Estimation of inbreeding rate by pedigree and marker methods and its effect on the accuracy of genomic prediction in simulation data

Document Type : (original research)

Author

Department of Animal Science, Faculty of Agriculture, Ilam University, Ilam, Iran

Abstract

In the past, pedigree relationships were used to control and monitor inbreeding because genomic relationships among selection candidates were not available until recently. These consequences were measured by genetic gain, pedigree- and genome-based rates of inbreeding, and local inbreeding across the genome. Their effect on the accuracy of genomic predictions was also investigated using simulation data. A baseline population of 1000 animals for 4000 generations was simulated using QMsim software. The number of ten chromosomes and 1000 SNP markers on each chromosome was simulated and the total number of QTLs on ten chromosomes was 1000. The results of the present study showed that the rate of genetic improvement in the genomic breeding value (GBLUP) method was estimated to be 13 percent higher than the TBLUP method. The rate of pedigree inbreeding method in GBLUP method was much lower than TBLUP method, although in the method of inbreeding estimated by IBD this rate was very small. The difference in the accuracy of genomic prediction for the method in which inbreeding was estimated to be low marker was 24 units higher than the pedigree method. In general, the results of the present study showed that the estimate of inbreeding was less accurate and its effect on the accuracy of genomic prediction was significant.

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References

  1. Barbato, M.; Orozco-terWengel, P.; Tapio, M. and Bruford, M.W., 2015. SNeP: a tool to estimate trends in recent effective population size trajectories using genome wide SNP data. Front Genet. Vol. 6, pp: 109.
  2. Daetwyler, H.D.; Pong-Wong, R.; Villanueva, B. and Woolliams, J.A., 2010. The impact of genetic architecture on genome-wide evaluation methods. Genetics. Vol. 185, pp: 1021-1031.
  3. Fernández, B.; Santiago, E.; Toro, M.A. and Caballero, A., 2000. Effect of linkage on the control of inbreeding in selection programmes. Genetics Selection Evolution. Vol. 32, pp: 249-264.
  4. Fisher, R.A., 1930. The Genetical Theory of Natural Selection. Oxford: Clarendon.
  5. Goddard, M.E., 2009. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica. Vol. 136, pp: 245-257.
  6. Grundy, B.; Villanueva, B. and Woolliams, J.A., 1998. Dynamic selection procedures for constrained inbreeding and their consequences for pedigree development. Genetics Research. Vol. 72, No. 2, pp: 159-168.
  7. Hayes, B. and Goddard, M.E., 2001. The distribution of the effects of genes affecting quantitative traits in livestock. Genetics Selection Evolution. Vol. 33, No. 3, pp: 209-229.
  8. Meuwissen, T.H.E.; Hayes, B.J. and Goddard, M.E., 2001. Prediction of total genetic value using enome-wide dense marker maps. Genetics. Vol. 157, pp: 1819-1829.
  9. Meuwissen, T.H.E., 1997. Maximizing the response of selection with a predefined rate of inbreeding. Journal of Animal Sciences. Vol. 75, No. 4, pp: 934-940.
  10. Ni, G.; Cavero, D.; Fangmann, A.; Erbe, M. and Simianer, H., 2017. Whole-genome sequence-based genomic prediction in laying chickens with different genomic relationship matrices to account for genetic architecture. Genetics Selection Evolution. Vol. 49, No. 8, pp:1-14.
  11. Pedersen, L.D.; Sørensen, A.C. and Berg, P., 2010. Marker-assisted selection reduces expected inbreeding but can result in large effects of hitchhiking. Journal of Animal Breeding. Genet. Vol. 127, No. 3, pp: 189-198.
  12. Schenkel, F.; Sargolzaei, M.; Kistemaker, G.; Jansen, G.; Sullivan, P.; Van Doormaal, B.J.; Vanraden, P.M. and Wiggans, G.R., 2009. Reliability of genomic evaluation of Holstein cattle in Canada. Interbull Bulletin. Vol. 39, pp:
    51-58.
  13. Sonesson, A.K.; Woolliams, J.A. and Meuwissen, T.H.E., 2012. Genomic selection requires genomic control of inbreedin. Genetics Selection Evolution. Vol. 44, No. 27, pp: 1-10.
  14. Sonesson, A.K. and Meuwissen, T.H.E., 2009. Testing strategies for genomic selection in aquaculture breeding programs. Genetics Selection Evolution. Vol. 41, No. 37, pp: 1-9.
  15. VanRaden, P.M., 2005. Inbreeding adjustments and effect on genetic trend estimates. Interbull Bull. Vol. 33, pp: 81-84.
  16. Wang, J.; Santiago, E. and Caballero, A., 2016. Prediction and estimation of effective population size. Hered. Vol. 117, pp: 193-206.
  17. Waples, R.S. and Antao, T., 2014. Intermittent breeding and constraints on litter size: consequences for effective population size per generation (Ne) and per reproductive cycle (Nb). Evolution. Vol. 68, pp: 1722-1734.
  18. Wright, S., 1931. Evolution in Mendelian populations. Genetics. Vol. 16, pp: 97-159.
  19. Yang, J.; Manolio, T.A.; Pasquale, L.R.; Boerwinkle, E.; Caporaso, N.; Cunningham, J.M.; de Andrade, M.; Feenstra, B.; Feingold, E.; Hayes, M.G.; Hill, W.G.; Landi, M.T.; Alonso, A.; Lettre, G.; Lin, P.; Ling, H.; Lowe, W.; Mathias, R.A.; Melbye, M.; Pugh, E.; Cornelis, M.C.; Weir, B.S.; Goddard, M.E. and Visscher, P.M., 2011. Genome partitioning of genetic variation for complex traits using common SNPs. Nature Genetics. Vol. 43, pp: 519-525.
Journal of Animal Environment
Volume 13, Issue 2
August 2021
Pages 49-54

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