Czech J. Anim. Sci., 2019, 64(10):405-410 | DOI: 10.17221/148/2019-CJAS

Impact of reference population and marker density on accuracy of population imputationOriginal Paper

Anita Kranjčevičová*,1,2, Eva Kašná2, Michaela Brzáková1, Josef Přibyl2, Luboš Vostrý1
1 Department of Genetics and Breeding, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic
2 Department of Genetics and Breeding of Farm Animals, Institute of Animal Science, Prague-Uhříněves, Czech Republic

The effect of the reference population size and the number of missing single nucleotide polymorphisms (SNPs) on imputation accuracy was determined. The population imputation method using the FImpute software was applied. The dataset used for the purpose of this study was taken from the database of the Holstein Cattle Breeders Association of the Czech Republic. It contains 1000 animals genotyped with the Illumina BovineSNP50 v.2 BeadChip. Two datasets were created, the first containing the original genotypes, including the missing SNPs, the second containing the same genotypes modified to avoid missing data. In these datasets, animals were randomly selected for a reference population (10, 25, 50 and 75%) and there were randomly selected SNPs for deletion (15, 30, 55, 70, and 95%) in animals that were not used as the reference population. Subsequently, the data accuracy was determined by two parameters: correlation between original and imputed SNPs and percentage of correctly imputed SNPs. Since animals and SNPs were randomly selected, the process including data imputation was repeated 100 times. Accuracy was determined as the average accuracy over all repetitions. It was found that the imputation accuracy is influenced by both parameters. If the size of the reference population is sufficient, the imputation accuracy is higher despite the large number of missing SNPs.

Keywords: cattle; genomics; marker density; missing SNPs; simulation

Published: October 31, 2019  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Kranjčevičová A, Kašná E, Brzáková M, Přibyl J, Vostrý L. Impact of reference population and marker density on accuracy of population imputation. Czech J. Anim. Sci. 2019;64(10):405-410. doi: 10.17221/148/2019-CJAS.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Browning S., Browning B. (2011): Haplotype phasing: Existing methods and new developments. Nature Reviews Genetics, 12, 703-714. Go to original source... Go to PubMed...
    2. Browning B., Zhou Y., Browning S. (2018): A one-penny imputed genome from next-generation reference panels. The American Journal of Human Genetics, 103, 338-348. Go to original source... Go to PubMed...
    3. Carvalheiro R., Boison S., Neves H., Sargolzaei M., Schenkel F., Utsunomiya Y., O'Brien A., Solkner J., McEwan J., Van Tassell C., Sonstegard T., Garcia J. (2014): Accuracy of genotype imputation in Nelore cattle. Genetics Selection Evolution, 46, 69. Go to original source... Go to PubMed...
    4. Daetwyler H., Wiggans G., Hayes B., Woolliams J., Goddard M. (2011): Imputation of missing genotypes from sparse to high density using long-range phasing. Genetics, 189, 317-327. Go to original source... Go to PubMed...
    5. Ghoreishifar S.M., Moradi-Shahrbabak H., Moradi-Shahrbabak M., Nicolazzi E.L., Williams J.L., Iamartino D., Nejati-Javaremi A. (2018): Accuracy of imputation of single-nucleotide polymorphism marker genotypes for water buffaloes (Bubalus bubalis) using different reference population sizes and imputation tools. Livestock Science, 216, 174-182. Go to original source...
    6. Gurgul A., Sienko K., Zukowski K., Pawlina-Tyszko K., Bugno M. (2014): Imputation accuracy of bovine spongiform encephalopathy-associated PRNP indel polymorphisms from middle-density SNPs arrays. Czech Journal of Animal Science, 59, 224-249. Go to original source...
    7. Hayes B., Bowman P., Chamberlain A., Goddard M. (2009): Invited review: Genomic selection in dairy cattle. Journal of Dairy Science, 92, 433-443. Go to original source... Go to PubMed...
    8. Hickey J., Kinghorn B., Tier B., Wilson J., Dunstan N., van der Werf J. (2011): A combined long-range phasing and long haplotype imputation method to impute phase for SNP genotypes. Genetics Selection Evolution, 43, 12. Go to original source... Go to PubMed...
    9. Mulder H., Calus M., Druet T., Schrooten C. (2012): Imputation of genotypes with low-density chips and its effect on reliability of direct genomic values in Dutch Holstein cattle. Journal of Dairy Science, 95, 876-889. Go to original source... Go to PubMed...
    10. Nicolazzi E., Biffani S., Jansen G. (2013): Short communication: Imputing genotypes using PedImpute fast algorithm combining pedigree and population information. Journal of Dairy Science, 96, 2649-2653. Go to original source... Go to PubMed...
    11. Sargolzaei M., Chesnais J., Schenkel F. (2014): A new approach for efficient genotype imputation using information from relatives. BMC Genomics, 15, 12. Go to original source... Go to PubMed...
    12. Schaeffer L. (2006): Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics, 23, 218-223. Go to original source... Go to PubMed...
    13. VanRaden P., O'Connell J., Wiggans G., Weigel K. (2011): Genomic evaluations with many more genotypes. Genetics Selection Evolution, 43, 10. Go to original source... Go to PubMed...
    14. VanRaden P., Sun C., O'Connell J. (2015): Fast imputation using medium or low-coverage sequence data. BMC Genetics, 16, 12. Go to original source... Go to PubMed...
    15. Ventura R., Lu D., Schenkel F.S., Wang Z., Li C., Miller S.P. (2014): Impact of reference population on accuracy of imputation from 6K to 50K single nucleotide polymorphism chips in purebred and crossbreed beef cattle. Journal of Animal Science, 92, 1433-1444. Go to original source... Go to PubMed...
    16. Wang Y., Lin G., Li C., Stothard P. (2016): Genotype imputation methods and their effects on genomic predictions in cattle. Springer Science Reviews, 4, 79-98. Go to original source...
    17. Whalen A., Gorjanc G., Ros-Freixedes R., Hickey J. (2018): Assessment of the performance of hidden Markov models for imputation in animal breeding. Genetics Selection Evolution, 50, 4-10. Go to original source... Go to PubMed...
    18. Zhang Z., Druet T. (2010): Marker imputation with lowdensity marker panels in Dutch Holstein cattle. Journal of Dairy Science, 93, 5487-5494. Go to original source... Go to PubMed...

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content