Skip to main content
SpringerLink
Log in

Genomic Selection. I: Latest Trends and Possible Ways of Development

  • REVIEWS AND THEORETICAL ARTICLES
  • Published:
Russian Journal of Genetics Aims and scope Submit manuscript

Abstract

There is a forecast that global demand for foods of animal and plant origin will increase by 74% by 2050 (Food and Agriculture Organization of the United Nations). Satisfying this demand without a destructive effect on the environment is only possible while maintaining the principles of organic farming, as well as introducing new technologies in animal husbandry and crop production. Genomic selection as one of the most promising and safest methods for improving the genetic qualities of farm animals and plants can play a key role in this process. This review summarizes information on genomic selection, indicates possible growth points of this direction, demonstrates how a genomic estimation of the breeding value is constructed and what are the key conditions required for its implementation, and discusses the advantages and limitations of genomic and marker selection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.

Similar content being viewed by others

REFERENCES

  1. Serebrovskii, A.S., Geneticheskii analiz (Genetic Analysis), Moscow: Nauka, 1970.

  2. Khlestkina, E.K., Molecular markers in genetic studies and breeding, Vavilovskii Zh. Genet. Sel., 2013, vol. 17, no. 4/2, pp. 1044—1054.

  3. Ilska, J.J., Meuwissen, T.H.E., Kranis, A., and Woolliams, J.A., Use and optimization of different sources of information for genomic prediction, Genet. Sel. Evol., 2017, vol. 49, no. 90. https://doi.org/10.1186/s12711-017-0365-7

  4. Berry, D.P., Bermingham, M.L., Good, M., and More, S.J., Genetics of animal health and disease in cattle, Irish Vet. J., 2011, vol. 64, no. 5. https://doi.org/10.1186/2046-0481-64-5

  5. Legarra, A., Croiseau, P., Sanchez, M.P., et al., A comparison of methods for whole-genome QTL mapping using dense markers in four livestock species, Genet., Sel., Evol., 2015, vol. 47, no. 6. https://doi.org/10.1186/s12711-015-0087-7

  6. Chamberlain, A.J., McPartlan, H.C., and Goddard, M.E., The number of loci that affect milk production traits in dairy cattle, Genetics, 2007, vol. 177, no. 2, pp. 1117—1123. https://doi.org/10.1534/genetics.107.077784

    Article  PubMed  PubMed Central  Google Scholar 

  7. Cai, Z., Guldbrandtsen, B., Lund, M.S., and Sahana, G., Prioritizing candidate genes for fertility in dairy cows using gene-based analysis, functional annotation and differential gene expression, BMC Genomics, 2019, vol. 20, no. 255, pp. 255—265. https://doi.org/10.1186/s12864-019-5638-9

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kiser, J.N., White, S.N., Johnson, K.A., et al., Identification of loci associated with susceptibility to Mycobacterium avium subspecies paratuberculosis (Map) tissue infection in cattle, J. Anim. Sci., 2017, vol. 95, no. 3, pp. 1080—1091. https://doi.org/10.2527/jas.2016.1152

    Article  CAS  PubMed  Google Scholar 

  9. Hu, Z.L., Park, C.A., and Reecy, J.M., Building a livestock genetic and genomic information knowledgebase through integrative developments of animal QTLdb and CorrDB, Nucleic Acids Res., 2019, vol. 47, no. D1, pp. D701—D710. https://doi.org/10.1093/nar/gky1084

    Article  CAS  PubMed  Google Scholar 

  10. Lusk, J.L., Association of single nucleotide polymorphisms in the leptin gene with body weight and backfat growth curve parameters for beef cattle, J. Anim. Sci., 2007, vol. 85, no. 8, pp. 1865—1872. https://doi.org/10.2527/jas.2006-665

    Article  CAS  PubMed  Google Scholar 

  11. Barendse, W., Bunch, R.J., Kijas, J.W., and Thomas, M.B., The effect of genetic variation of the retinoic acid receptor-related orphan receptor C gene on fatness in cattle, Genetics, 2007, vol. 175, no. 2, pp. 843—853. https://doi.org/10.1534/genetics.106.064535

