The marker effects of a single-step random regression model for 4 test-day traits in German Holsteins.

The single-step genomic model has become the golden standard for routine evaluation in livestock species, such as Holstein dairy cattle. The single-step genomic model with direct estimation of marker effects has been proven to be efficient in accurately accounting for millions of genotype records. For diverse applications including frequent genomic evaluation updates on a weekly basis, estimates of the marker effects from the single-step evaluations play a central role in genomic prediction. In this study we focused on exploring the marker effect estimates from the single-step evaluation. Phenotypic, genotypic, and pedigree data were taken from the official evaluation for German dairy breeds in April 2021. A multilactation random regression test-day model was applied to more than 242 million test-day records separately for 4 traits: milk, fat, and protein yields, and somatic cell scores (SCS). Approximately one million genotyped Holstein animals were considered in the single-step genomic evaluations including ∼21 million animals in pedigree. Deregressed multiple across-country breeding values of Holstein bulls having daughters outside Germany were integrated into the national test-day data to increase the reliability of genomic breeding values. To assess the stability and bias of the marker effects of the single-step model, test-day records of the last 4 yr were deleted, and the integrated bulls born in the last 4 yr were truncated from the complete phenotypic dataset. Estimates of the marker effects were shown to be highly correlated, with correlations ∼0.9, between the full and truncated evaluations. Regression slope values of the marker-effect estimates from the full on the truncated evaluations were all close to their expected value, being ∼1.03. Calculated using random regression coefficients of the marker effect estimates, drastically different shapes of the genetic lactation curve were seen for 2 markers on chromosome 14 for the 4 test-day traits. The contribution of individual chromosomes to the total additive genetic variances seemed to follow the polygenic inheritance mode for protein yield and SCS. However, chromosome 14 was found to make an exceptionally large contribution to the total additive genetic variance for milk and fat yields because of markers near the major gene DGAT1. For the first lactation test-day traits, we obtained ∼0 correlations of chromosomal direct genomic values between any pair of the chromosomes; no spurious correlations were found in our analysis, thanks to the large reference population. For trait milk yield, chromosomal direct genomic values appeared to have a large variation in the between-lactation correlations among the chromosomes, especially between first and second or third lactations. The optimal features of the random regression test-day model and the single-step marker model allowed us to track the differences in the shapes of genetic lactation curves down to the individual markers. Furthermore, the single-step random regression test-day model enabled us to better understand the inheritance mode of the yield traits and SCS (e.g., variable chromosomal contributions to the total additive genetic variance and to the genetic correlations between lactations).

Comparison of a single-step with a multistep single nucleotide polymorphism best linear unbiased predictor model for genomic evaluation of conformation traits in German Holsteins.

Genomic evaluation based on a single-step model uses all available data of phenotype, genotype, and pedigree; therefore, it should provide unbiased genomic breeding values with a higher correlation of prediction than the current multistep genomic model. Since 2019, a mixed reference population of cows and bulls has been applied to the routine multistep genomic evaluation in German Holsteins. For a fair comparison between the single-step and multistep genomic models, the same phenotype, genotype, and pedigree data were used. Because of its simple structure of the standard multitrait animal model used for German Holstein conventional evaluation, conformation traits were chosen as the first trait group to test a single-step SNP BLUP model for the large, genotyped population of German Holsteins. Genotype, phenotype, and pedigree data were taken from the official August 2020 conventional and genomic evaluation. Because of the same trait definition in national and multiple across-country evaluation for the conformation traits, deregressed multiple across-country evaluation estimated breeding value (EBV) of foreign bulls were treated as a new source of data for the same trait in the genomic evaluations. Due to a short history of female genotyping in Germany, the last 3 yr of youngest cows and bulls were deleted, instead of 4 yr, to perform a genomic validation. In comparison to the multistep genomic model, the single-step SNP BLUP model resulted in a higher correlation and greater variance of genomic EBV according to 798 national validation bulls. The regression of genomic prediction of the current, full evaluation on the earlier, truncated evaluation was slightly closer to 1 than the multistep model. For the validation bulls or youngest genomic artificial insemination bulls, correlation of genomic EBV between the 2 models was, on average, 0.95 across all the conformation traits. We did not find overprediction of young animals by the single-step SNP BLUP model for the conformation traits in German Holsteins.

