CSIRO Publishing blank image blank image blank image blank imageBooksblank image blank image blank image blank imageJournalsblank image blank image blank image blank imageAbout Usblank image blank image blank image blank imageShopping Cartblank image blank image blank image You are here: Journals > Animal Production Science   
Animal Production Science
Journal Banner
  Food, Fibre and Pharmaceuticals from Animals
 
blank image Search
 
blank image blank image
blank image
 
  Advanced Search
   

Journal Home
About the Journal
Editorial Board
Contacts
Content
Online Early
Current Issue
Just Accepted
All Issues
Special Issues
Research Fronts
Virtual Issues
Reviews
Sample Issue
For Authors
General Information
Notes for Authors
Submit Article
Open Access
For Referees
Referee Guidelines
Review Article
For Subscribers
Subscription Prices
Customer Service
Print Publication Dates

blue arrow e-Alerts
blank image
Subscribe to our Email Alert or RSS feeds for the latest journal papers.

red arrow Connect with us
blank image
facebook twitter youtube

 

Open Access Article << Previous     |     Next >>   Contents Vol 52(3)

A review of how dairy farmers can use and profit from genomic technologies

Jennie Pryce A C and Ben Hayes A B

A Biosciences Research Division, Department of Primary Industries, 1 Park Drive, Bundoora, Vic. 3083, Australia.
B La Trobe University, Bundoora, Vic. 3086, Australia.
C Corresponding author. Email: jennie.pryce@dpi.vic.gov.au

Animal Production Science 52(3) 180-184 http://dx.doi.org/10.1071/AN11172
Submitted: 9 August 2011  Accepted: 7 October 2011   Published: 6 March 2012


 
 Full Text
 PDF (142 KB)
 Export Citation
 Print
  
Abstract

New genomic technologies can help farmers to (1) achieve higher annual rates of genetic gain through using genomically tested bulls in their herds, (2) select for ‘difficult’ to measure traits, such as feed conversion efficiency, methane emissions and energy balance, (3) select the best heifers to become herd replacements, (4) sell pedigree heifers at a premium, (5) use mating plans to optimise rates of genetic gain while controlling inbreeding, (6) achieve certainty in parentage of individual cows and (7) avoid genetic defects that could arise from mating cows to bulls that are known carriers of genetic diseases that are the result of a single lethal mutation. The first use does not require genotyping females and could approximately double the net income per cow that arises due to genetic improvement, mainly through a reduction in generation interval. On the basis of current rates of genetic gain, the net profit from using genotyped bulls could be worth AU$20/cow per year and is permanent and cumulative. One of the most powerful uses of genomic selection is to select for economically important, yet difficult- or expensive-to-measure traits, such as residual feed intake or energy balance. Provided the accuracy of genomic breeding values is high enough (i.e. correlation between the true and estimated breeding values), these traits lend themselves well to genomic selection. For selecting replacement heifers, if genotyping costs are AU$50/cow, the net profit of genotyping 40 heifers to select the top 20 as replacements (per 100 cows) would be worth approximately AU$41 per cow. However, using parent average estimated breeding-value information is free and can already be used to select replacement heifers. So, genotyping costs would need to be very low to be more profitable than selecting on parent average estimated breeding value. However, extra value from genotyping can also be captured by using other strategies. For example, mating plans that use genomic relationships rather than pedigree relationships to capture inbreeding are superior in terms of reducing progeny inbreeding at a desired level of genetic gain, although pedigree does an adequate job. So, again, the benefits of genotyping are small (<AU$10). Ascertainment of pedigree is an additional use of genotyping and is potentially worth ~AU$30 per cow. Avoidance of genetic diseases and selling of pedigree heifers have a value that should be estimated case-by-case. Because genotyping costs continue to fall, it may become increasingly popular to capture the extra value from genotyping females.



References

Australian Dairy Herd Improvement Scheme (2010) Ranges and means for bull ABV’s, August 2011. Available at http://www.adhis.com.au/v2/sitev2.nsf/(ContentByKey)/BreedingValuesBullABVs?open [Verified August 2010]

Boichard D, Ducrocq V, Fritz S, Colleau JJ (2010) Where is dairy breeding going? A vision of the future. Interbull Bulletin 41, 63–68.

