Assessment of genetic diversity among 51 inbred sunflower lines

Sujatha, H.L.; Chikkadevaiah, C; Nandini, R.

doi:10.2298/hel0237101s

Cited by 5 publications

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“…It was noted that in one major cluster, there were only restorer lines, while the other major cluster contains A-lines, B-lines and SFP line combined. This trend of clear separation of restorer lines from A, B and SFP lines had previously been monitored in sunflower 12 , 14 , 27 . Efficiency of HCA has been well documented in diversity paneling.…”

Section: Discussionsupporting

confidence: 70%

“…At present, it is the fourth most important oilseed crop in the world. Reason for the widespread cultivation of sunflower is its ability to grow in wider range of environments, high seed yielding capacity, potential of having two crops in a calendar year 12 , 13 . To have cultivars with greater yield potential, it is established fact the F 1 s produced from distantly related high yield inbred lines are more transgressively superior to their parents or crosses attempted in closely related genotypes 14 .…”

Section: Introductionmentioning

confidence: 99%

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Application of machine learning for identification of heterotic groups in sunflower through combined approach of phenotyping, genotyping and protein profiling

Ibrar,

Khan,

Raza

et al. 2024

Sci Rep

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Application of machine learning in plant breeding is a recent concept, that has to be optimized for precise utilization in the breeding program of high yielding crop plants. Identification and efficient utilization of heterotic grouping pattern aided with machine learning approaches is of utmost importance in hybrid cultivar breeding as it can save time and resources required to breed a new plant hybrid/variety. In the present study, 109 genotypes of sunflower were investigated at morphological, biochemical (SDS-PAGE) and molecular levels (through micro-satellites (SSR) markers) for heterotic grouping. All the three datasets were combined, scaled, and subjected to unsupervised machine learning algorithms, i.e., Hierarchical clustering, K-means clustering and hybrid clustering algorithm (hierarchical + K-means) for assessment of efficiency and resolution power of these algorithms in practical plant breeding for heterotic grouping identification. Following the application of machine learning unsupervised clustering approach, two major groups were identified in the studied sunflower germplasm, and further classification revealed six smaller classes in each major group through hierarchical and hybrid clustering approach. Due to high resolution, obtained in hierarchical clustering, classification achieved through this algorithm was further used for selection of potential parents. One genotype from each smaller group was selected based on the maximum seed yield potential and hybridized in a line × tester mating design producing 36 F1 cross combinations. These F1s along with their parents were studied in open field conditions for validating the efficacy of identified heterotic groups in sunflowers genetic material under study. Data for 11 agronomic and qualitative traits were recorded. These 36 F1 combinations were tested for their combining ability (General/Specific), heterosis, genotypic and phenotypic correlation and path analysis. Results suggested that F1 hybrids performed better for all the traits under investigation than their respective parents. Findings of the study validated the use of machine learning approaches in practical plant breeding; however, more accurate and robust clustering algorithms need to be developed to handle the data noisiness of open field experiments.

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Section: Discussionsupporting

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Application of machine learning for identification of heterotic groups in sunflower through combined approach of phenotyping, genotyping and protein profiling

Ibrar,

Khan,

Raza

et al. 2024

Sci Rep

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show abstract

“…The popularity of sunflower cultivation is due to wider adaptability, self-incompatibility, high yield, and uniformity along with two growing seasons in a calendar year (Sujatha and Nandini, 2002a ; Kaya and Atakisi, 2004 ). With the intention to increase the productivity of a plant, a breeder can opt for two strategies: one is to develop a plant variety that can withstand elevated levels of biotic and abiotic stresses and the second one is to breed high-performing hybrids by crossing superior inbred lines (Falconer and Mackay, 1996 ).…”

