September 2006 - CGIL

There seemed to be no clear relationship between the percentage of daughters with modified data
and the ranking of sires. A question was posed as to whether it would be possible to apply a
different method for outliers to yield traits than to SCS, since there tended to be more erroneous
yield data compared with SCS. This method would be possible, but one would always have to
keep in mind that there is a built-in correlation structure between the 4 traits in the model.
DHI manipulates and screens data before sending it to CDN for inclusion in genetic evaluation. In
addition, they also place holds on records until the subsequent test so that outlying records from
the previous test can be validated. Record screening is only performed on yield traits, not on SCS.
It was noted that this screening is only performed on a cow-to-cow comparison, not cow-to-herd
basis. It was estimated that DHI screens approximately 3-5% of the records for inclusion in
genetic evaluation. Calculating the percentage of records expected to be outliers would not easy
since outlying records are defined by DIM and are specific to each cow and her predicted
lactation curve. Producers are aware of DHI’s editing process so it is no longer beneficial to
attempt to manipulate yields on one test day. This manipulation must be constantly conducted in
order to go unrecognized. Due to owner-supervised testing, it is no longer beneficial to
manipulate owner tests, since supervised tests will tend not to pass the editing process and be
removed every time.
Breeding Values are accounted for with the robust procedure, animals therefore animals that are
outliers because of their superior genetics would not be discounted. The adjustment for
heterogeneous variance and robust estimation procedures are interconnected. If heterogeneous
adjustments were performed prior to estimation, then fewer outliers would be expected. It is
possible to have a different k value for different traits.
1.2 Phantom Genetic Groups and Genetic Trend
Schaeffer
At the previous technical meeting, the possibility of smoothing the effects of phantom genetic
groups was proposed. In this study, a small (non-species specific) population was simulated to
investigate the effects of different genetic grouping methods. Eight generations of selection (for
two correlated traits) and matings were performed. Missing parents were randomly assigned,
therefore results from this study mat not be as bias as they might be in the actual dairy cattle
population. Four different models were investigated; a simple animal model, a model that
included a year of birth effect, a model including phantom parent groupings, and lastly a model
that included year of birth effects and phantom parent groupings. Three different percentages of
unknown parents (0%, 10% and 25%) were simulated for each model. Variance components,
breeding values and genetic trends were estimated.
Correlations between the true BV and estimated BV were calculated. Only small differences
between models were realized. The highest correlation was found for the simple animal (without
year of birth effects or phantom groups). As the percentage of unknown parents increased, the
correlation decreased. When year of birth effects are added to the model, unknown parents cause
problems in estimating breeding values.
Average inbreeding coefficients for animals born were summarized. As the percentage of
unknown parents increased, the inbreeding coefficient decreased. Therefore when an increasing
amount of the pedigree was missing, the inbreeding rate was significantly underestimated. No
statistical differences between models were found when comparing genetic and residual variances
and heritability. The simple animal model gave unbiased estimation for all pedigree structures.