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If clicking does not initiate a download, try right clicking or control clicking and choosing "Save" or "Download".(The run link is disabled for this model because it was made in a version prior to NetLogo 6.0, which NetLogo Web requires.)


This model is a simplistic simulation of the spread of a genetic disease with an autosomal recessive inheritance pattern in a small, isolated human population. It illustrates how allelic frequencies vary from generation to generation.

Genetic inheritance has risen to prominence as medical professionals have realized the implications of family health knowledge. Genetic diseases, indicators of heart, blood. and addiction problems, and even certain cancers can be predicted and measures can be taken to prevent the onset of these medical issues. Although genetic diseases are considered rare, twenty percent of all infant deaths are due to birth defects and genetic conditions.There are between 6,000 and 18,000 known single-gene disorders, affecting one in every 200 births, and only a small fraction of these have treatments. Additional study in this field could lead to the production of a wider range of more effective treatments and perhaps even cures for genetic diseases.

The model examines the effects of certain variables on the allelic frequencies of the gene pool. The user controls several aspects of the child-bearing relationships modeled. Exploration of these variables can provide insight into the genetic trends scientists have observed in natural populations and show why recessive genes cannot become completely extinct.


The model uses "couples" to represent two people in a committed relationship. Individuals wander around the world when they are not in couples. Upon coming into contact with a partner, the two individuals "couple" together. When this happens, the two individuals no longer move about, and instead stand next to each other holding hands as a representation of their child-bearing relationship.

Once in a couple, the two genotypes of the "parents" are considered before a child is born, and the child is then assigned a genotype based on Punnett square predictions of the parents' offspring. It is a 50/50 chance between the child being born a girl or boy.

The genotypes of the individuals in the population are represented by the colors of the individuals. Three colors are used: blue individuals are homozygous dominant (AA), green individuals are heterozygous (Aa), and yellow individuals are homozygous recessive (aa).


The SETUP button creates individuals with certain genotypes and randomly distributes them throughout the world. Once the simulation has been setup, it is now ready to run. The GO button starts the simulation and runs it continuously, with a carrying capacity set at 2,000 individuals.

Monitors show the percentage of individuals with each genotype as well as the frequency of each allele in the population. In this model each timestep is considered one month; the number of months that have passed is shown in the toolbar.

Here is a summary of the sliders in the model. They are explained in more detail below.

- numpeople
- avg-num-children
- avg-couple-length
- mutation-rate
- avg-num-couples

Numpeople is used to determine the initial population at the start of the simulation. Smaller populations occasionally exhibit genetic drift (tendency towards either homozygous dominant or recessive).

Avg-num-children is used to determine the average number of children a couple will have before splitting up. More children means more variety.

Avg-couple-length is used to determine the amount of time a couple has spent in a relationship.

Mutation rate is used to determine how often a de novo mutation arises in a "healthy" individual, causing them to contract the disease.

Avg-num-couples is used to determine how many child-bearing relationships one person will be in before they die.

The model's plot shows the total number of individuals (color), the number of homozygous dominant individuals (blue), the number of heterozygous individuals (green), and the number of homozygous recessive individuals (yellow).


If certain variables are set at low values, the population could eventually die out.
As in real life, the dominant allele usually becomes more frequent in the gene pool than the recessive allele unless the mutation rate is unnaturally high. However, the recessive allele never truly becomes extinct due to de novo mutations.


Run a number of experiments with the GO button to find out the effects of different variable on the dominant and recessive allele frequencies. In addition to changing one variable at a time, try changing multiple variables together to see if those factors interact at all.

Form hypotheses about how allelic frequencies will change with the adjustment of different variables and then test these to see if the results match your expectations.

A few real-world values for some of the variables are included below:

U.S.A - between 1 and 2
Latin America - between 2 and 3
Middle East - between 3 and 5

avg-num-couples: roughly 1

avg-couple-length: 45 years (USA)

mutation-rate: 2.22%


Like all computer simulations of human behaviors, this model has necessarily simplified its subject area substantially. The model therefore provides numerous opportunities for extension:

The model depicts a very simplistic gene at one locus controlled entirely by two alleles which exhibit complete dominance. In the real world, many genetic diseases are caused by a myriad of different factors.

The model depicts an autosomal genetic disease. To extend the model further, one could model an x-linked disease or a case of genomic imprinting (expression of allele in offspring depends on whether allele is inherited from male or female parent).


All statistics in the Info tab come from the Centers for Disease Control and Prevention.
The AIDS model in the NetLogo library was particularly helpful in setting up the coupling procedure:
Wilensky, U. (1997). NetLogo AIDS model. Center for Connected Learning and Computer-Based Modeling, Northwestern Unversity, Evanston, IL.
None of this would have been possible without the internship opportunity provided by the SEAP program and my mentor, Prof. Sanchez.

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