Section: Introductionmentioning

confidence: 99%

“…Phenotypic and genotypic variabilities are the most vital attributes to distinguish the efficiency of various oil and yield-linked traits correlation (Resende and de Duarte, 2007 ). Variation in a plant/line can be assessed morphologically and then their phenotypic and genotypic coefficients of variation are computed to assess the presence of genetic variability among different agronomic characters (Sujatha and Nandini, 2002a ; Al-Mehemdi et al, 2020 ). Assessing the genetic diversity by agro-morphological characters is often time-consuming and laborious, especially when no prior knowledge of the variability is present (Lochner, 2011 ; Ikram et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Molecular markers-based DNA fingerprinting coupled with morphological diversity analysis for prediction of heterotic grouping in sunflower (Helianthus annuus L.)

et al. 2022

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Cultivated sunflower holds a very narrow genetic base and the efficient utilization of available genetic diversity is very important for a successful breeding program. In the present study, 109 sunflower genotypes were assessed for diversity paneling through a combined approach of morphological and molecular markers analysis. Morphological parameters including days to flower initiation, days to flower completion, plant height, stem curvature, number of leaves per plant, leaf area, head diameter, hundred seed weight, and seed yield per plant were studied. Simple sequence repeats (40 DNA markers) were deployed for diversity profiling. Data were analyzed by both univariate and multivariate statistics. SD and coefficient of variation confirm the presence of significant amounts of genetic variation for all the morphological parameters. Cluster Analysis and Principal Component Analysis further confirm the presence of distinct grouping patterns in the studied material. Cluster analysis of both morphological and molecular analysis revealed that restorer lines tend to group separately from A, B, and open-pollinated lines. Further grouping, at the sub-cluster level, revealed six distinct sub-clusters in each of the two major clusters. In total, 12 genotypes, 6 CMS lines (CMS-HAP-12, CMS-HAP-54, CMS-HAP-56, CMS-HAP-99, CMS-HAP-111, and CMS-HAP-112) and 6 restorer lines (RHP-38, RHP-41, RHP-53, RHP-68, RHP-69, and RHP-71) could be used as potential parents for hybrid development. As genotypes of similar genetic backgrounds tend to group closer, it is deduced that one genotype with the highest seed yield per plant could be used for further hybrid breeding programs in sunflowers.

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Genetic diversity analysis for agro-morphological traits in sunflower (Helianthus annuusL.)

Madhavilatha¹,

Prasad²,

Neelima³

2017

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SUMMARY :Genetic diversity analysis was performed on 70 genotypes of sunflower through Mahalanobis D 2 statistics. Based on the results, the genotypes were categorized into 10 clusters connoting the existence of ample genetic diversity in the material evaluated. Cluster I was the largest with 56 genotypes. This was followed by cluster IV with four genotypes; cluster VII with three genotypes and the remaining were monogenotypic clusters. Maximum inter-cluster distance was observed between cluster VI and VIII (19.02) implying that utilization of the genotypes in those clusters might result in desired F 1 's upon hybridization.The study also revealed that the traits in the genotypes viz., SCMR (30.31%) followed by hull content (24.39%) contributed more to the total genetic divergence. Five genotypes belonging to monogenotypic clusters viz., DRM-342, R-45, CPI-1, NDI-16 and CMS-17B can be utilized in future breeding programme to harness desired heterotic F 1 s.How to cite this article : Madhavilatha, K., Prasad, A.V.S. Durga and Neelima, S. (2017). Genetic diversity analysis for agro-morphological traits in sunflower (Helianthus annuus L.).

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Assessment of genetic diversity among 51 inbred sunflower lines

Cited by 5 publications

References 0 publications

Application of machine learning for identification of heterotic groups in sunflower through combined approach of phenotyping, genotyping and protein profiling

Application of machine learning for identification of heterotic groups in sunflower through combined approach of phenotyping, genotyping and protein profiling

Molecular markers-based DNA fingerprinting coupled with morphological diversity analysis for prediction of heterotic grouping in sunflower (Helianthus annuus L.)

Genetic diversity analysis for agro-morphological traits in sunflower (Helianthus annuusL.)

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