Порівняння методів

	Selection Sweep (Tajima's D)	F-статистики (FST)	Тест HKA
Галузь	Генетика	Генетика	Генетика
Родина	Process / pipeline	Process / pipeline	Process / pipeline
Рік появи	1989	1951	1987
Автор методу	Fumio Tajima	Sewall Wright	Richard Hudson, Martin Kreitman & Montserrat Aguade
Тип	Neutrality test	Population differentiation measure	Statistical test
Основоположне джерело	Tajima, F. (1989). Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics, 123(3), 585–595.	Wright, S. (1951). The genetical structure of populations. Annals of Eugenics, 15(4), 323–354.	Hudson, R. R., Kreitman, M., & Aguadé, M. (1987). A test of neutral molecular evolution based on nucleotide data. Genetics, 116(1), 153–159.
Інші назви	Tajima's D test, Selective sweep analysis, Neutrality test	FST, Wright's F-statistics, Population differentiation index	HKA test, Polymorphism divergence test
Пов'язані	4	4	4
Підсумок	Tajima's D is a statistical test designed to detect selective sweeps—recent, rapid fixation of advantageous mutations—from patterns of genetic variation in DNA sequences. Developed by Fumio Tajima in 1989, this test measures deviations from neutrality by comparing different measures of DNA sequence diversity. A significant Tajima's D value indicates departure from neutral evolution, suggesting positive selection, population structure, or demographic events.	F-statistics are a family of measures developed by Sewall Wright to quantify population genetic structure and the degree of genetic differentiation between populations. FST, the most widely used F-statistic, measures the proportion of total genetic variation attributable to differences between populations versus within populations. FST ranges from zero (no differentiation) to one (complete differentiation). These statistics have become fundamental tools for understanding population structure, detecting population admixture, and analyzing the evolutionary forces shaping genetic variation.	The Hudson-Kreitman-Aguade (HKA) test is a statistical method that tests for neutral evolution by comparing levels of within-population polymorphism and between-population divergence at multiple loci. Developed by Hudson, Kreitman, and Aguade in 1987, this test uses the principle that neutral loci should show expected relationships between polymorphism and divergence. Loci deviating from these relationships are candidates for selection. The HKA test is particularly useful for detecting selection in genome-wide surveys because it uses relative comparisons across loci rather than requiring external calibration.