Genetic Control of Wellness in Dairy Cattle

Abstract

With increased selection pressure on milk production, many dairy populations are experiencing reduced fertility and disease resistance. Reducing susceptibility to metabolic diseases, such as ketosis, displaced abomasum, retained placenta, metritis, mastitis, and lameness, has long been excluded from genetic improvement programs, due to low heritability of those traits. However, research has shown that using large producer-recorded data, genomic information, and suitable statistical models can result in accurate genomic predictions for metabolic diseases, enabling producers to select animals with improved disease resistance early in life. Improving wellness in dairy herds not only increases economic efficiency of dairy herds, but also improves overall animal welfare as well as product quality and public perception of dairy farming. This chapter describes the development of genomic predictions for wellness traits in Holstein dairy cows in the United States and presents examples of validation of those predictions in commercial dairy populations in the United States and other countries.

1. Introduction

Over the last 50 years, genetic selection to improve milk production in dairy herds has been very successful. In many developed countries, the milk production per cow has more than doubled. About half of that progress can be contributed to genetics [1]. Along with increase in production, dairy farming has become more intensive. While the number of dairy farms is decreasing globally, the average herd size is increasing [2]. Selection pressure for higher yields and intensive farming have been linked to reduced welfare and an increased incidence of many common diseases in dairy cows, mostly due to genetic antagonisms between production and health traits [3, 4, 5]. Consequently, dairy cows are becoming less “robust,” which have negative consequences for the health and fertility of the modern day dairy cow [6, 7].

Profitable dairy cows are productive, fertile, and mostly “invisible”—they do not require extra attention or intervention to maintain their health through all phases of production. Having a larger proportion of mature cows that are productive and healthy during multiple lactations can enhance profitability of dairy operations. To reach their full potential and longevity, animals need to remain healthy from birth until calving, and then stay healthy and structurally sound, in addition to regularly calving and producing milk. Dairy animals that experience adverse health events negatively affect herd profitability through increased culling, veterinary expenses, and labor, as well as monetary losses through reduced milk sales [8]. The costs per case of the common dairy cow diseases were estimated to range from $181 for ketosis to $391 for displaced abomasum [9].

Dairy researchers and producers have made progress on providing the best environment for animals to reduce health events through nutrition, management, and housing. Additionally, genetic improvement of health and wellness traits in dairy cows is an attractive option for dairy producers because genetic gains are permanent and cumulative from one generation to another [10].

Genetic evaluation and selection for improved health traits has been lagging compared with selection for production and reproduction in dairy cows due to low heritability of health traits and the lack of centralized recording. Most health events in dairy herds have not been recorded by trained veterinarians, but rather by producers themselves using herd management software. However, research has shown that, given the large amount of data, availability of genomic information, and advanced statistical methodology, it is possible to provide accurate genetic and genomic predictions that producers can use as a tool to improve health and wellness of their herds.

In many countries, including the United States, the most frequently cited reason for not using health data in genetic evaluation of dairy cattle is the lack of a centralized national system to collect health record data. Although most dairy producers record health information of their animals using herd management software, the subjectivity of diagnosis and the user-defined terminology of health events contribute to increased difficulty in using health data in a genetic evaluation due to insufficient accuracy and inconsistency of recording [11]. However, several studies based on large amounts of producer-recorded data have shown that genetic selection for wellness traits can be effective in improving herd health in dairy cattle as long as the recording protocols within a herd are fairly consistent [8, 12, 13].

Genetic evaluation of health traits has a long tradition in countries with routine health data recording. In Scandinavian countries, health traits have been included in breeding programs since the mid-1970s [14]. Currently, over 97% of Norwegian dairy cows are included in the recording system [15, 16]. In other countries, the use of direct health data in genetic evaluation is progressing rapidly. Routine data collection and genetic evaluation for health traits in Germany and Austria started in 2006 [17]. In France, clinical mastitis has been included in routine genetic evaluation since 2010 [18]. In 2014, genetic evaluation for mastitis resistance was introduced for Canadian dairy cows; the evaluation is based on clinical mastitis incidence recorded in the first and second lactation and SCS [19, 20]. In Canada, genetic evaluation for ketosis and displaced abomasum was implemented in December 2016, followed by metritis and retained placenta, hoof health and lameness, and other functional traits in the following years [21].

The advances in molecular genetics and genome sequences have created unprecedented opportunities to select for genetically superior animals and increase the speed of genetic improvement of production, reproduction, and, especially, health traits in farm animals. In March 2016, Zoetis Genetics launched CLARIFIDE Plus, the first commercially available genomic test for wellness traits of dairy cattle. Today, CLARIFIDE Plus provides genomic predictions for 14 health and wellness traits in cows and calves of Holstein and Jersey breeds.

