POLYMORPHISM OF SOMATROPIN CASCADE GENES IN JERSEY COWS D. V. Zarubezhnova , R. R. Shaydullin, T. M. Akhmetov, F. F. Zinnatov, S. V. Tyulkin

DOI 10.12737/2782-490X-2025-4-3-59-64 УДК 636.084:636.082.12

Authors

Keywords:

cows, Jersey breed, polymorphism, somatotropin, prolactin, PIT1, genetic markers, breeding

Abstract

This article presents the results of a study of somatotropin cascade gene polymorphisms (GH - somatotropin, PRL - prolactin, PIT1 - pituitary transcription factor) in first-calf Jersey cows. Allelic variants of PRL, PIT1 and GH
genes were identified by polymerase chain reaction followed by restriction fragment length polymorphism (PCR-RFLP),
using the restriction endonucleases Alu I, RsaI and HinfI. Restriction results were identified by agarose gel electrophoresis. The frequency of alleles and genotypes was determined according to E.K.Merkurieva, the observed and expected distributions were calculated using the Hardy-Weinberg method, and the indices of genetic equilibrium, inbreeding coefficients and polymorphism informativeness were calculated. It was established that A (0.71) allele and AA (48.3%) and BB
(44.8%) genotypes predominate for PRL gene, B (0.78) allele and BB (63.2%) genotype predominate for PIT1 gene, L and
V allele frequencies are 0.53 and 0.47, respectively, for GH gene with LV genotype being dominant (52.9%). All the studied loci are in the Hardy-Weinberg equilibrium state (χ² < 3.84). Negative values of inbreeding coefficients indicate an
excess of heterozygotes, which may be associated with the manifestation of heterotic effect and selection in favor of heterozygous animals. The most polymorphic locus among the studied loci is the GH locus (PIC=0.374), followed by the PRL
(PIC=0.329) and PIT1 (PIC=0.287) loci. All three loci are characterized by moderate informativeness for breeding. The
obtained results can be used to develop marker-assisted selection programs for Jersey breed.

For citation: Polymorphism of somatotropin cascade genes in Jersey cows. Zarubezhnova D.V., Shaydullin R.R.,
Akhmetov T.M. [et al.]. Agrobiotechnologies and digital farming. 2025; 3 (15). 59-64 p. http://dx.doi.org/10.12737/2782-
490X-2025-4-3-59-64

Author Biographies

Diana Viktorovna Zarubezhnova, Kazan State Agrarian University, Kazan, Russia.

 Postgraduate Student, e-mail: diana.zarubezhnova.99@mail.ru

Radik Rafailovich Shaydullin, Kazan State Agrarian University, Kazan, Russia.

Doctor of Agricultural Sciences, Professor of the Department of Technology of Production and Processing of Agricultural Products, e-mail: tppi-kgau@bk.ru, ORCID: https://orcid.org/0000-0003-3172-3327

Takhir Munavirovich Akhmetov, Kazan State Agrarian University, Kazan, Russia.

Doctor of Biological Sciences, Professor of the Department of Biology, Genetics, General and Biological Chemistry, e-mail: ahmetov-tahir@mail.ru, ORCID: https://orcid.org/0000-0003-3495-2432

Farit Fatikhovich Zinnatov, Kazan State Agrarian University, Kazan, Russia.

Candidate of Biological Sciences (PhD in Biology), Associate Professor of the Department of Biology, Genetics, General and Biological Chemistry, e-mail: ffzinnatov@mail.ru, ORCID: https://orcid.org/0000-0003-0081-2810

Sergey Vladimirovich Tyulkin, Federal Research Center for Food Systems named after V.M. Gorbatov of the Russian Academy of Sciences, Moscow, Russia.

Doctor of Biological Sciences, Senior Researcher, e-mail: tulsv@mail.ru, ORCID: https://orcid.org/0000-0001-5379-237X

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Published

2026-07-22