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Matsuhashi, T., Maruyama, S., Uemoto, Y., et al., Effects of bovine fatty acid synthase, stearoyl-coenzyme A desaturase, sterol regulatory element-binding protein 1, and growth hormone gene polymorphisms on fatty acid composition and carcass traits in Japanese Black cattle, J. Anim. Sci., 2011, vol. 89, no. 1, pp. 12—22. https://doi.org/10.2527/jas.2010-3121

    Article  CAS  PubMed  Google Scholar 

  13. Marzanov, N.S., Turbina, I.S., Eskin, G.V., et al., Screening of the gene of the leukocyte-adhesion deficiency in Black-and-White holsteinized cattle, S.-kh. Biol., 2003, no. 6, pp. 23—30.

  14. Thomsen, B., Horn, P., Panitz, F., et al., A missense mutation in the bovine SLC35A3 gene, encoding a UDP-N-acetylglucosamine transporter, causes complex vertebral malformation, Genome Res., 2006, vol. 16, no. 1, pp. 97—105. https://doi.org/10.1101/gr.3690506

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ryan, M.T., Hamill, R.M., O’Halloran, A.M., et al., SNP variation in the promoter of the PRKAG3 gene and association with meat quality traits in pig, BMC Genet., 2012, vol. 13, no. 66. https://doi.org/10.1186/1471-2156-13-66

  16. Song, Y., Xu, L., Chen, Y., et al., Genome-wide association study reveals the PLAG1 gene for knuckle, biceps and shank weight in Simmental beef cattle, PLoS One, 2016, vol. 11, no. 12. e016831. https://doi.org/10.1371/journal.pone.0168316

    Article  CAS  Google Scholar 

  17. Wang, Z., Chen, Q., Liao, R., et al., Genome-wide genetic variation discovery in Chinese Taihu pig breeds using next generation sequencing, Anim. Genet., 2017, vol. 48, no. 1, pp. 38—47. https://doi.org/10.1111/age.12465

    Article  CAS  PubMed  Google Scholar 

  18. Cochran, S.D., Cole, J.B., Null, D.J., and Hansen, P.J., Discovery of single nucleotide polymorphism in candidate genes associated with fertility and production traits in Holstein cattle, BMC Genet., 2013, vol. 14, no. 49. https://doi.org/10.1186/1471-2156-14-49

  19. Goddard, M.E. and Hayes, B.J., Mapping genes for complex traits in domestic animals and their use in breeding programs, Nat. Rev. Genet., 2009, vol. 10, pp. 381—391. https://doi.org/10.1038/nrg2575

    Article  CAS  PubMed  Google Scholar 

  20. Meuwissen, T.H., Hayes, B.J., and Goddard, M.E., Prediction of total genetic value using genome-wide dense marker maps, Genetics, 2001, vol. 157, no. 4, pp. 1819—1829.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Barton, N.H., Etheridge, A.M., and Véber, A., The infinitesimal model: definition, derivation, and implications, Theor. Popul. Biol., 2017, vol. 118, pp. 50—73. https://doi.org/10.1016/j.tpb.2017.06.001

    Article  CAS  PubMed  Google Scholar 

  22. Zuidhof, M.J., Schneider, B.L., Carney, V.L., et al., Growth, efficiency, and yield of commercial broilers from 1957, 1978, and 2005, Poult. Sci., 2014, vol. 93, no. 12, pp. 2970—2982. https://doi.org/10.3382/ps.2014-04291

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Matukumalli, L.K., Lawley, C.T., Schnabel, R.D., et al., Development and characterization of a high density SNP genotyping assay for cattle, PLoS One, 2009, vol. 4, no. 4. e5350. https://doi.org/10.1371/journal.pone.0005350

