J. ten Napel

Alternative Somatic Cell Count Traits as Mastitis Indicators for Genetic Selection

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.

Temperature manipulation during layer chick embryogenesis

The current study investigated the effects of temperature manipulation (TM) during late embryogenesis on temperature preference, response to high environmental temperature, behavior, and performance in young layer chicks. Control (CC) embryos (n = 96) were incubated at 37.8 degrees C eggshell temperature throughout incubation. Thermally manipulated embryos (n = 96) were incubated at 37.8 degrees C eggshell temperature throughout incubation and were exposed to 40 degrees C for 4 h/d from embryonic d 14 to 18 (TM chicks). After hatch, chicks from each treatment were divided into 3 subgroups (n = 32 per group) and were subjected to a temperature preference test at d 1, 7, or 33. One day after the temperature preference test, each subgroup was exposed to 1 thermal challenge for 4 h (d 2, 40 degrees C; d 8, 40 degrees C; or d 34, 35 degrees C). Effects of TM on (fearfulness) behavior of chicks were investigated in a tonic immobility test and during home pen observations. Temperature manipulation decreased incubation time with 7 h (P < 0.0001) and body temperature at hatch with 0.2 degrees C (P = 0.002). The TM chicks preferred a lower ambient temperature in the temperature preference test (P < 0.05) and showed a higher body temperature response than CC chicks to the thermal challenge at d 2 and 8 (P < 0.05). No effects of TM on behavior and performance were observed. Because most TM studies are conducted in broilers, this study is the first attempt to unravel the effects of TM during late embryogenesis on posthatch environmental adaptation in layer chicks. The results demonstrated that effects of our TM on postnatal temperature preference and response to high environmental temperatures are only found until d 8 of age. This may suggest 1 of 3 options: a) the timing or the level, or both, of TM and duration were not at the sensitive period of embryogenesis or not sufficient, or both, respectively; b) the level of the postnatal thermal challenge was not strong enough to induce a hyperthermic response; and c) the postnatal effects of TM in layers are limited in time.

Combining somatic cell count traits for optimal selection against mastitis

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.

Characterization of distributions of somatic cell counts.

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.

Genetic parameters for androstenone, skatole, indole, and human nose scores as measures of boar taint and their relationship with finishing traits

The purpose of this study was to evaluate measures of boar (Sus scrofa) taint as potential selection criteria to reduce boar taint so that castration of piglets will become unnecessary. Therefore, genetic parameters of boar taint measures and their genetic correlations with finishing traits were estimated. In particular, the usefulness of a human panel assessing boar taint (human nose score) was compared with chemical assessment of boar taint compounds, androstenone, skatole, and indole. Heritability estimates for androstenone, skatole, and indole were 0.54, 0.41, and 0.33, respectively. The heritability for the human nose score using multiple panelists was 0.12, and ranged from 0.12 to 0.19 for individual panelists. Genetic correlations between scores of panelists were generally high up to unity. The genetic correlations between human nose scores and the boar taint compounds ranged from 0.64 to 0.999. The boar taint compounds and human nose scores had low or favorable genetic correlations with finishing traits. Selection index estimates indicated that the effectiveness of a breeding program based on human nose scores can be comparable to a breeding program based on the boar taint compounds themselves. Human nose scores can thus be used as a cheap and fast alternative for the costly determination of boar taint compounds, needed in breeding pigs without boar taint.

Estimation of genetic parameters and breeding values across challenged environments to select for robust pigs

Robustness is an important issue in the pig production industry. Since pigs from international breeding organizations have to withstand a variety of environmental challenges, selection of pigs with the inherent ability to sustain their productivity in diverse environments may be an economically feasible approach in the livestock industry. The objective of this study was to estimate genetic parameters and breeding values across different levels of environmental challenge load. The challenge load (CL) was estimated as the reduction in reproductive performance during different weeks of a year using 925,711 farrowing records from farms distributed worldwide. A wide range of levels of challenge, from favorable to unfavorable environments, was observed among farms with high CL values being associated with confirmed situations of unfavorable environment. Genetic parameters and breeding values were estimated in high- and low-challenge environments using a bivariate analysis, as well as across increasing levels of challenge with a random regression model using Legendre polynomials. Although heritability estimates of number of pigs born alive were slightly higher in environments with extreme CL than in those with intermediate levels of CL, the heritabilities of number of piglet losses increased progressively as CL increased. Genetic correlations among environments with different levels of CL suggest that selection in environments with extremes of low or high CL would result in low response to selection. Therefore, selection programs of breeding organizations that are commonly conducted under favorable environments could have low response to selection in commercial farms that have unfavorable environmental conditions. Sows that had experienced high levels of challenge at least once during their productive life were ranked according to their EBV. The selection of pigs using EBV ignoring environmental challenges or on the basis of records from only favorable environments resulted in a sharp decline in productivity as the level of challenge increased. In contrast, selection using the random regression approach resulted in limited change in productivity with increasing levels of challenge. Hence, we demonstrate that the use of a quantitative measure of environmental CL and a random regression approach can be comprehensively combined for genetic selection of pigs with enhanced ability to maintain high productivity in harsh environments.

