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

Goedhart PW, van der Voet H, Baldacchino F, Arpaia S - Ecol Evol (2014)

Bottom Line: Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment.A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart.Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared.

View Article: PubMed Central - PubMed

Affiliation: Biometris, Plant Research International, Wageningen University and Research Centre P.O. Box 16, 6700 AA, Wageningen, The Netherlands.

ABSTRACT
Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess-zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided.

No MeSH data available.


Examples of probabilities of statistical distributions for counts for means μ = 1, 4, and 10. The variance of the overdispersed Poisson distribution equals ϕμ. The variance of the negative binomial and Poisson-lognormal equals μ + ωμ2.
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fig01: Examples of probabilities of statistical distributions for counts for means μ = 1, 4, and 10. The variance of the overdispersed Poisson distribution equals ϕμ. The variance of the negative binomial and Poisson-lognormal equals μ + ωμ2.

Mentions: The basic distribution for counts is the Poisson distribution. The Poisson distribution arises when events occur independently of each other but at a fixed rate in time or space. The number of events in a fixed time- or space-interval then follows a Poisson distribution. The theoretical variance of the Poisson distribution equals the mean μ. Examples of three Poisson distributions are given in Figure 1. The Poisson distribution assumes a fixed rate of events in time or space. However frequently this rate might vary in different time- or space-intervals. A common way to model this is to assume inter-subject variability, also called mixing. It is then assumed that a count X follows a Poisson distribution with mean Z, where Z itself is a random variable with mean μ and variance say τ2. The marginal mean of the distribution of X is then given by μ and the variance equals μ + τ2. Consequently the resulting distribution has a variance which is larger than the mean and this is termed over-dispersion. Three common ways to specify the mixing distribution of Z result in the overdispersed Poisson distribution, the negative binomial distribution and the Poisson-Lognormal distribution. These are described below.


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

Goedhart PW, van der Voet H, Baldacchino F, Arpaia S - Ecol Evol (2014)

Examples of probabilities of statistical distributions for counts for means μ = 1, 4, and 10. The variance of the overdispersed Poisson distribution equals ϕμ. The variance of the negative binomial and Poisson-lognormal equals μ + ωμ2.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4020688&req=5

fig01: Examples of probabilities of statistical distributions for counts for means μ = 1, 4, and 10. The variance of the overdispersed Poisson distribution equals ϕμ. The variance of the negative binomial and Poisson-lognormal equals μ + ωμ2.
Mentions: The basic distribution for counts is the Poisson distribution. The Poisson distribution arises when events occur independently of each other but at a fixed rate in time or space. The number of events in a fixed time- or space-interval then follows a Poisson distribution. The theoretical variance of the Poisson distribution equals the mean μ. Examples of three Poisson distributions are given in Figure 1. The Poisson distribution assumes a fixed rate of events in time or space. However frequently this rate might vary in different time- or space-intervals. A common way to model this is to assume inter-subject variability, also called mixing. It is then assumed that a count X follows a Poisson distribution with mean Z, where Z itself is a random variable with mean μ and variance say τ2. The marginal mean of the distribution of X is then given by μ and the variance equals μ + τ2. Consequently the resulting distribution has a variance which is larger than the mean and this is termed over-dispersion. Three common ways to specify the mixing distribution of Z result in the overdispersed Poisson distribution, the negative binomial distribution and the Poisson-Lognormal distribution. These are described below.

Bottom Line: Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment.A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart.Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared.

View Article: PubMed Central - PubMed

Affiliation: Biometris, Plant Research International, Wageningen University and Research Centre P.O. Box 16, 6700 AA, Wageningen, The Netherlands.

ABSTRACT
Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess-zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided.

No MeSH data available.