18 research outputs found

    Etude de la prédiction génomique chez les caprins : faisabilité et limites de la sélection génomique dans le cadre d'une population multiraciale et à faible effectif

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    La sĂ©lection gĂ©nomique, qui a rĂ©volutionnĂ© la sĂ©lection gĂ©nĂ©tique des bovins laitiers notamment, est dĂ©sormais envisagĂ©e dans d’autres espĂšces comme l’espĂšce caprine. La clĂ© du succĂšs de la sĂ©lection gĂ©nomique rĂ©side dans la prĂ©cision des Ă©valuations gĂ©nomiques. Chez les caprins laitiers français, le gain de prĂ©cision attendu avec la sĂ©lection gĂ©nomique Ă©tait un des questionnements de la filiĂšre en raison de la petite taille de la population de rĂ©fĂ©rence disponible (825 mĂąles et 1945 femelles gĂ©notypĂ©s sur une puce SNP 50K). Le but de cette Ă©tude est d’évaluer comment augmenter la prĂ©cision des Ă©valuations gĂ©nomiques dans l’espĂšce caprine. Une Ă©tude de la structure gĂ©nĂ©tique de la population de rĂ©fĂ©rence caprine constituĂ©e d’animaux de races Saanen et Alpine, a permis de montrer que la population de rĂ©fĂ©rence choisie est reprĂ©sentative de la population Ă©levĂ©e sur le territoire français. En revanche, les faibles niveaux de dĂ©sĂ©quilibre de liaison (0,17 entre deux SNP consĂ©cutifs) de consanguinitĂ© et de parentĂ© au sein de la population, similaires Ă  ceux trouvĂ©s en ovins Lacaune, ne sont pas idĂ©aux pour obtenir une bonne prĂ©cision des Ă©valuations gĂ©nomiques. De plus, malgrĂ© l’origine commune des races Alpine et Saanen, leurs structures gĂ©nĂ©tiques suggĂšrent qu’elles se distinguent clairement d’un point de vue gĂ©nĂ©tique. Les mĂ©thodes gĂ©nomiques (GBLUP ou BayĂ©siennes) « two-step », basĂ©es sur des performances prĂ©-corrigĂ©es (DYD, EBV dĂ©rĂ©gressĂ©es) n’ont pas permis une amĂ©lioration significative des prĂ©cisions des Ă©valuations gĂ©nomiques pour les caractĂšres Ă©valuĂ©s en routine (caractĂšres de production, de morphologie et de comptages de cellules somatiques) chez les caprins laitiers. La prise en compte des phĂ©notypes des mĂąles non gĂ©notypĂ©s permet d’augmenter les prĂ©cisions des Ă©valuations de 3 Ă  47% selon le caractĂšre. L’ajout des gĂ©notypes de femelles issues d’un dispositif de dĂ©tection de QTL amĂ©liore Ă©galement les prĂ©cisions (de 2 Ă  14%) que ce soit pour les Ă©valuations two steps ou les Ă©valuations basĂ©es sur les performances propres des femelles (single step). Les prĂ©cisions sont augmentĂ©es de 10 Ă  74% avec les Ă©valuations single step comparĂ©es aux Ă©valuations two steps, ce qui permet d’atteindre des prĂ©cisions supĂ©rieures Ă  celles obtenues sur ascendance. Les prĂ©cisions obtenues avec les Ă©valuations gĂ©nomiques multiraciales, bicaractĂšres et uniraciales sont similaires mĂȘme si la prĂ©cision des valeurs gĂ©nomiques estimĂ©es des candidats Ă  la sĂ©lection est plus Ă©levĂ©e avec les Ă©valuations multiraciales. La sĂ©lection gĂ©nomique est donc envisageable chez les caprins laitiers français Ă  l’aide d’un modĂšle gĂ©nomique multiracial single step. Les prĂ©cisions peuvent ĂȘtre lĂ©gĂšrement augmentĂ©es par l’inclusion de gĂšnes majeurs tels que celui de la casĂ©ine αs1 notamment Ă  l’aide d’un modĂšle « gene content » pour prĂ©dire le gĂ©notype des animaux non gĂ©notypĂ©s

