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The development of a commercial high density (HD) SNP chip of 600,000 SNPs enabled the implementation of genomic selection in layer and broiler. However, genotyping costs with this tool still remain high for a routine use on the selection candidates. Concurrently, new sequencing technologies (NGS) allow to consider other solutions than SNP chip for genomic selection. One of the main goal of genomic selection is to develop less expensive tools for genotyping or sequencing the selection candidates. Then, from the HD genotypes of a reference population, it is possible with different imputation methods to deduce the HD genotypes of the candidates. Firstly, the aim was to investigate the impact of different factors concerning the development of low density SNP chips or the constitution of the reference population on imputation accuracy. The impact of the use or not of imputation on genomic evaluation was also studied. The results showed that an equidistant methodology, for a SNP density higher than 5K, is suitable to get good imputation accuracy and good genomic evaluation accuracy. For an SNP density higher than 5K, it is also possible to use low density SNP chips without imputation. Low density genotypes were then replaced by genotypes from simulated RAD-Seq methods. Depending on the restriction enzyme used, studies have shown that RAD-Seq methods could be an interesting alternative to low density SNP chips.; Le développement d’une puce à SNP commerciale haute densité (HD) de 600 000 SNP en 2013 a permis la mise en place de la sélection génomique dans les filières poules de ponte et poulets de chair. Toutefois cet outil reste cher pour une utilisation en routine sur les candidats de la sélection. En parallèle, de nouvelles techniques de séquençage NGS permettent d’envisager des solutions autres que les puces à SNP pour la sélection génomique. Un des enjeux de la sélection génomique est donc de développer des outils de génotypage ou de séquençage des candidats à la sélection à moindre coût. À partir des génotypes HD d’une population de référence, il est alors possible avec des méthodes d’imputation de déduire les génotypes HD des candidats. Un premier travail a consisté à étudier l’impact de différents facteurs concernant le développement des puces à SNP basse densité (BD) ou la constitution de la population de référence sur l’efficacité de l’imputation. L’impact de l’utilisation ou non de l’imputation sur l’évaluation génomique des candidats à la sélection a également été analysé. Les résultats montrent qu’une méthodologie équidistante, pour une densité supérieure à 5K SNP, est adaptée pour obtenir de bons résultats d’imputation et une bonne précision d’évaluation génomique. Pour une densité supérieure à 5K SNP, il est également possible d’utiliser les puces BD sans imputation. Les génotypages BD ont ensuite été remplacés par des génotypages issus de méthodes RAD-Seq par simulation. En fonction de l’enzyme de restriction utilisée, les études ont montré que les méthodes RAD-Seq semblent être une alternative intéressante aux puces BD.

Improved performances are partly due to heterosis effects. One of the basis of heterosis is dominance, which cannot be inherited.However, it can be exploited to boost the total genetic merit of the animals. This has a special interest in avian selection schemeswhere commercial animals are crossbred. In this study, we have estimated additive and dominance genetic variances for severalegg quality traits in pure-line layers.Around 10,500 egg quality performances were used, collected from 1,148 female Rhode Island layers, phenotyped at 70 weeksold and genotyped using a 600K high density SNP chip. Five egg quality traits were analysed: egg weight (EW), egg shell color(ESC), egg shell strength (ESS), albumen height (AH) and egg shell shape (ESShape). Additive and dominance genetic varianceswere estimated via EM-REML with univariate models. That included an inbreeding coefficient and an additive and a dominancerandom effect. Dominance variance explained a small fraction of the phenotypic variance (between 2 to 4 % across all traits).However, it represented a relevant fraction of the total genetic variance for some of the traits (16%, 10%, 35%, 2.4% and 15% ofthe total genetic variance for EW, ESC, ESS, AH, ESShape, respectively).Further research will estimate additive and dominance genetic correlations between the traits to maximize the total genetic gainof these traits simultaneously. In addition, a genomic BLUP with dominance effects is envisaged for the joint analyses of purebredand crossbred performances, to evaluate the potential to generate superior crossbred performances.

To reduce the cost of genomic selection, low density SNP chip can be used in combination with imputation for genotyping the selection candidates instead of using high density (HD) SNP chip. Concurrently, next-generation sequencing (NGS) techniques has been increasingly used in livestock species but remain expensive to be routinely used in selection. An alternative and costefficient solution is the Genotyping by Genome Reducing and Sequencing (GGRS) techniques to sequence only a fraction of the genome by using restriction enzymes. This approach was simulated from sequences of 1027 individuals in a pure layer line, using four enzymes (EcoRI, TaqI, AvaII and PstI). Imputation accuracy on HD genotypes was assessed as the mean correlation between true and imputed genotypes. Egg weight, egg shell color, egg shell strength and albumen height were evaluated with single-step GBLUP methodology. The impact of imputation errors on the genomic estimated breeding values (GEBV) was also investigated.AvaII or PstI led to the detection of more than 10K SNPs in common with the HD SNP chip resulting in imputation accuracy higher than 0.97. The impact of imputation errors on the ranking of the selection candidates was reduced with Spearman correlation (between GEBV calculated on true and imputed genotypes) higher than 0.97 for AvaII and PstI. Finally, the GGRS approach can be an interesting alternative to low density SNP chip for genomic selection. However, with real data, heterogeneity between individuals with missing data has to be taken into account.

