BackgroundSize 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.MethodsThis 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.ResultsCombining 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%.ConclusionsGenomic selection programs benefit greatly from combining data from several closely related populations into a single large reference population.

A whole-genome scan to detect quantitative trait loci (QTL) for functional traits was performed in the German Holstein cattle population. For this purpose, 263 genetic markers across all autosomes and the pseudoautosomal region of the sex chromosomes were genotyped in 16 granddaughter-design families with 872 sons. The traits investigated were deregressed breedingvalues for maternal and direct effects on dystocia (DYSm, DYSd) and stillbirth (STIm, STId) as well as maternal and paternal effects on nonreturn rates of 90 d (NR90m, NR90p). Furthermore, deregressed breeding values for functional herd life (FHL) and daughter yield deviation for somatic cell count (SCC) were investigated. Weighted multimarker regression analyses across families and permutation tests were applied for the detection of QTL and the calculation of statistical significance. A ten percent genomewise significant QTL was localized for DYSm on chromosome 8 and for SCC on chromosome 18. A further 24 putative QTL exceeding the 5% chromosomewise threshold were detected. On chromosomes 7, 8, 10, 18, and X/Yps, coincidence of QTL for several traits was observed. Our results suggest that loci with influence on udder health may also contribute to genetic variance of longevity. Prior to implementation of these QTL in marker assisted selection programs for functional traits, information about direct and correlated effects of these QTL as well as fine mapping of their chromosomal positions is required.

BackgroundThe 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.MethodsDirect 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.ResultsAs 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.ConclusionsGenetic 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.

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.

Posttranslational geranylgeranylation of Rab GTPases is catalyzed by Rab geranylgeranyltransferase (RabGGTase), which consists of a catalytic alpha/beta heterodimer and an accessory Rab escort protein (REP). The crystal structure of isoprenoid-bound RabGGTase complexed to REP-1 has been solved to 2.7 A resolution. The complex interface buries a surprisingly small surface area of ca. 680 A and is unexpectedly formed by helices 8, 10, and 12 of the RabGGTase alpha subunit and helices D and E of REP-1. We demonstrate that the affinity of RabGGTase for REP-1 is allosterically regulated by phosphoisoprenoid via a long-range trans-domain signal transduction event. Comparing the structure of REP-1 with the closely related RabGDI, we conclude that the specificity of the REP:RabGGTase interaction is defined by differently positioned phenylalanine residues conserved in the REP and GDI subfamilies.

Posttranslational modification of proteins with farnesyl and geranylgeranyl isoprenoids is a widespread phenomenon in eukaryotic organisms. Isoprenylation is conferred by three protein prenyltransferases: farnesyl transferase (FTase), geranylgeranyl transferase type-I (GGTase-I), and Rab geranylgeranyltransferase (RabGGTase). Inhibitors of these enzymes have emerged as promising therapeutic compounds for treatment of cancer, viral and parasite originated diseases, as well as osteoporosis. However, no generic nonradioactive protein prenyltransferase assay has been reported to date, complicating identification of enzyme-specific inhibitors. We have addressed this issue by developing two fluorescent analogues of farnesyl and geranylgeranyl pyrophosphates {3,7-dimethyl-8-(7-nitro-benzo[1,2,5]oxadiazol-4-ylamino)-octa-2,6-diene-1}pyrophosphate (NBD-GPP) and {3,7,11-trimethyl-12-(7-nitro-benzo[1,2,5]oxadiazo-4-ylamino)-dodeca-2,6,10-trien-1} pyrophosphate (NBD-FPP), respectively. We demonstrate that these compounds can serve as efficient lipid donors for prenyltransferases. Using these fluorescent lipids, we have developed two simple (SDS-PAGE and bead-based) in vitro prenylation assays applicable to all prenyltransferases. Using the SDS-PAGE assay, we found that, in contrast to previous reports, the tyrosine phosphatase PRL-3 may possibly be a dual substrate for both FTase and GGTase-I. The on-bead prenylation assay was used to identify prenyltransferase inhibitors that displayed nanomolar affinity for RabGGTase and FTase. Detailed analysis of the two inhibitors revealed a complex inhibition mechanism in which their association with the peptide binding site of the enzyme reduces the enzyme's affinity for lipid and peptide substrates without competing directly with their binding. Finally, we demonstrate that the developed fluorescent isoprenoids can directly and efficiently penetrate into mammalian cells and be incorporated in vivo into small GTPases.

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.

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.