Reducing methane (CH4) emissions from agriculture, among other sectors, is a key step to reduce global warming. There are many strategies to reduce CH4 emissions in ruminant animals, including genetic selection, which yields cumulative and permanent genetic gains over generations. A single-step genomic evaluation for Methane Efficiency (ME) was officially implemented in April 2023 for the Canadian Holstein breed, aiming to reduce CH4 emissions without impacting production levels. This evaluation was achieved by using milk mid-infrared (MIR) spectral data to predict individual cow CH4 production. The genetic evaluation model included milk MIR predicted CH4 (CH4MIR), along with milk yield (MY), fat yield (FY), and protein yield (PY), as correlated traits. Traits were expressed in kg/day (MY, FY, and PY) or g/day (CH4MIR). The MiX99 software was used to fit the single-step, 4-trait animal model. Genomic breeding values for CH4MIR were then obtained by re-parameterization, using recursive genetic linear regression coefficients on MY, FY, and PY, giving a measure of ME that is genetically independent of the production traits. The estimated breeding values were expressed as Relative Breeding Values (RBV) with a mean of 100 and standard deviation of 5 for the genetic base population, where a higher value indicates the animal produces lower predicted CH4. This national genomic evaluation is another tool that will lower the dairy industry's carbon footprint by reducing CH4 emissions without impacting production traits.

The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries, i.e., Australia [AUS], Canada [CAN], Denmark [DNK], Germany [DEU], Spain [ESP], Switzerland [CHE], and United States of America [USA] contribute with genotypes and phenotypes including DMI and CH4. However, combining data is challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis.

Genetic improvement of health, welfare, efficiency, and fertility traits is challenging due to expensive and fuzzy phenotypes, the polygenic nature of traits, antagonistic genetic correlations to production traits and low heritabilities. Nevertheless, many organizations have introduced large-scale genetic evaluations for such traits in routine selection indexes. Medium and high-density arrays can be applied in genomic selection strategies to improve breeding value accuracy, and also in genome-wide association studies (GWAS) to identify causative mutations responsible for economically important traits. Genomic information is particularly helpful when traits have low heritability. The objective here is to provide a framework for including health, welfare, efficiency, and fertility traits taken from large-scale genetic and genomic analyses and identifying areas of potential improvement in terms of trait definition and performance testing. General tendencies between trait groups confirmed that a number of moderate unfavourable correlations (+/-0.20 or higher) exist between economically important trait complexes and health, welfare, and fertility traits. A number of trait complexes were identified in which “closer-to-biology” phenotypes could provide clear improvements to routine genetic and genomic selection programs. Here we outline development of these phenotypes and describe their collection. While conventional variance component estimation methods have underpinned the genomic component of some traits of economic interest, performance testing for health, welfare, efficiency, and fertility traits remains an elusive goal for breeding programs. Although our results are encouraging, there is much to be done in terms of trait definition and obtaining better measures of physiological parameters for wide-scale application in breeding programs. Close collaboration between veterinarians, physiologists, and geneticists is necessary to attain meaningful advancement in such areas. We would like to acknowledge the support and funding from all national and international partners involved in the RDGP project through the Large Scale Applied Research Project program from Genome Canada

