|With genomic testing, Holstein reliabilities have shot up from 25% to 30% for net merit, milk and fat yield, productive life and type.
For the past year, USDA sire summaries have been incorporating genomic information in AI sire proofs, increasing the accuracy of young sire proofs and the potential genetic gain. Kent Weigel, an Extension dairy specialist with the University of Wisconsin, gives the concept a solid "B.”
"Genomics is a big step forward, but we still need daughter information,” he says. "We're not yet at the stage where we can identify the next sires of sons without using conventional genetics tools.”
After evaluating about 240 young bulls, Weigel says genomics is certainly better than simply using parent average. "We can identify the better bulls, but we still don't know if any single bull is superior,” he says. So producers need to spread their risk by using a number of these higher-ranking, higher-reliability bulls.
Weigel's analysis shows a 62% to 70% correlation between genomic evaluations and daughter yield deviations for traits such as milk, fat and protein yields. Those correlations dip to 53% for somatic cell score (SCS) and 34% for daughter pregnancy rate (DPR). But the correlations between parent average and daughter yield deviations are substantially lower: 45% to 55% for milk, fat and protein yields, 37% for SCS and 21% for DPR.
Roy Wilson, associate vice president of the Large Herd Business Center in Genex's Domestic Marketing Division, is pleased with the progress genomics offers. "The genetic gain is like getting the gain of multiple sire summaries in one, with no change in the genetic base,” he says.
In other words, genomics allows analysts to more accurately select which young sires to bring into service, cutting the generation interval of young sires by more than two years.
With conventional sire selection and progeny testing, it takes six years from the time a bull mother is selected for mating until her son is proven. With genomics, AI firms can identify which bull from a flush is more likely to have received a better sampling of genes within six to eight months of birth.
That bull can be brought into active service as early as 18 months of age. (AI firms typically bring genomically evaluated bulls into full service at 24 to 30 months, when they can produce larger quantities of semen.) Conventionally proven bulls have to wait for their daughters to mature and begin milking before their performance can be evaluated, Wilson says.
The other big advantage is in sorting out which bulls to select for AI sampling. On average, full-sibling brothers are expected to share half of their genes from each parent. "In reality, they may share 40% to 60% because each inherits a different mixture of chromosome segments from the parents,” Wilson explains.
For example, Genex had three full-sibs sired by Ramos: Cash, Cassino and Chester. The pedigree parent average net merit of the three bulls was 638 with a reliability of 35%. However, when the bulls' genomics were evaluated, Cash had a genomic net merit of 528, Cassino 918 and Chester 573. All three had a genomic reliability of about 73%.
"Not every flush of brothers will rank this differently,” Wilson says. "But 75% of the time, there is a clear-cut winner. The first choice of a litter of full-sibs is now the only choice.”
Knowing the winner eliminates the expense of sampling and housing young sires of unknown merit for five years. Plus, it theoretically reduces in-breeding because it reduces the number of females bred to a sire-mating embryo flush. That's because only one bull, rather than multiple bulls from the flush, is being sampled.
AI firms and university geneticists now need to refine how they're using genomics. "We need to let the dust settle and determine the best way to proceed,” Weigel says.
"Do we need a more powerful, but more expensive, genomic chip? Probably not. Do we need a better, cheaper screening chip to allow us to look at more animals? Or do we need to refine data collection methods to better utilize the genomic information we already have?” he asks.
INBREEDING A CONCERN
|Cheaper genomic tests might soon be available to screen females for potent, outlier pedigrees.
As AI firms intensify their use of genomic selection, they might be increasing the level of inbreeding, particularly in Holsteins.
"Reduced cow fertility is a key consequence of inbreeding, because inbred embryos are more likely to be nonviable and lost,” says Les Hansen, a University of Minnesota dairy geneticist.
In 1990, the average level of inbreeding in Holsteins stood at 2.5%. Since that time, as selection intensity focused primarily on production, the level has risen to 5.8%.
Inbreeding becomes a real concern at 6.25%, and if present practices continue, that level could be reached in the next decade.
Elevation and Chief, bulls born in the 1960s, together comprise about 30% of the Holstein genetics. Blackstar, a bull born in 1983, already has a relationship of 16% to the Holstein breed. "Many of Blackstar's sons and grandsons are now having a large impact on the breed,” Hansen says.
U.S. Holstein genes have also been exported around the world for decades. "Essentially, no ‘outcross' Holstein genetics exists globally. Genomics [which concentrates the gene pool even tighter] might be the last thing the breed needs,” Hansen says.
Genex's Roy Wilson agrees—if AI firms continue to use genomics as they have. But there is hope.
The problem is the high cost of DNA testing, he says. The test for a high-density DNA analysis is $250. So it's prohibitive to DNA type the top 1,000 or 1,500 cows in a 3,000-cow dairy.
A less accurate, lower-density test is on the horizon, however. Priced at about $35, it is expected to be released early in 2010.
With that test, an AI firm or producer can screen the top half of a herd for one-seventh of the cost today. Once the best half dozen individuals are found, further testing with the more accurate test can screen the top half dozen females.
In this way, high-performing, out-cross females might be found that can be used as bull mothers of outcross sires.
Intro to genomics
Can you believe genomic proofs?