doi: 10.15389/agrobiology.2017.2.367eng

UDC 636.52/.58:636.082.2:577.21

The study was carried out with the equipment of «Biotechnology» center (All-Russian Research Institute of Agricultural Biotechnology).
Supported financially by Federal Agency of Scientific Organizations, the additional state assignment for Breeding and Genetic Center «Smena» № 007 of August 5, 2016 in the framework of the Federal Scientific and Technical Program of agricultural development for 2017-2025 (Sub 2 «Breeding competitive crosses of domestic meat poultry»).



Ya.I. Alekseev1, 2, А.М. Borodin3, А.V. Nikulin1, Zh.V. Emanuilova4,
D.N. Efimov4, V.I. Fisinin5

1All-Russian Research Institute of Agricultural Biotechnology, Federal Agency of Scientific Organizations, 42, ul. Timiryazevskaya, Moscow, 127550 Russia, e-mail (corresponding author);
2JSC «Syntol», 42, ul. Timiryazevskaya, Moscow, 127550 Russia, e-mail;
3Non-proffn Partnership Institute of Medico-Biological Research, 10, ul. Studenaya, Nizhnii Novgorod, 603000 Russia, e-mail;
4Breeding and Genetic Center «Smena», Federal Agency of Scientific Organizations, pos. Bereznyaki, Moscow Province, 141327 Russia, e-mail;
5Federal Scientific Center All-Russian Research and Technological Poultry Institute RAS, Federal Agency of Scientific Organizations,10, ul. Ptitsegradskaya, Sergiev Posad, Moscow Province, 141315 Russia, e-mail,

The authors declare no conflict of interests

Fisinin V.I.

Received October 5, 2016


Traditional breeding is time and material consuming. Modern laboratory techniques significantly speed up and reduce the costs for breeding animals with the desired properties. A test based on the quantitative real-time PCR (qPCR) technology was developed to distinguish between homozygous and heterozygous state of the genes from alleles K and k which are responsible for the rate of wing feather growth in day-old chicks. The use of quantitative real-time PCR for the analysis of genotypes is aimed at the discrimination between one and two copies of the target gene in a genome. To obtain reliable results, certain rules must be followed when conducting the assay: the efficiency of the PCR should be close to the maximum; it is possible to obtain a significant number of false results without the appropriate statistical analysis. A new assay algorithm was proposed to overcome the limitations of qPCR: all samples are subjected to two successive independent analyses in parallel with the reference samples of both genotypes; if the two runs produce divergent results then the assay is repeated and the previous results are discarded. This approach allows to reduce assay error probability down to zero. The new system consists of three (instead of four) primers for amplification of two genes and two probes, allowing efficient analysis of various allele K genotypes. Quantitative real-time PCR data analysis was performed by ΔΔCt method using the statistics software package SPSS for ROC analysis. Using the method developed, the percentage of KK, Kk and kk genotypes was determined in 145 cocks of original lines B5, B6, B7 and B9 of domestic meat chicken of cross Smena 8. It was shown that 19 cocks of line B5 and 15 cocks of line B6 had kk genotype. From the 46 cocks of line B7, none had kk genotype, 17 cocks (37 %) had Kk genotype, and 29 cocks (63 %) had KK genotype. From the 65 cocks of line B9, none had kk genotype, 17 cocks (26 %) had Kk genotype, and 48 cocks (74 %) had KK genotype. Analyzed fragments were sequenced to exclude the effects of possible nucleotide sequence variability on the assay. The sequences did not contain any nucleotide substitutions in the sites of the primers and probes annealing. The data obtained will accelerate selection of new domestic meat chicken breeds with possibility of sexing based on feather length in day-old chicken. Further breeding work involves the assessment of the offspring using traditional and molecular genetic methods.

Keywords: real-time polymerase chain reaction, qPCR, genotype, gene copy number, autosex chickens, poultry selection, meat chickens.