Genomic evaluation was developed for feed efficiency for Canadian Holsteins, with the first official release in April 2021. The model defines all traits in two periods of first lactation: 5-60 and 61-305 days in milk. Traits are: a) Metabolic Body Weight (MBW), calculated as (body weight) 0.75 ; b) Energy Corrected Milk (ECM), calculated as 0.25*Milk + 12.2*Fat + 7.7*Protein; and c) Dry Matter Intake (DMI). All traits are weekly averages expressed in kg/day (ECM and DMI) or kg 0.75 (MBW). Single-step method is used to fit the multiple-trait linear animal model for 6 traits (ECM, MBW, and DMI, in two DIM intervals) with genotypic information, using the MiX99 software. GEBV of DMI are re-parameterized using linear regressions of DMI on ECM and MBW, giving a measure of feed efficiency (RFI) genetically independent of ECM and MBW. Genetic parameters were estimated using 99,713 weekly records on 4,952 cows. Heritability of RFI was 0.10 and 0.05 for early and later periods in first lactation, respectively, and were smaller than estimates for DMI (0.29 and 0.27). By definition, RFI and the energy sink traits were genetically uncorrelated. Correlations between DMI and RFI were 0.50 and 0.37 for first and second DIM intervals, respectively. Finally, RFI in 5-60 DIM was genetically less correlated with RFI in 61-305 DIM compared to DMI between these two DIM intervals (0.63 vs. 0.88). Estimated breeding values (GEBV) for RFI are reversed in sign and proofs are expressed as RBV (mean = 100 and SD = 5, for base bulls). Proofs for RFI in 61 – 305 DIM, labeled as Feed Efficiency (FE), are considered a principal selection criterion for feed efficiency in Canadian Holsteins. Average reliability of FE for young genomic bulls was 0.41.

Genomic evaluation was developed for resistance to fertility disorders in Canadian Ayrshire, Holstein and Jersey breeds, with the first official release in December 2020. The evaluation model includes the following traits: CO = Cystic Ovaries, MET = Metritis, and RP = Retained Placenta. All traits are scored as 0 (no case) or 1 (at least one case) in the period from calving to 305 d, 150 d, and 14 d after calving, for CO, MET and RP, respectively. First and later lactation traits are treated as different but correlated traits. Observations from lactations >2 are repeated records of lactation 2. The model is a multiple-trait (6 traits) linear animal model. Genomic information is utilized in additive relationships among animals via single-step method implemented in the MiX99 software. Genetic parameters were estimated using a subset (N= 76,082) of Holstein data. Heritability for fertility disorders ranged from 0.02 to 0.03. Genetic correlation between fertility disorders expressed in first and later lactation cows were between 0.55 (CO) and 0.70 (MET). This confirmed that disease resistance to fertility disorders are genetically different traits in first and later parities. Resistance to CO was genetically uncorrelated with resistance to 2 other disorders within and across parities (correlations did not statistically differ from 0). MET and RP were moderately genetically correlated (0.44 to 0.54). Estimated genomic breeding values for all traits are reversed in sign. Three additional evaluations are created by combining proofs for first and later lactation for a given disorder with equal weights. All proofs are expressed as RBV (mean = 100 and SD = 5, for base bulls).

Hoof lesions represent an important issue in modern dairy herds, with reported prevalence in different countries ranging from 40 to 70%. This high prevalence of hoof lesions has both economic and social consequences, resulting in increased labor expenses and decreasing animal production, longevity, reproduction, health, and welfare. Therefore, a key goal of dairy herds is to reduce the incidence of hoof lesions, which can be achieved both by improving management practices and through genetic selection. The Canadian dairy industry has recently released a hoof health sub-index. This national genetic evaluation program for hoof health was achieved by creating a centralized data collection system that routinely transfers data recorded by hoof trimmers into a coherent and sustainable national database. The 8 most prevalent lesions (digital dermatitis, interdigital dermatitis, interdigital hyperplasia, heel horn erosion, sole hemorrhage, sole ulcer, toe ulcer, and white line lesion) in Canada are analyzed with a multiple-trait model using a single-step genomic BLUP method. Estimated genomic breeding values for each lesion are combined into a sub-index according to their economic value and prevalence. In addition, data recorded within this system were used to create an interactive management report for dairy producers by Canadian DHI, including the prevalence of lesions on farm, their trends over time, and benchmarks with provincial and national averages.

