The Interbull Bulletin has become a well known reference for genetic evaluation of bovine cattle worldwide. It contains the state-of-the-art in genetic evaluation methods, as well as the most recent information on national and international implementations. Authors have an ideal forum to discuss new ideas and to challenge current genetic models. Following the Interbull Bulletin over the years presents a rich opportunity to understand how the current models were developed and how new traits were incorporated into genetic evaluations.
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Interbull Portfolio: Expansion of Traits
A total of 10 traits for three trait groups of claw health, metabolic diseases and calving were included in a new research run performed in October 2024 with the aim to expand the multiple across-country evaluation (MACE) portfolio. The traits in each trait group were as follows: digital dermatitis (dde), interdigital dermatitis (idd), interdigital hyperplasia (idh), sole hemorrhage (soh), sole ulcer (sou) and white line disease (wld) for claw health; clinical ketosis (cke), sub-clinical ketosis (sck) and milk fever (mfe) for metabolic diseases and direct gestation length (ges) for calving trait group. Although a total of 13 countries provided data for the six different breeds evaluated internationally, the research run could be performed only for four breeds (Holstein (HOL), Brown Swiss (BSW), Red Dairy Cattle (RDC) and Jersey (JER)) as only one country did provide data for Guernsey (GUE) and Simmental (SIM). The research results for across-countries correlations, international EBVs and reliabilities were all promising for all evaluated traits. Gestation length was included in the Interbull May 2025 test run and subsequently in the official August 2025 routine run as the fifth trait in the MACE calving trait group alongside direct and maternal calving ease and still birth traits. The implementation of claw health and metabolic diseases traits in the MACE portfolio is currently pending a slightly higher participation rate from countries and it is therefore aimed to happen in the near future
Genomic Evaluation for Calf Health in Canada
A genomic evaluation for calf health traits was developed for the Holstein breed in Canada effective August 2025. The new Calf Health index aims to increase resistance to the two most prevalent calf diseases on Canadian farms, respiratory problems (RESP) and diarrhea (DIAR). Producer-recorded respiratory problems and diarrhea health events recorded in the first 180d and 60d, respectively, of a heifer calf’s life, are used in the genetic evaluation. RESP and DIAR, coded as binary traits, are used in a two-trait linear animal model considering a fixed year-season effect and random herd-year-season, animal, and residual effects for both traits. Genetic parameters were estimated by the MC EM REML method using 310 662 calf records from 1 179 herds. Heritability estimates were 0.05 for RESP and 0.04 for DIAR, with a genetic correlation of 0.53 between the traits. A Single-step genomic evaluation was implemented using the MiX99 software. A June 2025 evaluation test run had 355 355 records for RESP and 144 495 for DIAR, collected from 1 442 Canadian herds from 2007 to 2025. There were 74 013 calves with health records that were genotyped and a total of 119 715 genotyped animals in the reference population. The overall prevalence for RESP and DIAR was 19.5% and 21.1%, respectively. The Calf Health index combines genomic estimated breeding values for RESP and DIAR at equal weightings. Calf Health evaluations are published as a relative breeding value, with a mean of 100 and standard deviation of 5 for base bulls, where higher values represent greater resistance to calf health diseases. No genetic trend was observed and only weak relationships with other routinely evaluated traits were present. From a sire comparison analysis, clear differences were found when comparing high and low RBV sires in terms of daughter disease rates, highlighting the potential of the evaluation. Genetic selection for improved calf health is a valuable tool for animal welfare, lifetime animal production, and overall herd profitability.
Genomic predictions for dairy calf health traits
Healthy calves are important to the productivity and welfare of dairy herds. They are potential herd replacements as well as a source of livestock trading income. Further, healthy calves are important to the continuous improvement of animal welfare that is valued by farmers and consumers. In our dataset of ~20,000 calves with health records, the prevalence of stillbirth, preweaning mortality and scours was 4%, 2% and 6% respectively suggesting that there are opportunities to improve calf health. The aim of this study was to estimate variance components for novel calf traits and gather the perspectives of farmers about the relative importance of these traits. Univariate linear models that included a genomic relationship matrix were used to estimate variance components for stillbirth, preweaning mortality, scours, respiratory disease and calf vitality where heritability (h2) estimates ranged from 1% to 11% depending on the trait. Calf vitality is a new, subjectively-scored trait where farmers describe calves on a scale from A (vigorous) to E (dead). The models included herd-year-season, sex, parity group and calving ease as fixed effects and these were found to be significant for most breed and trait combinations. Our survey found that calf traits were valued by farmers similarly to cow survival. They preferred new traits to be published separately, rather than in multi-trait indexes. As genetic variation in several calf health traits was measured and the value to farmers has been tested, we conclude that there is an opportunity to introduce new traits into routine evaluations that target genetic gain for calf health.
Update of genetic parameters and inclusion of Swedish and Finnish β-hydroxybutyrate and acetone measurements to the Nordic General Health evaluation model
The Nordic (Denmark, Finland, Sweden) General Health (GH) evaluation model was introduced in 2008 and significantly revised between 2017 and 2019. The current GH index includes reproductive disorders, feet and leg disorders, clinical ketosis, and other metabolic diseases recorded as veterinary treatments. Acetone and β-hydroxybutyrate (BHB) measurements from milk mid-infrared (MIR) spectra are used as correlated traits in mixed model equations and supplied primarily from Danish herds. Although the collection of Swedish acetone and BHB measurements began in 2018, this data has not yet been incorporated into official evaluations. Furthermore, new Finnish measurements are available only for BHB and predicted from MIR using a different equation than those used in Denmark and Sweden. Swedish BHB and acetone, along with Finnish BHB data, were integrated into the official Nordic evaluation pipeline. Genetic correlations between Finnish and Swedish/Danish BHB were estimated at around 0.8. Genetic parameters were newly estimated for Holstein, Red Dairy Cattle (RDC), and Jersey breeds. The largest changes in heritability and genetic correlations between clinical and subclinical ketosis were observed for RDC and Jersey. Correspondingly, the largest changes in breeding values were observed for RDC and JER Nordic AI bulls. The updated model is planned to be implemented in November 2025.
Genetic evaluation of twinning rate in Italian Holstein
Twinning in Holstein cattle is unfavorably linked to calving difficulties, abortions, milk production and reduced calf survival. The twinning rate in the Italian Holstein population is 2.2% and appears relatively stable over time; however, this figure does not account for early abortions and thus it is underestimated. This study aimed to establish a routine genetic evaluation of twinning rate in the aforementioned population. The phenotype of interest was the type of calving (0 = singleton; 1 = twins). The statistical model employed included, as random, herd-year of conception, permanent environmental effect and the cow’s additive genetic effect. Fixed effects comprised year-season of conception, herd, synchronization protocol (classified into three categories: yes, partial or no), days in milk class and parity-age-year of conception. The dataset included 11,329,160 records after filtering, with age at calving restricted to 18-77 months and parity limited to maximum three. Only fixed effects levels comprising at least 100 observations were retained. The minimum number of contemporaries was set to 10. Data editing was loop-based to simultaneously meet all the described restrictions. Genetic parameters were estimated on a sample of 500 randomly selected herds using THRGIBBS1F90 software. Posterior mean of heritability for twinning rate was 1%. To validate the accuracy and stability of the predictions a genomic validation was conducted. Genomic validation yielded a dispersion of 0.94 and validation reliability of 0.18. This study has laid the foundation for the implementation of a routine genetic evaluation of twinning rate in the Italian Holstein breed.
