Cargando…

A statistical approach to quantification of genetically modified organisms (GMO) using frequency distributions

BACKGROUND: According to Regulation (EU) No 619/2011, trace amounts of non-authorised genetically modified organisms (GMO) in feed are tolerated within the EU if certain prerequisites are met. Tolerable traces must not exceed the so-called ‘minimum required performance limit’ (MRPL), which was defin...

Descripción completa

Detalles Bibliográficos
Autores principales: Gerdes, Lars, Busch, Ulrich, Pecoraro, Sven
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4279603/
https://www.ncbi.nlm.nih.gov/pubmed/25496015
http://dx.doi.org/10.1186/s12859-014-0407-x
_version_ 1782350726062669824
author Gerdes, Lars
Busch, Ulrich
Pecoraro, Sven
author_facet Gerdes, Lars
Busch, Ulrich
Pecoraro, Sven
author_sort Gerdes, Lars
collection PubMed
description BACKGROUND: According to Regulation (EU) No 619/2011, trace amounts of non-authorised genetically modified organisms (GMO) in feed are tolerated within the EU if certain prerequisites are met. Tolerable traces must not exceed the so-called ‘minimum required performance limit’ (MRPL), which was defined according to the mentioned regulation to correspond to 0.1% mass fraction per ingredient. Therefore, not yet authorised GMO (and some GMO whose approvals have expired) have to be quantified at very low level following the qualitative detection in genomic DNA extracted from feed samples. As the results of quantitative analysis can imply severe legal and financial consequences for producers or distributors of feed, the quantification results need to be utterly reliable. RESULTS: We developed a statistical approach to investigate the experimental measurement variability within one 96-well PCR plate. This approach visualises the frequency distribution as zygosity-corrected relative content of genetically modified material resulting from different combinations of transgene and reference gene Cq values. One application of it is the simulation of the consequences of varying parameters on measurement results. Parameters could be for example replicate numbers or baseline and threshold settings, measurement results could be for example median (class) and relative standard deviation (RSD). All calculations can be done using the built-in functions of Excel without any need for programming. The developed Excel spreadsheets are available (see section ‘Availability of supporting data’ for details). In most cases, the combination of four PCR replicates for each of the two DNA isolations already resulted in a relative standard deviation of 15% or less. CONCLUSIONS: The aims of the study are scientifically based suggestions for minimisation of uncertainty of measurement especially in —but not limited to— the field of GMO quantification at low concentration levels. Four PCR replicates for each of the two DNA isolations seem to be a reasonable minimum number to narrow down the possible spread of results. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0407-x) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-4279603
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-42796032015-01-22 A statistical approach to quantification of genetically modified organisms (GMO) using frequency distributions Gerdes, Lars Busch, Ulrich Pecoraro, Sven BMC Bioinformatics Research Article BACKGROUND: According to Regulation (EU) No 619/2011, trace amounts of non-authorised genetically modified organisms (GMO) in feed are tolerated within the EU if certain prerequisites are met. Tolerable traces must not exceed the so-called ‘minimum required performance limit’ (MRPL), which was defined according to the mentioned regulation to correspond to 0.1% mass fraction per ingredient. Therefore, not yet authorised GMO (and some GMO whose approvals have expired) have to be quantified at very low level following the qualitative detection in genomic DNA extracted from feed samples. As the results of quantitative analysis can imply severe legal and financial consequences for producers or distributors of feed, the quantification results need to be utterly reliable. RESULTS: We developed a statistical approach to investigate the experimental measurement variability within one 96-well PCR plate. This approach visualises the frequency distribution as zygosity-corrected relative content of genetically modified material resulting from different combinations of transgene and reference gene Cq values. One application of it is the simulation of the consequences of varying parameters on measurement results. Parameters could be for example replicate numbers or baseline and threshold settings, measurement results could be for example median (class) and relative standard deviation (RSD). All calculations can be done using the built-in functions of Excel without any need for programming. The developed Excel spreadsheets are available (see section ‘Availability of supporting data’ for details). In most cases, the combination of four PCR replicates for each of the two DNA isolations already resulted in a relative standard deviation of 15% or less. CONCLUSIONS: The aims of the study are scientifically based suggestions for minimisation of uncertainty of measurement especially in —but not limited to— the field of GMO quantification at low concentration levels. Four PCR replicates for each of the two DNA isolations seem to be a reasonable minimum number to narrow down the possible spread of results. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0407-x) contains supplementary material, which is available to authorized users. BioMed Central 2014-12-14 /pmc/articles/PMC4279603/ /pubmed/25496015 http://dx.doi.org/10.1186/s12859-014-0407-x Text en © Gerdes et al.; licensee BioMed Central. 2014 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Gerdes, Lars
Busch, Ulrich
Pecoraro, Sven
A statistical approach to quantification of genetically modified organisms (GMO) using frequency distributions
title A statistical approach to quantification of genetically modified organisms (GMO) using frequency distributions
title_full A statistical approach to quantification of genetically modified organisms (GMO) using frequency distributions
title_fullStr A statistical approach to quantification of genetically modified organisms (GMO) using frequency distributions
title_full_unstemmed A statistical approach to quantification of genetically modified organisms (GMO) using frequency distributions
title_short A statistical approach to quantification of genetically modified organisms (GMO) using frequency distributions
title_sort statistical approach to quantification of genetically modified organisms (gmo) using frequency distributions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4279603/
https://www.ncbi.nlm.nih.gov/pubmed/25496015
http://dx.doi.org/10.1186/s12859-014-0407-x
work_keys_str_mv AT gerdeslars astatisticalapproachtoquantificationofgeneticallymodifiedorganismsgmousingfrequencydistributions
AT buschulrich astatisticalapproachtoquantificationofgeneticallymodifiedorganismsgmousingfrequencydistributions
AT pecorarosven astatisticalapproachtoquantificationofgeneticallymodifiedorganismsgmousingfrequencydistributions
AT gerdeslars statisticalapproachtoquantificationofgeneticallymodifiedorganismsgmousingfrequencydistributions
AT buschulrich statisticalapproachtoquantificationofgeneticallymodifiedorganismsgmousingfrequencydistributions
AT pecorarosven statisticalapproachtoquantificationofgeneticallymodifiedorganismsgmousingfrequencydistributions