Bayesian statistical treatment of the fluorescence of AFLP bands leads to accurate genetic structure inference.
Ever since the introduction of allozymes in the 1960s, evolutionary biologists and ecologists have continued to search for more powerful molecular markers to estimate important parameters such as effective population size and migration rates and to make inferences about the demographic history of populations, the relationships between individuals and the genetic architecture of phenotypic variation (Bensch & Akesson 2005; Bonin et al. 2007). Choosing a marker requires a thorough consideration of the trade-offs associated with the different techniques and the type of data obtained from them. Some markers can be very informative but require substantial amounts of start-up time (e.g. microsatellites), while others require very little time but are much less polymorphic. Amplified fragment length polymorphism (AFLP) is a firmly established molecular marker technique that falls in this latter category. AFLPs are widely distributed throughout the genome and can be used on organisms for which there is no a priori sequence information (Meudt & Clarke 2007). These properties together with their moderate cost and short start-up time have made them the method of choice for many molecular ecology studies of wild species (Bensch & Akesson 2005). However, they have a major disadvantage, they are dominant. This represents a very important limitation because many statistical genetics methods appropriate for molecular ecology studies require the use of codominant markers. In this issue, Foll et al. (2010) present an innovative hierarchical Bayesian method that overcomes this limitation. The proposed approach represents a comprehensive statistical treatment of the fluorescence of AFLP bands and leads to accurate inferences about the genetic structure of natural populations. Besides allowing a quasi-codominant treatment of AFLPs, this new method also solves the difficult problems posed by subjectivity in the scoring of AFLP bands.
Laboratoire d'Ecologie Alpine (LECA), CNRS UMR 5553, Université Joseph Fourier, 38041 Grenoble Cedex 09, France. oscar.gaggiotti@ujf-grenoble.fr
Oscar E
OE
Gaggiotti
D054458:Amplified Fragment Length Polymorphism Analysis / Q000379:methods*; D001499:Bayes Theorem; D003198:Computer Simulation; D005453:Fluorescence; D005828:Genetics, Population; D005838:Genotype; D008957:Models, Genetic; D015233:Models, Statistical