Johannes Baptist Cornelis Henricus Maria van Kaam
Genetic correlation: a heritable parameter
Breeding programs rely on selection of individuals through their breeding values to simultaneously improve multiple traits of commercial value. In order to adequately select candidates to breed for a next generation, the genetic relationships between traits are considered in the selection index that summarizes all the traits for each selection candidate. The methods deployed in genetic evaluations rely strongly in gaussian distributions describing the data, and consider the genetic relationships between traits in the form of genetic correlations determining the joint distribution of breeding values from different traits. In this manner, genetic correlations are treated as parameters, estimated on a base population for reference. However, genetic correlations depend on the involved traits’ architecture, thus depending on the genotype presented by each individual, and therefore, different individuals may present different potential for genetic correlations. Moreover, different potential for genetic correlations may partially represent a latent physiological trait responsible to balance the phenotypic expression of the measurable production traits. In practice, individual-specific genetic correlations (iSGC) can be obtained for individuals with many phenotyped descendants, as the expressed genetic correlation between the estimated breeding values among their offspring. Since the expressed iSGC depends on the involved traits’ genetic architecture, part of an individual’s iSGC can be transmitted to the offspring. In order to study the heritability of iSGC, two-trait genetic evaluations were performed on every pairwise combination of five traits from a French Holstein dairy cattle population: milk and protein yield (MY and PY), milking speed (MSPD), somatic cell score (SCS), and conception rate (CR). The iSGC between every pair of the five traits were obtained for ~1200 bulls with more than 500 phenotyped daughters in this population, and these iSGC were each evaluated as a phenotype with a single-trait model. This study confirmed the hypothesis that genetic correlations, when expressed as iSGC, are heritable parameters, with significant heritabilities ranging from 0.11 (iSGC between SCS and CR) to 0.51 (iSGC between PY and SCS).
Implementation of single-step genetic evaluation in Poland
In April 2025, Centre for Genetics of Polish Federation of Cattle Breeders and Dairy Farmers (CGen) implemented single-step genomic evaluation for Polish Holstein-Friesian population. The process of implementing the new evaluation system started in December 2024 when CGen became the official provider of dairy genetic evaluations in Poland calculating selection indexes based on breeding values estimated by the National Institute of Animal Production in Balice using a two-step approach. To increase the accuracy of breeding value estimations, CGen developed single-step pipelines using BLUPF90 family of programs. In January 2025 conventional EBVs for all traits were submitted for Interbull test run and successfully passed the evaluation. Next, the results of genomic evaluations were submitted for GEBV test and also passed for all traits. In April 2025 single-step evaluation results were officially published for the industry. In this paper we share the experience from the work undertaken to implement the single-step approach in Poland. The changes to the genetic evaluation models are highlighted. The methodology implemented for the integration of external information (MACE proofs) in single-step evaluations is discussed and results presented. Technical aspects of implementation are also discussed including model running times for individual traits. Highlights from the validation work undertaken are also included. The most noticeable impact on genetic evaluation results of the transition from the two-step evaluation system to the single-step method was increase in reliabilities. For example, for production traits, reliability increased by 12 percentage points for bulls and 10 percentage points for cows. The most significant improvement was observed for longevity, with a reliability increase of 40 percentage points for bulls and 35 percentage points for cows. The new system also demonstrated substantial improvement in the correlation between Polish and international breeding values, as estimated by the Interbull Centre. The most noticeable improvement was for longevity, where correlation increases with some countries reached 46 percentage points.
Implementation of a Single-Step genomic evaluation system for dairy cattle in Wallonia, Belgium
For several years, dairy cattle breeders in the Walloon Region of Belgium have had access to locally estimated breeding values (EBV) for traits of interest. These evaluations enable Wallonia to contribute to the Multiple Across Countries Evaluation (MACE) conducted by Interbull. In the current local genomic evaluation framework, genomic and pedigree data are integrated with local EBV and external information, MACE-derived EBV (MACE EBV), through a pseudo-single-step genomic evaluation system, producing genomically enhanced EBV (GEBV). However, this approach may introduce biases. To address this, the present study aimed to implement a single-step genomic BLUP (ssGBLUP) that simultaneously incorporates all available national data alongside MACE information and validate this method using milk production traits. The proposed strategy first involves defining "pseudo-traits" that represent MACE traits (i.e., 305-day averages for milk, fat, and protein yields over the first three lactations). MACE EBV are then transformed into adjusted pseudo-phenotypes (i.e., deregressed proofs) and effective contributions but avoiding double-counting of Walloon data within MACE EBV. Next, the variance-covariance matrices from the local random regression test-day model were modified to include the three MACE pseudo-traits as correlated traits. Finally, a single-step genomic evaluation was performed, jointly analyzing test-day records and MACE pseudo-phenotypes. Validation of both pedigree-based and single-step genomic evaluations, both integrating MACE information, was carried out using data from the official Walloon genetic evaluations of April 2022. Results show that MACE information is adequately integrated in the local evaluations, because Pearson correlations between MACE EBV and the integrated EBV were higher than 0.97 across traits. The addition of genomic information in single-step evaluations resulted in small changes for all individuals, as illustrated by Pearson correlations ranging from 0.975 and 0.986 for sires with MACE information.
Introduction of single-step genomic evaluations in German Holsteins
In April 2025, we introduced single-step genomic evaluations for all traits that are subject to routine genetic evaluations in German Holstein. With all models, we estimate the same main effects as with the former conventional genetic evaluations of Holsteins. In addition, a fixed regression on the inbreeding coefficient was added to all the models. With the introduction of single-step models, reliabilities of GEBVs increased for all traits. This increase is especially pronounced in young animals with no own or offspring performance. As expected, the increase in reliability was greatest for many functional traits: longevity and direct calving index: +0.11, maternal calving index: +0.13, young stock survival and health index: +0.14, while it was lower for production traits: production index: +0.04. Additionally, validations with 2 and 4 years of right-truncated data confirm a substantial increase in the predictive ability of genomic GEBVs compared to the previous multi-step model: correlations of purely genomic GEBVs of young bulls with their later daughter-proven GEBVs are higher for all traits with the single-step model. Again, this increase in predictive ability is highest for the functional traits and lower for the production traits. With publication dates in April, August, and December, we conduct main runs with updated phenotypic information three times a year. In these main runs, we include MACE information from the respective current Interbull MACE run. In addition to these full runs, we conduct weekly genomic evaluations, for which we use the estimates of the SNP-effects and the residual polygenic effects from the main runs and apply them to the newly genotyped animals.
