Extent of linkage disequilibrium, consistency of gametic phase, and imputation accuracy within and across Canadian dairy breeds

Genomic selection requires a large reference population to accurately estimate single nucleotide polymorphism (SNP) effects. In some Canadian dairy breeds, the available reference populations are not large enough for accurate estimation of SNP effects for traits of interest. If marker phase is highly consistent across multiple breeds, it is theoretically possible to increase the accuracy of genomic prediction for one or all breeds by pooling several breeds into a common reference population. This study investigated the extent of linkage disequilibrium (LD) in 5 major dairy breeds using a 50,000 (50K) SNP panel and 3 of the same breeds using the 777,000 (777K) SNP panel. Correlation of pair-wise SNP phase was also investigated on both panels. The level of LD was measured using the squared correlation of alleles at 2 loci (r2), and the consistency of SNP gametic phases was correlated using the signed square root of these values. Because of the high cost of the 777K panel, the accuracy of imputation from lower density marker panels [6,000 (6K) or 50K] was examined both within breed and using a multi-breed reference population in Holstein, Ayrshire, and Guernsey. Imputation was carried out using FImpute V2.2 and Beagle 3.3.2 software. Imputation accuracies were then calculated as both the proportion of correct SNP filled in (concordance rate) and allelic R2. Computation time was also explored to determine the efficiency of the different algorithms for imputation. Analysis showed that LD values >0.2 were found in all breeds at distances at or shorter than the average adjacent pair-wise distance between SNP on the 50K panel. Correlations of r-values, however, did not reach high levels (0.94) when the average pair-wise distances using the 777K SNP panel were examined. High concordance rate (0.968–0.995) and allelic R2 (0.946–0.991) were found for all breeds when imputation was carried out with FImpute from 50K to 777K. Imputation accuracy for Guernsey and Ayrshire was slightly lower when using the imputation method in Beagle. Computing time was significantly greater when using Beagle software, with all comparable procedures being 9 to 13 times less efficient, in terms of time, compared with FImpute. These findings suggest that use of a multi-breed reference population might increase prediction accuracy using the 777K SNP panel and that 777K genotypes can be efficiently a