Theoretical and Applied Genetics

, Volume 87, Issue 4, pp 446–454 | Cite as

Long-term effects of selection based on the animal model BLUP in a finite population

  • E. Verrier
  • J. J. Colleau
  • J. L. Foulley
Article

Abstract

Monte Carlo simulation was used to assess the long-term effects of truncation selection within small populations using indices (I=ωf+m) combining mid-parent [f=(ai+ad)/2] and Mendelian-sampling (m=a-f) evaluations provided by an animal model BLUP (a=f+m). Phenotypic values of panmictic populations were generated for 30 discrete generations. Assuming a purely additive polygenic model, heritability (h2) values were 0.10, 0.25 or 0.50. Two population sizes were considered: five males and 25 females selected out of 50 candidates of each sex (small populations, S) and 50 males and 250 females selected out of 500 candidates in each sex (large populations, L). Selection was carried out on the index defined above with ω = 1 (animal model BLUP), ω=1/2, or ω=0 (selection on within-family deviations). Mass selection was also considered. Selection based on the animal model BLUP (ω=1) maximized the cumulative genetic gain in L populations. In S populations, selection using ω = 1/2 and mass selection were more efficient than selection under an animal model (+ 3 to + 7% and + 1 to + 4% respectively, depending on h2). Selection on within-family deviations always led to the lowest gains. In most cases, the variance of response to selection between replicates did not depend on the selection method. The within-replicate genetic variance and the average coefficient of inbreeding (F) were highly affected by selection with ω=1 or 1/2, especially in populations of size S. As expected, selection based on within-family deviations was less detrimental in that respect. The number of copies of founder neutral genes at a separate locus, and the probability vector of origin of the genes in reference to the founder animals, were also observed in addition to F values. The conclusion was that selection procedures placing less emphasis on family information might be interesting alternatives to selection based on animal model BLUP, especially for small populations with long-term selection objectives.