Transition of the UK dairy national evaluation to across-breed and single-step genomic evaluation: somatic cell counts as a case trait
Current UK genomic evolutions follow a two-step approach; initially, a genetic evaluation based solely on pedigree information, followed by a Single Nucleotide Polymorphism Best Linear Unbiased Prediction (SNPBLUP) analysis for genotyped animals using de-regressed proofs, including MACE proofs from Interbull. Nowadays, with recent advances in computational feasibility and growing interest in across-breed genomic evaluations for dairy herds, there is a compelling need to adopt a single-step across-breed genomic evaluation approach within the UK dairy industry. The single-step method offers notable advantages by simultaneously incorporating genotypes and both recent and historical pedigree and phenotypic data into a single analysis. This integration enhances the accuracy of genetic predictions across diverse breeds, accelerates genetic progress, and improves selection efficiency. This study aims to evaluate the impact of using genomic information and compare the prediction ability of single-step genomic evaluation (using ssSNPBLUP method) and pedigree-based genetic evaluation (PedBLUP) employing cross-validation techniques (Linear Regression method). The trait analysed was somatic cell count (SCC), using data from the UK national evaluations as of the December 2024 official run. The dataset included 11,271,959 animals in the pedigree and 19,056,954 SCC records from 7,527,712 cows. Foreign information was incorporated for 182,844 bulls, with adjustments made to avoid double counting of domestic data. Genotypic data was available for 891,480 animals, imputed at 79,051 SNPs using findhap.f90 V3. Analyses were performed using the MiX99 V23.1026 software, applying an ssSNPBLUP model with 10% polygenic effects. The validation group comprises bulls born after 2016 and cows born after 2018, whose records are set to missing. Results showed a genomic accuracy improvement of up to 54% in cows when comparing ssSNPBLUP to PedBLUP. Among bulls, the greatest gain was observed in Holsteins (+33%), followed by Guernsey and Ayrshire (+30%), and Jersey (+20%). Level bias and dispersion bias was slightly reduced in ssSNPBLUP relative to PedBLUP. Overall, the findings demonstrate that single-step genomic evaluation is a promising and efficient approach for enhancing prediction accuracy in UK dairy cattle.
One Moo-ve Closer: Single-Step Genomic Predictions for Crossbred Holstein and Jersey Cattle Using Metafounders
The study examined the impact of incorporating metafounders (MF) in single-step genomic BLUP (ssGBLUP) models for the genetic evaluation of Holstein (HO) and Jersey (JE) cattle with their crossbreds (CROSS). The dataset included 23,736,975 records on 8,560,986 cows. Genotypic data on 181,379 JE, 1,905,292 HO, and 53,799 CROSS animals was used for the evaluation. The genetic evaluation included five production traits, namely milk yield (MY), protein yield (PY), fat yield (FY), somatic cell score (SCS), and daughter pregnancy rate (DPR), which were analyzed using a five-trait repeatability model using ssGBLUP with or without MF. Three different MF scenarios were tested: 4MF (based on breed), 24MF (based on the combination of breed, sex, and year of birth), and 32MF (similar to 24MF but with CROSS as a separate genetic group). The three MF scenarios were compared to a conventional ssGBLUP model that did not include metafounders (NO_MF). Forward‐in‐time validation was carried out to evaluate predictability, inflation, and stability. For purebred Holstein and Jersey cows, the truncated dataset included phenotypes through December 2018, whereas for crossbreds the cutoff was December 2015; the complete dataset extended through December 2022. Validation targeted genotyped cows lacking records in their respective truncated dataset but with at least one record in the complete dataset, yielding 96, 295 Holsteins 26, 436 Jerseys, and 5,099 crossbreds for analysis. Results showed that including MF affected prediction metrics differently depending on the trait, breed, and MF configuration. While certain MF classifications (e.g., 4MF) reduce bias and improved predictability in crossbreds for some traits, others showed minimal effects, particularly in purebred Holsteins. For low heritability traits (SCS, DPR), MF scenarios provided better predictive ability in CROSS animals. In contrast, for high heritability traits (MY, PY, FY), stability tended to decrease in MF models, suggesting possible overfitting due to added model complexity. Overall, MF offers a promising strategy to address pedigree gaps in multibreed evaluations, but its application should be carefully tailored to trait architecture and population composition to avoid overfitting and ensure accurate genetic predictions.
Single-step genetic evaluation for claw health traits in Switzerland
In Switzerland, a resource project was launched in 2019 to improve claw health in Swiss cattle. This project marked the beginning for the development of the first genetic evaluation for claw health traits in Swiss dairy cattle. Data recorded by claw trimmers during routine care was used to develop a single-step genetic evaluation for the most common dairy cattle breeds Holstein, Swiss Fleckvieh, Simmental, Brown Swiss and Original Braunvieh. A key advantage of this dataset is its comprehensive inclusion of all healthy cows observed during routine care. From 2019 to 2024, a total of 104,276 records were collected for the multi-breed evaluation of Holstein cattle (encompassing Holstein, Swiss Fleckvieh, and Simmental), while 33,464 records were documented for Brown Swiss (Brown Swiss and Original Braunvieh). Breeding values were predicted for four distinct traits: dermatitis digitalis (DD), white-line disease (WL), other infectious claw diseases (INF), and other non-infectious claw diseases (NINF). The prevalence rates of DD, WL, INF, and NINF were observed as 20.9%, 9.9%, 45.8%, and 20.7% respectively in the Holstein evaluation, while in the Brown Swiss evaluation, they were found as 7.8%, 10.2%, 37.6%, and 13.5%. A multi-trait animal model with binary coding of the trait was fit in the ssGTaBLUP evaluation, with heritability estimates ranging from 3% to 9% determined for the four traits across both populations. A top-bottom comparison revealed that daughter prevalence among sires with the highest and lowest estimated breeding values (EBV) varied by 14% to 35%. An index incorporating breed-specific weights for the four traits has been developed and is now published for all Swiss breeders. A primary challenge in developing the genetic evaluation was the scarcity of phenotypic data alongside a substantial population of genotyped animals that exhibited limited genetic correlation with individuals supplying phenotypic records.
An optimized single-step SNP BLUP model for calf fitness in German Holstein
A single-step SNP BLUP was introduced in routine official evaluation of German Holstein in April 2025 for all traits, including an early-measured trait, calf fitness, defined as calf survival between day 3 and 15 months after birth. Prior to the single-step model implementation, a mixed reference population of bulls and calves was set up for the calf fitness genomic evaluation using a multi-step genomic model. During the testing phase of the single-step model, an unrealistic, strong genetic trend of calf fitness was observed in genotyped animals, when compared to the multi-step genomic model or pedigree-based conventional model. Having searched for plausible causes for the overestimation, we detected a much lower mortality rate for genotyped calves than non-genotyped ones, particularly for the early periods from day 3 to 120 after birth. Although all female calves were genotyped under the whole-herd genotyping scheme in Germany, farmers did not always take genotyping sample right after the birth of a calf, causing a delay in genotyping for the early periods of the trait calf fitness. In addition, there were limited economic incentives for farmers to genotype dead calves. To solve the overprediction bias of the calf fitness evaluation, we developed a new single-step model by using only genotypes of sires of all female calves with phenotypic data. Genomic breeding values of the genotyped calves and all other genotyped animals were indirectly predicted based on SNP effect estimates and residual polygenic effect estimates of all the genotyped sires from the new single-step model. Genomic validation showed a slightly higher accuracy of the new single-step model using sire genotypes than the original model using genotypes of all animals. In comparison to a significant over-prediction for the original model, the new single-step model using only the sire genotypic records gave an almost unbiased genomic prediction. Genetic trends in genotyped AI bulls or female animals were no longer overestimated with the new single-step model. The problem of inflated genomic prediction of the original single-step model seems to be solved by using only the genotypic data of sires of female calves.
