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This article is part of the supplement: Research from the Eleventh International Workshop on Network Tools and Applications in Biology (NETTAB 2011)

Open Access Research

Imputation reliability on DNA biallelic markers for drug metabolism studies

Vladan Mijatovic1, Ilaria Iacobucci2, Marco Sazzini3, Luciano Xumerle1, Antonio Mori1, Pier Franco Pignatti1, Giovanni Martinelli2 and Giovanni Malerba1*

Author affiliations

1 Dep. of Life and Reproductions Sciences, Sec. of Biology and Genetics - University of Verona, strada Le grazie 8, 37134 Verona - Italy

2 Dep. of Hematology and Oncological Sciences - University of Bologna, Via Massarenti, 9 40138 Bologna, Italy

3 Dep. of Experimental Evolutionary Biology - University of Bologna, via Selmi 3, 40126 Bologna, Italy

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Citation and License

BMC Bioinformatics 2012, 13(Suppl 14):S7  doi:10.1186/1471-2105-13-S14-S7

Published: 7 September 2012

Abstract

Background

Imputation is a statistical process used to predict genotypes of loci not directly assayed in a sample of individuals. Our goal is to measure the performance of imputation in predicting the genotype of the best known gene polymorphisms involved in drug metabolism using a common SNP array genotyping platform generally exploited in genome wide association studies.

Methods

Thirty-nine (39) individuals were genotyped with both Affymetrix Genome Wide Human SNP 6.0 (AFFY) and Affymetrix DMET Plus (DMET) platforms. AFFY and DMET contain nearly 900000 and 1931 markers respectively. We used a 1000 Genomes Pilot + HapMap 3 reference panel. Imputation was performed using the computer program Impute, version 2. SNPs contained in DMET, but not imputed, were analysed studying markers around their chromosome regions. The efficacy of the imputation was measured evaluating the number of successfully imputed SNPs (SSNPs).

Results

The imputation predicted the genotypes of 654 SNPs not present in the AFFY array, but contained in the DMET array. Approximately 1000 SNPs were not annotated in the reference panel and therefore they could not be directly imputed. After testing three different imputed genotype calling threshold (IGCT), we observed that imputation performs at its best for IGCT value equal to 50%, with rate of SSNPs (MAF > 0.05) equal to 85%.

Conclusions

Most of the genes involved in drug metabolism can be imputed with high efficacy using standard genome-wide genotyping platforms and imputing procedures.