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In agreement with the literature [ 14, 19, 24, 25], imputation accuracy tended to increase as the relatedness between imputed and reference animals increased for both imputation methods.
Previous studies showed that increasing the number of close relatives between imputed and reference individuals increased imputation accuracy [ 9- 11, 32].
Another factor that influenced imputation accuracy was the level of relatedness between imputed and reference animals.
Regardless of the imputation software used, accuracy tended to increase as the relatedness between imputed and reference animals increased, especially for the 7 K chip.
Imputation accuracies tended to be higher as the relatedness between imputed and reference animals increased.
Previous studies indicated that the relationship between imputed and reference individuals is one of the major factors that affects performance of imputation [ 3, 6, 20]; Hayes et al. [ 3] reported that it could account for up to 64%% of the variation in accuracy of imputation in sheep.
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In this scenario, the quality of imputation was assessed by the correlation between imputed and masked true genotypes per individual and/or per SNP.
We excluded SNPs with minor allele frequency of <0.01 and those with poor imputation quality based on the estimated correlation between imputed and true genotypes (r < 0.3).
Our previous study has shown that imputation accuracy is primarily determined by the LD between imputed and typed SNPs, and their MAF.
If imputation was based on a selected reference set, imputation accuracies were also based on the Pearson correlation coefficient of each imputed SNP across test individuals, as well as on the Pearson correlation coefficient between imputed and observed genotypes for each test individual.
Nonetheless, genes that were lowly expressed in all samples showed poor correlations between imputed and observed expression across the 52 samples we used to test the imputation model.
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