Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-20T02:24:52.523Z Has data issue: false hasContentIssue false

Size of population required for artificial selection

Published online by Cambridge University Press:  14 April 2009

F. W. Nicholas
Affiliation:
Department of Animal Husbandry, University of Sydney, N.S.W. 2006, Australia
Rights & Permissions [Opens in a new window]

Summary

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

It is widely acknowledged that genetic drift is an important source of variation in response to artificial directional selection. How large should a selection line be in order to reduce the effect of genetic drift to an acceptably low level?

This paper investigates two criteria that can be used to answer this question in relation to short-term response to selection. The first criterion is coefficient of variation of response, and the second criterion is chance of success, where a successful selection programme is one in which the observed response is greater than a certain proportion, β, of expected response.

For a simple mass selection programme with intensity i and heritability h2, the size of population required in order for the coefficient of variation of response to be γ after t generations, is approximately 2/(γih)2t, and the size required for the chance of success to be α after t generations is approximately 2{zα/(β−l)ih}2/t, where zα is the standard normal deviate corresponding to the probability α.

As an example, suppose it is required that after t generations the coefficient of variation of response be 10% or that there be a 90% chance of achieving at least 9/10 of expected response. Since ih ≤ 2 in most selection programmes, the size of population required is at least 50/t or 82/t respectively. If ih ≤ 1, the corresponding sizes are 200/t and 328/t.

Results are extended to enable the calculation of size of population required for any type of artificial directional selection programme, including those in which generations overlap.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1980

References

REFERENCES

Avery, P. J. & Hill, W. G. (1977). Variability in genetic parameters among small populations. Genetical Research 29, 193213.CrossRefGoogle ScholarPubMed
Bohren, B. B. (1975). Designing artificial selection experiments for specific objectives. Genetics 80, 205220.CrossRefGoogle ScholarPubMed
Bulmer, M. G. (1976). The effect of selection on genetic variability: a simulation study. Genetical Research 28, 101118.CrossRefGoogle ScholarPubMed
Comstock, R. E. (1974). Consequences of genetic linkage. Proceedings of the 1st World Congress on Genetics Applied to Livestock Production 1, 353364.Google Scholar
Comstock, R. E. (1977). Quantitative genetics and the design of breeding programs. In Proceedings of the International Conference on Quantitative Genetics (ed. Pollak, E., Kempthorne, O. and Bailey, T. B.), pp. 705718. Ames: Iowa State University Press.Google Scholar
Hill, W. G. (1971). Design and efficiency of selection experiments for estimating genetic parameters. Biometrics 27, 293311.CrossRefGoogle ScholarPubMed
Hill, W. G. (1972 a). Estimation of realised heritabilities from selection experiments. I. Divergent selection. Biometrics 28, 747765.CrossRefGoogle ScholarPubMed
Hill, W. G. (1972 b). Estimation of realised heritabilities from selection experiments. II. Selection in one direction. Biometrics 28, 767780.CrossRefGoogle ScholarPubMed
Hill, W. G. (1972 c). Estimation of genetic change. I. General theory and design of control populations. Animal Breeding Abstracts 40, 115.Google Scholar
Hill, W. G. (1977 a). Variation in response to selection. In Proceedings of the International Conference on Quantitative Genetics (ed. Pollak, E., Kempthorne, O. and Bailey, T. B.), pp. 343365. Ames: Iowa State University Press.Google Scholar
Hill, W. G. (1977 b). Selection with overlapping generations. In Proceedings of the International Conference on Quantitative Genetics (ed. Pollak, E., Kempthorne, O. and Bailey, T. B.), pp. 367378. Ames: Iowa State University Press.Google Scholar
Hill, W. G. (1978). Design of selection experiments for comparing alternative testing regimes. Heredity 41, 371376.CrossRefGoogle Scholar
Hopkins, I. R. & James, J. W. (1978). Theory of nucleus breeding schemes with overlapping generations. Theoretical and Applied Genetics 53, 1724.CrossRefGoogle ScholarPubMed
James, J. W. (1977 a). A note on selection differential and generation interval when generations overlap. Animal Production 24, 109112.Google Scholar
James, J. W. (1977 b). Open nucleus breeding systems. Animal Production 24, 287305.Google Scholar
James, J. W. (1978). Effective population size in open nucleus breeding schemes. Acta Agriculturae Scandanavia 28, 387392.CrossRefGoogle Scholar
Johnson, D. L. (1977). Variance-covariance structure of group means with overlapping generations. In Proceedings of the International Conference on Quantitative Genetics (ed. Pollak, E., Kempthorne, O. and Bailey, T. B.), pp. 851858. Ames: Iowa State University Press.Google Scholar
Kimura, M. (1957). Some problems of stochastic processes in genetics. Annals of Mathematical Statistics 28, 883901.CrossRefGoogle Scholar
Ollivier, L. (1974). Optimum replacement rates in animal breeding. Animal Production 19, 257271.Google Scholar
Robertson, A. (1954). Artificial insemination and livestock improvement. Advances in Genetics 6, 451472.CrossRefGoogle ScholarPubMed
Robertson, A. (1960). A theory of limits in artificial selection. Proceedings of the Royal Society of London B 153, 234249.Google Scholar
Robertson, A. (1977). Artificial selection with a large number of linked loci. In Proceedings of the International Conference on Quantitative Genetics (ed. Pollak, E., Kempthorne, O. and Bailey, T. B.), pp. 307322. Ames: Iowa State University Press.Google Scholar
Soller, M. & Genizi, A. (1967). Optimum experimental designs for realised heritability estimates. Biometrics 23, 361365.CrossRefGoogle Scholar