Computational intelligence for studies on genetic diversity between genotypes of biomass sorghum

Silva, Michele Jorge da; Júnior, Antonio Carlos Oscar; Cruz, Cosme Damião; Nascimento, Moysés; Oliveira, Marciane da Silva; Schaffert, Robert Eugene; Parrella, R. A. da C.

doi:10.1590/s1678-3921.pab2020.v55.01723

Cited by 5 publications

(5 citation statements)

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“…Maintainers of soybean cultivars in more distant clusters (Figure 5) show a more significant genetic dissimilarity between the cultivars released by the respective maintainers. Clusters are composed of individuals resembling the neighboring class; the most divergent classes constitute the extreme regions, and the intermediate classes form the center of the map (Santos et al., 2019; Silva et al., 2020). Thus, it is possible to infer the profile of each maintainer and its genetic population base for the development of cultivars.…”

Section: Discussionmentioning

confidence: 99%

“…Although not yet widely used for plant breeding, SOM has proved to be a powerful tool for capturing genetic diversity (da Silva Oliveira et al., 2020; Sant'Anna et al., 2021). Its efficiency has been proven in studies of the genetic diversity of genotypes in irrigated rice ( Oryza sativa L.) (Santos et al., 2019) and biomass sorghum [ Sorghum bicolor (L.) Moench] (Silva et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

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Genetic diversity and interaction between the maintainers of commercial soybean cultivars using self‐organizing maps

et al. 2022

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Information on the genetic diversity of commercial cultivars is of fundamental importance for crop improvement. In addition, information about possible interactions between the maintainers developing these cultivars can help design a breeding program. The objective of this work was to study the genetic diversity of soybean [Glycine max (L.) Merr.] cultivars released in Brazil from 1998 to 2017 and compare the similarity between maintainers of these cultivars based on the phenotypic information disclosed. Data was collected on 1,587 soybean cultivars registered in the National Register of Cultivars of the Ministry of Agriculture, Livestock, and Supply belonging to 59 different maintainers. Among these cultivars, 12 agromorphological traits were evaluated. To perform the grouping and select the main discriminating traits of the cultivars, the Random Forest method was used. Multiple correspondence analysis was performed to obtain the variance explanation percentage of the traits evaluated in each dimension and coordinates among the maintainers. The maintainers were organized through Kohonen self‐organizing map (SOM) through the respective coordinates. There is a wide variety of maintainers of soybean cultivars in Brazil with different objectives for launching new cultivars. Among the maintainers, Soymax presented the most restricted genetic base. The other private companies showed more remarkable similarities and a broader genetic base. The public institution EMBRAPA presented the greatest genetic diversity in its population base. Federal University of Uberlândia and Federal University of Viçosa can be partners to launch soybean cultivars given the proximity between neurons in Kohonen analysis. The high diversity of traits shows that the genetic base of soybean in Brazil is large.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genetic diversity and interaction between the maintainers of commercial soybean cultivars using self‐organizing maps

et al. 2022

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“…9B). Several studies have shown Artificial Neural Networks (ANNs) to be quite promising as an efficient alternative for the selection of divergent genotypes in Carica papaya L (Barbosa et al 2011), for classification studies of alfalfa genotypes , in the evaluation of genetic resources in germplasm databases (Moura et al 2015), in studies on genetic diversity among biomass sorghum (Sorghum bicolor) genotypes (Silva et al 2020), to distinguish dissimilarity among colored fiber cotton genotypes (Pimentel 2021), and in identifying genetic similarities among BC1F3 dwarf tomato populations, among others.…”

Section: Groupsmentioning

confidence: 99%

Artificial Neural Networks in the Perception of Genetic Differentiation Caused by Migration

Pigaiani,

Oliveira,

Vidon

et al. 2024

Preprint

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Understanding genetic diversity is exceptionally important in order to ensure variability and viability among populations of the same species. Genetic diversity among populations is a consequence of different evolutionary mechanisms that act on them, resulting in different genotypic and allelic frequencies between generations and their future generations; among the different evolutionary mechanisms is migration or gene flow. Self-Organizing Maps (SOM) is an interesting tool to organize and map populations, in addition to highlighting the effects of genetic diversity caused by different evolutionary mechanisms, including migration. The objective of this work was to verify the effects of migration along the generations, analyzing them according to the Conventional Techniques of Biostatistics - Nei, Hedrick and Tocher Cluster Statistics - and, later, to analyze if the self-organizing maps are able to map the effects caused by it. This way, base populations were generated in Hardy-Weinberg equilibrium with 1000 individuals each, 100 codominant diallelic loci, and allelic frequencies equal to p = q = 0.50, which were used to simulate the effects of migration. The simulation is justified because it allows for the control of the generated effects. The SOM were able of capturing the diversity patterns generated for different quantities of migrants over different generations in various replicates. We concluded that SOMs are sensitive to detecting genetic variability and provide additional information on population organization.

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“…However, common restrictions in the evaluation of germplasms kept in banks, such as financial limitations and the lack of human resources, generally limit this evaluation 21 . The application of computational intelligence, and more specifically of artificial neural networks (ANNs), is a promising tool for evaluating and managing plant germplasms conserved in banks 22 , 23 . This tool has aroused interest because it can map non-linear systems, extracting the particularities of these systems from information such as measurements, samples, or patterns.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks optimize the establishment of a Brazilian germplasm core collection of winter squash (Cucurbita moschata D.)

Gomes,

Machado Júnior,

de Almeida

et al. 2024

Sci Rep

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With widespread cultivation, Cucurbita moschata stands out for the carotenoid content of its fruits such as β and α-carotene, components with pronounced provitamin A function and antioxidant activity. C. moschata seed oil has a high monounsaturated fatty acid content and vitamin E, constituting a lipid source of high chemical–nutritional quality. The present study evaluates the agronomic and chemical–nutritional aspects of 91 accessions of C. moschata kept at the BGH-UFV and propose the establishment of a core collection based on multivariate approaches and on the implementation of Artificial Neural Networks (ANNs). ANNs was more efficient in identifying similarity patterns and in organizing the distance between the genotypes in the groups. The averages and variances of traits in the CC formed using a 15% sampling of accessions, were closer to those of the complete collection, particularly for accumulated degree days for flowering, the mass of seeds per fruit, and seed and oil productivity. Establishing the 15% CC, based on the broad characterization of this germplasm, will be crucial to optimize the evaluation and use of promising accessions from this collection in C. moschata breeding programs, especially for traits of high chemical–nutritional importance such as the carotenoid content and the fatty acid profile.

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Computational intelligence for studies on genetic diversity between genotypes of biomass sorghum

Cited by 5 publications

References 23 publications

Genetic diversity and interaction between the maintainers of commercial soybean cultivars using self‐organizing maps

Genetic diversity and interaction between the maintainers of commercial soybean cultivars using self‐organizing maps

Artificial Neural Networks in the Perception of Genetic Differentiation Caused by Migration

Artificial neural networks optimize the establishment of a Brazilian germplasm core collection of winter squash (Cucurbita moschata D.)

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