Consequences of selection for improving production traits on the frequency of deleterious alleles for fitness

In this study the effect of artificial selection on BLUP EBV for production traits on the allele frequencies of a pleiotropic QTL affecting both production and disease susceptibility was investigated. Stochastic simulations were used to model artificial selection on a production trait that is controlled, in part, by a biallelic QTL that also controls susceptibility to disease. The QTL allele increasing production also increased susceptibility to disease. Different modes of action and proportions of variation accounted for by the QTL were assessed for the production trait. The main results indicated that alleles that confer susceptibility to the disease could be maintained in the population over a long period, depending on the mode of action of the QTL. In addition, the results of the study indicate that, under various conditions, it is possible to find pleiotropic QTL that control 2 traits despite these traits appearing to be uncorrelated. Therefore, in practice, an estimate of the genetic correlation between 2 traits may be misleading when the presence of such a QTL exists. The results of this study have implications for breeding programs. For example, if a pleiotropic QTL exists that favors heterozygotes for a production trait, it would be very difficult to remove disease susceptibility alleles via traditional selection methods.     

Erythrocyte osmotic fragility testing and the prediction of canine malignant hyperthermia susceptibility

A Doberman-German Shepherd cross-bred male dog, previously diagnosed as malignant hyperthermia susceptible, was mated to an unrelated nonsusceptible German Shepherd cross-bred female. The resultant litter was subjected to hematological, biochemical and erythrocyte osmotic fragility testing in an endeavor to predict the susceptibility of individuals to malignant hyperthermia. Laboratory evaluations were repeated at one year of age and the litter subjected to the halothane challenge test. No significant difference in erythrocyte osmotic fragility was found between malignant hyperthermia susceptible and nonsusceptible siblings at six weeks or at one year of age. Erythrocyte osmotic fragility, in both malignant hyperthermia susceptible and nonsusceptible animals, increased between six weeks and one year of age. Dantrolene sodium was an effective treatment for malignant hyperthermia in the dog when administered early in an episode and in adequate dosage. The initial sign of a malignant hyperthermia episode was a very rapid increase in end tidal partial pressure of carbon dioxide. This finding reinforces the value of capnographic monitoring in anesthesia.     

Genetic and phenotypic relationships of farrowing and weaning survival to birth and placental weights in pigs

Data obtained during 4 generations of divergent selection for placental efficiency were used to determine factors influencing survival at farrowing and weaning in litters produced by first-parity females. Data were collected from 193 litters and included records on 2,053 individuals. Farrowing survival (FS) and weaning survival (WS) were considered traits of the piglet and were scored 1 if the individual was alive at a time point or 0 if dead. Estimates of (co)variance components for direct and maternal additive genetic effects for FS and WS were obtained using an animal model and computed with the MTDFREML program. Estimates of direct heritability were 0.16 for FS and 0.18 for WS. Estimates of maternal heritability were 0.14 for FS and 0.10 for WS. Genetic correlation estimates between direct and maternal effects were high and negative for both traits. The direct genetic correlation between FS and WS was 0.92. Variables associated with FS and WS were determined using logistic regression procedures. Birth weight (BRW), placental weight, their interaction, and total born can be used as predictors of survival at farrowing in the absence of estimates of genetic merit for survival. The same model, excluding total number born, was the best model for predicting WS. In the presence of BRW information, placental efficiency did not improve the prediction of survival. While it was clearly disadvantageous for a piglet to be below the litter mean in BRW, being above the mean did not provide a substantial advantage in survival. Results from this analysis suggest that it is possible to select for increased survival at farrowing and at weaning. Information on a piglet's BRW, placental weight, litter average BRW, and deviation from litter average BRW can be used to optimize those values at levels resulting in high survival probability.     

Genetic analysis of sow longevity and sow lifetime reproductive traits using censored data

Sow longevity is a key component for efficient and profitable pig farming; however, approximately 50% of sows are removed annually from a breeding herd. There is no consensus in the scientific literature regarding a definition for sow longevity; however, it has been suggested that it can be measured using several methods such as stayability and economic indicators such as lifetime piglets produced. Sow longevity can be improved by genetic selection; however, it is rarely included in genetic evaluations. One reason is elongated time intervals required to collect complete lifetime data. The effect of genetic parameter estimation software in handling incomplete data (censoring) and possible early indicator traits were evaluated analysing a 30% censored data set (12 725 pedigreed Landrace × Large White sows that included approximately 30% censored data) with DMU6, THRGIBBS1F90 and GIBBS2CEN. Heritability estimates were low for all the traits evaluated. The results show that the binary stayability traits benefited from being analysed with a threshold model compared to analysing with a linear model. Sires were ranked very similarly regardless if the program handled censoring when all available data were included. Accumulated born alive and stayability were good indicators for lifetime born alive traits. Number of piglets born alive within each parity could be used as an early indicator trait for sow longevity.     

