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Open Access Research article

Evaluation of the imputation performance of the program IMPUTE in an admixed sample from Mexico City using several model designs

S Krithika1, Adán Valladares-Salgado2, Jesus Peralta2, Jorge Escobedo-de La Peña3, Jesus Kumate-Rodríguez4, Miguel Cruz2 and Esteban J Parra1*

Author Affiliations

1 Department of Anthropology, University of Toronto at Mississauga, 3359 Mississauga Road North, Mississauga, ON, Canada

2 Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, IMSS, Av. Cuauhtemoc 330, Col. Doctores, C.P. 06720, Mexico City, Mexico

3 Unidad de Investigacion en Epidemiologia Clinica, Hospital General Regional 1, Dr Carlos McGregor, IMSS, Mexico City, Mexico

4 Fundacion IMSS, Mexico City, Mexico

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BMC Medical Genomics 2012, 5:12  doi:10.1186/1755-8794-5-12

Published: 1 May 2012

Abstract

Background

We 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 posterior genotype probability thresholds on imputation performance and (d) imputation performance in common vs. rare markers.

Methods

The 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.

Results

The 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.

Conclusions

The 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.