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AFLP-SURV


Written by Xavier Vekemans


Description

AFLP-SURV estimates genetic diversity and population genetic structure from population samples analysed with AFLP or RAPD methods and computes genetic distance matrices between populations. The program starts by estimating allelic frequencies at each marker locus in each population assuming they are dominant and have only two alleles (a dominant marker allele coding for the presence of a band at a given position, and a recessive null allele coding for the absence of the band).

The user has to specify whether Hardy-Weinberg genotypic proportions can be assumed, or in contrast whether the organism is completely homozygous at marker loci (highly self-fertilising species or haploid species), or alternatively whether there are some known deviations from Hardy-Weinberg genotypic proportions. Based on these estimates of allelic frequencies, the program uses the approach of Lynch and Milligan (1994) to estimate genetic diversity and population genetic structure, which uses the average expected heterozygosity of the marker loci, or Nei's gene diversity, as a measure of genetic diversity.

The program also produces matrices of pairwise genetic distances between populations (with bootstraps) and of pairwise relatedness coefficients between individuals, and makes various tests of significance based on random permutations. It also computes the correlation between AFLP fragment sizes and frequencies when fragment sizes are provided in order to test the occurrence of size homoplasy (see Vekemans et al. 2002).


Computer requirement

AFLP-SURV runs on PC under Windows 9x or later versions, or under VirtualPC for Macintosh user's.


How to run AFLP-SURV

Sorry, AFLP-SURV has no nice user-friendly windows but it does not require installation. Just bring the AFLPsurv.exe file and the data files in some folder and double-click on AFLP.exe, the rest is written on the screen, but we suggest that you first have a look at the manual.


  1. AFLPsurv.exe program

  2. AFLPsurv.pdf manual







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Genetic data analysis or simulation

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