Predicting the Accuracy of Breeding Values Using High Density Genome Scans

  • Lee, Deuk-Hwan (Department of Animal Life and Environment Sciences, Hankyong National University) ;
  • Vasco, Daniel A. (Animal Genomics, Division of Animal Sciences, University of Missouri)
  • Received : 2010.04.26
  • Accepted : 2010.09.19
  • Published : 2011.02.01


In this paper, simulation was used to determine accuracies of genomic breeding values for polygenic traits associated with many thousands of markers obtained from high density genome scans. The statistical approach was based upon stochastically simulating a pedigree with a specified base population and a specified set of population parameters including the effective and noneffective marker distances and generation time. For this population, marker and quantitative trait locus (QTL) genotypes were generated using either a single linkage group or multiple linkage group model. Single nucleotide polymorphism (SNP) was simulated for an entire bovine genome (except for the sex chromosome, n = 29) including linkage and recombination. Individuals drawn from the simulated population with specified marker and QTL genotypes were randomly mated to establish appropriate levels of linkage disequilibrium for ten generations. Phenotype and genomic SNP data sets were obtained from individuals starting after two generations. Genetic prediction was accomplished by statistically modeling the genomic relationship matrix and standard BLUP methods. The effect of the number of linkage groups was also investigated to determine its influence on the accuracy of breeding values for genomic selection. When using high density scan data (0.08 cM marker distance), accuracies of breeding values on juveniles were obtained of 0.60 and 0.82, for a low heritable trait (0.10) and high heritable trait (0.50), respectively, in the single linkage group model. Estimates of 0.38 and 0.60 were obtained for the same cases in the multiple linkage group models. Unexpectedly, use of BLUP regression methods across many chromosomes was found to give rise to reduced accuracy in breeding value determination. The reasons for this remain a target for further research, but the role of Mendelian sampling may play a fundamental role in producing this effect.


Simulationbased Inference;Prediction Accuracy;Breeding Value;High Density Genome Scan;Single Nucleotide Polymorphism


