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.

AMPK promotes tolerance to Ras pathway inhibition by activating autophagy.

Targeted inhibitors of oncogenic Ras (rat sarcoma viral oncogene)-Raf signaling have shown great promise in the clinic, but resistance remains a major challenge: 30% of tumors with pathway mutations do not respond to targeted inhibitors, and of the 70% that do respond, all eventually develop resistance. Before cancer cells acquire resistance, they respond to initial drug treatment either by undergoing apoptosis ('addiction') or by surviving treatment albeit with reduced growth ('tolerance'). As these drug-tolerant cells serve as a reservoir from which resistant cells eventually emerge, inhibiting the pathways that confer tolerance could potentially delay or even prevent recurrence. Here, we show that melanomas and other cancers acquire tolerance to Ras-Raf pathway inhibitors by activating autophagy, which is mediated by the cellular energy sensor AMP-activated protein kinase (AMPK). Blocking this AMPK-mediated autophagy sensitizes drug-tolerant melanomas to Ras-Raf pathway inhibitors. Conversely, activating AMPK signaling and autophagy enables melanomas that would otherwise be addicted to the Ras-Raf pathway to instead tolerate pathway inhibition. These findings identify a key mechanism of tolerance to Ras-Raf pathway inhibitors and suggest that blocking either AMPK or autophagy in combination with these targeted inhibitors could increase tumor regression and decrease the likelihood of eventual recurrence.

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.

Considering genetic characteristics in German Holstein breeding programs.

Recently, several research groups have demonstrated that several haplotypes may cause embryonic loss in the homozygous state. Up to now, carriers of genetic disorders were often excluded from mating, resulting in a decrease of genetic gain and a reduced number of sires available for the breeding program. Ongoing research is very likely to identify additional genetic defects causing embryonic loss and calf mortality by genotyping a large proportion of the female cattle population and sequencing key ancestors. Hence, a clear demand is present to develop a method combining selection against recessive defects (e.g., Holstein haplotypes HH1-HH5) with selection for economically beneficial traits (e.g., polled) for mating decisions. Our proposed method is a genetic index that accounts for the allele frequencies in the population and the economic value of the genetic characteristic without excluding carriers from breeding schemes. Fertility phenotypes from routine genetic evaluations were used to determine the economic value per embryo lost. Previous research has shown that embryo loss caused by HH1 and HH2 occurs later than the loss for HH3, HH4, and HH5. Therefore, an economic value of € 97 was used against HH1 and HH2 and € 70 against HH3, HH4, and HH5. For polled, € 7 per polled calf was considered. Minor allele frequencies of the defects ranged between 0.8 and 3.3%. The polled allele has a frequency of 4.1% in the German Holstein population. A genomic breeding program was simulated to study the effect of changing the selection criteria from assortative mating based on breeding values to selecting the females using the genetic index. Selection for a genetic index on the female path is a useful method to control the allele frequencies by reducing undesirable alleles and simultaneously increasing economical beneficial characteristics maintaining most of the genetic gain in production and functional traits. Additionally, we applied the genetic index to real data and found a decrease of the genetic trend for the birth years 1990 to 2006. Since 2010 the genetic index has increased due to a strong increase in the polled frequency. However, further investigation is needed to better understand the biology to determine the correct time of embryo loss and the economic value of fertility disorders.

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.

Genome-wide association analysis to identify genotype × environment interaction for milk protein yield and level of somatic cell score as environmental descriptors in German Holsteins.

Genotype by environment interaction (G × E) has been widely reported in dairy cattle. If the environment can be measured on a continuous scale, reaction norms can be applied to study G × E. The average herd milk production level has frequently been used as an environmental descriptor because it is influenced by the level of feeding or the feeding regimen. Another important environmental factor is the level of udder health and hygiene, for which the average herd somatic cell count might be a descriptor. In the present study, we conducted a genome-wide association analysis to identify single nucleotide polymorphisms (SNP) that affect intercept and slope of milk protein yield reaction norms when using the average herd test-day solution for somatic cell score as an environmental descriptor. Sire estimates for intercept and slope of the reaction norms were calculated from around 12 million daughter records, using linear reaction norm models. Sires were genotyped for ~54,000 SNP. The sire estimates were used as observations in the association analysis, using 1,797 sires. Significant SNP were confirmed in an independent validation set consisting of 500 sires. A known major gene affecting protein yield was included as a covariable in the statistical model. Sixty (21) SNP were confirmed for intercept with P ≤ 0.01 (P ≤ 0.001) in the validation set, and 28 and 11 SNP, respectively, were confirmed for slope. Most but not all SNP affecting slope also affected intercept. Comparison with an earlier study revealed that SNP affecting slope were, in general, also significant for slope when the environment was modeled by the average herd milk production level, although the two environmental descriptors were poorly correlated.

