SUMMARY Polymorphisms in the myostatin (MSTN) gene were detected in 150 female Iranian Markhoz goats. Two 573 base pairs (bp) and 475 bp fragments of the MSTN gene, which contains a deletion 5 bp indel (206 TTTTA/), in the region of exon 1 encoding the 5′ untranslated region (UTR) of the MSTN transcript, and two single-nucleotide polymorphisms (SNPs) of substitution (339T/A, 169T/G) in exon 1 and 3 regions, respectively, were amplified. The polymerase chain reaction (PCR) products were digested separately using restriction enzyme endonuclease DraI, HinIII and HindIII. The digestion results indicated AA and AB genotypes in the region of exon 1 encoding the 5′UTR of the MSTN transcript, AA, AT and TT genotypes in exon 1 and TT, TG and GG genotypes in exon 3. The SNPs loci were in Hardy–Weinberg disequilibrium but the deletion locus showed equilibrium in the Markhoz goat population. Evaluation of associations between the polymorphisms with the studied growth traits showed that the AA and GG genotypes of exons 1 and 3 have a significant positive effect on weight at 6 months of age (W6) and average daily gain (ADG) traits, but genotypes in the region of exon 1 encoding the 5′UTR of the MSTN transcript did not have any significant effect on the studied growth traits. The statistical analyses showed a positive and significant effect of the 339A allele (exon 1) for W6 and negative and significant effect of the 169G allele (exon 3) for ADG trait. Therefore, these results suggest that the MSTN gene could be a potential candidate gene that affects ADG and W6 traits in goats. More studies are needed to simultaneously consider variants of this region in a larger population to better understand MSTN gene effects on the economic traits in goat.

Background: The study of growth traits is of interest to many animal scientists, regardless of specialization, due to the economic importance of growth rate, mature weight and other related traits. Objective: This study aimed to compare six non‐linear models for describing the growth of Lori‐Bakhtiari sheep. Methods: In order to collect weight data, 85 lambs (41 males and 44 females) were reared from birth to 140 days of age, and their growth patterns were recorded by measuring their body weight at 10‐day intervals. Various mathematical functions, including the negative exponential, Brody, Gompertz, Logistic, Morgan–Mercer–Flodin (MMF) and Weibull, were used to model the relationship between body weight records and age. Results: The results showed that the MMF and Gompertz models provided the best fit to the body weight data, whereas the negative exponential model exhibited the worst fit. In all models, the asymptotic weight of male lambs was higher than females. The research also revealed differences in growth patterns between male and female lambs. Overall, females had a lower absolute growth rate than males, but they reached their peak growth at an earlier period, and their growth rate declined faster. Conclusions: The differences in growth patterns between males and females indicate the importance of analysing male and female data separately when describing growth. As a result, Gompertz model can be recommended to Lori‐Bakhtiari female and male lamb breeders to determine more accurate growth traits. In addition, it should be considered that feeding male and female lambs separately according to absolute growth rate values may increase growth performance.

The main objectives of this study were to compare the prediction accuracy of different Bayesian methods for traits with a wide range of genetic architecture using simulation and real data and to assess the sensitivity of these methods to the violation of their assumptions. For the simulation study, different scenarios were implemented based on two traits with low or high heritability and different numbers of QTL and the distribution of their effects. For real data analysis, a German Holstein dataset for milk fat percentage, milk yield, and somatic cell score was used. The simulation results showed that, with the exception of the Bayes R, the other methods were sensitive to changes in the number of QTLs and distribution of QTL effects. Having a distribution of QTL effects, similar to what different Bayesian methods assume for estimating marker effects, did not improve their prediction accuracy. The Bayes B method gave higher or equal accuracy rather than the rest. The real data analysis showed that similar to scenarios with a large number of QTLs in the simulation, there was no difference between the accuracies of the different methods for any of the traits.