Sammenlign metoder

	Selection Sweep (Tajima's D)	F-statistikker (FST)	HKA-test	McDonald-Kreitman-testen
Fagområde	Genetik	Genetik	Genetik	Genetik
Familie	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Oprindelsesår	1989	1951	1987	1991
Ophavsperson	Fumio Tajima	Sewall Wright	Richard Hudson, Martin Kreitman & Montserrat Aguade	James McDonald & Martin Kreitman
Type	Neutrality test	Population differentiation measure	Statistical test	Hypothesis test
Oprindelig kilde	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.	McDonald, J. H., & Kreitman, M. (1991). Adaptive protein evolution at the Adh locus in Drosophila. Nature, 351(6328), 652–654.
Aliasser	Tajima's D test, Selective sweep analysis, Neutrality test	FST, Wright's F-statistics, Population differentiation index	HKA test, Polymorphism divergence test	MK test, Positive selection test
Relaterede	4	4	4	4
Resumé	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.	The McDonald-Kreitman (MK) test is a statistical method for detecting adaptive evolution by comparing ratios of synonymous and nonsynonymous substitutions within and between species. Developed by James McDonald and Martin Kreitman in 1991, this test exploits the key insight that neutral mutations accumulate at similar rates within and between species, while adaptive (nonsynonymous) substitutions should be enriched between species if they have been fixed by positive selection. The MK test has become a standard tool in molecular evolutionary biology for identifying genes under natural selection.