Determination of the relationship between class IV sirtuin genes and growth traits in Chinese black Tibetan sheep

The polymorphism of the ovine insulin like growth factor-2 (IGF2) gene and their associations with growth related traits in Tibetan sheep

In the current study, the role of the ovine IGF2 as a potential candidate gene was investigated as though marker-assisted selection in Chinese Tibetan sheep. The Sanger DNA sequencing method explored five single nucleotide polymorphisms (SNPs) in 5'UTR of the ovine IGF2 gene (C15640T, G15801A, G15870A, C15982G and G15991A) in Chinese Tibetan sheep. The frequencies of four SNPs were within the Hardy-Weinberg Equilibrium (chi-square test) except C15982G. The statistical analysis indicated that the C15640T and G15801A were significantly associated with body height, body length, chest circumference, and body weight (P < 0.05 or P < 0.01). Furthermore, C15982G variant exhibited significant correlation with the body weight (P < 0.01). These findings suggests that the promoter variants of IGF2 gene could be used as a candidate gene through marker-assisted selection for the body weight and body measurement traits in Tibetan sheep breeding program.

Modelling the growth curve and estimation of associated genetic parameters in Munjal sheep

The study of growth curves in sheep is an effective approach to monitor the animal development for predicting the growth rate and improving overall flock performance. The purpose of the present work was to study the growth curve traits of Munjal sheep using different non-linear models and to estimate the genetic parameters of those traits for the possibility of inclusion of them under selection strategy. Total 2285 weight records at birth, 3, 6 and 12 months of age of 706 lambs born to 48 sires and 149 dams were collected from the registers maintained from 2004 to 2019. Various non-linear growth models viz., Brody, Gompertz, logistic, Bertalanffy and negative exponential were fitted to targeted growth curve traits and then evaluated using goodness of fit criteria such as adjusted R², root means square error (RMSE), Akaike's information criterion (AIC) and Bayesian information criterion (BIC). The genetic parameters of growth curve traits were estimated using animal model. The results suggested that Brody model was best fitted to the data than other models. The growth curve estimates for mature weight (A), inflexion point (B) and rate of maturation (k) under Brody model were 25.82 ± 1.72, 0.84 ± 0.04, and 0.21 ± 0.04, respectively for female lambs, and 29.55 ± 2.04, 0.86 ± 0.03 and 0.19 ± 0.04, respectively for male lambs. The males showed superiority for mature weights whereas female lambs had higher maturation rates. The estimates of direct heritability for A, B and k were 0.33, 0.41 and 0.10, respectively. The moderate estimate of direct heritability of A and its negative genetic correlation with k indicated the scope of genetic improvement through selection based on mature weights. Therefore, it was concluded from the present findings that Brody model was the one that best describes the growth curve in the Munjal sheep and the selection based on mature weights can be employed for genetic improvement of Munjal flock.

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.