Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data.

Abstract

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.

Mixed-Mating Model of Reproduction Revealed in European Phytophthora cactorum by ddRADseq and Effector Gene Sequence Data

“…The separation of all groups identified by structure analysis is apparent in the results of Fst according to Weir and Cockerham [48] (Table 4). Similar results were reached through the use of other calculation methods-Φst [46] (Appendix A, Supplementary Table S2), Fst [47] (Appendix A, Supplementary Table S3) and DST [49] (Appendix A, Supplementary Table S4). Since all of those methods provide the measure of population differentiation, the ascertained values of calculated coefficients provide the conclusion that all five groups (S, C1, C2, F and H) are rather separated.…”

“…Caption to Table S2. The values of fixation index Φ st (Excoffier et al 1992). Populations of the S group are marked by their ID number, and the matrix also includes the genetic groups originating from woody hosts-C1, C2, F and H. Caption to Table S3.…”

“…For the purpose of evaluating the genetic differentiation among populations, the F-statistics were calculated. Since different methods of calculation emphasise different properties of population data, analogues of the F-statistics were calculated using four distinct methods to obtain more reliable information about population structure: the Φ st statistics were calculated according to Excoffier et al [46] and, as other analogues of this statistic, the F st according to Meirmans [47], F st according to Weir and Cockerham [48] and D st according to Jost [49] were calculated. The information of genetic differentiation among populations was complemented by the determination of the "number of private alleles" in each group, which also provided information about the mutual isolation between populations.…”

Population Genomics of American Mink Using Whole Genome Sequencing Data

“…In addition, analysis of molecular variance (AMOVA) was performed using StAMPP package to describe the genetic variation among samples at different hierarchical levels. The AMOVA was computed based on the Nei's genetic distance matrix using the stamppAmova() function following the procedure described by Excoffier et al [47].…”

Ocean Currents Drove Genetic Structure of Seven Dominant Mangrove Species Along the Coastlines of Southern China

“…Priors of 2-100 and 2-1,000 were also tested to assess whether the results were robust to changes in these assumed values. Analysis of molecular variance [AMOVA; (Excoffier et al, 1992)] was performed according to Arlequin version 3.5 (Excoffier and Lischer, 2010), with 1,000 permutations. We used all the populations of each species to estimate the variances attributable to differences between populations within species and among individuals within populations.…”