Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora.

ABSTRACT: Many methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions about the relationships between inputs and the output allowing great flexibility to handle different types of complex non-additive effects, such as dominance and epistasis. Despite this advantage, the biological interpretability of ANNs is still limited. The aim of this research was to estimate the heritability and markers effects for two traits in Coffea canephora using an additive-dominance architecture ANN and to compare it with genomic best linear unbiased prediction (GBLUP). The data used consists of 51 clones of C. canephora varietal Con

SUBMITTER: Coelho de Sousa I 

PROVIDER: S-EPMC8791507 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature