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Additional file 1: Text S1. Sampling. Details on the sampling procedure. Text S2. SNP calling. Details on the SNP calling procedure. Text S3. Local score approach. Details on the local score approach. Text S4. Identification of causal variants under selection. Details on the approach used to detect causal variants, and on the results obtained with this approach. Text S5. Private alleles. Details on the results obtained regarding private alleles. Text S6. Biological functions under selection. Details on the results obtained regarding the biological functions under selection. Text S7. Insight from a previous gene expression study. Details on the approach used to explore gene expression in candidate regions, and on the results obtained with this approach. Text S8. Molecular mechanisms mediating selection. Discussion on the most likely molecular mechanism (regulatory or protein-coding) mediating selection in each the 10 div regions. Text S9. Candidate genes under selection. Details on several interesting candidate genes under selection. Figure S6. Distribution of p values obtained by the time-LWD and time-LWS tests. High-quality SNPs (HQSNP set) with a MAF greater than 0.1 were considered. Figure S7. Correlation of expression levels in the muscle measured in [40] for 76 probes corresponding to 48 genes found under selection in our study. Correlation between two probes was computed based on normalized expression for Large White sire (n = 10) and dam lines (n = 41) at these two probes. The ordering of probes on the graph follows from a hierarchical clustering of the correlation matrix. Only significant correlations (p value < 0.05) are shown. Figure S8. Boxplot of muscle expression levels in LWD and LWS for the 12 genes found differentially to be expressed between the two lines. All genes are differentially regulated between the two lines with a p value < 0.05. None of these genes have an adjusted p value < 0.05. Additional File 1 cites the following references: 2, 24, 25, 40, 48–61.

Additional file 2: Figure S1. MDS analysis of the genetic diversity observed in French LW pigs. High-quality SNPs (HQSNP set) with a MAF greater than 0.1 were considered. Figure S2. Summary of the genome-wide scan for recent selection in French LW pigs, chromosomes 1 to 9. Each panel of the figure shows the results of the three tests for selection for a different autosome. P values of the time-LWD (blue), time-LWS (green) and hapFLK (red) tests are plotted in log10 scale (y axis) according to genomic position (x axis). Opposite signs are used for time-LWD and time-LWS on the one hand and hapFLK on the other hand, i.e. low p values correspond to highly negative points for time-LWD and time-LWS and to highly positive points for hapFLK. Significant p values are shown in darker color for the three tests. For time-LWD and time-LWS, SNPs ranked as significant by the local score approach (see “Methods” for further details) may be hidden by non significant SNPs with higher individual p values, so significant regions for these tests were also highlighted by small triangle marks below the p value plot. Figure S3. Summary of the genome-wide scan for recent selection in French LW pigs, chromosomes 10 to 18. See Figure S2 for details. Figure S4. Top 20 significant GO terms for Biological Processes and association between these terms and groups of selection signature categories. See Fig. 5 for details. Figure S5. Top 20 significant KEGG terms and association between these terms and groups of selection signature categories. See Fig. 5 for details. Table S1. Determination of candidate genes in several selection signatures. This table reports all the genes that (i) can be considered as good candidate genes within their region (based on criteria described below) and (ii) are associated to one of the enriched biological functions listed in Table 3 (or more generally whose biological function is consistent with selection objectives in LW). For a given region of interest, genes that represent even better candidates than those selected based on condition (ii) are also included. Column ‘Nb. genes’ gives the number of genes in a region, and column ‘Gene ID’ lists the candidate genes. Column ‘Local peak’ indicates whether the most relevant test statistic for the region shows a local peak within or close to the gene (see Additional file 4 for summary plots of the four test statistics in all candidate regions); this column takes value NA when no or a single gene is observed in a region. Column ‘Diff. expr.’ indicates whether the gene was found differentially expressed between LWS and LWD in [40]; this column takes value ‘no data’ for genes that were not included in this study. Column ‘Causal variant’ indicates whether a causal variant under selection is known for this gene, or if a small number of potential candidate variants have been identified in our study (these candidates may be functional or not, see the text for more details). Based on all these observations, column ‘Overall evidence’ quantifies the evidence for each candidate gene to be the one under selection in a given region.

