Sentence examples for imputation of error from inspiring English sources

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Whether one sees this suggested explanation as a step forward, it should by now be clear that we have moved beyond considerations of the contextualist's imputation of error to ordinary speakers per se.

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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%).

We now derive several measurements of imputation error to evaluate the difference in imputation accuracy between the internal and external panels.

Figure  1 shows the number of validation animals per class of imputation error rate, for method C and datasets 1 and 2. The distribution was skewed, with the largest number of animals in the lower imputation error rate classes.

The second measure of imputation error was the agreement between genotypes determined by sequencing and genotypes determined by imputation for a subset of stage 1 cases and controls for whom both genotypes were available.

The objective of this research was to quantify the potential of GBS for GS in outbred populations of livestock in the absence of imputation and error correction methods.

Deviations from this assumption provide a source of imputation error that was not treated in our analysis.

This result is inconsistent with previous studies on genomic predictions using imputed 54K genotype data from a 3K marker panel in Nordic and French [ 27] and German Holstein populations [ 28], in which, on average, each 1% of imputation allele error rate resulted in a loss in prediction reliability of 1.3% points.

Therefore, for GBS to be a viable alternative to current genotyping approaches with SNP arrays in livestock, it needs to be competitive in the absence of imputation and error correction methods.

Figure  4 presents the relationship of the imputation error rate with the degree of genetic relationship between training and validation populations (RT/V).

Algorithms and software for imputation and error correction of NGS data in livestock have yet to be developed to capitalize on their unique population structure and the available information (e.g., pedigree information, large family sizes and close relatives).

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