Register      Login
Animal Production Science Animal Production Science Society
Food, fibre and pharmaceuticals from animals
RESEARCH ARTICLE (Open Access)

Genomics and the global beef cattle industry1

E. J. Pollak A B , G. L. Bennett A , W. M. Snelling A , R. M. Thallman A and L. A. Kuehn A
+ Author Affiliations
- Author Affiliations

A USDA/ARS2-US Meat Animal Research Center, PO Box 166, Clay Center, NE 68933, USA.

B Corresponding author. Email: e.john.pollak@ars.usda.gov

Animal Production Science 52(3) 92-99 https://doi.org/10.1071/AN11120
Submitted: 21 June 2011  Accepted: 12 January 2012   Published: 20 February 2012

Journal Compilation © CSIRO Publishing 2012 Open Access CC BY-NC-ND

Abstract

After two decades of developing DNA-based tools for selection, we are at an interesting juncture. Genomic technology has essentially eliminated the potentially large negative impact of spontaneous single-mutation genetic defects as the management of recent examples in beef cattle have demonstrated. We have the ability to perform more accurate selection based on molecular breeding values (MBVs) for animals closely related to the discovery population. Yet the amount of genetic variation explained falls short of expectations held for the technology. Tests are less effective in distant relatives within a breed and are not robust enough for across-breed use. It is hypothesised that ‘larger single-nucleotide polymorphism (SNP) panels’ will help extend the effective use of tests to more distantly related animals and across breeds. Sequencing and imputing sequences across individuals will enable us to discover causative mutations or SNPs in perfect harmony with the mutation. However, the investment to revisit discovery populations will be large. We can ill afford to duplicate genotyping or sequencing activities for prominent individuals. Hence, a global strategy for genotyping and sequencing becomes an attractive proposition as many of our livestock populations are related. As we learned more of the complexities of the genome, the number of animals in discovery populations necessary to achieve high levels of predictability has grown dramatically. No one organisation has the resources to assemble the animals needed, especially for novel, expensive or hard to measure phenotypes. This scenario is fertile ground for increased international collaboration in all livestock species.


References

Aguilar I, Misztal I, Johnson DL, Legarra A, Tsuruta S, Lawlor TJ (2010) A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science 93, 743–752.
A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXht1CjtbY%3D&md5=2492a3d6a1616186be64ad6e6abb4fd2CAS |

Bolormaa S, Hayes BJ, Savin K, Hawken R, Barendse W, Arthur PF, Herd RM, Goddard ME (2011a) Genome-wide association studies for feedlot and growth traits in cattle. Journal of Animal Science 89, 1684–1697.
Genome-wide association studies for feedlot and growth traits in cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXnsVWktr0%3D&md5=2d87660a340ad5b0c8374e656685e5e4CAS |

Bolormaa S, Porto Neto LR, Zhang YD, Bunch RJ, Harrison BE, Goddard ME, Barendse W (2011b) A genome-wide association study of meat and carcass traits in Australian cattle. Journal of Animal Science 89, 2297–2309.
A genome-wide association study of meat and carcass traits in Australian cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXps1yqtbY%3D&md5=28b65730dce0e13791c727a4f8e577bcCAS |

Casas E, Shackelford SD, Keele JW, Stone RT, Kappes SM, Koohmaraie M (2000) Quantitative trait loci affecting growth and carcass composition of cattle segregating alternate forms of myostatin. Journal of Animal Science 78, 560–569.

Casas E, Stone RT, Keele JW, Shackelford SD, Kappes SM, Koohmaraie M (2001) A comprehensive search for QTL affecting growth and carcass composition of cattle segregating alternative forms of the myostatin gene. Journal of Animal Science 79, 854–860.

Casas E, Shackelford SD, Keele JW, Koohmaraie M, Smith TPL, Stone RT (2003) Detection of quantitative trait loci for growth and carcass composition in cattle. Journal of Animal Science 81, 2976–2983.

Casas E, White SN, Shackelford SD, Wheeler TL, Koohmaraie M, Bennett GL, Smith TPL (2007) Assessing the association of single nucleotide polymorphisms at the thyroglobulin gene with carcass traits in beef cattle. Journal of Animal Science 85, 2807–2814.
Assessing the association of single nucleotide polymorphisms at the thyroglobulin gene with carcass traits in beef cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhtlWitbrO&md5=3e6d4f8c26cf43049b07af53f4e6ab2fCAS |

Charlier C, Coppieters W, Rollin F, Desmecht D, Agerholm JS, Cambisano N, Carta E, Dardano S, Dive M, Fasquelle C, Frennet J-C, Hanset R, Hubin X, Jorgensen C, Karim L, Kent M, Harvey K, Pearce BR, Simon P, Tama N, Nie H, Vandeputte S, Lien S, Longeri M, Fredholm M, Harvey RJ, Georges M (2008) Highly effective SNP-based association mapping and management of recessive defects in livestock. Nature Genetics 40, 449
Highly effective SNP-based association mapping and management of recessive defects in livestock.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXjslCgtbY%3D&md5=f85a84f961b12c241f058869bf58bdaaCAS |

Cleveland MA, Forni S, Deeb N, Maltecca C (2010) Genomic breeding value prediction using three Bayesian methods and application to reduced density marker panels. BioMed Central Proceedings 4, S6

Garrick DJ, Taylor JF, Fernando RL (2009) Deregressing estimated breeding values and weighting information for genomic regression analyses. Genetics, Selection, Evolution. 41, 55
Deregressing estimated breeding values and weighting information for genomic regression analyses.Crossref | GoogleScholarGoogle Scholar |

Goddard ME (2009) How can we best use DNA data in selection of cattle? In ‘Proceedings of Beef Improvement Federation 41st annual research symposium’, 30 April–3 May 2009, Sacramento, CA. pp. 81–91.

