Research Article Analysis of genetic divergence in sweet corn genotypes through hierarchical optimization methods

Carvalho, Filipe; Maciel, Gabriel Mascarenhas; Marques, Odair José; Silva, I.G. da; Braga, Gisele da Silva Ferreira; Marquez, Guilherme Repeza; Siquieroli, Ana Carolina Silva; Neto, Luciano Dias Cabral

doi:10.4238/gmr18384

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“…These genotypes may be used as potential donors to transfer the traits to develop salt-tolerant cultivars (Table 2). Carvalho et al (2019) studied the package NbClust which was used to determine the best number of clusters, based on 24 indexes that establish the criteria of compactness and separation between clusters. The data was analyzed using R software version 3.4.1.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of morphological and molecular characterization of rice (Oryza sativa L.) through SSR marker under saline stress condition

Ivin¹,

Kayathri²,

Ch³

et al. 2022

JEB

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Aim: To assess 43 rice genotypes for their resistance to salinity and to assess the hereditary variety by 10 quantitative characteristics just as 40 SSR markers. Methodology: The seedlings were scored on the basis of 1-9 scale of SES following 10 days of 12 dS/m EC. The morphological traits (10 quantitative traits) were evaluated under natural saline soils (EC: 3.1; pH 7.8) to assess the diversity. At molecular level, 40 Simple Sequence Repeat (SSR) markers were also used to evaluate the diversity in different rice genotypes. Results: The cluster analysis of genotypes based on the morphological traits, identified the Amogh, Vasundhara, AC39040, PS336, and Paiyur-1 clustered in the Pokkali and Nona Bokra clusters. A total of 111 alleles were produced from 26 polymorphic SSR markers, with an average of 4.26 alleles per locus. The PIC value ranged from 0.314 (RM 13) to 0.824 (RM 6283) with an average of 0.633 and the expected heterozygosity varied from 0.498 to 0.844 with a mean of 0.690. Clustering of genotypes based on the molecular data clubbed Pokkali, AC35534, TRY-1, AC39014, Paiyur-1, JGL3844, Vasundhara, White Ponni, PS 349, AC39040, CR143202, and Nona Bokra in one cluster. Interpretation: The combination of screening at the seedling stage, morphological diversity, and molecular diversity clearly show the Vasundhara, AC39040, and Paiyur-1 as new sources of salinity. These genotypes may be further explored to identify novel genes/(QTLs) Quantitative Trait Loci, which could increase the production under salt-affected soils. Key words: Morphological diversity, Molecular diversity, Saline stress, Salinity screening

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Section: Resultsmentioning

confidence: 99%