Cameron ND (1997) ‘Selection indices and prediction of genetic merit in animal breeding.’ (CAB International: Wallingford, UK)

de Roos APW, Schrooten C, Veerkamp RF, van Arendonk JAM (2011) Effects of genomic selection on genetic improvement, inbreeding, and merit of young versus proven bulls. Journal of Dairy Science 94, 1559–1567.
CrossRef | CAS |

Falconer DS, Mackay TFC (1996) ‘Introduction to quantitative genetics.’ Edn 4. (Longmans Green: Harlow, UK)

Habier D, Fernando RL, Dekkers JC (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177, 2389–2397.
| CAS |

Haile-Mariam ME, Bowman PJ, Goddard ME (2007) A practical approach for minimising inbreeding and maximising genetic gain in dairy cattle. Genetics, Selection, Evolution 39, 369–389.
CrossRef |

Hayes BJ (2011) Technical note: efficient parentage assignment and pedigree reconstruction with dense single nucleotide polymorphism data. Journal of Dairy Science 94, 2114–2117.
CrossRef | CAS |

Hayes BJ, van der Werf JHJ, Pryce JE (2011) Economic benefit of genomic selection for residual feed intake (as a measure of feed conversion efficiency) in Australian dairy cattle. Recent Advances in Animal Nutrition 18, 31–36.

Kinghorn BP (1998) Managing genetic change under operational and cost constraints. In ‘36th national congress of the South African Association of Animal Science’, 5–8 April 1998. pp. 9–16. (University of Stellenbosch)

Legarra A, Aguillar I, Misztal I (2009) A relationship matrix including full pedigree and genomic information. Journal of Dairy Science 92, 4656–4663.
CrossRef | CAS |

Man WYN (2004) Inbreeding in Australian Holstein Friesian cattle. PhD Thesis, University of Sydney.

Pryce JE, Daetwyler HD (2012) Designing dairy cattle breeding schemes under genomic selection: a review of international research. Animal Production Science 52, 107–114.
CrossRef |

Pryce JE, Arias J, Bowman PJ, Davis SR, Macdonald KA, Waghorn GC, Wales W, Williams YJ, Spelman RJ, Hayes BJ (2012a) Accuracy of genomic predictions of residual feed intake and 250 day bodyweight in growing heifers using 625 000 SNP markers. Journal of Dairy Science (in press).

Pryce JE, Hayes BJ, Goddard ME (2012b) Novel strategies to minimise progeny inbreeding while maximising genetic gain using genomic information. Journal of Dairy Science (in press).

Shuster DE, Kehrli ME, Ackermann MR, Gilbert RO (1992) Identification and prevalence of a genetic defect that causes leukocyte adhesion deficiency in Holstein cattle. Proceedings of the National Academy of Sciences, USA 89, 9225–9229.
CrossRef | CAS |

Smith LA, Cassell BG, Pearson RE (1998) The effects of inbreeding on lifetime performance of dairy cattle. Journal of Dairy Science 81, 2729–2737.
CrossRef | CAS |

Sonesson AK, Woolliams JA, Meuwissen THE (2010) Maximising genetic gain whilst controlling rates of genomic inbreeding using genomic optimum contribution selection. In ‘Proceedings of the 9th world congress on genetics applied to livestock production’.

Tajima M, Irie M, Kirisawa R, Hagiwara K, Kurosawa T, Takahashi K (1993) The detection of a mutation of CD18 gene in bovine leukocyte adhesion deficiency (BLAD). The Journal of Veterinary Medical Science 55, 145–146.
CrossRef | CAS |

Thomsen B, Horn P, Panitz F, Bendixen E, Petersen AH, Holm L-E, Nielsen VH, Agerholm JS, Arnbjerg J, Bendixen C (2006) A missense mutation in the bovine SLC35A3 gene, encoding a UDP-N-acetylglucosamine transporter, causes complex vertebral malformation. Genome Research 16, 97–105.
CrossRef | CAS |


   
 


    
Legal & Privacy | Contact Us | Help

CSIRO

© CSIRO 1996-2014