Calf mortality leads to economic losses for the farmer and is an animal welfare issue. Currently, only calf mortality within the first 24 h is accounted for in the Danish breeding goal for beef x dairy calves. However, survival throughout the rearing period is also of upmost importance. Therefore, the aim of this study was to estimate genetic parameters for young stock survival, to evaluate if it is feasible to implement such a trait. Data on 90,926 crossbred calves was extracted from the Danish Cattle Database and was provided by the Danish research center, SEGES. Two traits were defined, young stock survival from 1 to 30 d and 31–200 d after birth. The traits were analyzed with a univariate animal model using the AI-REML algorithm in the DMU package. The model contained a fixed effect of year × month of birth, herd, sex, breed combination, parity of the dam, transfer and a random effect of the calf and herd × year. The pedigree was traced back 5 generations, for both the sires and dams. Results showed low but significant heritabilities (0.045–0.075) for both survival traits. Breeding values were calculated using DMU4. Breed combinations with Belgium Blue cattle sires outperformed all other sire breeds. The lowest survival rates were found for breed combinations with Jersey dams or Blonde d’Aquitaine sires. Sufficient genetic variation between sires for young stock survival was found. The breeding values of the sires had an effect on young stock survival that ranged from −2.5 to 3.5% and −5.4 to 4.7% for survival from 1 to 30 d and 31–200 respectively. It is therefore feasible to implement young stock survival traits in a genetic evaluation for beef × dairy crossbred calves. This will increase the survival rate of the calves and hereby increase animal welfare and decrease economic loss for the farmers.

Digital dermatitis represents the most prevalent hoof lesion in Canada, with almost 20% of cows affected. A data collection system of hoof lesions, which uses standardized and reliable scores, was developed in Canada within a four-year project started in 2014. Hoof trimmers willing to share data and to develop a standard protocol were identified across Canada. Consecutively, a pipeline for a routine flow of hoof lesion records from hoof trimmers to Canadian DHI and to Canadian Dairy Network (CDN) was developed. The data collected through this pipeline were then used to develop a herd management report provided by DHI, and a national genomic evaluation for digital dermatitis offered by CDN. The genomic evaluation was introduced in December 2017, using hoof lesions recorded by hoof trimmers between 2006 and 2017. Heritability and repeatability estimates for digital dermatitis were 0.08 and 0.20, respectively. Breeding values were estimated for Holstein cattle with a univariate linear animal model. Other possible indicator traits for digital dermatitis, such as selected conformation traits, were not included due to low genetic correlations, and low contribution to increase in prediction reliability. Single-step genomic evaluation was implemented using a reference population of 19,459 animals (5,268 sires and 14,191 cows, respectively). The average reliability for bulls in the reference population was 77%. Correlations between GEBV for resistance to digital dermatitis and traits currently under selection were all favorable.

Genetic evaluation was developed for resistance to metabolic disease traits in Canadian Ayrshire, Holstein and Jersey breeds, with the first official release scheduled for December 2016. The model is a 9-trait animal linear model including producer-recorded data on clinical ketosis ( CK ) and displaced abomasum ( DA ), sub-clinical ketosis ( SCK ) defined as a level of milk β-hydroxybutyrate, and 2 indictor traits: fat to protein ratio ( F:P ) and first lactation body condition score ( BCS ) from the conformation classification. First and later (up to the 5 th ) lactations are considered as different (but correlated) traits. Genetic parameters were estimated using a subset (records on 35,575 cows) of the Holstein data. Heritabilities for CK and DA ranged from 0.02 to 0.06. Higher heritabilities were estimated for SCK and indicator traits, from 0.08 (SCK in later lactations) to 0.30 (BCS). Genetic correlations of clinical disease traits between first and later lactations were strong (0.70 for CK and 0.79 for DA), correlations for SCK and F:P were 0.50 and 0.70, respectively. First lactation CK was strongly correlated with DA (0.77) and SCK (0.68); lower correlations were estimated with BCS (-0.56) and F:P (0.42). Genetic links between DA in first and lactations and indicator traits were weaker. EBVs for CK, DA and SCK are published as relative breeding values, with a mean of 100 and standard deviation of 5, where higher values are desirable. The overall Metabolic Disease Resistance ( MDR ) index includes SCK, CK and DA, with weights of 50%, 25% and 25%, respectively, and the component EBVs are the averages of first and later lactation EBV for each trait. The MDR index is standardized in the same way as EBVs for individual metabolic disease traits.