Additional file 1: Table S1. Composition (%) of the conventional (CO) and the high fiber (HF) diets. Description of the ingredient composition for pigs fed a conventional and a high-fiber diet. Table S2. Variance components for random effects included in the model with the microbial covariance matrix with 14,366 OTU (Scenario 1), 2399 OTU (Scenario 2) and 803 OTU (Scenario 3) using a Bayesian approach for feed and digestive efficiency traits in growing pigs fed a conventional (CO) diet or a high fiber (HF) diet, along with their posterior standard deviation. Table S3. Variance components for random effects included in the model with microbiota (model Micro), genetics (model Gen) and microbiota and genetics jointly (model Micro+Gen) using a Bayesian approach for feed and digestive efficiency traits in growing pigs fed a conventional (CO) diet or a high fiber (HF) diet, along with their posterior standard deviation. Table S4. Rank correlations of estimated microbiota values (EMV) between the model with only microbiota and the model with genetic and microbiota for feed and digestive efficiency traits records in the conventional diet and in the high fiber diet, along with their 95% confidence intervals (CI). Rank correlations of estimated microbiota values were compared between two models, with and without genomic information in two diets.

Additional file 2: Script S1. Script to implement the genetic-by-diet interaction model—example for ADG. Detailed script for the example of the G × D interaction for ADG.

Recently, digestive efficiency (DE) was proposed as a trait of interest to improve feed efficiency (FE) in pigs, especially when they are fed with alternative feeding resources. Both are influenced by the host genetics, and also by the gut microbiota composition. The goal of this study was to quantify the impact of faecal microbial information on the prediction accuracies of genomic estimated breeding values (GEBVs) of FE and DE traits for pigs fed conventional or fiber diets. For DE traits, gains in prediction accuracy of GEBVs were increased by about 18% when microbial information was included in linear mixed models. In addition, these gains of prediction accuracy were very similar in both diets. For FE traits, no improvement was observed. Thus, the addition of microbial information in breeding programs is promising to better estimate GEBVs for DE traits.

Classical methods as genomic BLUP performs well for genomic prediction of polygenic trait, but does not consider interaction between genes or between genes and other information such as host genetic or microbial data. This study aims at comparing several methods including parametric and machine learning methods to predict digestive coefficient using genomic, microbial and both genomic and microbial information. Considering only microbial data led to the best prediction accuracies for digestive coefficients, whereas considering only genomic data performed worst. BLUP, RKHS and GSVM gave the best prediction accuracies except when combined genomic and microbial data was used. Combining microbial and genomic data did not improve prediction accuracies for all traits and methods considered in this study. Thus, considering microbial information is crucial to predict digestive efficiency and interactions between host genetic and faecal microbial information seem to be limited.

International audience; Breeding pigs that can efficiently digest alternative diets with increased fibre content is a viable strategy to mitigate the feed cost volatility in pig production. This study aimed at determining the relative contribution of microbiome and genetics to the phenotypic variability of three digestibility coefficients (DC). Faecal samples were collected at sixteen weeks of age to sequence the V3-V4 regions of the 16S RNA gene and to predict DC with near-infrared spectrometry for energy, organic matter, and nitrogen. Data were available for 1,564 Large White pigs, and 14,366 identified Operational Taxonomic Units (OTUs) were identified. The proportions of phenotypic variance explained by the microbiome, or ‘microbiability’, and by additive genetic effects were first estimated independently and then jointly using a Bayesian approach. The microbiability was estimated under three scenarios: without filtering the OTUs, by filtering the OTUs presented in more than five samples and with an average abundance higher than 0.001% (2,399 OTUs) or greater than 0.01% (803 OTUs). Estimates of microbiability for the traits were reduced with increasingly stringent filtering, from 54±2 to 66±2% in the first scenario and from 25±1 to 35±1% for 803 OTUs. With intermediate filtering, the estimated microbiability ranged from 37±1 to 46±2% and was higher than heritability (from 26±1 to 29±1%) when estimated independently from genetics effects. The joint estimation of microbiability and heritability will point out how the two random effects overlap in explaining the trait variability. To conclude, our results show that the filtering of OTUs is important and influences microbiability. The microbiome explained a significant proportion of the phenotypic variance of digestive efficiency, which was even larger than heritability estimates.