    SNP-based mate allocation strategies to maximize total genetic value in pigs

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    International audienceAbstractBackgroundMate allocation strategies that account for non-additive genetic effects can be used to maximize the overall genetic merit of future offspring. Accounting for dominance effects in genetic evaluations is easier in a genomic context, than in a classical pedigree-based context because the combinations of alleles at loci are known. The objective of our study was two-fold. First, dominance variance components were estimated for age at 100 kg (AGE), backfat depth (BD) at 140 days, and for average piglet weight at birth within litter (APWL). Second, the efficiency of mate allocation strategies that account for dominance and inbreeding depression to maximize the overall genetic merit of future offspring was explored.ResultsGenetic variance components were estimated using genomic models that included inbreeding depression with and without non-additive genetic effects (dominance). Models that included dominance effects did not fit the data better than the genomic additive model. Estimates of dominance variances, expressed as a percentage of additive genetic variance, were 20, 11, and 12% for AGE, BD, and APWL, respectively. Estimates of additive and dominance single nucleotide polymorphism effects were retrieved from the genetic variance component estimates and used to predict the outcome of matings in terms of total genetic and breeding values. Maximizing total genetic values instead of breeding values in matings gave the progeny an average advantage of − 0.79 days, − 0.04 mm, and 11.3 g for AGE, BD and APWL, respectively, but slightly reduced the expected additive genetic gain, e.g. by 1.8% for AGE.ConclusionsGenomic mate allocation accounting for non-additive genetic effects is a feasible and potential strategy to improve the performance of the offspring without dramatically compromising additive genetic gain

    Study on genomic predictions in dairy goats : Benefits and limits of genomic selection in a small size multibreed population

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    La sĂ©lection gĂ©nomique, qui a rĂ©volutionnĂ© la sĂ©lection gĂ©nĂ©tique des bovins laitiers notamment, est dĂ©sormais envisagĂ©e dans d’autres espĂšces comme l’espĂšce caprine. La clĂ© du succĂšs de la sĂ©lection gĂ©nomique rĂ©side dans la prĂ©cision des Ă©valuations gĂ©nomiques. Chez les caprins laitiers français, le gain de prĂ©cision attendu avec la sĂ©lection gĂ©nomique Ă©tait un des questionnements de la filiĂšre en raison de la petite taille de la population de rĂ©fĂ©rence disponible (825 mĂąles et 1945 femelles gĂ©notypĂ©s sur une puce SNP 50K). Le but de cette Ă©tude est d’évaluer comment augmenter la prĂ©cision des Ă©valuations gĂ©nomiques dans l’espĂšce caprine. Une Ă©tude de la structure gĂ©nĂ©tique de la population de rĂ©fĂ©rence caprine constituĂ©e d’animaux de races Saanen et Alpine, a permis de montrer que la population de rĂ©fĂ©rence choisie est reprĂ©sentative de la population Ă©levĂ©e sur le territoire français. En revanche, les faibles niveaux de dĂ©sĂ©quilibre de liaison (0,17 entre deux SNP consĂ©cutifs) de consanguinitĂ© et de parentĂ© au sein de la population, similaires Ă  ceux trouvĂ©s en ovins Lacaune, ne sont pas idĂ©aux pour obtenir une bonne prĂ©cision des Ă©valuations gĂ©nomiques. De plus, malgrĂ© l’origine commune des races Alpine et Saanen, leurs structures gĂ©nĂ©tiques suggĂšrent qu’elles se distinguent clairement d’un point de vue gĂ©nĂ©tique. Les mĂ©thodes gĂ©nomiques (GBLUP ou BayĂ©siennes) « two-step », basĂ©es sur des performances prĂ©-corrigĂ©es (DYD, EBV dĂ©rĂ©gressĂ©es) n’ont pas permis une