Uses of genomics in livestock agriculture
M. E. GoddardDepartment of Agriculture and Food System, University of Melbourne, Parkville, Vic. 3010, Australia and Biosciences Research Division, Department of Primary Industries, Bundoora, Vic. 3083, Australia and Cooperative Research Centre for Beef Genetic Technologies, Armidale, NSW 2351, Australia. Email: mike.goddard@dpi.vic.gov.au
Animal Production Science 52(3) 73-77 https://doi.org/10.1071/AN11180
Submitted: 17 August 2011 Accepted: 24 January 2012 Published: 6 March 2012
Journal Compilation © CSIRO Publishing 2012 Open Access CC BY-NC-ND
Abstract
World demand for livestock products is likely to increase in coming decades but the cost of production could escalate faster than the price due to competition for land, water, grain and fertiliser and the effects of climate change and its mitigation. To remain competitive for these resources, livestock agriculture has to dramatically increase in efficiency of production. Genetic gain is one mechanism to achieve increased efficiency and there is the opportunity to utilise the scientific advances in genomics. Three ways in which genomics can be used are in additive genetic improvement, exploitation of non-additive genetic variance and management which exploits genotype by environment interactions to optimise management. Genomic selection is already being widely implemented in dairy cattle and beef cattle and sheep will follow in the future once the accuracy of genomic selection is high enough. The accuracy of equations that predict breeding value from DNA genotypes can be increased by increasing the size of the reference population from which the equations are estimated, increasing the density of markers, using genome sequences instead of markers, using more appropriate statistical procedures and incorporating biological information into the prediction. In the long term, genomic selection combined with reproductive technology that reduces the minimum age at breeding will greatly increase the rate of genetic gain. This will allow long-term increases in biological efficiency and short-term tailoring of livestock to meet the demands of particular markets and opportunities.
References
Carlborg O, Haley CS (2004) Epistasis: too often neglected in complex trait studies? Nature Reviews. Genetics 5, 618–625.| Epistasis: too often neglected in complex trait studies?Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXlvVSjt7g%3D&md5=f1a1b934bf0c19a3436168b314b51e07CAS |
Delgado C, Rosegrant M, Steinfeld H, Ehui S, Courbois C (1999) Livestock to 2020 – the next food revolution. Food, agriculture and the environment, draft discussion paper 28. International Food Policy Research Institute, Food and Agriculture Organization of the United Nations and International Livestock Research Institute.
de Roos APW, Hayes BJ, Spelman RJ, Goddard ME (2008) Linkage disequilibrium and persistence of phase in Holstein Friesian, Jersey and Angus cattle. Genetics 179, 1503–1512.
| Linkage disequilibrium and persistence of phase in Holstein Friesian, Jersey and Angus cattle.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD1cvmsFeltw%3D%3D&md5=a6c49348245b1efce69bcdfe46d3a613CAS |
de Roos APW, Hayes BJ, Goddard ME (2009) Reliability of genomic breeding values across multiple populations. Genetics 183, 1545–1553.
| Reliability of genomic breeding values across multiple populations.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD1MfhtFSqsg%3D%3D&md5=b9d5bd700e46018d2915502cf78fc7daCAS |
FAO Commission on Genetic Resources for Food and Agriculture (2007) In ‘The state of the world’s animal genetic resources for food and agriculture’. (Eds B Rischkowsky, D Pilling) pp. 141–142. (FAO: Rome)
Goddard ME (2008) The use of high density genotyping in animal health. In ‘Animal genomics for animal health’. (Eds M-H Pinard, C Gay, P-P Pastoret, B Dodet) pp. 383–389. (Developmental Biology Karger 132: Basel, Switzerland)
Goddard ME (2009) Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136, 245–257.
| Genomic selection: prediction of accuracy and maximisation of long term response.Crossref | GoogleScholarGoogle Scholar |
Goddard ME, Ahmed AM (1982) The use of the genetic distance between cattle breeds to predict the heterosis in crosses. In ‘Proceedings of the 2nd world congress on genetics applied to livestock production’. 8, 377–382.
Goddard ME, Meuwissen THE, Hayes BJ (2010) Genomic selection in farm animal species – lessons learnt and future perspectives. In ‘Proceedings of the 9th world congress on genetics applied to livestock production’, Leipzig. Paper 0701.
Goddard ME, Hayes BJ, Meuwissen THE (2011) Using the genomic relationship matrix to predict the accuracy of genomic selection. Journal of Animal Breeding and Genetics 128, 409–421.
Hayes BJ, Pryce J, Chamberlain AJ, Goddard ME (2010) Genetic architecture of complex traits and the accuracy of genomic prediction: proportion of black in Holstein Friesian cattle as a model trait. PLOS Genetics 6, e1001139
| Genetic architecture of complex traits and the accuracy of genomic prediction: proportion of black in Holstein Friesian cattle as a model trait.Crossref | GoogleScholarGoogle Scholar |
Hughes L (2003) Climate change and Australia: trends, projections and impacts. Austral Ecology 28, 423–443.
| Climate change and Australia: trends, projections and impacts.Crossref | GoogleScholarGoogle Scholar |
Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, et al (2010) Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838.
| Hundreds of variants clustered in genomic loci and biological pathways affect human height.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXht1ajtbfF&md5=376a4a6f16ac9a22d37b8fc868255d7cCAS |
Meuwissen THE, Goddard ME (1996) The use of marker haplotypes in animal breeding schemes. Genetics, Selection, Evolution. 28, 161–176.
| The use of marker haplotypes in animal breeding schemes.Crossref | GoogleScholarGoogle Scholar |
Meuwissen THE, Goddard ME (2010) Accurate prediction of genetic values for complex traits by whole genome resequencing. Genetics 185, 623–631.
| Accurate prediction of genetic values for complex traits by whole genome resequencing.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhsFOnu73M&md5=c56d09b168ce31d29423ea5dee9edc63CAS |
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.
Muir WM (2007) Comparison of genomic and traditional BLUP estimated breeding value accuracy and selection response under alternative trait and genomic parameters. Journal of Animal Breeding and Genetics 124, 342–355.
| Comparison of genomic and traditional BLUP estimated breeding value accuracy and selection response under alternative trait and genomic parameters.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD2sjjvFyhsg%3D%3D&md5=b28246fcedab60152671f67f39515ed9CAS |
Pryce JE, Hayes BJ, Goddard ME (2012) Novel strategies to minimise progeny inbreeding while maximising genetic gain using genomic information. Journal of Dairy Science 95, 377–388.
VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS (2009) Invited review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16–24.
| Invited review: reliability of genomic predictions for North American Holstein bulls.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXlsVOrsw%3D%3D&md5=e20b7d5db6872bfe41fe1c7cba312240CAS |
Yang J, Beben B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PF, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM (2010) Missing heritability of human height explained by genomic relationships. Nature Genetics 42, 565–569.
| Missing heritability of human height explained by genomic relationships.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXns1GisL8%3D&md5=c9db73a48ec56a558783f98f92da10efCAS |
