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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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The term in front of the sums guarantees an unbiased estimator, which does not depend on how many sequences you sample. [2] Nucleotide diversity is a measure of genetic variation. It is usually associated with other statistical measures of population diversity, and is similar to expected heterozygosity.
This paper described a method to use the site frequency spectrum to estimate whether a population is evolving neutrally, evolving under directional selection, or evolving under balancing selection. This test statistic, which is known as Tajima's D, became a widely used test for neutrality among population geneticists. [8] [9]
Watterson's estimator is commonly used for its simplicity. When its assumptions are met, the estimator is unbiased and the variance of the estimator decreases with increasing sample size or recombination rate. However, the estimator can be biased by population structure.
Thus "long branches" in a dN/dS analysis can lead to underestimates of both dN and dS, and the longer the branch, the harder it is to correct for the introduced noise. [3] Of course, the ancestral sequence is usually unknown, and two lineages being compared will have been evolving in parallel since their last common ancestor.
An SDRF file is a tab-delimited file describing the relationships between samples, arrays, data, and other objects used or produced in a microarray investigation. For simple experimental designs, constructing the SDRF file is straightforward, and even complex loop designs can be expressed in this format.
He did this via the relative rate test and then, using this data, he was able to construct a phylogeny using various methods, including parsimony and maximum likelihood. [6] He took the same approach in another experiment to compare humans to other primates, and found no significant difference in evolutionary rates.