Direct,maternal and nurse sow genetic effects on farrowing-, pre-weaning- and total piglet survival

Peri- and postnatal survival data, including birth weights and cross-foster information from two line/farm combinations with 33 717 and 29 200 piglets, respectively, were analyzed to find the best genetic model to describe piglet survival. This was done in terms of direct (piglet), maternal and nurse sow genetic effects, maternal to cover uterine quality and nurse sow to cover mothering ability. The two component traits, farrowing and pre-weaning survival and — birth weight, the most important factor for survival — were similarly analyzed. As fixed effects, Year/Season, cross, parity, birth weight in classes of 100 g, litter size as such, and sex were included in the analyses. Models combining the different genetic effects were compared on the basis of the log-likelihood. A maternal/nurse sow model fitted the data best for pre-weaning survival, a direct/maternal model for birth weight, a direct model for farrowing survival in the dam line and a direct/maternal model for farrowing survival in the sire line. Including nurse sow effect in a model for piglet survival as a whole gave erratic results, making it difficult to define an optimal model. Estimated heritabilities for pre-weaning survival, measured on the binary scale, in the dam line were 0.02±0.005 for both maternal and nurse sow effects. Heritabilities for birth weight were, on average for the two lines, 0.04±0.01 for the direct effect and 0.20±0.03 for the maternal effect. In conclusion, selection for increased component traits of piglet survival is possible.     

Genetic parameters for early lamb survival and growth in Scottish Blackface sheep

The objectives of this study were 1) to estimate the heritability of lamb survival and growth in the Scottish Blackface breed; 2) to examine the relationship between lamb survival and live BW; and 3) to investigate the possibility of using lamb survival in a breeding program for this breed. The data used for the analyses contained information about survival and live BW at different ages on 4,459 animals. The records were collected from 1988 to 2003 in a Scottish Blackface flock. Live BW was recorded every 4 wk from birth to 24 wk. Survival was defined either by perinatal or postnatal mortality (up to weaning at 12 wk), or as cumulative survival to 1, 4, 8, and 12 wk. The pedigree file comprised 1,416 dams and 178 sires. A sire model was used to estimate genetic parameters for binary survival traits. Heritabilities of BW traits, and phenotypic and genetic correlations between BW and between survival and BW were estimated by fitting an animal model. Further, correlations of survival with live BW were estimated by using a Markov chain Monte Carlo threshold model, implemented by Gibbs sampling. The heritability estimates for cumulative lamb survival declined from birth onward (from 0.33 to 0.08), and postnatal survival had a heritability of 0.01. The direct and maternal heritabilities for BW traits ranged from 0.08 to 0.26 and from 0.06 to 0.21, respectively, whereas the maternal environmental component was between 0.04 and 0.16. The genetic correlations between BW traits at different ages were high. The genetic and phenotypic correlations between survival and BW were always positive (ranging from 0.04 to 0.54), so there was no antagonism between these traits. Therefore, it is possible to simultaneously improve both survival and live BW in a breeding program for this breed.     

Generalized marker regression and interval QTL mapping methods for binary traits in half-sib family designs

A Generalized Marker Regression Mapping (GMR) approach was developed for mapping Quantitative Trait Loci (QTL) affecting binary polygenic traits in a single-family half-sib design. The GMR is based on threshold-liability model theory and regression of offspring phenotype on expected marker genotypes at flanking marker loci. Using simulation, statistical power and bias of QTL mapping for binary traits by GMR was compared with full QTL interval mapping based on a threshold model (GIM) and with a linear marker regression mapping method (LMR). Empirical significance threshold values, power and estimates of QTL location and effect were identical for GIM and GMR when QTL mapping was restricted to within the marker interval. These results show that the theory of the marker regression method for QTL mapping is also applicable to binary traits and possibly for traits with other non-normal distributions. The linear and threshold models based on marker regression (LMR and GMR) also resulted in similar estimates and power for large progeny group sizes, indicating that LMR can be used for binary data for balanced designs with large families, as this method is computationally simpler than GMR. GMR may have a greater potential than LMR for QTL mapping for binary traits in complex situations such as QTL mapping with complex pedigrees, random models and models with interactions.     

