Improving Dairy Bull Selection--Genetic Markers Reduce Guesswork

Susan McGinley & Lorraine Kingdon
Sept. 25, 2000


To get a good dairy cow, you start with a good bull. The dairy industry relies on identifying bulls that have all the right genes for producing milk in the next generation of cows.

Artificial insemination (AI) companies take the lead in this search by selecting and maintaining the kinds of bulls their customers need and want. This process can be expensive and time-consuming because it involves mating the top 1 percent of the nation's cows with the very best bulls, using multiple ovulation and embryo transfer. Bull calves from these matings then enter a progeny testing program to "prove" their worth as sires.

""If we could improve the chances of selecting sires with the best genetic merit, then AI companies would see a substantial drop in investment per bull," says Sue Denise, a professor in the UA animal sciences department. "We are now accumulating evidence about regions of chromosomes that affect performance characteristics in cattle."

Currently, proving the value of young bulls in the dairy industry costs AI companies between $25,000 and $45,000 per bull and requires about a five-year wait for their daughters to grow up, get pregnant and start producing milk. The average investment in every successful AI sire is between $225,000 and $405,000.

Molecular biology will allow companies to identify important regions of chromosomes, identify important genes, and improve the accuracy of selecting young sires," DeNise says. "Also, companies will be able to narrow their testing to include only those potential bull mothers that possess the best genetic combinations."

In her research, DeNise extracts DNA from samples of milk, blood and tissue taken from daughters of bulls that have milk production records and from the sire himself. Chromosomes come in pairs and each daughter will only inherit half the chromosomes of a bull. The chromosomes are identified using molecular markers, so that DeNise can determine which chromosomal segment was inherited by each daughter.

She then evaluates each cow's performance record using these chromosomal segments. If DeNise finds differences between daughters that inherit different chromosomes, then there must be genes nearby in the sequence that influence performance. She can use this information in selecting among bulls of future generations, to choose those likely to have inherited favorable genes. By reducing the guesswork, this method improves the probability that each animal selected will be a successful AI bull.

The same technology can be used to identify bulls that are carriers of genetic defects. DeNise has developed a marker test to identify animals that may carry the gene for Weaver Syndrome, a neurological genetic disease in Brown Swiss cattle. Her lab in the UA College of Agriculture and Life Sciences is the official test location for the Brown Swiss Cattle Breeder's Association.

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