Full article (Rus)

Full text (Eng)



  1. Serebrovsky A.S. Crossing-over involving three sex-linked genes in chickens. Amer. Nat., 1922, 56: 571-572 CrossRef
  2. Warren D.C. Inheritance of rate of feathering in poultry. J. Hered., 1925, 16(1): 13-18 CrossRef
  3. Somes R.G. Jr. Delayed feathering, a third allele at the K locus in the domestic fowl. J. Hered., 1969, 60(5): 281-288 CrossRef
  4. Jones D.G., Hutt F.B. Multiple alleles affecting feathering in the fowl. J. Hered., 1946, 37(7): 197-205CrossRef
  5. Warren D.C. Retarded feathering in the fowl. A new factor affecting manner of feathering. J. Hered., 1933, 24(11): 431-434 CrossRef
  6. Warren D.C. Developing early-feathering strains in heavy breeds of poultry. Agricultural Experiment Station, Kansas State College of Agriculture and Applied Science, 1944.
  7. McGibbon W.H. A sex-linked mutation affecting rate of feathering in chickens. Poult. Sci., 1977, 56(3): 872-875 CrossRef
  8. Elferink M.G., Vallee A.A.A., Jungerius A.P., Crooijmans R.P.M.A., Groenen M.A.M. Partial duplication of the PRLR and SPEF2 genes at the late feathering locus in chicken. BMC Genomics, 2008, 9: 391 CrossRef
  9. Zhao J., Yao J., Li F., Yang Z., Sun Z., Qu L., Wang K., Su Y., Zhang A., Montgomery S.A., Geng T., Cui H. Identification of candidate genes for chicken early- and late-feathering. Poult. Sci., 2016, 95(7): 1498-1503 CrossRef
  10. Bu G., Huang G., Fu H., Li J., Huang S., Wang Y. Characterization of the novel duplicated PRLR gene at the late-feathering K locus in Lohmann chickens. J. Mol. Endocrinol., 2013, 51: 261-276 CrossRef
  11. Bole-Feysot C., Goffin V., Edery M., Binart N., Kelly P.A. Prolactin (PRL) and its receptor: actions, signal transduction pathways and phenotypes observed in PRL receptor knockout mice. Endocrine Reviews, 1998, 19(3): 225-268 CrossRef
  12. Cui J.-X., Du H-L., Liang Y., Deng X.-M., Li N., Zhang X.-Q. Association of polymorphisms in the promoter region of chicken prolactin with egg production. Poult. Sci., 2006, 85(1): 26-31 CrossRef
  13. Juhn M., Harris P.C. Molt of capon feathering with prolactin. Exp. Biol. Med., 1958, 98(3): 669-672 CrossRef
  14. International Chicken Genome Sequencing Consortium. Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature, 2004, 432: 695-717 CrossRef
  15. Bubner B., Baldwin I.T. Use of real-time PCR for determining copy number and zygosity in transgenic plants. Plant. Cell Rep., 2004, 23: 263-271 CrossRef
  16. Mieog J.C., Howitt C.A., Ral J.P. Fast-tracking development of homozygous transgenic cereal lines using a simple and highly flexible real-time PCR assay. BMC Plant Biol., 2013, 13: 71 CrossRef
  17. Stefano B., Patrizia B., Matteo C., Massimo G. Inverse PCR and quantitative pcr as alternative methods to Southern blotting analysis to assess transgene copy number and characterize the integration site in transgenic woody plants. Biochem. Genet., 2016, 54(3): 291-305 CrossRef
  18. Yuan J.S., Burris J., Stewart N.R., Mentewab A., Stewart C.N. Jr. Statistical tools for transgene copy number estimation based on real-time PCR. BMC Bioinformatics, 2007, 8(Suppl 7): S6 CrossRef
  19. Huang Y., Yin X., Zhu C., Wang W., Grierson D., Xu C., Chen K. Standard addition quantitative real-time PCR (SAQPCR): a novel approach for determination of transgene copy number avoiding PCR efficiency estimation. PLoS ONE, 2013, 8(1): e53489 CrossRef
  20. Sochivko D.G., Fedorov A.A., Varlamov D.A., Kurochkin V.E., Petrov R.V. Simulation of the PCR amplification as two-type-particle branching process. Dokl. Biochem. Biophys., 2010, 434: 239-241 CrossRef
  21. Sochivko D.G., Fedorov A.A., Lavrov V.V., Varlamov D.A., Kurochkin V.E., Petrov R.V. Stochastic modeling of polymerase chain reaction kinetic curves. Dokl. Biochem. Biophys., 2011, 439: 188-191 CrossRef
  22. Livak K.J., Schmittgen T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods, 2001, 25(4): 402-408 CrossRef
  23. Bryman A., Cramer D. Quantitative data analysis with IBM SPSS 17, 18 and 19: A guide for social scientists. Routledge, NY, 2011 (ISBN 978-0-415-57918-6).
  24. Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters, 2006, 27(8): 861-874 CrossRef
  25. Saito T., Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE, 2015, 10(3): e0118432 CrossRef