The objective of this study was to investigate if milk β-hydroxybutyrate (BHBA) can be used as an indicator of ketosis. The different measures of milk BHBA were moderately genetically correlated with ketosis. The results imply that milk BHBA could be used to indirectly select animals that are more resistant to ketosis.

Producer-recorded health data for metabolic disease traits and fertility disorders on 35,575 Canadian Holstein cows were jointly analyzed with selected indicator traits. Metabolic diseases included clinical ketosis (KET) and displaced abomasum (DA); fertility disorders were metritis (MET) and retained placenta (RP); and disease indicators were fat-to-protein ratio, milk β-hydroxybutyrate, and body condition score (BCS) in the first lactation. Traits in first and later (up to fifth) lactations were treated as correlated in the multiple-trait (13 traits in total) animal linear model. Bayesian methods with Gibbs sampling were implemented for the analysis. Estimates of heritability for disease incidence were low, up to 0.06 for DA in first lactation. Among disease traits, the environmental herd-year variance constituted 4% of the total variance for KET and less for other traits. First- and later-lactation disease traits were genetically correlated (from 0.66 to 0.72) across all traits, indicating different genetic backgrounds for first and later lactations. Genetic correlations between KET and DA were relatively strong and positive (up to 0.79) in both first- and later-lactation cows. Genetic correlations between fertility disorders were slightly lower. Metritis was strongly genetically correlated with both metabolic disease traits in the first lactation only. All other genetic correlations between metabolic and fertility diseases were statistically nonsignificant. First-lactation KET and MET were strongly positively correlated with later-lactation performance for these traits due to the environmental herd-year effect. Indicator traits were moderately genetically correlated (from 0.30 to 0.63 in absolute values) with both metabolic disease traits in the first lactation. Smaller and mostly nonsignificant genetic correlations were among indicators and metabolic diseases in later lactations. The only significant genetic correlations between indicators and fertility disorders were those between BCS and MET in both first and later lactations. Results indicated a limited value of a joint genetic evaluation model for metabolic disease traits and fertility disorders in Canadian Holsteins.

Pro$ (pronounced Pro Dollars) was recently developed by Canadian Dairy Network (CDN) as a second national index that targets dairy producers who generate essentially all of their farm revenue from milk sales. Actual cow profitability data provided to producers by dairy herd improvement (DHI) agencies in Canada, namely CanWest DHI and Valacta, was used as the basis for deriving the new profit-based genetic selection index. Economic parameters used to calculate profitability for each cow are updated annually by economists to reflect changes in milk pricing as well as the associated expenses, including overhead, maintenance feed costs, marginal feed costs and quota opportunity costs. Data used was the accumulated profit to 6 years of age for 672,254 registered Holstein cows with known sire identification, born from January 2005 to September 2008. For cows not surviving to 6 years of age, accumulated profit to the date they left the herd was considered as lifetime profit. For each sire, the average accumulated profit of daughters to 6 years of age was computed.  A total of 830 sires with at least 100 daughters with profit data were used to conduct the two-step multiple trait regression analysis to determine the contribution of sire EBVs for three production, four major type, and eight functional traits in predicting the average daughter profit to 6 years of age. Adjusted R-squared of the Pro$ prediction equation was .6167, which can be applied to any dairy breed with the appropriate scaling factors. Relative to LPI, selection for Pro$ in Holsteins has an stronger expected response for Milk and Protein Yields as well as various functional traits, including Herd Life, while both indexes have similar selection responses for Fat Yield, Daughter Fertility, Mastitis Resistance and Rump. Effective August 2015, Pro$ will be available in the Holstein and Jersey breeds and will be expressed in dollar terms as a deviation from breed average.  For other dairy breeds, the research behind the development of Pro$ was used to modify the LPI formula effective August 2015 to better reflect expected average daughter profit from milk sales.

