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In the context of climate change, livestock production systems face the challenge of ensuring that, although more and more exposed to heat-stress conditions, animals will be able to remain healthy while maintaining production in satisfactory quality and levels, responding to consumers demand. As part of the European project RUMIGEN, France (INRAE, Idele), Spain (INIA, IRIAF), and the Netherlands (WUR) studied the response of different dairy traits to heat-stress conditions, for Holstein cattle breed.Performances of production (milk, fat and protein yields) and udder health (somatic cell score) traits were investigated under different meteorological conditions. These meteorological conditions were represented by the temperature humidity index (THI) averaged over three days around the day of record. A THI=50 was considered as neutral condition (i.e. no heat/cold-stress) and used as reference for level comparisons. Specific rates of changes in production/health traits at a given THI were measured as the slopes (first derivative) of the estimated reaction-norm curves for each trait. Genetic evaluations were performed by each country using test-day records of cows in their first lactation between 2010 and 2020, to estimate both levels and slopes for each trait. Breeding values (EBVs) and reliabilities were obtained for sires with at least 20 daughters.A meta-analysis was performed to estimate the genetic correlations between the three countries. For each country, deregressed proofs and effective record contributions were computed using a single trait model from the EBVs, reliabilities, and variance components estimated at a national level for specific THI values. For both levels and slopes, the genetic correlations between the three countries were then estimated using the MACE approach (Schaeffer, 1994).For the levels at THI=50, the estimated genetic correlations between countries ranged from 0.85 to 0.98. The low heritability of the slopes and the small proportion of pe

Computing time and memory requirements increase with single-step methods to estimate genomic breeding values when the number of genotyped animals increases. Computational costs can be reduced by omitting genotypes of animals without phenotype and progeny, often the candidate animals for selection. Indirect prediction of a candidate animal GEBV can be based on the animal’s genotype and SNP marker solutions (DGV). Alternatively, the sum of DGV and residual polygenic (RPG) effect can be computed, denoted GRV (Genomic and Residual polygenic Value). We applied indirect genomic prediction for a 6 trait calving difficulty evaluation. There were 1.50 million genotyped animals of which 36% were considered candidate animals. Based on our results, DGV showed high accuracy but also bias due to omitting the RPG effect. The GRV prediction had high accuracy and low bias. Computing time was reduced by 33%.

The software package MiXBLUP 3.0 allows to efficiently estimate (genomic enhanced) breeding values in livestock using various linear mixed effect models. MiXBLUP aims to be user-friendly while supporting efficient algorithms for solving large genomic evaluations. Originally developed for pedigree-based models, MiXBLUP has been extended to four different approaches for single-step genomic evaluations, allowing simultaneous analyses of all information for both genotyped and ungenotyped individuals. For all approaches, shared-memory parallelism is supported using tailor-made procedures and parallel libraries. It has been tested on some of the largest genomic datasets in the world. The software is developed by Wageningen University and Research in collaboration with the Natural Resources Institute Finland (Luke).

Defining multiple base populations for a genetic evaluation is subjective and there is generally no feedback on the suitability of the defined base populations prior to model validation. We present a number of statistics that can be used to evaluate defined base populations. Application of these statistics to one simulated and two practical datasets showed that in practical datasets the number of base animals per base population was in one case very low. In many cases the available genotype information for different base populations was coming from exactly the same genotyped animals. Both issues are likely to complicate genomic evaluation. A tool to estimate these statistics is available in the MiXBLUP software suite.

Beef cattle international evaluations, led by Interbeef, combine data from different countries to compute international estimated breeding values (EBVINT) which are expressed on the same country scale as national ones, allowing participating countries to compare national and foreign sires. These two EBV might be in disagreement due to differences in the information used in national and international evaluations. This can be overcome by integrating the EBVINT into the national evaluations to obtain a ‘blended’ EBV. Here we tested and validated a general procedure to integrate publishable sires EBVINT at the national level. Using evaluations for Limousin weaning weight and the integration for Italy as a case study, we show that the integration procedure accurately blends international information at the national level while avoiding double-counting of national information which are used as input to compute EBVINT in Interbeef evaluations. This procedure can also be used with other traits and breeds.

