Quantitative trait loci (QTL) associated with resistance of rainbow trout Oncorhynchus mykiss against the parasitic ciliate Ichthyophthirius multifiliis

The parasitic ciliate Ichthyophthirius multifiliis has a low host specificity eliciting white spot disease (WSD) in a wide range of freshwater fishes worldwide. The parasite multiplies rapidly whereby the infection may reach problematic levels in a host population within a few days. The parasite targets both wild and cultured fish but the huge economic impact of the protozoan is associated with mortality, morbidity and treatment in aquacultural enterprises. We have investigated the potential for genetic selection of WSD-resistant strains of rainbow trout. Applying the DNA typing system Affymetrix® and characterizing the genome of the individual fish by use of 57,501 single nucleotide polymorphisms (SNP) and their location on the rainbow trout chromosomes, we have genetically characterized rainbow trout with different levels of natural resistance towards WSD. Quantitative trait loci (QTL) used for the selection of breeders with specific markers for resistance are reported. We found a significant association between resistance towards I. multifiliis infection and SNP markers located on the two specific rainbow trout chromosomes Omy 16 and Omy 17. Comparing the expression of immune-related genes in fish-with and without clinical signs-we recorded no significant difference. However, trout surviving the infection showed high expression levels of genes encoding IgT, T-cell receptor TCRβ, C3, cathelicidins 1 and 2 and SAA, suggesting these genes to be associated with protection.

Differential effects of epidermal growth factor (EGF) receptor ligands on receptor binding, downstream signalling pathways and DNA synthesis in hepatocytes

Hepatocytes are responsive to mitogenic effects of several ligands acting via EGFR. Studying primary cultures of rat hepatocytes, we found that, as compared to EGF, HB-EGF had a markedly higher affinity of the EGFR, while AR and TGFα had lower affinity. HB-EGF was also more potent compared to the other growth factors regarding phosphorylation of EGFR, Shc, ERK1/2 and Akt. All ligands induced phosphorylation of ErbB2, indicating receptor heterodimerization. TGFα, despite having much lower receptor affinity, was about equally potent and efficacious as HB-EGF as a stimulator of DNA synthesis. In contrast, EGF had relatively high affinity but markedly lower efficacy in stimulation of DNA synthesis. The results suggest that amplifying and/or inhibitory mechanisms may modulate the mitogenic responses downstream of the initial signalling steps, and that this may affect the effects of the EGFR ligands differentially.

Genetic relationships between boar feed efficiency and sow piglet production, body condition score, and stayability in Norwegian Landrace pigs

Both feed efficiency and sow production are economically important traits in pig breeding. One challenge in a maternal line such as Norwegian Landrace is to breed for highly feed efficient fattening pigs and, at the same time, produce sows with high daily feed intake to maintain their BCS in multiple parities. The aim of this study was to estimate genetic correlations among novel feed efficiency measurements on Norwegian Landrace boars and piglet production, stayability, and body condition in Norwegian Landrace sows. The feed efficiency measurements were lean meat and fat efficiency. These measurements were calculated using an extended residual feed intake model where total feed intake in the test period was the response variable and fat (kg) and lean meat (kg) on the carcass were included as both fixed and random regressions. The random regression coefficients that resulted from this model were breeding values, which represented the amount of feed used to produce an extra kilogram of lean meat and fat. The sow traits were stayability of the sow from first to second parity, BCS at weaning, litter weight at 3 wk, and total number of piglets born. All traits were recorded on first parity purebred Norwegian Landrace and analyzed using multivariate animal models. All genetic correlations between fat efficiency and sow traits were low. Significant genetic correlations were found only between fat efficiency and stayability (0.21 ± 0.11) and between fat efficiency and total litter weight at 3 wk (0.21 ± 0.10). The results indicate that selection for efficient deposition of fat could give poor stayability and lower litter weight at 3 wk in first parity sows. The genetic correlations between lean meat efficiency and sow traits were not significantly different from 0 and signified no genetic relationships between these traits. Selection for efficient deposition of lean meat should not affect the sow traits and is, therefore, beneficial.

