Review: Towards the agroecological management of ruminants, pigs and poultry through the development of sustainable breeding programmes. II. Breeding strategies

Agroecology uses ecological processes and local resources rather than chemical inputs to develop productive and resilient livestock and crop production systems. In this context, breeding innovations are necessary to obtain animals that are both productive and adapted to a broad range of local contexts and diversity of systems. Breeding strategies to promote agroecological systems are similar for different animal species. However, current practices differ regarding the breeding of ruminants, pigs and poultry. Ruminant breeding is still an open system where farmers continue to choose their own breeds and strategies. Conversely, pig and poultry breeding is more or less the exclusive domain of international breeding companies which supply farmers with hybrid animals. Innovations in breeding strategies must therefore be adapted to the different species. In developed countries, reorienting current breeding programmes seems to be more effective than developing programmes dedicated to agroecological systems that will struggle to be really effective because of the small size of the populations currently concerned by such systems. Particular attention needs to be paid to determining the respective usefulness of cross-breeding v. straight breeding strategies of well-adapted local breeds. While cross-breeding may offer some immediate benefits in terms of improving certain traits that enable the animals to adapt well to local environmental conditions, it may be difficult to sustain these benefits in the longer term and could also induce an important loss of genetic diversity if the initial pure-bred populations are no longer produced. As well as supporting the value of within-breed diversity, we must preserve between-breed diversity in order to maintain numerous options for adaptation to a variety of production environments and contexts. This may involve specific public policies to maintain and characterize local breeds (in terms of both phenotypes and genotypes), which could be used more effectively if they benefited from the scientific and technical resources currently available for more common breeds. Last but not least, public policies need to enable improved information concerning the genetic resources and breeding tools available for the agroecological management of livestock production systems, and facilitate its assimilation by farmers and farm technicians

Use of partial least squares regression to impute SNP genotypes in Italian Cattle breeds

Background The objective of the present study was to test the ability of the partial least squares regression technique to impute genotypes from low density single nucleotide polymorphisms (SNP) panels i.e. 3K or 7K to a high density panel with 50K SNP. No pedigree information was used. Methods Data consisted of 2093 Holstein, 749 Brown Swiss and 479 Simmental bulls genotyped with the Illumina 50K Beadchip. First, a single-breed approach was applied by using only data from Holstein animals. Then, to enlarge the training population, data from the three breeds were combined and a multi-breed analysis was performed. Accuracies of genotypes imputed using the partial least squares regression method were compared with those obtained by using the Beagle software. The impact of genotype imputation on breeding value prediction was evaluated for milk yield, fat content and protein content. Results In the single-breed approach, the accuracy of imputation using partial least squares regression was around 90 and 94% for the 3K and 7K platforms, respectively; corresponding accuracies obtained with Beagle were around 85% and 90%. Moreover, computing time required by the partial least squares regression method was on average around 10 times lower than computing time required by Beagle. Using the partial least squares regression method in the multi-breed resulted in lower imputation accuracies than using single-breed data. The impact of the SNP-genotype imputation on the accuracy of direct genomic breeding values was small. The correlation between estimates of genetic merit obtained by using imputed versus actual genotypes was around 0.96 for the 7K chip. Conclusions Results of the present work suggested that the partial least squares regression imputation method could be useful to impute SNP genotypes when pedigree information is not available

A criterion for measuring the degree of connectedness in linear models of genetic evaluation

International audienc

Approximate restricted maximum likelihood and approximate prediction error variance of the Mendelian sampling effect

In an Expectation-Maximization type Restricted Maximum Likelihood (REML) procedure, the estimation of a genetic (co-)variance component involves the trace of the product of the inverse of the coefficient matrix by the inverse of the relationship matrix. Computation of this trace is usually the limiting factor of this procedure. In this paper, a method is presented to approximate this trace in the case of an animal model, by using an equivalent model based on the Mendelian sampling effect and by simplifying its coefficient matrix and its inversion. This approximation appeared very accurate for low heritabilities but was downwards biased when the heritability was high. Implemented in a REML procedure, this approximation reduced dramatically the amount of computation, but provided downwards biased estimates of genetic variances. Several examples are presented to illustrate the method.Dans certaines procédures de Maximum de Vraisemblance Restreint (REML), l’estimation des composantes de (co)variance génétique implique le calcul de la trace du produit de l’inverse de la matrice des coefficients par l’inverse de la matrice de parentés, calcul qui constitue généralement le facteur limitant de ce type de procédure. Nous présentons dans cet article une méthode visant à obtenir une valeur approchée de cette trace dans le cadre d’un modèle animal, en utilisant un modèle équivalent basé sur l’aléa de méiose, en simplifiant sa matrice des coefficients et en en calculant une inverse approchée. Cette approximation est très précise lorsque l’héritabilité du caractère est faible mais elle tend à sous-estimer la trace vraie lorsque l’héritabilité est élevée. Intégrée dans une procédure de REML, cette méthode en réduit considérablement le coût mais fournit en général des valeurs sous-estimées de variance génétique. Divers exemples sont présentés à titre d'illustratio

A simulation study of the effect of connectedness on genetic trend

International audienc