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Assistance in Determining Net Fitness Component Values

Assistance in Determining Net Fitness Component Values

For additional information, go to Program Library


About This Program
This program is a prototype for a model developed by Dr. William Muir and Dr. Richard Howard. Muir and Howard have reduced overall fitness to six critical control points: juvenile and adult viability, age at sexual maturity, female fecundity, male fertility, and mating success. They then incorporated these components into a mathematical model that integrates them into a single prediction of risk.

Information Systems for Biotechnology is working with Dr. Muir to make the model available for testing. The immediate objective is to test the validity of the model and to refine it. One long-term goal is to adapt the model to other organisms, specifically plants, so that the risk of releasing transgenic organisms to the environment can be assessed by addressing the probability of exposure to the hazard, rather than the probability of harm given exposure.

For additional information, click to the "Library" link at the top of any page, or Go There Now.


Existing Models

The Model names listed in the Existing Models select box are previously defined parameter sets that have been stored in your storage area. You may click on one of them to load the stored parameter values and use as they are, or change any parameters to create a new model. The new model values, under a new name, will be stored in your storage area. The word "example" is a reserved word and cannot be used as part of a new model name.


Example Models

The Model names listed in the Example Models select box are permanent parameter values that can be used as a starting point for designing your own models. You may click on one of them to load the stored parameter values and use as they are, or change any parameters to create a new model. The new model values, under a new name, will be stored in your storage area. The word "example" is a reserved word and cannot be used as part of a new model name.


Overwrite if model name already exists?

If you have created a storage area for your models, you can choose to overwrite existing model names, or change the name to save modifications to the loaded model under a new name. You must rename any file selected from the Examples List before it can be saved, since the word "example" is a reserved word and cannot be used as part of a new model name.


Terminology
The model is based on the assumption that transgene expression is completely dominant and that individuals hemizygous for the transgene (Tw) have the same phenotype as homozygous (TT) individuals. Heterozygous individuals are termed hemizygous because there is no complementary allele for the transgene. Nevertheless, the absent allele at that locus may be represented as w and the transgene allele may be represented as T. In the following notation, the transgenics' genotypes TT and Tw are designated as subscripts 2 and 3, respectively.

ww - Wild typeTw - Hemizygous for the transgeneTT - Homozygous for the transgene

Relative Mating Success (mj) and (fi )
As with juvenile viability, mating success of each genotype can be determined either directly or indirectly. Direct observation is the simplest, but may not be possible in all species. With direct observation, staged mating trials are conducted that allow for both mate competition and mate choice. The mating frequency of each genotype is recorded.4,5 Because the effect of the transgene on mating success can vary with its relative frequency, such trials should also be performed using different ratios of genotypes.

With indirect observation, transgenic and wild type males and females in various ratios are placed together in a setting and allowed to mate. Muir and Howard call these trials `mating sets.' Several different mating sets, and replications of mating sets, are conducted. After mating, the identity of the male parent is inferred from the genotypes of the progeny. In the simplest case, a single wild-type female would be introduced into a tank containing a transgenic and wild-type male. Any transgenic offspring would immediately identify the genotype of the male parent. Assume for this example an equal mating success of transgenic and wild-type males and females. (Values must be positive for males)


ww - Wild typeTw - Hemizygous for the transgeneTT - Homozygous for the transgene

Male Fertility (rj )
Fertility is more difficult to determine than fecundity and is inferred from the number of eggs fertilized by alternative genotypes. We examined the ability of the male genotype to fertilize eggs with a simple, completely randomized design experiment in which 10 transgenic males and 10 wild-type females were randomly single-pair mated with wild-type females in separate 40-liter tanks for eight days. The first three egg masses produced by each female in that eight-day period were collected and incubated in a hatching tank. Twenty-four hours later, the eggs were examined under a dissecting microscope and classified as fertile or infertile based on presence or absence of embryo development. Assume for the medaka example that both wild-type and transgenic males have fertility rates of 95%. A value of 1.0 indicates 100% success; .9, 90%; and so on. (Values must be greater than or equal to 0 and less than or equal to 1)

ww - Wild typeTw - Hemizygous for the transgeneTT - Homozygous for the transgene

Female Fecundity (cj)
Fecundity is straightforward. For medaka, estimating fecundity is a simple matter of counting the number of eggs produced from each genotype. The genotypes should be the same age and several fish should be measured. For this example, assume wild-type fish produce an average of 8.8 eggs per spawn and transgenic produce 11.4. (Values must be greater than or equal to 2)

ww - Wild typeTw - Hemizygous for the transgeneTT - Homozygous for the transgene

Number Of Males or Females In Initial Population
Wild type (ww): Values must be greater than or equal to 1000;
Tw and TT values must be greater than or equal to 0.

ww - Wild typeTw - Hemizygous for the transgeneTT - Homozygous for the transgene

Juvenile Viability (vj)
Juvenile viability is simply defined as survival from the embryo to the age of sexual maturity (or approaching sexual maturity). There are several ways to estimate this component. The simplest experiment would be to establish two pure breeding lines (TT and ww) and, starting with a known number of fertile eggs, count the number that survive to sexual maturity. The experiment should be conducted under environmental conditions that closely approximate the natural environment into which they might escape. This experiment should be replicated several times (2 to 10). The average percent survival of each genotype (v'j) is converted to per day viabilities (vj) occurring between consecutive census time periods (at+1 and at) by assuming a log-linear reduction in daily viability between time periods. Viability can then be described by the following equation:


For example, if 10 replicates of 1000 fry results in an average of 175 transgenic and 250 wild-type individuals surviving to 56 days of age, the per day survival rate is calculated thus:

An alternative method that addresses the issue of background genotype, but does not require the offspring to be genotyped, is given by Muir and Howard.1,2 This method is based on the theory that the only difference between survival of an intercross and a backcross is the segregation ratio of 3:1 vs. 1:1. If the viabilities of each genotype are the same, then the expected survival for each cross would be the same. If viability of the transgenic genotype is less than that of wild type, then total survival of the intercross will be less than that of a backcross. Preferably the wild-type line is representative of the native fish in the area into which the fish might escape. In this way, the background genotype of both transgenic and wild-type fish are taken into account.