Comparison of random regression and repeatability models to predict breeding values from test-day records of Norwegian goats

One aim of the research was to challenge a previously selected repeatability model with 2 other repeatability models. The main aim, however, was to evaluate random regression models based on the repeatability model with lowest mean-squared error of prediction, using Legendre polynomials up to third order for both animal additive genetic and permanent environmental effects. The random regression and repeatability models were compared for model fit (using likelihood-ratio testing, Akaike information criterion, and the Bayesian information criterion) and the models' mean-squared errors of prediction, and by cross-validation. Cross-validation was carried out by correlating excluded observations in one data set with the animals' breeding values as predicted from the pedigree only in the remaining data, and vice versa (splitting proportion: 0.492). The data was from primiparous goats in 2 closely tied buck circles (17 flocks) in Norway, with 11,438 records for daily milk yield and 5,686 to 5,896 records for content traits (fat, protein, and lactose percentages). A simple pattern was revealed; for daily milk yield with about 5 records per animal in first lactation, a second-order random regression model should be chosen, whereas for content traits that had only about 3 observations per goat, a first-order polynomial was preferred. The likelihood-ratio test, Akaike information criterion, and mean-squared error of prediction favored more complex models, although the results from the latter and the Bayesian information criterion were in the direction of those obtained with cross-validation. As the correlation from cross-validation was largest with random regression, genetic merit was predicted more accurate with random regression models than with the repeatability model.

A method for the prediction of multitrait breeding values for use in stochastic simulation to compare progeny-testing schemes, with large progeny groups for proven sires

A method of approximating estimated breeding values (EBV) from a multivariate distribution of true breeding values (TBV) and EBV is proposed for use in large-scale stochastic simulation of alternative breeding schemes with a complex breeding goal. The covariance matrix of the multivariate distributions includes the additive genetic (co)variances and approximated prediction error (co)variances at different selection stages in the life of the animal. The prediction error (co)variance matrix is set up for one animal at a time, utilizing information on the selection candidate and its offspring, the parents, as well as paternal and maternal half- sibs. The EBV are a regression on TBV taking individual uncertainty into account, but with additional 'free' variation drawn at random. With the current information included in the calculation of the prediction error variance of a selection candidate, it is concluded that the method can be used to optimize progeny-testing schemes, where the progeny-tested sires are utilized with large progeny groups, e.g. through artificial insemination.

Quantitative genetics of disease resistance in vaccinated and unvaccinated Atlantic salmon (Salmo salar L.)

Furunculosis (Aeromonoas salmonicida) is an important disease in Atlantic salmon (Salmo salar) farming. Vaccination and selective breeding for increased resistance to the disease on the basis of challenge tests of unvaccinated fish are used as complementary prophylactic methods. An important issue is whether genetic predisposition to infection is consistent across vaccinated and unvaccinated fish. Hence, the main objective of this study was to determine the magnitude of the genetic associations (correlations) between resistance to furunculosis in vaccinated and unvaccinated fish, and to estimate the magnitude of the correlation of resistance to furunculosis with resistance to the viral diseases infectious pancreatic necrosis (IPN) and infectious salmon anaemia (ISA). Sub-samples of unvaccinated and vaccinated salmon from 150 full-sib families were subjected to separate cohabitation challenge tests. Substantial genetic variation was found in resistance to furunculosis in both the unvaccinated (heritabilities of 0.51 ± 0.05) and vaccinated (0.39 ± 0.06) fish. However, the genetic correlation between resistance to furunculosis in the two groups was low (0.32 ± 0.13), indicating a weak genetic association between resistance in the two groups. Hence, the current selection strategy on the basis of challenge tests of unvaccinated fish is likely to produce low genetic improvement in resistance to furunculosis under field conditions, where fish are vaccinated with an effective vaccine. Evidence was found of significantly favourable genetic associations of resistance to furunculosis in unvaccinated (but less so for vaccinated) fish with resistance to both IPN and ISA (unvaccinated fish), indicating that vaccination 'mask' genetic associations between resistance to different diseases.

