Creation of a panel of genetic markers for breeding of a large white breed pigs on the reproductive fitness

The article presents studies of the effect of SNP of genes PRLR, LEP, ESR, FSHb, MC4R, LIF, POU1F1, GH on the reproductive performance of large white pigs. The analysis was carried out using linear models of mixed type. For each variant of SNP, a separate model was used; as a fixed factor in model the genotype for the gene in question was. Another fixed factor in all models was the order of number of farrowing. A randomized factor was individual sow genotypes. As a result of the analysis a significant superiority of the LEP/CC genotype over the LEP/TT genotype was established for all indicators of reproductive qualities: by the number of pigs at birth on 1.55 h. (p = 0.02), for a piglets born alive on 1.8 h. (p = 0.009), by litter weight at birth on 1.91 kg (p = 0.05). According to the ESR gene animals with the ESR/BB genotype also outperformed all their analogues with the ESR/AA genotype (by the number of pigs at birth on 1.36 h. (p = 0.14), by piglets born alive on 0.93 h. (p = 0.005), by the litter weight at birth on 2.24 kg (p = 0.15) The LIF gene was used to determine the genotype LIF/BB (the number of piglets at birth + 1.35 h. (p = 0 , 15), piglets born alive + 1.9 h. (p = 0.04), litter weight at birth + 2.24 kg (p = 0.24) relative to the LIF/AA genotype.) The superiority of the PRLR/55 genotype was established for the PRLR gene (number of piglets at birth + 0.42 head (p = 0.4), piglets born alive + 0.57 h. (p = 0.25), litter weight at birth + 1.96 kg (p = 0.01) relative to the PRLR/AB genotype.) For other genes there are no significant differences in the reproductive qualities between the genotypes. Based on the obtained results a panel of genetic markers for evaluating reproductive qualities of large white pigs was developed, which includes the LEP, ESR, LIF, and PRLR genes.

SNP панель, PRLR, LEP, ESR, FSHb, MC4R, LIF, POU1F1, GH, SNP panel, pigs, markers, PRLR, LEP, ESR, FSHb, MC4R, LIF, POU1F1, GH, linear models

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