On the performance of procedures to determine genetic population structure

ANDRE PEÑA MALAVERA; FERNÁNDEZ, ELMER ANDRÉS; MÓNICA G BALZARINI

Kobe

Congreso; International Biometric Conference Kobe 2012.; 2012

International Biometric Society

Variability analysis to determine population genetic structure (PGS) in germplasm collections is a crucial step in the formation of core collections for the conservation and use of genetics resources, as well as for association studies. From the algorithmic point of view, statistical and bioinformatics methods can be used to infer genetic structure. In this work, we used simulated and real data to compare 6 alternative procedures to estimate genetic structure. In different scenarios, characterized by levels of genetic divergence (Fst) and numbers of populations (3 and 5) were compared hierarchical clustering (UPGMA and Ward), Ward method using significant principal components according to the Tracy-Widom statistic (PCA+Ward), K-means clustering, the relative position self organizing maps (RP-Q-SOM) and a Bayesian method. The methods were comparing using the proportion clustering error (PCE). The results showed that the RP-Q-SOM algorithm, the Bayesian method implemented in the software STRUCTURE and the non hierarchical K-means clustering method are the procedures that perform best (PCE