Combining ability analysis in sunflower (Helianthus annuus L.) genotypes

Habib, Sheikh Hasna; Akanda, Abdul Latif; Hossain, Kausar; Alam, Mufid

doi:10.5897/jco2020.0221

Cited by 5 publications

(7 citation statements)

References 17 publications

(8 reference statements)

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“…To increase the yield in sunflower vertically, development of hybrids with better yield potential and stability is required. Parents with diverse genetic makeup would generally produce superior transgressive hybrids 48 . To make a breeding program fruitful, the first step is to select the parental lines to be used for hybridization.…”

Section: Discussionmentioning

confidence: 99%

“…The occurrence of both significant GCA and SCA effects in the present study indicates the presence of both additive and non-additive gene effects in the expression of plant measured traits. GCA effects generally lead to the selection of suitable parents for population improvement or development of synthetics or composite cultivars, as these may be the preference of some growers because their seed can be used for more than one year 48 . In present study, high and significant GCA effects for both among CMS and restore lines in desirable directions i.e., negative for flowering, maturity, and plant height traits and positive for the rest of traits measured was observed.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

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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“…11 & 12). Habib et al (2021) and Khan et al (2008) had reported variability for no of achenes per head in sunflower.…”

Section: No Of Achenes Per Headmentioning

confidence: 96%

“…Crosses showing largest head diameter might be utilized in producing a greater number of achenes per head and ultimately high yielding hybrids. Habib et al (2021) and Khan et al (2008) had reported variability for head diameter in sunflower ranged from 8.51 to 24.46 cm. The above listed findings are in comparisons with these researchers.…”

Section: Fresh Head Diameter (Cm)mentioning

confidence: 97%

Genetic Variability Analysis for Achene Yield and Its Related Traits in Sunflower

2023

IJAB

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Pakistan is deficient in edible oil production. The sunflower being an important oilseed crop due to better oil composition, short duration as a cheapest source of energy and major component of our daily diet can be used for overcoming the gap. In this experiment combining ability was computed for achene yield and its related trait. 15 Genotypes of sunflower were crossed by using line × tester pattern. Out of 15 genotypes, A6, A7, A10, A11, A12, A17, A19, A20, A21 and A26 were taken as females (lines) and A8, A9, A16, A18 and A22 were taken as male (testers) in this experiment. 50 Crosses were made. The parental and F1 cross seeds was sown in randomized complete block design in 3 replications in next growing season. The data was recorded for number of leaves/plant, leaf area, plant height, head angle, fresh head diameter, dry head diameter, No of achenes/head, average yield/plant, 100 achene weight and harvest index. The recorded data was used to estimate the genetic variation among the genetic material. Line × tester analysis was used for estimating combining ability effects of sunflower genotypes. Analysis of variance, Line × Tester and Crosses vs parents showed highly significant differences among the sunflower accessions. Significant and positive GCA was found among lines A10, A21 for average yield and A17, A26 for 100 achene weight. Among testers significant and positive GCA values were found in A9 for average yield, A18 for 100 achene weight. Dominance variance represented that the ratio of GCA: SCA variance for all of the investigated traits is more than unity. All the lines had highest contribution to the total variance which indicated the presence of maternal effects. The findings suggest that crosses can produce heterotic effects which can be utilized for developing sunflower hybrids with high yield potential.

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“…After canola, palm, and soybean, it is the fourth most significant oilseed crop (Farrokhi et al, 2011). In 2019-20, despite a minor decline in cottonseed and sunflower seed production, global oilseed production was virtually unchanged at 610.42 million tonnes (Sheikh et al, 2021). It includes between 40 and 50 percent oil and 23 percent protein, which is a substantial amount.…”

Section: Introductionmentioning

confidence: 99%

Combining Ability Analysis for Various Morphological Traits in Sunflower (Helianthus Annuus L.)

MUSTAFA

TARIQ

ZAFAR

et al. 2023

Biol Clin Sci Res J

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The present research was conducted in the Department of Plant Breeding and Genetics, University of Agriculture Faisalabad. Six lines and four testers were sown, and 24 crosses were generated. The seeds of these crosses were sown in a randomized complete block design (RCBD) with three replications and data for several morphological traits, including the number of days to flowering, plant height, number of whorls per head, head diameter, number of seeds per head, number of leaves per plant, achene yield per plant, 100-seed weight, oil content and protein content were recorded. Data were analyzed using analysis of variance and line × tester analysis. General combining ability (GCA) and specific combining ability (SCA) for traits mentioned above were evaluated. Lines with crosses showing high values of GCA and SCA were selected for further breeding programs. The recorded data were further subjected to analysis of variance, GCA and SCA to determine genetic variability and association among the characters. The results showed that the parental lines A-1, A-2, A-7 and A-8 showed good potential for most of the traits so that they could be taken to further breeding programs. The crosses A-5×A-10 and A-2×A-7 could be used for hybrid development.

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Combining ability analysis in sunflower (Helianthus annuus L.) genotypes

Cited by 5 publications

References 17 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

Genetic Variability Analysis for Achene Yield and Its Related Traits in Sunflower

Combining Ability Analysis for Various Morphological Traits in Sunflower (Helianthus Annuus L.)

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