International audience

International audience; This study compared the fecal microbiota of growing pigs fed a conventional diet (CO) or a diet with increased fiber content (F). For each animal, fecal microbiota were collected at sixteen weeks of age to sequence the V3-V4 region of the 16S RNA gene. Microbiota composition data were available for 752 Large White pigs fed the F diet and 812 full-siblings fed the CO diet, with a total of 14,366 identified Operational Taxonomic Units (OTUs) and 231 genera. Pigs fed the F diet had higher microbial diversity than pigs fed the CO diet according to the Shannon diversity index (P < 0.001). The genera Lactobacillus and Streptococcus, which belong to the Firmicutes phylum, were predominantly represented in the CO diet. Lactobacillus and Prevotella 9, which belong to the Firmicutes and Bacteroidetes phyla, respectively, were the genera predominantly represented in pigs fed the F diet. Differences in microbiota composition between diets were confirmed by an Adonis test (P < 0.001). The genera Ruminococcus 1, Lachnospira and Lachnoclostridium 12 discriminated most the individuals fed the two diets. Characterizing the diversity of the microbiota within and between the two diets is an important first step before assessing the role of gut microbiota on the variability in feed efficiency traits in various dietary contexts.

International audience; This study compared the fecal microbiota of growing pigs fed a conventional diet (CO) or a diet with increased fiber content (F). For each animal, fecal microbiota were collected at sixteen weeks of age to sequence the V3-V4 region of the 16S RNA gene. Microbiota composition data were available for 752 Large White pigs fed the F diet and 812 full-siblings fed the CO diet, with a total of 14,366 identified Operational Taxonomic Units (OTUs) and 231 genera. Pigs fed the F diet had higher microbial diversity than pigs fed the CO diet according to the Shannon diversity index (P < 0.001). The genera Lactobacillus and Streptococcus, which belong to the Firmicutes phylum, were predominantly represented in the CO diet. Lactobacillus and Prevotella 9, which belong to the Firmicutes and Bacteroidetes phyla, respectively, were the genera predominantly represented in pigs fed the F diet. Differences in microbiota composition between diets were confirmed by an Adonis test (P < 0.001). The genera Ruminococcus 1, Lachnospira and Lachnoclostridium 12 discriminated most the individuals fed the two diets. Characterizing the diversity of the microbiota within and between the two diets is an important first step before assessing the role of gut microbiota on the variability in feed efficiency traits in various dietary contexts.

International audience; The use of early biomarkers is an interesting approach to improve selection efficiency for traits that are difficult to measure. The aim of this study was to validate juvenile IGF1 and leptin as biomarkers of growth and feed efficiency in Large White (LW), Duroc and Piétrain purebred lines. To determine plasma concentrations of IGF1 and leptin, blood samples were obtained from 8-9-week-old piglets at the end of the post-weaning phase. After data curation, 337 LW, 192 Duroc and 189 Piétrain pigs had both plasma concentrations and production performance recorded during the growing-finishing phase, namely average daily gain (ADG), daily feed intake (DFI) and feed conversion ratio (FCR). Nested linear mixed models including successively the biomarker concentration and the weight at bleeding (BBW) as covariables in addition to the usual effects were compared using a likelihood ratio test. In the three lines, adding IGF1 concentration in prediction models of growth and feed efficiency traits increased the goodness-of-fit, with the covariable being significant (P < 0.001), except for FCR in the Piétrain line. Augmenting this model with BBW increased the model likelihood in the LW and Duroc breeds for any traits, as BBW was highly significant (P < 0.001) although IGF1 was not significant anymore. These results suggested that IGF1 is a biomarker of growth and feed efficiency that captured the same information as BBW. In the Piétrain line, IGF1 contributed to increase the likelihood of the model along with BBW for ADG and DFI. Hence, IGF1 seems to be a relevant biomarker for the Piétrain breed but to have limited utility for the LW and Duroc breeds if pigs can be weighed in the post-weaning phase. The same approach was applied to juvenile leptin, but it was found not to be a useful biomarker of growth or feed efficiency for the three lines.

