Abstract
Evolution is a random process. Random events enter in many ways, from errors in copying genetic material to small and large scale environmental changes, but the most basic source of randomness that we must understand is due to reproduction in a finite population leading to random genetic drift. The simplest model of random genetic drift was developed independently by Sewall Wright and R.A. Fisher and is known as the Wright–Fisher model.We consider a population in which every individual is equally likely to mate with every other and in which all individuals experience the same conditions. Such a population is called panmictic.We also suppose that the population is neutral (everyone has an equal chance of reproductive success). Most species are either haploid meaning that they have a single copy of each chromosome (for example, most bacteria), or diploid meaning that they have two copies of each chromosome (for example, humans). We suppose that the population is haploid, so that each individual has exactly one parent. Although in a diploid population individuals have two parents, each gene can be traced to a single parental gene in the previous generation and so it is customary in this setting to model the genes in a diploid population of size N as a haploid population of size 2N.1 As we shall see in Sect. 5.6, this device fails once we are interested in tracing several genes at the same time.
Keywords
- Effective Population Size
- Fisher Model
- Diploid Population
- Random Genetic Drift
- Much Recent Common Ancestor
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© 2011 Springer-Verlag Berlin Heidelberg
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Etheridge, A. (2011). Mutation and Random Genetic Drift. In: Some Mathematical Models from Population Genetics. Lecture Notes in Mathematics(), vol 2012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16632-7_2
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DOI: https://doi.org/10.1007/978-3-642-16632-7_2
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Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-642-16632-7
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