Estimating the probability of freedom of classical swine fever virus of the East-Belgium wild-boar population.

A report of the Scientific Committee on Animal Health and Animal Welfare of the European Commission (CEC, 1999.) includes recommendations for setting up monitoring programmes for classical swine fever (CSF) infection in a wild-boar population, based on the assumption that one would detect at least 5% prevalence in a CSF-infected wild-boar population. This assumption, however, is not science based. We propose an alternative method to provide evidence for a wild-boar population being free of CSF and evaluate the efficiency of a surveillance programme that was implemented in Belgium in 1998. In our study, the probability of freedom of CSF-virus was estimated based on 789 samples; these were collected from wild-boars within the surveillance programme (within the three provinces which include 95% of the Belgian wild-boar population) and examined by three diagnostics methods (antibody detection, virus detection and virus RNA detection). A Bayesian framework was used for the estimation, accounting for the diagnostic test characteristics without the assumption of the presence of a gold standard. The median probability of freedom of CSF-virus was estimated at 0.970, with a 95% credibility interval of 0.149-1.000. Independent on the choice of the prior information, the posterior distributions for the probability of freedom of CSF-virus were always skewed close to the upper boundary of 1. This represents a big gain of knowledge since we did not use any prior information for the probability of freedom of CSF-virus and took the uncertainty about the accuracy of the diagnostic methods into account.

Genomic prediction in French Charolais beef cattle using high-density single nucleotide polymorphism markers.

The objective of the study was to develop a genomic evaluation for French beef cattle breeds and assess accuracy and bias of prediction for different genomic selection strategies. Based on a reference population of 2,682 Charolais bulls and cows, genotyped or imputed to a high-density SNP panel (777K SNP), we tested the influence of different statistical methods, marker densities (50K versus 777K), and training population sizes and structures on the quality of predictions. Four different training sets containing up to 1,979 animals and a unique validation set of 703 young bulls only known on their individual performances were formed. BayesC method had the largest average accuracy compared to genomic BLUP or pedigree-based BLUP. No gain of accuracy was observed when increasing the density of markers from 50K to 777K. For a BayesC model and 777K SNP panels, the accuracy calculated as the correlation between genomic predictions and deregressed EBV (DEBV) divided by the square root of heritability was 0.42 for birth weight, 0.34 for calving ease, 0.45 for weaning weight, 0.52 for muscular development, and 0.27 for skeletal development. Half of the training set constituted animals having only their own performance recorded, whose contribution only represented 5% of the accuracy. Using DEBV as a response brought greater accuracy than using EBV (+5% on average). Considering a residual polygenic component strongly reduced bias for most of the traits. The optimal percentage of polygenic variance varied across traits. Among the methodologies tested to implement genomic selection in the French Charolais beef cattle population, the most accurate and less biased methodology was to analyze DEBV under a BayesC strategy and a residual polygenic component approach. With this approach, a 50K SNP panel performed as well as a 777K panel.

Genetic structure of the European Charolais and Limousin cattle metapopulations using pedigree analyses.

Pedigree collected by the Interbeef service allowed genetic diversity to be assessed by using pedigree analyses for the European Charolais (CHA) and Limousin (LIM) cattle populations registered in national herdbooks in Denmark (DNK), France (FRA), Ireland (IRL), Sweden (SWE), and, solely for the LIM breed, the United Kingdom (UK). The CHA data set included 2,563,189 calves with weaning performance, of which 96.1% were recorded in FRA, 3.0% in SWE, 0.5% in IRL, and 0.4% in DNK. The LIM data set included 1,652,734 calves with weaning performance, of which 91.9% were recorded in FRA, 4.9% in UK, 1.8% in DNK, 0.9% SWE, and 0.5% in IRL. Pedigree files included 3,191,132 CHA and 2,409,659 LIM animals. Gene flows were rather limited between populations, except from FRA toward other countries. Pedigree completeness was good in all subpopulations for both breeds and allowed the pedigree to be traced back to the French population. A relatively high level of genetic diversity was assessed in each CHA and LIM subpopulation by estimating either effective population sizes (N(e) >244 and N(e) >345 in the CHA and LIM subpopulations, respectively), relationship coefficients within subpopulations (<1.3% in both breeds), or probability of gene origins. However, in each subpopulation, it was shown that founders and also ancestors had unbalanced genetic contributions, leading to a moderate but continuous reduction in genetic diversity. Analyses between populations suggested that all European CHA and LIM populations were differentiated very little. The Swedish CHA population was assessed as genetically more distant from the other CHA populations because of fewer gene flows from other countries and because of the use of North American sires to introgress the polled phenotype. In each European subpopulation, most of the main ancestors, which explained 50% of gene origin, were born in FRA. However, those main ancestors were different between countries. Moreover, in both breeds, the main ancestors, which explained 50% of the gene origin in DNK, IRL, SWE, and UK for the LIM breed, were found to be infrequently used in FRA. Those results were consistent with the low relationship coefficients estimated between subpopulations (<0.6% in both the CHA and LIM breeds). Therefore, in both breeds, each subpopulation may constitute a reservoir of genetic diversity for the other ones.

Use of structured antedependence models for the genetic analysis of growth curves.

Growth curve analysis is an important issue for many agricultural and laboratory species, for both phenotypic and genetic studies. The aim of this paper is to present the use of a novel statistical approach, namely the structured antedependence (SAD) models, to deal with this issue. The basic idea of these models is that an observation at time t can be explained by the previous observations. These models are especially appropriate to deal with cumulative traits such as growth, as BW at age t clearly depends on BW measures at ages (t -1), (t -2), etc. These models were applied on an INRA experimental Charolais herd data set. The data comprised BW records for 560 cows born over an 11-yr period (from 1988 to 1998) from 60 sires and 369 dams. The proposed SAD models were compared with the well-known random regression (RR) models that are already widely used in various areas of longitudinal data analysis. It was found that the SAD models fit the growth process better with far fewer parameters than the RR models (9 instead of 16 covariance parameters for the phenotypic analysis, and 14 instead of 21 for the genetic analysis). Despite this smaller number of covariance parameters, the likelihood value was found to be much higher with the SAD vs. the RR models, with a difference of 262.9 for the phenotypic analysis with a quartic polynomial for the RR and 751.5 for the genetic analysis with a cubic polynomial for both the genetic and environmental parts of the RR model. The SAD models also proved to be better able to interpolate missing values. Heritability, genetic, and environmental correlation coefficients were estimated for weights from birth to adulthood. The structured antedependence models proved, in this study, to be very appropriate to model growth data in a parsimonious and flexible way.