Estimating population structure from AFLP amplification intensity.
In the last decade, amplified fragment length polymorphisms (AFLPs) have become one of the most widely used molecular markers to study the genetic structure of natural populations. Most of the statistical methods available to study the genetic structure of populations using AFLPs consider these markers as dominant and are thus unable to distinguish between individuals being heterozygous or homozygous for the dominant allele. Some attempts have been made to treat AFLPs as codominant markers by using AFLP band intensities to infer the most likely genotype of each individual. These two approaches have some drawbacks, the former discarding potentially valuable information and the latter being sometimes unable to correctly assign genotypes to individuals. In this study, we propose an alternative likelihood-based approach, which does not attempt at inferring the genotype of each individual, but rather incorporate the uncertainty about genotypes into a Bayesian framework leading to the estimation of population-specific F(IS) and F(ST) coefficients. We show with simulations that the accuracy of our method is much higher than one using AFLP as dominant markers and is generally close to what would be obtained by using the same number of Single-Nucleotide Polymorphism (SNP) markers. The method is applied to a data set of four populations of the common vole (Microtus arvalis) from Grisons in Switzerland, for which we obtained 562 polymorphic AFLP markers. Our approach is very general and has the potential to make AFLP markers as useful as SNP data for nonmodel species.
Foll
Matthieu
M
Computational and Molecular Population Genetics (CMPG), Institute of Ecology and Evolution, University of Bern, Baltzerstrasse 6, CH-3012 Bern, Switzerland.
Fischer
Martin C
MC
Heckel
Gerald
G
Excoffier
Laurent
L
D054458:Amplified Fragment Length Polymorphism Analysis / Q000379:methods*; D000818:Animals; D003411:Arvicolinae / Q000235:genetics; D001499:Bayes Theorem; D003198:Computer Simulation; D005828:Genetics, Population; D005838:Genotype; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide