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Funding Acknowledgements Type of funding sources: None. Background It has been shown recently that ablation of paroxysmal and persistent atrial fibrillation (AF) using very high-power-short duration (vHPSD) ablation protocol is safe and effective. Data for vHPSD ablation protocol in adult patients with congenital heart disease (ACHD) are lacking. We searched our database for ACHD patients undergoing AF ablation using vHPSD ablation regarding periprocedural safety and outcome. Methods The data of 62 consecutive ACHD patients with mild (n=43; 69.4%), moderate (n=15; 24.2%) or complex (n= 4; 6.4%) CHD were analysed (Table 1 ). Mean age was 60,43 ± 12,7 years ( 46% male) with a Cha2ds2-VASc2 Score median of 2. Patients were diagnosed with paroxysmal AF (n= 24; 38%) or persistent AF (n= 38; 61%). In 6 patients (9,6 %), a previous conventional pulmonary vein isolation (PVI) had been performed. Settings used for vHPSD ablation were 70 W/5-7 s or 60 W/7-10 s using irrigated-tip catheters. Circumferential PVI was performed in all 62 patients; in 30 patients (48%) additional substrate ablation was performed. The 30-day incidence of adverse events was analyzed and patients followed-up in regular visits at our outpatient clinic using repetitive 7 days Holter ECGs every 3 months. Results No adverse events such as cardiac tamponade, pericardial effusion >10mm, transient ischemic attack/stroke, atrio-esophageal fistula, cardiac arrest or death occurred. Vascular access complications were detected in 7/62 patients (11%), including 4 patients with a need of intervention (surgical n=2, thrombin injection n=2). Complete PVI was performed in all patients (mean procedure time 129 ± 42min, mean fluoroscopy time 9,09 ± 5.44 min. Radiofrequency (RF) time was 127.27 ± 7,25 min (111,43 ± 27,83 min PVI only, 148,76 ± 46,07 min PVI + substrate ablation). After a mean follow up of 249 days, freedom of any atrial arrhythmia off antiarrhythmic drugs (AAD) after a single ablation procedure was present in 26/47 patients (55.3%). Success rate was significantly higher in patients with paroxysmal AF (n=16/18; 88%) compared to patients with persistent AF (n=10/29; 34.5%; p< 0.001; Figure 1) Conclusion: In this first study using vHPSD in ACHD patients of variable complexity with paroxysmal or persistent AF, the technique was safe and effective. Major periprocedural complications were rare and limited to vascular access problems. No vHPSD ablation modality-related complications occurred. vHPSD was fast in all patintes regardless of CHD complexity. Results in the paroxysmal CHD group were excellent and encouraging for persistent AF.

Routine genomic evaluations in animal breeding are usually based on either a BLUP with genomic relationship matrix (GBLUP) or single nucleotide polymorphism (SNP) BLUP model. For a multi-step genomic evaluation, these 2 alternative genomic models were proven to give equivalent predictions for genomic reference animals. The model equivalence was verified also for young genotyped animals without phenotypes. Due to incomplete linkage disequilibrium of SNP markers to genes or causal mutations responsible for genetic inheritance of quantitative traits, SNP markers cannot explain all the genetic variance. A residual polygenic effect is normally fitted in the genomic model to account for the incomplete linkage disequilibrium. In this study, we start by showing the proof that the multi-step GBLUP and SNP BLUP models are equivalent for the reference animals, when they have a residual polygenic effect included. Second, the equivalence of both multi-step genomic models with a residual polygenic effect was also verified for young genotyped animals without phenotypes. Additionally, we derived formulas to convert genomic estimated breeding values of the GBLUP model to its components, direct genomic values and residual polygenic effect. Third, we made a proof that the equivalence of these 2 genomic models with a residual polygenic effect holds also for single-step genomic evaluation. Both the single-step GBLUP and SNP BLUP models lead to equal prediction for genotyped animals with phenotypes (e.g., reference animals), as well as for (young) genotyped animals without phenotypes. Finally, these 2 single-step genomic models with a residual polygenic effect were proven to be equivalent for estimation of SNP effects, too.

Technical advances and development in the market for genomic tools have facilitated access to whole-genome data across species. Building-up on the acquired knowledge of the genome sequences, large-scale genotyping has been optimized for broad use, so genotype information can be routinely used to predict genetic merit. Genomic selection (GS) refers to the use of aggregates of estimated marker effects as predictors which allow improved individual differentiation at young age. Realizable benefits of GS are influenced by several factors and vary in quantity and quality between species. General characteristics and challenges of GS in implementation and routine application are described, followed by an overview over the current status of its use, prospects and challenges in important animal species. Genetic gain for a particular trait can be enhanced by shortening of the generation interval, increased selection accuracy and increased selection intensity, with species- and breed-specific relevance of the determinants. Reliable predictions based on genetic marker effects require assembly of a reference for linking of phenotype and genotype data to allow estimation and regular re-estimation. Experiences from dairy breeding have shown that international collaboration can set the course for fast and successful implementation of innovative selection tools, so genomics may significantly impact the structures of future breeding and breeding programmes. Traits of great and increasing importance, which were difficult to improve in the conventional systems, could be emphasized, if continuous availability of high-quality phenotype data can be assured. Equally elaborate strategies for genotyping and phenotyping will allow tailored approaches to balance efficient animal production, sustainability, animal health and welfare in future.

