Genetic parameters for body weight and different definitions of residual feed intake in broiler chickens

International audienceBackground :The objectives of this study were to (1) simultaneously estimate genetic parameters for BW, feed intake (FI), and body weight gain (Gain) during a FI test in broiler chickens using multi-trait Bayesian analysis; (2) derive phe-notypic and genetic residual feed intake (RFI) and estimate genetic parameters of the resulting traits; and (3) compute a Bayesian measure of direct and correlated superiority of a group selected on phenotypic or genetic residual feed intake. A total of 56,649 male and female broiler chickens were measured at one of two ages ( t or t−6 days). BW, FI, and Gain of males and females at the two ages were considered as separate traits, resulting in a 12-trait model. Phenotypic RFI ( RFIP ) and genetic RFI ( RFIG ) were estimated from a conditional distribution of FI given BW and Gain using partial phenotypic and partial genetic regression coefficients, respectively.Results : Posterior means of heritability for BW, FI and Gain were moderately high and estimates were significantly different between males and females at the same age for all traits. In addition, the genetic correlations between male and female traits at the same age were significantly different from 1, which suggests a sex-by-genotype interaction. Genetic correlations between RFIP and RFIG were significantly different from 1 at an older age but not at a younger age.Conclusions :The results of the multivariate Bayesian analyses in this study showed that genetic evaluation for pro-duction and feed efficiency traits should take sex and age differences into account to increase accuracy of selection and genetic gain. Moreover, for communicating with stakeholders, it is easier to explain results from selection on RFIGthan selection on RFIP , since RFIG is genetically independent of production traits and it explains the efficiency of birds in nutrient utilization independently of energy requirements for production and maintenanc

Use of genomic information to exploit genotype-by-environment interactions for body weight of broiler chicken in bio-secure and production environments

International audienceAbstractBackgroundThe increase in accuracy of prediction by using genomic information has been well-documented. However, benefits of the use of genomic information and methodology for genetic evaluations are missing when genotype-by-environment interactions (G × E) exist between bio-secure breeding (B) environments and commercial production (C) environments. In this study, we explored (1) G × E interactions for broiler body weight (BW) at weeks 5 and 6, and (2) the benefits of using genomic information for prediction of BW traits when selection candidates were raised and tested in a B environment and close relatives were tested in a C environment.MethodsA pedigree-based best linear unbiased prediction (BLUP) multivariate model was used to estimate variance components and predict breeding values (EBV) of BW traits at weeks 5 and 6 measured in B and C environments. A single-step genomic BLUP (ssGBLUP) model that combined pedigree and genomic information was used to predict EBV. Cross-validations were based on correlation, mean difference and regression slope statistics for EBV that were estimated from full and reduced datasets. These statistics are indicators of population accuracy, bias and dispersion of prediction for EBV of traits measured in B and C environments. Validation animals were genotyped and non-genotyped birds in the B environment only.ResultsSeveral indications of G × E interactions due to environmental differences were found for BW traits including significant re-ranking, heterogeneous variances and different heritabilities for BW measured in environments B and C. The genetic correlations between BW traits measured in environments B and C ranged from 0.48 to 0.54. The use of combined pedigree and genomic information increased population accuracy of EBV, and reduced bias of EBV prediction for genotyped birds compared to the use of pedigree information only. A slight increase in accuracy of EBV was also observed for non-genotyped birds, but the bias of EBV prediction increased for non-genotyped birds.ConclusionsThe G × E interaction was strong for BW traits of broilers measured in environments B and C. The use of combined pedigree and genomic information increased population accuracy of EBV substantially for genotyped birds in the B environment compared to the use of pedigree information only

Novel genes upregulated when NOTCH signalling is disrupted during hypothalamic development.

International audienceBACKGROUND: The generation of diverse neuronal types and subtypes from multipotent progenitors during development is crucial for assembling functional neural circuits in the adult central nervous system. It is well known that the Notch signalling pathway through the inhibition of proneural genes is a key regulator of neurogenesis in the vertebrate central nervous system. However, the role of Notch during hypothalamus formation along with its downstream effectors remains poorly defined. RESULTS: Here, we have transiently blocked Notch activity in chick embryos and used global gene expression analysis to provide evidence that Notch signalling modulates the generation of neurons in the early developing hypothalamus by lateral inhibition. Most importantly, we have taken advantage of this model to identify novel targets of Notch signalling, such as Tagln3 and Chga, which were expressed in hypothalamic neuronal nuclei. CONCLUSIONS: These data give essential advances into the early generation of neurons in the hypothalamus. We demonstrate that inhibition of Notch signalling during early development of the hypothalamus enhances expression of several new markers. These genes must be considered as important new targets of the Notch/proneural network

Comparing the intestinal transcriptome of Meishan and Large White piglets during late fetal development reveals genes involved in glucose and lipid metabolism and immunity as valuable clues of intestinal maturity

Background: Maturity of intestinal functions is critical for neonatal health and survival, but comprehensive description of mechanisms underlying intestinal maturation that occur during late gestation still remain poorly characterized. The aim of this study was to investigate biological processes specifically involved in intestinal maturation by comparing fetal jejunal transcriptomes of two representative porcine breeds (Large White, LW; Meishan, MS) with contrasting neonatal vitality and maturity, at two key time points during late gestation (gestational days 90 and 110). MS and LW sows inseminated with mixed semen (from breed LW and MS) gave birth to both purebred and crossbred fetuses. We hypothesized that part of the differences in neonatal maturity between the two breeds results from distinct developmental profiles of the fetal intestine during late gestation. Reciprocal crossed fetuses were used to analyze the effect of parental genome. Transcriptomic data and 23 phenotypic variables known to be associated with maturity trait were integrated using multivariate analysis with expectation of identifying relevant genes-phenotypic variable relationships involved in intestinal maturation. Results: A moderate maternal genotype effect, but no paternal genotype effect, was observed on offspring intestinal maturation. Four hundred and four differentially expressed probes, corresponding to 274 differentially expressed genes (DEGs), more specifically involved in the maturation process were further studied. In day 110-MS fetuses, Ingenuity® functional enrichment analysis revealed that 46% of DEGs were involved in glucose and lipid metabolism, cell proliferation, vasculogenesis and hormone synthesis compared to day 90-MS fetuses. Expression of genes involved in immune pathways including phagocytosis, inflammation and defense processes was changed in day 110-LW compared to day 90-LW fetuses (corresponding to 13% of DEGs). The transcriptional regulator PPARGC1A was predicted to be an important regulator of differentially expressed genes in MS. Fetal blood fructose level, intestinal lactase activity and villous height were the best predicted phenotypic variables with probes mostly involved in lipid metabolism, carbohydrate metabolism and cellular movement biological pathways. [b]Conclusions[/b]: Collectively, our findings indicate that the neonatal maturity of pig intestine may rely on functional development of glucose and lipid metabolisms, immune phagocyte differentiation and inflammatory pathways. This process may partially be governed by PPARGC1A

Cartographie de QTL et évaluation génomique chez la poule pondeuse dans un contexte alimentaire changeant

