Increased genetic gains in multi-trait sheep indices using female reproductive technologies combined with optimal contribution selection and genomic breeding values
T. Granleese A B D , S. A. Clark A B , A. A. Swan A C and J. H. J. van der Werf A BA Sheep Cooperative Research Centre, Armidale, NSW 2351, Australia.
B School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia.
C Animal Genetics and Breeding Unit, Armidale, NSW 2351, Australia.
D Corresponding author. Email: tgranle2@une.edu.au
Animal Production Science 57(10) 1984-1992 https://doi.org/10.1071/AN15440
Submitted: 18 January 2016 Accepted: 17 July 2016 Published: 28 November 2016
Abstract
Female reproductive technologies such as multiple ovulation and embryo transfer (MOET) and juvenile in vitro fertilisation and embryo transfer (JIVET) can produce multiple offspring per mating in sheep and cattle. In breeding programs this allows for higher female selection intensity and, in the case of JIVET, a reduction in generation interval, resulting in higher rates of genetic gain. Low selection accuracy of young females entering JIVET has often dissuaded producers from using this technology. However, genomic selection (GS) could increase selection accuracy of candidates at a younger age to help increase rates of genetic gain. This increase might vary for different traits in multiple trait breeding programs depending on genetic parameters and the practicality of recording, particularly for hard to measure traits. This study used both stochastic (animals) and deterministic (GS) simulation to evaluate the effect of reproductive technologies on the genetic gain for various traits in sheep breeding programs, both with and without GS. Optimal contribution selection was used to manage inbreeding and to optimally assign reproductive technologies to individual selection candidates. Two Australian sheep industry indexes were used – a terminal sire index that focussed on growth and carcass traits (the ‘Lamb 2020’ index) and a Merino index that focuses on wool traits, bodyweight, and reproduction (MP+). We observed that breeding programs using artificial insemination or natural mating (AI/N) + MOET, compared with AI/N alone, yielded an extra 39% and 27% genetic gain for terminal and Merino indexes without GS, respectively. However, the addition of JIVET to AI/N + MOET without GS only yielded an extra 1% genetic gain for terminal index and no extra gain in the Merino index. When GS was used in breeding programs, we observed AI/N + MOET + JIVET outperformed AI/N + MOET by 21% and 33% for terminal and Merino indexes, respectively. The implementation of GS increased genetic gain where reproductive technologies were used by 9–34% in Lamb 2020 and 37–98% in MP+. Individual trait response to selection varied in each breeding program. The combination of GS and reproductive technologies allowed for greater genetic gain in both indexes especially for hard to measure traits, but had limited effect on the traits that already had a large amount of early age records.
Additional keywords: JIVET, MOET, inbreeding management.
References
Armstrong DT, Kotaras PJ, Earl CR (1997) Advances in production of embryos in vitro from juvenile and prepubertal oocytes from the calf and lamb. Reproduction, Fertility and Development 9, 333–339.| Advances in production of embryos in vitro from juvenile and prepubertal oocytes from the calf and lamb.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaK2svgsFaisw%3D%3D&md5=a77cc064e3df7a53e8b66f3761643dc1CAS |
Bijma P (2000) Long-term genetic contributions. PhD thesis, Wageningen University, The Netherlands.
Bijma P (2012) Accuracies of estimated breeding values from ordinary genetic evaluations do not reflect the correlation between true and estimated breeding values in selected populations. Journal of Animal Breeding and Genetics 129, 345–358.
| Accuracies of estimated breeding values from ordinary genetic evaluations do not reflect the correlation between true and estimated breeding values in selected populations.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC38blsFKktA%3D%3D&md5=e59a84111f6f2a9f7eaddc4d4c3b5f69CAS |
Brash LD, Wray NR, Goddard ME (1996) Use of MOET in Merino breeding programmes: a practical and economic appraisal. Animal Science 62, 241–254.
| Use of MOET in Merino breeding programmes: a practical and economic appraisal.Crossref | GoogleScholarGoogle Scholar |
Brown DJ, Swan AA (2016a) Genetic importance of fat and eye muscle depth in Merino breeding programs. Animal Production Science 56, 690–697.
| Genetic importance of fat and eye muscle depth in Merino breeding programs.Crossref | GoogleScholarGoogle Scholar |
Brown DJ, Swan AA (2016b) Genetic parameters for liveweight, wool and worm resistance traits in multi-breed Australian meat sheep. 2. Genetic relationships between traits. Animal Production Science 56, 1449–1453.