Habier D, Fernando RL, Kizikaya K, Garrick DJ (2011) Extension of the Bayesian alphabet for genomic selection. BioMed Central Bioinformatics 12, 186

Kachman SD (2008) Parameters needed to add genomics to genetic prediction. In ‘Proceedings of the 9th genetic prediction workshop’, Kansas City, MO, pp. 92–98. (Beef Improvement Federation: Raleigh, NC)

Kim JJ, Farnir F, Savell J, Taylor JF (2003) Detection of quantitative trait loci for growth and beef carcass fatness traits in a cross between Bos taurus (Angus) and Bos indicus (Brahman) cattle. Journal of Animal Science 81, 1933–1942.

Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124, 743

MacNeil MD, Nkrumah JD, Woodward BW, Northcutt SL (2010) Genetic evaluation of Angus cattle for carcass marbling using ultrasound and genomic indicators. Journal of Animal Science 88, 517–522.
Genetic evaluation of Angus cattle for carcass marbling using ultrasound and genomic indicators.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXktVOqtrk%3D&md5=427b7fa6c3248e30cb2b362fc131e8d1CAS |

Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 15, 1819–1829.

Meyers SN, McDaneld TG, Swist SL, Marron BM, Steffen DJ, O’Toole D, O’Connell JR, Beever JE, Sonstegard TS, Smith TP (2010) A deletion mutation in bovine SLC4A2 is associated with osteopetrosis in Red Angus cattle. BioMed Central Genomics 11, 337

Miller S, Lu D, Vander Voort G, Sargolzaei M, Caldwell T, Wang Z, Mah J, Plastow G, Moore S (2010) Beef tenderness QTL on BTA25 from a whole genome scan with the BovineSNP50 Beadchip. In ‘Proceedings of the 9th world congress on genetics applied to livestock production’, 1–6 August 2010, Leipzig, Germany. CD-ROM Communication 0675.

Saatchi M, McClure MC, McKay SD, Rolf MM, Kim JW, Decker JE, Taxis TM, Chapple RH, Ramey HR, Northcutt SL, Bauck S, Woodward B, Dekkers JCM, Fernando RL, Schnabel RD, Garrick DJ, Taylor JF (2011) Accuracies of genomic breeding values in American Angus beef cattle using k-means clustering for cross-validation. Genetics, Selection, Evolution. 43, 40
Accuracies of genomic breeding values in American Angus beef cattle using k-means clustering for cross-validation.Crossref | GoogleScholarGoogle Scholar |

Snelling WM, Allan MF, Keele JW, Kuehn LA, McDaneld T, Smith TPL, Sonstegard TS, Thallman RM, Bennett GL (2010) Genome-wide association study of growth in crossbred beef cattle. Journal of Animal Science 88, 837–848.
Genome-wide association study of growth in crossbred beef cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXktVOqurw%3D&md5=b0c361c36448a3aa767f22d82b13649cCAS |

Snelling WM, Allan MF, Keele JW, Kuehn LA, Thallman RM, Bennett GL, Ferrell CL, Jenkins TG, Freetly HC, Nielsen MK, Rolfe KM (2011) Partial-genome evaluation of postweaning feed intake and efficiency of crossbred beef cattle. Journal of Animal Science 89, 1731–1741.
Partial-genome evaluation of postweaning feed intake and efficiency of crossbred beef cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXnsVWktrk%3D&md5=2523dccb7b2a914f24fbb2af156d9855CAS |

Thallman RM, Kuehn LA, Allan MF, Bennett GL, Koohmaraie M (2008) Opportunities for collaborative phenotyping for disease resistance traits in a large beef cattle resource population. Developments in Biologicals 132, 327–330.
Opportunities for collaborative phenotyping for disease resistance traits in a large beef cattle resource population.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXmt1Cksg%3D%3D&md5=067954327496d355343aab8aa2194148CAS |

Thallman RM, Hanford KJ, Quaas RL, Kachman SD, Templeman RJ, Fernando RL, Kuehn LA, Pollak EJ (2009) Estimation of the proportion of genetic variation accounted for by DNA tests. In ‘Proceedings of the Beef Improvement Federation 41st annual research symposium and annual meeting’, 30 April–3 May 2009, Sacramento, CA. pp. 184–209.

Van Eenennaam AL, Li J, Thallman RM, Quaas RL, Dikeman ME, Gill CA, Franke DE, Thomas MG (2007) Validation of commercial DNA tests for quantitative beef quality traits. Journal of Animal Science 85, 891–900.
Validation of commercial DNA tests for quantitative beef quality traits.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXjslWlu7o%3D&md5=185ded947a2309529cd86c8e06d17acdCAS |

Van Eenennaam AL, Thallman RM, Quaas RL, Hanford K, Pollak EJ (2009) Validation and estimation of additive genetic variation associated with DNA tests for quantitative beef cattle traits. Proceedings of the Association for Advancement of Animal Breeding and Genetics 18, 129–132.