Key words

Animal model BLUP Genetic response Genetic variability Inbreeding 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Avery PJ, Hill WG (1977) Variation in genetic parameters in small populations. Genet Res 29:198–213Google Scholar
  2. Belonsky GM, Kenedy BW (1988) Selection on individual phenotype and best linear unbiased predictor of breeding value in a closed swine herd. J Anim Sci 66:1124–1131Google Scholar
  3. Bulmer MG (1971) The effect of selection on genetic variability. Am Nat 105:201–211CrossRefGoogle Scholar
  4. Bulmer MG (1976) The effect of selection on genetic variability: a simulation study. Genet Res 28:101–117Google Scholar
  5. Burrows PM (1984) Inbreeding under selection from unrelated families. Biometrics 40:357–366Google Scholar
  6. Chevalet C, Rochambeau H de (1985) Predicting the genetic drift in small populations. Livest Prod Sci 13:207–218Google Scholar
  7. Crow JF, Kimura M (1970) An introduction to population genetics theory. Harper and Row, New YorkGoogle Scholar
  8. Dempfle L (1975) A note on increasing the limit of selection through selection within families. Genet Res 24:127–135Google Scholar
  9. Dempfle L (1990) Statistical aspects of design of animal breeding programmes: a comparison among various selection strategies. In: Gianola D, Hammond K (eds) Advances in statistical methods for genetic improvement of livestock, Springer-Verlag, Berlin, 109–128Google Scholar
  10. Fernando R, Gianola D (1986) Optimal properties of the conditional mean as a selection criterion. Theor Appl Genet 72:822–825Google Scholar
  11. Foulley JL, Chevalet C (1981) Méthode de prise en compte de la consanguinité dans un modèle simple de simulation des performances. Ann Génét Sél Anim 13:189–196Google Scholar
  12. Goffinet B, Elsen JM (1984) Critère optimal de sélection: quelques résultats généraux. Génét Sél Evol 13:307–318Google Scholar
  13. Henderson CR (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31:423–449Google Scholar
  14. Henderson CR (1976) A simple method for computing the inverse of a numerator relationship matrix used for prediction of breeding values. Biometrics 32, 69–83Google Scholar
  15. Hill WG (1977) Variation in response to selection. In: Pollak E, Kempthorne O, Bailey TB (eds) Proc Int Conf Quanti Genet. Iowa State University Press, pp 343–366Google Scholar
  16. Hill WG (1985) Fixation probabilities of mutant genes with artificial selection. Génét Sél Evol 17:351–358Google Scholar
  17. Kennedy BW, Sorensen DA (1988) Properties of mixed model methods for predicting genetic merit. Proc 2nd Int Conf Quant Genet Raleigh/North Carolina 1987, pp 91–103Google Scholar
  18. Kennedy BW, Schaeffer LR, Sorensen DA (1988) Genetic properties of animal models. Proc Anim Model Workshop, Edmonton, Alberta, 1988, J Dairy Sci 71 (suppl. 2):17–26Google Scholar
  19. Langlois B (1990) Réflexions sur l'incidence de la sélection et des croisements raisonnes sur les paramètres du modèle génétique aditif. Genet Sel Evol 22:119–132Google Scholar
  20. Lush JL (1945) Animal breeding plans, 3rd edn. Iowa State University Press, Ames/Iowa, pp 141–143Google Scholar
  21. Lush JL (1946) Chance as a cause of gene frequency within pure breeds of livestock. Am Nat 80:318–342Google Scholar
  22. Meuwissen THE (1991) Expectation and variance of genetic gain in open and closed nucleus and progeny testing schemes. Anim Prod 53:133–141Google Scholar
  23. Nicholas FW (1980) Size of population required for artificial selection. Genet Res 35:85–105Google Scholar
  24. Quinton M, Smith C, Goddard ME (1992) Comparison of selection methods at the same level of inbreeding. J Anim Sci 70:1060–1067Google Scholar
  25. Robertson A (1961) Inbreeding in selection programmes. Genet Res 2:189–194Google Scholar
  26. Toro MA, Perez-Enciso M (1990) Optimizing selection under restricted inbreeding. Genet Sel Evol 22:93–107Google Scholar
  27. Van Raden PM (1990) Potential improvements in animal model evaluation systems. World congress on Genetics applied to Livestock Production, Edinburgh, pp 357–363Google Scholar
  28. Verrier E, Colleau JJ, Foulley JL (1989a) Peut-on prédire l'évolution de la variance génétique en vue d'optimiser les programmes de sélection sur le moyen ou le long terme? In: Molenat M, Verrier E (eds) La gestion des ressources génétiques des espèces animales domestiques. Bureau des ressources génétiques, Paris, pp 159–170Google Scholar
  29. Verrier E, Colleau JJ, Foulley JL (1989b) Effect of mass selection on the within-family genetic variance in finite populations. Theor Appl Genet 77:142–148Google Scholar
  30. Verrier E, Colleau JJ, Foulley JL (1990) Predicting cumulated response to directional selection in finite panmictic populations. Theor Appl Genet 79:833–840Google Scholar
  31. Verrier E, Colleau JJ, Foulley JL (1991) Methods for predicting response in small populations under additive genetic models: a review. Livest Prod Sci 29:93–114Google Scholar
  32. Wray NR, Thompson R (1990) Prediction of inbreeding in selected populations. Genet Res 55:41–54Google Scholar
  33. Wright S (1931) Evolution in Mendelian populations. Genetics 16:97–159Google Scholar

Copyright information

© Springer-Verlag 1993

Authors and Affiliations

  • E. Verrier
    • 1
  • J. J. Colleau
    • 2
  • J. L. Foulley
    • 2
  1. 1.Département des Sciences animalesInstitut National Agronomique Paris-GrignonParis CedexFrance
  2. 2.INRA, Station de Génétique Quantitative et AppliquéeJouy-en-Josas CedexFrance

Personalised recommendations