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. 2015 Mar 11;16(1):168. doi: 10.1186/s12864-015-1366-y

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© He et al.; licensee BioMed Central. 2015

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Imputing improves accuracy of prediction of genomic selection based on GBS-like data sets. Correlation between true and predicted genotypic values applying genomic selection for the original 90 k SNP data set (GBLUP, 0%), as well as for genotype-by-sequencing-like data sets where missing values were not imputed (GBLUP) or were imputed with map-dependent (Beagle, FImpute and IMPUTE2) and map-independent (Random Forest) algorithms for rates of missing values of 72.8%, 61.5%, 38.8% and 16.1% in the total population of 371 lines.