1. Impact of the size of the reference population and kinship degree on low density genotyping strategies for genotype imputation in layer chickens
- Author
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Burlot, Thierry, Herry, Florian, Hérault, Frédéric, Picard--Druet, David, Varenne, Amandine, Le Roy, Pascale, Allais, Sophie, Novogen, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), ANR-10-GENOM_BTV-015 UtOpIGe, Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), AGROCAMPUS OUEST, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)
- Subjects
Animal biology ,polymorphisme nucléotidique simple (SNP) ,Imputation accuracy ,Layer chickens ,Reference population ,Kinship degree ,[SDV.BA]Life Sciences [q-bio]/Animal biology ,laying hen ,évaluation génomique ,poule pondeuse ,génotypage ,Biologie animale ,déséquilibre de liaison ,précision de sélection - Abstract
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.
- Published
- 2018