In laying hens farming, candidates for selection are evaluated according to their Estimated Breeding Value (EBV), which is estimated, using a statistic model which considers all the available phenotype of their relative (BLUP). The recent development of a high density genotyping array (600K Affymetrix® Axiom® HD), could allow the development of genomic selection in this farming. The Genomic Estimated Breeding Value (GEBV) could be potentially more accurate than the EBV, available at the birth of the individual and for probably a larger number of candidates, increasing the rates of genetic progress. Besides, a same genetic type of laying hens is widely distributed around the world, so animals produce in various environments (alimentation, temperature, hygiene standards…). So, genotype – environment interactions could affect the estimation of breeding values of the candidates for selection. The first objective of this work is to determine of the impact of these interactions on a large panel of egg production and egg quality traits. Moreover, the consequences of genetic architecture of these traits on breeding value estimation have been studied. Thus, 438 pureline males have been genotyped, and the phenotype of their 31 381 crossbred daughters has been measured for egg production and egg quality traits. Half of the daughters were fed ad libitum with a low-energy diet (LE) (2,455 kcal) and the other half were fed ad libitum with a “high-energy diet (HE)” (2,881 kcal). With GWAS analyses done on mean and standard-deviation of performances of these hens, QTL in interaction with the diet and the age have been detected. For the study of mean, 45% of the QTL have a significant interaction with the diet and/or with the age, whereas, for the study of standard-deviation, 98% of the QTL have a significant interaction with the environment. So, it seems that the variance of traits is more sensitive to the genotype – environment interactions than the mean of traits. Thereafter, we have studied the impact of these interactions on genetic or genomic evaluation of the candidates for selection. Thus, roosters have been evaluated by 3 different methods (genotypic, genetic and genomic) for 3 traits which have different genetic architecture (number of QTL and number of QTL in interaction with diet). We showed that the genotype – regime interactions did not influence the rank of the candidates. Genetic or genomic correlation between traits measured for both diets were superior to 0.95 in all cases. We also showed that genomic evaluation could, for some case, give an evaluation more accurate for candidates. In conclusion, it seems that the environment influence the genetic architecture of traits, because QTL, which have a different effect depending on the environment, have been detected. Nevertheless, the genotype – regime interactions did not influence the rank of candidates for selection. So, a specific selection adapted to the diet is not necessary. The implementation of genomic selection in laying hens farming seems to be encouraging, because it could give more accurate breeding values, and at an earlier age, than the evaluation based on pedigree.Dans la filière « poule pondeuse », les candidats à la sélection sont actuellement évalués à partir de leur valeur génétique estimée (Estimated Breeding Value, EBV) en appliquant un modèle statistique prenant en compte l’ensemble des phénotypes disponibles sur leurs apparentés (BLUP). Le développement récent d’une puce de génotypage à haute densité (600K Affymetrix® Axiom® HD), permet d’envisager la mise en place d’une sélection génomique dans cette filière. La valeur génomique estimée (Genomic Estimated Breeding Value, GEBV) serait potentiellement plus précise que l’EBV, disponible dès la naissance de l’individu et potentiellement pour un plus grand nombre de candidats, engendrant ainsi un gain de progrès génétique. Par ailleurs, un même type génétique de poule pondeuse étant largement diffusé à travers le monde, les animaux produisent dans des environnements différents (alimentation, température, normes d’hygiène…). Des interactions génotype – environnement pourraient donc affecter l’estimation des valeurs génétiques des candidats à la sélection. L’objectif premier de ce travail est de préciser l’impact de celles-ci sur un panel large de caractères de production et de qualité des oeufs. De plus, les conséquences de l’architecture génétique des caractères sur l’estimation des valeurs génétiques ont été étudiées. Ainsi, 438 coqs de lignée pure ont été génotypés et les phénotypes de 31 381 de leurs filles croisées ont été mesurés pour des caractères de production et de qualité des oeufs. Une moitié de leur descendantes a été nourrie avec un régime bas en énergie métabolisable (2455 kcal) et l’autre moitié avec un régime haut en énergie métabolisable (2881 kcal). Par des analyses GWAS réalisées sur la moyenne et l’écart-type des performances de ces poules, nous avons pu mettre en évidence l’existence de QTL en interaction avec le régime et avec l’âge. Alors que pour la moyenne, 45% des QTL présentaient une interaction significative avec le régime alimentaire et/ou l’âge, 98% des QTL détectés dans l’étude sur l’écart-type présentaient une interaction significative avec l’environnement. Il semble donc que la variance des caractères soit plus sensible aux interactions génotype – environnement que la moyenne. Par la suite, nous avons étudié l’impact de ces interactions sur l’évaluation génétique ou génomique des candidats à la sélection. Pour cela, des coqs ont été évalués par 3 méthodes différentes (génotypique, génétique et génomique) pour 3 caractères d’architectures génétiques différentes (nombre de QTL et interactions avec le régime). Nous avons mis en évidence que les interactions génotype – régime n’influençaient pas le classement des candidats, les corrélations génétiques ou génomiques entre les caractères mesurés dans chacun des régimes étant supérieures à 0,95 dans tous les cas. Nous avons également montré que l’évaluation génomique permettrait, dans certains cas, une évaluation plus précise des candidats. En conclusion, il semble que l’environnement influence l’architecture génétique des caractères, car des QTL ayant des effets différents en fonction de l’environnement ont été identifiés. Néanmoins, les interactions génotype – régime mises en évidence n’impactent pas le classement des candidats à la sélection. La mise en place d’une sélection spécifique au régime ne semble donc pas nécessaire. L’implémentation de la sélection génomique dans la filière « poule pondeuse » semble prometteuse, car elle permettrait une estimation plus précise des valeurs génétiques, et cela plus précocement dans la vie des individus, que l’évaluation sur pedigree