A novel instrument for quantitative nanoanalytics involving complementary X-ray methodologies.

A novel ultra-high vacuum instrument for X-ray reflectometry and spectrometry-related techniques for nanoanalytics by means of synchrotron radiation has been constructed and commissioned. This versatile instrument was developed by the Physikalisch-Technische Bundesanstalt, Germany's national metrology institute, and includes a 9-axis manipulator that allows for an independent alignment of the samples with respect to all degrees of freedom. In addition, a rotational and translational movement of several photodiodes as well as a translational movement of an aperture system in and out of the beam is provided. Thus, the new instrument enables various analytical techniques based on energy dispersive X-ray detectors such as reference-free X-ray fluorescence analysis (XRF), total-reflection XRF, grazing-incidence XRF in addition to optional X-ray reflectometry measurements or polarization-dependent X-ray absorption fine structure analyses. With this instrument samples having a size of up to 100 mm × 100 mm can be analyzed with respect to their mass deposition, elemental or spatial composition, or the species in order to probe surface contamination, layer composition and thickness, the depth profile of matrix elements or implants, the species of nanolayers, nanoparticles or buried interfaces as well as the molecular orientation of bonds. Selected applications of this advanced ultra-high vacuum instrument demonstrate both its flexibility and capability.

Reaction norms and genotype-by-environment interaction in the German Holstein dairy cattle.

Reaction norm random regression sire models were used to study genotype-by-environment interactions (G × E) in the German Holstein dairy cattle population. Around 2300 sires with a minimum of 50 daughters per sire and seven first-lactation test day observations per daughter were analysed. Corrected test day records for milk yield, protein yield, fat yield and somatic cell score (SCS) were used. Herd test day solutions for milk traits, milk energy yield or SCS were used as environmental descriptors. Second-order orthogonal polynomial regressions were applied to the sire effects. The results revealed significant slope variances of the reaction norms, which caused a non-constant additive genetic variance across the environmental ranges considered. This pointed to the presence of minor G × E effects. The additive genetic variance increased when the environment improved, that is, higher (lower) herd test day solutions for milk traits (SCS). This was also influenced by pure scaling effects, because the non-genetic variance increased in an improved environment and the heritability was less influenced by the environment. The G × E effects caused very little reranking of the sires for the environmental range considered in this study.

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.

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.

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.

Exploration of relationships between production and fertility traits in dairy cattle via association studies of SNPs within candidate genes derived by expression profiling.

The objective of this work was to integrate findings from functional genomics studies with genome-wide association studies for fertility and production traits in dairy cattle. Association analyses of production and fertility traits with SNPs located within or close to 170 candidate genes derived from two gene expression studies and from the literature were performed. Data from 2294 Holstein bulls genotyped for 39557 SNPs were used. A total of 111 SNPs were located on chromosomal segments covered by a candidate gene. Allele substitution effects for each SNP were estimated using a mixed model with a fixed effect of marker and a random polygenic effect. Assumed covariance was derived either from marker or from pedigree information. Results from the analysis with the kinship matrix built from marker genotypes were more conservative than from the analysis with the pedigree-derived relationship matrix. From sixteen SNPs with significant effects on both classes of traits, ten provided evidence of an antagonistic relationship between productivity and fertility. However, we found four SNPs with favourable effects on fertility and on yield traits, one SNP with favourable effects on fertility and percentage traits, and one SNP with antagonistic effects on two fertility traits. While most quantitative genetic studies have proven genetic antagonisms between yield and functional traits, improvements in both production and functionality may be possible when focusing on a few relevant SNPs. Investigations combining input from quantitative genetics and functional genomics with association analysis may be applied for the identification of such SNPs.

Refined mapping of quantitative trait loci for somatic cell score on BTA02 in the German Holstein.