amĂ©lioration significative des prĂ©cisions des Ă©valuations gĂ©nomiques pour les caractĂšres Ă©valuĂ©s en routine (caractĂšres de production, de morphologie et de comptages de cellules somatiques) chez les caprins laitiers. La prise en compte des phĂ©notypes des mĂąles non gĂ©notypĂ©s permet d’augmenter les prĂ©cisions des Ă©valuations de 3 Ă  47% selon le caractĂšre. L’ajout des gĂ©notypes de femelles issues d’un dispositif de dĂ©tection de QTL amĂ©liore Ă©galement les prĂ©cisions (de 2 Ă  14%) que ce soit pour les Ă©valuations two steps ou les Ă©valuations basĂ©es sur les performances propres des femelles (single step). Les prĂ©cisions sont augmentĂ©es de 10 Ă  74% avec les Ă©valuations single step comparĂ©es aux Ă©valuations two steps, ce qui permet d’atteindre des prĂ©cisions supĂ©rieures Ă  celles obtenues sur ascendance. Les prĂ©cisions obtenues avec les Ă©valuations gĂ©nomiques multiraciales, bicaractĂšres et uniraciales sont similaires mĂȘme si la prĂ©cision des valeurs gĂ©nomiques estimĂ©es des candidats Ă  la sĂ©lection est plus Ă©levĂ©e avec les Ă©valuations multiraciales. La sĂ©lection gĂ©nomique est donc envisageable chez les caprins laitiers français Ă  l’aide d’un modĂšle gĂ©nomique multiracial single step. Les prĂ©cisions peuvent ĂȘtre lĂ©gĂšrement augmentĂ©es par l’inclusion de gĂšnes majeurs tels que celui de la casĂ©ine αs1 notamment Ă  l’aide d’un modĂšle « gene content » pour prĂ©dire le gĂ©notype des animaux non gĂ©notypĂ©s.Genomic selection which is revolutionizing genetic selection in dairy cattle has been tested in several species like dairy goat. Key point in genomic selection is accuracy of genomic evaluation. In French dairy goats, gain in accuracy using genomic selection was questioning due to the small size of the reference population (825 males and 1 945 females genotyped). The aim of this study was to investigate how to reach adequate genomic evaluation accuracy in French dairy goat population. The study of reference population structure (Alpine and Saanen breeds) showed that reference population is similar to the whole population of French dairy goats. But the weak level of linkage disequilibrium (0.17 between two consecutive SNP), inbreeding and relationship between reference and candidate population were not ideal to maximize genomic evaluation accuracy. Despite their common origin, genetic structure of Alpine and Saanen breeds suggested that they were genetically distant. Two steps genomic evaluation (GBLUP, Bayesian) based on performances corrected for fixed effect (DYD, deregressed EBV) did not improve genetic evaluation accuracy compared to classical evaluations for milk production traits, udder type traits and somatic cells score classically selected in French dairy goat. Taking into account phenotypes of ungenotyped sires increased genomic evaluation from 3 to 47% depending on the trait considered. Adding female genotypes also improved genomic evaluation accuracies from 2 to 4% depending on the method (two steps or single step) and on the trait. When using gemomic evaluation directly based on female performances (single step), accuracy of genomic evaluation reach the level obtained from ascendance in classic evaluation which was not the case using two steps evaluations. Genomic evaluation accuracies were similar when using multiple-trait model, multi-breed or single breed evaluation. But accuracies derived from prediction error variances were better when using multi-breed genomic evaluations. Genomic selection is feasible in French dairy goats using single step multi-breed genomic evaluations. Accuracies could be slightly improved integrating major gene as αs1 casein especially when using « gene content » approach to predict genotypes of ungenotyped animals

