Artificial Neural Networks in the Prediction of Genetic Merit to Flowering Traits in Bean Cultivars

Rosado, Renato Domiciano Silva; Cruz, Cosme Damião; Barili, Leiri Daiane; Carneiro, José Eustáquio de Souza; Carneiro, Pedro Crescêncio Souza; Carneiro, Vinícius Quintão; Silva, Jackson Tavela da; Nascimento, Moysés

doi:10.3390/agriculture10120638

Cited by 13 publications

(12 citation statements)

References 49 publications

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“…While the crop model approach used derived phenotypes (estimated model parameters) to obtain QTL information, the mixed-effects model used QTL information obtained from genetic analysis of observed phenotypes. These results also demonstrated that the overall approach used in many crop models for computing rate of progress toward first flowering can be used in a statistical approach in which G, E, and Genomic selection [30] has been adopted by plant breeding programs world-wide because it is an affective methodology to predict the phenotype, including time-to-flowering [40,41] and in combination with artificial neural networks [42]. However, there are fundamental differences between genomic selection and QTL analysis.…”

Section: Discussionmentioning

confidence: 92%

Dynamic QTL-Based Ecophysiological Models to Predict Phenotype from Genotype and Environment Data

Vallejos

Jones

Bhakta

et al. 2022

Preprint

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BackgroundPredicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework. ResultsWe describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype, Environment, and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model.ConclusionsThe dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.

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Section: Discussionmentioning

confidence: 92%

Dynamic QTL-Based Ecophysiological Models to Predict Phenotype from Genotype and Environment Data

Vallejos

Jones

Bhakta

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Genomic selection [ 30 ] has been adopted by plant breeding programs world-wide because it is an affective methodology to predict the phenotype, including time-to-flowering [ 40 , 41 ] and in combination with artificial neural networks [ 42 ]. However, there are fundamental differences between genomic selection and QTL analysis.…”

Section: Discussionmentioning

confidence: 99%

Dynamic QTL-based ecophysiological models to predict phenotype from genotype and environment data

et al. 2022

View full text Add to dashboard Cite

Background Predicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework. Results We describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype, Environment, and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model. Conclusions The dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.

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“…In GS, these functions automatically identify factors such as epistasis or dominance in genomic marker information. Moreover, it does not require any assumptions about the phenotypic distribution, and applying ANN to GS enables effective estimations of the effects of complex interactions ( Rosado et al., 2020 )…”

Section: Digitalizing Plant Breedingmentioning

confidence: 99%

Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction

Jeon

Kang²,

Lee³

et al. 2023

Front. Plant Sci.

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As the world’s population grows and food needs diversification, the demand for cereals and horticultural crops with beneficial traits increases. In order to meet a variety of demands, suitable cultivars and innovative breeding methods need to be developed. Breeding methods have changed over time following the advance of genetics. With the advent of new sequencing technology in the early 21st century, predictive breeding, such as genomic selection (GS), emerged when large-scale genomic information became available. GS shows good predictive ability for the selection of individuals with traits of interest even for quantitative traits by using various types of the whole genome-scanning markers, breaking away from the limitations of marker-assisted selection (MAS). In the current review, we briefly describe the history of breeding techniques, each breeding method, various statistical models applied to GS and methods to increase the GS efficiency. Consequently, we intend to propose and define the term digital breeding through this review article. Digital breeding is to develop a predictive breeding methods such as GS at a higher level, aiming to minimize human intervention by automatically proceeding breeding design, propagating breeding populations, and to make selections in consideration of various environments, climates, and topography during the breeding process. We also classified the phases of digital breeding based on the technologies and methods applied to each phase. This review paper will provide an understanding and a direction for the final evolution of plant breeding in the future.

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Artificial Neural Networks in the Prediction of Genetic Merit to Flowering Traits in Bean Cultivars

Cited by 13 publications

References 49 publications

Dynamic QTL-Based Ecophysiological Models to Predict Phenotype from Genotype and Environment Data

Dynamic QTL-Based Ecophysiological Models to Predict Phenotype from Genotype and Environment Data

Dynamic QTL-based ecophysiological models to predict phenotype from genotype and environment data

Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction

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