Impact of reference population on accuracy of imputation from 6K to 50K single nucleotide polymorphism chips in purebred and crossbreed beef cattle

Genotyping with lower density but lower cost panels enables more animals to be genotyped for genomic selection. Imputation enables the determination of missing SNP genotypes in animals genotyped with a low-density panel by using information from a reference population genotyped with a higher density panel, which should increase accuracy of genomic EBV. In this study, population imputation, using linkage disequilibrium among markers, was implemented using the software BEAGLE, FIMPUTE 2.2, and IMPUTE2 in a multibreed, crossbred taurine beef cattle population genotyped with the Illumina SNP50. Different combinations of reference populations and imputed animals were defined based on breed composition. Number of animals (n = 250 to 4,932) and the presence of closer relatives in the reference population (only for Angus animals) were investigated. The overall average imputation accuracy for purebred animals ranged from 94.20 to 97.93% using FIMPUTE, from 95.35 to 98.31% using IMPUTE2, and from 90.02 to 96.38% when BEAGLE software was used. Imputation accuracy of crossbred animals ranged from 54.15 to 97.53% (FIMPUTE), from 57.04 to 97.46% (IMPUTE2), and from 54.35 to 95.64% (BEAGLE). Higher imputation accuracies were obtained when closer relatives along with the breed composition of imputed animals was well represented in the reference population. Within breed imputation from 6K to 50K did not improve when an additional purebred population was added to the reference population. FIMPUTE reduced the run time by 13 to 52 times compared to BEAGLE and 51 to 108 times compared to IMPUTE2.