Picking the best herd sire or selecting replacement heifers is getting easier for producers who use genomically enhanced expected progeny differences.
The predictions of valuable traits like carcass merit, longevity and calving ease may not be 100 per cent accurate but new computing power is making the job easier.
Why it matters: Genomics can be a powerful tool in making genetic selection by providing more confidence in results.
DNA is the basis of inheritance and genes are responsible for different traits in animals, humans and plants. DNA information can help make better selections but there is a variation, so some traits may not be expressed as hoped. Also, a trait may be correlated to a different trait, so selecting for one desirable trait could affect how another trait is expressed.
Research is ongoing to find new traits and figure out correlations between different traits.
“The value of buying a genomically enhanced bull is critical. It provides more confidence,” said Shane Bedwell, head of breed improvement for the American Hereford Association.
Genetic information also provides parentage validation so it is known which bull is responsible for passing on valuable traits that affect calving ease, birthweight, weaning weight, yearling weight, scrotal size, mature cow weight, udder suspension, teat size, carcass weight, fat, ribeye area and marbling.
Cattle are also rated with indexes. The first indexes were developed in 1943 but it took decades to be adopted in beef sire selection.
“The whole premise in the beginning was to simplify sire selection and to be able to combine the relevant economic traits and weight them based on their economic impact in a given production scenario,” Bedwell said in a recent genomics webinar sponsored by the U.S. National Cattlemen’s Beef Association.
High index values do not mean a complete suite of superior traits. A bull may rank very high for one trait but is average for something else.
All this information has improved the modern animal and should lead to more profitability when selecting economically relevant traits that affect costs or profit.
“To determine which economically relevant traits that you need to select for in a suite of EPDs or to figure out if a selection index is the one you need to use for a given breed, it is important to start with your breeding objectives and your marketing goals and identify which strengths and weaknesses your operation has today,” Bedwell said.
Genomic testing improves accuracy of young unproven bulls to the tune of 18 to 25 progeny equivalents. This helps select bulls that can produce offspring that work in a particular environment.
“For some operators there is no doubt environment can constrain or dictate the potential level of performance expected,” he said.
DNA collection does not replace phenotypic data, said Kelly Retallick of Angus Genetics, a subsidiary of the American Angus Association that also works with other breeds like the Canadian Angus Association and the Canadian and American Charolais associations.
“The only reason our genomics work so well is because producers have connected performance data to genomic tools,” she said.
Genomic analysis also redefines the traditional pedigree.
A traditional pedigree shows an animal is an even 50-50 split between parents and is 25 per cent related to grandparents.
New information shows inheritance is not that streamlined.
“We don’t all get equal chunks of DNA from our grandparents,” Retallick said.
An individual may be 50 per cent related to parents but could be related more to one grandparent than another.
“We know even in our own families full sibs don’t look alike,” she said.
When making changes to a cow herd, it is important to know what key traits exist and the relationship between negative and positive traits. For example, those who want to add more marbling to the carcass should know what impact that may have on rib eye size.
Research has shown there is a low correlation, so by increasing the rib eye size, fat thickness may decrease, said Retallick.
Further research may provide more information about the relationship between progeny and grandparent. More advanced models may also fit different correlations for different breeds, she said.