Alternative Approaches to handling of missing parents in genetic evaluation of dairy cattle using single-step test-day SNP-BLUP model
In many countries, single-step genomic models are replacing conventional pedigree-based models for routine valuation. Those models use all available information on the animals’ phenotype, genotype, and pedigree. Pedigree data still has a huge impact on estimated genomic breeding values (GEBV), and it is also important to consider information about the structure of the pedigree. The foremost aspect of pedigree editing is dealing with missing parents' information. The choice of method of handling missing parents can affect the prediction of breeding values. This work investigates three scenarios of pedigree data: 1) Pedigree_real (P_Real) – pedigree from the routine evaluation, 2) Pedigree_2010 (P_2010) – at least 20 and 10 percent of dams and sires born before 2019 were set randomly to missing, respectively, 3) Pedigree_4020 (P_4020) – at least 40 and 20 percent of dams and sires born before 2019 were set randomly to missing, respectively. Moreover, for those pedigrees, three approaches to defining missing parents were used: 1) Raw pedigree (RP) – missing parents IDs set to missing, 2) Genetic groups (GG) – missing parents replaced by unrelated GG, which are defined based on year of birth, sex, and country of origin, 3) Metafounders (MF) – missing parents replaced by MF, which correspond to genetic groups. Relationships within and between metafounders were estimated from genomic information of descendants. The genomic breeding values for fat yield were estimated using the single-step test-day SNP-BLUP model, implemented by the MiXBLUP software. Although GEBV prediction was similar across scenarios, expressing missing parents by GG or MF impacts the genetic trend, especially in situations of limited pedigree completeness. Removing parent information led to reduced precision results across the methods of handling missing parents, since P_Real scenario demonstrated highest accuracy results. Compared to RP and GG, MF scenarios resulted in higher genetic trends. Insufficient pedigree completeness, especially among ungenotyped individuals, leads to an overestimation of the genetic trend. Completeness of pedigree information and a large number of genotyped individuals improve the reliability of evaluations. Modeling missing sires with MFs is less effective than assuming unrelated GGs if pedigree information is very incomplete. Therefore, the best method to model missing parents depends on completeness of pedigree.
Integration of single step DGV in conventional genetic evaluations using DGV-PBLUP
Routine single-step genomic evaluations can be costly in time and computer resources. Hence, newly genotyped animals initially receive a genomic prediction of their direct genomic values (DGV). If genomic predictions of DGVs of animals become available it may be convenient to estimate GEBV of such animals using some form of integration into conventional pedigree BLUP evaluations. DGV-PBLUP is a novel method of integration of DGV from genomic predictions, into a conventional pedigree BLUP (PBLUP) evaluation. This is done by setting the prior mean of the animal genetic effect (which usually is zero in linear mixed models) to the DGV to be incorporated. In this paper we report on the application of this methodology to the Dutch-Flemish genetic evaluation. Results showed a high correlation (0.99 or higher) between GEBV of animals associated with a genotype in single step SNP-BLUP and a GEBV in DGV-PBLUP. Run time of DGV-PBLUP evaluations were comparable to conventional pedigree evaluations and much shorter than routine single-step SNP-BLUP evaluations. DGV-PBLUP promises to be a convenient method of integration of genomic information into pedigree BLUP evaluations, without the need for sharing or accessing SNP genotypes.
Lazy computation of Residual Polygenic effects
Single step methods use a blended relationship matrix that contains a fraction (typically 5%-20%) of pedigree-based relationships, called Residual Polygenic Effect (RPG). Indirect predictions of animals not included in the Mixed Model Equations (MME) are composed by a Direct Estimated Genomic Value (DGV), a sum of SNP readings times their solutions, and an RPG. Computation of RPG is not straightforward, and involves some complicated algebra and software, including separate relationships for genotyped and non-genotyped animals. We propose an alternative, equivalent computation that infers RPG for genotyped animals in the MME as RPG=GEBV-DGV, and then solves BLUP equations with RPG as “record” with heritability close to 1. The solution is the RPG for all animals in the MME, from which Parent Average can be used for Indirect Predictions. We show feasibility in practice with a US data set with millions of animals genotyped and in pedigree.
Comparing methods for approximating reliabilities in large-scale single-step genomic evaluations
Accurate approximation of genomic estimated breeding value (GEBV) reliabilities is vital in single-step genomic prediction as reliable predictions of GEBV facilitate effective selection decisions. However, calculating exact reliabilities by inverting the left-hand side matrix of the mixed model equations is computationally infeasible for large datasets. In this study, we compared two approaches from Luke and Interbull for approximating genomic reliabilities for both genotyped and non-genotyped animals. The Luke approach uses effective record contributions (ERC) derived from the conventional EBV reliabilities as weights to approximate GEBV reliabilities for genotyped animals. A blended approach is used to implicitly account for residual polygenic (RPG) effects. Subsequently, genomic information is propagated to non-genotyped animals using ERC weights derived from the reliabilities of the genotyped animals. In contrast, the Interbull approach requires the derivation of a constant parameter, denoted , which is the genomic effective daughter contribution (EDC) gain via the Interbull GEBV test. This parameter is used to propagate genomic information to non-genotyped relatives through the pedigree. The final genomic reliabilities are obtained by combining conventional reliabilities with the genomic reliability gain. Notably, accuracy of reliabilities by this method highly depends on the precise estimation and regular updating of . In addition, this approach requires validation-based adjustments to correct inflated theoretical reliabilities observed in extremely large reference populations. In this study, both approaches were assessed and compared against exact reliabilities using a real dataset from the Finnish Red dairy population under a single trait model. The results demonstrated that the approximated reliabilities from both approaches were in close agreement with the exact reliabilities. Thus, both approaches can offer effective strategies for obtaining the reliabilities of GEBV in practical large-scale single-step evaluations.
Many publications requiring access to large datasets from commercial conditions, such as dairy genetics, note challenges with access to and quantity of data. Challenges include insufficient records and inconsistent trait definitions. This could be improved through developing closer relationships between farmers and researchers. In the same way that scientists innovate to develop the fields of genomics, phenomics, metabolomics, etc., can we advance our research by developing ‘farmeromics’ – defined as the study of farmer-driven, biological data recording, at scale? The purpose of this investigation was to compile examples of research studies that involved farmers in their co-design, including the calf vitality project, Feeding the Genes and ImProving Herds These examples could spark discussion on ways to strengthen collaboration between farmers, scientists and stakeholders to reach shared data-gathering objectives.
Integration of MACE breeding values into Swiss multi-trait test-day model evaluation
In this study, we integrated Multiple Across Country Evaluation (MACE) information for Interbull (ITB) bulls into the Swiss Test-day model (TDM). The 9-trait TDM includes test-day records of milk, protein and fat from the first three lactations, while total yield indices submitted to ITB are averages of 305d yields for lactations. A bull was considered to have relevant MACE information if its reliabilities for all indices in MACE were at least 0.1 units higher than its reliabilities from the Swiss TDM. With this integration, the Swiss TDM gained information for round 5,800 bulls with MACE index reliabilities exceeding 0.5. The integration process had three steps. 1) For selected bulls, the multitrait reversed reliability approximation was used to estimate effective record contributions (ERC) for Swiss and MACE yield indices, based on their respective reliabilities. 2) Yield indices and ERCs were used to calculate multitrait deregressed proofs (DRP) separately for Swiss and MACE evaluation. Correlations between the evaluated indices and pedigree relationships were accounted during the ERC and DRP calculations. 3) Based on the DRPs and ERCs for domestic and MACE indices, pseudo-observations approximating the additional information in the MACE evaluation were calculated for the selected bulls. As a result, for each selected bull a DRP and ERC for milk, protein, and fat were obtained. The original Swiss TDM describes breeding values using 45 random regression coefficients. The DRP was included in the model as a separate trait, weighted by its ERC. The genetic correlation between pseudo trait and lactation averages of the original traits was assumed to be 1. MACE inclusion improved correlations between MACE and Swiss indices to 0.99 (from 0.78–0.80 for milk, fat, protein). This demonstrates a good alignment between the two evaluation systems. Integration of MACE is now implemented successfully in the Swiss single-step routine genetic evaluation.