Many traits of economical relevance in beef cattle are influenced in their phenotypic expression by the dam. Genetic evaluations of maternally affected traits require to model direct, maternal and direct-maternal genetic (co)variances next to non-genetic effects. In Interbeef beef cattle international evaluations, direct-maternal genetic correlations (rdm) may be different both within countries (rdm_WC) and between countries (rdm_BC). rdm_WC for growth traits up to weaning are often reported to be negative and significantly different from zero. As rdm_BC are difficult to estimate, these are currently assumed to be equal to zero in Interbeef evaluations. The objective of this study was to evaluate the impact of using estimated values for rdm_BC instead of assuming them to be zero, on international estimated breeding values (IEBV). We implemented two scenarios that differed only in the modelling of rdm_BC: A) the current Interbeef evaluation with assumes rdm_BC to be 0 and fits estimated rdm_WC, and B) an Interbeef evaluation in which both estimated rdm_WC and rdm_BC were fitted. Weaning weight phenotypes and pedigree information were available for more than 3 million Limousin beef cattle males and females, born between 1972 and 2017, and distributed across ten European countries. We evaluated the impact of ignoring rdm_BC on different groups of animals by comparing animals’ direct and maternal IEBV between scenarios A and B. Ignoring rdm_BC resulted in no re-ranking for direct IEBV, and limited re-ranking for maternal IEBV. Less re-ranking in maternal IEBV was observed with increasing reliability. Moreover, ignoring rdm_BC resulted in no re-ranking for publishable sires, i.e. of sires with IEBV that can be exchanged across countries. Our study suggests that the current practice of ignoring rdm_BC has limited impact on Interbeef evaluations when rdm_BC are close to 0 on average (ranging from +0.14 to -0.14) as is the case for weaning weight.

This study demonstrated the feasibility of a genomic evaluation for the dairy cattle population for which the small national training population can be complemented with foreign information from international evaluations. National test-day milk yield data records for the Slovenian Brown Swiss cattle population were analyzed. Genomic evaluation was carried out using the single-step genomic best linear unbiased prediction method (ssGBLUP), resulting in genomic estimated breeding values (GEBV). The predominantly female group of genotyped animals, representing the national training population in the single-step genomic evaluation, was further augmented with 7,024 genotypes of foreign progeny-tested sires from an international Brown Swiss InterGenomics genomic evaluation (https://interbull.org/ib/whole_cop). Additionally, the estimated breeding values for the altogether 7,246 genotyped domestic and foreign sires from the 2019 sire multiple across-country evaluation (MACE), were added to the ssGBLUP as external pseudophenotypic information. The ssGBLUP method, with integration of MACE information by avoiding double counting, was then performed, resulting in MACE-enhanced GEBV (GEBVM). The methods were empirically validated with forward prediction. The validation group consisted of 315 domestic males and 1,041 domestic females born after 2012. Increase, inflation, and bias of the GEBV(M) reliability (REL) were assessed for the validation group with a focus on females. All individuals in the validation benefited from genomic evaluations using both methods, but the GEBV(M) REL increased most for the youngest selection candidates. Up to 35 points of GEBV REL could be assigned to national genomic information, and up to 17 points of GEBVM REL could additionally be attributed to the integration of foreign sire genomic and MACE information. Results indicated that the combined foreign progeny-tested sire genomic and external MACE information can be used in the single-step genomic

Allele frequencies are used for several aspects of genomic prediction, with the assumption that these are equal to the allele frequency in the base generation of the pedigree. The current standard method, however, calculates allele frequencies from the current genotyped population. We compared the current standard method with BLUP and general least squares (GLS) methods explicitly targeting the base population to determine whether there is a more accurate and still efficient method of calculating allele frequencies that better represents the base generation. A data set based on a typical dairy population was simulated for 325,266 animals; the last 100,078 animals in generations 9 to 12 of the population were genotyped, with 1,670 SNP markers. For the BLUP method, several SNP genotypes were analyzed with a multitrait model by assuming a heritability of 0.99 and no genetic correlation among them. This method was limited by the time required for each BLUP to converge (approximately 6 min per BLUP run of 15 SNP). The GLS method had 2 implementations. The first implementation, using imputation on the fly and multiplication of sparse matrices, was very efficient and required just 49 s and 1.3 GB of random access memory. The second implementation, using a dense fullA22 −1 matrix, was very inefficient and required more than 1 d of wall clock time and more than 118.2 GB of random access memory. When no selection was considered in the simulations, all methods predicted equally well. When selection was introduced, higher correlations between the estimated allele frequency and known base generation allele frequency were observed for BLUP (0.96 ± 0.01) and GLS (0.97 ± 0.01) compared with the current standard method (0.87 ± 0.01). The GLS method decreased in accuracy when introducing incomplete pedigree, with 25% of sires in the first 5 generations randomly replaced as unknown to erroneously identify founder animals (0.93 ± 0.01) and a further decrease for 8 generations (0.91 ± 0