Over the last decades, several genetic disorders have been discovered in cattle. However, the genetic background of disorders in calves is less reported. Recently, German cattle farmers reported on calves from specific matings with chronic diarrhea and retarded growth of unknown etiology. Affected calves did not respond to any medical treatment and died within the first months of life. These calves were underdeveloped in weight and showed progressive and severe emaciation despite of normal feed intake. Hallmark findings of the blood biochemical analysis were pronounced hypocholesterolemia and deficiency of fat-soluble vitamins. Results of the clinical and blood biochemical examination had striking similarities with findings reported in human hypobetalipoproteinemia. Postmortem examination revealed near-complete atrophy of the body fat reserves including the spinal canal and bone marrow. To identify the causal region, we performed a genome-wide association study with 9 affected and 21,077 control animals genotyped with the Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA), revealing a strong association signal on BTA 11. Subsequent autozygosity mapping identified a disease-associated haplotype encompassing 1.01 Mb. The segment of extended homozygosity contains 6 transcripts, among them the gene APOB, which is causal for cholesterol disorders in humans. However, results from multi-sample variant calling of 1 affected and 47 unaffected animals did not detect any putative causal mutation. The disease-associated haplotype has an important adverse effect on calf mortality in the homozygous state when comparing survival rates of risk matings vs. non-risk matings. Blood cholesterol values of animals are significantly associated with the carrier status indicating a codominant inheritance. The frequency of the haplotype in the current Holstein population was estimated to be 4.2%. This study describes the identification and phenotypic manifestation of a new Holstein haplotype characterized by pronounced hypocholesterolemia, chronic emaciation, growth retardation, and increased mortality in young cattle, denominated as cholesterol deficiency haplotype. Our genomic investigations and phenotypic examinations provide additional evidence for a mutation within the APOB gene causing cholesterol deficiency in Holstein cattle.

With the availability of single nucleotide polymorphism (SNP) marker chips, such as the Illumina BovineSNP50 BeadChip (50K), genomic evaluation has been routinely implemented in dairy cattle breeding. However, for an average dairy producer, total costs associated with the 50K chip are still too high to have all the cows genotyped and genomically evaluated. To study the accuracy of cheaper low-density chips, genotypes were simulated for 2 low-density chips, the Illumina Bovine3K BeadChip (3K) and BovineLD BeadChip (6K), according to their original marker maps. Simulated missing genotypes of the 50K chip were imputed using the programs Beagle and Findhap. Three genotype data sets were used to study imputation accuracy: the EuroGenomics data set, with 14,405 reference bulls (data set I); the smaller EuroGenomics data set, with 11,670 older reference bulls (data set II); and the data set of all genotyped German Holsteins, with 31,597 reference animals (data set III). Imputed genotypes were compared with their original ones to calculate allele error rate for validation animals in the 3 data sets. To evaluate the loss in accuracy of genomic prediction when using imputed genotypes, a genomic evaluation was conducted only for EuroGenomics data set II. Furthermore, combined genome-enhanced breeding values calculated from the original and imputed genotypes were compared. Allele error rate for EuroGenomics data set II was highest for the Findhap program on the 3K chip (3.3%) and lowest for the Beagle program on the 6K chip (0.6%). Across the data sets, Beagle was shown to be about 2 times as accurate as Findhap. Compared with the real 50K genotypes, the reduction in reliability of the genomic prediction when using the imputed genotypes was highest for Findhap on the 3K chip (5.3%) and lowest for Beagle on the 6K chip (1%) when averaged over the 12 evaluated traits. Differences in genome-enhanced breeding values of the original and imputed genotypes were largest for Findhap on the 3K chip, whereas Beagle on the 6K chip had the smallest difference. The low-density chip, 6K, gave markedly higher imputation accuracy and more accurate genomic prediction than the 3K chip. On the basis of the relatively small reduction in accuracy of genomic prediction, we would recommend the BovineLD 6K chip for large-scale genotyping as long as its costs are acceptable to breeders.

Several arguments exist for breeding organizations to focus on cooperative herds for progeny testing, but an efficient methodology addressing herd selection strategies is lacking. In this study, a new approach based on yield deviations (YD) to identify the most informative cooperator herds in terms of genetic differentiation was evaluated. Data comprised YD from 717,377 first-lactation cows from 2 regions in East and West Germany calving between January 2003 and January 2008. Daughters were ranked and classified within sire according to their YD for protein yield, fat yield, milk yield, and somatic cell score. Cows in created YD classes were merged with respective herd-calving year (HCY) characteristics. Cows of extreme YD classes (i.e., such classes including the most extreme daughter contributions), belonged to herds characterized by a high HCY production level, a low value for HCY somatic cell count, and a low HCY age at first calving (AFC). Cows with low extremes for YD in protein yield were associated with the lowest HCY production level, a high value for HCY somatic cell count, and a late HCY AFC. Ranks of HCY and ranks of herds considering HCY over the whole analyzed period were calculated by averaging YD percentages within HCY, and within herds, respectively. The YD percentages (in absolute values so that negative and positive daughter contributions were treated equally) were derived from the rank of the YD of a daughter within sire in relation to all daughters of a sire. A further partitioning of ranks of herds into quartiles revealed the following results: herds in the first quartile had the highest average protein yield, the highest intra-herd standard deviation for the national production index, and the lowest AFC. Correlations between herd rankings for different production traits ranged between 0.64 and 0.86, and were 0.65 for West Germany and 0.62 for East Germany between HCY 2006 and the average herd rank of all calving years. Correlations between daughter yield deviations for the highest and the lowest herd quartile of 0.87 for protein yield disproved concerns regarding genotype by environment interaction between test and production environment. The suggested methodology to identify informative cooperator herds is easy to implement, holds for regions with small herd sizes, and thus, may help in implementing sustainable and competitive dairy cattle breeding programs.

