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Computing time and memory requirements increase with single-step methods to estimate genomic breeding values when the number of genotyped animals increases. Computational costs can be reduced by omitting genotypes of animals without phenotype and progeny, often the candidate animals for selection. Indirect prediction of a candidate animal GEBV can be based on the animal’s genotype and SNP marker solutions (DGV). Alternatively, the sum of DGV and residual polygenic (RPG) effect can be computed, denoted GRV (Genomic and Residual polygenic Value). We applied indirect genomic prediction for a 6 trait calving difficulty evaluation. There were 1.50 million genotyped animals of which 36% were considered candidate animals. Based on our results, DGV showed high accuracy but also bias due to omitting the RPG effect. The GRV prediction had high accuracy and low bias. Computing time was reduced by 33%.

The software package MiXBLUP 3.0 allows to efficiently estimate (genomic enhanced) breeding values in livestock using various linear mixed effect models. MiXBLUP aims to be user-friendly while supporting efficient algorithms for solving large genomic evaluations. Originally developed for pedigree-based models, MiXBLUP has been extended to four different approaches for single-step genomic evaluations, allowing simultaneous analyses of all information for both genotyped and ungenotyped individuals. For all approaches, shared-memory parallelism is supported using tailor-made procedures and parallel libraries. It has been tested on some of the largest genomic datasets in the world. The software is developed by Wageningen University and Research in collaboration with the Natural Resources Institute Finland (Luke).

Precision livestock farming tools, such as sensor and automation techniques, enable animal breeders to define novel traits. New challenges are data storage and handling and the development of genetic evaluation. Traditionally, the development of genetic evaluation for novel traits requires several steps, i.e. data curation, trait definition, variance component estimation (ASReml), genetic evaluation (MiXBLUP), and validation of the estimated breeding values (EBVs), requiring many iterations to optimize the genetic evaluations. We combined these steps in a cloud solution to make this entire process more efficient. An experiment was run with 1,782,373,113 daily milk yield records of 1,120,550 cows, all the way from data curation to trait definition and validation of the EBV. The resulting wall-time, i.e. elapsed actual time from start to finish ofthe entire process, was ~23 hours. The flexible cloud solution can be easily modified or adapted to develop novel traits.

Defining multiple base populations for a genetic evaluation is subjective and there is generally no feedback on the suitability of the defined base populations prior to model validation. We present a number of statistics that can be used to evaluate defined base populations. Application of these statistics to one simulated and two practical datasets showed that in practical datasets the number of base animals per base population was in one case very low. In many cases the available genotype information for different base populations was coming from exactly the same genotyped animals. Both issues are likely to complicate genomic evaluation. A tool to estimate these statistics is available in the MiXBLUP software suite.

Resilience is increasingly recognized as an important trait for dairy cattle to improve their functioning and welfare. The log transformed variance (LnVar) and autocorrelation (rauto) of daily milk yield deviations from an expected lactation curve have been studied as potential resilience indicators, so far only using data of Holstein Friesian cattle. The aim of this research was to compare the resilience indicators between different breeds present at Dutch dairy farms and to estimate the effect of crossbreeding on these indicators. Significant differences in LnVar and rauto were found across twelve breeds, with the breed effects on LnVar different from the effects on rauto. We estimated negligible heterosis effects for rauto and LnVar. This study suggests that different breeds respond differently to environmental disturbances and that different breeds might have different levels or types of resilience.

Beef cattle international evaluations, led by Interbeef, combine data from different countries to compute international estimated breeding values (EBVINT) which are expressed on the same country scale as national ones, allowing participating countries to compare national and foreign sires. These two EBV might be in disagreement due to differences in the information used in national and international evaluations. This can be overcome by integrating the EBVINT into the national evaluations to obtain a ‘blended’ EBV. Here we tested and validated a general procedure to integrate publishable sires EBVINT at the national level. Using evaluations for Limousin weaning weight and the integration for Italy as a case study, we show that the integration procedure accurately blends international information at the national level while avoiding double-counting of national information which are used as input to compute EBVINT in Interbeef evaluations. This procedure can also be used with other traits and breeds.

