方法对比

	HKA检验	F统计量 (FST)	麦克唐纳-克莱特曼检验	Tajima's D (选择扫描)
领域	遗传学	遗传学	遗传学	遗传学
方法族	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1987	1951	1991	1989
提出者	Richard Hudson, Martin Kreitman & Montserrat Aguade	Sewall Wright	James McDonald & Martin Kreitman	Fumio Tajima
类型	Statistical test	Population differentiation measure	Hypothesis test	Neutrality test
开创性文献	Hudson, R. R., Kreitman, M., & Aguadé, M. (1987). A test of neutral molecular evolution based on nucleotide data. Genetics, 116(1), 153–159.	Wright, S. (1951). The genetical structure of populations. Annals of Eugenics, 15(4), 323–354.	McDonald, J. H., & Kreitman, M. (1991). Adaptive protein evolution at the Adh locus in Drosophila. Nature, 351(6328), 652–654.	Tajima, F. (1989). Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics, 123(3), 585–595.
别名	HKA test, Polymorphism divergence test	FST, Wright's F-statistics, Population differentiation index	MK test, Positive selection test	Tajima's D test, Selective sweep analysis, Neutrality test
相关	4	4	4	4
摘要	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.	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 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.	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.