Step 1: Data formatting/loading required packages If your data contains any populations with only one individual, these must be removed before uploading your dataset into R. The R package poppr can be used to generate an AMOVA, and requires that a distance matrix be calculated from the data and the data to be divided into different stratifications (e.g., populations or subpopulations). Your null hypothesis would be that the population means for all of the populations in your data set are equal, and your alternative hypothesis would be that at least one mean differs from the others. For codominant markers, like the microsatellite data used for this tutorial, this is done on a locus by locus approach, where a distance matrix is generated for each locus 5.Īn AMOVA will compare molecular variance across the different strata (i.e., populations in this case) and look to see if the population means differ from one another. When running an AMOVA, a matrix of squared Euclidean distances between all pairs of individuals is calculated to determine the within and between-groups sums of squares 1, 4. This is not necessary if running a spatial AMOVA 3, however for the analyses run in this tutorial, strata must be set before running. Population and subpopulation hierarchy must be known previously, so if populations/other strata are not known before, then a clustering analysis (e.g., STRUCTURE) must be run prior to running the AMOVA 2. This tutorial will focus on microsatellite data, however a number of different marker types can be used.ĪMOVA is a popular method to use for calculating F-statistics as it makes it possible to test for the presence of hierarchical population structure when your dataset has three or more populations 1. AMOVA (Analysis of MOlecular VAriance) is a method used to describe population differentiation using data generated via molecular markers 1 (Excoffier, Smouse & Quattro, 1992).
0 Comments
Leave a Reply. |