SNPs were found as SNP1, valine > proline; SNP2, leucin > phenylalanine; and SNP3, threonine > valine. Allelic and genotypic frequencies' analysis exhibited that all three SNPs were following the Hardy-Weinberg equilibrium (HWE: P < 0.05). All the three SNPs showed medium PIC value and gene heterozygosity. The SNPs located on different position of exon 7 of CSN2 gene exhibited associations with some of the performance traits and milk composition. Higher daily milk yield of 9.86 ± 0.43 L and the peak milk yield of 13.80 ± 0.60 L were found in response to SNP3 followed by SNP2 and SNP1. The percentage of milk fat and protein was found significantly higher (P ≤ 0.05) in relation to SNP3 followed by SNP2 and SNP1 given as 7.88 ± 0.41, 7.48 ± 0.33, and 7.15 ± 0.48 for fat% and 4.00 ± 0.15, 3.73 ± 0.10 and 3.40 ± 0.10 for protein%. It was concluded that Azi-Kheli buffalo milk contains A2 genetic variant along with other useful novel variants indicating quality milk for human health. Genotypes of SNP3 should be given preference in selection both in indices and nucleotide polymorphism.

Applications of Omics Technology for Livestock Selection and Improvement

Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals’ actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.