Genomics Has Reduced Generation Interval
Progeny testing has been a successful strategy to identify the best bulls for widespread use in AI and as sires of bulls for the next gener-ation. Progeny testing, however, takes more than three years, is expensive and limits the number of bulls evaluated. In contrast, genomic selection allows AI studs to evaluate many more bulls than is feasible through a traditional progeny testing program.
Genomic selection refers to selection decisions based on genomic breeding values. The discovery of thousands of DNA markers plus the development of predicted breeding values based on marker data has allowed for accurate genomic selection of dairy sires by AI centers.
Consequently, selecting dairy sires for use in AI has shifted from progeny testing to genomic predictions of milk production, fertility and health traits. With genomic selection, the sire and dam of a next generation sire are selected on the basis of DNA marker profiles and mated soon after puberty. The next generation sire is marker-analyzed for desirable and undesirable traits as an in vitro fertilization (IVF) embryo or shortly after birth.
Genomic selection has reduced the generation interval, or the average age of parents when offspring are born. Males and females both contribute to the reduced interval, as elite genomic heifers 6 to 8 months of age undergo ovum pick up, followed by IVF and embryo transfer.
According to scientists in USDA’s Animal Genomics and Improvement laboratory, the generation interval of sires of bulls has decreased from approximately seven to two and a half years, from 2010 to 2015, respectively. Over the same period, the generation interval of dams of bulls has decreased from four to two and a half years.
A reduction in generation interval translates into more rapid genetic progress. Indeed, genomic selection has increased the rate of improvement in economically important traits such as daughter pregnancy rate, productive life and somatic cell score.
A common misconception is genomics will replace DHIA milk testing. This is not true, as genomics is based on the relationship of the phenotype and genotype. Genomics requires accurate data collection to establish the reference population to calibrate genomic results and to continually update the reference population. The flow of data (milk testing, type classification and health traits) into the system is critical as the size of the reference population affects the accuracy of genomic predictions. Genomics, similar to many other new technologies, adds information but does not replace information.
PROGENY TESTS STILL VALUABLE
Genomic evaluations provide more accurate information on young bulls than what was previously available. Although the percentage of semen sold from bulls without progeny data has risen steadily from 2008 to today, progeny-tested sires remain in demand as they compete favorably with young genomic bulls. This is likely a result of the critical mass of data generated via progeny test, which facilitates the accurate evaluation necessary for continued widespread use.
As a result of the reduction in generation interval and increased rates of genetic improvement, the competitiveness of young genomic sires will likely improve. Progeny testing can continue to provide competitive progeny-proven sires to satisfy market demand.
Joseph C. Dalton, Ph.D., is a professor at the University of Idaho. His research is focused on increasing the efficiency of AI in dairy cattle, including heat detection accuracy, synchronization programs and AI technician proficiency.
Note: This article appears in the September 2018 issue of Dairy Herd Management.
Tue, 09/18/2018 – 12:57
Genomics makes it possible to make selection decisions at an early age, shortening the generation interval and speeding progress.
Source: Dairy Herd