The procedure is to cross the homozygous transgenic line with a wild-type strain to produce the F1 generation. The cross F1 is then intercrossed to produce the F2 generation and the F1 is also backcrossed to the wild type stock to produce the BC1 generation. The number of fish that survive from hatching to sexual maturity (or approaching sexual maturity) is recorded. The relative viabilities are then found by the method of maximum likelihood as shown by Muir and Howard.1


ww - Wild typeTw - Hemizygous for the transgeneTT - Homozygous for the transgene

Adult Viability (uj)
Ideally, adult survival of each genotype would be measured in as natural an environment as possible until most fish die. Because this may take a very long time, an alternative method is to assume a log-linear reduction in daily viability and observe only enough time periods to establish a trend.

For example, assume 1000 fish of each genotype are observed for 100 days past sexual maturity. The proportion surviving at the termination of the experiment of each genotype (u'j) is 90% for wild-type and transgenic individuals. The daily reduction in survival (uj) is determined using the following equation:


In our example, at+1- at = 100 days. Thus

ww - Wild typeTw - Hemizygous for the transgeneTT - Homozygous for the transgene

Age at Sexual Maturity (sj)
Age at sexual maturity can be straightforward or difficult, depending on the species. For medaka, age at sexual maturity was recorded as the age at which females first produced eggs. We assumed the males to be mature at the same age. For other species, it may be necessary to sacrifice the animals at various ages and observe gonadal development. Assume for this example that ages at sexual maturity are 56 and 49 days after hatching for wild type and transgenic fish, respectively. Values must be greater than or equal to 1.

ww - Wild typeTw - Hemizygous for the transgeneTT - Homozygous for the transgene

Reproductive Longevity
Currently defaulted to 300 days.

Exponential Decay Parameter (b Constant)
Before starting to calculate fitness, one usually assumes a stable wild-type population age distribution. The initial age distribution is set using an exponential decay parameter `b'. The value of this parameter is found by trial and error—the fitness values are set for transgenic fish the same as for wild-type, i.e., the initial population consists only of wild-type fish, or at least fish that do not differ in their fitness. The value of b is found such that the initial and stable age distribution, determined after many generations, is similar. This parameter is not critical to the program, but establishes at least a reasonable starting distribution. Using these above components, the constant value of b for a stable age distribution was found to be 0.93.

Use Proportional Constraint
This is a complex parameter that will be explained in the future. Unless you are thoroughly familiar with its function, it should be left ON.

Getting Started
The basic sequence is as follows:
1. The program provides an initial set of values as a reference.
2. Provide appropriate values based on your data.
3. Supply a name for the model.
4. Click on to see the resulting graph.

The values are used to calculate x,y values. These values are then used to generate graphs of the data.


More Detail:
1. The initial values provided are for medaka. These values can be changed.

2. Click on any underlined parameter value for additional information.

3. Supply a new name for the model. The word "example" is not allowed in model names.

4. If you are only interested in testing the program, click . Your model values will not be saved, Or,
5. If you wish to save your model values or create different sets of model values for future use or comparison, create a Storage Area as described below.

Creating a Storage Area

6. Example models, like medaka, and your stored value files (listed under "Existing Models" will be displayed at the top of the model values screen. You may select any one of these to load for further modification, and either overwrite the filename or enter a new name.

7. After you have specified the values, click on . The program should then display a graph based on the values you have supplied.

8. Once the graph is displayed, you can select multiple models to graph simultaneously for comparison, or select different data types to graph (Gene Frequency, Population Size, or Age Distribution.) Available models will be listed at the top of each screen.

9. Links to files of calculated points for each model, and minimum and maximum x and y values for each graph are available below that graph. The file can be downloaded and the minimum and maximum x, y values can be imported into other graphing programs, spreadsheets, and statistical programs. Though every point is calculated, a decimation factor is applied for graph files to optimize speed and accuracy.


Library

Articles and Overviews:

Part 1: Potential Environmental Risks and Hazards of Biotechnology, from ISB News Report, November 2001

Part 2: Potential Environmental Risks and Hazards of Biotechnology, from ISB News Report, February 2002

Beware of Trojans bearing fish. TRENDS in Ecology and Evolution. August 2001.

Net Fitness Approach to Risk Assessment Powerpoint Presentation (63Mb).

Article and Abstract from Purdue News


Published Papers:

Assessment of possible ecological risks and hazards of transgenic fish with implications for other sexually reproducing organisms. Transgenic Research. 2002.

Possible ecological risks of transgenic organism release when transgenes affect mating success: Sexual selection and the Trojan gene hypothesis. Proceedings of the National Academy of Sciences. November 1999.

Fitness Components and Ecological Risk of Transgenic Release: A Model Using Japanese Medaka (Oryzias latipes). The American Naturalist. July 2001.


Associated Links:

Dr. Muir's Web Site with Additional Information