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Additional file 1: Table S1. Pearson correlations, regression coefficients, and level bias for direct and maternal GEBV computed from the full ssSNPBLUP versus from the indirect prediction approaches for 251,332 genotyped selection candidates with both parents genotyped. Table S2. Pearson correlations, regression coefficients, and level bias for direct and maternal GEBV computed from the full ssSNPBLUP versus from the indirect prediction approaches for 155,675 genotyped selection candidates with only one genotyped parent. Table S3. Pearson correlations, regression coefficients, and level bias for direct and maternal GEBV computed from the full ssSNPBLUP versus from the indirect prediction approaches for 50,164 genotyped selection candidates with no genotyped parents. Table S4. Pearson correlations, regression coefficients, and level bias for direct and maternal GEBV computed from the full ssGTABLUP versus from the indirect prediction approaches for 251,332 genotyped selection candidates with both parents genotyped. Table S5. Pearson correlations, regression coefficients, and level bias for direct and maternal GEBV computed from the full ssGTABLUP versus from the indirect prediction approaches for 155,675 genotyped selection candidates with only one genotyped parent. Table S6. Pearson correlations, regression coefficients, and level bias for direct and maternal GEBV computed from the full ssGTABLUP versus from the indirect prediction approaches for 50,164 genotyped selection candidates with no genotyped parents.

Additional file 1: Table S1. Number of phenotypes (N), minimum, mean, maximum, and phenotypic standard deviation ( $${\sigma }_{P}$$ σ P ) of males and females per country. Table S2. List of environmental effects in the national model of each country. Table S3. Direct and maternal genetic covariances (below diagonal), genetic variances (diagonal) and genetic correlations (above diagonal) within and across countries. Table S4. National genetic, environmental and residual (co)variances. Table S5. Level bias ( $${\widehat{\Delta }}_{p}$$ Δ ^ p ), dispersion bias ( $${\widehat{b}}_{p}$$ b ^ p ) and accuracy of partial EBV ( $${\widehat{acc}}_{p}$$ acc ^ p ) of direct and maternal EBV for animals with phenotypes and no genotypes born from 2014 onwards, in each scenario and for each country. Table S6. Increases in population accuracy ( $$inc\_acc$$ i n c _ a c c ) of moving from each scenario to ssSNPBLUPINT for direct and maternal EBV for animals with phenotypes and no genotypes born from 2014 onwards for each country. Table S7. Countries sending the genotypes for Common Bulls (CB) and Common Maternal Grand-Sires (CMGS). Table S8. Level bias ( $${\widehat{\Delta }}_{p}$$ Δ ^ p ), dispersion bias ( $${\widehat{b}}_{p}$$ b ^ p ) and accuracy of partial EBV ( $${\widehat{acc}}_{p}$$ acc ^ p ) of direct and maternal EBV for the focal group, in each scenario and for each country. Standard errors reported within parenthesis.

Additional file 4: Figure S3. Direct and maternal EBV realized Mendelian sampling (RMS) trends in each countrya expressed in genetic standard deviation (GSD) for genotyped and non-genotyped animalsb computed with pedigree-based BLUP international (PBLUPINT) and single-step SNP-BLUP international (ssSNPBLUPINT) models. aCZE Czech Republic, DFS Denmark, Finland and Sweden, IRL Ireland, DEU Germany, CHE Switzerland. bGenotyped animals: animals with genotype that appear in the pseudo-national pedigree (with or without phenotype in the country). Non-genotyped animals: animals that appear in the pseudo-national pedigree without genotype and with phenotype in the country.

Additional file 2: File S1. Findhap instruction file. File S2. International pedigree-based BLUP MiXBLUP instruction file. File S3. International single-step SNPBLUP MiXBLUP instruction file.

Additional file 3: Figure S1. Plot of pedigree-based (x-axis) and genomic-based (y-axis) relationships between genotyped animals. The red dots indicate the relationships of the 41 genotypes removed due to pedigree incompatibilities. Figure S2. Differences in genetic trends between ssSNPBLUPINT and PBLUPINT per country for sires with at least 10 recorded offspring in the country (only birth years with at least 5 sires are included).