| Genetic parameters for liveweight, wool and worm resistance traits in multi-breed Australian meat sheep. 2. Genetic relationships between traits.Crossref | GoogleScholarGoogle Scholar |
Brown DJ, Swan AA, Gill SJ, Ball AJ, Banks RG (2016) Genetic parameters for liveweight, wool and worm resistance traits in multi-breed Australian meat sheep. 1. Description of traits, fixed effects, variance components and their ratios. Animal Production Science 56, 1442–1448.
| Genetic parameters for liveweight, wool and worm resistance traits in multi-breed Australian meat sheep. 1. Description of traits, fixed effects, variance components and their ratios.Crossref | GoogleScholarGoogle Scholar |
Buch LH, Sørensen MK, Berg P, Pedersen LD, Sørensen AC (2012) Genomic selection strategies in dairy cattle: strong positive interaction between use of genotypic information and intensive use of young bulls on genetic gain. Journal of Animal Breeding and Genetics 129, 138–151.
| Genomic selection strategies in dairy cattle: strong positive interaction between use of genotypic information and intensive use of young bulls on genetic gain.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC383ptFWgsQ%3D%3D&md5=9ad52be87fd9f409f8ceed4b909cccfeCAS |
Bulmer MG (1971) The effect of selection on genetic variability. American Naturalist 105, 201–211.
| The effect of selection on genetic variability.Crossref | GoogleScholarGoogle Scholar |
Bunter KL, Brown DJ (2013) Yearling and adult expressions of reproduction in maternal sheep breeds are genetically different traits. In ‘Proceedings of the 20th association for advancement in animal breeding and genetics 2013’. Napier, New Zealand, Vol. 20, pp. 82–85.
Clark SA, Kinghorn BP, Hickey JM, van der Werf JHJ (2013) The effect of genomic information on optimal contribution selection in livestock breeding programs. Genetics, Selection, Evolution. 45, 44
| The effect of genomic information on optimal contribution selection in livestock breeding programs.Crossref | GoogleScholarGoogle Scholar |
Dekkers JCM (2007) Prediction of response to marker-assisted and genomic selection using selection index theory. Journal of Animal Breeding and Genetics 124, 331–341.
| Prediction of response to marker-assisted and genomic selection using selection index theory.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD2sjjvFyiuw%3D%3D&md5=378a6af03399aa4b706b9b267d7ddae0CAS |
Gilmour AR, Gogel BJ, Cullis BR, Welham SJ, Thompson R (2009) ‘ASReml user guide. Release 3.0.’ (VSN International Ltd: Hemel Hempstead, UK)
Granleese T, Clark SA, Swan AA, van der Werf JHJ (2015) Increased genetic gains in sheep, beef and dairy breeding programs from using female reproductive technologies combined with optimal contribution selection and genomic breeding values. Genetics, Selection, Evolution 47, 70
| Increased genetic gains in sheep, beef and dairy breeding programs from using female reproductive technologies combined with optimal contribution selection and genomic breeding values.Crossref | GoogleScholarGoogle Scholar |
Horton B (1996) A method of using a genetic algorithm to examine the optimum structure of the Australian sheep breeding industry: open-nucleus breeding systems, MOET and AI. Australian Journal of Experimental Agriculture 36, 249–258.
| A method of using a genetic algorithm to examine the optimum structure of the Australian sheep breeding industry: open-nucleus breeding systems, MOET and AI.Crossref | GoogleScholarGoogle Scholar |
Huisman AE, Brown DJ (2008) Genetic parameters for bodyweight, wool, and disease resistance and reproduction traits in Merino sheep. 2. Genetic relationships between bodyweight traits and other traits. Australian Journal of Experimental Agriculture 48, 1186–1193.
| Genetic parameters for bodyweight, wool, and disease resistance and reproduction traits in Merino sheep. 2. Genetic relationships between bodyweight traits and other traits.Crossref | GoogleScholarGoogle Scholar |
Huisman AE, Brown DJ, Ball AJ, Graser H (2008) Genetic parameters for bodyweight, wool, and disease resistance and reproduction traits in Merino sheep. 1. Description of traits, model comparison, variance components and their ratios. Australian Journal of Experimental Agriculture 48, 1177–1185.
