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. 2021 Jul 15;16(7):e0254413.
doi: 10.1371/journal.pone.0254413. eCollection 2021.

Genetic divergence for adaptability and stability in sugarcane: Proposal for a more accurate evaluation

Affiliations

Genetic divergence for adaptability and stability in sugarcane: Proposal for a more accurate evaluation

João de Andrade Dutra Filho et al. PLoS One. .

Erratum in

Abstract

The best agro-industrial performance presented by a crop genotype in one environment may not be reproduced in another owing to complex edaphoclimatic variations. Therefore, breeding programs are constantly attempting to obtain, through artificial hybridization, novel genotypes with high adaptability and stability potential. The objective of this study was to analyze genetic divergence in sugarcane based on the genotypic values of adaptability and stability. A total of 11 sugarcane genotypes were analyzed for eight agro-industrial traits. The genotypic values of the traits were determined using mixed model methodology, and the genetic divergence based on phenotypic and genotypic values was measured using the Mahalanobis distance. The distance matrices were correlated using the Mantel test, and the genotypes were grouped using the Tocher method. Genetic divergence is more accurate when based on genotypic values free of genotype-environment interactions and will differ from genetic divergence based on phenotypic data, changing the genotype allocations in the groups. The above methodology can be applied to assess genetic divergence to obtain novel sugarcane genotypes with higher productivity that are adapted to intensive agricultural systems using diverse technologies. This methodology can also be tested in other crops to increase accuracy in selecting the parents to be crossed.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Graphic dispersion of pairs of sugarcane genotypes determined based on dissimilarity measurements obtained from phenotypic (x axis) and genotypic (y axis) data.
The correlation between the matrices is illustrated. **p = 0.01, as determined using a t test; ++p = 0.01, as determined using the Mantel test based on 1,000 simulations.

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