National audience; Genomic evaluation of French maternal lines, set up in 2016, has helped increase genetic progress, especially for reproductive traits. However, computational problems have emerged for genomic evaluation of certain production traits for which phenotyping capacity is limited. This particularly concerns genotyped candidates on breeding farms that have no phenotypes and only a few phenotyped relatives. This data structure seems to pose convergence problems for predicting genomic breeding values. To check this hypothesis, we simulated such a situation, based on a set of actual phenotype data measured for all farm candidates and genotypes. The simulation consisted of deleting phenotypes of the animals measured on-farm in order to reproduce the data structure encountered for the traits recorded at the FGPorc/INRAE test station in Le Rheu. Phenotypes were then progressively added in different scenarios to identify whether prediction accuracy improved and to estimate the number of phenotypes required. The simulations showed that the unbalanced structure between genotypes and phenotypes was responsible for the computational problems that led to low accuracy of genomic predictions. Phenotyping 12% of all pigs phenotyped at 100 kg each year made it possible to solve the computational problems observed and to recover 61% of the maximum expected accuracy. In conclusion, these results highlight the importance of collecting large-scale phenotypes in the context of genomic selection schemes. Further studies will be conducted to study the impact of genotyping animals measured at the station.

National audience; Measuring feed intake is a key for accurate selection of feed efficiency. However, recording feed efficiency is expensive and depends on multiple components, including production level, activity, behaviour, responses to stress and digestion. The relative importance of these components is partly driven by the production system, especially diet and housing. This paper summarizes results obtained in the Feed-a-Gene project for new measures for feed efficiency in pigs, with a focus on how they can be used for sustainable breeding schemes. Measures related to pig activity (including feeding behaviour), measures to quantify welfare (injuries and inter-individual interactions) and stress level, and measures of individual digestibility were evaluated. The genetic impact of other animals in the pen (indirect genetic effects, IGE) on individual performance was also considered as a potential approach to better estimate individual breeding values. Some of the most promising measures (feeding activity, pig interactions, digestibility, IGE) had moderate to high heritabilities (from 0.18-0.70). Gains in genetic progress achievable when including these measures in breeding schemes were evaluated analytically to identify the best procedures to implement on-farm; Les mesures de l’ingéré sont les clés d’une sélection précise de l’efficacité alimentaire. Elles sont cependant coûteuses. De plus, ces performances dépendent de nombreuses composantes, telles que le niveau de production, l’activité et le comportement des animaux, leur aptitude à répondre à des stress, et leur capacité à digérer l’aliment. L’importance relative de ces composantes dépend du système de production, en particulier de la séquence alimentaire et des conditions de logement des animaux. Cette communication propose de résumer les résultats obtenus dans le projet Feed-a-Gene sur l’évaluation de nouvelles mesures pour la sélection de l’efficacité alimentaire, en se concentrant sur les mesures les moins coûteuses qui peuvent contribuer à une plus grande durabilité de la production. Des mesures liées à l’activité des porcs, dont le comportement alimentaire, des indicateurs de bien-être (comptage des lésions et qualification des interactions entre animaux) et de stress, et des mesures d’efficacité digestive ont été évaluées. L’impact de l’effet génétique des autres individus de la loge (effets génétiques indirects) sur les performances individuelles a aussi été testé comme levier pour obtenir des valeurs génétiques plus précises. Les mesures les plus prometteuses (comportement alimentaire, interactions entre porcs, efficacité digestive, effets génétiques indirects) avaient des héritabilités comprises entre 0,18 et 0,70. Les gains de progrès génétique possibles par l’utilisation de telles mesures dans les schémas de sélection ont été évalués analytiquement pour identifier les meilleurs enregistrements à déployer en ferme

