What do artificial neural networks tell us about the genetic structure of populations? The example of European pig populations.
General and genetic statistical methods are commonly used to deal with microsatellite data (highly variable neutral genetic markers). In this paper, the self-organizing map (SOM) that belongs to the unsupervised artificial neural networks (ANNs) was applied to analyse the structure of 58 European and two Chinese pig populations (Sus scrofa) including commercial lines, local breeds and cosmopolitan breeds. Results were compared with other unsupervised classification or ordination methods such as factorial correspondence analysis, hierarchical clustering from an allele sharing distance and the Bayesian genetic model and with principal components analysis and neighbour joining from allelic frequencies and genetic distances between populations. Like other methods, SOMs were able to classify individuals according to their breed origin and to visualize similarities between breeds. They provided additional information on the within- and between-population diversity, allowed differences between similar populations to be highlighted and helped differentiate different groups of populations.
Nikolic
Natacha
N
Laboratoire de Génétique Cellulaire (UMR 444), INRA-ENVT, Castanet Tolosan, France.
Park
Young-Seuk
YS
Sancristobal
Magali
M
Lek
Sovan
S
Chevalet
Claude
C
D000818:Animals; D002681:China; D016000:Cluster Analysis; D005060:Europe; D040342:Genetic Structures; D014644:Genetic Variation; D005828:Genetics, Population; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D010802:Phylogeny; D013045:Species Specificity; D034421:Sus scrofa / Q000145:classification / Q000235:genetics*