Validation of simultaneous deregression of cow and bull breeding values and derivation of appropriate weights

Training of genomic prediction in dairy cattle may use deregressed proofs (DRP) as phenotypes. In this case, DRP should be estimated breeding values (EBV) corrected for information of relatives included in the data used for genomic prediction, and adjusted for regression to the mean (i.e., their reliability). Deregression is especially important when combining animals with EBV with low reliability, as commonly the case for cows, and high reliability. The objective of this paper, therefore, was to compare the performance of different deregression procedures for data that include both cow and bull EBV, and to develop and test procedures to obtain the appropriate deregressed weights for the DRP. Considered DRP were EBV: without any adjustment, adjusted for information of parents and regression to the mean, or adjusted for information of all relatives and regression to the mean. Considered deregressed weights were weights of initial EBV: without any adjustment, adjusted for information of parents, or adjusted for information of all relatives. The procedures were compared using simulated data based on an existing pedigree with 1,532 bulls and 13,720 cows that were considered to be included in the data used for genomic prediction. For each cow, 1 to 5 records were simulated. For each bull, an additional 50 to 200 daughters with 1 record each were simulated to generate a source of data that was not used for genomic prediction. The simulated trait had either a heritability of 0.05 or 0.3. The validation involved 3 steps: (1) computation of initial EBV and weights, (2) deregression of those EBV and weights, (3) using deregressed EBV and weights to compute final EBV, (4) comparison of the initial and final EBV and weights. The methods developed to compute appropriate weights for the DRP were either very precise and computationally somewhat demanding for larger data sets, or were less precise but computationally trivial due their approximate nature. Adjusting DRP for all relatives, known as matrix deregression, yields by definition final EBV that are identical to the original EBV. Matrix deregression is therefore preferred over other approaches that only correct for information of parents or not performing any deregression at all. It is important to use appropriate weights for the DRP, properly corrected for information of relatives, especially when individual reliabilities of final EBV are computed based on the prediction error variance of the model.

Estimating challenge load due to disease outbreaks and other challenges using reproduction records of sows

A method was developed and tested to estimate challenge load due to disease outbreaks and other challenges in sows using reproduction records. The method was based on reproduction records from a farm with known disease outbreaks. It was assumed that the reduction in weekly reproductive output within a farm is proportional to the magnitude of the challenge. As the challenge increases beyond certain threshold, it is manifested as an outbreak. The reproduction records were divided into 3 datasets. The first dataset called the Training dataset consisted of 57,135 reproduction records from 10,901 sows from 1 farm in Canada with several outbreaks of porcine reproductive and respiratory syndrome (PRRS). The known disease status of sows was regressed on the traits number born alive, number of losses as a combination of still birth and mummified piglets, and number of weaned piglets. The regression coefficients from this analysis were then used as weighting factors for derivation of an index measure called challenge load indicator. These weighting factors were derived with i) a two-step approach using residuals or year-week solutions estimated from a previous step, and ii) a single-step approach using the trait values directly. Two types of models were used for each approach: a logistic regression model and a general additive model. The estimates of challenge load indicator were then compared based on their ability to detect PRRS outbreaks in a Test dataset consisting of records from 65,826 sows from 15 farms in the Netherlands. These farms differed from the Canadian farm with respect to PRRS virus strains, severity and frequency of outbreaks. The single-step approach using a general additive model was best and detected 14 out of the 15 outbreaks. This approach was then further validated using the third dataset consisting of reproduction records of 831,855 sows in 431 farms located in different countries in Europe and America. A total of 41 out of 48 outbreaks detected using data analysis were confirmed based on diagnostic information received from the farms. Among these, 30 outbreaks were due to PRRS while 11 were due to other diseases and challenging conditions. The results suggest that proposed method could be useful for estimation of challenge load and detection of challenge phases such as disease outbreaks.

Breeding replacement gilts for organic pig herds.

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 × Landrace cross had the highest margin per sow place (€779), followed by RotBS with Yorkshire × Landrace cross (€706) and FlowerBS with Yorkshire sow line (€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 (€3.72/slaughter pig per generation), followed by RotBS and OrgBS (€3.60/slaughter pig per generation). For FlowerBS, additional costs will be involved for maintaining a dedicated breeding programme. In conclusion, OrgBS using conventional genetics is economically the most viable option for the organic pig sector. However, this structure has clear disadvantages in terms of risks with regard to disease transmission and market demand. FlowerBS using a dedicated purebred organic line will only be cost-effective if sow population size is sufficiently large. RotBS might be a viable alternative, especially in combination with artificial insemination (AI) boars that are ranked according to an organic selection index. Regardless of breeding structure, the Yorkshire sow line gave the highest prolificacy and the highest economic returns on organic herds.

Early life experiences affect the adaptive capacity of rearing hens during infectious challenges.

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 rearing hens to cope with an Eimeria and IB infection at an older age, in which performance of chicks is best maintained after optimized incubation plus hatch followed by enriched rearing. This suggests that the development of adaptive capacity to infectious challenges can be influenced with management during a short period in pre- or early postnatal life, but that effects last for a considerable time after cessation of the specific management.