Genotype by environment interaction for growth due to altitude in United States Angus cattle

The objectives of this study were to determine if sires perform consistently across altitude and to quantify the genetic relationship between growth and survival at differing altitudes. Data from the American Angus Association included weaning weight (WW) adjusted to 205 (n = 77,771) and yearling weight adjusted to 365 (n = 39,450) d of age from 77,771 purebred Angus cattle born in Colorado between 1972 and 2007. Postweaning gain (PWG) was calculated by subtracting adjusted WW from adjusted yearling weight. Altitude was assigned to each record based upon the zip code of each herd in the database. Records for WW and PWG were each split into 2 traits measured at low and high altitude, with the records from medium altitude removed from the data due to inconsistencies between growth performance and apparent culling rate. A binary trait, survival (SV), was defined to account for censored records at yearling for each altitude. It was assumed that, at high altitude, individuals missing a yearling weight either died or required relocation to a lower altitude predominantly due to brisket disease, a condition common at high altitude. Model 1 considered each WW and PWG measured at 2 altitudes as separate traits. Model 2 treated PWG and SV measured as separate traits due to altitude. Models included the effects of weaning contemporary group, age of dam, animal additive genetic effects, and residual. Maternal genetic and maternal permanent environmental effects were included for WW. Heritability estimates for WW in Model 1 were 0.28 and 0.26 and for PWG were 0.26 and 0.19 with greater values in low altitude. Genetic correlations between growth traits measured at different altitude were moderate in magnitude: 0.74 for WW and 0.76 for PWG and indicate possibility of reranking of sires across altitude. Maternal genetic correlation between WW at varying altitude of 0.75 also indicates these may be different traits. In Model 2, heritabilities were 0.14 and 0.27 for PWG and 0.36 and 0.47 for SV. Genetic correlation between PWG measured at low and high altitude was 0.68. Favorable genetic correlations were estimated between SV and PWG within and between altitudes, suggesting that calves with genetics for increased growth from weaning to yearling also have increased genetic potential for SV. Genetic evaluations of PWG in different altitudes should consider preselection of the data, by using a censoring trait, like survivability to yearling.     

Relationship between antimicrobial susceptibility of clinical mastitis pathogens and treatment outcome in cows

OBJECTIVE: To determine whether there was any association between results of in vitro antimicrobial susceptibility testing of pathogens isolated from cows with mild or moderate clinical mastitis and outcome of treatment. DESIGN: Observational study. ANIMALS: 133 cows with mild or moderate mastitis in a single quarter. PROCEDURE: Cows were treated by means of intramammary infusion of pirlimycin (50 mg) in the affected quarter once daily for 2 days; additional intramammary treatments with the same product were administered if the milk continued to appear abnormal. Duration of treatment and days until clinical cure were recorded. Bacterial isolates were tested for antimicrobial susceptibility by means of a broth micro-dilution technique. RESULTS: Environmental streptococci, coliforms, and coagulase-negative Staphylococcus spp were the most commonly isolated pathogens. Duration of treatment and days until clinical cure were not significantly different for cows from which pathogens that were susceptible or resistant to pirlimycin were isolated. Bacteriologic cure rates 14 and 21 days after treatment were not significantly different for cows with mastitis caused by susceptible or resistant bacteria. Similar results were found when data only from cows with mastitis caused by gram-positive isolates were analyzed. CONCLUSIONS AND CLINICAL RELEVANCE: In the present study, differences in clinical outcome for cows with mild or moderate mastitis that could be attributed to differences in results of in vitro susceptibility testing were not identified. The use of in vitro susceptibility testing to guide intramammary mastitis treatment cannot be recommended on the basis of results of this study.     

