Animal Production Science Animal Production Science Society
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RESEARCH ARTICLE

Ability of sire breeding values to predict progeny bodyweight, fat and muscle using various transformations across environments in terminal sire sheep breeds

A. E. Huisman A C , D. J. Brown A and N. M. Fogarty B D
+ Author Affiliations
- Author Affiliations

A Animal Genetics and Breeding Unit1, University of New England, Armidale, NSW 2351, Australia.

B NSW Department of Primary Industries, Orange Agricultural Institute, Orange, NSW 2800, Australia.

C Present address: Hendrix Genetics Research, Technology and Services B.V., Research and Technology Centre, Boxmeer, The Netherlands.

D Corresponding author. Email: neal.fogarty@dpi.nsw.gov.au

Animal Production Science 56(1) 95-101 https://doi.org/10.1071/AN14666
Submitted: 30 June 2014  Accepted: 22 September 2014   Published: 2 December 2014

Abstract

Data used for the genetic evaluation of the terminal sire sheep breeds in Australia originate from a large range of genotypes and environments. This means there are large differences in the level of production and therefore contemporary group means and variances within the data. This study examined four transformations to account for the heterogeneity of variance in the observed data and their effect on the ability of estimated breeding values of sires (sire EBV) to predict progeny performance. This predictive ability was described by regressing offspring performance on sire EBV. The expected value of this regression is 0.5, which indicates that half of the sire EBV differences can be expected in the progeny. The transformations of observed data were investigated in low, medium and high production environments for weight and ultrasound scan traits (fat and muscle) in terminal sire sheep breeds. There were records from over 300 000 sheep in the LAMBPLAN terminal sire dataset, predominately from Poll Dorset, Texel, Suffolk and White Suffolk breeds. The transformation methods applied to the observed data were: traits expressed as a percentage of the contemporary group mean; traits re-scaled to a common contemporary group mean in units of measurement; a logarithmic transformation; and a square root transformation. The heritabilities and other variance ratios estimated from the transformed traits were not significantly different from those using the observed data. Phenotypes transformed to a proportion of the contemporary group mean, either as a percentage or in units of measurement, resulted in the most consistent EBV across all production environments for weight and fat traits, with little effect of transformations for muscle traits. The transformation of data to the contemporary mean in units of measurement for weight and fat traits has been implemented in the Sheep Genetics evaluation system. The consistency of the progeny–sire EBV regressions around 0.5 in the data from these purebred industry flocks is heartening for terminal sire evaluation.

Additional keywords: bodyweight, breeding value estimation, heterogeneity, transformation, ultrasound scan.


References

Banks RG (1990) LAMBPLAN: an integrated approach to genetic improvement for the Australian lamb industry. Proceedings of the Australian Association of Animal Breeding and Genetics 8, 237–240.

Brown DJ, Tier B (2003) Alternate methods of estimating breeding values for faecal egg count data from Merino studs across Australia. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 15, 115–118.

Brown DJ, Tier B, Reverter A, Banks R, Graser HU (2000) OVIS: a multiple trait breeding value estimation program for genetic evaluation of sheep. International Journal of Sheep and Wool Science 48, 285–297.

Brown DJ, Atkins KD, Huisman AE (2005) Expression of body weight, fleece weight and fibre diameter in across flock genetic evaluation. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 16, 84–87.

Brown DJ, Huisman AE, Swan AA, Graser HU, Woolaston RR, Ball AJ, Atkins KD, Banks RG (2007) Genetic evaluation for the Australian sheep industry. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 17, 187–194.

Dodenhoff J, Swalve HH (1998) Heterogeneity of variances across regions of northern Germany and adjustment in genetic evaluation. Livestock Production Science 53, 225–236.
Heterogeneity of variances across regions of northern Germany and adjustment in genetic evaluation.CrossRef |

Eady S (1995) Implications of non-normal distribution of faecal egg count for measuring worm resistance in Merino sire evaluation schemes. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 11, 79–83.

Fogarty NM (1995) Genetic parameters for live weight, fat and muscle measurements, wool production and reproduction in sheep: a review. Animal Breeding Abstracts 63, 101–143.

Gilmour AR, Gogel BJ, Cullis BR, Welham SJ, Thompson R (2002) ‘ASReml user guide. Release 1.0.’ (VSN International Ltd: Hemel Hempstead, UK)

Hegarty RS, Neutze SA, Oddy VH (1999) Effects of protein and energy supply on the growth and carcass composition of lambs from differing nutritional histories. The Journal of Agricultural Science 132, 361–375.
Effects of protein and energy supply on the growth and carcass composition of lambs from differing nutritional histories.CrossRef |

Hill WG (1984) On selection among groups with heterogeneous variance. Animal Production 39, 473–477.
On selection among groups with heterogeneous variance.CrossRef |

Meuwissen THE, de Jong G, Engel B (1996) Joint estimation of breeding values and heterogeneous variances of large data files. Journal of Dairy Science 79, 310–316.
Joint estimation of breeding values and heterogeneous variances of large data files.CrossRef | 1:CAS:528:DyaK28Xit1Klur8%3D&md5=68cdd7118c88d1a3dc8416e24dc43aedCAS |

Nakaoka H, Narita A, Ibi T, Sasae Y, Miyake T, Yamada T, Sasaki Y (2007) Effectiveness of adjusting for heterogeneity of variance in genetic evaluation of Japanese Black cattle. Journal of Animal Science 85, 2429–2436.
Effectiveness of adjusting for heterogeneity of variance in genetic evaluation of Japanese Black cattle.CrossRef | 1:CAS:528:DC%2BD2sXhtFSjtrrE&md5=c36731e155d5221ea0033bde9dfdcb54CAS | 17565062PubMed |

Nikolaou M, Kominakis AP, Rogdakis E, Zampitis S (2004) Effect of mean and variance heterogeneity on genetic evaluations of Lesbos dairy sheep. Livestock Production Science 88, 107–115.
Effect of mean and variance heterogeneity on genetic evaluations of Lesbos dairy sheep.CrossRef |

Reverter A, Tier B, Johnson DJ, Graser HU (1997) Assessing the efficiency of multiplicative mixed model equations to account for heterogeneous variance across herds in carcass scan traits from beef cattle. Journal of Animal Science 75, 1477–1485.

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.
A review of genetic parameter estimates for wool, growth, meat and reproduction traits in sheep.CrossRef |

Santana ML, Bignardi AB, Eler JP, Cardoso FF, Ferraz JBS (2013) Genotype by environment interaction and model comparison for growth traits of Santa Ines sheep. Journal of Animal Breeding and Genetics 130, 394–403.

Visscher PM, Thompson R, Hill WG (1991) Estimation of genetic and environmental variances for fat yield in individual herds and an investigation into heterogeneity of variance between herds. Livestock Production Science 28, 273–290.
Estimation of genetic and environmental variances for fat yield in individual herds and an investigation into heterogeneity of variance between herds.CrossRef |

Zwald NR, Weigel KA, Lawlor TJ (2005) Genetic parameters for conformation traits in herds that differ in mean final score and completeness of pedigree and performance data. Journal of Dairy Science 88, 386–391.
Genetic parameters for conformation traits in herds that differ in mean final score and completeness of pedigree and performance data.CrossRef | 1:CAS:528:DC%2BD2MXmslWn&md5=2d461e5b8eacb66b7f699c129fd686e5CAS | 15591403PubMed |



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