    Étude de la prĂ©diction gĂ©nomique chez les caprins : IntĂ©rĂȘt et limites de la sĂ©lection gĂ©nomique dans le cadre d'une population multiraciale Ă  faible effectif

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    Genomic selection which is revolutionizing genetic selection in dairy cattle has been tested in several species like dairy goat. Key point in genomic selection is accuracy of genomic evaluation. In French dairy goats, gain in accuracy using genomic selection was questioning due to the small size of the reference population (825 males and 1 945 females genotyped). The aim of this study was to investigate how to reach adequate genomic evaluation accuracy in French dairy goat population. The study of reference population structure (Alpine and Saanen breeds) showed that reference population is similar to the whole population of French dairy goats. But the weak level of linkage disequilibrium (0.17 between two consecutive SNP), inbreeding and relationship between reference and candidate population were not ideal to maximize genomic evaluation accuracy. Despite their common origin, genetic structure of Alpine and Saanen breeds suggested that they were genetically distant. Two steps genomic evaluation (GBLUP, Bayesian) based on performances corrected for fixed effect (DYD, deregressed EBV) did not improve genetic evaluation accuracy compared to classical evaluations for milk production traits, udder type traits and somatic cells score classically selected in French dairy goat. Taking into account phenotypes of ungenotyped sires increased genomic evaluation from 3 to 47% depending on the trait considered. Adding female genotypes also improved genomic evaluation accuracies from 2 to 4% depending on the method (two steps or single step) and on the trait. When using gemomic evaluation directly based on female performances (single step), accuracy of genomic evaluation reach the level obtained from ascendance in classic evaluation which was not the case using two steps evaluations. Genomic evaluation accuracies were similar when using multiple-trait model, multi-breed or single breed evaluation. But accuracies derived from prediction error variances were better when using multi-breed genomic evaluations. Genomic selection is feasible in French dairy goats using single step multi-breed genomic evaluations. Accuracies could be slightly improved integrating major gene as αs1 casein especially when using « gene content » approach to predict genotypes of ungenotyped animals.La sĂ©lection gĂ©nomique, qui a rĂ©volutionnĂ© la sĂ©lection gĂ©nĂ©tique des bovins laitiers notamment, est dĂ©sormais envisagĂ©e dans d’autres espĂšces comme l’espĂšce caprine. La clĂ© du succĂšs de la sĂ©lection gĂ©nomique rĂ©side dans la prĂ©cision des Ă©valuations gĂ©nomiques. Chez les caprins laitiers français, le gain de prĂ©cision attendu avec la sĂ©lection gĂ©nomique Ă©tait un des questionnements de la filiĂšre en raison de la petite taille de la population de rĂ©fĂ©rence disponible (825 mĂąles et 1945 femelles gĂ©notypĂ©s sur une puce SNP 50K). Le but de cette Ă©tude est d’évaluer comment augmenter la prĂ©cision des Ă©valuations gĂ©nomiques dans l’espĂšce caprine. Une Ă©tude de la structure gĂ©nĂ©tique de la population de rĂ©fĂ©rence caprine constituĂ©e d’animaux de races Saanen et Alpine, a permis de montrer que la population de rĂ©fĂ©rence choisie est reprĂ©sentative de la population Ă©levĂ©e sur le territoire français. En revanche, les faibles niveaux de dĂ©sĂ©quilibre de liaison (0,17 entre deux SNP consĂ©cutifs) de consanguinitĂ© et de parentĂ© au sein de la population, similaires Ă  ceux trouvĂ©s en ovins Lacaune, ne sont pas idĂ©aux pour obtenir une bonne prĂ©cision des Ă©valuations gĂ©nomiques. De plus, malgrĂ© l’origine commune des races Alpine et Saanen, leurs structures gĂ©nĂ©tiques suggĂšrent qu’elles se distinguent clairement d’un point de vue gĂ©nĂ©tique. Les mĂ©thodes gĂ©nomiques (GBLUP ou BayĂ©siennes) « two-step », basĂ©es sur des performances prĂ©-corrigĂ©es (DYD, EBV dĂ©rĂ©gressĂ©es) n’ont pas permis une amĂ©lioration significative des prĂ©cisions des Ă©valuations gĂ©nomiques pour les caractĂšres Ă©valuĂ©s en routine (caractĂšres de production, de morphologie et de comptages de cellules somatiques) chez les caprins laitiers. La prise en compte des phĂ©notypes des mĂąles non gĂ©notypĂ©s permet d’augmenter les prĂ©cisions des Ă©valuations de 3 Ă  47% selon le caractĂšre. L’ajout des gĂ©notypes de femelles issues d’un dispositif de dĂ©tection de QTL amĂ©liore Ă©galement les prĂ©cisions (de 2 Ă  14%) que ce soit pour les Ă©valuations two steps ou les Ă©valuations basĂ©es sur les performances propres des femelles (single step). Les prĂ©cisions sont augmentĂ©es de 10 Ă  74% avec les Ă©valuations single step comparĂ©es aux Ă©valuations two steps, ce qui permet d’atteindre des prĂ©cisions supĂ©rieures Ă  celles obtenues sur ascendance. Les prĂ©cisions obtenues avec les Ă©valuations gĂ©nomiques multiraciales, bicaractĂšres et uniraciales sont similaires mĂȘme si la prĂ©cision des valeurs gĂ©nomiques estimĂ©es des candidats Ă  la sĂ©lection est plus Ă©levĂ©e avec les Ă©valuations multiraciales. La sĂ©lection gĂ©nomique est donc envisageable chez les caprins laitiers français Ă  l’aide d’un modĂšle gĂ©nomique multiracial single step. Les prĂ©cisions peuvent ĂȘtre lĂ©gĂšrement augmentĂ©es par l’inclusion de gĂšnes majeurs tels que celui de la casĂ©ine αs1 notamment Ă  l’aide d’un modĂšle « gene content » pour prĂ©dire le gĂ©notype des animaux non gĂ©notypĂ©s

    Including caseine αs1 major gene effect on genetic and genomic evaluations of French dairy goats