Accuracy of genome-wide evaluation for disease resistance in aquaculture breeding programs

Current aquaculture breeding programs aimed at improving resistance to diseases are based on challenge tests, where performance is recorded on sibs of candidates to selection, and on selection between families. Genome-wide evaluation (GWE) of breeding values offers new opportunities for using variation within families when dealing with such traits. However, up-to-date studies on GWE in aquaculture programs have only considered continuous traits. The objectives of this study were to extend GWE methodology, in particular the Bayes B method, to analyze dichotomous traits such as resistance to disease, and to quantify, through computer simulation, the accuracy of GWE for disease resistance in aquaculture sib-based programs, using the methodology developed. Two heritabilities (0.1 and 0.3) and 2 disease prevalences (0.1 and 0.5) were assumed in the simulations. We followed the threshold liability model, which assumes that there is an underlying variable (liability) with a continuous distribution and assumed a BayesB model for the liabilities. It was shown that the threshold liability model used fits very well with the BayesB model of GWE. The advantage of using the threshold model was clear when dealing with disease resistance dichotomous phenotypes, particularly under the conditions where linear models are less appropriate (low heritability and disease prevalence). In the testing set (where individuals are genotyped but not measured), the increase in accuracy for the simulated schemes when using the threshold model ranged from 4 (for heritability equal to 0.3 and prevalence equal to 0.5) to 16% (for heritability and prevalence equal to 0.1) when compared with the linear model.     

Quantitative analysis of production traits in saltwater crocodiles (Crocodylus porosus): I. reproduction traits

Repeatability and phenotypic correlations were estimated for saltwater crocodile reproductive traits. No pedigree information was available to estimate heritability or genetic correlations, because the majority of breeder animals on farms were wild‐caught. Moreover, as the age of the female breeders could not be accounted for, egg‐size measurements were used as proxies. The reproductive traits investigated were clutch size (total number of eggs laid), number of viable eggs, number of eggs that produced a live, healthy hatchling, hatchability, average snout–vent length of the hatchlings and time of nesting. A second data set was also created comprising binary data of whether or not the female nested. Repeatability estimates ranged from 0.24 to 0.68 for the measurable traits, with phenotypic correlations ranging from ?0.15 to 0.86. Repeatability for whether a female nested or not was 0.58 on the underlying scale. Correlations could not be estimated between the measurement and binary traits because of confounding. These estimates are the first published for crocodilian reproduction traits.     

A resource allocation model describing consequences of artificial selection under metabolic stress

Long-term selection on production results in increased environmental sensitivity. This often is expressed through decreased fertility and increased health problems. The phenomenon has been described in all common farm animal species. One theory is that potential resource intake is insufficient to express production potential. Additional resources are drawn away from fitness-related traits, such as fertility and health, to further increase observed production. In addition, resources for maintaining fitness depend on the demands by the environment. In a harsh environment, more resources are required for fitness-related traits than in an optimal environment. Literature results show that selection in an optimal environment will increase sensitivity to less optimal environments. The objectives of this paper were to increase understanding of the underlying mechanism behind the development of environmental sensitivity and to gain insight into correlated response(s) when selection is on observed production. A resource allocation model was defined where observed production depended on production potential, resource intake potential, and the allocation of resources to production or fitness, including maintenance, health, and reproduction. Penalties for reproductive performance and probability of survival were included when the proportion of resources assigned to fitness dropped below a certain, environment-related, threshold. Mass selection was practiced on observed production during 40 generations using stochastic simulation. Depending on the heritabilities of the underlying components and on the environment, selection on observed production resulted in a decrease in reproductive rate and in the development of environmental sensitivity when resource intake becomes limiting. Correlations of observed production with underlying components changed across generations, following a nonlinear pattern. The proposed model is simple, but increases the understanding of underlying mechanisms and consequences of selection for production when resources are limiting.     

Product versus additive model for studying artificial insemination results in several livestock populations

