This review outlines revisions in the AHA genetic evaluation released December 4, 2017. Previous AHA evaluations utilized Molecular Breeding Values (MBVs) to predict the genomic contribution by correlating the genetic relationship between MBVs and traits of interest. The new evaluation more precisely evaluates the genomic differences between animals using an analytical approach called the Marker Effects Model. Like phenotypes, the marker effects of a genotype directly impact an animal’s Expected Progeny Difference (EPD). Like most breed associations, AHA has conducted a full multi-trait genetic evaluation with all traits correlated to one another, either through a favorable or unfavorable relationship. With the new evaluation, traits have been grouped into subsets optimal for inclusion allowing more frequent and efficient evaluations and better estimating traits of interest rather than building these traits through correlation.
The main Growth model stayed the same, but because the new analysis uses the single step Marker Effects Model, some animals’ EPDs changed as a primary result of increased accuracy and reduced bias. Scrotal circumference is correlated with weaning weight, but only Scrotal Circumference EPDs are reported. There is a decrease of correlated data impacting Scrotal EPDs, and even though this impact is small, the new handling of the genomic component caused changes to some animals’ Scrotal Circumference EPDs.
Birth weight is used as the correlated trait with carcass traits to account for selection. Weaning and yearling weight are no longer included as correlated traits with carcass fat and carcass marbling. Instead scan fat and intra-muscular fat along with birth and carcass weight are used to resolve traits of interest when real carcass data has not been collected. This creates some changes in Carcass Fat and Marbling score EPDs, the two traits reported from this model. The variance components for this analysis were re-estimated for all carcass traits and the correlation between scan intramuscular fat and carcass marbling score decreased from 0.70 to 0.54 which combined with the new handling of the genomic component, causes changes to some animals’ EPDs. Weaning weight is used as a correlated trait, however, yearling weight will no longer be included as a correlated trait with carcass weight and carcass rib-eye area to account for selection. Instead scan rib-eye area and weaning weight are included with available carcass data to calculate the traits of interest. Carcass rib-eye area and carcass weight are the only two traits reported from this model. Correlations comparing the previous carcass trait EPDs to the new ones are very high, but can be different for some animals because of the new handling of the genomic component.
The updated AHA genetic evaluation uses a random regression statistical procedure to calculate calving ease and maternal calving ease. Birth weight is used as the correlated trait in this model but is not reported from this model as it is reported from the Growth analysis. This updated evaluation moves away from the previous threshold model for calving ease, in part because the old model required that observations that are the same within a contemporary group or herd could not be used. This means that the new analysis uses all observations regardless of score as well as using the corresponding birth weight phenotype. Even though this evaluation uses only data from two-year-old heifers, more observations result in a more comprehensive calving ease evaluation than in the past. Again, the new handling of the genomic component changes some animals’ EPDs.
Mature cow weight is analyzed with weaning weight, but only mature cow weight will be reported out of this model. The Weaning Weight EPD comes from the Growth analysis. Even though the new MCW EPDs are highly correlated to the old, changes occur because yearling weight is no longer used as a correlated trait and the new handling of the genomic component. The udder and teat evaluation remains the same model as it has been with the exception that it now includes the genomic information to increase the accuracy of prediction.
Sustained Cow Fertility is analyzed by itself, without a correlated trait, using random regression statistical model to predict female fertility and longevity. SCF EPD trait predicts a female’s ability to stay in the herd through the age of twelve for ten calving after calving as a two-year-old heifer. The EPD is reported on a probability scale meaning that a higher EPD for a sire means his daughters are more likely to remain fertile and produce more calves in their lifetime. Dry Matter Intake includes weaning and yearling weight as correlated traits, but dry matter intake is the only trait reported from this model. The DMI EPD predicts the daily consumption of feed. Because of the limited number of phenotypes collected for these traits that have a corresponding genotype, the genomic component is not included in either of these models.
In the updated AHA evaluation, phenotypes are used only from progeny born after 2001, the start of Whole Herd Total Performance Reporting (TPR) and the pedigree data includes at least three generations of pedigree from the observations through great-grandparents. This data window reduces the biases from possible incomplete reporting of data prior to Whole Herd TPR which is based on a cow inventory system since previous data points were collected only when a breeder chose to register a calf. Sires or dams that had progeny born on both sides of Whole Herd TPR may have seen EPD and accuracy values change.
The computing power of BOLT more precisely calculates accuracy, the hardest piece of a genetic evaluation to correctly compute. In the past, an approximation technique was used to calculate accuracy by all breed organizations. Improved computing known as sampling allows the direct calculation of key variables used in calculating accuracy rather than from approximating these variables. Previous approximations overestimated the accuracy of EPDs, especially for young animals, and accuracy changes occur because this direct method does not contain the bias from the approximations.
Both Dry Matter Intake (DMI) and Sustained Cow Fertility (SCF) are now to be included in the AHA economic indexes along with other key Economically Relevant Traits (ERT’s) Carcass Weight (CW) and Mature Cow Weight (MCW). Adding these ERT’s into the economic indexes provides a more robust and comprehensive selection tool for commercial producers to select Hereford bulls to be used on Angus based cows. DMI and CW are included in all three AHA economic indexes to help predict the cost associated with feed inputs and to measure the end-product pounds that are critical for profit. SCF replaces scrotal circumference as the predictor of fertility and is a large contributor to both maternal indexes. Because of the inclusion of these key ERT’s, some animal index values have changed.