@article{excoffier_analysis_1992,
	title = {Analysis of molecular variance inferred from metric distances among {DNA} haplotypes: application to human mitochondrial {DNA} restriction data.},
	volume = {131},
	issn = {1943-2631},
	shorttitle = {Analysis of molecular variance inferred from metric distances among {DNA} haplotypes},
	url = {https://academic.oup.com/genetics/article/131/2/479/6007328},
	doi = {10.1093/genetics/131.2.479},
	abstract = {We present herea framework for the study ofmolecularvariationwithin a singlespecies. Information on DNA haplotypedivergence is incorporated into an analysisofvariance format, derived from a matrix of squared-distancesamong all pairs of haplotypes.This analysis of molecular variance (AMOVA) produces estimates of variance components and F-statistic analogs, designated here as @-statisticsr,eflecting the correlation of haplotypic diversityat different levels of hierarchical subdivision. The method is flexible enough toaccommodateseveral alternative input matrices, corresponding to different types of moleculardata, as well asdifferent types of evolutionary assumptions, without modifyingthe basic structure of the analysis. The significance of the variance components and @-statisticsis tested using a permutational approach, eliminating the normality assumption that is conventional for analysis of variance but inappropriate for molecular data. Application of AMOVA to human mitochondrialDNA haplotype data shows that population subdivisionsare better resolved whensomemeasureofmolecular differences among haplotypes is introduced into the analysis. Atthe intraspecific level, howevetrh, e additionalinformation provided by knowing the exact phylogenetic relations among haplotypes or by a nonlinear translation of restriction-site changeinto nucleotide diversity does not significantly modify the inferred population genetic structure. Monte Carlo studies show that site sampling doesnot fundamentally affectthe significance of the molecular variance components. The AMOVA treatment is easily extended in several different directions and it constitutesa coherent and flexible frameworkfor the statistical analysis of moleculardata.},
	language = {en},
	number = {2},
	urldate = {2024-01-21},
	journal = {Genetics},
	author = {Excoffier, L and Smouse, P E and Quattro, J M},
	month = jun,
	year = {1992},
	pages = {479--491},
}

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Information on DNA haplotypedivergence is incorporated into an analysisofvariance format, derived from a matrix of squared-distancesamong all pairs of haplotypes.This analysis of molecular variance (AMOVA) produces estimates of variance components and F-statistic analogs, designated here as @-statisticsr,eflecting the correlation of haplotypic diversityat different levels of hierarchical subdivision. The method is flexible enough toaccommodateseveral alternative input matrices, corresponding to different types of moleculardata, as well asdifferent types of evolutionary assumptions, without modifyingthe basic structure of the analysis. The significance of the variance components and @-statisticsis tested using a permutational approach, eliminating the normality assumption that is conventional for analysis of variance but inappropriate for molecular data. Application of AMOVA to human mitochondrialDNA haplotype data shows that population subdivisionsare better resolved whensomemeasureofmolecular differences among haplotypes is introduced into the analysis. Atthe intraspecific level, howevetrh, e additionalinformation provided by knowing the exact phylogenetic relations among haplotypes or by a nonlinear translation of restriction-site changeinto nucleotide diversity does not significantly modify the inferred population genetic structure. Monte Carlo studies show that site sampling doesnot fundamentally affectthe significance of the molecular variance components. The AMOVA treatment is easily extended in several different directions and it constitutesa coherent and flexible frameworkfor the statistical analysis of moleculardata.","language":"en","number":"2","urldate":"2024-01-21","journal":"Genetics","author":[{"propositions":[],"lastnames":["Excoffier"],"firstnames":["L"],"suffixes":[]},{"propositions":[],"lastnames":["Smouse"],"firstnames":["P","E"],"suffixes":[]},{"propositions":[],"lastnames":["Quattro"],"firstnames":["J","M"],"suffixes":[]}],"month":"June","year":"1992","pages":"479–491","bibtex":"@article{excoffier_analysis_1992,\n\ttitle = {Analysis of molecular variance inferred from metric distances among {DNA} haplotypes: application to human mitochondrial {DNA} restriction data.},\n\tvolume = {131},\n\tissn = {1943-2631},\n\tshorttitle = {Analysis of molecular variance inferred from metric distances among {DNA} haplotypes},\n\turl = {https://academic.oup.com/genetics/article/131/2/479/6007328},\n\tdoi = {10.1093/genetics/131.2.479},\n\tabstract = {We present herea framework for the study ofmolecularvariationwithin a singlespecies. Information on DNA haplotypedivergence is incorporated into an analysisofvariance format, derived from a matrix of squared-distancesamong all pairs of haplotypes.This analysis of molecular variance (AMOVA) produces estimates of variance components and F-statistic analogs, designated here as @-statisticsr,eflecting the correlation of haplotypic diversityat different levels of hierarchical subdivision. The method is flexible enough toaccommodateseveral alternative input matrices, corresponding to different types of moleculardata, as well asdifferent types of evolutionary assumptions, without modifyingthe basic structure of the analysis. The significance of the variance components and @-statisticsis tested using a permutational approach, eliminating the normality assumption that is conventional for analysis of variance but inappropriate for molecular data. Application of AMOVA to human mitochondrialDNA haplotype data shows that population subdivisionsare better resolved whensomemeasureofmolecular differences among haplotypes is introduced into the analysis. Atthe intraspecific level, howevetrh, e additionalinformation provided by knowing the exact phylogenetic relations among haplotypes or by a nonlinear translation of restriction-site changeinto nucleotide diversity does not significantly modify the inferred population genetic structure. Monte Carlo studies show that site sampling doesnot fundamentally affectthe significance of the molecular variance components. 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