ABSTRACT.
A bayesian approach to inferring the genetic population structure of sugarcane accessions from INTA (Argentina)

Mariana Inés Pocovi1*, and Jorge Alberto Mariotti1
 
Understanding the population structure and genetic diversity in sugarcane (Saccharum officinarum L.) accessions from INTA germplasm bank (Argentina) will be of great importance for germplasm collection and breeding improvement as it will identify diverse parental combinations to create segregating progenies with maximum genetic variability for further selection. A Bayesian approach, ordination methods (PCoA, Principal Coordinate Analysis) and clustering analysis (UPGMA, Unweighted Pair Group Method with Arithmetic Mean) were applied to this purpose. Sixty three INTA sugarcane hybrids were genotyped for 107 Simple Sequence Repeat (SSR) and 136 Amplified Fragment Length Polymorphism (AFLP) loci. Given the low probability values found with AFLP for individual assignment (4.7%), microsatellites seemed to perform better (54%) for STRUCTURE analysis that revealed the germplasm to exist in five optimum groups with partly corresponding to their origin However clusters shown high degree of admixture, FST values confirmed the existence of differences among groups. Dissimilarity coefficients ranged from 0.079 to 0.651. PCoA separated sugarcane in groups that did not agree with those identified by STRUCTURE. The clustering including all genotypes neither showed resemblance to populations find by STRUCTURE, but clustering performed considering only individuals displaying a proportional membership > 0.6 in their primary population obtained with STRUCTURE showed close similarities. The Bayesian method indubitably brought more information on cultivar origins than classical PCoA and hierarchical clustering method.
Keywords: AFLP, bayesian clustering, hierarchical clustering, principal coordinate analysis, Saccharum officinarum, SSR.
1Universidad Nacional de Salta, Facultad de Ciencias Naturales, Avenida Bolivia 5150, 4400 Salta, Argentina. *Corresponding author (fcn13161@gmail.com)