Over the past several years, high-density genotyping has led to major advances in genomic selection and understanding of genomes. This is also the case in laying hens with the availability of the Affymetrix HD 600K chip since 2013. Nowadays, NGS costs are becoming more and more affordable. Based on the HD SNP chip, the use of imputation to go back to the sequence level then makes it possible to obtain information on millions of polymorphisms for a large number of individuals. In this study, we analyzed the quality of the imputation of the HD chip genotypes on sequence genotypes for 90 individuals of a layer line presenting the 2 types of information. After quality control, these individuals presented genotypes for 290K and more than 6 million SNPs respectively with 600K genotyping and 20X sequencing. We carried out a cross-validation with 8 draws of 5 individuals. The correlation between true and imputed genotypes was between 0.96 and 0.99 depending on the individual.Correlations were greater than 0.90 for all chromosomes except chromosomes 16 and 25. After this validation work, we imputed an additional set of 95 individuals to perform GWAS. These analyses confirmed some QTLs of interest as a QTL of egg shell color on chromosome 7 and a QTL of egg shell strength on chromosome 1.

National audience; Avian breeders are currently putting in place genomic selection in their selection schemes. One key parameter to optimize selection schemes, and so genetic gain of selected populations, is to correctly predict the accuracy of genomic evaluation. In a previous study, it has been shown that genomic selection at birth was a very promising strategy for breeders selection in purebred. As crossbred between pure lines is commonly used in avian selection schemes, raises the question about the use of crossbred offspring in breeders evaluation, and their impact on evaluation accuracy. In this study, we had 2 goals. First, estimation of genetic correlation, for the same egg quality trait, between performance from pure line, and cross line layers. Then, estimation of the differences in evaluation accuracy, by adding crossbred information to pure line evaluation. Results shown that genetic correlation between purebred and crossbred trait is strong, but did not allow to consider that it is the same trait. Adding crossbred performances into evaluations allowed to obtain better results, globally, than in separated evaluations.; Depuis quelques années, la sélection génomique commence à se mettre en place dans la filière avicole. Dans le but d’optimiser les schémas de sélection, et donc le progrès génétique des populations sélectionnées, il est important de connaître la précision de l’évaluation génomique, comparativement à l’évaluation génétique. Dans une étude précédente, il avait été observé que l’usage de la sélection génomique à la naissance était une stratégie très prometteuse pour la sélection des reproducteurs en race pure. Le croisement entre lignées pures étant utilisé à large échelle par les schémas de sélection avicoles, il s’est posé la question de l’utilisation des données des descendantes croisées sur l’évaluation des reproducteurs, et de leur influence sur la précision de l’évaluation. Dans le cadre de cette étude, 2 objectifs ont été poursuivis. Tout d’abord, estimer la corrélation génétique, pour un même caractère de qualité d’œuf, entre la performance exprimée par des poules de lignée pure, et celle des poules croisées. Ensuite, estimer les différences de précision apportées par l’intégration des données des croisées à l’évaluation en lignée pure. Les résultats ont montré que la corrélation génétique entre caractère pur et croisé est forte, mais ne permet pas de considérer qu’il s’agit du même caractère. L’ajout des performances de croisées aux évaluations a permis d’obtenir des résultats globalement meilleurs qu’en évaluations séparées.

