ArticleBriefings in bioinformatics2015
Systematic assessment of imputation performance using the 1000 Genomes reference panels.
Article in Briefings in bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Genome-wide association meta-analysis of childhood ADHD symptoms and diagnosis identifies new loci and potential effector genes.Nature genetics · 2025Pooled it
- False positive findings during genome-wide association studies with imputation: influence of allele frequency and imputation accuracy.Human molecular genetics · 2021Pooled it
- Genome-wide association study of INDELs identified four novel susceptibility loci associated with lung cancer risk.International journal of cancer · 2020Pooled it
- Meta-analysis of sequence-based association studies across three cattle breeds reveals 25 QTL for fat and protein percentages in milk at nucleotide resolution.BMC genomics · 2017Pooled it
- Establishing Best Practices for Clinical GWAS: Tackling Imputation and Data Quality Challenges.International journal of molecular sciences · 2025Review
- Genotype imputation accuracy and the quality metrics of the minor ancestry in multi-ancestry reference panels.Briefings in bioinformatics · 2023Article
- Extent to which array genotyping and imputation with large reference panels approximate deep whole-genome sequencing.American journal of human genetics · 2022Article
- Exploring the optimal strategy of imputation from SNP array to whole-genome sequencing data in farm animals.Frontiers in genetics · 2022Article
- Gene-Based Variant Analysis of Whole-Exome Sequencing in Relation to Eosinophil Count.Frontiers in immunology · 2022Article
- Imputation accuracy to whole-genome sequence in Nellore cattle.Genetics, selection, evolution : GSE · 2021Article
- Assessment of Imputation Quality: Comparison of Phasing and Imputation Algorithms in Real Data.Frontiers in genetics · 2021Article
- Identification of intermediate-sized deletions and inference of their impact on gene expression in a human population.Genome medicine · 2019Article
- Odyssey: a semi-automated pipeline for phasing, imputation, and analysis of genome-wide genetic data.BMC bioinformatics · 2019Article
- Accuracy of imputation to whole-genome sequence in sheep.Genetics, selection, evolution : GSE · 2019Article
- Rare Variants Imputation in Admixed Populations: Comparison Across Reference Panels and Bioinformatics Tools.Frontiers in genetics · 2019Article
- Whole-genome sequence-based genomic prediction in laying chickens with different genomic relationship matrices to account for genetic architecture.Genetics, selection, evolution : GSE · 2017Article
- Comparison among three variant callers and assessment of the accuracy of imputation from SNP array data to whole-genome sequence level in chicken.BMC genomics · 2015Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Genotype imputation has been widely adopted in the postgenome-wide association studies (GWAS) era. Owing to its ability to accurately predict the genotypes of untyped variants, imputation greatly boosts variant density, allowing fine-mapping studies of GWAS loci and large-scale meta-analysis across different genotyping arrays. By leveraging genotype data from 90 whole-genome deeply sequenced individuals as the evaluation benchmark and the 1000 Genomes Project data as reference panels, we systematically examined four important issues related to genotype imputation practice. First, in a study of imputation accuracy, we found that IMPUTE2 and minimac have the best imputation performance among the three popular imputing software evaluated and that using a multi-population reference panel is beneficial. Second, the optimal imputation quality cutoff for removing poorly imputed variants varies according to the software used. Third, the major contributing factors to consistently poor imputation are low variant heterozygosity, high sequence similarity to other genomic regions, high GC content, segmental duplication and being far from genotyping markers. Lastly, in an evaluation of the imputability of all known GWAS regions, we found that GWAS loci associated with hematological measurements and immune system diseases are harder to impute, as compared with other human traits. Recommendations made based on the above findings may provide practical guidance for imputation exercise in future genetic studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.