**4.1 Validation of genomic predictions for wellness traits in other countries**

To date, demonstration studies for the wellness traits have been conducted in multiple countries using similar methodology as described in [34] (Anthony McNeel and Fernando Di Croce, Zoetis Genetics Technical Services, personal communication, 2021). In 2020, a demonstration study was conducted using 1053 animals across four farms in the United Kingdom [36]. **Table 4** shows disease incidence (marginal means) of the best and worst third (33%) of the animals when animals are ranked by genomic standardized transmitting abilities (STA) and the estimated disease cost per 100 cows. In this study, a 43% relative reduction in the incidence of mastitis was observed between the bottom and top third of cows ranked on the MAST STA. Translated into economic terms, this equates to £38 a cow per lactation. Similarly, a 42% reduction in the incidence of lameness was observed between the bottom and top third of animals ranked on the LAME STA, equating to £13 a cow per lactation.

These observations have important implications for the sustainability of animal agriculture as fewer health events translate into less antibiotic usage. **Table 5** shows the results for antibiotic use for mastitis treatment in the genomic groups (quartiles) when animals are ranked by standardized transmitting abilities (STA) for MAST. The animals in the best genetic group required almost three times fewer the intramammary antibiotic tubes compared with worst genetic group ranking animals.

Another demonstration study using similar methodology as in [34] was conducted in 2019 across multiple European countries (Anthony McNeel and Fernando Di Croce, Zoetis Genetics Technical Services, personal communication, 2021). Over 4000 animals from 29 dairy herds in 7 different countries (France, Germany, Russia, Poland, Spain, Ukraine, and the Netherlands) were sampled for the study. First and second lactation animals that produced a usable genotype, passed breed check and calved within the desired time frame (April 1st to


**Table 4.**

*Results of the independent demonstration study conducted in the United Kingdom in 2020 [36].*


#### **Table 5.**

*Antibiotic use for mastitis treatment in the genomic groups for MAST [36].*


#### **Table 6.**

*Summary of results obtained in the validation study conducted in 7 European countries in 2019 (McNeel and Di Croce, personal communication, 2021).*

September 30th, 2018) were included in the analysis. The incidence of the respective health events and the costs associated with disease were calculated. **Table 6** contains average STA, disease incidence (marginal means), and the estimated disease cost per 100 cows of the genetic groups (quartiles) when animals are ranked by standardized transmitting abilities (STA).

#### **5. Why do genomic predictions for wellness traits work so well?**

The validation studies performed in the US commercial herds as well as in the UK and European herds showed consistent results, regardless of differences in location, herd size, and farm management. Genomic predictions for wellness traits in Holstein have been created using data from US commercial herds and they have been shown to accurately predict performance of the animals in Holstein herds not only in the USA, but also in other countries, in herds that did not contribute phenotypic data for development of genomic predictions. How is that possible?

The Holstein population in the United States is genetically fairly homogeneous. A study of genetic variation on the Y chromosome has revealed that more than 99% of all known Holstein artificial insemination (AI) bulls in the United States can be traced through their male lineage to just two bulls born in the 1950s, Round Oak Rag Apple Elevation (Elevation) and Pawnee Farm Arlinda Chief (Chief) [37]. Therefore, the

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

**Figure 2.**

*(a) Principal component analysis of purebred animals; (b) magnified Holstein cluster [Tiago Passafaro, personal communication, 2021].*

genomic relationships among all Holstein animals are strong in the United States, as well as in other countries that have imported Holstein genetics (mostly *via* frozen semen). Animals that are well connected to the population used to develop genomic predictions will have accurate predictions for wellness traits even without having their own phenotypes, or phenotypes of their herdmates, in the genetic evaluation.

A small number of animals registered as Holstein may not be well connected to the rest of the population. Crossbred animals or Holstein animals from other countries from populations that did not use Holstein bulls imported from the United States may show loose relationships to the rest of the population, which results in poor predictions and low reliabilities of wellness traits gPTAs, even if the animal has a high-quality genotype in the evaluation. **Figure 2(a)** shows the population structure characterized by principal component analysis (PCA) of purebred animals distributed across the first two principal components, obtained using about 40,000 SNP markers. Breeds included in the analysis were Holstein, Jersey, Brown Swiss, Ayrshire, Guernsey, and the beef breed Angus. It is clearly visible that the individual breeds form distinct clusters, with the Holstein cluster being the largest (due to the largest number of Holstein genotypes in the analysis). However, when magnified (**Figure 2(b)**), the Holstein cluster shows several outliers, that is, animals that fall outside the main cluster, likely due to mild crossbreeding with Jersey. The genomic predictions for wellness traits for those animals may be less accurate than the predictions for animals within the main cluster, due to their poor connection to the rest of the Holstein population.

#### **6. Conclusions**

This chapter describes the development of genomic predictions for wellness traits in US Holstein cattle using large producer-recorded data, genomic

information, and sophisticated statistical methodology designed to handle large amounts of phenotypic, pedigree, and genomic data. Genomic predictions for wellness traits have been successfully validated in commercial herds in the United States, UK, and several European countries. These results indicate that genomic data of young calves and heifers can be used to effectively predict future health performance as long as the target population is genetically connected to the population used for developing those predictions. Improving health traits, commonly referred to as functional or wellness traits, through direct genetic selection presents a compelling opportunity for dairy producers to help manage disease incidence and improve profitability when coupled with sound management practices. Genetic selection for improved wellness traits will result in a permanent and cumulative improvement of herd health, as opposed to temporary relief achieved using antibiotics, vaccinations, and other management interventions. Including genomic predictions for wellness traits in an index, along with existing predictions for other economically relevant traits, could provide dairy producers with a more complete tool for selecting potentially most profitable animals.