Binational genetic evaluation between Germany and France were performed for each type trait using a single-trait MACE (multiple across-country evaluation) model. Daughter yield deviations (DYD) of bulls having 30 equivalent daughter contributions or more were the data for parameter estimation. Full pedigree information of bulls was used via sire and dam relationships. In general, across-country genetic correlation estimates were in agreement with what is observed by Interbull. The estimated correlations were over 0.93 for stature, rump angle, udder depth, front teat placement, teat length and rear teat placement. These traits have been classified in both countries for a long period of time. However, some other type traits were included later in the French type classification system (most of them since 2000): chest width, body depth, angularity, rump width, rear leg rear view, fore udder and rear udder height. The estimated correlations for these traits were relatively low. In order to check changes in genetic correlations over time, data from bulls born until the end of 1995 were discarded. Higher genetic correlation estimates between both countries were obtained by using more recent data especially for traits having lower genetic correlation, e.g. body depth correlation increased from 0.55 to 0.83. Once genetic correlations were estimated, binational genetic evaluation between Germany and France were performed for each type trait using DYD of bulls. The rankings of bulls obtained from this evaluation had some differences with Interbull rankings but a similar proportion of bulls from each country was found. Finally, more computationally demanding binational evaluations were performed using yield deviations of cows for binational cow comparison. The rankings obtained were influenced by the number of daughters per bull and heritabilities used in each country.

A multitrait, multiple across-country evaluation (MT-MACE) model permitting a variable number of correlated traits per country allows international genetic evaluation models to more closely match national models. Before the MT-MACE evaluation can be applied, genetic (co)variance components within and across country must be estimated. An approximate REML algorithm for parameter estimation was developed and was validated via simulation. This method is based on the expectation maximization REML (EM-REML) algorithm. Because obtaining the inverse of co-efficient matrix is not usually feasible for large amounts of data, an algorithm using the multiple-trait effective daughter contribution (EDC) is proposed to provide approximate diagonal elements of the inverse matrix. The accuracy of the approximate EM-REML was tested with simulated data and compared with an average information REML (AI-REML) from available software. Two simulation studies were performed. First, data of 2 countries were simulated using a single-trait model. Estimates of across-country genetic correlations with the developed algorithm were unbiased and very precise. The precision, however, depended on the percentage of bulls with data in both countries. The results obtained with the approximate EM-REML software were very close to those obtained with the AI-REML software regarding estimated genetic correlations and bulls' estimated breeding values. The second simulation assumed a multiple trait model and the same number of traits, pedigree structure, EDC, and pattern of missing records as for actual observations for milk yield obtained from French and German national Holstein evaluations. As with the single-trait scenarios, the approximate EM-REML gave nearly unbiased and very precise estimates of within- and across-country genetic correlations. The results obtained in both simulation studies confirmed the suitability of the MT-MACE model and approximate EM-REML software in a wide range of situations. Even when the genetic trend was incorrectly estimated by the national evaluations, a joint analysis including a time effect in the MT-MACE model adequately corrected for this bias.

The experimental power of a granddaughter design to detect quantitative trait loci (QTL) in dairy cattle is often limited by the availability of progeny-tested sires, by the ignoring of already identified QTL in the statistical analysis, and by the application of stringent experimentwise significance levels. This study describes an experiment that addressed these points. A large granddaughter design was set up that included sires from two countries (Germany and France), resulting in almost 2000 sires. The animals were genotyped for markers on nine different chromosomes. The QTL analysis was done for six traits separately using a multimarker regression that included putative QTL on other chromosomes as cofactors in the model. Different variants of the false discovery rate (FDR) were applied. Two of them accounted for the proportion of truly null hypotheses, which were estimated to be 0.28 and 0.3, respectively, and were therefore tailored to the experiment. A total of 25 QTL could be mapped when cofactors were included in the model—7 more than without cofactors. Controlling the FDR at 0.05 revealed 31 QTL for the two FDR methods that accounted for the proportion of truly null hypotheses. The relatively high power of this study can be attributed to the size of the experiment, to the QTL analysis with cofactors, and to the application of an appropriate FDR.

