Research Article

Heritability profiles defined by hierarchal models and artificial neural networks for dual-purpose wheat attributes

Published: August 30, 2019
Genet. Mol. Res. 18(3): GMR18266 DOI:
Cite this Article:
I.R. Carvalho, J.A.G. Da Silva, L.L. Ferreira, V.E. Bubans, M.H. Barbosa, R.B. Mambrin, S.M. Fachi, G.G. Conte, V.Q. de Souza (2019). Heritability profiles defined by hierarchal models and artificial neural networks for dual-purpose wheat attributes. Genet. Mol. Res. 18(3): GMR18266.


Dual purpose wheat could be a good alternative for helping overcome the need to import this cereal in Brazil. To achieve this, development of cultivars with high yield is necessary. The contribution of genetics in defining traits is very important for directing breeding programs for the development of cultivars that provide the desired agronomic ideotype. We estimated heritability for 36 characters of agronomic importance in dual-purpose wheat. The inheritable genetic patterns were examined using linear trends, a Euclidean algorithm, factor analysis and artificial neural networks. The study was carried out during the crop seasons of 2011, 2012 and 2013. The experimental design was randomized block, arranged in a factorial scheme with three growing seasons (2011, 2012 and 2013) and five dual-purpose wheat genotypes (BRS Tarumã, BRS Umbu, BRS Figueira, BRS Guatambu and BRS 277) x three cuttings (first cutting, second cutting and third cutting), with three replicates. Deviance analysis or maximum likelihood was significant for the 36 characters. The length of the head of the main plant, plant height before the first second cutting and dry mass of the seedlings showed high variability. The 36 characters expressed linear genetic dependence based on the Euclidean Algorithm; similar to what was found with the Tocher Optimized Clustering and Artificial Neural Networks K-means methods. Similar genetic trends for heritability profiles were obtained with factor analysis and Artificial Neural Networks by the Kohonem method. The use of Artificial Neural Networks through the Kohonem method gave the greatest efficacy in the definition of the genetic profiles needed to develop the recommended agronomic ideotype for the improvement of dual-purpose wheat.