Cattle producers stand on the threshold of knowing genetic worth more quickly and accurately.
Imagine knowing the genetic merit of individual cattle the day they're born, with more accuracy than knowing the genetic worth of their parents.
Imagine knowing the genetic merit in calves for Economically Relevant Traits (ERTs), even though there's no estimate for those traits in the calves' parents. Think of hard-to-measure traits like feed efficiency.
That's the promise of genomic selection (GS), a tool that should begin bearing fruit this year.
In the most basic of terms, GS (also known as whole-genome selection) involves calculating breeding value based upon an animal's genome.
“We've had EPDs and estimates of genetic merits based on animal phenotypes, relatives and pedigrees,” explains Jerry Taylor, professor and Wurdack Chair in Animal Genomics at the University of Missouri (MU). “Whole-genome selection seeks to establish genetic merit, too, but through identifying genomic markers that have a beneficial or detrimental effect on the phenotypic performance of specific traits.”
Single assay, big numbers
Such evaluation was made possible by new technology last year that enables probing an animal for 54,000 single-nucelotide polymorphisms (SNPs) in a single assay. According to Taylor, up to 42,000 of those SNPs have been proven to be informative in individual breeds.
The assay — the BovineSNP50 BeadChip — was developed by the iBMAC consortium, which included genomics researchers from MU, USDA's Beltsville Agricultural Research Center (BARC), University of Alberta, U.S. Meat Animal Research Center (USMARC) and Illumina, Inc., which manufactures and sells the BeadChip.
This approach runs counter to previous genomic efforts where the aim was to unequivocally identify variation in specific genes affecting one or more traits, and then develop tests that allowed selection for or against variation in specific genes (see “Fast-Forward Genetics,” BEEF, February 2008, page 38).
“The physical work is nearly complete. The project is moving along as we'd hoped,” says Gary Bennett, USMARC research leader of genetics and breeding.
Bennett is referring to the process begun last year by a collaboration including USMARC and 16 breed associations. USMARC has assayed all but about 200 of 5,600 animals. Of that group, 2,000 represent the most influential current sires provided by the 16 breeds. The balance is extensively phenotyped cattle from the USMARC Germplasm Evaluation Project. Of the USMARC cattle, 2,600 represent a meticulous cross of F1 sires and dams conducive to finding chunks of chromosomes segregating ERTs.
“Our goal by the end of 2009 is to predict genetic merit with BeadChip data for at least some traits for the 2,000 influential sires provided by the breed associations,” says Mark Thallman, USMARC research geneticist. BARC researchers unveiled the first such estimates in the dairy industry a year ago.
At MU, more than 11,000 animals have been genotyped, including 2,000 registered Angus bulls, 2,000 registered Limousin bulls and 4,000 Angus steers from Circle A Angus Ranch at Iberia, MO. That number also includes 3,000 steers from the industry's Carcass Merit Project represented by the breed associations for Angus, Charolais, Hereford, Limousin and Simmental. According to Taylor, markers are being identified to produce GS tests for growth, fertility, carcass traits, feed efficiency and meat tenderness.
In total, Taylor explains the iBMAC consortium has assayed more than 31,000 animals that will provide the foundation of GS breeding estimates in the dairy and beef industries.
Independently, a committee for the National Beef Cattle Evaluation Consortium is working to merge estimates from GS, along with other types of DNA tests, into the system that computes Expected Progeny Differences (EPDs) from pedigrees and phenotypes.
Thallman cautions that the infrastructure required for industry-wide GS predictions will take more than another year. As well, he points out the cost of whole-genome assays will likely limit their use for the time being to herd-sire and donor-cow prospects.
Down the road, though, Thallman explains, “We would expect that as we determine which SNPs have the strongest relationship to ERTs, lower-cost products would be developed using only a few hundred SNPs. That would open the door to marker-assisted management (MAM), a fundamentally different application of the technology.”
For instance, with MAM, enabled by GS, Thallman explains calves entering the feedlot could be sorted into different expected outcome groups matched to feeding regimens and marketing objectives. Or, cow-calf producers could more accurately select replacement heifers, increasing production and decreasing costs.