The goals of this chapter are (1) to describe the research leading to the development of genomic predictions for wellness traits mastitis (MAST), metritis (METR), retained placenta (RETP), displaced abomasum (DA), ketosis (KETO), and lameness (LAME) based on large producer-recorded data, genomic information, and sophisticated statistical methodology and (2) to present examples of studies focused on assessing efficacy of genomic predictions for wellness traits in independent commercial dairy herds in the United States and other countries.

2. Creating genomic predictions for wellness traits

2.1 Data

Phenotypic data have mostly been collected directly from producers upon obtaining their signed permissions. The main source of data was backup files from herd management software DairyComp 305 (Valley Agricultural Software, Tulare, CA), PC Dart (Dairy Records Management Systems, Raleigh, NC), and DHI Plus (DHI Computing Services Inc., Provo, UT). Backup files are processed using internally written scripts, and information on pedigree, production, reproduction, and health events is extracted. Terminology used to record the health events varies across different herds, which was standardized as described [12, 34]. About 300 herds from around the United States have been participating in providing data.

The majority of genotypes used in genomic evaluation have been obtained in the Zoetis genotyping lab. Samples from animals from commercial herds (hair, blood, ear tissue, or semen for males) submitted to Zoetis for genomic testing were analyzed. Upon DNA extraction, genotyping was performed using Illumina BeadArray SNP chips with a number of SNP markers ranging from about 3000 to over 80,000. Raw genotypes were edited following the criteria as described previously [22, 23]. All animals genotyped with lower-density chips (<40,000 markers) were imputed using the program FImpute [24] to a set of 45,245 markers selected based on their call rates and minor allele frequencies that are used in genomic evaluation.

2.2 Trait definition

Health events of interest were extracted from the herd management software backup files. Wellness traits mastitis (MAST), metritis (METR), retained placenta (RETP), displaced abomasum (DA), ketosis (KETO), and lameness (LAME) were considered.

Each wellness trait was defined as a binary event, having a value of one if a respective health event has been recorded at least once during the lactation and zero otherwise. Animal were required to have a lactation record with a valid calving date and lactation number, with a calving interval ranging from 250 to 999 days [23]. Lactations of the same cow without recorded disorders, as well as lactations of all herdmates of an animal without recorded health events, were added as “healthy” records. Phenotype records were checked against the pedigree, and all animals recorded as male as well as those having incompatible birth and calving dates were removed. Records were also removed if an animal in her most recent lactation did not reach an opportunity period, which was defined as a number of days in milk (DIM) by which 90% of all cases of a particular disorder have been recorded, or if the health event was recorded after the highest number of DIM when the occurrence of a disorder was biologically plausible. Animals not reaching the opportunity period were removed from the analysis regardless of whether they were healthy or sick.

Contemporary groups were created by combining the herd, year, and season of calving. Each group was required to have a minimum of 20 lactation records and at least one “sick” and one “healthy” record; otherwise, the entire group was discarded.

2.3 Methodology

Single-step genomic BLUP (ssGBLUP) was the method of choice for creating genomic predictions for wellness traits. ssGBLUP combines all available sources of information–pedigree, phenotypes, and genotypes–into one single evaluation, without the need of post-analysis processing, and incorporating information on genotyped and non-genotyped animals in this method in a straightforward manner [25].

The data were analyzed for each trait separately, using the following threshold model [23]:

$$\lambda =\mathit{X\beta }+Z_{h}h+Z_{a}a+Z_{p}p+e,$$E1

where λrepresents a vector of the unobserved liabilities for the given disorder; βis the vector of fixed effect of parity; parities 1, 2, 3, 4,and 5+ were considered; his the random effect of herd, and year and season of calving, where $h\sim N\left(0,I{\sigma }_h^2\right)$with the variance ${\sigma }_h^2$; four seasons were defined within each year: Winter (Dec-Feb), Spring (Mar-May), Summer (Jun-Aug), and Fall (Sep-Nov); ais the random animal effect, with $a\sim N\left(0,A{\sigma }_a^2\right)$, where ${\sigma }_a^2$is the additive genetic variance and A is the pedigree relationship matrix; pis the random effect of permanent environment with $p\sim N\left(0,I{\sigma }_{\mathit{pe}}^2\right),$and eis the random residual, where $e\sim N\left(0,I\right).$X, $Z_h$, $Z_a$, and $Z_p$are incidence matrices corresponding to the fixed effect in $\mathit{X\beta }$and the random effects of HYS, animal, and permanent environment, respectively.

