Copy-number variation genotyping of GSTT1 and GSTM1 gene deletions by real-time PCR

Rose-Zerilli, M.J., Barton, S.J., Henderson, A.J., Shaheen, S.O. and Holloway, J.W. (2009) Copy-number variation genotyping of GSTT1 and GSTM1 gene deletions by real-time PCR Clinical Chemistry, 55, (9), pp. 1680-1685. (doi:10.1373/clinchem.2008.120105).


Full text not available from this repository.


Background: Structural variation in the human genome is increasingly recognized as being highly prevalent and having relevance to common human diseases. Array-based comparative genome-hybridization technology can be used to determine copy-number variation (CNV) across entire genomes, and quantitative PCR (qPCR) can be used to validate de novo variation or assays of common CNV in disease-association studies. Analysis of large qPCR data sets can be complicated and time-consuming, however.
Methods: We describe qPCR assays for GSTM1 (glutathione S-transferase mu 1) and GSTT1 (glutathione S-transferase theta 1) gene deletions that can genotype up to 192 samples in duplicate 5-µL reaction volumes in <2 h on the ABI Prism 7900HT Sequence Detection System. To streamline data handling and analysis of these CNVs by qPCR, we developed a novel interactive, macro-driven Microsoft Excel® spreadsheet. As proof of principle, we used our software to analyze CNV data for 1478 DNA samples from a family-based cohort.
Results: With only 8 ng of DNA template, we assigned CNV genotypes (i.e., 2, 1, or 0 copies) to either 96% (GSTM1) or 91% (GSTT1) of all DNA samples in a single round of PCR amplification. Genotyping accuracy, as ascertained by familial inheritance, was >99.5%, and independent genotype assignments with replicate real-time PCR runs were 100% concordant.
Conclusions: The genotyping assay for GSTM1 and GSTT1 gene deletion is suitable for large genetic epidemiologic studies and is a highly effective analysis system that is readily adaptable to analysis of other CNVs

Item Type: Article
Digital Object Identifier (DOI): doi:10.1373/clinchem.2008.120105
ePrint ID: 69838
Date :
Date Event
July 2009Published
Date Deposited: 08 Dec 2009
Last Modified: 18 Apr 2017 21:09
Further Information:Google Scholar

Actions (login required)

View Item View Item