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Comparative Study
. 2008;3(10):e3551.
doi: 10.1371/journal.pone.0003551. Epub 2008 Oct 29.

Analyses and comparison of accuracy of different genotype imputation methods

Yu-Fang Pei  1 , Jian Li, Lei Zhang, Christopher J Papasian, Hong-Wen Deng
Affiliations
Comparative Study

Analyses and comparison of accuracy of different genotype imputation methods

Yu-Fang Pei et al. PLoS One. 2008.

Abstract

The power of genetic association analyses is often compromised by missing genotypic data which contributes to lack of significant findings, e.g., in in silico replication studies. One solution is to impute untyped SNPs from typed flanking markers, based on known linkage disequilibrium (LD) relationships. 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. Therefore, we investigated and compared the performance of five popular genotype imputation methods, MACH, IMPUTE, fastPHASE, PLINK and Beagle, to assess and compare the effects of factors that affect imputation accuracy rates (ARs). Our results showed that a stronger LD and a lower MAF for an untyped marker produced better ARs for all the five methods. We also observed that a greater number of haplotypes in the reference sample resulted in higher ARs for MACH, IMPUTE, PLINK and Beagle, but had little influence on the ARs for fastPHASE. In general, MACH and IMPUTE produced similar results and these two methods consistently outperformed fastPHASE, PLINK and Beagle. Our study is helpful in guiding application of imputation methods in association analyses when genotype data are missing.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Effects of LD level on accuracy rates.
The results are based on 90 reference haplotypes and a medium marker density (one SNP per 6 kb).
Figure 2
Figure 2. Effects of MAF of untyped SNPs on accuracy rates.
The results are based on 90 reference haplotypes and the medium marker density (1 SNP per 6 kb). (a) Low LD level; (b) Medium LD level; (c) High LD level.
Figure 3
Figure 3. Effects of marker density on accuracy rates.
The results are based on 90 reference haplotypes at the medium LD level. X-axis represents marker density: low marker density: one SNP per 10 kb; medium marker density: one SNP per 6 kb and high marker density: one SNP per 3 kb.
Figure 4
Figure 4. Effects of sample size of reference samples on accuracy rates under various conditions.
(a) Low LD level and high marker density (one SNP per 3 kb); (b) Medium LD level and medium marker density (one SNP per 6 kb); (c) High LD level and low marker density (one SNP per 10 kb).
Figure 5
Figure 5. Performance of the imputation methods under various conditions using real data sets.
Each label along x-axis represents a specific combination of LD level and marker density. Within each label, “L”, “M”, and “H” refer to, respectively, low, medium and high LD level when they are the first letter or marker density when they are the second letter.
Figure 6
Figure 6. Effects of MAF of untyped SNPs on accuracy rates in real datasets.
The results are based on the medium marker density (1 SNP per 6 kb). (a) Low LD level; (b) Medium LD level; (c) High LD level.
See this image and copyright information in PMC

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