doi: 10.15389/agrobiology.2017.6.1166eng

UDC 636.52/.58:575.174.5

Acknowledgements:
Supported financially by Russian Science Foundation, project № 16-16-04060. Chicken blood samples were provided by Genetic collection of rare and endangered breeds of chickens (ARRIFAGB) under the program of Federal Agency of Scientific Organizations for support of bio resource collections

 

STUDYING THE STRUCTURE OF A GENE POOL POPULATION
OF THE RUSSIAN WHITE CHICKEN BREED
BY GENOME-WIDE SNP SCAN

N.V. Dementeva1, M.N. Romanov1, 2, A.A. Kudinov1, O.V. Mitrofanova1,
O.I. Stanishevskaya1, V.P. Terletsky1, E.S. Fedorova1, E.V. Nikitkina1,
K.V. Plemyashov1

1All-Russian Research Institute for Farm Animal Genetics and Breeding — branch of L.K. Ernst Federal Science Center for Animal Husbandry,Federal Agency of Scientific Organizations, 55А, Moskovskoe sh., pos. Tyarlevo, St. Petersburg—Pushkin, 196625 Russia, e-mail dementevan@mail.ru (corresponding author);
2School of Biosciences, University of Kent, Canterbury, Kent, UK, CT2 7NJ,
e-mail m.romanov@kent.ac.uk


ORCID:
Dementeva N.V. orcid.org/0000-0003-0210-9344
Kudinov A.A. orcid.org/0000-0002-7811-576X
Stanishevskaya O.I. orcid.org/0000-0001-9504-3916
Fedorova E.S. orcid.org/0000-0002-1618-6271
Plemyashov K.V. orcid.org/000-0002-3658-5886
Romanov M.N. orcid.org/0000-0003-3584-4644
Mitrofanova O.V. orcid.org/000-0003-4702-2736
Terletsky V.P. orcid.org/0000-0003-4043-3823
Nikitkina E.V. orcid.org/0000-0002-8496-5277

Received August 11, 2017

 

A population of the Russian White chickens, bred at the gene pool farm of ARRIFAGB for 25 generations using individual selection, is characterized by resistance to a lowered temperature in the early postnatal period and white colour of the embryonic down. In 2002-2012, breeding was carried out by panmixia, and by now a new population of the Russian White chickens has been formed on the basis of the surviving stock. Comparison of the genetic variability of this population and the archival DNA of representatives of the 2001 population using microarray screening technology will help to assess the population structure and the preservation of the unique characteristics of its genome. The material for the study was DNA extracted from 162 chicken blood samples. Two groups of the Russian White breed were studied, the 2001 population and the current population. Genome-wide analysis using single nucleotide markers (SNP) included screening by means of the Illumina Chicken 60K SNP iSelect BeadChip microarray. Quality control of genotyping, determination of the population genetic structure by multidimensional scaling (MDS), calculation of linkage disequilibrium (LD) and allele frequency in the groups were carried out using PLINK 1.9 software program. The construction of a cluster delimitation model based on SNP genotypes was carried out using the ADMIXTURE program. According to the MDS analysis results, the current population can be divided into four MDS groups, which, when compared to the data of the pedigree, adequately reflect the origin of the studied individuals. The representatives of the ancestral population were genetically similar to the MDS3 group of the current population. Using the F-statistic of the two-way analysis of variance, a significant effect of the group, chromosome, chromosome in the group, and the distance between SNP markers on LD (r2) values was observed. In the 2001 group, the maximum r2 and the high incidence of LD equal to 1 were observed for all chromosomes, with a distance between SNP markers being 500-1000 Kb. There was also the greatest number of monomorphic alleles in this group. Based on the SNP analysis, we may conclude that the current Russian White chicken population is characterized by the disintegration of long LD regions of the ancestral population. Modelling clusters using the ADMIXTURE program revealed differences between the current population groups determined by MDS analysis. The groups composed of individuals included in MDS1 and MDS2 had a homogeneous structure and differed from each other at K = 4 and K = 5. The MDS4 group formed a genetically heterogeneous cluster different from the MDS1 and MDS2 groups at K of 2-5. The MDS3 group was phylogenetically close to the 2001 population (at K of 2-5). In general, the analysis of the current gene pool population of the Russian White chickens showed its heterogeneity while one of its groups (MDS3) was similar to the ancestral population of 2001, which in turn is characterized by a large number of monomorphic alleles and a high frequency of long LD regions. Thus, SNP scanning allowed evaluating the genetic similarity of individuals and the population structure of the Russian White chicken breed. Understanding the genetic structure is an important point in the panmictic breeding and tracking of historical changes in the molecular organization of the genome of a gene pool population with a limited number of animals.

Keywords: population structure, genetic diversity, SNP genotyping, Russian White breed of chickens.

 

Full article (Rus)

Full article (Eng)

 