Genetic and genomic relationships among Canadian Holstein dairy cattle population and international Holstein bulls
The increasing level of inbreeding in dairy cattle populations can be a concern for researchers, producers, and artificial insemination (AI) companies. High inbreeding levels can lead to the accumulation of deleterious recessive variants, depression of the mean value of economically important traits, and a reduction in available genetic diversity in the population. Advancements in reproductive technologies and the integration of genomic information into genetic evaluations have contributed to rising inbreeding levels. Given the global interest in inbreeding, there is a need to monitor inbreeding trends and develop strategies to manage its adverse effects while ensuring continued genetic progress. The objective of this study was to measure the current trends in genetic and genomic relationships among Canadian Holstein cows and international Holstein bulls. Pedigree and genotype data were provided by Lactanet Canada (Guelph, ON). Genotype information was available for 168,995 animals, for which a pedigree of 616,258 animals was extracted. Among genotyped animals, 8,491 bulls were born between 2000 and 2023, and 131,139 cows were born between 2010 and 2024. The average pedigree completeness index of all genotyped animals was greater than 99%, with a maximum pedigree depth of 30 generations. Genetic relationship values were estimated using pedigree data (R-value) and genomic data (GR-value). R-value and GR-value represent the expected and realized percentage of shared DNA between an animal and a defined reference population, respectively. This was done by iteratively tracing back the gene contribution of an animal to the reference population. In this study, the reference population was defined as currently active cows and heifers enrolled in milk recording without a documented left-herd date on the latest test day in April 2024. Results show a yearly increase in relationship within the reference and bull populations. The average genetic relationship between bulls and the reference population ranged from 9.3% to 26.5% (R-value) and from 12.9% to 40.8% (GR-value). Among bulls, those with United States registration codes had the highest relationship value with active Canadian cows and heifers, with R-value and GR-value estimates of 20.8% and 30.4%, respectively. Conversely, bulls registered in the Czech Republic had the lowest average relationship values, with R-value and GR-value estimates of 17.1% and 24.3%, respectively. Selecting sires with low average relationship values among defined reference populations as a mating strategy could reduce or maintain inbreeding at acceptable levels while preserving genetic diversity. Key words: Genetic relationship, inbreeding diversity, Holstein, dairy cattle
Improving the Stability of the Test-Day Repeatability Model for Production Traits in Italian Brown Swiss
The genetic evaluation of Brown Swiss cattle has undergone a significant methodological improvement with the introduction of a new algorithm designed to enhance the accuracy and stability of genetic indices for productive traits. This innovation addresses issues observed in previous models, particularly the significant variability in early evaluations of genomic bulls. The updated model incorporates a classification system for herds based on productivity differences between primiparous and multiparous cows, ensuring more precise genetic assessments. The new approach has resulted in greater stability in genetic indices and reduced the impact of initial data distribution biases. This advancement strengthens the reliability of genetic evaluations, supporting breeders in achieving their productivity goals.
Foundational Review of U.S. Female Fertility Trait Evaluations
The Council on Dairy Cattle Breeding provides four female fertility evaluations for U.S. dairy producers: daughter pregnancy rate (DPR), cow conception rate (CCR), heifer conception rate (HCR), and early first calving (EFC). These evaluations were first introduced in 2004 for DPR, 2009 for CCR and HCR, and 2019 for EFC. Currently, these traits are expressed on six different breed bases: Ayrshire, Brown Swiss, Guernsey, Holstein, Jersey, and Milking Shorthorn. Over time, the data and methods used to calculate these traits have evolved in response to changes in availability, recording practices, and management systems. In recent tri-annual evaluations, unexpected season fluctuations have been observed in the Sire Estimated Breeding Values (EBV) of recently born bulls. The objective of this project was to identify the cause of these fluctuations and implement changes to improve stability across evaluations. In collaboration with the USDA Animal Genomics and Improvement Laboratory, this project also involves the research and development of a potential new trait to be added to the fertility evaluation, First Service to Conception (FSC), and re-estimation of genetic parameters for all five traits. Comprehensive tests were conducted to refine models, pre-adjustments, and data edits, including the use of both truncated and full datasets. Although data truncation showed promise in mitigating historical biases, it introduced higher variability in smaller breeds (Guernsey and Ayrshire). Additional tested changes included stricter calving year restrictions, improved data extraction procedures, updated CCR and HCR pre-adjustments, the inclusion of a days-in-milk covariate at first insemination for CCR and FSC, and the addition of a random herd-by-sire effect. Tests also examined whether modeling days open to pregnancy rate as a linear or non-linear trait, modeling traits as uncorrelated, performing unweighted analyses, or stricter convergence criteria of the traditional evaluation mixed model equations solver were appropriate. While the findings suggest that current methodologies provide a robust foundation, ongoing work is required to address the persistent slight negative trends reported in young bulls, where the underlying causes remain unclear. The project team is well-positioned to further enhance the stability of female fertility trait evaluations for U.S. dairy producers.
Female Inclusive MACE for Improved Genetic Evaluations in Small Populations
Abstract Small reference populations hinder prediction accuracy in minor breeds, thereby limiting genetic progress. In response, the World Ayrshire Federation (WAF) initiated an international collaboration through the Interbull Centre (IBC) to enhance genomic evaluations for Ayrshire-based populations across countries. This initiative is primarily to ensure breed sustainability by addressing the challenge of limited reference population sizes in individual countries, which affects the reliability of national genomic evaluations. The aim of this study is to adapt IBC’s Multiple Across-Country Evaluation (MACE) to include female estimated breeding values (EBVs) alongside male data while leveraging the Interbull Data Exchange Area (IDEA) and GenoEx-GDE platforms for data management. The participating countries initially include Australia, Canada, Colombia, the United Kingdom, New Zealand, South Africa, and the United States. A total of 22,383 genotyped Ayrshire animals (4,403 males and 18,880 females) have been identified, with Canada contributing 46% of the genotypes. Adding cow data is envisioned to accelerate reference population growth, boost genotyping returns and improve the reliability of both national and international genetic and genomic evaluations over time. The first phase of implementation involves integrating cow EBVs into MACE while sharing genotypes among participating countries using the GenoEx-GDE platform. The second phase envisions extending these evaluations through InterGenomics for interested countries and those that cannot perform their own national genomic evaluations. This collaboration paves the way to faster growth of reference population sizes and improves genetic and genomic prediction accuracy for not only Ayrshire sustainability, but also the opportunity exists for other small breed populations like Guernsey, among others. Key words: Ayrshire, genomic evaluation, MACE, small populations, international evaluation
Variance components estimation for methane emission in smallholders’ dairy farms
Enteric methane (CH4​) emissions from cattle account for 70% of livestock GHG emissions in Sub-Saharan Africa. Also, climate change has impact on smallholder livestock-based food systems in terms of feed resources and emergence of new diseases. Direct selection for CH4​ is one of the approaches to mitigate the effects of climate change and this requires estimation of genetic parameters. Moreover, the amount of CH4​ emitted is influenced by the activity status (ACTs) of the cow such as feeding, ruminating, sleeping, and standing idle during time of measurement. The aim of this study was to evaluate CH4​ emissions under different activities, estimate variance components and compare accuracies of predicting CH4​ emissions using MIR information.The data consistent of over 14500 point-measurement of methane emissions measured by laser methane detectors with minimum duration of 3 minutes from 940 cows in 29 small-holders dairy farms in Ethiopia under various cow activities from July 2023 to March 2025. Records obtained under different ACTs for feeding, ruminating, sleeping, and standing idle were 2382, 7885, 660, and 3494 respectively. Pedigree information was also available for 435 cows with observation for CH4​ and the remaining 459 cows were genotyped using a 90k SNP chip. Overall average CH4 production was 341 g/day. CH4 production in feeding status was highest with 517 g/day on average. Pedigree BLUP (PBLUP), and single step combining both pedigree and genomic information (HBLUP) were applied to estimate variance components (VCs) using different modelling approaches. A repeatability animal model (full model (FM)) was fitted with ACTs, year-season, and average farm milk yield as fixed factors and permanent environmental effects a random effect in addition to animal. Also, records averaged within year-season subclasses (average model) were also analyzed with fixed effects of year-season and average farm milk yield and random effects of animal and permanent environmental effects. Heritability estimates for the FM were 0.09 (0.03), and 0.10(0.02) for PBLUP and HBLUP, respectively. The corresponding estimates for the average model were 0.14 (0.06), and 0.19 (0.04). For the indirect prediction of CH4, a partial least square modelling approach was applied using milk mid-infrared data obtained in one week period around the CH4 measurements. The model with data restricted only to cows feeding gave higher prediction accuracy of 0.41 compared to 0.28 when using all data. In summary, heritabilities were low and consistent with published estimates, indirect predictions accuracy of CH4 were moderate. In general, feeding status not only had the highest production average but also highest prediction accuracy and has influence on genetic parameters.
