Feed Efficiency and Genomics

The importance of raising efficient animals

The profitability of beef production is based on both input costs and output values. Feed costs have a major impact on profitability as they represent about 60 to 80% of the total cost of beef production systems. Improving feed efficiency by just 1% would cut feed costs by $30 million per year for beef production in Canada. Thus, improving feed efficiency traits offers great potential for increasing the profitability of production systems and must be included in the breeding objectives of beef cattle producers. However, while the target of improving feed efficiency is clearly defined, it is not that easy to achieve.

Feeding Time

Figure 1. Imagine how hard and time consuming to measure intake of each animal.

The difficulty of selecting for feed efficiency

A major challenge for beef producers to select for more efficient animals is the difficulty in measuring individual feed intake (Figure 1). Measuring feed intake is also an expensive process, particularly if feed intake data is collected on large numbers of animals for long periods1. Sophisticated technologies such as wireless radio frequency identification (RFID) units [i.e. Insentec© (figure 2) or Growsafe© technology2] are required to measure individual feed intake accurately. Nonetheless, technology such as Growsafe© has been used to measure individual animal consumption, feeding behavior and animal weight in some beef research stations3,4. During the last few years, individual feed intake has been measured on bulls in performance testing stations in many parts of the world, including Ontario5. Utilization of automated feeding technology for bull evaluation may increase the benefit obtainable from economic investment in feed intake recording equipment.

Autoamted Feeding Station

Figure 2. Automated feeding station in Elora Beef Cattle Research Centre, University of Guelph.

Measuring feed efficiency

Feed intake by itself does not accurately reflect the feed utilization efficiency of animals because animal feed intake is highly associated with body weight and the level of production6. We need a method of accounting for these effects in order to identify animals that have an inherently superior ability to convert feed to weight gain, which they will be able to pass on to their progeny. The concept of net feed efficiency (also called residual feed intake, or RFI), possesses most of the desired properties in feed efficiency measurement. It gives us the power to select for animals which use less feed for a given rate of gain, and it evaluates feed intake independently of growth rate and body size. With RFI, an individual animal's actual feed intake is compared to that predicted to be required by an average animal of the same weight which is growing at the same rate7. Residual feed intake is the difference between the animal's actual intake and what we predicted it would be. In other words, RFI = Actual Intake - Predicted Intake. Animals with a low RFI are desirable (more efficient) and animals with a high RFI are undesirable (less efficient).

Towards genetic improvement of feed efficiency using genomics

There is a lot of genetic variation for feed efficiency between animals of the same breed, and amongst animals of different breeds8. We can maximize progress in selection for feed efficiency by combining conventional performance selection with genomic methods. With genomics, we can examine the animal's actual genetic material and use both the relationships between an animal's specific genes or genetic markers and its actual performance to calculate its genetic merit. [For an introduction to genomics, please see the two previous articles in this series, written by Dr. Stephen P. Miller, University of Guelph and published in earlier editions of OMAFRA Virtual Beef9,10]. One type of genetic marker is called a "single nucleotide polymorphism" or SNP for short. These are specific locations on the DNA where alternate versions of a "genetic code letter" are found in different animals. Sometimes, one version of the code letter is associated with a measurable difference in the performance of the animal. If these can be identified and mapped, then a test can be developed to screen for its presence in large numbers of animals. Those that have the favorable version of the code letter (SNP) are more likely to be higher performing animals than those that have the unfavorable versions. This can be used as a pre-selection tool to decide which animals should enter a performance testing program, or for selection of breeding stock which do not have performance records themselves.

Genomic approaches: fantasy or fact?

In the last decade, improving feed efficiency traits using genetic markers has progressed from using a few SNPs to thousands of SNPs. Earlier studies focused on using few SNPs, in particular with genes associated with the biology of feed utilization. The science supporting the usefulness of SNPs has steadily grown. Validation for the use of SNPs includes: discovery of the link between SNPs in the leptin gene with feed intake and feeding behavior11; between SNPs in uncoupling protein 2 and protein 3 genes with feed efficiency12; between SNPs in the gene for growth hormone receptor13 as well as several others. At the University of Guelph we have developed a small SNP panel for feed efficiency which includes SNPs within 9 different genes14.

Increasing the number of SNPs in the evaluation of a complex trait controlled by many genes (such as feed efficiency) should improve the accuracy of genetic selection15. To this end, the number of SNPs has increased to the thousands, covering the whole genome. This has resulted in great progress being made in identifying specific genes and locations on the chromosomes that are strongly associated with feed efficiency.

However, the full implementation of genetic markers identified in previous studies into selection programs is facing two barriers16. The first is the lack of understanding of the interactions of the genes' effects on residual feed intake. Second, the number of animals in the validation data set is small and these data are collected from different environments.

