Abstract:
Determining body weight from growth attributes is the best, cheapest, and simplest approach, especially in rural areas where farmers lack the resources to purchase weighing instruments to assess an animal's body weight. Gene polymorphisms are regarded as elements to evaluate the animal’s economic value and thus serve as a viable technique in improving traits of critical economic importance. The study sought to uncover genetic markers of the growth hormone gene that could be utilized for selection during breeding to improve Dorper sheep growth traits. A total of fifty Dorper sheep (forty-three ewes and seven rams) between the ages of one and two years old were used in the study. Pearson’s correlation, simple linear regression analysis, PCR-FRLP, DNA sequences and marker-traits association were used for analysis to achieve the objectives. Coefficient of determination (R2) and mean square error (MSE) were used as the goodness of fit criteria to select the best regression model. Growth traits were measured from all randomly selected Dorper sheep. Growth traits: Heart girth (HG), Body length (BL), Withers height (WH), Sternum height (SH) and Rump height (RH) were measured with a centimetre-calibrated measuring tape (cm). At the same time, Body weight (BW) of each sheep was individually weighed in kilograms (kg) using a balanced weighing scale. Blood samples (2-3ml) were collected from each animal via the external jugular vein in the morning and DNA was isolated and purified using the Noegen's Genomic DNA Isolation kit process. Correlation coefficients (r) in ewes indicated that BW had a positive significant relationship with HG (r = 0.51), WH (r = 0.49) and BL (r = 0.41). Whereas in rams, BW showed to be positively and statistically correlated to WH (r= 0.78) and SH (r= 0.78). Simple linear regression results in ewes demonstrated the highest R2 value with the lowest MSE on HG while in rams SH and WH had the highest R2 with the lowest MSE. PCR-RFLP and DNA sequence findings showed a synonymous SNP (T/A) on position 735 of the coding region of the growth hormone gene in exon 4 and were named T735A. Moreover, Marker-trait association results showed that there was no statistical relationship between genotypes (AA and AB) and growth traits except for withers height whereby genotype AA had the highest impact on withers height. Correlation results suggest that increasing HG, WH and BL in ewes might cause BW to increase and an increase in WH and SH might result in an increase in BW in rams. Ram’s regression equation with SH shows that an increase in one centimetre of SH will increase BW by 0.84 kg and a model consisting of wither height reveals that an increase in one cm of WH will lead to a body weight increase of 0.60 kg. In ewes, the model with HG implies that an increase in one cm of HG will result in 0.62 kg of body weight. PCR-RFLP and DNA sequence results suggest that animals with genotype AA of growth hormone gene might be used when improving withers height. In conclusion, the findings of the current study will assist breeders in advising rural farmers who lack weighing equipment on how to predict the body weight of their animals using growth traits for a variety of reasons, including feeding, medication and breeding purposes. Furthermore, the findings will assist breeders in selecting animals based on molecular genetic markers to optimize withers height. Farmers should be educated on the association between body weight and growth traits, single nucleotide polymorphisms, and the significance of body weight in making good management decisions when feeding, medicating, marketing, and selecting replacement animals. However, more research on growth hormone gene polymorphisms and their association with growth traits need to be done with bigger sample size and more growth traits.