| Genetic parameters for bodyweight, wool, and disease resistance and reproduction traits in Merino sheep. 1. Description of traits, model comparison, variance components and their ratios.Crossref | GoogleScholarGoogle Scholar |
Kinghorn BP (2011) An algorithm for efficient constrained mate selection. Genetics Selection Evolution 43,
Kinghorn BP, Smith C, Dekkers JCM (1991) Potential genetic gains in dairy cattle with gamete harvesting and in vitro fertilization. Journal of Dairy Science 74, 611–622.
| Potential genetic gains in dairy cattle with gamete harvesting and in vitro fertilization.Crossref | GoogleScholarGoogle Scholar |
Lillehammer M, Meuwissen THE, Sonesson AK (2011) A comparison of dairy cattle breeding designs that use genomic selection. Journal of Dairy Science 94, 493–500.
| A comparison of dairy cattle breeding designs that use genomic selection.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXjslCms7w%3D&md5=7f82fd94971cfdceb77a9ff799d8f06fCAS |
Meuwissen THE (1997) Maximizing the response of selection with a predefined rate of inbreeding. Journal of Animal Science 75, 934–940.
Nicholas FW (1996) Genetic improvement through reproductive technology. Animal Reproduction Science 42, 205–214.
| Genetic improvement through reproductive technology.Crossref | GoogleScholarGoogle Scholar |
Price K, Storn R (1997) Differential evolution. Dr. Dobb’s Journal 78, 18–24.
Pryce JE, Goddard ME, Raadsma HW, Hayes BJ (2010) Deterministic models of breeding scheme designs that incorporate genomic selection. Journal of Dairy Science 93, 5455–5466.
| Deterministic models of breeding scheme designs that incorporate genomic selection.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXnvVSjtQ%3D%3D&md5=b46623c200af5038475b1aa919f35be8CAS |
Shariflou MR, Wade CM, Windsor PA, Tammen I, James JW, Nicholas FW (2011) Lethal genetic disorder in Poll Merino/Merino sheep in Australia. Australian Veterinary Journal 89, 254–259.
| Lethal genetic disorder in Poll Merino/Merino sheep in Australia.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC3Mnisl2nuw%3D%3D&md5=36c84a6a435c4ba5149c4c0944052b78CAS |
Smith C (1967) Improvement of metric traits through specific genetic loci. Animal Science 9, 349–358.
Smith C (1986) Use of embryo transfer in genetic improvement of sheep. Animal Science 42, 81–88.
Smith LA, Cassell BG, Pearson RE (1998) The effects of inbreeding on the lifetime performance of dairy cattle. Journal of Dairy Science 81, 2729–2737.
| The effects of inbreeding on the lifetime performance of dairy cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK1cXnt1Sms7Y%3D&md5=bedf9d7ab6eafd06c59a006c30d634b2CAS |
Swan AA, Brown DJ, Daetwyler HD, Hayes BJ, Kelly M, Moghaddar N, van der Werf JHJ (2014) Genomic evaluations in the Australian sheep industry. In ‘Proceedings 10th world congress on genetics applied to livestock production’. Vancouver, Canada.
Swan AA, Pleasants T, Pethick D (2015) Breeding to improve meat eating quality in terminal sire sheep breeds. In ‘Proceedings of the 21st association for advancement in animal breeding and genetics 2015’. Lorne, Australia, Vol. 21, p. 44.
van Arendonk JAM, Bijma P (2003) Factors affecting commercial application of embryo technologies in dairy cattle in Europe – a modelling approach. Theriogenology 59, 635–649.
| Factors affecting commercial application of embryo technologies in dairy cattle in Europe – a modelling approach.Crossref | GoogleScholarGoogle Scholar |
Van Raden PM, Olson KM, Null DJ, Hutchison JL (2011) Harmful recessive effects on fertility detected by absence of homozygous haplotypes. Journal of Dairy Science 94, 6153–6161.
| Harmful recessive effects on fertility detected by absence of homozygous haplotypes.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhsFClsbjM&md5=27ed4819c9648ce8b8d71d39c7014a44CAS |
Wray NR, Goddard ME (1994) Increasing long-term response to selection. Genetics, Selection, Evolution 26, 431–451.
| Increasing long-term response to selection.Crossref | GoogleScholarGoogle Scholar |
