ArticleBMC medical genomics2012
Evaluation of the imputation performance of the program IMPUTE in an admixed sample from Mexico City using several model designs.
Article in BMC medical genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Population-specific optimization of polygenic risk scores for breast cancer in East Asians.Genetics in medicine open · 2026Article
- AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.Nature communications · 2024Article
- ImputAccur: fast and user-friendly calculation of genotype-imputation accuracy-measures.BMC bioinformatics · 2022Article
- Iam hiQ-a novel pair of accuracy indices for imputed genotypes.BMC bioinformatics · 2022Article
- Genotype imputation performance of three reference panels using African ancestry individuals.Human genetics · 2018Article
- Validation of genotype imputation in Southeast Asian populations and the effect of single nucleotide polymorphism annotation on imputation outcome.BMC medical genetics · 2018Article
- GenomeLaser: fast and accurate haplotyping from pedigree genotypes.Bioinformatics (Oxford, England) · 2015Article
- GIGI: an approach to effective imputation of dense genotypes on large pedigrees.American journal of human genetics · 2013Article
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7 authors.
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Abstract
backgroundWe explored the imputation performance of the program IMPUTE in an admixed sample from Mexico City. The following issues were evaluated: (a) the impact of different reference panels (HapMap vs. 1000 Genomes) on imputation; (b) potential differences in imputation performance between single-step vs. two-step (phasing and imputation) approaches; (c) the effect of different INFO score thresholds on imputation performance and (d) imputation performance in common vs. rare markers.
methodsThe sample from Mexico City comprised 1,310 individuals genotyped with the Affymetrix 5.0 array. We randomly masked 5% of the markers directly genotyped on chromosome 12 (n=1,046) and compared the imputed genotypes with the microarray genotype calls. Imputation was carried out with the program IMPUTE. The concordance rates between the imputed and observed genotypes were used as a measure of imputation accuracy and the proportion of non-missing genotypes as a measure of imputation efficacy.
resultsThe single-step imputation approach produced slightly higher concordance rates than the two-step strategy (99.1% vs. 98.4% when using the HapMap phase II combined panel), but at the expense of a lower proportion of non-missing genotypes (85.5% vs. 90.1%). The 1,000 Genomes reference sample produced similar concordance rates to the HapMap phase II panel (98.4% for both datasets, using the two-step strategy). However, the 1000 Genomes reference sample increased substantially the proportion of non-missing genotypes (94.7% vs. 90.1%). Rare variants (<1%) had lower imputation accuracy and efficacy than common markers.
conclusionsThe program IMPUTE had an excellent imputation performance for common alleles in an admixed sample from Mexico City, which has primarily Native American (62%) and European (33%) contributions. Genotype concordances were higher than 98.4% using all the imputation strategies, in spite of the fact that no Native American samples are present in the HapMap and 1000 Genomes reference panels. The best balance of imputation accuracy and efficiency was obtained with the 1,000 Genomes panel. Rare variants were not captured effectively by any of the available panels, emphasizing the need to be cautious in the interpretation of association results for imputed rare variants.
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