Quantifying the effective contribution of phenotypic records to genetic evaluations: a case study on enteric methane emissions
In the context of data sharing for genetic evaluation, such as enteric methane emissions in cattle, quantifying the effective contributions of phenotypic records to genetic evaluations is essential. This research introduces a framework for estimating the effective contribution of phenotypic records to genetic evaluations, using the concept of effective record contributions (ERCs). Our three-step approach involves: 1) computing reliabilities of a pedigree-based genetic evaluation using phenotypic information, 2) approximating ERCs due to own records, free of contributions due to relationships, from reliabilities of phenotyped animals using a reverse reliability algorithm, and 3) calculating the total effective contribution of phenotypic records as the sum of ERCs associated with all phenotyped animals. We apply this approach to a Dutch dataset comprising 187 219 records of weekly enteric methane emissions from 8 668 Holstein cows measured between March 2019 and April 2024. The pedigree spans five generations. Estimated heritability and repeatability were 0.18 and 0.47, respectively. We evaluate the effective contribution of weekly enteric methane emission records using: 1) the entire dataset, 2) a subset spanning until October 2023, instead of April 2024, 3) a dataset reduced by over 30% and limited to 20 records per animal, and 4) the entire dataset but considering the weekly enteric methane trait as an indicator trait genetically correlated to an hypothetical trait of interest with an heritability of 0.20 and a genetic correlation of 0.80. Results show that the entire dataset corresponds to 12 671 ERCs for the weekly enteric methane emission trait, which remains similar after reducing the number of weekly records by over 30%. The subset spanning until October 2023 corresponds to 10 870 ERCs. The reduction of ERCs can be explained by a smaller amount of records, but also by a smaller amount of recorded animals. Finally, when calculating the effective contribution to a correlated trait of interest, the entire dataset with weekly methane emission records corresponds to only 3 286 ERCs. Our approach provides a flexible framework for quantifying the effective contribution of phenotypic records to genetic evaluations. The proposed framework can be extended for optimizing data collection schemes when aiming to optimize the accuracy of genetic evaluations.
Genomic prediction of methane emissions in Danish Holstein using single step and multi-trait prediction models
Enteric methane emissions from ruminants are a major contributor to global greenhouse gas emissions and pose a significant challenge to the sustainability of livestock production. To mitigate these emissions, breeding strategies have been mentioned as a promising tool, but prediction accuracies of methane emission traits are still limited by the size of datasets with records. Hence, using methane concentrations (MeC) in Danish Holstein cows as target trait, this study evaluated the predictive performance of pedigree-based BLUP (pBLUP) and single-step genomic BLUP (ssGBLUP) in univariate and multi-trait models, the latter including milk production traits. Previously, both ssGBLUP as well as multi-trait models have been shown to enhance prediction accuracies. The dataset included 1,744 primiparous (PP) and 2,989 multiparous (MP) cows from 15 Danish dairy farms, with over 600,000 daily records of MeC, fat yield (FY), and energy-corrected milk yield (ECM). Methane concentrations were measured using sniffers, and milk production data was acquired from milking robots and national milk recording data. At first, a pedigree-based variance component estimation revealed heritabilities between 0.17 (SE=0.03) for MeC in PP and MP cows to 0.38 (SE=0.06) for ECM in PP cows. Similarly, repeatabilities ranged from 0.32 (MeC, SE=0.01) to 0.81 (ECM, SE=0.01). Genetic correlations between MeC and production traits were positive but unfavorable, i.e., in a range from 0.15 (SE=0.13) between MeC and ECM in PP cows to 0.41 (SE=0.09) between MeC and ECM in MP cows, indicating a genetic antagonism between reducing emissions and maintaining milk yield. Prediction accuracies were generally higher for ssGBLUP compared to pBLUP models (up to 61.90% increase), and for MP cows compared to PP cows. Multi-trait models outperformed univariate models, particularly when phenotypic data for FY and ECM were available in both the reference and validation populations. The highest accuracy for MeC prediction in PP cows was 0.38 (ssGBLUP), while MP cows reached up to 0.51, both for the multi-trait model including both, ECM and FY. While incorporating FY and ECM improved MeC prediction, the unfavorable genetic correlations highlight the risk of compromising milk production when selecting for reduced emissions. Therefore, future breeding strategies should aim to expand methane phenotyping, develop methane traits independent of milk production, and implement multi-trait selection indices that balance environmental and economic goals. This study demonstrates the potential of multi-trait genomic prediction to enhance the genetic evaluation of methane emissions and supports its integration into sustainable dairy cattle breeding programs.
Sustainable dairy breeding: reducing methane emissions in the Netherlands and Flanders
Dairy cows contribute to the emission of methane (CH4), a strong greenhouse gas, into the atmosphere. Reducing CH4 emissions from dairy cows will lower the impact of livestock on global warming. Breeding could be an effective reduction method, and estimating breeding values was the objective of this work. The CH4 emission of 11,595 dairy cows in 89 Dutch herds was measured with sniffers in parts per million (ppm) of exhaled air. The CH4 emission of 397 dairy cows from 1 Dutch herd was measured in grams per day (g/d) of exhaled air using GreenFeed. CH4 measurements took place from 2019 to 2025 (sniffer) and from 2022 to 2025 (GreenFeed). All observations during a week on a cow were averaged into week observations. There were 226,449 week observations for ppm and 11,824 week observations for g/d. Genetic parameters were estimated with ASReml 4.2 using an animal multi-trait repeatability model. Heritabilities (h2) were 0.14, 0.14 and 0.19 for ppm and 0.34, 0.37 and 0.37 for g/d, for respectively parity 1, parity 2 and parity 3 and later (3+). Genetic correlations between different parities for ppm were 0.74, 0.47 and 0.79, and for g/d 0.73, 0.38 and 0.69, between respectively 1 and 2, 1 and 3+, and 2 and 3+. An overall breeding value was calculated for g/d based on traits in parity 1, 2 and 3+. By using a selection index, extra information was added to the overall breeding value in g/d. Traits in the selection index were kg milk production, kg fat production, feed intake and body weight with genetic correlations of respectively 0.39, 0.19, 0.20 and 0.09. The average CH4 emission of a dairy cow was 435 gram per day with a genetic standard deviation of 36 grams per day. The heritability of the trait, the size of the genetic standard deviation, and the fact that genetic correlations with health traits were estimated to be small, makes breeding an effective and powerful tool to mitigate CH4 emissions from dairy cattle in the Netherlands and Flanders. The overall breeding value for CH4 in grams per day is published in the Netherlands and Flanders from April 2025 onwards.
