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Tajima's D is a population genetic test statistic created by and named after the Japanese researcher Fumio Tajima. [1] Tajima's D is computed as the difference between two measures of genetic diversity: the mean number of pairwise differences and the number of segregating sites, each scaled so that they are expected to be the same in a neutrally evolving population of constant size.
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Fumio Tajima was born in Ōkawa, in Japan's Fukuoka prefecture, in 1951. [1] [2] He graduated from high school in 1970, completed his undergraduate degree at Kyushu University in 1976, and received a Master's degree from the same institution in 1978. [3]
The allele frequency spectrum can be written as the vector = (,,,,), where is the number of observed sites with derived allele frequency .In this example, the observed allele frequency spectrum is (,,,,), due to four instances of a single observed derived allele at a particular SNP loci, two instances of two derived alleles, and so on.
Tajima's D is based on the expectation that S = theta * x where x is the sum of 1/i for i from 1 to N. Thus, we turn this into a method to estimate theta by noting that theta = E(S)/x. The current version suggests that S/x part is a "normalized" version of segregating sites, and this leads to a mistake in the calculation of D in the example.
Another approach offered by MEGA is Tajima's relative rate test. This method compares the number of substitutions per site between different sequences. If the resulting numbers differ by a large factor, the molecular clock hypothesis may not be valid for the given data set. [36]
A number of different Markov models of DNA sequence evolution have been proposed. [1] These substitution models differ in terms of the parameters used to describe the rates at which one nucleotide replaces another during evolution.
Fay and Wu's H is a statistical test created by and named after two researchers Justin Fay and Chung-I Wu. [1] The purpose of the test is to distinguish between a DNA sequence evolving randomly ("neutrally") and one evolving under positive selection.