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To distinguish between these two cases, we computed the accuracy of imputing alleles as the percentage of correctly predicted alleles, and the allelic error rate of imputation as the percentage of incorrectly predicted alleles i.e. mean allelic error rate = number of incorrectly predicted alleles / total number of alleles imputed in the test set × 100.
The lower accuracies under scenario B are more evident for Bovine3K which has much higher mean allelic error rate (5.52%).
Genome-wide mean allelic error rate is less than 1% for both the methods (0.79% for IMPUTE2 and 0.99% for Beagle).
Although we cannot compare these accuracies with the actual 800K genotypes in this study, however, mean allelic error rate of imputation up to 800K using 50K in the test samples was very small (0.79%).
The mean allelic error rate increases as the number of animals in the reference set decreases.
The respective mean allelic error rates of imputing 50K specific (1324 SNPs) genotypes are 2.75%, 2.75% and 4.55%.
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In general allelic error rates are just slightly more than half of genotypic error rates.
Allelic diversity within each population was the mean allelic diversities among the 114 loci.
Mean allelic richness varied from 1.33 to 10.70.
50K specific genotypes were imputed with moderate allelic error rates from 15K (2.85%) and 25K (2.75%) genotypes.
Figure 2 presents the root mean square error (RMSE) of male- and female-specific allelic effect estimates at the causal variant for the same range of models.
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