Test-day milk, fat, protein yield, and somatic cell score (SCS) were analyzed separately using data from the first 3 lactations and a random regression model. Data used in the model were from Austria, Germany, and Luxembourg and from Holstein, Red, and Jersey dairy cattle. For reliability approximation, a multiple-trait effective daughter contribution (MTEDC) method was developed under general multiple trait models, including random regression test-day models, by extending the single-trait daughter equivalents concept. The MTEDC was applied to the very large dairy population, with about 15.5 million animals. The calculation of reliabilities required less computer memory than the corresponding iteration program and a significantly lower computing time equivalent to 24 rounds of iteration. A formula for daughter-yield deviations was derived for bulls under multiple-trait models. Reliability associated with daughter-yield deviations was approximated using the MTEDC method. Both the daughter-yield deviation formula and associated reliability method were verified in a simulation study using the random regression test-day model. Correlations of lactation daughter-yield deviations with estimated breeding values calculated from a routine genetic evaluation were 0.996 for all bulls and 0.95 for young bulls having only daughters with short lactations.

Genes determining the bovine erythrocyte antigens were mapped by linkage analysis. In total 9591 genotypes of 20 grandsire families with 1074 sires from a grand-daughter design were elucidated for the genes determining the erythrocyte antigens EAA, EAB, EAC, EAF, EAJ, EAL, EAM, EAN', EAR', EAS, EAT', and EAZ according to standard paternity testing procedures in the blood typing laboratories. Linkage analyses were performed with 248 microsatellite markers, eight SSCP markers and four polymorphic proteins and enzymes covering the 29 autosomes and the pseudoautosomal region of the sex chromosomes. The number of informative meioses for the blood group systems ranged from 76 to 947. Blood group systems EAM and EAT' were non-informative. Most of the erythrocyte antigen loci showed significant linkage to a single chromosome and were mapped unequivocally. The genes determining erythrocyte antigen EAA, EAB, EAC, EAL, and EAS were mapped to chromosomes 15, 12, 18, 3, and 21, respectively. Lod-score values ranged from 11.43 to 107.83. Moreover, the EAF system could be mapped to chromosome 17. However, the EAN' system previously known as part of the EAF system could be mapped to chromosome 5. In addition, the blood group systems EAJ, the new EAN', EAR', and EAZ, showed significant linkage to microsatellite markers on various chromosomes and also to other blood groups. The appearance of a single blood group system might be therefore either dependent on the existence of other blood group systems or because of an interaction between different loci on various chromosomes as is known in humans and in pigs.

An important issue in quantitative trait loci (QTL) detection is the use of phenotypic measurement as a dependent variable. Daughter yield deviations (DYDs) as the unit of choice are not available for all traits of interest. The use of de-regressed proofs (DRPFs) of estimated breeding values (EBVs) is an alternative to using daughter yield deviations. The objective of this study was to examine possible differences between DYDs and DRPFs within the use of QTL detection. The pedigree used was part of the granddaughter design of the German QTL effort. Consisting marker maps for livestock species were derived from all available data of 16 German Holstein paternal half-sib families with a total of 872 sires. The number of progeny ranged from 19 to 127. A whole genome scan was performed using weighted and unweighted multimarker regression with DYDs, DRPFs and EBVs as dependent variables for the traits milk, fat and protein yields. Results were compared with respect to the number of QTL detected. A similar number of QTL was detected with DRPFs and DYDs. Also, when dependent variables were weighted according to the variance of the trait, a higher number of QTL was detected at the desired level of significance as compared to using unweighted variables.

Statistical models were presented to estimate daily yields from either morning or evening test results. The 64,451 test-day records from 10,392 lactations of 8800 cows were available for analysis from experiments that were designed to investigate the accuracy of an alternate morning and evening four-weekly milk-testing scheme. The experiments were conducted in 152 herds from six German states and covered a span from 1994 to 1998. Milk yield, fat, and protein percentage were recorded for all of the morning and evening milkings. Seven statistical models were fitted to the data to derive formulas for estimating daily yields from morning or evening yields. In general, use of evening milkings less accurately estimated yields than did use of morning milkings. Among the three yield traits the lowest accuracy of estimation of daily yield was found for fat yield. Although the models do not differ much in the correlation between estimated and true daily yields, systematic under- and overestimation of daily yield at the beginning and end of lactation were observed in all models with the exception of model 6, which accounted for heterogeneous variances by parity class, milking interval class, and lactation stage by fitting separate regression formulas within each combination of the three factors. A study to validate the models showed that model 6 is also robust for the analyzed populations. Smoothing model 6 regression formulas across lactation stages caused a systematic pattern of estimation error, although loss in accuracy was minimal by fitting far fewer parameters in the regression formulas. Differences in the accuracy of alternate milking schemes to predict daily yields were found between traits, between morning and evening milkings, and between parity classes. Compared with true daily yields from different lactation stages, variances and correlations of the estimated yields were reduced, which must be accounted for in genetic evaluation. The use of estimated daily yields from morning or evening milkings has a smaller impact on estimated breeding values of bulls than cows. As a result of lower heritability and repeatability of estimated daily yields than true daily yields, the weight on own test-day records for estimating cows' breeding values is lower when cows are in a.m.-p.m. than conventional monthly testing schemes. However, the difference in the weights between estimated and true daily yields decreases as lactation progresses. Use of estimated daily yields is less reliable for estimating breeding value than use of true daily yields.

