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The 2006 08 dataset contained sufficient missing data to warrant imputation (between 8% and 14% for most covariates).
To compare the performance of imputation between purebred and crossbred animals, first imputation from 5 K to 8 K was evaluated, which was applied to the common set of 7940 SNPs.
Imputation has been shown to be accurate: imputation between two different 50 k chips with approximately 10 k SNPs in common resulted in an allelic imputation error rate of 1.0% when using DAGPHASE, and when approximately 1000 animals had genotypes on both chips [ 2].
Imputation between the two recombination hotspots encompassing each of the 24 loci that showed evidence of association in Phase I was accomplished with Impute v2 using two reference panels: 1000 Genomes Project (b36) for wide coverage, and HapMap3 (r2 b36) for deep coverage [ 16, 20, 21].
When the testing individuals were genotyped with the L6k panel, there was little difference in accuracy of imputation between genotyping dams and granddams with the high-density panel, the low-density L6k, L3k, L384 panels or not genotyping them at all (0.981 - 0.996).
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The second aspect of interest is that age and female have the smallest within-imputation and between-imputation variances.
Increasing the SNP density of the LDP serves to reduce the considerable range in imputation accuracy between SNPs and between horses.
Accuracy of imputation ranged between -1 (opposite genotype imputed) and +1 (correct genotype imputed).
The estimated variance of the overall MI estimate allows for within-imputation (i.e. the uncertainty in the estimate within each completed dataset) and between- imputation (i.e. the uncertainty between the estimates across the completed datasets) variability [ 15, 16].
Imputation efficacy between platforms corresponds to the percentage of imputable SNPs which passed imputation QC (r > 0.3, MAF > 0.1, call rate > 0.95; Table 4).
We observed large differences in imputation accuracy between breeds.
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