Trend of bias in prediction of genomic estimated breeding values due to selective genotyping in genomic selection schemes in consecutive generations

Document Type : Original Research Article (Regular Paper)


1 Department of Animal Science, Faculty of Animal and Aquatic Science, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.

2 Department of Animal Science, Tarbiat Modares University, Tehran, Iran.


The aim of this study was to investigate the trend of bias in genomic estimated breeding values (GEBVs) arising from selective genotyping of the candidate population in an ongoing selection scheme. The bias was calculated as the regression of true breeding values (TBVs) on GEBVs. A simulation study was performed under two scenarios with selection intensities (SI) of 0.798 and 1.755 for three traits with heritability (h2) of 0.1, 0.25 and 0.4 in 10 consecutive generations. Regression of TBVs on GEBVs was close to one for the first generation when selective genotyping was random, and it continuously receded from one as selection shifted to choose animals with high EBVs from generations 2 to 10. Biasedness became larger with increased SI and decreased h2. Further, biasedness increased over the generations but the rate of change in biasedness decreased dramatically after the second generation and became almost steady after generation 4 which may be due to Bulmer effect. The findings showed that scaling down the GEBVs, using a scale parameter, might help removing biasedness in generation 4 onwards.


Main Subjects

  • Bijma, P., 2012. Accuracies of estimated breeding values from ordinary genetic evaluations do not reflect the correlation between true and estimated breeding values in selected populations. Journal of Animal Breeding and Genetics  129, 345-358.
  • Bulmer, M.G., 1976. The effect of selection on genetic variability. Genetic Research 28, 101-17.
  • Cesarani, A., Pocrnic, I., Macciotta, N.P.P., Fragomeni, B.O.,  Misztal, I., Lourenco D.A.L., 2019. Bias in heritability estimates from genomic restricted maximum likelihood methods under different genotyping strategies. Journal of Animal Breeding and Genetics 136, 40-50.
  • Ducrocq, V., 2011. Evidence of biases in genetic evaluations due to genomic preselection in dairy cattle. Journal of Dairy Science 94, 1011-1020.
  • Ehsani, A., Janss, L., Christensen, O., 2010.  Effects of selective genotyping on genomic prediction. Processings of the 9th World Congress on Genetics Applied to Livestock Production. Germany.
  • Falconer, D.S., 1996. Introduction to Quantitative Genetics. Prentice Hall, Harlow, England.
  • Gowane, G.R., Lee, S.H., Clark, S., Moghaddar, N., Al-Mamun, H.A., van der Werf, J.H.J.,  2019. Effect of selection and selective genotyping for creation of reference on bias and accuracy of genomic prediction. Journal of Animal Breeding and Genetics 136, 390-407.
  • Henderson, C.R., 1975.  Best Linear Unbiased Estimation and Prediction under a Selection Model. Biometrics 31, 423-447.
  • Hsu, W.L., Garrick, D.J., Fernando, R.L., 2017. The accuracy and bias of single-step genomic prediction for populations under selection. G3: Genes, Genomes, Genetics 7, 2685-2694.
  • Kuehn, L.A., Lewis, R.M., Notter, D.R., 2007. Managing the risk of comparing estimated breeding values across flocks or herds through connectedness: a review and application. Genetics Selection Evolution 39, 225-247.
  • Meuwissen, T.H.E., Hayes, B.J., Goddard, M.E., 2001. Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps. Genetics  157,  1819-1829.
  • Meuwissen, T.H., Goddard, M.E., 2004. Mapping multiple QTL using linkage disequilibrium and linkage analysis information and multitrait data. Genetics Selection Evolution 36, 261-279.
  • Patry, C., Ducrocq,V.,  2011. Evidence of biases in genetic evaluations due to genomic preselection in dairy cattle. Journal of Dairy Science 94, 1011-1020.
  • Powell, J.E., Visscher, P.M., Goddard, M.E., 2010. Reconciling the analysis of IBD and IBS in complex trait studies. Nature Reviews Genetics 11, 800-805.
  • Reverter, A.,  Golden, B.L.,  Bourdon, R.M., Brinks, J.S., 1994. Technical note: detection of bias in genetic predictions. Journal of Animal Science 72, 34-37.
  • Robinson, G.K., 1991. That BLUP is a good thing: the estimation of random effects. Statistical Science  6, 15-32.
  • Sargolzaei, M., Schenkel F.S., 2009. QMSim: a large-scale genome simulator for livestock. Bioinformatics 25, 680-681.
  • VanRaden, P.M., Van Tassell, C.P., Wiggans, G.R., Sonstegard, T.S., Schnabel, R.D., Taylor, J.F., Schenkel, F.S., 2009. Invited review: Reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16-24.
  • Van Grevenhof, E.M., Van Arendonk, J.A., Bijma, P., 2012. Response to genomic selection: The Bulmer effect and the potential of genomic selection when the number of phenotypic records is limiting. Genetics Selection Evolution  44, 26.
  •  Vitezica, Z.G., Aguilar, I., Misztal, I., Legarra, A., 2011. Bias in genomic predictions for populations under selection. Genetics Research  9,  357-366.
  • Zhao, Y., Gowda, M., Longin, F.H., Würschum, T., Ranc, N., Reif, J.C.,  2012. Impact of selective genotyping in the training population on accuracy and bias of genomic selection. Theoretical and Applied Genetics 125, 707-713.