Estimation and consequences of direct-maternal genetic and environmental covariances in models for genetic evaluation in broilers

Abstract Background Maternal effects influence juvenile traits such as body weight and early growth in broilers. Ignoring significant maternal effects leads to reduced accuracy and inflated predicted breeding values. Including genetic and environmental direct-maternal covariances into prediction models in broilers can increase the accuracy and limit inflation of predicted breeding values better than simply adding maternal effects to the model. To test this hypothesis, we applied a model accounting for direct-maternal genetic covariance and direct-maternal environmental covariance to estimate breeding values. Results This model, and simplified versions of it, were tested using simulated broiler populations and then was applied to a large broiler population for validation. The real population analyzed consisted of a commercial line of broilers, for which body weight at a common slaughter age was recorded for 41 selection rounds. The direct-maternal genetic covariance was negative whereas the direct-maternal environmental covariance was positive. Simulated populations were created to mimic the real population. The predictive ability of the models was assessed by cross-validation, where the validation birds were all from the last five selection rounds. Accuracy of prediction was defined as the correlation between the predicted breeding values estimated without the phenotypic records of the validation population and a predictor. The predictors were the breeding values estimated using all the phenotypic information and the phenotypes corrected for the fixed effects, and for the simulated data, the true breeding values. In the real data, adding the environmental covariance, with or without also adding the genetic covariance, increased the accuracy, or reduced deflation of breeding values compared with a model not including dam–offspring covariance. Nevertheless, in the simulated data, reduction in the inflation of breeding values was possible and was associated with a gain in accuracy of up to 6% compared with a model not including both forms of direct-maternal covariance. Conclusions In this paper, we propose a simple approach to estimate the environmental direct-maternal covariance using standard software for REML analysis. The genetic covariance between dam and offspring was negative whereas the corresponding environmental covariance was positive. Considering both covariances in models for genetic evaluation increased the accuracy of predicted breeding values

Correction to: Use of genomic information to exploit genotype-by-environment interactions for body weight of broiler chicken in bio-secure and production environments

After publication of this work [1], we noticed that there was an error: the formula to calculate the standard error of the estimated correlation.</p

Évaluation génomique chez la poule pondeuse en interaction avec le régime alimentaire