Advancing Genomic Evaluation for Methane Efficiency in Walloon Holstein Cattle towards Implementation
For several years, dairy cattle breeding in the Walloon Region of Belgium has increasingly focused on sustainability, including strategies for reducing methane emissions. Genetic selection provides a viable long-term approach to mitigating methane emissions while maintaining economic viability. The current study aimed to present a single-step genomic evaluation framework for methane efficiency (ME) based on predicted methane (PCH4) derived from milk mid-infrared (MIR) spectra and its integration into the existing genomic evaluation system for Holstein dairy cattle. The study incorporated data from 285 530 first-parity, 224 643 second-parity, and 160 226 third-parity Holstein cows across 1 520 herds. Genomic information from 9 631 animals, including 1 823 bulls, was integrated using a single-step GBLUP approach with a three-trait model (PCH4 across three parities). The predictive accuracy of the genomic evaluation framework was validated using a set of 2 038 youngest genotyped animals. Approximate genetic correlations (AGC) were calculated between PCH4 and 37 traits included in the Walloon breeding goal. Three methane efficiency (ME) indices were evaluated: relative ME based on production (RMEP), relative ME based on functionality (RMEF), and relative ME based on a global economic index (RMEG). The results demonstrated that the mean daily PCH4 ranged from 324 to 367 g/day, with mean daily heritability estimates between 0.20 and 0.23 for the first three lactations. The genomic prediction accuracy for PCH4-GEBV was 0.83. The AGC between PCH4 and the 37 traits ranged from -0.16 (milk yield) to 0.53 (fat percentage), highlighting the importance of balancing methane reduction with economic performance. Among the three ME indices, RMEG exhibited the most favorable balance, supporting its integration into genomic evaluations. Bulls with higher ME indices produced progeny with lower methane emissions, demonstrating the potential for genetic selection to contribute to sustainability goals. In light of these findings, we propose that INTERBULL considers methane for international genetic evaluations as many countries start to generate breeding values. These and other MACE breeding values would allow us to generate ME indices locally. Further discussions should focus on integrating reducing methane into breeding programs while maintaining productivity and functionality traits, as well as exploring strategies to incorporate direct methane measurements. Alternative thinking and use of tools like desired gain index will be required, but most important will be better knowledge about economic value of methane and its genetic relationship to other traits of interest. These initiatives will support sustainable dairy breeding strategies, aligning environmental and economic objectives for the future.
Weighted single-step genome-wide association studies for methane intensity in Chinese Holstein cattle
Reducing methane emissions from dairy cows has been a key area of research in recent decades. This study aimed to identify genomic regions associated with methane intensity (MeI) in Chinese Holstein cattle. MeI phenotype was either predicted by mid-infrared spectra (MIRS, R2cv= 0.66) or directly measured by sniffer. Data were collected from eight commercial farms in Beijing between 2017–2020 and 2024. A weighted single-step genome-wide association study (WssGWAS) was performed based on 1,120 genotypes, 4,995 phenotypic records, and pedigree of 10 911 individuals. The mean MeI was 7.67 ± 1.52 (g/kg milk yield). The estimated heritability of MeI was 0.15±0.04, and the repeatability was 0.42±0.02. Eleven 10-SNP windows harboring 19 protein encoding genes explained 2.17% of the genomic variance, with genomic regions on BTA1, 5, 8, 15, 19, 20, 24, 26, and 27. Five of the windows were also associated with milk production or milk component traits, while one window contained the QTL linked to metabolic body weight. The region explaining the highest proportion of variance (0.34%) was located on BTA15, which included five protein encoding genes. Among them, SCN4B and MPZL3 are proposed as candidate genes. In total, the preliminary results show that MeI is a heritable, repeatable, and polygenic trait in Chinese Holstein population. The identified MeI-related genomic regions provide an insight for breeding dairy cows with lower methane emissions.
Genetic evaluation for longevity of dairy cattle in the Netherlands
Longevity of dairy cattle is an important trait from an economic and welfare perspective, as well as from a societal and government perspective. For a farmer it is beneficial to keep older cows, as it will reduce costs of rearing. The Dutch government aims to reduce the environmental impact of livestock, and for that it is also beneficial to keep older cows. Older cows produce more on average, and feed is converted more efficiently. In the Dutch-Flemish genetic evaluation of functional longevity a random regression animal model with a fifth order Legendre polynomial is used, where within-herd production level is fitted as an explanatory effect. Published breeding values for longevity are partially based on predictor traits. The breeding values for milk production, as well as udder health, claw health and locomotion are added as predictor traits through selection index theory. In this paper the impact of the introduction of genetic evaluation of longevity is assessed. Over the past 25 years all statistics on longevity metrics have been favorable. Productive life increased with 337 days to 1,445 days for cows culled in 2024. Together with a reduced amount of youngstock, the rearing period reduced 40 days to 763 days. The mean number of calvings increased by 0.8 to 3.9 calvings. Lifetime production increased in 25 years by 14,329 kg to 38,283 kg of milk (with 4.40% fat and 3.58% protein), resulting in 1,684 kg fat and 1,369 kg protein. Production per day of life increased by 4.8 kg to 17.1 kg of milk. Longevity is a result of management (e.g. feeding, housing and culling decisions), environment, and genetics. Genetically, longevity increased by 600 days, which suggests that the full genetic potential is not yet utilized. The strong increase of the genetic trend for longevity was supported by selection on udder health, claw health and feet & legs. Culling decisions can also be affected by governmental changes in regulations. The genetic trend is more consistent over the years than the phenotypic trend. Long-term trends show that all these factors together resulted in significant improvements for longevity and lifetime production. With the continuing improvement of production and health traits further improvement of longevity is expected.
Modernizing Canada’s Lifetime Performance Index (LPI)
National selection indexes combining important traits are frequently used by dairy farmers, breeders, and A.I. companies to achieve their breeding goals. The Canadian dairy industry has made significant genetic progress with two national selection indexes, the Lifetime Performance Index (LPI) and Pro$, which are now double the rate compared to the pre-genomics era. Since its introduction in 1991 the LPI formula has changed alongside the expansion of national breeding objectives. With the introduction in recent years of genetic evaluations for its portfolio of traits related to sustainability, the timing was right for Lactanet to modernize several aspects of Canada’s LPI, effective April 2025. A key strategic change is the creation of six subindexes, which are each published on their own using a standardized scale with an average of 500 and standard deviation of 100. Subindexes include the Production Index (PI), Longevity & Type Index (LTI), Health & Welfare Index (HWI), Reproduction Index (RI), Milkability Index (MI), and Environmental Impact Index (EI). For the Holstein breed, the relative weights placed on these subindexes are 40% PI, 32% LTI, 8% HWI, 10% RI, 5% MI, and 5% EI. The six other dairy breeds evaluated have differing relative weights in accordance with the respective breed objectives. A second important change is an increased focus on presenting the genetic response over the next five years that can be expected for each trait based on the average level of selection gain realized for LPI. While this approach recognizes the impact of direct inclusion of a trait in one of the six LPI subindexes, it also reflects the expected response for correlated traits. Defining six subindexes that contribute to LPI demonstrates the increased diversity of traits currently evaluated and acknowledges the continued expansion of Canada’s overall breeding goal for dairy cattle breeds.