The proportion of unpigmented coat on the trunk was determined from photographs of 38 German Simmental and 627 German Holstein bulls distributed over three generations. All 665 animals were members of 18 Holstein and 3 Simmental half-sib families. A Bayesian estimation of heritability yielded a posterior mean of 0.88 and a standard error of 0.08. A quantitative trait loci (QTL) scan over all chromosomes covered by 229 microsatellite marker loci (2926 cM) was performed by fitting a multiple marker regression model to 625 observations from the youngest generation in 18 families. On chromosome 6 a QTL for the proportion of white coat with large effects (experiment-wise error probability < .0001) was found and a less important one on chromosome 3 (chromosome-wise error probability < .009). Chromosome 6 is known to harbor the KIT locus (receptor tyrosinase kinase), which is associated with various depigmentation phenotypes in mice, humans, and pigs. Similarity of phenotypic KIT effects in other species and synteny with the reported QTL suggest that KIT is a serious candidate gene for the degree of spotting in cattle. The results are also discussed with respect to resistance to solar radiation, heat stress, and photosensitization.

Compared with the currently widely used multi-step genomic models for genomic evaluation, single-step genomic models can provide more accurate genomic evaluation by jointly analyzing phenotypes and genotypes of all animals and can properly correct for the effect of genomic preselection on genetic evaluations. The objectives of this study were to introduce a single-step genomic model, allowing a direct estimation of single nucleotide polymorphism (SNP) effects, and to develop efficient computing algorithms for solving equations of the single-step SNP model. We proposed an alternative to the current single-step genomic model based on the genomic relationship matrix by including an additional step for estimating the effects of SNP markers. Our single-step SNP model allowed flexible modeling of SNP effects in terms of the number and variance of SNP markers. Moreover, our single-step SNP model included a residual polygenic effect with trait-specific variance for reducing inflation in genomic prediction. A kernel calculation of the SNP model involved repeated multiplications of the inverse of the pedigree relationship matrix of genotyped animals with a vector, for which numerical methods such as preconditioned conjugate gradients can be used. For estimating SNP effects, a special updating algorithm was proposed to separate residual polygenic effects from the SNP effects. We extended our single-step SNP model to general multiple-trait cases. By taking advantage of a block-diagonal (co)variance matrix of SNP effects, we showed how to estimate multivariate SNP effects in an efficient way. A general prediction formula was derived for candidates without phenotypes, which can be used for frequent, interim genomic evaluations without running the whole genomic evaluation process. We discussed various issues related to implementation of the single-step SNP model in Holstein populations with an across-country genomic reference population.

The present study estimated variance components for test day records of somatic cell score and production traits. Data consisted of 235,100 test day observations recorded between 1986 and 1994 on lactations 1 to 3 of 15,922 Holstein cows from 143 herds. Records were considered as repeated observations within a lactation and as different traits across lactations. The multiple-trait animal model for analysis included random animal additive genetic and permanent environment effects by lactation. Fixed effects included herd test date and a set of four covariables for days in lactation, estimated by parity, age, and season, which accounted for the shape of the lactation curve. Gibbs sampling chains were carried out separately for somatic cell score and milk production and fat and protein yields. Heritabilities of somatic cell score for lactations l to 3 were .09, .09, and .11, respectively. Genetic correlations between lactations were high (.88, .79, and .95 between lactations 1 and 2, 1 and 3, and 2 and 3, respectively). Correlations between permanent environment effects were smaller (.29, .19, and .46 between lactations 1 and 2, 1 and 3, and 2 and 3, respectively). Heritabilities and correlations between permanent environment effects were higher for production traits than for somatic cell score. Genetic correlations between lactations for production traits were similar to those for somatic cell score. Variances between lactations differed significantly, indicating that observations from different lactations should not be considered as repeated observations of the same trait.

A multiple lactation model for test day data was applied to predict genetic merit for somatic cell scores of Canadian Holsteins. The model for genetic evaluation included a fixed effect for herd test date, fixed regressions on functions of days of lactation, random effects of permanent environment within lactation, random genetic effects of animal, and residual effects. Records from the first three lactations were used and treated as different traits. Procedures for this model, developed for national genetic evaluation for somatic cell score in Canada, were found to be practical. Use of starting values from the previous genetic evaluation reduced the number of rounds necessary to reach convergence. Test day models were compared with several single-trait models based on lactation average of somatic cell score in terms of computing efficiency and ranking of animals. Differences between EBV from the test day model and EBV from a repeatability model for lactation average were small for bulls with many daughters, but differences were large with EBV from a single-trait model for first lactation average of somatic cell count. Associations were desirable for EBV for somatic cell score with EBV for some udder conformation traits, but undesirable for EBV for milk and protein yield.

