Sentence examples for differences in the imputation from inspiring English sources
These three populations allowed us to ascertain population-specific differences in the imputation quality of SNPs.
The additional variance caused by differences in the imputed values between the various copies reflects the uncertainty of the imputation.
Our confidence intervals for this parameter are wider than those of NIOSH/NCI which must be due to differences in the method of imputation for the large percentages of nondetected CO samples (30%—61%, Table 4).
Thus, we conclude that having a genotyped sire is not the main cause of the differences in imputation accuracies for crossbred animals when imputation used a reference population that consisted of Landrace pigs only compared to Yorkshire pigs only.
In order to explore possible causes of differences in imputation accuracy between SNPs, imputation accuracy and average pairwise r g 2 were plotted against SNP position (bp).
Large differences in the computational requirements for the imputation methods were observed (Table 3).
The differences in imputation quality when using the combined reference set for more frequent alleles were either very small or not significant (Supplementary Figure S1, Supplementary Tables S2 and S3).
None of these studies has reported substantial differences in the performances of joint modelling imputation and chained equations imputation.
The following issues were evaluated in this project: (a) the impact of different reference panels (HapMap and 1000 Genomes) on imputation; (b) potential differences in imputation performance between single-step vs. two-step (phasing and imputation) approaches; (c) the impact of different INFO score thresholds on imputation performance and (d) imputation performance in common vs. rare markers.
The following issues were evaluated: (a) the impact of different reference panels (HapMap vs. 1000 Genomes) on imputation; (b) potential differences in imputation performance between single-step vs. two-step (phasing and imputation) approaches; (c) the effect of different INFO score thresholds on imputation performance and (d) imputation performance in common vs. rare markers.
Given the indication from our analysis (Table 2) that the different imputations can give important differences in the estimated slope, we conducted a multiple imputation approach based on maximum-likelihood estimation to assign values for CO that were below the limit of detection.
