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Initially, research focused mainly on estimation of genomic breeding values [ 1- 3] and more recently, on imputation from low-density marker sets to denser marker sets [ 4- 6].
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This has not been specifically investigated, but some studies have focused on the impact of imputation from low-density panels on reliability of genomic selection [ 14, 30] and concluded that an imputation error rate between 2 and 3% leads to a mean loss of reliability of 2%.
Because there are more markers on longer haplotypes, accuracy of imputation from long haplotypes is higher compared to short haplotypes.
The objective of this study was to investigate the effect of reference population size used and the imputation method and its parameters on the error rate for imputation from the BovineSNP50 to the BovineHD chip.
Previous studies on combining reference populations for imputation from 50 k to High Density (HD) marker panels in cattle have shown no gain in accuracy [ 8, 11], Brøndum et al. however concluded that the imputation accuracy could be improved for animals with a mixed genetic background without decreasing the accuracy for the purebred animals when using a combined breed reference population [ 6].
Sep 2018 Voxelmorph (Probabilistic Diffeomorphic registration) is a Finalist for best paper (Young Scientist) award at MICCAI! Sep 2018 Our paper, led by Paolo Casale, on using Gaussian Process Priors in Variational Autoencoders, accepted at NeurIPS! Jul 2018 Our paper on Medical Image Imputation from Image Collections accepted at IEEE TMI: Transactions on Medical Imaging.
Correlations between DGV and DGV_IMP were, on average, 0.96 for all traits for imputation from 7K to 50K, and 0.89 for imputation from 3K to 50K.
The imputation method was validated against parallel Affymetrix microarray data [ 9], and revealed that imputation based on 60 landmark genes could accurately approximate global transcriptional patterns, with minimal improvement in accuracy for imputation from more than 60 genes.
The effect of imputation software and its parameters on imputation errors was obtained from analysis of dataset 1.
Table 3 shows the influence of marker information from relatives on imputation error rate and imputation accuracy.
Because we were missing some health plan costs, we relied on imputation to derive expected costs from the health plan perspective.
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