The aim of this study was to estimate phenotypic and genetic correlations between methane production (MP) and milk fatty acid contents of first-parity Walloon Holstein cows throughout lactation. Calibration equations predicting daily MP (g/d) and milk fatty acid contents (g/100 dL of milk) were applied on milk mid-infrared spectra related to Walloon milk recording. A total of 241,236 predictions of MP and milk fatty acids were used. These data were collected between 5 and 305 DIM in 33,555 first-parity Holstein cows from 626 herds. Pedigree data included 109,975 animals. Bivariate (i.e., MP and a fatty acid trait) random regression test-day models were developed to estimate phenotypic and genetic parameters of MP and milk fatty acids. Individual short-chain fatty acids (SCFA) and groups of saturated fatty acids, SCFA, and medium-chain fatty acids showed positive phenotypic and genetic correlations with MP (from 0.10 to 0.16 and from 0.23 to 0.30 for phenotypic and genetic correlations, respectively), whereas individual long-chain fatty acids (LCFA), and groups of LCFA, monounsaturated fatty acids, and unsaturated fatty acids showed null to positive phenotypic and genetic correlations with MP (from -0.03 to 0.13 and from -0.02 to 0.32 for phenotypic and genetic correlations, respectively). However, these correlations changed throughout lactation. First, de novo individual and group fatty acids (i.e., C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, SCFA group) showed low phenotypic or genetic correlations (or both) in early lactation and higher at the end of lactation. In contrast, phenotypic and genetic correlations between MP and C16:0, which could be de novo synthetized or derived from blood lipids, were more stable during lactation. This fatty acid is the most abundant fatty acid of the saturated fatty acid and medium-chain fatty acid groups of which correlations with MP showed the same pattern across lactation. Phenotypic and genetic correlations between MP and C17:0 and

Animals that are robust to environmental changes are desirable in the current dairy industry. Genetic differences in micro-environmental sensitivity can be studied through heterogeneity of residual variance between animals. However, residual variance between animals is usually assumed to be homogeneous in traditional genetic evaluations. The aim of this study was to investigate genetic heterogeneity of residual variance by estimating variance components in residual variance for milk yield, somatic cell score, contents in milk (g/dL) of 2 groups of milk fatty acids (i.e., saturated and unsaturated fatty acids), and the content in milk of one individual fatty acid (i.e., oleic acid, C18:1 cis-9), for first-parity Holstein cows in the Walloon Region of Belgium. A total of 146,027 test-day records from 26,887 cows in 747 herds were available. All cows had at least 3 records and a known sire. These sires had at least 10 cows with records and each herd × test-day had at least 5 cows. The 5 traits were analyzed separately based on fixed lactation curve and random regression test-day models for the mean. Estimation of variance components was performed by running iteratively expectation maximization-REML algorithm by the implementation of double hierarchical generalized linear models. Based on fixed lactation curve test-day mean models, heritability for residual variances ranged between 1.01 × 10-3 and 4.17 × 10-3 for all traits. The genetic standard deviation in residual variance (i.e., approximately the genetic coefficient of variation of residual variance) ranged between 0.12 and 0.17. Therefore, some genetic variance in micro-environmental sensitivity existed in the Walloon Holstein dairy cattle for the 5 studied traits. The standard deviations due to herd × test-day and permanent environment in residual variance ranged between 0.36 and 0.45 for herd × test-day effect and between 0.55 and 0.97 for permanent environmental effect. Therefore, nongenetic effects also contrib

The aim of this research was to compare different Bayesian procedures to integrate information from outside a given evaluation system, hereafter called external information, and in this context estimated breeding values (EBV), into this genetic evaluation, hereafter called internal evaluation, and to improve the Bayesian procedures to assess their potential to combine information from diverse sources. The 2 improvements were based on approximations of prior mean and variance. The first version of modified Bayesian evaluation considers all animals as animals associated with external information. For animals that have no external information (i.e., internal animals), external information is predicted from available external information. Thereby, propagation of this external information through the whole pedigree is allowed. Furthermore, the prediction of external information for internal animals allows large simplifications of the computational burden during setup and solving of mixed model equations. However, double counting among external animals (i.e., animals associated with available external information) is not avoided. Double counting concerns multiple considerations of contributions due to relationships by integration of external EBV for related external animals and is taken into account by the second version of modified Bayesian evaluation. This version includes the estimation of double counting before integration of external information. To test the improvements, 2 dairy cattle populations were simulated across 5 generations. Milk production for the first lactation for each female was simulated in both populations. Internal females were randomly mated with internal males and 50 external males. Results for 100 replicates showed that rank correlations among Bayesian EBV and EBV based on the joint use of external and internal data were very close to 1 for both external and internal animals if all internal and external animals were associated with external infor