With the advent of a 50,000 SNP panel (Figure 3) in Dec. 2007, different beef groups around the world tested their records with phenotypes available. Promising results have been obtained from 50k SNP panel17,18,19. Combining phenotypes from different breeds is required as the gene responsible for the trait always has one rare allele20. Of course, phenotypic (actual animal performance) data for feed intake is required to be accumulated from different breeds and from different populations within breeds.

The Illumina Bovine Beadchips

Figure 3. The Illumina BovineSNP50© Beadchips

Genomic assisted selection for feed efficiency

The aim of adopting new technologies such DNA testing is to increase the accuracy of selection, which is a source of motivation for competition between the breed associations21. Implementing genomics in the genetic evaluation programs leads to more accurate EPDs where the molecular breeding values of offspring can be accurately predicted using only their genotypes22. Genomic-enhanced EPDs for all economic traits are the current and future target for beef cattle genetic improvement programs. Recently, the American Angus Association has combined different sources of information (DNA Markers, pedigree, and phenotypes) into one EPD, termed Genomic-enhanced EPDs23. Currently Genomic-enhanced EPDs for multiple carcass traits are available for Angus breeders. On October 5, 2010 the American Angus Association and Angus Genetics Inc. released residual average daily gain (RADG) expected progeny differences (EPDs) as a selection tool for feed efficiency, using the 50K SNP panel.

Our understanding of the genetic architecture of feed efficiency will deepen dramatically in the next two years. Moving from the 50K SNP panel to an 800k panel (BovineHD©, High Density Bovine BeadChip), will provide even more accurate genomic EPDs. As well, using whole genome sequencing data in genetic evaluation is currently possible24.

Genomics is the future. The future is now!


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9Miller, S. P. 2008. Genomics - A Fast Train Coming. OMAFRA Virtual Beef.

10Miller, S. P. 2009. Harnessing the Power of Genomics for Beef - A Collaborative Approach. OMAFRA Virtual Beef

11Nkrumah, J. D., Li, C., Yu, J., Hansen, C., Keisler, D. H., S. S. Moore, S. S. 2005. Polymorphisms in the bovine leptin promoter associated with serum leptin concentration, growth, feed intake, feeding behavior, and measures of carcass merit. J. Anim Sci. 83: 20-28

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13Sherman, E. L., J. D. Nkrumah, B. M. Murdoch, C. Li, Z. Wang, A. Fu and S. S. Moore. 2008. Polymorphisms and haplotypes in the bovine neuropeptide Y, growth hormone receptor, ghrelin, insulin-like growth factor 2, and uncoupling proteins 2 and 3 genes and their associations with measures of growth, performance, feed efficiency, and carcass merit in beef cattle. J. Anim. Sci., 86: 1-16.

14Abo-Ismail, M. K.; M. J. Kelly; E. J. Squires; K. C. Swanson1; J. D. Nkrumah; and S. P. Miller. 2009. Identification of single nucleotide polymorphisms influencing feed efficiency and performance in multi-breed beef cattle using a candidate gene approach.J. Anim. Sci. Vol. 87, E-Suppl. 2.

15Goddard, M. E. and B. J. Hayes. 2007. Genomic selection. J Anim Breed Genet.124(6):323-30.

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17Sherman, E. L., J. D.Nkrumah, S. S. Moore, 2010. Whole genome single nucleotide polymorphism associations with feed intake and feed efficiency in beef cattle. J. Anim Sci. 2010 88: 16-22

18Abo-Ismail, M.K.; E. J. Squires, K. C. Swanson, D. Lu, Z. Wang, J. Mah, G. Plastow, S. Moore and S. P. Miller, 2010. Fine mapping QTL and candidate genes discovery for residual feed intake on Chromosomes 5, 15, 16, and 19 in beef cattle. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production.

19Lu, D., S. Miller, M. Sargolzaei, G. Vander Voort, T. Caldwell, Abo-Ismail, M.K., Z. Wang, J. Mah, G. Plastow, S. Moore Genome Wide Association Scan for Signals of Recent Selection in Angus Beef Cattle. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production.

20Goddard, M.E. 2009. Genomic selection: prediction of accuracy and maximisation of long-term response. Genetica, 136:245-257

21Golden, B. L., D. J. Garrick and L. L. Benyshek. 2009. Milestones in beef cattle genetic evaluation. J. Anim. Sci. 87 (E. Suppl.):3-10.

22Kizilkaya, K., R.L. Fernando and D.J. Garrick (2010): Genomic prediction of simulated multibreed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes. J. Anim. Sci. 88: 544-551.

23Northcutt, S. 2010. Pulling it all together:Genomic-enhanced EPDs.

24Meuwissen, T. H.E., M.E. Goddard. 2010. Accurate Prediction of Genetic Values for Complex Traits by Whole Genome Resequencing. Genetics, 185: 623-631

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Author: Mohammed Abo-Ismail - University of Guelph
Last Reviewed: November 2010

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