ArticleBMC bioinformatics2020
imputeqc: an R package for assessing imputation quality of genotypes and optimizing imputation parameters.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Knockoff-Based Fine Mapping of MS-Associated SNPs in Sardinian Trios.Biochemical genetics · 2026Article
- Genomic insights into population structure, genetic diversity, and key traits of Jiangxi indigenous pigs.BMC genomics · 2025Article
- Genome-wide scans for signatures of selection in North African sheep reveals differentially selected regions between fat- and thin-tailed breeds.Animal genetics · 2025Article
- Whole genome sequencing reveals signals of adaptive admixture in Creole cattle.Scientific reports · 2023Article
- A comprehensive analysis of the genetic diversity and environmental adaptability in worldwide Merino and Merino-derived sheep breeds.Genetics, selection, evolution : GSE · 2023Article
- Genome-wide mapping of signatures of selection using a high-density array identified candidate genes for growth traits and local adaptation in chickens.Genetics, selection, evolution : GSE · 2023Article
- Integrative bioinformatics analysis of potential therapeutic targets and immune infiltration characteristics in dilated cardiomyopathy.Annals of translational medicine · 2022Article
- Selection signature analysis and genome-wide divergence of South African Merino breeds from their founders.Frontiers in genetics · 2022Article
- Identification of Genetic Loci for Sugarcane Leaf Angle at Different Developmental Stages by Genome-Wide Association Study.Frontiers in plant science · 2022Article
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2 authors.
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Abstract
backgroundThe imputation of genotypes increases the power of genome-wide association studies. However, the imputation quality should be assessed in each particular case. Nevertheless, not all imputation softwares control the error of output, e.g., the last release of fastPHASE program (1.4.8) lacks such an option. In this particular software there is also an uncertainty in choosing the model parameters. fastPHASE is based on haplotype clusters, which size should be set a priori. The parameter influences the results of imputation and downstream analysis.
resultsWe present a software toolkit imputeqc to assess the imputation quality and/or to choose the model parameters for imputation. We demonstrate the efficacy of toolkit for evaluation of imputations made with both fastPHASE and BEAGLE software for HapMap and 1000 Genomes data. The discordance of genotypes received correlated well in both methods. Using imputeqc, we also shown how to choose the optimal number of haplotype clusters and expectation-maximization cycles for fastPHASE program. The found number of haplotype clusters of 25 was further applied for hapFLK testing that revealed signatures of selection at LCT region on chromosome 2. We also demonstrated how to decrease the computational time in the case of hapFLK testing from 3 days to 20 h.
conclusionsThe toolkit is implemented as an R package imputeqc and command line scripts. The code is freely available at https://github.com/inzilico/imputeqc under the MIT license.
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