REFERENCES

  1. Sokolova A.N. Genetiko-selektsionnye metody sozdaniya populyatsii kur s povyshennoi ustoichivost'yu k neoplazmam. Avtoreferat doktorskoi dissertatsii [Genetic and selection in chicken breeding for resystance to neoplastic growth]. St. Petersburg—Pushkin, 1999 (in Russ.).   
  2. Weigend S., Romanov M.N. Current strategies for the assessment and evaluation of genetic diversity in chicken resources. World Poultry Sci. J., 2001, 57(3): 275-288 CrossRef
  3. Soller M., Weigend S., Romanov M.N., Dekkers J.C.M., Lamont S.J. Strategies to assess structural variation in the chicken genome and its associations with biodiversity and biological performance. Poultry Sci., 2006, 85(12): 2061-2078 CrossRef
  4. Romanov M.N., Weigend S. Analysis of genetic relationships between various populations of domestic and jungle fowl using microsatellite markers. Poultry Sci., 2001, 80(8): 1057-1063 CrossRef
  5. Dunn I.C., Miao Y.-W., Morris A., Romanov M.N., Wilson P.W., Waddington D. A study of association between genetic markers in candidate genes and reproductive traits in one generation of a commercial broiler breeder hen population. Heredity, 2004, 92(2): 128-134 CrossRef
  6. Tyshchenko V.I., Mitrofanova O.V., Dement'eva N.V., Terletskii V.P. Estimation of genetic variability in the breeds and hen experimental populations by DNA-fingerprinting. Sel’skokhozyaistvennaya Biologiya [Agricultural Biology], 2007, 4: 29-33 (in Russ.).   
  7. Mekchay S., Supakankul P., Assawamakin A., Wilantho A., Char-
    eanchim W., Tongsima S. Population structure of four Thai indigenous chicken breeds. BMC Genet., 2014, 15: 40 CrossRef
  8. Qanbari S., Hansen M., Weigend S., Preisinger R., Simianer H. Linkage disequilibrium reveals different demographic history in egg laying chickens. BMC Genet., 2010, 11: 103 CrossRef
  9. Yakovlev A.F., Smaragdov M.G. Zootekhniya, 2011, 5: 2-4 (in Russ.).   
  10. Khanyile K.S., Dzomba E.F, Muchadeyi F.C. Population genetic structure, linkage disequilibrium and effective population size of conserved and extensively raised village chicken populations of Southern Africa. Front. Genet., 2015, 6: 13 CrossRef
  11. Smaragdov M.G., Saksa E.I., Kudinov A.A., Dementeva N.V., Mitrofanova O.V., Plemyashov K.V. Genome-wide analysis of across herd Fst heterogeneity in holsteinized cattle. Russ. J. Genet., 2016, 52(2): 173-179 CrossRef
  12. Habier D., Fernando R.L., Dekkers J.C.M. The impact of genetic relationship information on genome-assisted breeding values. Genetics, 2007, 177(4): 2389-2397 CrossRef
  13. Weng Z., Wolc A., Shen X., Fernando R.L., Dekkers J.C., Arango J., Settar P., Fulton J.E., O'Sullivan N.P., Garrick D.J. Effects of number of training generations on genomic prediction for various traits in a layer chicken population. Genet. Sel. Evol., 2016, 48: 22 CrossRef
  14. Meuwissen T.H., Hayes B.J., Goddard M.E. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 2001, 157(4): 1819-1829.
  15. Solberg T.R., Sonesson A.K., Woolliams J.A., Odegard J., Meuwis-
    sen T.H. Persistence of accuracy of genome-wide breeding values over generations when including a polygenic effect. Genet. Sel. Evol., 2009, 41: 53 CrossRef
  16. Fariello M.I., Boitard S., Naya H., SanCristobal M., Servin B. Detecting signatures of selection through haplotype differentiation among hierarchically structured populations. Genetics, 2013, 193(3): 929-941 CrossRef
  17. Muir W.M., Wong G.K.-S., Zhang Y., Wang J., Groenen M.A.M., Crooijmans R.P.M.A., Megens H.-J., Zhang H., Okimoto R., Vereijken A., Jungerius A., Albers G.A.A., Lawley C.T., Delany M.E., MacEachern S., Cheng H.H. Genome-wide assessment of worldwide chicken SNP genetic diversity indicates significant absence of rare alleles in commercial breeds. PNAS, 2008, 105(45): 17312-17317 CrossRef
  18. Fragomeni B.D.O., Misztal I., Lourenco D.L., Aguilar I., Okimoto R., Muir W.M. Changes in variance explained by top SNP windows over generations for three traits in broiler chicken. Front. Genet., 2014, 5: 332 CrossRef
  19. Beynon S.E., Slavov G.T., Farré M., Sunduimijid B., Waddams K., Davies B., Haresign W., Kijas J., MacLeod I.M., Newbold C.J., Davies L., Larkin D.M. Population structure and history of the Welsh sheep breeds determined by whole genome genotyping. BMC Genet., 2015, 16: 65 CrossRef
  20. Saitou N., Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol., 1987, 4(4): 406-425.
  21. Pritchard J.K., Stephens P., Donnelly P. Inference of population structure using multilocus genotype data. Genetics, 2000, 155(2): 945-959.
  22. Evanno G., Regnaut S., Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol., 2005, 14(8): 2611-2620 CrossRef
  23. Chang C.C., Chow C.C., Tellier L.C., Vattikuti S.S., Purcell S.M., Lee J.J. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 2015, 4: 7 CrossRef
  24. RStudio. Available http://www.rstudio.com. No date.
  25. Alexander D.H., Novembre J., Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res., 2009, 19(9): 1655-1664 CrossRef
  26. Deniskova T.E., Dotsev A.V., Bagirov V.A., Vimmers K., Reier Kh., Brem G., Zinov'eva N.A. Biodiversity assessment in interspecies hybrids of the genus ovis using STR and SNP markers. Agricultural Biology, 2017, 52(2): 251-260 CrossRef
  27. Wragg D., Mwacharo J., Alcalde J., Hocking P.M., Hanotte O. Analysis of genome-wide structure, diversity and fine mapping of Mendelian traits in traditional and village chickens. Heredity, 2012, 109(1): 6-18 CrossRef
  28. Ardlie K.G., Kruglyak L., Seielstad M. Patterns of linkage disequilibrium in the human genome. Nat. Rev. Genet., 2002, 3(4): 299-309 CrossRef
  29. Andreescu C., Avendano S., Brown S. R., Hassen A., Lamont S.J., Dekkers J.C. Linkage disequilibrium in related breeding lines of chickens. Genetics, 2007, 177(4): 2161-2169 CrossRef

back