Table of Contents
According to the first step, the evolutionary parameters were set to zero in order to meet the Hardy-Weinberg equilibrium. Also, the rate of migration has been set as zero in addition of Migrant R Allele Prop., rates of mutation set to zero. Here, relative fitness of the genotypes has been set as 1 and assertive mating as zero. The initial R Allele proportion has been set to 0.5, which is represented numerically as p here and is the dominant allele’s frequency. The runtime duration of the experiment has been considered as about 500 generations. Here, the process can be speeded up as well. Once these parameters are set, the results are then recorded. The next step is to reset the experiment and change the initial R proportion of Allele to 0.8. The experiment again set to run for about 500 generations, and the results are then to be recorded (Chen et al. 2019).
In case a chosen population is in Hardy-Weinberg equilibrium for a gene, it does not go through the process of evolution and the frequencies here will tend to stay at the same state across generations. Therefore, it can be concluded from this theory that across 500 generations there will be no change in spite of the different paraments being set and changed from time to time. While speeding up the process it has been noticed that there is not evolution on the selected samples, which holds true for Hardy-Weinberg equilibrium. In Hardy-Weinberg equilibrium, there are five fundamental assumptions, which are as follows- non-random mating, mutation, finite population size, gene flow, and natural selection. In case the assumptions listed here are not met for a given gene sample, there might be evolution of the population for that particular gene. However, here, the allele frequencies of the gene may differ or get changed.
The tendency of different population found across the environment is to evolve and proceed from their previous position of survival via evolution and natural development. Some of the examples include the evolution of apes to eventually human. Primarily, during the process of evolution, the genes of the evolving species gets altered gradually leading to certain changes in their bodies. Other parameters like the way of survival depends on this as well. This process occurs over generations and take millions and billions of years to reach a stagnant state. The genetic makeup is primarily altered during this change, which may at times be subtle (Fountain et al. 2016). Generally, the changes take place due to natural circumstances. However, there might be some rare occurrences where migration of new organisms into a given population or occurrence of random events might influence these changes in genetic constituency and give rise to evolution. This is often referred as the term “luck of the draw”. There are certain criteria that needs to be fulfilled in order to initiate evolution, and the process of which may get hindered by certain principles, one of them being Hardy-Weinberg equilibrium.
The second experiment deals with the circumstances that is related to the scenario “if Hardy-Weinberg equilibrium is not met”. In this case, the proportions of Allele can be changed, or new sets of the same might get introduced.
In order to prove this hypothesis theoretically, the experiment has been started anew. Excluding the rate of migration, all the parameters of evolution are kept at zero, which is considered the ideal initializing stage for any species. The migration rate is changed to 0.5 along with the Migrant R Allele Prop set to 1. This represents migration of orange fish having genotype RR within the fishbowl. Here, the initial ‘R’ Allele Proportion was set to 0.5 and the experiment was made to run for approximately 500 generations, after which the results are recorded and analyzed.
There are some conditions that needs to be applied in this scenario, one of them being the result obtained in case the initial frequency of the dominant allele is higher. In order to determine the result, the experiment was reset and the initial R allele proportion was changed to 0.8. Furthermore, the experiment was set to run for approximately 500 generations. Once all these parameters are set and the experiment has been run successfully, the results are then recorded and analyzed (Gonzalez-Galarza et al. 2018).
The second condition here is related to the scenario and the result if less numbers of orange RR fish are migrating into the bowl. In this case the experiment is again reset and the initial R allele proportion is changed to 0.5 along with setting the Migration rate to 0.1. Similar to the previous situation, the experiment is allowed to run for about 500 generations after which the records are analyzed.
Similarly, the fourth section here deals with the case when the number of fish migrating into the fishbowl have the similar sets of frequencies as that of the fishes that have already migrated into the fishbowl. Therefore, in order to obtain a fresh set of results, the experiment is reset and the initial R allele proportion has been set to 0.5, which is similar to that of the previous scenario. Furthermore, the rate of migration is set to 0.5 and the migrant R allele prop set to 0.5 as well. Again, the experiment was run for about 500 generations, after which the results are recorded and analyzed.
Unlike the previous experiment, in this experiment changes and evolution of the species will be seen as Hardy-Weinberg equilibrium is not at play here. The specific sets of parameters that has been set under different conditions will lead to different result. Although the results and rate of evolution of species for each scenario might differ, all of them will surely show some kind of evolution and changes as the experiment has been allowed to run for about 500 generations. The species of fish under this scenario as can be seen from the results were under a continuous evolution phase throughout the process. Random mating along with other characteristics among the species like mutation, gene flow, and increase in population size has been noticed here for all the given conditions and scenarios (Harpak, Bhaskar & Pritchard, 2016).
Moreover, when the case of migration is concerned, the migrants become larger with the increase in the difference between resident’s allele frequencies and allele frequencies of the migrant individuals. Therefore, the effect of migrants increases here as well with an increased chance of altercations in the genetic constituencies of the resident population. The change in allele frequencies within a given set of species falls under the category of microevolution, which also reflects the changes in DNA of these species over time. All these changes might be possible due to factors like mutations, which is responsible for introducing new sets of alleles into the chosen population set. In other terms, microevolution can also be referred commonly as evolution that takes place in a small scale, and where altercations in the frequency of gene variants along with mutations and other changes can be experienced on the population sample. The way the population’s change over time is referred as population genetics according to biology (Linck & Battey, 2019).