Because of the relatively high levels of genetic relationships among potential bull sires and bull dams, innovative selection tools should consider both genetic gain and genetic relationships in a long-term perspective. Optimum genetic contribution theory using official estimated breeding values for a moderately heritable trait (production index, Index-PROD), and a lowly heritable functional trait (index for somatic cell score, Index-SCS) was applied to find optimal allocations of bull dams and bull sires. In contrast to previous practical applications using optimizations based on Lagrange multipliers, we focused on semi-definite programming (SDP). The SDP methodology was combined with either pedigree ( a ij ) or genomic relationships ( f ij ) among selection candidates. Selection candidates were 484 genotyped bulls, and 499 preselected genotyped bull dams completing a central test on station. In different scenarios separately for PROD and SCS, constraints on the average pedigree relationships among future progeny were varied from a ij =0.08 to a ij =0.20 in increments of 0.01. Corresponding constraints for single nucleotide polymorphism-based kinship coefficients were derived from regression analysis. Applying the coefficient of 0.52 with an intercept of 0.14 estimated for the regression pedigree relationship on genomic relationship, the corresponding range to alter genomic relationships varied from f ij = 0.18 to f ij = 0.24. Despite differences for some bulls in genomic and pedigree relationships, the same trends were observed for constraints on pedigree and corresponding genomic relationships regarding results in genetic gain and achieved coefficients of relationships. Generally, allowing higher values for relationships resulted in an increase of genetic gain for Index-PROD and Index-SCS and in a reduction in the number of selected sires. Interestingly, more sires were selected for all scenarios when restricting genomic relationships compared with restricting pedigree relationships. For example, at constraint of f ij =0.185 and selection on Index-PROD, the number of selected sires was 35. In contrast, only 21 sires were selected at the comparable constraint on additive genetic relationship of a ij =0.09. A further reduction in relationships is possible when using SDP output (i.e., suggested genetic contributions of selected parents) and applying a simulated annealing algorithm to define specific mating plans. However, the advantage of this strategy is limited to a short-term perspective and probably not successful in the period of genomic selection allowing a substantial reduction of generation intervals.

Background Size of the reference population and reliability of phenotypes are crucial factors influencing the reliability of genomic predictions. It is therefore useful to combine closely related populations. Increased accuracies of genomic predictions depend on the number of individuals added to the reference population, the reliability of their phenotypes, and the relatedness of the populations that are combined. Methods This paper assesses the increase in reliability achieved when combining four Holstein reference populations of 4000 bulls each, from European breeding organizations, i.e. UNCEIA (France), VikingGenetics (Denmark, Sweden, Finland), DHV-VIT (Germany) and CRV (The Netherlands, Flanders). Each partner validated its own bulls using their national reference data and the combined data, respectively. Results Combining the data significantly increased the reliability of genomic predictions for bulls in all four populations. Reliabilities increased by 10%, compared to reliabilities obtained with national reference populations alone, when they were averaged over countries and the traits evaluated. For different traits and countries, the increase in reliability ranged from 2% to 19%. Conclusions Genomic selection programs benefit greatly from combining data from several closely related populations into a single large reference population.

A genetic evaluation system was developed for 5 fertility traits of dairy cattle: interval from first to successful insemination and nonreturn rate to 56 d of heifers, and interval from calving to first insemination, nonreturn rate to 56 d, and interval first to successful insemination of cows. Using the 2 interval traits of cows as components, breeding values for days open were derived. A multiple-trait animal model was applied to evaluate these fertility traits. Fertility traits of later lactations of cows were treated as repeated measurements. Genetic parameters were estimated by REML. Mixed model equations of the genetic evaluation model were solved with preconditioned conjugate gradients or the Gauss-Seidel algorithm and iteration on data techniques. Reliabilities of estimated breeding values were approximated with a multi-trait effective daughter contribution method. Daughter yield deviations and associated effective daughter contributions were calculated with a multiple trait approach. The genetic evaluation software was applied to the insemination data of dairy cattle breeds in Germany, Austria, and Luxembourg, and it was validated with various statistical methods. Genetic trends were validated. Small heritability estimates were obtained for all the fertility traits, ranging from 1% for nonreturn rate of heifers to 4% for interval calving to first insemination. Genetic and environmental correlations were low to moderate among the traits. Notably, unfavorable genetic trends were obtained in all the fertility traits. Moderate to high correlations were found between daughter yield-deviations and estimated breeding values (EBV) for Holstein bulls. Because of much lower heritabilities of the fertility traits, the correlations of daughter yield deviations with EBV were significantly lower than those from production traits and lower than the correlations from type traits and longevity. Fertility EBV were correlated unfavorably with EBV of milk production traits but favorably with udder health and longevity. Integrating fertility traits into a total merit selection index can halt or reverse the decline of fertility and improve the longevity of dairy cattle.

Since many countries use multiple lactation random regression test day models in national evaluations for milk production traits, a random regression multiple across-country evaluation (MACE) model permitting a variable number of correlated traits per country should be used in international dairy evaluations. In order to reduce the number of within country traits for international comparison, three different MACE models were implemented based on German daughter yield deviation data and compared to the random regression MACE. The multiple lactation MACE model analysed daughter yield deviations on a lactation basis reducing the rank from nine random regression coefficients to three lactations. The lactation breeding values were very accurate for old bulls, but not for the youngest bulls with daughters with short lactations. The other two models applied principal component analysis as the dimension reduction technique: one based on eigenvalues of a genetic correlation matrix and the other on eigenvalues of a combined lactation matrix. The first one showed that German data can be transformed from nine traits to five eigenfunctions without losing much accuracy in any of the estimated random regression coefficients. The second one allowed performing rank reductions to three eigenfunctions without having the problem of young bulls with daughters with short lactations.