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. McCue, M.E., Bannasch, D.L., Petersen, J.L., et al., A high-density SNP array for the domestic horse and extant Perissodactyla: utility for association mapping, genetic diversity, and phylogeny studies, PLoS Genet., 2012, vol. 8, no. 1. e1002451. https://doi.org/10.1371/journal.pgen.1002451

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wiggans, G.R., Cole, J.B., Hubbard, S.M., and Sonstegard, T.S., Genomic selection in dairy cattle: the USDA experience, Annu. Rev. Anim. Biosci., 2017, vol. 5, pp. 309—327. https://doi.org/10.1146/annurev-animal-021815-111422

    Article  PubMed  Google Scholar 

  26. Dekkers, J.C., Application of genomics tools to animal breeding, Curr. Genomics, 2012, vol. 13, no. 3, pp. 207—212. https://doi.org/10.2174/138920212800543057

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Georges, M., Charlier, C., and Hayes, B., Harnessing genomic information for livestock improvement, Nat. Rev. Genet., 2019, vol. 20, no. 3, pp. 135—156. https://doi.org/10.1038/s41576-018-0082-2

    Article  CAS  PubMed  Google Scholar 

  28. Mrode, R., Ojango, J.M.K., Okeyo, A.M., and Mwacharo, J.M., Genomic selection and use of molecular tools in breeding programs for indigenous and crossbred cattle in developing countries: current status and future prospects, Front. Genet., 2019, vol. 9, no. 694. https://doi.org/10.3389/fgene.2018.00694

  29. Brookes, A.J., The essence of SNPs, Gene, 1999, vol. 234, no. 2, pp. 177—186. https://doi.org/10.1016/s0378-1119(99)00219-x

    Article  CAS  PubMed  Google Scholar 

  30. Sachidanandam, R., Weissman, D., Schmidt, S.C., et al., International SNP map working group: a map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms, Nature, 2001, vol. 409, pp. 928—933.

    Article  CAS  PubMed  Google Scholar 

  31. Nicolazzi, E.L., Biffani, S., Biscarini, F., et al., Software solutions for the livestock genomics SNP array revolution, Anim. Genet., 2015, vol. 46, no. 4, pp. 343—353. https://doi.org/10.1111/age.12295

    Article  CAS  PubMed  Google Scholar 

  32. Keeble-Gagnère, G., Rigault, P., Tibbits, J., et al., Optical and physical mapping with local finishing enables megabase-scale resolution of agronomically important regions in the wheat genome, Genome Biol., 2018, vol. 19, no. 112. https://doi.org/10.1186/s13059-018-1475-4

  33. Daetwyler, H.D., Capitan, A., Pausch, H., et al., Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle, Nat. Genet., 2014, vol. 46, no. 8, pp. 858—865. https://doi.org/10.1038/ng.3034

    Article  CAS  PubMed  Google Scholar 

  34. Kadri, N.K., Harland, C., Faux, P., et al., Coding and non-coding variants in HFM1, MLH3, MSH4, MSH5, RNF212 and RNF212B affect recombination rate in cattle, Genome Res., 2016, vol. 26, no. 10, pp. 1323—1332. https://doi.org/10.1101/gr.204214.116

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kaler, A.S. and Purcell, L.C., Estimation of a significance threshold for genome-wide association studies, BMC Genomics, 2019, vol. 20, no. 1, p. 618. https://doi.org/10.1186/s12864-019-5992-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Van Raden, P.M., Efficient methods to compute genomic predictions, J. Dairy Sci., 2008, vol. 91, no. 11, pp. 1414—1423. https://doi.org/10.3168/jds.2007-0980

    Article  CAS  Google Scholar 

  37. Kemper, K.E., Bowman, P.J., Hayes, B.J., et al., A multi-trait Bayesian method for mapping QTL and genomic prediction, Genet. Sel. Evol., 2018, vol. 50, no. 10. https://doi.org/10.1186/s12711-018-0377-y

  38. Clark, S.A. and van der Werf, J., Genomic best linear unbiased prediction (gBLUP) for the estimation of genomic breeding values, Methods Mol. Biol., 2013, vol. 1019, pp. 321—330. https://doi.org/10.1007/978-1-62703-447-0_13. 38a. Stolpovskii, Yu.A., Svishcheva, G.R., Piskunov, A.K., Genomic selection: II. Promising directions, Russ. J. Genet., 2020, vol. 56, no. 10 (in press).