International audience; Feeding diets with increased dietary fibre content from alternative by-products, which are less digestible for pigs, is a solution usedto decrease the impact of increased feed costs on pig production. This study aimed to determine the impact of an alternative diet with increased fibre content (F) on pig digestibility and to estimate genetic parameters of digestibility. A total of 654 Large White pigs were fed a conventional diet (CO), and 588 of their full-sibs were fed a F diet. For each pig, digestibility coefficients (DC) of energy, organic matter, and nitrogen were predicted from faeces samples analysed with near infrared spectrometry. Individual daily feed intake (DFI), residual feed intake (RFI), average daily gain (ADG), feed conversion ratio (FCR), lean meat percentage (LMP), carcass yield (CY) and meat quality traits (MQT) were recorded for each pig. The three DC were significantly lower for pigs fed the F diet than pigs fed the CO diet (-6 to -7%). The three DC were moderately to highly heritable (from 0.38±0.12 to 0.40±0.12 and from0.54±0.15 to 0.56±0.15 for the CO and the F diet, respectively). The three DC were favourably correlated with FCR, DFI and RFI, but unfavourably with ADG and MQT. To conclude, digestibility could be a useful trait to include in breeding schemes to improve selection decisions about feed efficiency. However, it would have to be selected along with ADG and MQT to avoid adverse genetic trends on the latter traits.; L’utilisation de régimes avec des teneurs accrues en fibres provenant d’aliments alternatifs, moins digestibles pour les porcs, est une solution utilisée pour limiter l’impact du coût de l’aliment en production porcine. L’étude a pour objectif de déterminer l’impact d’une teneur accrue en fibres (F) dans l’aliment sur la digestibilité chezle porc en croissanceet d’estimer les paramètres génétiques de celle-ci. Un total de 654 porcs Large White a été nourri avec un régime conventionnel (CO) et 588 de leurs pleins-frères avec un régime F. Pour chaque porc, les coefficients d’utilisation digestive (CUD) de l’énergie, de la matière organique et de l’azote ont étéprédits à partir d’échantillons de fèces analysés par spectrométrie dans le proche infrarouge. La consommation moyenne journalière (CMJ) et résiduelle (CMJR), le gain moyen quotidien (GMQ), l’indice de consommation (IC), le taux de muscle des pièces (TMP), le rendement en carcasse (RDT) et des caractères de qualité de la viande (CQV) ont été enregistrés sur chaque animal. Les trois CUD étaient significativement plus faibles pour les porcs nourris avec le régime F que les porcs nourris avec le régime CO (-6 à -7 %). Les trois CUD étaient modérément à fortement héritables (de 0,38±0,12 à 0,40±0,12 et de 0,54±0,15 à 0,56±0,15, respectivement, dans les régimes CO et F). Les trois CUD étaient favorablement corrélés à l’IC, la CMJ et la CMJR, mais défavorablement corrélés avec le GMQ et les CQV. Pour conclure, la digestibilité serait un caractère intéressant à ajouter dans les schémas de sélection afin d’affiner les décisions de sélection concernant l’efficacité alimentaire. Cependant, les corrélations génétiques défavorables avec le GMQ et les CQV notamment devront être prises en compte.

National audience; To reduce genotyping costs for genomic selection, a Low-Density SNP (LD) chip, designed in 2016, is now used routinely. This panel is composed of approximately 1100 equidistant SNPs. The relevance of this chip has been studied in French populations of the Landrace, Large White and Pietrainpig breeds. The quality of imputation was estimated by the correlation between actual and imputed genotypes and error rates. The impact of imputation on the genomic evaluations was estimatedby the correlation between the genomic values obtained for the candidates with imputed genotypes, and those obtained with the high-densitygenotypes. Average error rates of imputation estimated on all the chromosomes were 0.03, 0.11 and 0.14for Landrace, Large White and Pietrain, respectively. The estimated correlations between actual and imputed genotypes were relativelyhigh at 0.93, 0.92 and 0.88forLandrace, Large White and Pietrain populations,respectively. Correlations between genomicbreedingvalues predicted with high-density genomicdata or imputed genomic data from the LD SNP panel rangedfrom0.89-0.97 for Large White and Landrace populations for reproductivetraits. They were higher than those obtained for the Pietrain population (0.80and 0.97for production traits, respectively). In conclusion, despite the limited number of SNPs on the low-density panel used in this study, the imputation accuracyis sufficient to use the imputed genotypes in the genomic evaluations. In practice, genotyping candidates with aLD chip is a solution for selecting future breeding pigsat lower cost.