Technical note: Equivalent genomic models with a residual polygenic effect.

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.

Identification of a haplotype associated with cholesterol deficiency and increased juvenile mortality in Holstein cattle.

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.

A single-step genomic model with direct estimation of marker effects.

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.

Genomic selection: Status in different species and challenges for breeding.

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.

Reliability of genomic prediction for German Holsteins using imputed genotypes from low-density chips.

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.

Identification of informative cooperator herds for progeny testing based on yield deviations.

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.

Controlling inbreeding and maximizing genetic gain using semi-definite programming with pedigree-based and genomic relationships.

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.

Genetic evaluation of fertility traits of dairy cattle using a multiple-trait animal model.

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.

Validation of an approximate REML algorithm for parameter estimation in a multitrait, multiple across-country evaluation model: a simulation study.

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.

Binational evaluation of type traits from Germany and France with a single-trait MACE animal model.

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.

Estimation of quantitative trait loci parameters for milk production traits in German Holstein dairy cattle population.

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.

Factors influencing international comparisons of dairy sires.

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.

Derivation and calculation of approximate reliabilities and daughter yield-deviations of a random regression test-day model for genetic evaluation of dairy cattle.

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.

Approaches to estimating daily yield from single milk testing schemes and use of a.m.-p.m. records in test-day model genetic evaluation in dairy cattle.

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.

Genetic evaluation for somatic cell score with a test day model for multiple lactations.

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 on 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. Association were desirable for EBV for somatic cell score with EBV for some udder conformation traits, but undesirable for EBV for milk and protein yield.

Prediction of daily milk, fat, and protein production by a random regression test-day model.

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 <1 yr and animals with very few tests in the evaluation file improved correlations between predicted and observed yields at the next test day (correlation of 0.864 for milk in first-lactation cows as compared with a correlation of 0.821 with no records eliminated). Correlations with the observed 305-d production ranged from 0.575 to 1 for predictions based on 0 to 10 test-day records, respectively. Similar results were found for second and third lactation records for milk and milk components. These findings demonstrate the predictive ability of a TDM.

Estimation of genetic parameters for test day records of somatic cell score.

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 1 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 no be considered as repeated observations of the same trait.

Crystallization and preliminary X-ray diffraction analysis of the Rab escort protein-1 in complex with Rab geranylgeranyltransferase.

Posttranslational prenylation of proteins is a widespread phenomenon and the majority of prenylated proteins are geranylgeranylated members of the Rab GTPase family. Geranylgeranylation is catalyzed by Rab geranylgeranyltransferase (RabGGTase) and is critical for the ability of Rab protein to mediate vesicular docking and fusion of various intracellular vesicles. RabGGTase consists of a catalytic alpha/beta heterodimer and an accessory protein termed Rab escort protein (REP-1) that delivers the newly prenylated Rab proteins to their target membrane. Mutations in the REP-1 gene in humans lead to an X-chromosome-linked defect known as choroideremia--a debilitating disease that inevitably culminates in complete blindness. Here we report in vitro assembly and purification of the stoichiometric ternary complex of RabGGTase with REP-1 stabilized by a hydrolysis-resistant phosphoisoprenoid analog--farnesyl phosphonyl(methyl)phoshonate. The complex formed crystals of extended plate morphology under low ionic-strength conditions. X-ray diffraction data were collected to 2.8 A resolution at the ESRF. The crystals belong to the monoclinic space group P2(1), with unit-cell parameters a = 68.7, b = 197.7, c = 86.1 A, beta = 113.4 degrees. Preliminary structural analysis revealed the presence of one molecule in the asymmetric unit.