De-regressed EBV are commonly used phenotypes in national genomic evaluation systems. This study compared REML estimates of variance for de-regressed MACE proofs of foreign sires on the Canadian scale, versus de-regressed national EBV of domestic sires. Variances for nearly all traits were higher for foreign than domestic sires. Ratios of SD for foreign relative to domestic sires, based on December 2010 Holstein data were; 1.05 and 1.07 for protein and fat yields, 1.17, 1.04 and 1.24 for mammary system, feet & legs and conformation, 1.50 for cow survival and 1.20 for cow non-return rate. Based on current data from December 2014, some of the more extreme ratios of SD were closer but still higher than 1. After applying a variance adjustment to de-regressed MACE proofs of foreign sires, genomic validation results improved for all traits. Slopes of regression, of the 2014 Canadian LPI index of trait EBV, on the 2010 genomic-enhanced parent averages (GPA), increased from 0.93 to 0.97, and biases of over-prediction for top young genomic bulls were accordingly reduced.

The overall goal of this study was to develop genetic evaluations for metabolic disease traits in Canadian dairy cattle. The specific objective was to estimate genetic parameters for metabolic diseases and their main predictors in Canadian Holsteins. Health data recorded by producers were available from the National Dairy Cattle Health System. Records from first to fifth lactation were considered for ketosis (KET), displaced abomasum (DA), milk fever (MF), fat to protein ratio (F:P) and milk shydroxybutyrate (BHBA), whereas for body condition score (BCS) only records from first lactation cows were available. Binary disease traits (0 = no case, 1 = at least one case), F:P and milk BHBA were treated as different traits in first and later lactations. Records for MF in first lactation were not considered in the present study as the frequency of this disease was near zero and a preliminary analysis revealed a heritability of zero. Bivariate and multivariate linear sire models were fitted using AI-REML. Heritability for metabolic disease traits ranged from 0.011 to 0.047. Higher heritabilities were found for BCS, F:P and milk BHBA, with estimates ranging from 0.10 to 0.22. First lactation KET was strongly correlated with DA (0.76) and milk BHBA (0.75), whereas lower genetic correlations were found with BCS and F:P (-0.54 and 0.37, respectively). Displaced abomasum in first lactation was moderately correlated with BCS (-0.40) and F:P (0.19). Similar genetic correlations were estimated in later lactation cows. Milk fever, which was only evaluated in second and later lactation cows, was moderately correlated with KET (0.39) and milk BHBA (0.33). Genetic correlations of disease traits between first and later lactations were relatively high (0.79 for KET and 0.86 for DA).

A routine genetic evaluation for mastitis resistance will be officially implemented in Canada in August 2014 for Holstein, Ayrshire and Jersey breeds. The model is a multiple-trait linear animal model including mastitis, average SCS in early lactation, standard deviation of SCS, excessive test-day SCC, fore udder attachment, udder depth and body condition score. Genetic evaluations for clinical mastitis in first lactation as well as in second and later lactations are calculated and expressed as relative breeding values with a mean of 100 and a standard deviation of 5, where higher values are desirable. An index for Mastitis Resistance was developed that includes both the two clinical mastitis traits and the official SCS evaluation, with equal weights. Hyperketonemia or ketosis is one of the most frequent diseases in dairy cattle and the level of milk s-hydroxybutyrate (BHBA) is an indicator of subclinical ketosis. Heritability estimates for milk BHBA in Canadian Holstein cows were between 0.13 and 0.29. Higher milk BHBA in early lactation was genetically associated with a higher frequency of clinical ketosis and displaced abomasum. Milk BHBA can be routinely analyzed in milk samples at test-days, and, therefore, provides a potential alternative for breeding cows with a lower susceptibility to hyperketonemia.

440 In vivo and in vitro heat shock proteins gene expression in cattle. A. C. A. P.M. Geraldo*1, L. J. Oliveira1, A. M. F. Pereira2, F. Moreira da Silva3, and E. A. L. Titto1, 1Faculdade de Zootecnia e Engenharia de Alimentos-Universidade de Sao Paulo, Pirassununga, Sao Paulo, Brazil, 2Universidade de Evora, Evora, Portugal, 3Universidade dos Acores, Angra do Heroismo-Terceira, Acores, Portugal.