Mutations has the capacity to alter the proportions of allele. Here, the new allele frequency previously was 0.0, which is now greater than the same figure. In the given scenario, the experiment has again been reset in order to study and analyze the effects of mutation over the allele frequencies within the population over certain time period. All the evolutionary parameters have been set to zero and the mutation rate has been changed to 0.01for R to r. Furthermore, the initial R allele population was set to 0.5 and the experiment was allowed to run for about 500 generations. The results are then recorded and analyzed.
While undergoing the process of mutation, change in DNA occurs at a specific locus point in a set of population or a single organism. It is considered as a weak force in the change or altercations of allele frequencies as seen from the results. However, it can be a strong force for the introduction of new sets of alleles. This is due to the fact that small sizes of populations have a smaller number of alleles which can be possible due to drifts in their genes. This can also be supported by the fact that fewer mutations occur in small sets of populations. All these facts and figures are supported by the result that has been generated in the given experiment.
The number of effective alleles in a given set of population is represented by 4Neu + 1. This is applicable for diploid organisms and here Ne represents the size of the effective populations and u is the rate of mutation (Warmuth & Ellegren, 2019).
This experiment is divided into two parts, which are related to natural selection and non-random mating respectively. For both of them the experiment criteria has been reset.
All the evolutionary parameters have been set to zero and the genotype relative fitness has been changed to 0.8 for Rr, 0.6 for RR, and 1 for rr. Furthermore, the initial R allele proportion has been set to 0.5 and the experiment was run for about 500 generations similar to the previous scenarios. The results are then recorded and analysed.
According to the results obtained, micro-evolution can be seen in the population sample, which can be caused die to the natural selection. Also, changes and altercations in the allele frequencies over time has been observed. As fitness increases, the alleles become more common within the population over the set number of generations, which here is around 500.
The genotype or phenotype are chosen by individuals according to their mate, that are relative to their own phenotype. It is likely that the individuals will try to find similar traits and characteristics within their partners. Here, the experiment has been reset and all the evolutionary parameters has been set to zero. However, this is not applicable for the strength of assortment, which is set to 0.6. Moreover, the initial R allele proportion has been set to 0.5, and the experiment is allowed to run for about 500 generations, after which the results are recorded.
According to the results, it can be seen that the non-random mating has the ability to alter the frequencies of genotypes, which can be for, or against the nature in certain scenarios (Warmuth & Ellegren, 2019).
Chen, N., Juric, I., Cosgrove, E. J., Bowman, R., Fitzpatrick, J. W., Schoech, S. J., ... & Coop, G. (2019). Allele frequency dynamics in a pedigreed natural population. Proceedings of the National Academy of Sciences, 116(6), 2158-2164. Retrieved from: https://www.pnas.org/content/pnas/116/6/2158.full.pdf
Fountain, T., Nieminen, M., Sirén, J., Wong, S. C., Lehtonen, R., & Hanski, I. (2016). Predictable allele frequency changes due to habitat fragmentation in the Glanville fritillary butterfly. Proceedings of the National Academy of Sciences, 113(10), 2678-2683. Retrieved from: https://www.pnas.org/content/pnas/113/10/2678.full.pdf
Gonzalez-Galarza, F. F., McCabe, A., dos Santos, E. J. M., Takeshita, L., Ghattaoraya, G., Jones, A. R., & Middleton, D. (2018). Allele frequency net database. In HLA Typing (pp. 49-62). Humana Press, New York, NY. Retrieved from: https://link.springer.com/protocol/10.1007/978-1-4939-8546-3_4
Harpak, A., Bhaskar, A., & Pritchard, J. K. (2016). Mutation rate variation is a primary determinant of the distribution of allele frequencies in humans. PLoS genetics, 12(12), e1006489. Retrieved from: https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006489
Linck, E., & Battey, C. J. (2019). Minor allele frequency thresholds strongly affect population structure inference with genomic data sets. Molecular Ecology Resources, 19(3), 639-647. Retrieved from: https://www.biorxiv.org/content/biorxiv/early/2017/09/14/188623.full.pdf
Warmuth, V. M., & Ellegren, H. (2019). Genotype‐free estimation of allele frequencies reduces bias and improves demographic inference from RADSeq data. Molecular ecology resources, 19(3), 586-596. Retrieved from: https://www.researchgate.net/profile/Vera_Warmuth/publication/293311858_Erratum_Reply_to_Forster_et_al_Quantifying_demic_movement_and_local_recruitment_in_the_spread_of_horse_domestication_Proceedings_of_the_National_Academy_of_Sciences_of_the_United_States_of_America_201/links/5d7636f6a6fdcc9961baaf0e/Erratum-Reply-to-Forster-et-al-Quantifying-demic-movement-and-local-recruitment-in-the-spread-of-horse-domestication-Proceedings-of-the-National-Academy-of-Sciences-of-the-United-States-of-America-201.pdf
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