Tajima's D Calculator

Tajima's D is a population genetics statistic that tests whether DNA sequence variation in a sample follows neutral evolution — a value near zero suggests no selection, while positive or negative values point to balancing or purifying/directional selection. Enter your sample size, number of segregating sites, nucleotide diversity (π), and sequence length into the Tajima's D Calculator to get the Tajima's D statistic and its biological interpretation. Secondary outputs include Watterson's Theta, Pi Estimate, and Variance of D, with an optional sliding window analysis mode. Also try the Molecular Clock Calculator.

Number of sequences or chromosomes sampled

Total number of polymorphic sites in the sequence

Average number of nucleotide differences per site between sequences

Total length of the analyzed sequence in base pairs

Size of sliding window for analysis (only for sliding window method)

Results

Tajima's D Statistic

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Watterson's Theta (θw)

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Pi Estimate (θπ)

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Variance of D

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Interpretation

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Frequently Asked Questions

What is Tajima's D statistic and what does it measure?

Tajima's D is a population genetics statistic that tests the neutral mutation hypothesis by comparing two estimates of genetic diversity: nucleotide diversity (π) and Watterson's theta (θw). It detects deviations from neutral evolution caused by selection or demographic changes. See also our Founder Effect Calculator.

How do I interpret Tajima's D values?

D = 0 indicates neutrality, D > 0 suggests balancing selection or population bottleneck, and D < 0 indicates directional selection or population expansion. Values outside ±2 are typically considered statistically significant.

What is the difference between nucleotide diversity (π) and Watterson's theta (θw)?

Nucleotide diversity (π) measures the average number of differences per site between sequences, while Watterson's theta (θw) estimates diversity based only on the number of segregating sites. Their comparison forms the basis of Tajima's D.

What sample size do I need for reliable Tajima's D calculations?

A minimum of 10-20 sequences is recommended, though larger samples (50+) provide more statistical power. Very small samples may give unreliable results due to high variance. You might also find our Coalescence Time Calculator useful.

When should I use sliding window analysis for Tajima's D?

Sliding window analysis is useful for detecting localized selection or demographic effects across a longer genomic region. It reveals spatial patterns of selection that might be masked in genome-wide averages.

Can Tajima's D detect different types of natural selection?

Yes, Tajima's D can detect balancing selection (positive values), directional/purifying selection (negative values), and demographic effects like population bottlenecks or expansions that mimic selection signatures.

What factors can affect Tajima's D besides selection?

Population structure, demographic history (bottlenecks, expansions), migration, recombination rate variation, and linkage to selected sites can all influence Tajima's D values independently of local selection.