  39. García-Ruiz, A., Cole, J.B., Van Raden, P.M., et al., Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection, Proc. Natl. Acad. Sci. U.S.A., 2016, vol. 113, no. 28, pp. 3995—4004. https://doi.org/10.1073/pnas.1519061113

    Article  CAS  Google Scholar 

  40. Fernando, R.L., Cheng, H., Golden, B.L., and Garrick, D.J., Computational strategies for alternative single-step Bayesian regression models with large numbers of genotyped and non-genotyped animals, Genet. Sel. Evol., 2016, vol. 48, no. 96. https://doi.org/10.1186/s12711-016-0273-2

  41. Hayes, B. and Goddard, M., Genome-wide association and genomic selection in animal breeding, Genome, 2010, vol. 53, no. 11, pp. 876—883. https://doi.org/10.1139/G10-076

    Article  CAS  PubMed  Google Scholar 

  42. Edwards, S.M., Buntjer, J.B., Jackson, R., et al., The effects of training population design on genomic prediction accuracy in wheat, Theor. Appl. Genet., 2019, vol. 132, no. 7, pp. 1943—1952. https://doi.org/10.1007/s00122-019-03327-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Eynard, S.E., Croiseau, P., Laloë, D., et al., Which individuals to choose to update the reference population? Minimizing the loss of genetic diversity in animal genomic selection programs, Genes, Genomes,Genet., 2018, vol. 8, no. 1, pp. 113—121. https://doi.org/10.1534/g3.117.11172018

    Article  Google Scholar 

  44. Dehnavi, E., Mahyari, S.A., Schenkel, F.S., and Sargolzaei, M., The effect of using cow genomic information on accuracy and bias of genomic breeding values in a simulated Holstein dairy cattle population, J. Dairy Sci., 2018, vol. 101, no. 6, pp. 5166—5176. https://doi.org/10.3168/jds.2017-12999

    Article  CAS  PubMed  Google Scholar 

  45. Boison, S.A., Utsunomiya, A.T.H., Santos, D.J.A., et al., Accuracy of genomic predictions in Gyr (Bos indicus) dairy cattle, J. Dairy Sci., 2017, vol. 100, no. 7, pp. 5479—5490. https://doi.org/10.3168/jds.2016-11811

    Article  CAS  PubMed  Google Scholar 

  46. Silva, R.M.O., Fragomeni, B.O., Lourenco, D.A.L., et al., Accuracies of genomic prediction of feed efficiency traits using different prediction and validation methods in an experimental Nelore cattle population, J. Anim. Sci., 2016, vol. 94, no. 9, pp. 3613—3623. https://doi.org/10.2527/jas.2016-0401

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

This work was supported by the Russian Science Foundation (grant no. 19-76-20061).

Author information

Authors and Affiliations

  1. Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991, Moscow, Russia

    Yu. A. Stolpovsky, A. K. Piskunov & G. R. Svishcheva

  2. Federal Research Center Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, 630090, Novosibirsk, Russia

    G. R. Svishcheva

Authors
  1. Yu. A. Stolpovsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. K. Piskunov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. G. R. Svishcheva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yu. A. Stolpovsky, A. K. Piskunov or G. R. Svishcheva.

Ethics declarations

The authors declare that they have no conflict of interest. This article does not contain any studies involving animals or human participants performed by any of the authors.

Additional information

Translated by A. Barkhash

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stolpovsky, Y.A., Piskunov, A.K. & Svishcheva, G.R. Genomic Selection. I: Latest Trends and Possible Ways of Development. Russ J Genet 56, 1044–1054 (2020). https://doi.org/10.1134/S1022795420090148

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1022795420090148

Keywords:

Search

Navigation