Two models can be used for studying binary results of AI. The additive threshold model proposes an underlying variable as summing the environmental and genetic effects from the 2 individuals involved in the mating, and the product threshold model assumes that the conditional probability of AI success is the product of the probabilities of success of 2 unobserved binary phenotypes (one is the male fertility; the other is the female fertility). The purpose of this paper is to compare the predictive ability of the product and the additive threshold models for studying AI results and to compare results obtained with the 2 models in 3 different species: cattle, sheep, and rabbits. Results showed that the predictive ability of the product model is similar to the additive model in sheep and rabbits but worst in cattle (percentage of wrong prediction = 42, 27, and 35% in the additive model; 43, 28, and 47% in the product model in sheep, rabbits, and cattle, respectively). Even when the 2 models have similar performance, they differed in their EBV (for instance, Pearson correlation between EBV predicted with the 2 models = 0.46 in sheep for male fertility). The product model can determine which sex is responsible for an AI failure. In sheep, the female was the responsible in 94% of the cases and male in 2% of them; in rabbits, the female was the responsible in 54% of the cases and the male in 39% of them. Different estimates of probabilities for male and female fertility success obtained with the product model in the 3 species suggest that male and female fertilities behave differently depending on the species and the uniqueness of the data sets. Although product model seems to provide additional information in the fertility process, further research is needed to understand the worst performance of the product model in cattle.     

Efficiency of estimation of variance and covariance components from full-sib group means for continuous or binary records

SUMMARY: High fecundity in fish allows for testing of large full-sib groups and testing of sub-samples of each group for different traits in different environments, and this benefit is especially useful when traits cannot be measured on the breeding candidate itself. A MIVQUE-method that compresses the information in such data, by estimating variance components based on full-sib mean data, is presented for a two-trait model. The efficiency of this method is compared in a simulation study with a model using an individual observation vector when estimating additive genetic and residual (co)variances. The effect on efficiency of continuous versus binary traits, of varied average number of full-sibs, of varied frequencies in the binary traits, of different combinations of frequencies in the two binary traits, of different heritabilities, of different genetic correlations, and of two different design sizes were studied. Generally, all parameters influenced the efficiency, and this influence was larger on the residual, than on the additive genetic variance component. The proposed methodology will be especially well-suited for analysing binary traits because the residual variance component can be calculated via the phenotypic variance, which can be estimated on basis of the frequency of the trait. ZUSAMMENFASSUNG: Effizienz der Schaetzung von Varianz- und Kovarianzkomponenten aus Vollgeschwistergruppen-Mitelwerten bei kontinuierlichen und dichotomen Merkmalen Die hohe Vermehrungsrate von Fischen erlaubt es, gro?e Vollgeschwistergruppen zu pruefen und auch Stichproben aus diesen für verschiedene Merkmale in verschiedenen Umwelten zu testen. Dies ist besonders hilfreich, wenn die interessierenden Merkmale nicht an den zu selektierenden Individuen erhoben werden k?nnen. Es wird ein MIVQUE-Ansatz zur Varianz- und Kovarianzkomponentensch?tzung fuer ein Zwei-Merkmalsmodell vorgestellt, der auf der Beruecksichtigung von Vollgeschwistergruppen-Mittelwerten beruht. Die Effizienz dieses Ansatzes wird in einer Simulationsstudie untersucht, indem dieser mit einem auf individuellen Leistungen basierenden Ansatz verglichen wird. Dabei wird untersucht, welche Unterschiede sich bei kontinuierlichen und dichotomen Variablen ergeben sowie der Einflu? verschiedener Gruppengroessen, verschiedener Frequenzen der dichotomen Variablen, unterschiedlicher Heritabilitaeten und genetischer Korrelationen sowie zweier unterschiedlicher Designs. Im Allgemeinen waren die Einfluesse auf die geschaetzte Restvarianz immer groesser als die auf die genetischen Varianzkomponenten. Die vorgeschlagene Methode erwies sich als besonders geeignet fuer die Auswertung dichotomer Merkmale, weil dort die Groesse der Restvarianz sehr einfach aus der phaenotypischen Varianz approximiert werden kann, welche wiederum aus der beobachteten Frequenz des Merkmals gut zu schaetzen ist.     

Considerations for analysis of time-to-event outcomes subject to competing risks in veterinary clinical studies

In veterinary medicine, prospective clinical trials are increasingly utilized to address questions regarding effectiveness of therapies and patient prognosis. A large number of these trials involve time-to-event (TTE) endpoints, which require special methods of analysis to handle data in which not all subjects are observed to have the event of interest. Analyses and interpretation of the results can be further complicated when an endpoint of interest is not observed in some patients because they incur a competing risk, such as death from an unrelated cause. Competing risks have been the source of confusion in many epidemiologic analyses leading to the potential for misinterpretation. In this article, we review key considerations for the TTE analysis in the setting of competing risks. We briefly review standard TTE tools, namely Kaplan–Meier survival curves and Cox regression. In the setting of outcomes with competing risks, we provide guidance on the appropriate analysis techniques, such as cumulative incidence curves, to estimate the risk of an event of interest. We also describe a common pitfall of treating competing risks as censoring in Kaplan–Meier survival curve analysis, which can overestimate the event rate of interest. We describe two common regression methods that examine associated risk factors in the presence of competing risks and highlight the different research questions these methods address. This article provides an introductory overview and illustrates concepts with examples from veterinary trials and with example data sets.     

