Comparer des méthodes

	Test HKA	Statistiques F (FST)
Domaine	Génétique	Génétique
Famille	Process / pipeline	Process / pipeline
Année d'origine	1987	1951
Auteur d'origine	Richard Hudson, Martin Kreitman & Montserrat Aguade	Sewall Wright
Type	Statistical test	Population differentiation measure
Source fondatrice	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.
Alias	HKA test, Polymorphism divergence test	FST, Wright's F-statistics, Population differentiation index
Apparentées	4	4
Résumé	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.