Analyses and Comparison of Accuracy of Different Genotype Imputation Methods

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.

Identification of loci affecting teat number by genome-wide association studies on three pig populations

“…More currently, genotype imputation and meta-analysis have been explored in GWAS of genetic diseases or phenotypic traits [15,18,19]. Genotype imputation is a reliable tool to equate different sets of markers from multiple platforms [18].…”

1000 Genomes-based imputation identifies novel and refined associations for the Wellcome Trust Case Control Consortium phase 1 Data

“…Both MaCH and BEAGLE have been recommended for practical use because of their user-friendly interface and computational efficiency. [23][24][25][26] We adopted a conservative genome-wide significance threshold 2.5Â10 À8 to guard against false positives particularly given that we are testing seven phenotypic traits instead of a single one. The fact that we only have genome-wide significant signals from four wellestablished regions suggests that our conservative threshold fulfilled its purpose.…”

Genome-wide association studies - A summary for theclinical gastroenterologist

“…Imputation has already been available in software packages such as PHASE and fastPHASE [42,43] , however recent implementations such as MACH (http://www.sph.umich.edu/csg/abecasis/MaCH/), IMPUTE [44] and Beagle [45] specifically aimed at GWAS data are recommended [46] and have been shown to produce accurate and reliable results [47] . There are some differences between the software packages, with MACH and IMPUTE having the edge [47,48] , but in general, they produce comparable results.…”