To reduce genotyping costs for genomic selection, a Low-Density SNP (LD) chip, designed in 2016, is now used routinely. This panel is composed of approximately 1 100 equidistant SNPs. The relevance of this chip has been studied in French populations of the Landrace, Large White and Pietrain pig breeds. The quality of imputation was estimated by the correlation between actual and imputed genotypes and error rates. The impact of imputation on the genomic evaluations was estimated by the correlation between the genomic values obtained for the candidates with imputed genotypes, and those obtained with the high-density genotypes. Average error rates of imputation estimated on all the chromosomes were 0.03, 0.11 and 0.14 for Landrace, Large White and Pietrain, respectively. The estimated correlations between actual and imputed genotypes were relatively high at 0.93, 0.92 and 0.88 forLandrace, Large White and Pietrain populations, respectively. Correlations between genomic breeding values predicted with high-density genomic data or imputed genomic data from the LD SNP panel ranged from 0.89-0.97 for Large White and Landrace populations for reproductive traits. They were higher than those obtained for the Pietrain population (0.80 and 0.97 for production traits, r espectively). In conclusion, despite the limited number of SNPs on the low-density panel used in this study, the imputation accuracy is sufficient to use the imputed genotypes in the genomic evaluations. In practice, genotyping candidates with a LD chip isa solution for selecting future breeding pigs at lower cost

International audience

National audience

International audience

La sélection génomique est un nouvel outil permettant d’augmenter la précision du choix des reproducteurs porcins par la prise en compte de l’information de leur génome dans l’évaluation génétique. Une population de référence de 1348 reproducteurs génotypés sur puces SNP haute - densité (panels Illumina 60K etGeneSeek Genomic Profiler Porcine HD) a été constituée dans la population Collective Landrace Français. A partir de ces données, une étude de validation a permis de mettre en évidence des gains de précision substantiels dans le choix des reproducteurs à l’issue du contrôle en ferme par rapport à l’évaluation génétique conventionnelle de type BLUP Modèle Animal. Les gains de fiabilité des valeurs génétiques, de l’ordre de 30% à 50%, ont été estimés pour des critères de reproduction clés comme le nombre de porcelets nés vivants, le nombre de porcelets sevrés ou le poids moyen des porcelets à la naissance. En effet, sur ces critères, aucune performance propre n’est disponible pour les candidats au moment de la sélection. L’information génomique se révèle donc être une information importante pour identifier les meilleurs reproducteurs. Sur la base de ces résultats, la sélection génomique a été déployée dans le schéma de sélection Landrace en 2016. Chaque semaine, une évaluation génomique combinant performances, généalogies et génotypages est réalisée. Les candidats à la sélection sont d’abord triés sur valeur génétique conventionnelle avant d’être génotypés pour choisir sur valeur génomique les reproducteurs à conserver pour le noyau de sélection.

National audience; L’amélioration de l’efficacité alimentaire des porcs est un objectif de sélection prioritaire car l’alimentation représente environ deux-tiers du coût de production (66% en 2014 selon IFIP, 2015). Le travail de sélection est actuellement réalisé sur la base d’un aliment riche en énergie et facilement digestible, alors que les aliments utilisés en élevage sont généralement moins digestibles, du fait de l’utilisation croissante de coproduits. Une étude conduite à l'Inra (Saint-Gilles) en 2012 suggère un déterminisme génétique de l’aptitude à digérer les nutriments et l’énergie d’un régime fibreux ouvrant la possibilité de sélectionner spécifiquement ce caractère afin d’améliorer l’efficacité alimentaire dans ce nouveau contexte (Noblet et al., 2013). Cependant, l’absence de méthodes rapides pour caractériser la capacité digestive des animaux ne permet pas de développer des stratégies de sélection adaptées à ces évolutions du contexte alimentaire de la production porcine. L’objectif de ce projet est donc de mettre au point une méthode de phénotypage à haut-débit de l’efficacité digestive des porcs s’appuyant sur la spectrométrie dans le proche infrarouge (SPIR). Cette méthode doit permettre, à terme, d’évaluer la variabilité de l’efficacité digestive entre animaux en élevage, et de proposer des stratégies de sélection adaptées à ce nouveau contexte de ressources alimentaires. Pour cela, nous envisageons d’évaluer, d’une part, la méthode de prétraitement des échantillons avant analyse (lyophilisation, séchage en étuve ou pas de prétraitement), et d’autre part, la fiabilité d’un marqueur indigestible mesurable par SPIR qui permettrait de s’affranchir de la collecte totale des fèces, au profit d’une collecte ponctuelle. Deux régimes différant par leur teneur en fibres (4 ou 8 % de cellulose brute) ont été formulés afin de créer une variabilité d’utilisation digestive. Les marqueurs utilisés sont le polyéthylène glycol (PEG, VWR), l’huile de silicone (Thermo Ficher Scientific), et une résine plastique (polyvinyle de fluorure, Arkema SA) qui sont mesurables par la SPIR. Au total, 48 porcs ont été placés en cages à digestibilité en 2 lots de 40 kg et 70 kg de poids vif. Chaque porc a reçu l’un des 4 régimes avec 2 semaines d’adaptation et 1 semaine de mesure précise de la quantité d’aliment ingéré et de collecte des fèces. Les fèces de la collecte totale et ponctuelle partielle ont été aliquotées séparément pour des analyses par SPIR sur produit frais et sur produit lyophilisé et pour des analyses de laboratoire sur produit lyophilisé (azote, matière organique, matière grasse, fibres, et énergie). Les premiers résultats montrent que les marqueurs choisis ne modifient ni la digestibilité de la matière sèche ni la production de fèces de l’animal.

