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Several imputation methods have been implemented and are widely used to impute genotypes from one chip to another.
Second, we developed and implemented several imputation methods for GBS data and evaluated the performance of prediction approaches using imputed data.
Several imputation methods are available and their usefulness in association studies has been demonstrated, but factors affecting their relative performance in accuracy have not been systematically investigated.
Several imputation methods using various statistical models such as the haplotype-clustering algorithm [12], the hidden Markov model (HMM) [5], and the Markov Chain model [13], have been proposed.
Several imputation methods should be avoided, in particular PMS imputation.
Several imputation methods are currently available, based on different statistical models.
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Several imputations methods produce bias on psychometrical indexes (generally, the imputation methods artificially improve the psychometric qualities of the scale).
Dealing with missing data will include various imputation methods.
We compared LD-kNNi, as implemented in LinkImpute, with several other imputation methods and programs that do not require a reference panel: Mode: The modal value of all other samples' genotypes at the SNP of interest.
When a higher proportion of patients have missing data, the FDA recommends the use of several different imputation methods (including a worst-case scenario in which missing data are assumed to be unfavorable for those on the investigational treatment and favorable for those in the control group) and an assessment of the consistency of the study results using each method.
Using genotyping-by-sequencing data from diverse and heterozygous accessions of apples, grapes, and maize, we compare LD-kNNi with several genotype imputation methods and show that LD-kNNi is fast, comparable in accuracy to the best-existing methods, and exhibits the least bias in allele frequency estimates.
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