手法を比較

	Selection Sweep (Tajima's D)	Coalescent Theory	HKAテスト
分野	遺伝学	遺伝学	遺伝学
系統	Process / pipeline	Process / pipeline	Process / pipeline
提唱年	1989	1982	1987
提唱者	Fumio Tajima	John Kingman	Richard Hudson, Martin Kreitman & Montserrat Aguade
種類	Neutrality test	Stochastic process model	Statistical test
原典	Tajima, F. (1989). Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics, 123(3), 585–595.	Kingman, J. F. C. (1982). The coalescent. Stochastic Processes and their Applications, 13(3), 235–248.	Hudson, R. R., Kreitman, M., & Aguadé, M. (1987). A test of neutral molecular evolution based on nucleotide data. Genetics, 116(1), 153–159.
別名	Tajima's D test, Selective sweep analysis, Neutrality test	Kingman Coalescent, n-coalescent	HKA test, Polymorphism divergence test
関連	4	4	4
概要	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.	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.	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.