Self-organizing maps in the study of genetic diversity among irrigated rice genotypes

Santos, Iara Gonçalves dos; Carneiro, Vinícius Quintão; Silva, Antônio Carlos da; Cruz, Cosme Damião; Soares, Plínio César

doi:10.4025/actasciagron.v41i1.39803

Cited by 17 publications

(29 citation statements)

References 21 publications

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“…Result concordant with the study by Silva et al (2019). The estimated coefficients of variation for the characteristic were compatible with those obtained in other rice studies (Costa et al, 2002;Hosan et al, 2010;Silva et al, 2011;Streck et al, 2017;Santos et al, 2019;, emphasizing the acceptable test quality.…”

Section: Resultssupporting

confidence: 90%

“…Rice (Oryza sativa) is one of the most consumed cereals in the world, characterizing it as main source of basic food (Walter et al, 2008;Santos et al, 2019;Silva et al, 2019) for more than half the world population (Irgsp, 2005;Jacquemin et al, 2013;Zhang et al, 2013;Muthayya et al, 2014). Thus, it has great potential to reduce food insecurity (Zhou et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Research Article Methods of adaptability and stability applied to the improvement of flooded rice

Júnior¹,

Carneiro²,

Santos³

et al. 2020

Genet. Mol. Res.

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Rice (Oryza sativa) is crop that adapts well to diverse soil and climate conditions; breeding programs have generally been committed to identifying and selecting genotypes that are stable and have high productivity in various environments. In this sense, studies of adaptability and stability are of paramount importance to aid in the recommendation of cultivars, since it allows growers to obtain detailed information about the behavior of the genotypes in each region. We evaluatde the adaptability and stability of irrigated rice genotypes grown with continuous flooding, for the selection and recommendation of cultivars for crops or breeding programs. Eighteen genotypes were evaluated for grain yield in four agricultural years at three sites, covering 12 environments. The adaptability and stability were assessed by the methods of Eberhart and Russell, multiple centroids and GGE biplot. Genotypes behaved differently regarding stability and adaptability in the different environments. Both methodologies identified BRA 02691 and MGI 0607-1 as promising to be released as cultivars; however, classification inconsistencies occurred, such as for the line BRA 031001. Multiple centroid and GGE biplot methods demonstrated greater sensitivity ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 19 (3): gmr18434 A.C. Silva Junior et al. 2 than the Eberhart and Russell method. Using the methods simultaneously provides an innovative approach to the interpretation of GxE interactions and is a viable alternative for genotype classification. The genotype MGI 0607-1 showed promising behavior independent of the methodology used.

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Section: Resultssupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Research Article Methods of adaptability and stability applied to the improvement of flooded rice

Júnior¹,

Carneiro²,

Santos³

et al. 2020

Genet. Mol. Res.

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“…In complex scenarios such as this in which the genetic similarity between genotypes can differ (siblings, half siblings, parents and grandparents), the SOM method allows the visualization of patterns of similarity and data classification based on the distances between genotypes 29 . This method is efficient, as noted by [30][31][32] . Thus, the agreement between most of the applied techniques, especially between the K-means and SOM methods, suggested that the Asiatic clones were best represented as two clusters, as were the IAC 400 series.…”

Section: Discussionmentioning

confidence: 99%

Genetic diversity associated with natural rubber quality in elite genotypes of the rubber tree

Sant’Anna

Gouvêa

Martins

et al. 2021

Sci Rep

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The objective of this study was to evaluate the genetic variability of natural rubber latex traits among 44 elite genotypes of the rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Müell. Arg.]. Multivariate analysis and machine learning techniques were used, targeting the selection of parents that demonstrate superior characters. We analyzed traits related to technological or physicochemical properties of natural rubber latex, such as Wallace plasticity (P0), the plasticity retention index [PRI (%)], Mooney viscosity (VR), ash percentage (Ash), acetone extract percentage (AE), and nitrogen percentage (N), to study genetic diversity. Multivariate [unweighted pair group method with arithmetic means (UPGMA) and Tocher)] and machine learning techniques [K-means and Kohonen’s self-organizing maps (SOMs)] were employed. The genotypes showed high genetic variability for some of the evaluated traits. The traits PRI, Ash, and PO contributed the most to genetic diversity. The genotypes were classified into six clusters by the UPGMA method, and the results were consistent with the Tocher, K-means and SOM results. PRI can be used to improve the industrial potential of clones. The clones IAC 418 and PB 326 were the most divergent, followed by IAC 404 and IAC 56. These genotypes and others from the IAC 500 and 400 series could be used to start a breeding program. These combinations offer greater heterotic potential than the others, which can be used to improve components of rubber latex quality. Thus, it is important to consider the quality of rubber latex in the early stage of breeding programs.

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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

Self Cite

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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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Self-organizing maps in the study of genetic diversity among irrigated rice genotypes

Cited by 17 publications

References 21 publications

Research Article Methods of adaptability and stability applied to the improvement of flooded rice

Research Article Methods of adaptability and stability applied to the improvement of flooded rice

Genetic diversity associated with natural rubber quality in elite genotypes of the rubber tree

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

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