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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.
This shows that the results do not depend on the imputation of missing values.
The potential for imputed genotypes to enhance an analysis of genetic data depends largely on the accuracy of imputation, which in turn depends on properties of the reference panel of template haplotypes used to perform the imputation.
The 'range' of such local sensitivity analyses will depend on the between imputation variance of the estimator, which is indirectly related to the proportion of missing observations.
Depending on the imputation algorithm used to predict missing SNP imputation methods vary in their error rates (Wang et al, 2012).
Interactions are treated differently, depending on which imputation method is used.
Depending on the imputation method used, 6−13% of the test results changed qualitatively at the 5% level.
The reason is that multiple imputation degrees of freedom not only depend on the number of imputations but also inversely relate to the RVI.
With RAN, Beagle and LDMIP gave very similar accuracies, but for the lists that depend on relationships, LDMIP achieved greater imputation accuracy; this might be as expected since LDMIP uses family information for the imputation.
The method makes maximal use of the available genotype data, is computationally efficient via use of matrix algebra and does not depend on problematic solutions such as imputation.
If we assume that missing values might depend on θ, we should include θ into the imputation model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com