FPG Simulates a population of constant size
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simulation simulate simulator constant cross constant volume constant signal constant population mutation population display population world population world population displayer population counter Population Clock population estimate population checker constant popups constant value Constant Name Lister List Constant Name Constant Name population genetics evolution population distribution Constant Calculator population genetics Simulate Population Bottleneck Population Bottleneck rate constant fitter population genetic simulator Population Simulator Population Dynamics population monitor inhibition constant determine constant autotetraploid population simulate constant acceleration population admixture estimate population parameters effective population size population genetic structure simulate population population simulation demographic population genetic population define population groups population group haploid population population stratification analyze population population analysis population-genetic simulation dissociation constant

FPG Description

The name FPG stands for Forward Population Genetic simulation. FPG simulates a population of constant size that is undergoing various evolutionary processes, including: mutation, recombination, natural selection, and migration. The meaning of "forward" in this context is simply that time, within the simulation, moves forward just as it does in the real world. This is in contrast to coalescent population genetic simulation in which time, as represented within the simulation, proceeds back into the past. Coalescent simulations have many advantages, but they are unwieldy if they incorporate natural selection on multiple sites. FPG is useful for assessing the impact of natural selection on patterns of genetic variation. It is designed so as to be able to approximate real world situations with fairly large population sizes and high mutation rates over long stretches of DNA. The mutation model is an infinite sites model, meaning that no site that is segregating in the population can receive another mutation. The simulation accommodates neutral, beneficial and deleterious mutations under several different fitness models, including additive, multiplicative and epistatic fitness models. The program generates a wide variety of analyses, including polymorphism levels, heterozygosity (observed and expected), fixation rates, and linkage disequilibrium - all conducted for each of several categories of mutation. When migration in invoked, several analyses regarding population structure are carried out.. The basic life cycle alternates between haploid genomes (gametes) and diploid genomes (zygotes), and proceeds as follows: - Gametes are randomly paired into zygotes - Fitness is measured for each zygote as a function of the mutations it carries. If dominance is set to 0.5, then the model is similar in most respects to a haploid selection model. - Reproductive success is the number of gametes contributed to the next generation, and is determined for every zygote by drawing from a multinomial distribution with expectations that are the relative fitness values of each fitness class. - Recombination and mutation occur during the production of gametes - If there are multiple populations, gametes are drawn at random from each population and placed at random into other populations. Those gametes that are displaced by migrants are lost, so migration adds an extra element of genetic drift. This is an island-model of migration - New zygotes are formed by random union of gametes. Generations are non-overlapping.

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