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Aufsatz
Enlarging a training set for genomic selction by imputation of un-genotyped animals in populations of varying genetic architecture
(BioMed Central, 2013)
Background: The most common application of imputation is to infer genotypes of a high-density panel of markers
on animals that are genotyped for a low-density panel. However, the increase in accuracy of genomic predictions
resulting from an increase in the number of markers tends to reach a plateau beyond a certain density. Another
application of imputation is to increase the size of the training set with un-genotyped animals. This strategy can be
particularly successful when a set of closely related individuals ...