Many traits of economical relevance in beef cattle are influenced in their phenotypic expression by the dam. Genetic evaluations of maternally affected traits require to model direct, maternal and direct-maternal genetic (co)variances next to non-genetic effects. In Interbeef beef cattle international evaluations, direct-maternal genetic correlations (rdm) may be different both within countries (rdm_WC) and between countries (rdm_BC). rdm_WC for growth traits up to weaning are often reported to be negative and significantly different from zero. As rdm_BC are difficult to estimate, these are currently assumed to be equal to zero in Interbeef evaluations. The objective of this study was to evaluate the impact of using estimated values for rdm_BC instead of assuming them to be zero, on international estimated breeding values (IEBV). We implemented two scenarios that differed only in the modelling of rdm_BC: A) the current Interbeef evaluation with assumes rdm_BC to be 0 and fits estimated rdm_WC, and B) an Interbeef evaluation in which both estimated rdm_WC and rdm_BC were fitted. Weaning weight phenotypes and pedigree information were available for more than 3 million Limousin beef cattle males and females, born between 1972 and 2017, and distributed across ten European countries. We evaluated the impact of ignoring rdm_BC on different groups of animals by comparing animals’ direct and maternal IEBV between scenarios A and B. Ignoring rdm_BC resulted in no re-ranking for direct IEBV, and limited re-ranking for maternal IEBV. Less re-ranking in maternal IEBV was observed with increasing reliability. Moreover, ignoring rdm_BC resulted in no re-ranking for publishable sires, i.e. of sires with IEBV that can be exchanged across countries. Our study suggests that the current practice of ignoring rdm_BC has limited impact on Interbeef evaluations when rdm_BC are close to 0 on average (ranging from +0.14 to -0.14) as is the case for weaning weight.

The development of genomic evaluation models over the last two decades has resulted in faster genetic improvement of animals, compared to when only pedigree-based genetic evaluation models were used. In large animal breeding programs, selection of parents of the next generation usually takes place in multiple stages, and the initial stages of this selection are collectively called preselection. Preselection takes place when selection candidates are young, sometimes even before they have records for any breeding goal trait. As the preselected animals grow older, they generally get records for more breeding goal traits, and they are re-evaluated in subsequent evaluations to select the final set of parents of the next generation. Impact of preselection on accuracy and bias of subsequent genomic evaluation of preselected animals is poorly understood. The same applies for the role of genotypes of preculled animals (i.e. animals removed from the breeding program at preselection stage) in subsequent genomic evaluation of their preselected sibs. In this thesis, I used single-step genomic best linear unbiased prediction (ssGBLUP) as the representative genomic evaluation model, and used simulated and real datasets to investigate the impact of i) types and intensities of preselection and ii) genotypes and phenotypes of different groups of animals, on accuracy and bias in ssGBLUP evaluation of preselected animals. I showed that preselection, regardless of its type and intensity, results in some accuracy loss in subsequent ssGBLUP evaluation of preselected animals, compared to a scenario without preselection. I explained that the accuracy loss is mainly due to loss of relatives with records. I also showed that ssGBLUP evaluates preselected animals without preselection bias, regardless of type and intensity of preselection. I further showed that genotypes of preculled animals are only needed in subsequent ssGBLUP evaluation of their genomically preselected sibs if some of their p

Background The prediction accuracy of several linear genomic prediction models, which have previously been used for within-line genomic prediction, was evaluated for multi-line genomic prediction. Methods Compared to a conventional BLUP (best linear unbiased prediction) model using pedigree data, we evaluated the following genomic prediction models: genome-enabled BLUP (GBLUP), ridge regression BLUP (RRBLUP), principal component analysis followed by ridge regression (RRPCA), BayesC and Bayesian stochastic search variable selection. Prediction accuracy was measured as the correlation between predicted breeding values and observed phenotypes divided by the square root of the heritability. The data used concerned laying hens with phenotypes for number of eggs in the first production period and known genotypes. The hens were from two closely-related brown layer lines (B1 and B2), and a third distantly-related white layer line (W1). Lines had 1004 to 1023 training animals and 238 to 240 validation animals. Training datasets consisted of animals of either single lines, or a combination of two or all three lines, and had 30 508 to 45 974 segregating single nucleotide polymorphisms. Results Genomic prediction models yielded 0.13 to 0.16 higher accuracies than pedigree-based BLUP. When excluding the line itself from the training dataset, genomic predictions were generally inaccurate. Use of multiple lines marginally improved prediction accuracy for B2 but did not affect or slightly decreased prediction accuracy for B1 and W1. Differences between models were generally small except for RRPCA which gave considerably higher accuracies for B2. Correlations between genomic predictions from different methods were higher than 0.96 for W1 and higher than 0.88 for B1 and B2. The greater differences between methods for B1 and B2 were probably due to the lower accuracy of predictions for B1 (~0.45) and B2 (~0.40) compared to W1 (~0.76). Conclusions Multi-line genomic prediction did not

