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Dr. Jamie Courter is your Mizzou Beef Genetics Extension Specialist

By Jared E. Decker Many of you have probably noticed that things have been a lot less active on the A Steak in Genomics™   blog, but you probably haven't known why. In January 2021, I was named the Wurdack Chair in Animal Genomics at Mizzou, and I now focus on research, with a little bit of teaching. I no longer have an extension appointment. But, with exciting news, the blog is about to become a lot more active! Jamie Courter began as the new MU Extension state beef genetics specialist in the Division of Animal Sciences on September 1, 2023. I have known Jamie for several years, meeting her at BIF when she was a Masters student. I have been impressed by Jamie in my interactions with her since that time.  Dr. Courter and I have been working closely together the last 6 weeks, and I am excited to work together to serve the beef industry for years to come! Jamie holds a bachelor’s degree in animal science from North Carolina State University and earned a master's degree in animal

BIF 2017: The Promise of Genomics for Beef Improvement

Daniela Lourenco
The University of Georgia

Before genomics, we were fairly happy with genetic prediction. Traditional evaluation combined pedigree, individual performance and progeny information. This was equal to summing all of an animal's genetics effects and dividing by 2.

SNPs are used as markers for genes, or regions of the genome that impact production. The color of a banana peel (green vs. yellow) is a marker for banana ripeness. SNP markers are used in the same way.

Marker assisted selection did not work. Why? Most traits are polygenic, meaning they are controlled by hundreds or thousands of genes. Trying to predict quantitative traits with a small number of DNA markers doesn't work well.

What is 100,000 times cheaper now than in 2001?
Airline flights are 2 times cheaper.
Computers are 5 times cheaper.
DNA sequencing is 100,000 times cheaper now than in 2001.

A Holstein bull named Freddy was the best bull according to genomic predictions in 2009. In 2012, when trait records on his daughters came in, he was still the best bull.

Genomic information is simply an extra source of information about an animal.
This information can either be used to predict genomic merit and use this prediction as an indicator or correlated trait in EPD prediction. This is called multi-step genomic prediction.

Another method is to use the genomic information to measure genetic similarity between animals.

There are two software implementations of single-step genomic prediction, Georgia's software and Bolt's software. Which software should be use?

One of the ways to improve genomic predictions is to have additional animals.
The US Holstein association has 1.6 million genotyped animals.

Increasing the number of DNA markers, 50,000 vs 500,000, we only see a 2% improvement in genomic predictions.

There are small gains with more SNPs. This does not help to improve relationship measurements, because 50,000 DNA markers is already enough. Further, with limited trait information (phenotype records), adding additional markers simply divides the pie into smaller slices.

Genomics does allow us to select animals at an earlier age. The genomic information has the same effect as more progeny information. This allows us to shorten the generation interval. This allows more rapid genetic improvement.

There is no magic here. Phenotypes are still essential. Only using genomic information is like running up a credit card bill with out earning cash. Phenotypes are the cash that keeps the system solvent.

More information always equals higher accuracy. Genomic-enhanced EPDs, which combine pedigree, performance and genomics, is more accurate than genomic predictions alone.

Before 2015, the max load for UGA's software was 150,000 animals. Their new APY method allows millions of genotyped animals.
Single-step with APY has been used by American Angus, Zoetis Holsteins, and US Holsteins.

"Keep genotyping and phenotyping if you want more reliable GE-EPDs," Lourenco said.

There is another parameter in genetic evaluation, which Lourenco calls delta. Delta is the trust in the genetic advisor. If there is no trust in the genetic adviser (delta = 0), then there is no genetic improvement. If there is great trust in the genetic adviser (delta = 1), then genetic improvement can be maximized.

When PIC switched to genomic prediction in 2013, their rate of genetic improvement increased 35%.


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