A sequential threshold cure model for genetic analysis of time-to-event data

In analysis of time-to-event data, classical survival models ignore the presence of potential nonsusceptible (cured) individuals, which, if present, will invalidate the inference procedures. Existence of nonsusceptible individuals is particularly relevant under challenge testing with specific pathogens, which is a common procedure in aquaculture breeding schemes. A cure model is a survival model accounting for a fraction of nonsusceptible individuals in the population. This study proposes a mixed cure model for time-to-event data, measured as sequential binary records. In a simulation study survival data were generated through 2 underlying traits: susceptibility and endurance (risk of dying per time-unit), associated with 2 sets of underlying liabilities. Despite considerable phenotypic confounding, the proposed model was largely able to distinguish the 2 traits. Furthermore, if selection is for improved susceptibility rather than endurance, the error of applying a classical survival model was nonnegligible. The difference was most pronounced for scenarios with substantial underlying genetic variation in endurance and when the 2 underlying traits were lowly genetically correlated. In the presence of nonsusceptible individuals, the method provides a novel and more accurate tool for utilization of time-to-event data, and has also been proven successful when applied to zero-inflated longitudinal binary data.

Validation of test-day models for genetic evaluation of dairy goats in Norway

Test-day data for daily milk yield and fat, protein, and lactose content were sampled from the years 1988 to 2003 in 17 flocks belonging to 2 genetically well-tied buck circles. In total, records from 2,111 to 2,215 goats for content traits and 2,371 goats for daily milk yield were included in the analysis, averaging 2.6 and 4.8 observations per goat for the 2 groups of traits, respectively. The data were analyzed by using 4 test-day models with different modeling of fixed effects. Model [0] (the reference model) contained a fixed effect of year-season of kidding with regression on Ali-Schaeffer polynomials nested within the year-season classes, and a random effect of flock test-day. In model [1], the lactation curve effect from model [0] was replaced by a fixed effect of days in milk (in 3-d periods), the same for all year-seasons of kidding. Models [2] and [3] were obtained from model [1] by removing the fixed year-season of kidding effect and considering the flock test-day effect as either fixed or random, respectively. The models were compared by using 2 criteria: mean-squared error of prediction and a test of bias affecting the genetic trend. The first criterion indicated a preference for model [3], whereas the second criterion preferred model [1]. Mean-squared error of prediction is based on model fit, whereas the second criterion tests the ability of the model to produce unbiased genetic evaluation (i.e., its capability of separating environmental and genetic time trends). Thus, a fixed structure with year (year, year-season, or possibly flock-year) was indicated to appropriately separate time trends. Heritability estimates for daily milk yield and milk content were 0.26 and 0.24 to 0.27, respectively.

A genome scan for quantitative trait locus by environment interactions for production traits

Genotype by environment interactions between milk production traits and production level have often been observed. To increase the power of quantitative trait loci (QTL) detection, QTL by environment interaction was included in QTL analyses for the milk, protein, and fat yields. The aim of the study was to detect QTL with interaction effects with the production environment. The QTL effects were modeled through random regression models for within-herd production level. All autosomes except Bos taurus autosome 6 were included in the analysis. A more detailed study of chromosome 6 is planned. For milk yield, 5 QTL were observed, 2 of which had interaction effects with production level (suggestive linkage). For protein yield, 5 QTL were observed, 3 of which had interaction effects (suggestive linkage). For fat yield, 3 QTL were observed, none of which had interaction effects with the environment (suggestive linkage). Thus, some QTL with interaction effects seemingly exist for milk yield and protein yield. For such QTL, estimated correlations between slope and intercept of the effect (close to 1 or -1) indicated that only 2 alleles were segregating. The study indicates that QTL by environment interactions exist, and that random regression models that describe the environment as herd production level can detect this interaction.