Quantifying the use of and the genetic progress from advanced mating strategies in US dairy herds
The use of mating technologies, including genomic testing and sexed semen, has recently increased in the breeding programs of commercial dairy herds, along with the use of beef semen. We aimed to quantify the utilization of advanced mating strategies in US dairy herds and the influence of these strategies on genetic merit. Breeding records (n = 35,124,479) that resulted in successful pregnancies of cows and heifers by semen type (conventional dairy, sexed dairy, and beef) and records of genomic testing of female dairy cattle were extracted from the National Cooperator Database for the years 2008 to 2023. Herds were categorized within year by semen type used and use of genomic testing of heifers and the genetic merit of heifers born in 2023 (n = 678,064) was compared by herd mating strategy. Female dairy cattle in the US are genotyped, on average, at 6 months of age. When the net merit of a genotyped heifer increased by one standard deviation, the odds that she remained in the herd through first lactation increased by 13%. Breeding values of net merit ($1,203) and most of the traits investigated were most favorable in heifers born in herds that used all mating strategies investigated (genotyping of heifers, and a combination of beef, sexed, and conventional semen). Calves born in herds that used a combination of sexed and conventional semen had the least net merit ($532) and generally had the least favorable breeding values across production, fertility, and longevity traits. Results confirm that the incorporation of advanced mating strategies has increased rapidly in US dairy herds. Heifers were more likely to enter the milking herd as their genomic merit increased and herds that incorporated all strategies investigated had the greatest genetic progress.
Renewed Genetic Evaluation of Heat Tolerance in Italian Holsteins
Heat stress is a significant and growing challenge for the Italian dairy industry, adversely affecting milk production, fertility, and animal welfare. This study presents a renewed genetic evaluation for heat tolerance in Italian Holstein cattle, expanding upon a previous 2021 index that focused solely on milk yield. The primary objective was to develop a more comprehensive selection tool by incorporating five production traits: milk yield (kg), fat yield (kg), protein yield (kg), fat percentage, and protein percentage. Test-day production records were augmented with meteorological data from 137 weather stations across Italy to calculate a 7-day average Temperature-Humidity Index (THI) for each record. A repeatability mixed model was first employed to identify the specific THI thresholds at which each of the five traits begins to decline. Subsequently, a random regression mixed model was implemented to estimate genetic parameters and calculate Estimated Breeding Values (EBVs) for both general production merit and specific heat tolerance. THI thresholds were identified for all traits, with milk yield declining above a THI of 70. Heritabilities for heat tolerance traits were found to be low to moderate, ranging from 0.12 for fat yield to 0.37 for protein percentage, indicating sufficient genetic variation for selection. An aggregate index, the Heat Tolerance Index (IHT), was developed by assigning economic weights to each trait Heat Tolerance EBV. Validation results demonstrated the index's efficacy: daughters of high-IHT bulls (+1 SD) lost 0.91 kg/d less milk during summer compared to daughters of low-IHT bulls (-1 SD). This renewed evaluation provides a robust tool to select for more resilient animals, offering a tangible strategy to mitigate economic losses and improve animal welfare in a warming climate.
Nordic Genetic Evaluation of Feed Efficiency – Updated Model for Saved Feed in Nordic Dairy Cattle Breeds.
Since 2019, the Nordic Cattle Genetic Evaluation (NAV) has published breeding values for Saved Feed for Holstein (HOL), Jersey (JER), and Red Dairy Cattle (RDC). This trait is integrated into the Nordic Total Merit (NTM) index. Previously, the genetic evaluation of energy use for maintenance was based on body weight and indicator traits such as stature, body depth, and body width. Metabolic efficiency was genetically evaluated by using a two-step model, where the first step is a pre-correction of phenotypes that introduced challenges. This study developed a one-step genomic model for Saved Feed to both address these challenges and improve the use of research and Cattle Feed InTake (CFIT) data. The dataset comprised 741,491 weekly records from 4,541 JER, 5,377 RDC, and 8,030 HOL cows. SNP genotypes from NAV were used to create breed-specific single-step genomic evaluations. We fitted two-trait random regression models for each of the traits of dry matter intake (DMI), energy-corrected milk (ECM), and body weight (BW) where we treated the first and later lactations as separate traits. The breeding values for BW change (ΔBW) were derived from the BW model. Across lactation (2–44 weeks in milk), the heritabilities ranged from moderate to moderately high for DMI and ECM (0.22–0.50 & 0.47- 0.52) and were moderately high for BW (0.46–0.61). The genetic correlations were strong across parities (≥0.82). These genetic parameters estimated with pedigree based BLUP, were used in three single-step GBLUP models, where lactation-wise breeding values for Saved Feed were calculated within each breed as: GEBVSaved Feed = 0.40 × GEBVECM + 4.0 × GEBVΔBW – GEBVDMI One index unit of Saved Feed corresponds to 18.3 kg of dry matter saved per 305-day lactation, or 183 kg for 10 index units. Among for candidate bulls born in year 2022, the breeding values for Saved Feed had moderate index correlations with the NTM (0.20–0.30), weak to moderate index correlations with yield trait (0.07–0.26), and low index correlations with female fertility, udder health, and general health (–0.14 to 0.12). For HOL and RDC, the index correlations between Saved Feed and frame size were moderately negative (–0.19 to –0.29), while for JER, this correlation was close to zero. In conclusion, NTM has been updated with the new NAV one-step Saved Feed index which should promote genetic progress for feed efficiency in the Nordic Dairy Cattle breeds.
The M3GE project: developing beef cattle multi-trait multi-breed multi-country genomic evaluations for sustainability traits and small populations
Sustainability traits, such as feed efficiency and enteric methane emissions, are difficult and expensive to measure. Establishing a large national reference population is therefore challenging, and pooling data across countries in a joint international evaluation would be beneficial. In beef cattle, data on sustainability traits are collected across multiple breeds and in small populations, including crossbred animals of various breed composition. In such scenarios, genomic prediction requires modelling the individuals’ different genetic background. Additionally, including available data on correlated indicator traits could improve the accuracy of genomic prediction for sustainability traits. However, current international beef cattle evaluations led by Interbeef are pedigree‐based, performed within each breed separately, and use data from one trait, or one group of traits, at a time. The “M3GE” project aims to develop multi-trait multi-breed multi-country genomic evaluations for beef cattle, focusing on sustainability traits and small populations. The project is the result of a collaboration between WUR, ICAR (the Netherlands), Interbull Centre (Sweden), ICBF (Ireland), AHDB, SRUC (Great Britain), and FedANA (Italy), involving six national breeding organisations from three countries. The aims of this paper are to: i) present the M3GE project and its objectives, ii) give an overview of the status of collecting and modelling feed efficiency across participating organisations, and iii) present the first results of the project. Pedigree, phenotypic, and genomic data for feed efficiency, longevity, and associated indicator traits have been collected using the Interbull Centre’s GenoEx-GDE and IDEA platforms. Initial work will focus on feed efficiency for which individual direct measures have been collected on ~13K phenotyped animals (~9K of which are genotyped), from over 15 different breeds and crossbreds recorded in Great Britain, Ireland, and Italy. The first steps include the imputation of collected genotypes to a common reference panel, population structure analysis, estimation of connectedness measures across populations, and estimation of genetic correlations across countries. The final step is to develop an international multi-breed single-step evaluation for feed efficiency including crossbred animals. This project contributes to the development of sustainable genomic evaluations in beef cattle for large and small populations.