Accuracy of genomic selection to improve litter traits in the French Landrace pig population. 67. Annual Meeting of the European Federation of Animal Science (EAAP)

National audience

Released in the 1980's and in the early 1990's, the statistical models used for genetic evaluations of beef cattle breeding stocks with on-farm performance recording (IBOVAL) and in central and progeny test stations rely on simple hypotheses which induce biases in the prediction of breeding values. These biases can imply a non optimal choice of breeding stocks and therefore reduce the short-term selection efficiency. They can also reduce long-term selection efficiency because they tend to designate breeding stocks to select among well-known parental lineages. Genetic diversity of the three main French beef cattle populations (Blonde d'Aquitaine, Charolais and Limousin) was also described using pedigree analyses. This study showed that genetic diversity is still large in these populations and sufficient to guarantee genetic gain in the next generations. The work achieved in this thesis also contributes to the improvement of beef cattle selection schemes by proposing new statistical models to eliminate some biases in beef cattle genetic evaluations., Mis en place dans les années 1980 et au début des années 1990, les modèles d'évaluation génétique des reproducteurs bovins allaitants en ferme (IBOVAL) et en stations de contrôle individuel (CI) et sur descendance (CD) reposent sur des hypothèses simples dont le non respect est à l'origine de biais dans la prédiction des valeurs génétiques. Ces biais peuvent induire un choix non optimal des reproducteurs et réduire ainsi l'efficacité de la sélection à court terme, mais aussi à plus long terme en privilégiant les reproducteurs issus de lignées familiales bien connues. Le travail présenté dans cette thèse contribue à l'amélioration des programmes de sélection allaitants en proposant de nouveaux modèles statistiques pour quantifier et éliminer certaines sources de biais des évaluations génétiques des reproducteurs bovins allaitants. Tout d'abord, un bilan détaillé de la diversité génétique des trois principales populations bovines allaitantes françaises a été réalisé à l'aide d'analyses de pedigree des animaux des bases de sélection Blonde d'Aquitaine, Charolaise et Limousine. Cette étude a montré que la diversité génétique est encore importante dans ces populations et largement suffisante pour garantir des marges de progrès génétique dans les générations futures. Elle a également permis de caractériser les populations d'animaux nés dans les élevages sélectionneurs, vendeurs de taureaux de monte naturelle ou approvisionnant en mâles les programmes de sélection des taureaux d'insémination artificielle (IA). Cette caractérisation a jeté les bases pour proposer de nouveaux modèles d'évaluation génétique des reproducteurs de bovins allaitants. Tout d'abord, le modèle IBOVAL actuel ne prend pas en compte correctement les différences de longueurs de pedigree existant dans les populations bovines allaitantes. En effet, il suppose que les parents inconnus proviennent d'une unique population de fondateurs génétiquement homogènes et défavorise ainsi les animaux aux généalogies les plus courtes en fixant leur valeur génétique prédite sur ascendance au niveau génétique de l'ensemble des fondateurs de la race. L'introduction de groupes de parents inconnus (GPI) dans le modèle d'évaluation permet de mieux intégrer les différences de niveau génétique existant dans la population des fondateurs. Une méthode a été élaborée et validée en race Charolaise pour définir des GPI robustes et homogènes d'après les flux de reproducteurs observés au sein de la population évaluée sur les performances au sevrage des veaux. L'inclusion de GPI dans le modèle IBOVAL s'avère utile pour améliorer l'efficacité à court terme de la sélection en race Charolaise, principalement sur la voie femelle. Elle contribue à améliorer sensiblement le choix des vaches pour le renouvellement des troupeaux ayant un taux élevé d'animaux nés de parents inconnus. En revanche, cela impacte peu le choix des reproducteurs mâles destinés à une large diffusion par IA parce que ces mâles sont essentiellement issus d'élevages spécialisés dans la vente de reproducteurs où les généalogies des animaux sont bien connues sur plusieurs générations. Les programmes de sélection des taureaux d'IA s'appuient sur une succession de trois étapes séquentielles d'évaluation et sélection, d'abord en ferme puis en stations de CI et de CD. A chaque étape, une évaluation génétique est réalisée à l'aide d'un modèle unicaractère ignorant les données qui ont servi à la sélection aux étapes précédentes. Un modèle multicaractère, combinant performances enregistrées en stations et performances homologues contrôlées en ferme dans les élevages approvisionnant le schéma IA, a été proposé pour éliminer les biais dus à la sélection des mâles entrant en stations. L'utilisation de ce modèle permet d'augmenter l'efficacité de la sélection par une meilleure précision des index, mais surtout par l'élimination des,biais dans la prédiction des valeurs génétiques en stations de CI et de CD et l'amélioration de la connexion entre séries évaluées au cours du temps. Si les reclassements des reproducteurs évalués en stations sont limités intra-série, ils peuvent être en revanche très sensibles entre séries évaluées en race Blonde d'Aquitaine ou Limousine, modulant l'utilisation des reproducteurs sélectionnés intra-série et donc la diffusion à large échelle de leur semence.

