Self-organizing maps: a powerful tool for capturing genetic diversity patterns of populations

Oliveira, Marciane da Silva; Santos, Iara Gonçalves dos; Cruz, Cosme Damião

doi:10.1007/s10681-020-2569-0

Cited by 6 publications

(9 citation statements)

References 17 publications

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“…Clusters that present high diversity among the traits may have a broader population base (da Silva Oliveira et al., 2020) such as the maintainers current in Clusters 10, 14, and 15. On the other hand, clusters that present low diversity, such as Cluster 5, may have a small population base, which can be harmful, mainly targeting crosses between divergent parents (Santos et al., 2019).…”

Section: Discussionmentioning

confidence: 99%

“…The SOMs method consists of a computational intelligence technique that allows for the visualization of similar patterns and data classification based on the distances between them (Kohonen, 1990). 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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“…KSOMs are an unsupervised learning neural network method that detects similarities between entry patterns through a competition process [ 25 , 49 , 50 ]. The value of each individual assessed for the 31 traits in each environment was used as an input.…”

Section: Methodsmentioning

confidence: 99%

Machine learning and statistics to qualify environments through multi-traits in Coffea arabica

et al. 2021

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Several factors such as genotype, environment, and post-harvest processing can affect the responses of important traits in the coffee production chain. Determining the influence of these factors is of great relevance, as they can be indicators of the characteristics of the coffee produced. The most efficient models choice to be applied should take into account the variety of information and the particularities of each biological material. This study was developed to evaluate statistical and machine learning models that would better discriminate environments through multi-traits of coffee genotypes and identify the main agronomic and beverage quality traits responsible for the variation of the environments. For that, 31 morpho-agronomic and post-harvest traits were evaluated, from field experiments installed in three municipalities in the Matas de Minas region, in the State of Minas Gerais, Brazil. Two types of post-harvest processing were evaluated: natural and pulped. The apparent error rate was estimated for each method. The Multilayer Perceptron and Radial Basis Function networks were able to discriminate the coffee samples in multi-environment more efficiently than the other methods, identifying differences in multi-traits responses according to the production sites and type of post-harvest processing. The local factors did not present specific traits that favored the severity of diseases and differentiated vegetative vigor. Sensory traits acidity and fragrance/aroma score also made little contribution to the discrimination process, indicating that acidity and fragrance/aroma are characteristic of coffee produced and all coffee samples evaluated are of the special type in the Mata of Minas region. The main traits responsible for the differentiation of production sites are plant height, fruit size, and bean production. The sensory trait "Body" is the main one to discriminate the form of post-harvest processing.

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“…The maintenance of genetic diversity is a essential factor for the conservation of population viability, and this can be measured by the genetic differences among individuals (Oliveira et al 2020). Genetic variability is based on the differentiation of allelic and genotypic frequencies among individuals of a particular population (Frankham 2001).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, migrants bring new alleles and consequently increase genetic diversity within the population, reducing the differences between populations. One way to map the changes generated by evolutionary factors is through multivariate techniques capable of organizing genetic diversity and highlighting the effects caused by them, such as Self-Organizing Maps (SOM) (Oliveira et al 2020).…”

Section: Introductionmentioning

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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Self-organizing maps: a powerful tool for capturing genetic diversity patterns of populations

Cited by 6 publications

References 17 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

Machine learning and statistics to qualify environments through multi-traits in Coffea arabica

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

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