Genetic analyses of piglet survival and individual birth weight on first generation data of a selection experiment for piglet survival under outdoor conditions

Genetic parameters of piglet survival traits and birth weight were estimated on the first generation data of a selection experiment aimed at improving piglet survival using a multiple trait linear and threshold model. Data on 5293 piglets for survival at birth, at day one after birth and during the entire nursing period, as well as individual birth weight and litter size, were recorded in an outdoor production system. Genetic effects of piglet survival traits and birth weight were estimated based on threshold and Gaussian models, respectively, using a Bayesian approach. The statistical model included as fixed effects selection group, parity, gender, fostering, gestation length and month of farrowing and, alternatively, an adjustment for litter size. Direct genetic effects (i.e. the piglet's genetic potential) for piglet survival and birth weight were estimated separately, whereas maternal genetic and environmental effects could only be estimated for the given data structure in a combined litter effect. Posterior means of heritabilities for direct genetic effects of survival at birth, at first day after birth and the entire nursing period, as well as birth weight, were 0.08, 0.07, 0.08 and 0.20, respectively. Genetic correlations among survival traits were in the range of 0.29 to 0.40 and indicate that these traits were mainly attributable to different genetic effects. Genetic correlations between direct effects of survival traits and birth weight ranged between 0.18 and 0.23 and were reduced when weights of stillborn piglets were omitted in the analysis or the traits were adjusted for litter size. The magnitudes of direct genetic effects of survival traits are substantially higher than estimates in the literature, which may indicate that these traits have a higher genetic influence under outdoor conditions. The use of birth weight in the multiple trait estimation provided important information for the estimation of survival traits due to its favourable genetic correlations with survival, its high heritability and its high information content as a continuously measured trait.     

Success at first insemination in Australian Angus cattle: analysis of uncertain binary responses

Field data from Australian Angus herds were used to investigate 2 methods of analyzing uncertain binary responses for success or failure at first insemination. A linear mixed model that included herd, year, and month of mating as fixed effects; unrelated service sire, additive animal, and residual as random effects; and linear and quadratic effects of age at mating as covariates was used to analyze binary data. An average gestation length (GL) derived from artificial insemination data was used to assign an insemination date to females mated to natural service sires. Females that deviated from this average GL led to uncertain binary responses. Two analyses were carried out: 1) a threshold model fitted to uncertain binary data, ignoring uncertainty (M1); and 2) a threshold model fitted to uncertain binary data, accounting for uncertainty via fuzzy logic classification (M2). There was practically no difference between point estimates obtained from M1 and M2 for service sire and herd variance; however, when uncertain binary data were analyzed ignoring uncertainty (M1), additive variance and heritability estimates were greater than with M2. Pearson correlations indicated that no major reranking would be expected for service sire effects and animal breeding values using M1 and M2. Given the results of the current study, a threshold model contemplating uncertainty is suggested for noisy binary data to avoid bias when estimating genetic parameters.     

Genetics of length of productive life and lifetime prolificacy in the Finnish Landrace and Large White pig populations