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    Casein αs1 gene has a major effect on protein content of dairy goat milk. All French bucks (Alpine and Saanen breeds) used for artificial insemination, 2 145 testing bucks and 2 983 dams of these bucks, have been genotyped for this gene since 1990. The casein αs1 gene is a multi-allelic gene with 6 different alleles. The idea of this study is to investigate how to include the casein αs1 gene effect in genetic or genomic evaluation. In French dairy goat, Casein αs1 genotype has a significant effect on protein content, milk yield and fat content. Parts of variances of casein αs1 genotype were estimated between 3% in milk yield in Saanen breed to 38% for protein content in Alpine breed. Genetic evaluations based on daughter yield deviations (DYD) were done per breed, including casein αs1 genotype as fixed or random effect. Validation correlations estimated on the 252 youngest bucks were slightly improved by considering the casein αs1 gene in Saanen breed (+18% in protein content). However considering the gene effect as fixed or random gave similar results. Adding the 50k bead chip genotypes of bucks (471 Alpine and 354 Saanen bucks) in the model already including casein αs1 genotype did not improved validation correlation. The most of females used for genetic evaluation were not genotyped for casein αs1 gene. Including casein αs1 gene effect in genetic evaluation based on female performances need to predict casein αs1 genotype for these females. Probabilities of each 19 genotypes were estimated for each female, from pedigree information and true genotype of animals. Several models were tested to include this probability in the model as random or fixed effect. The first results shown that validation correlations were not improved considering the genotype probability. Other methods or models are investigated to avoid the problem of imputation

    Vers une sélection génomique chez les caprins laitiers

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    La sĂ©lection gĂ©nomique, qui a rĂ©volutionnĂ© la sĂ©lection gĂ©nĂ©tique des bovins laitiers notamment, est dĂ©sormais envisagĂ©e dans d’autres filiĂšres animales. Chez les caprins laitiers français, le gain de prĂ©cision attendu des valeurs gĂ©nomiques Ă©tait un des questionnements de la filiĂšre en raison de la petite taille de la population de rĂ©fĂ©rence disponible (825 mĂąles et 1945 femelles gĂ©notypĂ©s sur une puce SNP 50K). Le but de cette Ă©tude est de tester diffĂ©rentes techniques d’évaluation gĂ©nomique afin d’obtenir les Ă©valuations gĂ©nomiques les plus prĂ©cises possibles. Une Ă©tude de la structure gĂ©nĂ©tique de la population de rĂ©fĂ©rence caprine constituĂ©e d’animaux de races Saanen et Alpine, a rĂ©vĂ©lĂ© de faibles niveaux de dĂ©sĂ©quilibre de liaison (0,17 entre deux SNP consĂ©cutifs), de consanguinitĂ© et de parentĂ© au sein de la population, ce qui n’est pas favorable Ă  une bonne prĂ©cision des Ă©valuations gĂ©nomiques. Les mĂ©thodes d’évaluations gĂ©nomiques (GBLUP ou BayĂ©siennes), basĂ©es sur des performances prĂ©-corrigĂ©es n’ont pas permis une amĂ©lioration significative des prĂ©cisions des Ă©valuations gĂ©nomiques pour les caractĂšres Ă©valuĂ©s en routine (caractĂšres de production, de morphologie et comptages de cellules somatiques). Cependant les Ă©valuations gĂ©nomiques basĂ©es sur les performances propres des femelles ont permis d’obtenir des prĂ©cisions supĂ©rieures Ă  celles obtenues sur ascendance. La sĂ©lection gĂ©nomique est donc envisageable chez les caprins laitiers français. Ces prĂ©cisions peuvent Ă©galement ĂȘtre lĂ©gĂšrement augmentĂ©es par l’inclusion de gĂšnes majeurs tels que celui de la casĂ©ine αs1.Genomic selection, which is revolutionizing genetic selection in dairy cattle is now considered in the breeding of other animals. In French dairy goats, gain in accuracy using genomic selection has been questionned due to the small size of the reference population (825 males and 1 945 females genotyped). The aim of this study was to investigate how to reach adequate genomic evaluation accuracy in the French dairy goat population. The study of a reference population structure (Alpine and Saanen breeds) showed that the level of linkage disequilibrium (0.17 between two consecutive SNP), inbreeding and the relationship between the reference and candidate population were not ideal to maximize genomic evaluation accuracy. Two steps genomic evaluation (GBLUP, Bayesian) based on performances corrected for fixed effects did not improve genetic evaluation accuracy compared to classical evaluations for milk production traits, udder type traits and somatic cells scores. When using genomic evaluation directly based on female performances (single step), accuracy of genomic evaluation is higher than the accuracy level obtained from the ascendance. Genomic selection is feasible in French dairy goats. Accuracies could be slightly improved integrating a major gene such as αs1 casein

    Importance du phénotypage pour maintenir la précision des prédictions génomiques des caractÚres mesurés en station

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    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

    Predicting pig digestibility coefficients with microbial and genomic data using machine learning prediction algorithms

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    International audienceClassical 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

    Utilisation d’une puce trĂšs basse densitĂ© (1 100 SNP) pour la sĂ©lection gĂ©nomique chez 3 races de porcs françaises

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    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 cos
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