In ssGBLUP, the inverse of the traditional pedigree relationship matrix, A⁻¹ is replaced by the inverse of H matrix, which is the pedigree relationship matrix augmented using genotypes [26, 27].

$$H^{-1}=A^{-1}+\left[\begin{array}{cc}0& 0\\ 0& G^{-1}-A_{22}^{-1}\end{array}\right]$$E2

where $G^{-1}$is an inverse of the genomic relationship matrix and $A_{22}^{-1}$is an inverse of the pedigree relationship matrix for genotyped animals only. The genomic relationship matrix G was constructed using allele frequencies for each of the 45,245 SNP markers as described in [28]. By using the “hybrid” relationship matrix H, the SNP markers are utilized to better define relationships among all animals in the analysis.

Prior to genetic evaluation, variance components for each trait were estimated using the same data and model, but without including genotype information. Heritability of each trait was expressed as the ratio of genetic variance (${\sigma }_a^2$) and the sum of all estimated variances:

$$h^2=\frac{{\sigma }_a^2}{{\sigma }_a^2+{\sigma }_{\mathit{pe}}^2+{\sigma }_h^2+{\sigma }_e^2}$$E3

2.4 Software

All analyses were performed using the BLUPF90 suite of programs created by Prof. Ignacy Misztal and his team at the University of Georgia in Athens (UGA) [29]. First, the data were formatted and renumbered using the program RENUMF90 v. 1.14. The variance components were estimated using the program THRGIBBS1F90 ver. 2.116. The genetic evaluation was performed with a program CBLUP90IOD2 version 3.21, which is appropriate for massive datasets as it uses iteration on data. To accommodate the large number of genotypes, the algorithm for proven and young animals (APY) was applied [30]. The APY algorithm generates the inverse of the genomic relationship matrix (G⁻¹) indirectly using recursion based on a proportionally small subset of animals (proven or core animals). Only the genomic relationship matrix for the core animals needs to be inverted; then, the elements of G⁻¹ for all other animals (young or non-core) are calculated linearly by recursion, which significantly reduces the computational requirements [30]. Computational details of the APY algorithm are described in [31, 32]. In our analysis, the core consisted of 25,000 animals selected at random. Each trait was run in a separate process, but with the same model and $H^{-1}$matrix. The reliabilities of estimated breeding values were approximated using the program ACCF90GS v. 2.54 that implements an algorithm that combines contributions of genotypes, pedigree, and phenotypes [33]. Reliability of estimated breeding values (EBV) is formally defined as the squared correlation between true (unknown) and estimated breeding value; in practice, reliability shows how well the estimate represents the true breeding value. Higher values of reliabilities indicate that EBV are more accurate and less likely to change over time, with the addition of new information. Reliability estimates depend on the amount of data available, heritability of the trait, connectedness among the animals in the population, as well as methodology used to estimate reliabilities. In our analyses with a very large number of genotypes, an approximation considers value of the diagonal of the G matrix, g_ii, as a proxy for the contribution from genotypes (Daniela Lourenco, University of Georgia, Athens, personal communication, 2016).

2.5 Expression of evaluation results

The solutions for the random animal effect obtained by the cblup90iod program represent raw estimated breeding values (EBV) on the liability scale. To make them easier to interpret, raw EBV for each trait were transformed into probabilities of exceeding the value of the threshold. The threshold value represents the estimated point of transition between the two categories of a binary trait (in the case of wellness traits, the transition from healthy to sick). Threshold values for all traits were estimated from the data. For each animal solution, the probability that a standard normal variable with a mean equal to that solution and a variance of 1 exceeds the threshold was calculated [23]. These probabilities were then transformed into percentages by multiplying by 100, divided by 2 to obtain predicted transmitting abilities (PTA), which are defined as a half of EBV, and expressed as the differences from the average of the reference population, that is, a group of animals selected to represent relevant individuals from current commercial herds. Higher values of PTA (or genomically enhanced PTA—gPTA—if the animal was genotyped) represent higher risk of having a disorder. For example, in a reference population with an average incidence of mastitis of 20%, an animal with a PTA for mastitis of 2.5 will have offspring with an estimated 22.5% chance of getting mastitis during lactation. Animals’ genetic merit for wellness traits is reported as standardized transmitting abilities (STA) [34] where;

$$\mathit{STA}=\left(\frac{\mathit{\text{gPTA}}-\mu }{\sigma }\times \left(-5\right)\right)+100$$E4

where μ and σ represent the mean and the standard deviation of gPTA, respectively. Therefore, a value of 100 represents the average expected disease risk, with animals at 95 or 105 being one standard deviation away from the mean. For wellness traits, larger STA are more desirable for all traits, because they represent lower expected average disease risk. Selecting for a higher STA is expected to result in reduced incidence of the respective disease.

