Genotypic variation of traits related to quality of cassava roots using affinity propagation algorithm

Abstract

The conservation, sustainable evaluation and use of cassava (Manihot esculenta Crantz) genetic resources are essential to the development of new commercial varieties. This study aimed to evaluate the quality of cassava roots and to estimate genetic variation and clustering in cassava germplasm using the Affinity Propagation algorithm (AP), which is based on the concept of "message passing" between data points. AP finds "exemplars" of each group and members of the input set representative of clusters. The genotypic data of 474 cassava accessions were evaluated over a period of two years for starch yield (StYi), root dry matter (DMC), amylose content (AML), and the level of cyanogenic compounds (CyC). The AP algorithm enabled the formation of nine diversity groups, whose number reflects the high genetic diversity of this germplasm. A high homogeneity of genetic distances was observed within all the groups, except for two groups in which there was a partial overlap caused mainly by a high variation of the CyC trait. In addition, no relationship between the genetic structure and CyC (sweet and bitter cassava) was observed. Analysis of variance of the nine clusters confirmed the presence of differences between the groups. Thus, the results of this study can be used in future breeding programs (hybridization or selection) to introduce new genetic variability into commercial cultivars to avoid problems related to low genetic variation and to improve the quality of cassava roots.