The objective of this study was to estimate direct and indirect selection potential for length of productive life and lifetime prolificacy in Finnish Large White and Landrace swine populations. To study the direct selection potential, the heritabilities of these traits were estimated. The genetic correlations of length of productive life and lifetime prolificacy with prolificacy traits and overall leg conformation were estimated to evaluate whether selection for these traits could indirectly improve measures of sow longevity. In addition, correlations between length of productive life, lifetime prolificacy, ADG, and backfat thickness were estimated. Records were used from Finnish purebred Landrace (n = 26,744) and Large White (n = 24,007) sows born on operations that perform on-farm production tests on all females. Heritabilities were estimated using both a survival analysis procedure and a linear model. Due to computational limitations, correlations were estimated with the linear model only. Estimated length of productive life heritabilities obtained from linear model analyses were less (0.05 to 0.10) than those obtained from survival analyses (0.16 to 0.19). This may be indicative of the superiority of survival analysis compared with linear model analysis methods when evaluating longevity or similar types of data. All the prolificacy traits were genetically correlated with length of productive life and lifetime prolificacy, and the correlations were greater than 0.13. These results indicate that selection for increased number of piglets weaned in the first litter and for short first farrowing interval is beneficial for sow longevity and also for sow's lifetime prolificacy. The genetic correlations between length of productive life and leg conformation score also were favorable (0.32 in Landrace and 0.17 in Large White). The heritability estimates indicate that survival analysis is likely the most appropriate method of evaluating longevity traits in swine. Because of computational problems, simultaneous analysis of linear traits and longevity is not currently possible. More research is needed to develop methods for multiple linear and survival trait analyses.     

Risk ratio as parameter for the genetic characterization of complex binary traits in cattle. A simulation study under various genetic models using halfsib families

The risk ratio (λ_R) is defined as ‘the recurrence risk for a relative of an affected individual divided by the prevalence in the general population’ and is considered as the most important parameter when designing mapping experiments for diseases in humans. In this paper, the risk ratio was introduced as a parameter to genetically characterize complex binary traits such as mastitis in cattle. Simulations were applied to evaluate the properties of λ_R under different genetic models (monogenic, digenic, polygenic and mixed models) and in dependency of their parameters for a design consisting of unbalanced halfsib families typically found in dairy cattle. Population prevalences of the simulated data ranged from 5 to 40% and λ_R was estimated on a phenotypic level. In the discrete loci models complexity of the traits was introduced through different levels of penetrance and the proportions of phenocopies within each genetic background. The risk ratio and the power to reject the null hypothesis of independent halfsibs (λ_R=1) were influenced by the prevalence in all genetic models chosen. Absolute values for λ_R were higher for lower prevalences, for example, λ_R=2.77 and 1.62 for a pure monogenic recessive model with 5 and 20% prevalence, respectively, whereas the power decreases in the case of lower prevalences. For all the prevalences investigated, higher risk ratios were found for discrete loci models compared with the polygenic models, with higher values for the monogenic relative to the digenic models in general. For the mixed models, λ_R was intermediate between the polygenic and discrete loci models. Genes with dominant relative to recessive inheritance for susceptibility caused higher risk ratios in monogenic and mixed models, for example, λ_R=5.16 and 2.77 for a pure monogenic model with 5% prevalence. In the discrete loci models, λ_R decreased with lower penetrance and a higher proportion of phenocopies. Risk ratios increased with the heritability in the polygenic and in addition with the effect of the major gene in mixed models. Consistent patterns of risk ratios were observed under the analysed genetic models and parameters, which indicate that the risk ratio is a parameter well suited to genetically characterize binary traits in unbalanced halfsib families.     

Mapping quantitative trait loci for metabolic and cytological fatness traits of connected F2 crosses in pigs

In the present study 3 connected F(2) crosses were used to map QTL for classical fat traits as well as fat-related metabolic and cytological traits in pigs. The founder breeds were Chinese Meishan, European Wild Boar, and Pietrain with to some extent the same founder animals in the different crosses. The different selection history of the breeds for fatness traits as well as the connectedness of the crosses led to a high statistical power. The total number of F(2) animals varied between 694 and 966, depending on the trait. The animals were genotyped for around 250 genetic markers, mostly microsatellites. The statistical model was a multi-allele, multi-QTL model that accounted for imprinting. The model was previously introduced from plant breeding experiments. The traits investigated were backfat depth and fat area as well as relative number of fat cells with different sizes and 2 metabolic traits (i.e., soluble protein content as an indicator for the level of metabolic turnover and NADP-malate dehydrogenase as an indicator for enzyme activity). The results revealed in total 37 significant QTL on chromosomes 1, 2, 4, 5, 6, 7, 8, 9, 14, 17, and 18, with often an overlap of confidence intervals of several traits. These confidence intervals were in some cases remarkably small, which is due to the high statistical power of the design. In total, 18 QTL showed significant imprinting effects. The small and overlapping confidence intervals for the classical fatness traits as well as for the cytological and metabolic traits enabled positional and functional candidate gene identification for several mapped QTL.