The importance of accounting for maternal genetic effects in Australian fine-wool Merino breeding
M. Asadi Fozi A C D E , J. H. J. Van der Werf A C and A. A. Swan B CA The Institute for Genetics and Bioinformatics, School of Rural Science and Agriculture, University of New England, Armidale, NSW 2351, Australia.
B CSIRO Livestock Industries, Armidale, NSW 2350, Australia.
C Australian Sheep Industry CRC, Chiswick New England Highway, Locked Bag 1, Armidale, NSW 2350, Australia.
D Department of Animal Science, Faculty of Agriculture, University of Kerman, Kerman, Iran.
E Corresponding author. Email: masadifo@une.edu.au
Australian Journal of Agricultural Research 56(8) 789-796 https://doi.org/10.1071/AR05006
Submitted: 24 January 2005 Accepted: 8 June 2005 Published: 25 August 2005
Abstract
(Co) variances for greasy fleece weight (GFW), clean fleece weight (CFW), mean fibre diameter (MFD), staple strength (SS), coefficient of variation of fibre diameter (CVFD), birthweight (BW), weaning weight (WW), and yearling weight (YW) were estimated for 5108 Australian Merino sheep from the CSIRO Fine Wool Project, born between 1990 and 1994. Covariances between these traits and number of lambs weaned per ewe joined (NLW) were also estimated. Significant maternal genetic effects were found for GFW, CFW, BW, WW, and YW. Estimates of heritability were biased upwardly when maternal effects were ignored. The maternal heritability estimates for GFW, CFW, BW, WW, and YW were 0.17, 0.15, 0.38, 0.28, and 0.13, respectively. Maternal effects were not important for MFD, CVFD, SS, and NLW. Direct-maternal genetic correlations within each fleece weight and bodyweight trait were estimated to be moderately negative (–0.26 to –0.48). The effect of ignoring maternal genetic effect was explored using selection index theory. Accounting for the maternal effects in both the selection criteria and breeding objective increased the overall response by 14.3%, 4.8%, 2.6%, 1.4%, and 0.0% in 3, 6, 12, 20 and 30% micron premium scenarios, respectively, compared with when the maternal effects were only included in breeding objective. Complete ignorance of the maternal effects led to overestimation in overall response of 2.8–35.7% for different micron premium scenarios in contrast to when the maternal effects were ignored in the selection index weight, but were included in the breeding objective. The results indicate that the maternal genetic effects of fleece weight and bodyweight should be considered in Merino breeding programs.
Additional keywords: sheep, genetic parameters, wool traits, selection index.
Acknowledgment
We are grateful to CSIRO for providing us data from the Fine Wool Project.
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The breeding objectives were defined as:
where H 1 is the breeding objective with maternal effects, v Di is direct relative economic value of the ith objective trait, g Di is the relative economic value of the ith maternal objective trait, and g mi is the breeding value of the ith maternal objective trait, and H 2 is the breeding objective without maternal effects.
The optimum response was calculated using the following equation:
where b is a vector with selection index weights calculated as P −1 Ga when maternal genetic effects as well as direct additive genetic effects were considered, G is a matrix of genetic covariances between the selection criteria and breeding objectives containing direct and maternal components, a is a vector of direct and maternal relative economic values, and P is phenotypic co(variance) matrix.
The elements of G = (Gd Gm) are derived as follows:
where X i is the phenotypic observation for trait i, A i is the direct additive genetic effect, M i is the maternal genetic effect, e i is the residual for trait i, and subscript j refers to these effects for trait j (measured on same animal).
where G nomat is genetic covariance matrix when maternal genetic effects were ignored. The variance components that were used in the Gnomat were obtained from a model without fitting maternal genetic effects; anomat is a vector of direct relative economic values (subset of a).
Expected response was calculated using the equation:
