ІНТЕГРАЦІЯ BLUP-МОДЕЛЕЙ І МОЛЕКУЛЯРНО-ГЕНЕТИЧНОГО АНАЛІЗУ В СЕЛЕКЦІЇ КРОЛІВ ПОРОДИ ПОЛТАВСЬКЕ СРІБЛО
DOI:
https://doi.org/10.37617/2708-0617.2026.12.55-60Keywords:
rabbits, index evaluation, BLUP, MSTN, PGR, breeding value, genetic polymorphismAbstract
The study presents the results of a comprehensive genetic and breeding evaluation of Poltava Silver breed rabbits using the BLUP AM methodology
combined with an analysis of polymorphisms in the myostatin (MSTN) and progesterone receptor (PGR) genes. The analysis of allele and genotype frequencies
demonstrated that the population meets Hardy–Weinberg equilibrium for the MSTN gene and revealed a two-cluster structure for the PGR gene, which is due to the
intensive selective use of certain lines, particularly the Hraf line. The frequency of the T allele (MSTN) significantly exceeded that of C, while the A allele (PGR)
predominated over G. A positive fixation index (F) for MSTN indicates a predominance of heterozygotes and an adequate level of genetic diversity.
The developed BLUP AM selection index, integrated with molecular-genetic data, enabled a comprehensive assessment of both meat productivity and
reproductive performance. According to the BLUP evaluation results (2022–2024), the highest breeding values were obtained for males 1727215, 2118127, 1847213,
1871817, and 1894136 (up to +0.157) with high reliability of estimation (REL up to 81.3%). A correlation was found between the number of daughters included in the
evaluation and EBV accuracy (r = 0.512). Line 1871817 showed the highest values for the MSTN gene (BLUP 1385, EBV 1.458) and superiority in reproductive traits
compared with line 1811231 (BLUP 5.81 vs. 5.68); both lines were characterized by high selection potential and stability of prediction (REL 0.603–0.615). The obtained
results confirm the effectiveness of integrating molecular-genetic markers into breeding programs for Poltava Silver breed rabbits and demonstrate the promise of
using BLUP and G-BLUP models to increase the accuracy of genetic evaluations and enable early prediction of animals’ breeding potential.