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Chantier qualité GA; In genetic evaluations, the definition of unknown parent groups (UPG) is usually based on time periods, selection path and flows of foreign founders. The definition of UPG may be more complex for populations presenting genetic heterogeneity due to both, large national expansion and coexistence of artificial insemination (AI) and natural service (NS). A UPG definition method accounting for beef bull flows was proposed and applied to the French Charolais cattle population. It assumed that, at a given time period, unknown parents belonged to the same UPG when their progeny were bred in herds that used bulls with similar origins (birth region and reproduction way). Thus, the birth period, region and AI rate of a herd were pointed out to be the three criteria reflecting genetic disparities at the national level in a beef cattle population. To deal with regional genetic disparities, 14 regions were identified using a factorial approach combining principal component analysis and Ward clustering. The selection nucleus of the French cattle population was dispersed over three main breeding areas. Flows of NS bulls were mainly carried out within each breeding area. On the contrary, the use and the selection of AI bulls were based on a national pool of candidates. Within a time period, herds of different regions were clustered together when they used bulls coming from the same origin and with an estimated difference of genetic level lower than 20% of genetic standard deviation (σg) for calf muscle and skeleton scores (SS) at weaning. This led to the definition of 16 UPG of sires, which were validated as robust and relevant in a sire model, meaning numerically stable and corresponding to distinct genetic subpopulations. The UPG genetic levels were estimated for muscle and SS under sire and animal models. Whatever the trait, differences between bull UPG estimates within a time period could reach 0.5 σg across regions. For a given time period, bull UPG estimates for muscle and SS were generally larger by 0.30 to 0.75 σg than those of cows. Including genetic groups in the evaluation model increased the estimated genetic trends by 20% to 30%. It also provoked re-ranking in favor of bulls and cows without pedigree.

Chantier qualité GA; 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 (Ne >244 and Ne >345 in the CHA and LIM subpopulations, respectively), relationship coefficients within subpopulations (