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Differences in the underlying structure [ 32] of the three populations may impact imputation accuracies.
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In contrast, allele frequency impacted imputation accuracy only marginally if the low density panel contained markers that exhibited at least moderate levels of LD (r > 0.5) with the imputed markers in the high density panel (Additional file 3: Figure S3).
To assess the variation in haplotype assignment and impact on imputation accuracy, imputation Scenario 3 was completed, by race, with only unphased genotypes for the sequenced samples (i.e., no phased reference haplotypes used).
The impact of imputation was assessed by examining imputed and non-imputed values, comparing with a complete-case analysis, and exploring the sensitivity of imputation models.
These results suggest that the impact of imputation accuracy on genomic prediction not only depends on imputation accuracy, but also on the number of markers in the lower density panel.
To evaluate the potential impact of imputation on ICER, plots from both the imputed sample and the complete case analysis were generated.
The impact of imputation error rate on the reliability of GEBV is expected to be relatively small, because the imputation error rate was small (0.41%).
This has not been specifically investigated, but some studies have focused on the impact of imputation from low-density panels on reliability of genomic selection [ 14, 30] and concluded that an imputation error rate between 2 and 3% leads to a mean loss of reliability of 2%.
We measured each method's impact on imputation by computing the MSE and Pearson correlation coefficient (r) between the imputed z-scores and true z-scores.
The impact of imputation on the coverage of low frequency variation is significant.
Mulder et al. [ 15] analyzed the impact of imputation errors on the reliability of GEBV.
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