Impact of the size of the reference population and kinship degree on low density genotyping strategies for genotype imputation in layer chickens

The main goal of selection is to choose breeders of the next generation among a set ofselection candidates. In genomic selection, the choice of breeders rests on the use of information onDNA polymorphisms, in particular SNP, in addition of performance measures. Since 2013, acommercial high density genotyping chip (600,000 markers) for chicken allowed theimplementation of genomic selection in layer and broiler breeding. However, genotyping costs withthis 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 SNPmarkers has to be selected to enable an imputation (prediction) of missing genotypes on a highdensity chip (HD chip). This imputation enables to predict missing genotypes of all selectioncandidates from high density genotyping of a reference population with phenotypes.In this perspective, according to the reference population, various simulation studies wereconducted to choose the best strategy for low density genotyping of laying hen lines. Two differentlow density genotyping chips of 10K SNP were designed according to two methodologies: a choiceof SNP depending on a clustering based on linkage disequilibrium threshold or a choice of SNP atregular intervals (kb) along each chromosome. Imputation accuracy was assessed as the meancorrelation between true and imputed genotypes. Focusing on populationnal factors that caninfluence imputation accuracy, it is shown that imputation accuracy improves with an increase inthe size of the reference population. By decreasing the kinship degree between reference andcandidate population, it is seen that imputation accuracy decreases. Most importantly, results showthat 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 linkagedisequilibrium methodology enables to get better results of imputation than with equidistantmethodology.