This study aimed to investigate whether suboptimal incubation (SI) temperature in weeks 1 and 3 of layer embryo incubation affects their development and post-hatch adaptive capacity during infectious challenges, by using Eimeria as a model infection under normal and immediately after more challenging environmental conditions of 72 h heat exposure. Eggs (n = 160 per treatment) were incubated at optimal (OI = 37.8°C continuously) or suboptimal eggshell temperature (36.7°C, 37.8°C and 38.9°C in weeks 1, 2 and 3, respectively). At day 33 of age, half the chickens of each incubation treatment were exposed to 72 h heat (35°C), whereas the other half remained under control conditions (21°C). At day 36 of age, all chickens were inoculated with 1 ml of a phosphate buffer saline solution containing 25 000 sporulated Eimeria acervulina oocysts/ml. The adaptive response to E. acervulina was measured by BW gain and FI from days 0 to 3 post infection (p.i.), days 3 to 5 p.i. and days 5 to 7 p.i., and by oocyst production (days 4 and 7 p.i.) and lesion scores in the duodenum (day 3, 4 and 7 p.i.). Our results demonstrated that SI temperatures in weeks 1 and 3 of incubation resulted in a reduction in yolk-free BW, chick length and navel condition. Moreover, SI temperature appeared to reduce the adaptive capacity to E. acervulina. This was demonstrated by tendencies to lower FI (P = 0.07) and BW gain (P = 0.08), more duodenal lesions (P = 0.09) and higher oocyst production (P = 0.02) after inoculation of E. acervulina. Higher lesion scores and faecal oocyst numbers were especially found when suboptimal incubation was combined with heat exposure preceding the infection. In conclusion, SI layer chickens tend to be less able to cope with an infectious challenge post hatch.

In this study, breeding structures and commercial sow lines were evaluated by economic and genetic simulation studies for their suitability to provide the Dutch organic pig sector with replacement gilts. Sow and litter performance from over 2000 crossbred sows from 2006 to 2007 were collected on 11 to 14 Dutch organic pig herds, respectively, and compared with conventional herds. Results showed that organic herds had lower farrowing rates (3.6% to 7.5%), more live born piglets per litter (0.4% to 1.2%) and higher preweaning mortality rates (7% to 13%) compared to conventional herds. These results were used to simulate economic performance of various combinations of breeding structures and sow lines under organic conditions, under the assumption of absence of genotype-environment interactions. Sow and litter performance data under organic conditions (total piglets born/litter, stillborn piglets/litter, mortality until weaning, lactation length, interval weaning-oestrus and sow culling rate) and the costprice calculation for the Dutch organic pig sector were used as input for the economic simulation studies. The expected genetic progress was simulated for three potential breeding structures of the organic sector: organic breeding herds producing F1 gilts (OrgBS), a flower breeding system (FlowerBS) and a two-line rotation breeding system (RotBS). In FlowerBS, an organic purebred sow line is bred, using on-farm gilt replacement. The OrgBS with a Yorkshire X Landrace cross had the highest margin per sow place ((sic)779), followed by RotBS with Yorkshire X Landrace cross ((sic)706) and FlowerBS with Yorkshire sow line ((sic)677). In case that an organic purebred sow population of 5000 sows would be available, FlowerBS gave the highest genetic progress in terms of cost price reduction ((sic)3.72/slaughter pig per generation), followed by RotBS and OrgBS ((sic)3.60/slaughter pig per generation). For FlowerBS, additional costs will be involved for maintaining a dedicated br

This study aimed to investigate whether pre- and early postnatal experiences of rearing hens contribute to the ability to cope with infectious challenges at an older age. In a 2 × 2 factorial arrangement, 352 Lohmann Brown chicks were exposed to either suboptimal or optimized incubation plus hatch conditions, and to cage or enriched rearing from week 0 to 7 of age. After week 7 all rearing conditions were similar until the end of the experiment. The development of adaptive capacity to infectious challenges was investigated by introducing an Eimeria and Infectious Bronchitis (IB) infection on day 53 and day 92, respectively. BW gain and feed intake during the infections, duodenal lesions and amount of positive stained CD4+ T cells, CD8+ T cells and macrophages at day 4 and day 7 after Eimeria infection, as well as the IB antibody titer throughout the experimental period were determined. The results showed a significant interaction between incubation plus hatch and rearing environment. Optimized incubation plus hatch conditions followed by an enriched rearing environment resulted in the least weight loss (P <0.05) and the highest feed intake (P <0.01) from day 3 to day 7 after the Eimeria infection (day 56 to 60 of age), compared with all other treatments. In addition, the optimized × enriched chicks had the highest BW gain from day 7 to day 14 after IB infection (day 99 to 106 of age), compared with chicks housed in a cage environment (P <0.01). Besides the interaction, optimized incubation plus hatch alone resulted in reduced macrophage numbers in the duodenal tissue at day 4 after Eimeria infection, compared with suboptimal incubation plus hatch, whereas the enriched rearing environment stimulated the recovery of intestinal damage caused by Eimeria (P <0.05) and reduced the production of specific antibodies after IB infection (P <0.05), compared with the cage environment. In conclusion, this study shows that early life experiences can indeed affect the capacity of

