Literature DB >> 21324199

A statistical assessment of differences and equivalences between genetically modified and reference plant varieties.

Hilko van der Voet1, Joe N Perry, Billy Amzal, Claudia Paoletti.   

Abstract

BACKGROUND: Safety assessment of genetically modified organisms is currently often performed by comparative evaluation. However, natural variation of plant characteristics between commercial varieties is usually not considered explicitly in the statistical computations underlying the assessment.
RESULTS: Statistical methods are described for the assessment of the difference between a genetically modified (GM) plant variety and a conventional non-GM counterpart, and for the assessment of the equivalence between the GM variety and a group of reference plant varieties which have a history of safe use. It is proposed to present the results of both difference and equivalence testing for all relevant plant characteristics simultaneously in one or a few graphs, as an aid for further interpretation in safety assessment. A procedure is suggested to derive equivalence limits from the observed results for the reference plant varieties using a specific implementation of the linear mixed model. Three different equivalence tests are defined to classify any result in one of four equivalence classes. The performance of the proposed methods is investigated by a simulation study, and the methods are illustrated on compositional data from a field study on maize grain.
CONCLUSIONS: A clear distinction of practical relevance is shown between difference and equivalence testing. The proposed tests are shown to have appropriate performance characteristics by simulation, and the proposed simultaneous graphical representation of results was found to be helpful for the interpretation of results from a practical field trial data set.

Entities:  

Mesh:

Year:  2011        PMID: 21324199      PMCID: PMC3050699          DOI: 10.1186/1472-6750-11-15

Source DB:  PubMed          Journal:  BMC Biotechnol        ISSN: 1472-6750            Impact factor:   2.563


  18 in total

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3.  Statistical analysis used in the nutritional assessment of novel food using the proof of safety.

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4.  Natural variability of metabolites in maize grain: differences due to genetic background.

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6.  Results of a 90-day safety assurance study with rats fed grain from corn rootworm-protected corn.

Authors:  B Hammond; J Lemen; R Dudek; D Ward; C Jiang; M Nemeth; J Burns
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7.  Compositional assessment of event DAS-59122-7 maize using substantial equivalence.

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8.  Comparison of broiler performance when fed diets containing corn grain with insect-protected (corn rootworm and European corn borer) and herbicide-tolerant (glyphosate) traits, control corn, or commercial reference corn.

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  13 in total

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2.  Problem formulation and phenotypic characterisation for the development of novel crops.

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3.  Comments on the paper "A statistical assessment of differences and equivalences between genetically modified and reference plant varieties" by van der Voet et al. 2011.

Authors:  Keith J Ward; Margaret A Nemeth; Cavell Brownie; Bonnie Hong; Rod A Herman; Regina Oberdoerfer
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4.  The use of statistical tools in field testing of putative effects of genetically modified plants on nontarget organisms.

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5.  Stacking transgenic event DAS-Ø15Ø7-1 alters maize composition less than traditional breeding.

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6.  Safety Assessments and Multiplicity Adjustment: Comments on a Recent Paper.

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Journal:  J Agric Food Chem       Date:  2018-02-26       Impact factor: 5.279

7.  Comparative analysis of genetically-modified crops: Part 1. Conditional difference testing with a given genetic background.

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Journal:  PLoS One       Date:  2019-01-16       Impact factor: 3.240

8.  Field performance of transgenic citrus trees: assessment of the long-term expression of uidA and nptII transgenes and its impact on relevant agronomic and phenotypic characteristics.

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Review 9.  What risk assessments of genetically modified organisms can learn from institutional analyses of public health risks.

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10.  A statistical simulation model for field testing of non-target organisms in environmental risk assessment of genetically modified plants.

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