  1. Aguitar, I. and I. Misztal. 2008. Technical note: Recursive algorithm for inbreeding coefficients assuming nonzero inbreeding of unknown parents. J. Dairy Sci. 91:1669-1672.
  2. Bailey, N. T. J. 1961. Introduction to the Mathematical Theory of Genetic Linkage. Oxford Universisty press.
  3. Barendse, W., D. Vaiman, S. J. Kemp, Y. Sugimoto, S. M. Armitage, J. L. Williams, H. S. Sun, A. Eggen, M. Agaba, S. A. Aleyasin, M. Band, M. D. Bishop, J. Buitkamp, K. Byrne, F. Collins, L. Cooper, W. Coppettiers, B. Denys, R. D. Drinkwater, K. Easterday, C. Elduque, S. Ennis, G. Erhardt, L. Ferretti, N. Flavin, Q. Gao, M. Georges, R. Gurung, B. Harlizius, G. Hawkins, J. Hetzel, T. Hirano, D. Hulme, C. Jorgensen, M. Kessler, B. W. Kirkpatrick, B. Konfortov, S. Kostia, C. Kuhn, J. A. Lenstra, H. Leveziel, H. A. Lewin, B. Leyhe, L. Lil, I. Martin Burriel, McGraw, J. R. Miller, D. E. Moody, S. S. Moore, S. Nakane, I. J. Nijman, I. Olsaker, D. Pomp, A. Rando, M. Ron, A. Shalom, A. J. Teale, U. Thieven, B. G. D. Urquhart, D.-I. Vage, A. Van de Weghe, S. Varvio, R. Velmala, J. Vilkki, R. Weikard, C. Woodside, J. E. Womack, M. Zanotti and Zaragoza. 1997. A medium-density genetic linkage map of the bovine genome. Mamm. Genome 8:21-28.
  4. Blouin, M. S. 2003. DNA-based methods for pedigree reconstruction and kinship analysis in natural populations, Trends Ecol. Evol. 18:503-511.
  5. Calus, M. P. L., T. H. E. Meuwissen, A. P. W. de Roos and R. F. Veerkamp. 2008. Accuracy of genomic selection using different methods to define haplotypes. Genetics 178:553-561.
  6. De Roos, A. P. W., B. J. Hayes, R. J. Spelman and M. E. Goddard. 2008. Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle. Genetics 179:1503-1512
  7. Falconer, D. S. and T. F. C. Mackay. 1996. Introduction to quantitative genetics. Longman Group, Essex, UK.
  8. Gasbarra, D., M. J. Sillanpaa and E. Arjas. 2005 Backward simulation of ancestors of sampled individuals. Theor. Popul. Biol. 67:75-83.
  9. Guttorp, P. 1995. Stochastic modeling of scientific data. Chapman and Hall, CRC press.
  10. Hayes, B. J. and M. E. Goddard. 2001. The distribution of the effects of genes affecting quantitative traits in livestock. Genet. Sel. Evol. 33:209-229.
  11. Hayes, B. J. and M. E. Goddard. 2008. Technical note: Prediction of breeding values using marker-derived relationship matrices. J. Anim. Sci. 86:2089-2092.
  12. Hill, W. G. and B. S. Weir. 2007. Prediction of multi-locus inbreeding coeffcients and relation to linkage disequilibrium in random mating populations. Theor. Popul. Biol. 72:179-185.
  13. Libiger, O. and N. J. Schork. 2007. A simulation-based analysis of chromosome segment sharing among a group of arbitrarily related individuals. Eur. J. Hum. Genet. 15:1260-1268.
  14. Liu, B-H. 1998. Statisitical genomics. CRC press.
  15. Lynch, M. and B. Walch. 1998. Genetics and analysis of quantitative traits. Sinauer Associates Inc, Sunderland, MA.
  16. Macleod, I. M., B. J. Hayes and M. E. Goddard. 2006 Efficiency of dense bovine single-nucleotide polymorphisms to detect and position quantitative trait loci. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Brazil, August 13-18, 2006. CD-ROM communication no. 20-04.
  17. Matukumalli, L. K., C. T. Lawley, R. D. Schnabel, J. F. Taylor, M. F. Allan, M. P. Heaton, J. O'Connell, T. S. Sonstegard, T. P. L. Smith, S. S. Moore and C. P. Van Tassell. 2009. Development and characterization of a high density SNP genotyping assay for cattle. PLoS One. (submitted).
  18. Meuwissen, T. H. E., B. Hayes and M. E. Goddard. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819-1829.
  19. Peng, B., C. I. Amos and M. Kimmel. 2007. Forward-time simulations of human populations with complex diseases. PLoS Genetics 3:e47.
  20. Peng, B. and M. Kimmel. 2005. SimuPOP: a forward-time population genetics simulation environment. Bioinformatics 21:3686-3687.
  21. Quass, R. L. 1976. Computing the diagonal elements and inverse of a large numerator relationship matrix. Biometrics 32:949-953.
  22. Schaeffer, L. R. 2006. Strategy for applying genome-wide selection in dairy cattle. J. Anim. Breed. Genet. 123:218-223.
  23. Solberg, T. R., A. K. Sonesson, J. A. Woolliams and T. H. E. Meuwissen. 2008. Genomic selection using different marker types and densities. J. Anim. Sci. 86:2447-2454.
  24. Strand, A. E. 2002. METASIM 1.0: an individual-based environment for simulating population genetics of complex population dynamics. Mol. Ecol. Notes 2:373-376.
  25. Tenesa, A., P. Navarro, B. J. Hayes, D. L. Duffy, G. M. Clarke, M. E. Goddard and P. M. Visscher. 2007. Recent human effective population size estimated from linkage disequilibrium. Genome Res. 17:520-526.
  26. TeMeerman, G. J. and M. A. Van der Meulen. 1997. Genomic sharing surrounding alleles identical by descent: effects of genetic drift and population growth. Genet. Epidemiol. 14:1125-1130.<1125::AID-GEPI94>3.0.CO;2-I
  27. VanRaden, P. M. 2007. Genomic measures of relationship and inbreeding. INTERBULL bulletin. 37: 33-36
  28. VanRaden, P. M. 2008. Efficient methods to compute genomic predictions. J. Dairy Sci. 91:4414-4423.
  29. VanRaden, P. M., C. P. Van Tassell, G. R. Wiggans, T. S. Sonstegard, R. D. Schnabel, J. F. Taylor and F. S. Schenkel. 2009. Invited review: Reliability of genomic prediction for north american Holstein bulls. J. Dairy Sci. 92:16-24.
  30. Van Tassell, C. P., T. P. L. Smith, L. K. Matukumallik, J. F. Taylor, R. D. Schnabel, C. T. Lawley, C. D. Haudenschild, S. S. Moore, W. C. Warren and T. S. Sonstegard. 2008. SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat. Methods 5:247-252.
  31. Villanueva, B., R. Pong-Wong, J. Fernandez and M. A. Toro. 2005. Benefits from marker-assisted selection under an additive polygenic genetic model. J. Anim. Sci. 83:1747-1752.
  32. Visscher, P. M., S. E. Medland, M. A. Ferreira, K. I. Morley, G. Zhu, B. K. Cornes, G. W. Montgomery and N. G. Martin. 2006. Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genet. 2:e4.
  33. Wu, R., C-X Ma and G. Casella. 2007. Statistical genetics of quantitative traits. Springer.