Test-day records of somatic cell counts (SCC) can be used to define alternative traits to decrease genetic susceptibility to clinical mastitis (CM) and subclinical mastitis (SCM). This paper examines which combination of alternative SCC traits can be used best to reduce both CM and SCM and whether direct information on CM is useful in this respect. Genetic correlations between 10 SCC traits and CM and SCM were estimated from 3 independent data sets. The SCC traits with the strongest correlations with CM differed from those with the strongest correlations with SCM. Selection index calculations were made for a breeding goal of 50% CM and 50% SCM resistance using these correlations. They indicated that a combination of 5 SCC traits (SCC early and late in lactation, suspicion of infection based on increased SCC, extent of increased SCC, and presence of a peak pattern in SCC) gave a high accuracy, almost without loss, compared with the full set of 10 SCC traits. The estimated accuracy of this index was 0.91, assuming that the correlations had been estimated without error. To take errors in estimation into account, correlations were resampled from a normal distribution with mean and standard errors as originally estimated. The accuracy of the index calculated with the original correlations was then recalculated using the resampled correlations. The average accuracy based on 50,000 resamplings decreased to 0.81. Use of direct information on CM improved the accuracy (uncorrected for errors in correlations) only slightly, to 0.92.

There is more useful information in distributions of somatic cell count (SCC) than is currently used in practice. Analysis of SCC of individual quarters (n = 450,834 quarter records of 133,102 cows) showed that the presence of pathogens did not change the peak of the SCC distribution. Instead, the percentages of observations in the tail changed. Probability density functions of specified sets of up to 5 standard distributions were then fitted on the number of records per class, using a maximum likelihood procedure. Analysis of cow SCC (2 data sets: n = 335,135 test-day records of 41,567 cows on 407 farms and n = 1,665,431 test-day records) showed that a mixture of a normal, a log-normal and an exponential density function (N+LN+E) best described the distribution of SCC. A mixture of 4 normal and an exponential distribution (4N+E) was also a good approximation. For this last mixture, each distribution could be associated with presence or absence of pathogens. The first 2 normal distributions appear to consist of uninfected cows and cows recovering from an infection, the third normal distribution may be associated with minor pathogens, and the fourth normal and the exponential distribution with major pathogens and persistent infections. Estimated percentages of records in each underlying distribution differed between parities, between stages of lactation, and between records with previous records being above or below 100,000 cells/mL. The categorical nature of cow-SCC can be utilized by deriving new traits such as the fraction of cow-SCC records in a lactation that are associated with an infection with a major pathogen

The aim of this study was to define alternative traits of somatic cell count (SCC) that can be used to decrease genetic susceptibility to clinical and subclinical mastitis (CM and SCM, respectively). Three kinds of SCC traits were evaluated: 1) lactation-averages of SCC, 2) traits derived from the proportion of test-day SCC above 150,000 cells/mL, and 3) patterns of peaks in SCC. Genetic parameters for these SCC traits and their genetic correlation with CM and SCM were estimated; CM and SCM were scored as binary traits. Two data sets (A and B) depending on CM recording were available. After editing, subset A contained 28,688 lactations from 21,673 cows in 394 herds. Subset B contained 56,726 lactations of 30,145 cows in 272 herds. Variance components for sire and permanent animal effects were estimated. Estimated heritabilities for all mastitis traits were around 0.03. Heritabilities for SCC traits ranged from 0.01 for patterns of peaks in SCC to 0.13 for lactation-average SCC. Genetic correlations between SCC traits and CM or SCM ranged from 0.55 to 0.93 for CM and from 0.55 to 0.98 for SCM. High genetic correlations were estimated between CM and SCC averaged over 250 d in milk (0.87), and between SCM and presence of test-day SCC >150,000 cells/mL (0.98) in subset A. In subset B, a high genetic correlation was estimated between CM and an SCC peak with a quick recovery (0.93) and between SCM and SCC averaged between 151 and 400 d (0.95). Partial genetic correlations were calculated to investigate the additional information of the alternative SCC traits, compared with lactation-average SCC. They showed that some traits remain informative for CM and others for SCM. Therefore, use of information from a combination of different SCC traits may be more successful in improving overall udder health than the traditional single SCC measure.