**3. Genomically enhanced PTA and STA**

**Table 1** shows the number of phenotypic records, the number of animals with phenotypic records, mean and standard deviation of the incidence, and the estimated heritability of wellness traits. The number of records for cow wellness traits ranged from about 3.2 million for KETO to almost 5.8 million for MAST. Large differences in the number of records available for individual traits were caused by variations in recording among the farms. The mean incidence of the disorders in our analysis varied from 2.6% for DA to 16.7% and 29.1% for LAME and MAST, respectively, indicating that MAST and LAME are the most common health problems in dairy herds.

The estimated heritabilities for wellness traits were in the narrow range from 0.079 (LAME) to 0.112 (RETP) and were comparable to those reported previously based on studies using similar data and methodology [8, 12]. Heritabilities under 10% are generally considered low, due to proportionally large effects of the environment and not to the lack of genetic variability within the population. Traits with low heritabilities require more data to produce accurate estimates of animals' breeding values.

#### **Figure 1.**

*Distribution of STA for MAST for all animals in the analysis. Animals with extremely low STAs are more likely to develop MAST. Animals with extremely high STAs are considered more resistant to MAST [Dianelys Gonzalez, personal communication, 2021].*


#### **Table 1.**

*Basic statistics and the estimated heritability of wellness traits [Dianelys Gonzalez, personal communication, 2021].*

*Genetic Control of Wellness in Dairy Cattle DOI: http://dx.doi.org/10.5772/intechopen.103819*


**Table 2.**

*Statistics of gPTA, STA, and reliabilities for wellness traits for genotyped animals (n = 1,512,546) [Dianelys Gonzalez, personal communication, 2021].*

**Table 2** shows descriptive statistics for gPTA, STA, and reliabilities for all genotyped animals (n = 1,512,546) in the current genetic evaluation. The average values of gPTA and STA were close to zero and 100, respectively, as expected. The variation of gPTA, expressed by their standard deviation and range, reflects the heritability of the trait and the incidence of the disorder. Traits with higher heritabilities and incidence (MAST, LAME) show higher amount of variation in gPTAs. Broader range of gPTA is preferable because it enables better segregation of animals of different genetic merit for wellness traits. Reliabilities for all traits averaged around 50%, but ranging from 0 to over 99%. The reliabilities reflect both the amount of data and the heritabilities of the traits. Very high reliabilities were obtained for bulls with large numbers of phenotyped daughters. A small number of genotyped animals had reliabilities equal to 0. Zero reliabilities for genotyped animals are not expected unless an animal belongs to a different breed or is poorly connected to the population and has an extreme value of the diagonal of the genomic relationship matrix. Animals with zero reliabilities were either crossbreds registered as Holsteins or Holstein animals from unrelated populations from other countries or their offspring without genotyped ancestors or relatives in our data.

### **4. Validation of genomic predictions for wellness traits**

Genomic prediction for wellness trait obtained at young age is considered a useful tool for selection and management for genetic progress and to assist with culling and breeding decisions in the existing herd. Genetically better heifers and cows can be bred with sexed semen, whereas genetically inferior animals can be sold for beef early on or bred with beef semen [35]. It is best practice for any genetic evaluation to assess the effectiveness of the genetic estimates to predict performance of the evaluated animals. For that matter, we conducted a validation study to determine the effectiveness of the wellness trait genomic predictions in US Holstein cows in an independent population of animals [34].

The study involved 11 large dairy herds distributed across the major dairyproducing regions of the United States. One of the criteria for including herds in the study was that they did not provide phenotypic data for the development of genomic predictions for wellness traits. This was important in order to mimic the experience of new customers who decide to genomically test their animals.

Tissue samples from 2875 animals from the 11 herds were genotyped (Zoetis Genetics, Kalamazoo, MI) after which their genotypes and pedigree information were included in the genetic evaluation for wellness traits. gPTA and STA were obtained

for six wellness traits—MAST, METR, RETP, DA, KETO, and LAME. Wellness trait predictions (STA) were used to rank and assign animals to 4 quartiles—genetic groups, for each trait (bottom 25, 26–50, 51–75, and top 25%). Animals were ranked within herd to account for the lack of independence between animal and herd.

Statistical analysis was performed using a GLIMMIX model with a binomial distribution in SAS (version 9.3, SAS Institute Inc., Cary, NC; SAS, 2011). The statistical model included the fixed effects of genetic group, lactation, and age group at the beginning of the study. Herd and animal nested within herd were included as the random effects. The marginal means (incidence) and odds ratios were obtained. The average cost per animal associated with each case of an adverse health event was calculated as a product of the estimated marginal mean and the previously published cost estimate per case of a health event [34].

**Table 3** shows the average incidence (marginal means) for the four genetic groups—quartiles—based on gSTAs, the estimated average costs of disease per animal, and the odds ratio compared to the best quartile. The differences in disease incidence between the top and bottom quartiles were 2.9% for retained placenta, 10.8% for metritis, 1.1% for displaced abomasum, 1.7% for ketosis, 7.4% for mastitis,


**Table 3.** *Results of the analysis of genetic groups for wellness traits in the validation animals [34].* and 3.9% for lameness. The differences in marginal means by genetic groups (disease incidence) translate into appreciable differences in expected economic costs.
