&lt;b&gt;Genetic dissimilarity among sweet potato genotypes using morphological and molecular descriptors

Andrade, Elisângela Knoblauch Viega de; Júnior, Valter Carvalho de Andrade; Laia, Marcelo Luiz de; Fernandes, José Sebastião Cunha; Oliveira, Altino Júnior Mendes; Azevedo, Alcinei Místico

doi:10.4025/actasciagron.v39i4.32847

Cited by 22 publications

(19 citation statements)

References 14 publications

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“…The assigning of group membership in DAPC is in agreement with the hierarchical cluster analysis; this finding is consistent with reports from other studies [ 36 , 57 , 58 ]. The high cophenetic correlation coefficient (>0.70) observed for the hierarchical cluster analysis indicates the reliability of this approach to summarize the information of dissimilarity matrices [ 34 ]. Moderate levels of admixture were observed among the inbred lines used in this study, this indicate shared ancestry among the inbred lines.…”

Section: Discussionmentioning

confidence: 99%

“…They have been widely used in plants to evaluate genetic diversity, construct linkage maps, and association analysis [ 31 , 32 ]. However, studies have shown that joint analysis of molecular and morphological data provides in-depth insight into population structure and genetic diversity, and this has been used in different crops [ 33 , 34 , 35 , 36 , 37 ]. Belalia et al [ 38 ] demonstrated the usefulness of this method in maize by assessing the genetic diversity of 56 populations using 14 agro-morphological traits and 18 simple sequence repeat (SSR) markers.…”

Section: Introductionmentioning

confidence: 99%

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Genetic Diversity and Population Structure of Maize Inbred Lines with Varying Levels of Resistance to Striga Hermonthica Using Agronomic Trait-Based and SNP Markers

Stanley

Menkir

Agre

et al. 2020

Plants

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Striga hermonthica is a serious biotic stress limiting maize production in sub-Saharan Africa. The limited information on the patterns of genetic diversity among maize inbred lines derived from source germplasm with mixed genetic backgrounds limits the development of inbred lines, hybrids, and synthetics with durable resistance to S. hermonthica. This study was conducted to assess the level of genetic diversity in a panel of 150 diverse maize inbred lines using agronomic and molecular data and also to infer the population structure among the inbred lines. Ten Striga-resistance-related traits were used for the phenotypic characterization, and 16,735 high-quality single-nucleotide polymorphisms (SNPs), identified by genotyping-by-sequencing (GBS), were used for molecular diversity. The phenotypic and molecular hierarchical cluster analyses grouped the inbred lines into five clusters, respectively. However, the grouping patterns between the phenotypic and molecular hierarchical cluster analyses were inconsistent due to non-overlapping information between the phenotypic and molecular data. The correlation between the phenotypic and molecular diversity matrices was very low (0.001), which is in agreement with the inconsistencies observed between the clusters formed by the phenotypic and molecular diversity analyses. The joint phenotypic and genotypic diversity matrices grouped the inbred lines into three groups based on their reaction patterns to S. hermonthica, and this was able to exploit a broad estimate of the actual diversity among the inbred lines. The joint analysis shows an invaluable insight for measuring genetic diversity in the evaluated materials. The result indicates that wide genetic variability exists among the inbred lines and that the joint diversity analysis can be utilized to reliably assign the inbred lines into heterotic groups and also to enhance the level of resistance to Striga in new maize varieties.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genetic Diversity and Population Structure of Maize Inbred Lines with Varying Levels of Resistance to Striga Hermonthica Using Agronomic Trait-Based and SNP Markers

Stanley

Menkir

Agre

et al. 2020

Plants

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“…Genetic improvement is one of the main tools to increase crop yield in a sustainable way [1]. However, for genotype selection in breeding programs, knowledge of genetic dissimilarity among the genotypes of the population to be improved is essential [2,3]. Several molecular techniques have been used for genetic improvement.…”

Section: Introductionmentioning

confidence: 99%

Self-organized map: the new aproach for study of genetic divergence in kale

Brito

Júnior

Azevedo

et al. 2020

Preprint

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The objective of this study was to study the genetic divergence between genotypes of kale, to propose a methodology for the use of neural networks of the SOM type and to test its efficiency through Anderson's discriminant analysis. We evaluated 33 families of half-siblings of kale and three commercial cultivars. The design was a randomized block with four replications with six plants per plot. A total of 14 plant-level quantitative traits were evaluated. Genetic values were predicted at family level via REML / BLUP. For the study of divergence, neural networks of the SOM type (Selforganizing Map) were adopted. We evaluated different network architectures, whose consistencies of the clusters were identified by the Anderson discriminant analysis and by the number of empty clusters. After selecting the best network configuration, a dissimilarity matrix was obtained, from which a dendrogram was constructed using the UPGMA method. The best network architecture was formed with five rows and one column, totaling five neurons and consequently five clusters. The greatest dissimilarity was established between clusters I and V. The crossing between the genotypes of cluster I and those belonging to clusters III and V are the most recommended, since they aim to recombine families with characteristics of interest to the improvement and high dissimilarity. Anderson's discriminant analysis showed that the genotype classification was 100% correct, indicating the efficiency of the methodology used. Map (SOM) neural networks are a type of exploratory multivariate analysis tool that allows, through artificial computational intelligence, to design high-dimensional data in a smaller dimensional space, without loss of information [12]. This new organization prioritizes maintaining the structure, such as clusters and information relationships [13]. This reinforces its constant use in several

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“…Determination of the genetic variation in breeding population facilitates identification of useful genetic divergence imperative for cassava population improvement. Genetic divergence in breeding population is evaluated by genetic markers (Andrade et al 2017). Genetic markers such as agro-morphological markers had been used frequently in preliminary studies because they are fast and easy approach for assessing the extent of diversity among germplasm (Asare et al 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Elias et al (2001) also reported that morphological traits have a heritable genetic variation. As knowledge in scientific research progressed, molecular markers were noted to unravel the genetic constitution and significance of traits through DNA fingerprinting, gene link detection, identification of genotypes, gene introgression, germplasm characterization, phylogenetic analysis, and indirect selection of agronomic traits (Souza 2015;Andrade et al 2017). Such knowledge underpins the use of appropriate and reliable agro-morphological descriptor and molecular markers for the evaluation of genetic diversity (Fukuda and Guevara 1998).…”

Section: Introductionmentioning

confidence: 99%

Genetic characterization of cassava (Manihot esculenta Crantz) genotypes using agro-morphological and single nucleotide polymorphism markers

Karim

Ifie

Dzidzienyo

et al. 2019

Physiol Mol Biol Plants

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Dearth of information on extent of genetic variability in cassava limits the genetic improvement of cassava genotypes in Sierra Leone. The aim of this study was to assess the genetic diversity and relationships within 102 cassava genotypes using agro-morphological and single nucleotide polymorphism markers. Morphological classification based on qualitative traits categorized the germplasm into five different groups, whereas the quantitative trait set had four groups. The SNP markers classified the germplasm into three main cluster groups. A total of seven principal components (PCs) in the qualitative and four PCs in the quantitative trait sets accounted for 79.03% and 72.30% of the total genetic variation, respectively. Significant and positive correlations were observed between average yield per plant and harvest index (r = 0.76***), number of storage roots per plant and harvest index (r = 0.33*), height at first branching and harvest index (0.26*), number of storage roots per plant and average yield per plant (r = 0.58*), height at first branching and average yield per plant (r = 0.24*), length of leaf lobe and petiole length (r = 0.38*), number of leaf lobe and petiole length (r = 0.31*), width of leaf lobe and length of leaf lobe (r = 0.36*), number of leaf lobe and length of leaf lobe (r = 0.43*), starch content and dry matter content (r = 0.99***), number of leaf lobe and root dry matter (r = 0.30*), number of leaf lobe and starch content (r = 0.28*), and height at first branching and plant height (r = 0.45**). Findings are useful for conservation, management, short term recommendation for release and genetic improvement of the crop.

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<b>Genetic dissimilarity among sweet potato genotypes using morphological and molecular descriptors

Cited by 22 publications

References 14 publications

Genetic Diversity and Population Structure of Maize Inbred Lines with Varying Levels of Resistance to Striga Hermonthica Using Agronomic Trait-Based and SNP Markers

Genetic Diversity and Population Structure of Maize Inbred Lines with Varying Levels of Resistance to Striga Hermonthica Using Agronomic Trait-Based and SNP Markers

Self-organized map: the new aproach for study of genetic divergence in kale

Genetic characterization of cassava (Manihot esculenta Crantz) genotypes using agro-morphological and single nucleotide polymorphism markers

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