Sensitivity of response of multi-trait index selection to changes in genetic correlations between production traits in sheep
E. Safari A , N. M. Fogarty A B and A. R. Gilmour AA Australian Sheep Industry Cooperative Research Centre, NSW Department of Primary Industries, Orange Agricultural Institute, Forest Road, Orange, NSW 2800, Australia.
B Corresponding author. Email: neal.fogarty@dpi.nsw.gov.au
Australian Journal of Experimental Agriculture 46(3) 283-290 https://doi.org/10.1071/EA04232
Submitted: 4 November 2004 Accepted: 27 June 2005 Published: 28 March 2006
Abstract
Complex sheep breeding objectives and the inclusion of more traits in genetic evaluation requires more accurate genetic parameters. There are few estimates of genetic correlations, especially between the major sheep production trait groups and they are generally variable with large standard errors. This study examines the sensitivity of response to multi-trait index selection for a range of genetic correlations between major traits (growth, wool, carcass, reproduction and worm resistance) under 3 breeding objectives (fine wool, dual-purpose wool/meat and meat) relevant to Australian sheep enterprises. Factor analysis identified the major factors explaining the underlying variability in the matrix of base genetic correlations for each breeding objective. Monte Carlo simulation studied the effect of changes in the genetic correlations under 3 index scenarios with the genetic and environmental variance–covariance matrices sampled independently from a Wishart distribution. The sensitivity of responses to selection over the range of correlations varied with the breeding objective. Selection responses were sensitive under all objectives for the range of correlations for reproduction with wool traits and in at least some objectives for reproduction with growth, worm resistance and carcass muscling. Selection responses in the fine wool objective were highly sensitive to the correlation between growth and fibre diameter. The results provide a logical basis for setting priorities for studies to obtain more accurate estimates of genetic correlations among sheep production traits.
Additional keywords: breeding objectives, dual-purpose, genetic correlations, meat, wool.
Acknowledgments
The authors wish to acknowledge the useful discussions with Dr K. D. Atkins and A/Prof. J. van der Werf during the course of this study.
Banks R
(2002) Enhancing the value of meat from wool sheep: Is there a need for specialisation? Wool Technology and Sheep Breeding 50, 584–595.
(verified 21 February 2006).
Mortimer SI, Atkins KD
(1993) Genetic evaluation of production traits between and within flocks of Merino sheep 2. Component traits of the hogget fleece. Australian Journal of Agricultural Research 44, 1523–1539.
| Crossref | GoogleScholarGoogle Scholar |
Safari E,
Fogarty NM, Gilmour AR
(2005) A review of genetic parameter estimates for wool, growth, meat and reproduction traits in sheep. Livestock Production Science 92, 271–289.
| Crossref | GoogleScholarGoogle Scholar |
Sales J, Hill WG
(1976a) Effects of sampling errors on efficiency of selection indexes. 1. Use of information from relatives for single trait improvement. Animal Production 22, 1–7.
Sales J, Hill WG
(1976b) Effects of sampling errors on efficiency of selection indexes. 2. Use of information on associated traits for improvement of a single important trait. Animal Production 23, 1–14.
Smith C
(1983) Effects on changes in economic weights on the efficiency of index selection. Journal of Animal Science 56, 1057–1064.
Smith WB, Hocking RR
(1972) Algorithm AS53, Wishart variate generator. Applied Statistics 21, 341–345.
Williams JS
(1962a) The evaluation of a selection index. Biometrics 18, 375–393.
Williams JS
(1962b) Some statistical properties of a genetic selection index. Biometrika 49, 325–337.
