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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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Linkage disequilibrium influences the accuracy of imputing missing values. The relationship between linkage disequilibrium (as measured by r² between 90 k SNPs and the respective most closely linked 9 k SNPs) and the average correlation between observed and imputed genotypic data, as calculated using map-dependent (Beagle, FImpute, and IMPUTE2) and map-independent (Random Forest) imputation algorithms, for a reference population size of 50 out of 371 lines. Trends are shown as boxplot displays separately for three minor allele frequency (MAF) classes.