Environmental Sensitivity of Milk Production in Extensive Environments: A Comparison of Simmental, Brown Swiss, and Tyrol Grey Using Random Regression Models

A total of 25,160 milk test-day records from 2,516 cows in first lactation of 3 dairy cattle breeds [Simmental (n = 1,900), Brown Swiss (n = 444), and Tyrol Grey (n = 172)] in Kosovo were analyzed using nested repeatability and random regression test-day models with varying (co)variance structures. The different models were compared based on likelihood-based criteria. The best model was a second-order random regression model, with heterogeneous cow variance per breed and heterogeneous residual variance per lactation month and breed, which was used for further analysis. The highest milk production was found in Brown Swiss, followed by Simmental and Tyrol Grey. Substantial breed differences were found for the trajectories of cow and residual variances by month of lactation, with the highest variances found for Brown Swiss, followed by Simmental and Tyrol Grey. High cow and residual variances indicated a high degree of environmental sensitivity on the macro- and microenvironmental levels, respectively. Thus, these results indicate increased environmental sensitivity for breeds with higher genetic potential for milk production. These results support the conclusion that dairy cattle production under the current environmental conditions of Kosovo should be based on a breed with moderate production that is robust to the diet offered (e.g., Tyrol Grey).

A Bayesian threshold-normal mixture model for analysis of a continuous mastitis-related trait

Mastitis is associated with elevated somatic cell count in milk, inducing a positive correlation between milk somatic cell score (SCS) and the absence or presence of the disease. In most countries, selection against mastitis has focused on selecting parents with genetic evaluations that have low SCS. Univariate or multivariate mixed linear models have been used for statistical description of SCS. However, an observation of SCS can be regarded as drawn from a 2- (or more) component mixture defined by the (usually) unknown health status of a cow at the test-day on which SCS is recorded. A hierarchical 2-component mixture model was developed, assuming that the health status affecting the recorded test-day SCS is completely specified by an underlying liability variable. Based on the observed SCS, inferences can be drawn about disease status and parameters of both SCS and liability to mastitis. The prior probability of putative mastitis was allowed to vary between subgroups (e.g., herds, families), by specifying fixed and random effects affecting both SCS and liability. Using simulation, it was found that a Bayesian model fitted to the data yielded parameter estimates close to their true values. The model provides selection criteria that are more appealing than selection for lower SCS. The proposed model can be extended to handle a wide range of problems related to genetic analyses of mixture traits.

Short Communication: Genetic Correlation Between Test-Day Electrical Conductivity of Milk and Mastitis

Electrical conductivity (EC) of milk is an indicator of mastitis. If EC shows genetic variation and is genetically correlated to mastitis, it could be used in a breeding program that includes selection for improved mastitis resistance. In this study, daily records of EC and mastitis from about 1,500 Holstein cows were analyzed. A bivariate animal model was used for estimation of (co)variance components, including fixed effects of age of calving, herd-test-day, and days in milk, in addition to random additive genetic effects and permanent environmental effects. For EC, the estimated heritability was moderate (0.22 to 0.39), whereas for mastitis, the heritability was low (0.013). The genetic correlation between EC and mastitis was estimated to be 0.75, and genetic improvement of mastitis resistance should be feasible through selection for reduced EC.

Short Communication: Bivariate Genetic Analysis of Clinical Mastitis and Somatic Cell Count in Norwegian Dairy Cattle

Clinical mastitis (CM) and lactation mean somatic cell score (LSCS) were analyzed with a bivariate linear sire model. Nearly 1.4 million primiparous cows of Norwegian Dairy Cattle from 2043 sires were used. The heritability estimates were 0.03 for CM and 0.11 for LSCS. The estimates of genetic and residual correlations between the 2 traits were 0.53 and 0.10, respectively. It is postulated that the genetic correlation probably is highly population-specific.

Quantitative trait loci affecting clinical mastitis and somatic cell count in dairy cattle

Norway has a field recording system for dairy cattle that includes recording of all veterinary treatments on an individual animal basis from 1978 onwards. Application of these data in a genome search for quantitative trait loci (QTL) verified genome-wise significant QTL affecting clinical mastitis on Chromosome (Chr) 6. Additional putative QTL for clinical mastitis were localized to Chrs. 3, 4, 14, and 27. The comprehensive field recording system includes information on somatic cell count as well. This trait is often used in selection against mastitis when direct information on clinical mastitis is not available. The absence of common QTL positions for the two traits in our study indicates that the use of somatic cell count data in QTL studies aimed for reducing the incidence of mastitis should be carefully evaluated.