Dans le cadre du volet avicole du programme ANR UtOpIGe, une expérience de sélection a été conduite, dans le cadre d’une collaboration entre l’INRA, le SYSAAF et la société NOVOGEN. Elle s’appuie sur l’évaluation génomique des performances de ponte de poules soumises à deux régimes alimentaires différant pour la quantité d’énergie métabolisable (2881 kcal pour le régime HE, «Haute énergie », vs. 2455 kcal pour le régime BE, « Basse énergie »). Quatre cent trente-huit mâles de la lignée A, issus de trois cheptels successifs (2010-1, 2010-2 et 2011-1), ont été génotypés à l’aide de la puce Affimetrix ® Axiom® HD 600k SNP afin de constituer la population de référence. Les phénotypes utilisés étaient les performances de ponte en élevage de production de 31381 de leurs filles croisées AxD conduites en cages collectives de 12 poules (3 ou 4 cages par coq dans chaque régime alimentaire). Six cents jeunes mâles, issus des 3 cheptels suivants (2011-2, 2012-1 et 2012-2), ont également été génotypés. Ces 600 candidats à la sélection n’avaient aucune fille mesurée pour la ponte au moment de l’évaluation de leur valeur génétique. Plusieurs caractères ont été évalués, dont le poids des œufs à 70 semaines (EW) pour lequel nous présentons ici quelques résultats. L’évaluation génomique a été menée avec la suite logicielle de I. Mizstal (renumf90 et gibbs2f90) qui permet une évaluation multicaractère par l’approche Single Step. Le modèle d’analyse considère le poids des œufs des poules croisées comme une performance répétée de leur père. Il corrige également pour les effets liés au positionnement de la cage dans le bâtiment. L’utilisation du Gibbs Sampling permet d’estimer un écart-type d’erreur pour les valeurs génétiques, dont on peut déduire un CD. L’évaluation a été réalisée en utilisant, soit la matrice de parenté pedigree (EBV), soit la matrice de parenté génomique (GEBV), en mono caractère ou en bicaractère (EWHE, EWBE). La distribution des performances n’est pas significativement différente entre les 2 régimes (60.6 g +/- 4.8 g en BE vs. 61.0 g +/4.9 g en HE) mais le classement des pères change en fonction du régime (la corrélation de rang étant de 0.73 entre performances les moyennes par père), ce qui laisse entrevoir des interactions GxE. Cependant la corrélation génétique entre les poids des œufs dans les deux régimes est élevée ( =0.95 dans l’analyse classique et =0.98 dans l’analyse génomique) Par comparaison aux EBV, les GEBV présentent un CD moyen plus élevé pour les cheptels 2011-2, 2012-1 et 2012-2, ce qui démontre l’intérêt d’une évaluation génomique permettant un choix précoce des mâles. Par ailleurs, 60 mâles parmi les candidats ont maintenant des filles croisées (AxB) mesurées en début de ponte qui constituent une première population de validation. Concernant le poids des œufs, la prédiction de la valeur génétique des mâles est meilleure avec la GEBV qu’avec l’EBV calculée pour l’évaluation génétique de routine en lignée pure

GWAS analyses reveal QTL in egg layers that differ in response to diet differences

Background: The genetic architecture of egg production and egg quality traits, i.e. the quantitative trait loci (QTL) that influence these traits, is still poorly known. To date, 33 studies have focused on the detection of QTL for laying traits in chickens, but less than 10 genes have been identified. The availability of a high-density SNP (single nucleotide polymorphism) chicken array developed by Affymetrix, i.e. the 600K Affymetrix® Axiom® HD genotyping array offers the possibility to narrow down the localization of previously detected QTL and to detect new QTL. This high-density array is also anticipated to take research beyond the classical hypothesis of additivity of QTL effects or of QTL and environmental effects. The aim of our study was to search for QTL that influence laying traits using the 600K SNP chip and to investigate whether the effects of these QTL differed between diets and age at egg collection. Results: One hundred and thirty-one QTL were detected for 16 laying traits and were spread across all marked chromosomes, except chromosomes 16 and 25. The percentage of variance explained by a QTL varied from 2 to 10 % for the various traits, depending on diet and age at egg collection. Chromosomes 3, 9, 10 and Z were overrepresented, with more than eight QTL on each one. Among the 131 QTL, 60 had a significantly different effect, depending on diet or age at egg collection. For egg production traits, when the QTL × environment interaction was significant, numerous inversions of sign of the SNP effects were observed, whereas for egg quality traits, the QTL × environment interaction was mostly due to a difference of magnitude of the SNP effects. Conclusions: Our results show that numerous QTL influence egg production and egg quality traits and that the genomic regions, which are involved in shaping the ability of layer chickens to adapt to their environment for egg production, vary depending on the environmental conditions. The next question will be to address what the impact of these genotype × environment interactions is on selection