Algorithms for population structure inference of molecular marker profiles

PEÑA MALAVERA ANDREA; FERNÁNDEZ ELMER A.; BALZARINI MÓNICA

Congreso; 2do Congreso Argentino de Bioinformática y Biología Computacional; 2011

In genetic studies is of interest to identify the underlying genetic structure of a set of individuals. When there are subgroups of individuals who differ systematically in allele frequencies of markers, it creates a genetic structure, not to be considered, increases the risk of detecting spurious associations between markers and the phenotype of interest. Several statistical and bioinformatics algorithms are used to determine the grouping of individuals from marker data. Among these are those based on hierarchical clustering algorithms (UPGMA and Ward) cluster nonhierarchical K-means [1], neural networks and self-organizing maps (SOM) [2] method based on Markov´s chains (STRUCTURE ) [3] and pre-eigenanalysis via Ward (ea-ward)hierarchical clustering using Euclidean distance based on principal components (PC), statistically significant [4].