Context. Genotype imputation is an effective method to increase the number of SNP markers available for an animal and thereby increase the overall power of genome-wide associations and accuracy of genomic predictions. It is also the key to achieve a common set of markers for all individuals when the original genotypes are obtained using multiple genotyping platforms. High accuracy of imputed genotypes is crucial to their utility.
Aims. In this study, we propose a method for the construction of a common set of medium density markers for imputation, which relies on keeping as much information as possible. We also investigated the impact of changing marker coordinates on the basis of the new bovine genome assembly, ARS-UCD 1.2, on imputation accuracy.
Methods. In total, 49 754 animals with 45 364 single nucleotide polymorphism markers were used in a 10-fold cross-validation to compare four different imputation scenarios. The four scenarios were based on two alternative designs for the reference datasets. (1) A traditional reference panel that was created using the overlapping SNP from five medium density arrays and (2) a composite reference panel created by combining SNPs across the five arrays. Each of the reference datasets was used to test imputation accuracy when the SNPs were aligned on the basis of two genome assemblies (UMD 3.1 and ARS-UCD 1.2).
Key results. Our results showed that a composite reference panel can achieve higher imputation accuracies than does a traditional overlap reference. Incorporating mapping information on the basis of the recent genome build slightly improved the imputation accuracies, especially for lower density chips.
Conclusions. Markers with unreliable mapping information and animals with low connectedness to the imputation reference dataset benefited the most from the ARS-UCD 1.2 assembly and composite reference respectively.
Implications. The presented method is straightforward and can be used to setup an optimal imputation for accurate inference of genotypes in Australian Angus cattle.