Сравнение методов

	Тест HKA	Коалесцентная теория	F-статистики (FST)
Область	Генетика	Генетика	Генетика
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Год появления	1987	1982	1951
Автор метода	Richard Hudson, Martin Kreitman & Montserrat Aguade	John Kingman	Sewall Wright
Тип	Statistical test	Stochastic process model	Population differentiation measure
Основополагающий источник	Hudson, R. R., Kreitman, M., & Aguadé, M. (1987). A test of neutral molecular evolution based on nucleotide data. Genetics, 116(1), 153–159.	Kingman, J. F. C. (1982). The coalescent. Stochastic Processes and their Applications, 13(3), 235–248.	Wright, S. (1951). The genetical structure of populations. Annals of Eugenics, 15(4), 323–354.
Другие названия	HKA test, Polymorphism divergence test	Kingman Coalescent, n-coalescent	FST, Wright's F-statistics, Population differentiation index
Связанные	4	4	4
Сводка	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.	Coalescent theory is a probabilistic framework that traces the genealogical history of DNA sequences backward in time to their most recent common ancestor. Developed by John Kingman in 1982, this method forms the foundation of modern population genetics, enabling researchers to understand demographic events, estimate genetic parameters, and reconstruct evolutionary histories from modern genetic data.	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.