Potential for Random Regression Models in Genetic Evaluation

Currently, 5%-6% of the weaning data being submitted is eliminated from National Cattle Evaluation (NCE) because the age of the calves falls outside the 160- to 250-day window recommended by Beef Improvement Federation (BIF) guidelines. Much of this data is from early-weaned calves, observed Keith Bertrand of the University of Georgia, Athens, during the Selection Decisions roundtable discussion at the 35th BIF Annual Meeting May 29, 2003, in Lexington, Ky.

The multi-trait model currently used for growth traits predicts traits at standard ages, Bertrand explained. The actual records are pre-corrected for age or discarded if they don’t fall within the designated age windows. With more producers weaning early as a management tool for reducing the nutritional requirements of the cow, many weights are being excluded due to age of the animal.

Using a random regression model would allow researchers to:
• treat a trait as a continuous function;
• consider records submitted at any age;
• make predictions for any age;
• include all data submitted for evaluation; and
• account for the differences in heritabilities, for example, in animals of different ages.

A simulation study in Australia showed some promise for using the random regression model, especially as more weights were available on an animal, Bertrand reported. But a follow-up study at the University of Georgia on actual field data yielded lower-than-expected correlations between multi-trait models and random regression models. Bertrand attributed the lower-than-expected correlations to poor parameters in the random regression models.

“If you have poor estimates of your heritabilities and correlations, the random regression model isn’t such a good thing to use,” Bertrand said. While the random regression models have potential, he added, work still needs to be done to develop parameters for the random regression models that will give us reasonable breeding values.

To access Bertrand’s Power Point presentation, visit the newsroom.

— by Shauna Rose Hermel