La structure du déséquilibre de liaison (DL) au sein des populations en sélection impacte fortement la précision obtenue lors des études de cartographie de QTL ou lors de l’évaluation génomique des reproducteurs. Chez les oiseaux, la structure hétérogène du génome nécessite de décrire précisément le DL pour optimiser la sélection. L’utilisation des puces SNP haute densité pour le génotypage des populations de volailles est une opportunité pour approfondir notre connaissance de la structure du DL de ces populations. L’objectif de cette étude est d’acquérir une connaissance haute résolution de la structure du DL au sein de populations de poules de types ponte et chair. Nous avons analysé les génotypes (puce 600 K Affymetrix® Axiom® HD SNP) de 1541 animaux issus de 3 populations. L’étendue et le niveau du DL ont été estimés par le r2 moyen à distance physique donnée entre SNP. Cette étude met en évidence des différences importantes de structure du DL entre lignées et entre chromosomes. L’étendue et le niveau du DL sont plus importants dans les lignées de type ponte ou pour les macro-chromosomes et le chromosome Z. Ce niveau important de DL peut faciliter la détection de QTL sur ces chromosomes, mais peut également compliquer la localisation fine de polymorphismes causaux. A l’inverse, le faible niveau de DL observé sur les micro-chromosomes nécessite l’utilisation d’une forte densité de SNP pour détecter une association avec un phénotype, mais devrait permettre la cartographie fine d’un polymorphisme causal. Ces différences sont à prendre en considération pour définir une stratégie de génotypage économique et efficace pour la cartographie fine de QTL ou l’évaluation génomique., Knowledge of the linkage disequilibrium (LD) pattern is useful in animal genetic studies as it underlies mapping studies and genomic selection. This is all the more important in birds given the heterogeneous structure of the avian karyotype. Recently, the availability of the high density 600 K Affymetrix® Axiom® HD SNP genotyping array allowed to assess an in-depth knowledge of the LD pattern in chicken genome. The aim of the present study was to assess a higher resolution of the LD pattern in chicken genome in layer and broiler lines. In this study, we analyzed genotypes of 1541 animals from layers and broiler commercial populations to characterize their LD pattern. LD was measured by the average r2 value at a given physical distance between SNP. LD extended over a larger region for layer lines than for broiler line. Most differences between lines appeared at small interval distances (< 0.5Mb). LD extent and decay differed considerably between chromosomes categories. Average r2 values were higher for Z chromosome than for macro, intermediates and microchromosomes. The extent of useful LD observed for autosomal chromosomes was at least ten-fold longer for layer lines than for broiler. Finally, this study shed light on high LD for Z chromosome. The differences in LD pattern observed between chromosomes and chicken lines should be taken into account to define an economically efficient genotyping strategy.

Avian breeders are actually putting in place genomic selection in their selection schemes. One key parameter to optimize selection schemes is to correctly predict the accuracy of genomic evaluation. This paper presents results obtained by comparing accuracy of genetic and genomic evaluations on multiple traits, using several reference populations with different amount of phenotypic informations. Genetic parameters (heritability and genetic correlations) were observed, and found very stable. EBVs and GEBVs accuracies showed that genomic evaluation was each time more accurate than genetic evaluation, especially when phenotypic information was restreint. It was also found that the use of sexual chromosome genotype had a negative impact on traits evaluation for dams (no effect found on sires). In any case, genomic evaluation of breeding birds at birth seems to be a promising strategy for layers.

The main goal of selection is to choose breeders of the next generation among a set of selection candidates. In genomic selection, the choice of breeders is based on the use of information on DNA polymorphisms, in particular SNP, in addition of performance measures. Since 2013, a commercial high density genotyping chip (600,000 markers) for chicken allowed the implementation of genomic selection in layer and broiler breeding. However, genotyping costs with this chip still remain high for a routine use on a large number of selection candidates. Consequently, it is interesting to develop, at a lower cost, low density genotyping chips. To do so, a set of SNP markers has to be selected to enable an imputation (prediction) of missing genotypes with high accuracy on a high density chip (HD chip). In this perspective, we conducted various simulation studies to choose the optimal strategy for low density genotyping of two different lines of laying hen. Different low density genotyping chips were designed according to two methodologies: a choice of SNP depending on a clustering of SNP based on linkage disequilibrium threshold or a choice of SNP at regular intervals (kb) along each chromosome. Imputation accuracy was assessed as the mean correlation between true and imputed genotypes. Results showed that correlations were more sensitive to false imputation of SNPs with low Minor Allele Frequency (MAF) with the equidistant methodology. Imputation accuracy improved with SNP density and when a higher LD threshold is used for SNP selection. Given the particular structure of the avian genome with chromosomes of very heterogeneous sizes and extents of LD, imputation accuracy differed according to the type of chromosome. All the simulation studies showed that linkage disequilibrium methodology enabled to get better results of imputation than with equidistant methodology.

