Updates to Growth Trait Predictions

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Updates to Growth Trait Predictions

Jackie Atkins Ph.D., Matt Spangler Ph.D., Bruce Golden Ph.D., and Wade Shafer Ph.D.

The genetic evaluation is constantly evolving with updates to models as new science is discovered and new technologies are available. One area under recent scrutiny is the prediction of growth traits (birth, weaning, and yearling weights, and milk). The IGS Genetic Evaluation Science Team is investigating the following five areas of potential improvement in the prediction of growth traits.

The goal of each of these changes is to improve the prediction of growth traits. Breeders and IGS partners should expect to see the implementation of improvements to growth trait prediction in the approaching months. 

 1. A new definition of contemporary groups based on the age of the dam.

Regardless of how users designate contemporary groups, the science team is considering placing all calves born from first-calf dams into separate contemporary groups (CG) from calves out of mature cows. Given the vast majority of producers actually manage this age group separately, it is reasonable to define their calves as their own CG. Handling these as separate CG is a valid way to reduce the environmental noise caused by different management strategies for this age group. 

2. Setting the genetic correlation between weaning weight maternal (milk) and weaning weight direct to 0 (compared to - 0.3).

The magnitude, and even direction, of the correlation between weaning weight direct and milk, has been long debated in scientific circles. In fact, there is a wide range of estimates that exist in the scientific literature. Given that, the science team feels the appropriate way to model these traits is to assume they are independent (i.e., genetic correlation of 0). We expect the impact of this change to be greatest for low accuracy animals.

3. Different variances for different sexes (heterogeneous variance).

Males usually have a higher growth potential than females simply due to gender. As a consequence, the variation associated with their weights also tend to be greater. This difference in the amount of variation between the sexes should be accounted for in genetic predictions. The IGS team is testing the validity of setting the variance of growth EPDs on a male base.

4. New DNA Marker subset.

As the number of genotyped animals has increased, so has our ability to estimate marker effects and identify subsets that are more predictive. Relative to growth traits, a new (and larger) subset of markers has been identified to add more accuracy to EPD.

5. Accounting for different birth weight collection methods.

When the IGS Science Team began looking into growth trait data, we discovered that not all birth weight CG followed expected amounts of variation in the weights. In some cases, weights are rounded to the nearest 2 lb increment or 5 lb increment. In other cases, the amount of variation was substantially reduced due to the use of hoof tapes (see the December 2019/January 2020 issue of the Register for more information on hoof tapes). There were also cases where the reported weights were clearly fabricated (for instance, all calves weighed 75 lb). Some of these data are useful, but they are clearly on a different scale and need to be treated appropriately. Dr. Bruce Golden developed a way to use machine learning to recognize unique features of each class of birth weight observation and predict how it was generated (e.g., scale weights, hoof tape, rounding, fabricated). By accounting for the various categories, the genetic evaluation is still able to use the records submitted that fall out of biological expectations for most scenarios, while more accurately accounting for different practices of collecting the weights. 

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