Spangler: beef industry should utilize genomic selection more frequentlyWritten by Christy Martinez
“The seedstock industry still provides adjusted measurements, ratios and EPDs (Expected Progeny Differences), which we need to use, but before the advent of genomics there wasn’t a nickel’s worth of accuracy amongst yearling bulls,” he explains.
Exploring complex traits
Before genomics began to be used for bull selection, Spangler notes they were mainly used for the “low-hanging fruit,” like genetic defects and parentage testing.
“Then we focused our attention on the complex traits – those that are controlled by numerous genes for which we have EPDs,” he says.
However, he says a question arose, asking why adjusted IMF (Intramuscular Fat) didn’t match up with its EPD and seemed to disagree with the results from the genomic test.
“The answer is that I don’t expect them to line up perfectly – they’re different,” he says. “One is an adjusted phenotype that has the influence of environment, one is and EPD, which separates the wheat from the chaff in terms of genetics only, and the other is a results of a marker test which, if it has any efficacy at all, only accounts for a fraction of the genetic variation, so they’re apples, oranges and pineapples.”
Three genomic strategies
To incorporate genomic information into national cattle evaluation through EPDs, Spangler says there are three general approaches.
“One is called blending, which is making an index of the traditional EPD and the genomic information, which is what the American Hereford Association (AHA) is doing,” he says.
The second approach is to use a genomic relationship, which is what the dairy industry uses.
“They use the 50,000 gene markers to supplement pedigree information,” notes Spangler. “To do that, industry has to have access to those genotypes.”
The third strategy, used by the American Angus Association, treats the molecular information as a correlated trait.
“The Angus Association is out front in the adoption of genomic predictions, and other breeds are quickly following,” says Spangler, noting that AHA is geared up to release genomic-enhanced EPDs, and that Simmental is quickly behind them.
Does it work?
“The adoption of genomic predictions has to center on the gain in EPD accuracy, and more accurate EPDs on younger animals,” states Spangler. “The gain in accuracy is directly related to the proportion of genetic variation explained.”
He adds that the answer to the question as to whether the technology works is always yes, but it’s a matter of how well it works.
“The adoption of this technology follows the breeding pyramid,” says Spangler. “This fundamental concept isn’t as well-accepted in beef cattle as other species, but the beef industry is still structured as nucleus breeders, multipliers and the commercial sector.”
“The nucleus breeders should first utilize new technology, and the benefits will trickle down through genetic lag,” he says, mentioning the four pathways of selection: sires of sires, sires of dams, dams of sires and dams of dams. “We need to focus on sires of sires – the breeders who produce the bulls that produce other bulls used in the seedstock industry – they drive your genetic change. As newer technologies come out, like genomic sequencing, they need to start in those nucleus herds.”
“Why do we want to increase accuracy?” asks Spangler of genomic predictions. “Think about flush mates, which have identical pedigree index EPDs, and how many different genetic combinations there could be among them. We want to increase EPD accuracy at a young age to differentiate among those animals.”
“A sire passes a random half of his alleles to the next generation, and a dam does the same thing, and we’re unsure of what’s been passed on. If we get some information on a phenotypic record on that animal, like ultrasound, the EPD accuracy increases. If the animal stays in a seedstock herd and records progeny, the EPD accuracy increases more, but if that animal goes to a commercial herd, we never see a change. We want DNA information early in life so we can see the young yearling bulls change, and get a better idea of their true genetic potential,” explains Spangler.
Genomic list to grow
“We’ll see new traits in the genomics era, whether it’s healthfulness of beef, disease susceptibility, adaptation, reproduction, and the list will continue to grow,” says Spangler. “That’s why using economic indexes for multiple trait selection will become more critical.”
He says traits like reproduction could benefit the most from genomic selection.
“So why did we start with weight and carcass instead? We need phenotypes to develop these tests, and there are a lot of phenotypes for weight and carcass, but not for reproduction and feed intake and efficiency,” he explains. “We need those phenotypes to do discovery, or training, and we also need them for validation before we send it out for commercial use. Collecting phenotypes is critical.”
“We have more technology available to us in beef cattle than other species, and we probably utilize it the worst,” he comments. “In an era of genomics where we’re rushing forward, we don’t have time and we shouldn’t waste time, resources and effort and leave money on the table by not utilizing what we already know to work – EPDs, economic indexes and crossbreeding.”
Testing increases accuracy
Currently, when an Angus producer gets a DNA sample it’s sent to the breed association, then to Pfizer or Merial IGENITY, and the company sends back the molecular breeding value, or the genomic prediction, and the association then incorporates it into the National Cattle Evaluation and releases a genomic-enhanced or marker-assisted EPD.
“That product is what we’re interested in,” says beef genetics specialist Matt Spangler from the University of Nebraska-Lincoln. “I don’t care about the molecular breeding value, all I care about is the marker-assisted EPD.”
“Better tests explain 40 percent of genetic variation, and that’s what we’re seeing in Angus and Hereford now,” says Spangler. “That impacts the low-accuracy animals – the yearling bulls. Even if a bull has a couple progeny, it still gives them increased accuracy.”
Even highly accurate animals, like AI sires, are genotyped so they can be used in the training process and to refine genomic predictions.