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.

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].

Trait	No records	No animals	Mean	SD	Heritability
MAST	5,768,760	2,770,872	0.291	0.454	0.097
METR	4,865,943	2,435,542	0.100	0.300	0.090
RETP	5,505,269	2,714,416	0.050	0.218	0.112
DA	4,489,831	2,262,183	0.026	0.158	0.089
KETO	3,221,467	1,735,818	0.057	0.232	0.081
LAME	4,336,602	2,247,900	0.167	0.373	0.079

Table 1.

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

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.

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.

Trait	gPTA				STA				Reliability
	Mean	SD	Min	Max	Mean	SD	Min	Max	Mean	SD	Min	Max
MAST	−0.543	4.49	−14.10	22.61	99.5	5.1	73	115	50.8	5.44	0	99.8
METR	−0.903	3.02	−9.31	20.75	101.4	5.0	66	115	50.0	5.53	0	99.7
RETP	0.111	2.71	−7.80	18.70	99.8	4.9	66	114	51.4	5.44	0	99.7
DA	−0.249	2.85	−6.77	24.03	100.0	4.5	62	110	46.1	5.52	0	99.7
KETO	−0.964	2.55	−7.05	18.83	101.7	4.8	65	113	46.5	5.64	0	99.6
LAME	0.395	3.73	−10.80	22.19	99.3	5.1	70	115	47.8	5.66	0	99.7

Table 2.

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

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 dairy-producing 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, and 3.9% for lameness. The differences in marginal means by genetic groups (disease incidence) translate into appreciable differences in expected economic costs.

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.

Trait	Genetic group	Incidence (marginal mean, %)	Disease cost per animal ($)	Odds ratio
MAST	Bottom 25	15.9	33.63	2.03
	26–50	11.2	23.65	1.35
	51–76	11.1	23.32	1.33
	Top 25	8.5	18.00	—
METR	Bottom 25	23.6	70.92	2.10
	26–50	18.5	55.47	1.54
	51–76	19.1	57.42	1.61
	Top 25	12.9	38.58	—
RETP	Bottom 25	4.5	9.30	2.94
	26–50	3.3	6.88	2.15
	51–76	2.5	5.10	1.58
	Top	1.6	3.26	—
DA	Bottom 25	1.1	5.58	17.05
	26–50	0.5	2.32	7.13
	51–76	0.1	0.64	1.95
	Top 25	0.1	0.35	—
KETO	Bottom 25	3.2	3.75	2.20
	26–50	2.5	2.87	1.67
	51–76	1.7	1.97	1.14
	Top 25	1.5	1.73	—
LAME	Bottom 25	11.4	20.23	1.58
	26–50	8.7	15.40	1.17
	51–76	8.6	15.28	1.16
	Top 25	7.6	13.37	—

Table 3.

Results of the analysis of genetic groups for wellness traits in the validation animals [34].

Trait	Incidence (%)		Economic losses per 100 animals (£)
	Best third	Worst third	Best third	Worst third
MAST	11.3	22.3	2083	4025
METR	1.2	5.1	307	1293
RETP	3.0	4.8	699	1137
LAME	23.0	38.2	3586	5949

Table 4.

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

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.

Genomic Groups	Mastitis STA Average	No. of tubes per group	No. of tubes per cow	Antibiotic use reduction compared to worst 25%
76–100% (Best)	105	178	0.68	−65%
51–75%	101	250	0.95	−52%
26–50%	98	480	1.83	−7%
0–25% (Worst)	93	518	1.96	0%

Table 5.

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

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 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).

Trait	STA quartile group	STA means	Disease prevalence (%) (Marginal mean)	Estimated disease cost (€*) per 100 cows
MAST	Worst 25%	91	42.2	7870
	26–50%	98	36.0	6720
	51–75%	102	36.3	6771
	Best 25%	107	32.7	6098
METR	Worst 25%	95	10.8	2854
	26–50%	100	11.4	3037
	51–75%	103	10.0	2663
	Best 25%	107	7.8	2073
RETP	Worst 25%	93	12.1	2202
	26–50%	99	10.1	1832
	51–75%	103	8.4	1533
	Best 25%	107	6.7	1212
DA	Worst 25%	93	4.5	1963
	26–50%	98	2.6	1113
	51–75%	102	2.0	873
	Best 25%	105	1.7	739
LAME	Worst 25%	92	17.7	2772
	26–50%	98	17.0	2668
	51–75%	101	15.8	2473
	Best 25%	105	15.0	2351

Table 6.

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

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 genomic relationships among all Holstein animals are strong in the United States, as well as in other countries that have imported Holstein genetics (mostly viafrozen 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.

Figure 2.

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

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.