Comparison of algorithms to infer genetic population structure from unlinked molecular markers

PEÑA-MALAVERA, A.; FERNANDEZ, E.; BRUNO, C.; BALZARINI, M.

STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY

BERKELEY ELECTRONIC PRESS

Año: 2014 vol. 13 p. 391 - 391

1544-6115

dentifying population genetic structure (PGS) is crucial for breeding and conservation. Several clustering algorithms are available to identify the underlying PGS to be used with genetic data of maize genotypes. In this work, six methods to identify PGS from unlinked molecular marker data were compared using simulated and experimental data consisting of multilocus-biallelic genotypes. Datasets were delineated under different biological scenarios characterized by three levels of genetic divergence among populations (low, medium, and high FST) and two numbers of sub-populations (K = 3 and K = 5). The relative performance of hierarchical and non-hierarchical clustering, as well as model-based clustering (STRUCTURE) and clustering from neural networks (SOM-RP-Q). We use the clustering error rate of genotypes into discrete sub- populations as comparison criterion. In scenarios with great level of divergence among genotype groups all methods perf