Genomic selection for feed efficiency in dairy cattle

Pryce, J. E., Wales, W. J., de Haas, Y., Veerkamp, R. F. and Hayes, B. J. (2014) Genomic selection for feed efficiency in dairy cattle. Animal, 8 1: 1-10. doi:10.1017/S1751731113001687

Author Pryce, J. E.
Wales, W. J.
de Haas, Y.
Veerkamp, R. F.
Hayes, B. J.
Title Genomic selection for feed efficiency in dairy cattle
Journal name Animal   Check publisher's open access policy
ISSN 1751-732X
Publication date 2014-01
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1017/S1751731113001687
Open Access Status Not yet assessed
Volume 8
Issue 1
Start page 1
End page 10
Total pages 10
Place of publication Cambridge, United Kingdom
Publisher Cambridge University Press
Language eng
Abstract Feed is a major component of variable costs associated with dairy systems and is therefore an important consideration for breeding objectives. As a result, measures of feed efficiency are becoming popular traits for genetic analyses. Already, several countries account for feed efficiency in their breeding objectives by approximating the amount of energy required for milk production, maintenance, etc. However, variation in actual feed intake is currently not captured in dairy selection objectives, although this could be possible by evaluating traits such as residual feed intake (RFI), defined as the difference between actual and predicted feed (or energy) intake. As feed intake is expensive to accurately measure on large numbers of cows, phenotypes derived from it are obvious candidates for genomic selection provided that: (1) the trait is heritable; (2) the reliability of genomic predictions are acceptable to those using the breeding values; and (3) if breeding values are estimated for heifers, rather than cows then the heifer and cow traits need to be correlated. The accuracy of genomic prediction of dry matter intake (DMI) and RFI has been estimated to be around 0.4 in beef and dairy cattle studies. There are opportunities to increase the accuracy of prediction, for example, pooling data from three research herds (in Australia and Europe) has been shown to increase the accuracy of genomic prediction of DMI from 0.33 within country to 0.35 using a three-country reference population. Before including RFI as a selection objective, genetic correlations with other traits need to be estimated. Weak unfavourable genetic correlations between RFI and fertility have been published. This could be because RFI is mathematically similar to the calculation of energy balance and failure to account for mobilisation of body reserves correctly may result in selection for a trait that is similar to selecting for reduced (or negative) energy balance. So, if RFI is to become a selection objective, then including it in an overall multi-trait selection index where the breeding objective is net profit is sensible, as this would allow genetic correlations with other traits to be properly accounted for. If genetic parameters are accurately estimated then RFI is a logical breeding objective. If there is uncertainty in these, then DMI may be preferable. Copyright
Keyword Feed efficiency
Dairy cow
Genomic selection
Residual feed intake
Dry matter intake
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collection: Queensland Alliance for Agriculture and Food Innovation
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Citation counts: TR Web of Science Citation Count  Cited 15 times in Thomson Reuters Web of Science Article | Citations
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