Analysis of molecular variance
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An Analysis of molecular variance is a statistical model of the molecular variance for a single species based on the ANOVA model.
- AKA: AMOVA.
- See: Variance, Statistical Model, ANOVA Algorithm.
References
2016
- (Wikipedia, 2016) ⇒ https://www.wikiwand.com/en/Analysis_of_molecular_variance Retrieved 2016-07-31
- Analysis of molecular variance (AMOVA), is a statistical model for the molecular variation in a single species, typically biological. The name and model are inspired by ANOVA. The method was developed by Laurent Excoffier, Peter Smouse and Joseph Quattro at Rutgers University in 1992.
- Since developing AMOVA, Excoffier has written a program for running such analyses. This program, which runs on Windows is called Arlequin, and is freely available on Excoffier's website. There is also an implementation by Sandrine Pavoine in R language in the ade4 package available on CRAN (Comprehensive R Archive Network). Another implementation is in Info-Gen, which also runs on Windows. The student version is free and fully functional. Native language of the application is Spanish but an English version is also available.
- An additional free statistical package, GenAlEx, is geared toward teaching as well as research and allows for complex genetic analyses to be employed and compared within the commonly used Microsoft Excel interface. This software allows for calculation of analyses such as AMOVA, as well as comparisons with other types of closely related statistics including F-statistics and Shannon's index, and more.
1992
- (Excoffier et al., 1992) ⇒ Excoffier, L., Smouse, P. E., & Quattro, J. M. (1992). Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics, 131(2), 479-491. http://www.ncbi.nlm.nih.gov/pubmed/1644282
- We present here a framework for the study of molecular variation within a single species. Information on DNA haplotype divergence is incorporated into an analysis of variance format, derived from a matrix of squared-distances among all pairs of haplotypes. This analysis of molecular variance (AMOVA) produces estimates of variance components and F-statistic analogs, designated here as phi-statistics, reflecting the correlation of haplotypic diversity at different levels of hierarchical subdivision. The method is flexible enough to accommodate several alternative input matrices, corresponding to different types of molecular data, as well as different types of evolutionary assumptions, without modifying the basic structure of the analysis. The significance of the variance components and phi-statistics is 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 mitochondrial DNA haplotype data shows that population subdivisions are better resolved when some measure of molecular differences among haplotypes is introduced into the analysis. At the intraspecific level, however, the additional information provided by knowing the exact phylogenetic relations among haplotypes or by a nonlinear translation of restriction-site change into nucleotide diversity does not significantly modify the inferred population genetic structure. Monte Carlo studies show that site sampling does not fundamentally affect the significance of the molecular variance components. The AMOVA treatment is easily extended in several different directions and it constitutes a coherent and flexible framework for the statistical analysis of molecular data.