Copy-number variation genotyping of GSTT1 and GSTM1 gene deletions by real-time PCR
Copy-number variation genotyping of GSTT1 and GSTM1 gene deletions by real-time PCR
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
1680-1685
Rose-Zerilli, M.J.
08b3afa4-dbc2-4c0d-a852-2a9f33431199
Barton, S.J.
4f674382-ca0b-44ad-9670-e71a0b134ef0
Henderson, A.J.
fed528f9-ccf9-4aca-85b8-e6d7da9cc8c3
Shaheen, S.O.
ae8e3194-c8a7-4f38-a71f-32da3ad0ea21
Holloway, J.W.
4bbd77e6-c095-445d-a36b-a50a72f6fe1a
July 2009
Rose-Zerilli, M.J.
08b3afa4-dbc2-4c0d-a852-2a9f33431199
Barton, S.J.
4f674382-ca0b-44ad-9670-e71a0b134ef0
Henderson, A.J.
fed528f9-ccf9-4aca-85b8-e6d7da9cc8c3
Shaheen, S.O.
ae8e3194-c8a7-4f38-a71f-32da3ad0ea21
Holloway, J.W.
4bbd77e6-c095-445d-a36b-a50a72f6fe1a
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), .
(doi:10.1373/clinchem.2008.120105).
Abstract
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
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Published date: July 2009
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Local EPrints ID: 69838
URI: http://eprints.soton.ac.uk/id/eprint/69838
PURE UUID: 84c1c15f-1f70-4b55-a4c2-5d7996ff6502
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Date deposited: 08 Dec 2009
Last modified: 14 Mar 2024 02:56
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Author:
A.J. Henderson
Author:
S.O. Shaheen
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