The main objective of this study was to estimate the proportion of total genetic variance attributed to a quantitative trait locus (QTL) on Bos taurus autosome 6 (BTA6) for milk production traits in the German Holstein dairy cattle population. The analyzed chromosomal region on BTA6 spanned approximately 70 cM, and contained 6 microsatellite markers. Milk production data were obtained from routine genetic evaluation for 4500 genotyped German Holstein bulls. Technical aspects related to the estimation of model parameters for a large data set from routine genotype recording were outlined. A fixed QTL model and a random QTL model were introduced to incorporate marker information into parameter estimation and genetic evaluation. Estimated QTL variances, expressed as the ratio of QTL to polygenic variances, were 0.04, 0.03, and 0.07 for milk yield; 0.06, 0.08, and 0.14 for fat yield; and 0.04, 0.04, and 0.11 for protein yield, in the first 3 parities, respectively. The estimated QTL positions, expressed as distances from the leftmost marker DIK82, were 18, 31, and 17 cM for milk yield; 25, 17, and 9 cM for fat yield; and 16, 30, and 17 cM for protein yield in the 3 respective parities. Because the data for the parameter estimation well represented the current population of active German Holstein bulls, the QTL parameter estimates have been used in routine marker-assisted genetic evaluation for German Holsteins.

Test-day genetic evaluation models have many advantages compared with those based on 305-d lactations; however, the possible use of test-day model (TDM) results for herd management purposes has not been emphasized. The aim of this paper was to study the ability of a TDM to predict production for the next test day and for the entire lactation. Predictions of future production and detection of outliers are important factors for herd management (e.g., detection of health and management problems and compliance with quota). Because it is not possible to predict the herd-test-day (HTD) effect per se, the fixed HTD effect was split into 3 new effects: a fixed herd-test month-period effect, a fixed herd-year effect, and a random HTD effect. These new effects allow the prediction of future production for improvement of herd management. Predicted test-day yields were compared with observed yields, and the mean prediction error computed across herds was found to be close to zero. Predictions of performance records at the herd level were even more precise. Discarding herds enrolled in milk recording for

Genome scans for quantitative trait loci (QTL) in farm animals have concentrated on primary production and health traits, and information on QTL for other important traits is rare. We performed a whole genome scan in a granddaughter design to detect QTL affecting body conformation and behavior in dairy cattle. The analysis included 16 paternal half-sib families of the Holstein breed with 872 sons and 264 genetic markers. The markers were distributed across all 29 autosomes and the pseudoautosomal region of the sex chromosomes with average intervals of 13.9 cM and covering an estimated 3155.5 cM. All families were analyzed jointly for 22 traits using multimarker regression and significance thresholds determined empirically by permutation. QTL that exceeded the experiment-wise significance threshold (5% level) were detected on chromosome 6 for foot angle, teat placement, and udder depth, and on chromosome 29 for temperament. QTL approaching experiment-wise significance (10% level) were located on chromosome 6 for general quality of feet and legs and general quality of udder, on chromosome 13 for teat length, on chromosome 23 for general quality of feet and legs, and on chromosome 29 for milking speed. An additional 51 QTL significant at the 5% chromosome-wise level were distributed over 21 chromosomes. This study provides the first evidence for QTL involved in behavior of dairy cattle and identifies QTL for udder conformation on chromosome 6 that could form the basis of recently reported QTL for clinical mastitis.

A joint analysis of five paternal half-sib Holstein families that were part of two different granddaughter designs (ADR- or Inra-design) was carried out for five milk production traits and somatic cell score in order to conduct a QTL confirmation study and to increase the experimental power. Data were exchanged in a coded and standardised form. The combined data set (JOINT-design) consisted of on average 231 sires per grandsire. Genetic maps were calculated for 133 markers distributed over nine chromosomes. QTL analyses were performed separately for each design and each trait. The results revealed QTL for milk production on chromosome 14, for milk yield on chromosome 5, and for fat content on chromosome 19 in both the ADR- and the Inra-design (confirmed within this study). Some QTL could only be mapped in either the ADR- or in the Inra-design (not confirmed within this study). Additional QTL previously undetected in the single designs were mapped in the JOINT-design for fat yield (chromosome 19 and 26), protein yield (chromosome 26), protein content (chromosome 5), and somatic cell score (chromosome 2 and 19) with genomewide significance. This study demonstrated the potential benefits of a combined analysis of data from different granddaughter designs.

Genes determining the bovine erythrocyte antigens were mapped by linkage analysis. In total 9591 genotypes of 20 grandsire families with 1074 sires from a grand-daughter design were elucidated for the genes determining the erythrocyte antigens EAA, EAB, EAC, EAF, EAJ, EAL, EAM, EAN', EAR', EAS, EAT', and EAZ according to standard paternity testing procedures in the blood typing laboratories. Linkage analyses were performed with 248 microsatellite markers, eight SSCP markers and four polymorphic proteins and enzymes covering the 29 autosomes and the pseudoautosomal region of the sex chromosomes. The number of informative meioses for the blood group systems ranged from 76 to 947. Blood group systems EAM and EAT' were non-informative. Most of the erythrocyte antigen loci showed significant linkage to a single chromosome and were mapped unequivocally. The genes determining erythrocyte antigen EAA, EAB, EAC, EAL, and EAS were mapped to chromosomes 15, 12, 18, 3, and 21, respectively. Lod-score values ranged from 11.43 to 107.83. Moreover, the EAF system could be mapped to chromosome 17. However, the EAN' system previously known as part of the EAF system could be mapped to chromosome 5. In addition, the blood group systems EAJ, the new EAN', EAR', and EAZ, showed significant linkage to microsatellite markers on various chromosomes and also to other blood groups. The appearance of a single blood group system might be therefore either dependent on the existence of other blood group systems or because of an interaction between different loci on various chromosomes as is known in humans and in pigs.

