Abstract
Criteria for design of experiments to estimate within-population genetic parameters such as heritabilities and correlations are reviewed. The design and relative efficiency of sib covariance, offspring-parent regression and combinations of these estimators are described. Problems of objectives and models for design and analysis of selection experiments are discussed. The design of field experiments, for example to estimate covariances between indicator and production traits, is reviewed in terms of convenience and efficiency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Atkins KD, Thompson R (1986) Predicted and realised responses to selection for an index of bone length and body weight in Scottish Blackface sheep. 1. Response in the index and component traits. Anim Prod 43:421–435
Blair HT, Pollak EJ (1984) Estimation of genetic trend in a selected population with and without the use of a control population. J Anim Sci 58:878–886
Bohren BB (1975) Designing artificial selection experiments for specific objectives. Genetics 80:205–220
Bulmer MG (1980) The mathematical theory of quantitative genetics. Clarendon Press, Oxford
Cameron ND, Thompson R (1986) Design of multivariate selection experiments to estimate genetic parameters. Theor Appl Genet 72:466–476
Eisen EJ (1967) Mating designs for estimating direct and maternal genetic variances and direct maternal covariances. Canad J Genet and Cytology 9:13–22
Falconer DS (1981) Introduction to quantitative genetics. 2nd edn Longman, London
Gianola D (1979) Estimation of genetic covariance from joint offspring-parent and sib-sib statistics. Genetics 93:1039–1049
Guiard V, Herrendorfer G (1977) Estimation of the genetic correlation coefficient by half-sib analysis if the characters are measured on different offsprings. Biometrical J 19:31–36
Hill WG (1970) Design of experiments to estimate heritability by regression of offspring on selected parents. Biometrics 26:566–571
Hill WG (1971) Design and efficiency of selection experiments for estimating genetic parameters. Biometrics 27:293–311
Hill WG (1972) Estimation of realised heritabilities from selection experiments. I. Divergent selection. Biometrics 28:747–765
Hill WG (1980a) Design of quantitative genetic selection experiments. In: Robertson A (ed) Selection experiments in laboratory and domestic animals. Common Agric Bur, Slough, pp 1–13
Hill WG (1980b) Experimental design in quantitative genetics and animal breeding. Unpublished notes for course in Göttingen, West Germany
Hill WG (1985) Detection and genetic assessment of physiological criteria of merit. In: Land RB, Robinson DW (eds) Genetics of reproduction in sheep. Butterworths, London, pp 319–331
Hill WG, Nicholas FW (1974) Estimation of heritability by both regression of offspring on parent and intra-class correlation of sibs in one experiment. Biometrics 30:447–468
Hill WG, Thompson R (1977) Design of experiments to estimate offspring-parent regression using selected parents. Anim Prod 24:163–168
James JW (1986) Cumulative selection differentials and realized heritabilties with overlapping generations. Anim Prod 42:411–415
Johnson DL (1977) Variance-covariance structure of group means with overlapping generations. In: Pollak E, Kempthorne O, Bailey TB Jr (eds) Proceedings of the International Conference on Quantitative Genetics. Iowa State Univ Press, Ames, pp 851–858
Land RB (1981) An alternative philosophy for animal breeding. Livest Prod Sci 8:95–99
Latter BDH, Robertson A (1960) Experimental design in the estimation of heritability by regression methods. Biometrics 16:348–353
Nicholas FW (1980) Size of population required for artificial selection. Genet Res 35:85–105
Reeve ECR (1955) The variance of the genetic correlation coefficient Biometrics 11:357–374
Robertson A (1959a) Experimental design in the evaluation of genetic parameters. Biometrics 15:219–226
Robertson A (1959b) The sampling variance of the genetic correlation coefficient. Biometrics 15:469–485
Sales J, Hill WG (1976a) Effect of sampling errors on efficiency of selection indices. 1. Use of information from relatives for single trait improvement. Anim Prod 22:1–17
Sales J, Hill WG (1976b) Effect of sampling errors on efficiency of selection indices. 2. Use of information on associated traits for improvement of a single important trait. Anim Prod 23:1–14
Sorensen DA, Kennedy BW (1983) The use of the relationship matrix to account for genetic drift variance in the analysis of genetic experiments. Theor Appl Genet 66:217–220
Tallis GM (1959) Sampling errors of genetic correlation coefficients calculated from analyses of variance and covariance. Aust J Stat 1:35–43
Taylor S CS (1976a) Multibreed designs. 1. Variation between breeds. Anim Prod 23:133– 144
Taylor S CS (1976b) Multibreed designs. 2. Genetic variation within and between breeds. Anim Prod 23:145–154
Taylor S CS, Hnizdo E (1987) Multibreed designs. 3. Inter-breed relationships. Anim Prod 44:39–53
Thompson R (1976a) Design of experiments to estimate heritability when observations are available on parents and offspring. Biometrics 32:283–304
Thompson R (1976b) The estimation of maternal genetic variances. Biometrics 32:903–917
Thompson R (1982) Methods of estimation of genetic parameters. In: Proc 2nd World Congr Genet Appl Livest Prod. Garsi, Madrid 5:95–103
Thompson R (1986) Estimation of realized heritability in a selected population using mixed model methods. Genet Sel Evol 18:475–483
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1990 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Hill, W.G. (1990). Considerations in the Design of Animal Breeding Experiments. In: Gianola, D., Hammond, K. (eds) Advances in Statistical Methods for Genetic Improvement of Livestock. Advanced Series in Agricultural Sciences, vol 18. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-74487-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-74487-7_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-74489-1
Online ISBN: 978-3-642-74487-7
eBook Packages: Springer Book Archive