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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.

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.