Friday General Session
How Can We Best Use DNA Data in the Selection of Cattle?
Abstract:
Some traits are controlled by single genes, such as many genetic abnormalities, and many cattlemen already use DNA tests for these genes. However, most economically important traits are controlled by many genes and are influenced by the environment.
Recently, panels of 50,000 genetic markers called SNPs have become available, and they provide an opportunity to select for all the traits we seek to improve. The first step in using these SNPs is to estimate the effect of each SNP on all the important traits. To do this requires a reference population of cattle that have been genotyped for the SNPs and measured for the traits. From this population a prediction equation is derived that predicts breeding value or progeny difference for a trait, say marbling, from the 50,000 SNP genotypes (breeding values are just twice the progeny difference). This prediction equation can then be applied to animals that have SNP genotypes but no phenotypic information to calculate a predicted breeding value that is sometimes called a molecular breeding value (MBV).
Usually animals will have some other source of information about their breeding value (e.g., pedigree and ultrasound scans) that is used at present to calculate a traditional estimated breeding value (EBV) or expected progeny difference (EPD). Therefore, the MBV should be combined with the traditional EBV to give a prediction of breeding value that is more accurate than either source of information alone. This new estimate has been called a genomic EBV (GEBV).
Obtaining a prediction equation that accurately predicts breeding value from SNP genotypes has proven difficult in beef cattle. We have often found that a prediction that works in one breed or herd does not work in other breeds and herds. Therefore we need to estimate the prediction equation from very large reference populations that include several breeds and then test or validate the prediction equations across large populations also comprising several breeds.
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About the speaker:
Mike Goddard is a professorial fellow in animal genetics with a joint appointment from the University of Melbourne and Victoria Department of Primary Industries. He graduated in veterinary science (1972) and did a doctorate on breeding guide dogs for the blind (1979) before becoming a lecturer in biometrics at James Cook University, Townsville (1977-1983), and then a scientist in the Department of Agriculture (1983-1993) and director of the Animal Genetics and Breeding Unit (1993-1998).
Goddard’s research on genetic improvement of dairy and beef cattle and pigs and application of genomics to genetic improvement has broad application and global recognition. In 2001, he published the first paper on genomic selection. It showed how to use dense SNPs covering the whole genome to accurately estimate the genetic merit of animals.
Currently, he serves as the chief scientist of the Cooperative Research Center (CRC) for Beef Genetic Technologies in Australia. This CRC is using the human, mouse and bovine genomes to improve the profitability and productivity of Australian and global beef businesses. It is one of 49 CRCs funded by the Australian Commonwealth Government.
BIF acknowledges the support of the National Research Initiative Grant no. 2009-55205-05062 from the USDA Cooperative State Research, Education and Extension Service Animal Genome program for sponsoring Goddard’s attendance at this meeting.
Editor’s Note: The above material is provided by and posted with permission of the Beef Improvement Federation. Please direct reprint requests to BIF via the “Contact BIF” page at www.beefimprovement.org.
