Classifying individuals among infra-specific taxa using microsatellite data and neural networks.

Abstract

The method of neural networks was tested for its ability to assign individuals on the basis of their multilocus genotypes, using a data collection of 430 honeybees and 8 microsatellite loci. This data set includes various taxonomical levels (populations within the same subspecies, various subspecies belonging to the same evolutionary lineage, and the 3 lineages of the species). Qualitative genotypic data have been submitted to 2 types of transformation (simple coding and coding plus factorial correspondence analysis), and they have been partitioned in 2 sets, a training set of 300 individuals and a testing set of 103 individuals. Two procedures ("leave one out" and "hold out") were applied to evaluate the quality of prediction. Compared to discriminant analysis, neural networks performed better in terms of correctly classified individuals at any taxonomical level. For instance, with the simple coding and the hold out procedure, the proportions of correctly assigned individuals from the testing set were 66.2%, 82.3% and 100% at the populations, subspecies and lineage level, respectively. The potential use of neural networks in populations genetics is discussed.