The main goal of selection is to choose breeders of the next generation among a set of selection candidates. In genomic selection, the choice of breeders rests on the use of information on DNA polymorphisms, in particular SNP, in addition of performance measures. Since 2013, a commercial high density genotyping chip (600,000 markers) for chicken allowed the implementation of genomic selection in layer and broiler breeding. However, genotyping costs with this chip still remain high for a routine use on a large number of selection candidates. Consequently, it is interesting to develop, at a lower cost, low density genotyping chips. To do so, a set of SNP markers has to be selected to enable an imputation (prediction) of missing genotypes on a high density chip (HD chip). This imputation enables to predict missing genotypes of all selection candidates from high density genotyping of a reference population with phenotypes. In this perspective, according to the reference population, various simulation studies were conducted to choose the best strategy for low density genotyping of laying hen lines. Two different low density genotyping chips of 10K SNP were designed according to two methodologies: a choice of SNP depending on a clustering based on linkage disequilibrium threshold or a choice of SNP at regular intervals (kb) along each chromosome. Imputation accuracy was assessed as the mean correlation between true and imputed genotypes. Focusing on populationnal factors that can influence imputation accuracy, it is shown that imputation accuracy improves with an increase in the size of the reference population. By decreasing the kinship degree between reference and candidate population, it is seen that imputation accuracy decreases. Most importantly, results show that a key point in getting good imputations is to have the direct parents in the reference population. Finally, all different genotyping strategies focused on population factors show that linkage disequilibrium methodology enables to get better results of imputation than with equidistant methodology.

Design of a low density SNP chip for genomic selection in layer chicken. 10. European Symposium on Poultry Genetics (ESPG)

La sélection génomique utilise des informations sur les polymorphismes de l’ADN, notamment SNP, en complément des mesures de performances, afin de choisir les futurs reproducteurs parmi un ensemble de candidats à la sélection. Depuis 2013, une puce commerciale de génotypages à haute densité (600 000 marqueurs) de l’espèce poule a permis le développement de la sélection génomique dans les filières ponte et chair. Toutefois, les coûts de génotypages avec cette puce restent élevés pour une utilisation en routine sur un grand nombre de candidats à la sélection. Un des enjeux actuels de la sélection génomique est donc le développement de puces de génotypage de basse densité (dites puces LD) à plus faible coût. Il s’agit pour cela de sélectionner un panel de marqueurs SNP permettant une imputation (prédiction) des génotypes manquants sur puce haute densité (puce HD). Dans cette optique, différentes études ont été menées afin de choisir la stratégie de génotypages basse densité la mieux adaptée à une lignée de poule pondeuse. Différentes densités de génotypages, plusieurs scenarii de populations et deux logiciels ont été testés. Les puces basse densité ont été construites selon deux stratégies : choix des SNP selon un clustering basé sur un seuil de déséquilibre de liaison ou choix de SNP à intervalles réguliers. Trois critères d’efficacité des imputations ont été comparés : taux d’erreur génotypique, taux d’erreur allélique et corrélation entre génotypes imputés et réels. Le taux d’erreur génotypique s’est révélé un critère plus discriminant que les autres. Le logiciel FImpute apparaît plus intéressant que le logiciel Beagle car c’est un meilleur compromis entre efficacité de l’imputation et temps de calcul. L’étude des différentes puces basse densité montre que les imputations sont meilleures lorsque la densité des SNP augmente, lorsque le seuil de DL augmente et lorsque des SNP avec une faible MAF (Fréquence Allélique Mineure) sont pris en compte. Les études de populations montrent que les imputations sont meilleures lorsque la taille de la population de référence augmente. L’influence du degré d’apparentement entre population de référence et candidats ainsi que l’impact sur la précision des évaluations génomiques restent à approfondir., Genomic selection uses information on DNA polymorphisms, in particular SNP, in addition of performance measures, in order to choose breeders of the next generation among a set of selection candidates. Since 2013, a commercial high density genotyping chip (600000 markers) for chicken allowed the implementation of genomic selection in layer and broiler breeding. However, genotyping costs with this chip still remain high for a routine use on a large number of selection candidates. Consequently, one of the current stakes of genomic selection is the development at a lower cost of low density genotyping chips (LD chips). To do so, a set of SNP markers has to be selected to enable an imputation (prediction) of missing genotypes on a high density chip (HD chip). [br/] In this regard, various studies were conducted to choose the low density genotyping strategy the best suited to laying hens line. Different genotyping densities, several population scenarii and two software were tested. Low density genotyping chips were built according to two strategies: a choice of SNP depending on a clustering based on linkage disequilibrium threshold or a choice of SNP at regular intervals. Three criteria of imputation accuracy were compared: genotypic error rate, allelic error rate and correlation. The genotypic error rate appeared to be a criterion more discriminating than the others. FImpute software is preferred to Beagle software because FImpute is a better compromise between imputation accuracy and calculation time. The study of different low density genotyping chips shows that imputations improve with SNP density, when the LD threshold increases and when SNP with low MAF (Minor Allele Frequency) are considered. Population studies show that imputations are better when the size of the reference population increases. The influence of the kinship degree between reference population and target population, and the impact on accuracy of genomic evaluations still remain to be deepened.

Métaprogramme Selgen; Select SNP 4 Imputation (SS4I) : un outil simple de sélection de SNP en fonction du DL. Réunion du projet Selgen INCoMINGS