The aim of this study was to more precisely map a previously reported quantitative trait locus (QTL) affecting somatic cell score on Bos taurus autosome 2 by increasing the number of markers fourfold, analysing more families and exploiting within-population linkage disequilibrium (LD). A granddaughter design of 10 German Holstein grandsire families with 1121 progeny tested sons was used. Twenty-six markers with an average marker spacing of 3.14 cM were genotyped along 81.6 cM. Linkage analysis (LA) was performed using variance-component methodology. The incorporation of LD was first done using variance-component methods followed by regression on marker alleles. LA revealed genome-wide significance (LOD > 3) at 15 contiguous marker-intervals, with the maximum test-statistic between DIK2862 and BMS778 and a 1-lod drop-off interval of 38 cM. While the variance-component methods could not detect any LD, two individual markers with a significant effect (ILSTS098, p < 0.05; BMS778, p < 0.01) were found by regression analysis. Compared with previous results QTL-localisation was substantially narrowed; further fine-mapping should focus on the close vicinity of BMS778.

Need for sharp phenotypes in QTL detection for calving traits in dairy cattle.

The aim of the study was to investigate whether parity-specific phenotypes provide a clearer picture of quantitative trait loci (QTL) affecting calving traits in German Holsteins than breeding values estimated across parities. In experiment I, approximate daughter yield deviations were calculated by applying a univariate sire model assuming unrelated sires used as phenotypes in a QTL mapping study. These results were compared with those obtained using deregressed estimated breeding values obtained from the routine German sire evaluation (experiment II). In experiment I, 17 chromosome-wise significant QTL were found for the first parity, but only 12 for the second parity. Only three QTL for maternal stillbirth, located on BTA7, 15 and 23, showed an experiment-wise significance. Experiment II revealed 15 chromosome-wise significant QTL. The results differed markedly between first and second parity within experiment I, as well as between experiment I and II. The present study showed that parity-specific daughter yield deviations are beneficial for mapping QTL for calving traits. Furthermore, it is expected that the use of sharper phenotypes will also be advantageous for QTL fine mapping and the identification of candidate genes.

Refined positioning of a quantitative trait locus affecting somatic cell score on chromosome 18 in the German Holstein using linkage disequilibrium.

Combined linkage and linkage disequilibrium analysis (LALD) was conducted to more accurately map a previously reported quantitative trait locus (QTL) affecting somatic cell score on bovine chromosome 18. A grand-daughter design consisting of 6 German Holstein grandsire families with 1,054 progeny-tested genotyped sons was used in this study. Twenty microsatellite markers, 5 single nucleotide polymorphisms, and an erythrocyte antigen marker with an average marker spacing of 1.95 cM were analyzed along a chromosomal segment of 50.80 cM. Variance components were estimated and restricted maximum likelihood test statistics were calculated at the midpoint of each marker interval. The test statistics calculated in single-QTL linkage analysis exceeded the genome-wide significance threshold at several putative QTL positions. Using LALD, we were successful in assigning a genome-wide significant QTL to a confidence interval of 10.8 cM between the markers ILSTS002 and BMS833. The QTL in this marker interval was estimated to be responsible for between 5.89 and 13.86% of the genetic variation in somatic cell score. In contrast to the single-QTL linkage analysis model, LALD analyses with a 2-QTL model confirmed the position of one QTL, but gave no conclusive evidence for the existence or position of a second QTL. Ultimately, the QTL position was narrowed down considerably compared with previous results with a refined confidence interval of less than 11 cM.

Evaluation of high-resolution X-ray absorption and emission spectroscopy for the chemical speciation of binary titanium compounds.

For the chemical speciation of binary compounds of tri- and tetravalent titanium, high-resolution X-ray absorption and emission spectra were recorded in different energy regimes in order to evaluate and to qualify both near-edge X-ray absorption fine structure (NEXAFS or XANES) spectroscopy and wavelength-dispersive X-ray emission spectroscopy (WDXES) as spectroscopic methods for this analytical task. A high resolving power in the excitation channel was ensured by use of monochromatic synchrotron radiation provided by BESSY II, where the soft X-ray emission spectra were recorded as well. In the hard X-ray range, emission measurements were performed at SPring-8. For a comparison of the information gained from the various methods, the titanium compounds were classified according to the bonded titanium's oxidation state. Thus, it was possible to distinguish between inner atomic effects due to different oxidation states and external effects related to the respective ligand and the surrounding structure. It becomes evident, that certain compounds, while hardly distinguishable in their Ti-K XANES spectra, still show significant differences in their emission characteristics. On the other hand, some compounds with little difference in their emission spectra are easily distinguished by their NEXAFS structures. Only the combined use of the complementary methods both in the soft and the hard X-ray range allows for a reliable speciation of tri- and tetravalent titanium compounds.

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.

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.