A method of dairy sire evaluation across multiple countries is described. Factors influencing this method are overestimation of genetic trends within countries, inclusion of evaluations of imported bulls, years of birth of the bulls included in the analysis, and estimates of genetic correlations between countries. Fall 1994 evaluations for milk, fat, and protein yields from Canada (4559 bulls), Germany (5894 bulls), and France (8419 bulls) were used to study the effect of these factors. After inclusion of ancestors there were 21,555 bulls in total. Eight data files were created based on combinations of three factors: 1) bulls born from 1970 to present versus bulls born from 1979 to present, 2) all bulls included versus imported bulls omitted, and 3) official Canadian evaluations for all lactations versus Canadian evaluations for first lactation only. Separate evaluations for two of the data files assumed a uniform genetic correlation of 0.995 between countries. Rankings of top bulls from analyses were affected by all factors to various degrees, depending on the country. Evaluations of imported bulls have an effect on bull rankings and probably should not be included. An assumed uniform genetic correlation between countries of 0.995 may not be appropriate. Proper methods and data for estimation of the genetic correlation between countries should be sought.

The gene, acyl-CoA:diacylglycerol acyltransferase1 (DGAT1), was recently identified as the one underlying the quantitative trait locus (QTL) for milk production traits in the centromeric region of the bovine chromosome 14. Until now, 2 alleles, the lysine variant (increasing fat yield, fat and protein percentage) and the alanine variant (increasing protein and milk yield), were postulated at DGAT1. This study investigated whether the diallelic DGAT1 polymorphism is responsible for all the genetic variation at the centromeric region of this chromosome for milk, fat, and protein yield and fat and protein percentage. A statistical model was applied to a granddaughter design to analyze 16 German Holstein families. The model included the diallelic DGAT1 effect and the QTL transition probability estimated for each chromosomal position by a multiple marker approach. Because the regression coefficient of this probability was corrected for the diallelic DGAT1 polymorphism, it represented a putative conditional QTL effect. The effect of the DGAT1 gene was always highly significant. The conditional QTL effect was significant genomewise for fat percentage at the proximal end of the chromosome and for protein percentage at a more distal chromosomal region. Additional chromosomewise significance was found for fat and protein yield. Our results suggest an additional source of genetic variance on this chromosome for these traits; either one or more additional alleles segregating at DGAT1 that were not previously detected, a second quantitative trait locus affecting these traits, or both.

The aim of this paper is to present the construction of a male genetic linkage map as a result of the bovine genome mapping project, which is a common effort of the German cattle breeding federation (ADR), four animal breeding institutes, three blood group laboratories and two animal data and breeding value evaluation centres. In total 20 grandsires with 1074 sires were provided from the German cattle population as reference families, 16 of these paternal half-sib groups are German Holstein families (DH), three are German Simmental (ST) families, and one is a Brown Swiss family (BS). Of 265 markers included in the linkage map, 248 were microsatellite markers, five were bovine blood group systems, eight SSCP markers and four proteins and enzymes. More than 239 000 genotypes resulted from typing the offspring for the respective markers and these were used for the construction of the map. On average 478 informative meioses were provided from each marker of the map. The summarized map length over all chromosomes was 3135.1 cM with an average interval size of 13.34 CM. About 17, 35.7 and 79.1% of the map intervals showed a maximum genetic distance between the adjacent markers of 5,10 and 20 cM, respectively. The number of loci ranged from two (pseudoautosomal region of the sex chromosome, BTAY) to 15 (BTA23) with an average of 8.8 markers per chromosome. Comparing the length of the chromosomes shows variation from 49.6 cM for BTA26 to 190.5 CM for BTA1 with a mean of 107.7 cM for all autosomes of the genetic linkage map. It was possible to identify chromosomal discrepancies in locus order and map intervals by comparison with other published maps. The map provided sufficient marker density to serve as a useful tool for a scan of segregating quantitative trait loci. (Less)

Background The purpose of this work was to study the impact of both the size of genomic reference populations and the inclusion of a residual polygenic effect on dairy cattle genetic evaluations enhanced with genomic information. Methods Direct genomic values were estimated for German Holstein cattle with a genomic BLUP model including a residual polygenic effect. A total of 17,429 genotyped Holstein bulls were evaluated using the phenotypes of 44 traits. The Interbull genomic validation test was implemented to investigate how the inclusion of a residual polygenic effect impacted genomic estimated breeding values. Results As the number of reference bulls increased, both the variance of the estimates of single nucleotide polymorphism effects and the reliability of the direct genomic values of selection candidates increased. Fitting a residual polygenic effect in the model resulted in less biased genome-enhanced breeding values and decreased the correlation between direct genomic values and estimated breeding values of sires in the reference population. Conclusions Genetic evaluation of dairy cattle enhanced with genomic information is highly effective in increasing reliability, as well as using large genomic reference populations. We found that fitting a residual polygenic effect reduced the bias in genome-enhanced breeding values, decreased the correlation between direct genomic values and sire's estimated breeding values and made genome-enhanced breeding values more consistent in mean and variance as is the case for pedigree-based estimated breeding values.