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Genomic Prediction Takes the “Guess” out of Guesswork

While crystal balls may have their place in seeing the future, they tend to raise eyebrows in a professional setting. Fortunately, researchers on the Genome Alberta project to improve carcass quality and feed efficiency in beef cattle have a far more powerful tool at their disposal in the form of genomic prediction.

“Genomic prediction is a new tool to beef cattle that we’re developing which uses associations between DNA markers and traits to predict the value of an animal,” said Dr. Changxi Li, a research scientist with Agriculture and Agri-Food Canada (AAFC) who serves as an AAFC Professor and Chair in Bovine Genomics at the University of Alberta.

Identification, please

Dr. Li’s research focuses on identification of genetics that affect beef cattle performance including growth, feed efficiency, carcass quality and fertility traits. That knowledge is applied in developing genomic tools to improve beef production efficiency and quality through genomic selection and genome-assisted management.

“Genomic prediction works by taking a reference population, analyzing their DNA and measuring and recording a number of traits. We then find correlations between the DNA markers and the traits that we can use to predict another animal’s traits or EPD (Expected Progeny Difference) when we only have their genotype. This is a highly effective tool because it allows us to predict an animal’s traits while it is still a calf, and to predict traits which are expensive or difficult to measure, such as feed efficiency.”

One of the greatest advantages of genomic prediction is that it doesn’t require a pedigree to predict an animal’s trait EPD. This means that unregistered cattle, crossbred cattle or cattle with no performance records can still have their traits predicted. The only catch is that the accuracy of prediction can be low if the animal isn’t closely related to the animals used to create the prediction equation. Even so, genetic prediction enables researchers to predict these animals’ feed efficiency, growth and carcass traits when they might otherwise have nothing.

The need for speed

In an industry where time is money, genomic prediction is a timely innovation.

“Collecting data on traits can take years when you have to let a bull grow to maturity, breed, and then wait for the calves to mature before collecting carcass data. Genomic prediction speeds up this process by making trait prediction available as early as a few weeks after calving. This will help ranchers make decisions faster on which bulls or heifers to keep and influence breeding strategies to get an optimized calf crop for the traits they desire most.”

At the same time, Dr. Li stresses that genetic improvement for traits such as feed efficiency in a herd requires a long-term, comprehensive genetic/genomic selection program with reliable prediction of molecular EPD. Scientists from Livestock Gentec are working on improving the prediction accuracy by exploring functional DNA markers and by building a reference population that is more closely related to commercial beef cattle.

This collaborative, “big picture” approach will be critical in maintaining progress for the beef industry.

“Genome Alberta and Genome Canada have made long term investments in genomics for livestock, which is very important in allowing researchers to develop useful tools that help farmers improve their production. We’re also working with Alberta Agriculture & Forestry, and we’d love to have more investment from other governments as well. Technology-wise, things are moving quickly, but we need more resources in the form of time, manpower and money to really enhance the reference data and maintain our progress.”

Additionally, Dr. Li and his colleagues are stressing the need for international collaboration, where beef cattle data is shared and countries work together to pinpoint the genes that influence beef performance.

If they can make it all happen, you won’t need a crystal ball to tell you that the future looks bright for all concerned.







Genomic Prediction Takes the “Guess” out of Guesswork

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