The aim of this study was to identify genomic regions associated with 305‐day milk yield and lactation curve parameters on primiparous (n = 9,910) and multiparous (n = 11,158) Holstein cows. The SNP solutions were estimated using a weighted single‐step genomic BLUP approach and imputed high‐density panel (777k) genotypes. The proportion of genetic variance explained by windows of 50 consecutive SNP (with an average of 165 Kb) was calculated, and regions that accounted for more than 0.50% of the variance were used to search for candidate genes. Estimated heritabilities were 0.37, 0.34, 0.17, 0.12, 0.30 and 0.19, respectively, for 305‐day milk yield, peak yield, peak time, ramp, scale and decay for primiparous cows. Genetic correlations of 305‐day milk yield with peak yield, peak time, ramp, scale and decay in primiparous cows were 0.99, 0.63, 0.20, 0.97 and −0.52, respectively. The results identified three windows on BTA14 associated with 305‐day milk yield and the parameters of lactation curve in primi‐ and multiparous cows. Previously proposed candidate genes for milk yield supported by this work include GRINA, CYHR1, FOXH1, TONSL, PPP1R16A, ARHGAP39, MAF1, OPLAH and MROH1, whereas newly identified candidate genes are MIR2308, ZNF7, ZNF34, SLURP1, MAFA and KIFC2 (BTA14). The protein lipidation biological process term, which plays a key role in controlling protein localization and function, was identified as the most important term enriched by the identified genes.

Improving nitrogen use efficiency (NUE) at both the individual cow and the herd level has become a key target in dairy production systems, for both environmental and economic reasons. Cost-effective and large-scale phenotyping methods are required to improve NUE through genetic selection and by feeding and management strategies. The aim of this study was to evaluate the possibility of using mid-infrared (MIR) spectra of milk to predict individual dairy cow NUE during early lactation. Data were collected from 129 Holstein cows, from calving until 50 d in milk, in 3 research herds (Denmark, Ireland, and the UK). In 2 of the herds, diets were designed to challenge cows metabolically, whereas a diet reflecting local management practices was offered in the third herd. Nitrogen intake (kg/d) and nitrogen excreted in milk (kg/d) were calculated daily. Nitrogen use efficiency was calculated as the ratio between nitrogen in milk and nitrogen intake, and expressed as a percentage. Individual daily values for NUE ranged from 9.7 to 81.7%, with an average of 36.9% and standard deviation of 10.4%. Milk MIR spectra were recorded twice weekly and were standardized into a common format to avoid bias between apparatus or sampling periods. Regression models predicting NUE using milk MIR spectra were developed on 1,034 observations using partial least squares or support vector machines regression methods. The models were then evaluated through (1) a cross-validation using 10 subsets, (2) a cow validation excluding 25% of the cows to be used as a validation set, and (3) a diet validation excluding each of the diets one by one to be used as validation sets. The best statistical performances were obtained when using the support vector machines method. Inclusion of milk yield and lactation number as predictors, in combination with the spectra, also improved the calibration. In cross-validation, the best model predicted NUE with a coefficient of determination of cross-validation of 0.74 and a relative error of 14%, which is suitable to discriminate between low- and high-NUE cows. When performing the cow validation, the relative error remained at 14%, and during the diet validation the relative error ranged from 12 to 34%. In the diet validation, the models showed a lack of robustness, demonstrating difficulties in predicting NUE for diets and for samples that were not represented in the calibration data set. Hence, a need exists to integrate more data in the models to cover a maximum of variability regarding breeds, diets, lactation stages, management practices, seasons, MIR instruments, and geographic regions. Although the model needs to be validated and improved for use in routine conditions, these preliminary results showed that it was possible to obtain information on NUE through milk MIR spectra. This could potentially allow large-scale predictions to aid both further genetic and genomic studies, and the development of farm management tools.