Molecular genetic diversity and inter-generation association parameters for yield attributes in the segregating generation of barnyard millet [Echinochloa frumentacea (Roxb.) Link] crosses
DOI:
https://doi.org/10.14719/pst.4805Keywords:
F2, F3, molecular diversity, parent-progeny regression, PIC, true heritabilityAbstract
Barnyard millet, recognized for its high nutritional and agronomic value, has garnered significant attention in recent times. However, no short-duration varieties of barnyard millet have been released so far in Tamil Nadu. To ad- dress this gap, a study was conducted at the Agricultural College and Re- search Institute, TNAU, Madurai, Tamil Nadu, India, during the summer of 2020 and 2021. The study aimed to evaluate the diversity among ten barn- yard millet parents, varying in duration used in various crosses, employing 30 EST-SSR and SSR markers. Twenty of the thirty primers used demon- strated polymorphism, highlighting molecular diversity. The Polymorphic Information Content (PIC) value extended from 0.18 (BMESSR 101 and BMESSR 114) to 0.62 (BMESSR 120). Two to three alleles per locus were pro- duced by these polymorphic markers. The ten parents were grouped into four clusters, based on Jaccard’s coefficient. The parents used for different crosses in the hybridization program were chosen from the distant clusters as confirmed by the parental diversity analysis. The intergeneration herita- bility parameters, including parent-progeny correlation, regression, and narrow-sense heritability, were analyzed between the F2 and F3 generations of crosses involving extra-early parents ACM-15-343 x IEc 82 and Co (Kv) 2 x IEc 107. Regression values for yield attributes were positive and highly sig- nificant, confirming the successful inheritance of traits with minimal envi- ronmental influence. High narrow-sense heritability estimates for all yield traits indicated the potential for developing early-maturing, high-yielding genotypes. This study highlights the molecular diversity and genetic poten- tial of barnyard millet, paving the way for the development of improved cultivars.
Downloads
References
Chandrkar L, Sahu T, Shahi S. A systematic review of barnyard millet. Afr J Bio Sci. 2024;6(9):2674?80. https://doi.org/10.33472/AFJBS.6.9.2024.2673-2680
Sood S, Khulbe RK, Gupta AK, Agrawal PK, Upadhyaya HD, Bhatt JC. Barnyard millet–a potential food and feed crop of future. Plant Breed. 2015;134(2):135?47. https://doi.org/10.1111/pbr.12243
Renganathan VG, Vanniarajan C, Renuka R, Veni K, Vetriventhan M. Current status and future prospects of omics strategies in barnyard millet. In: Pudake RN, Solanke AU, Sevanthi AM, Rajendrakumar P, editors. Omics of climate resilient small millets. Springer Nature, Singapore; 2022. p. 53?68. https://doi.org/10.1007/978-981-19-3907-5
Monika S, Vanniarajan C, Chandirakala R, Renuka R. Genetic variability and association analysis in the segregating population of extra early barnyard millet [Echinochloa frumentaceae (Roxb.) Link] involved crosses. Electron J Plant Breed. 2021;12(3):841?48. https://doi.org/10.37992/2021.1203.117
Vanniarajan C, Anand G, Kanchana S, Veeranan V, Giridhari A, Renganathan VG. A short duration high yielding culture-Barnyard millet ACM 10145. Agric Sci Dig. 2018;38(2):123?26. https://doi.org/10.18805/ag.D-4574
Rani R, Singh V, Punia M. Intergeneration correlation and parent-offspring regression in rust resistance derived F4 and F5 generations in bread wheat. Indian J Agric Sci. 2021;91(5):683?88. https://doi.org/10.56093/ijas.v91i5.112983
Dellaporta SL, Wood J, Hicks JB. A plant DNA minipreparation: version II. Plant Mol Biol Rep. 1983;1:19?21. https://doi.org/10.1007/BF02712670
Özkan G, Halilo?lu K, Türko?lu A, Özturk HI, Elkoca E, Poczai P. Determining genetic diversity and population structure of common bean (Phaseolus vulgaris L.) landraces from Türkiye using SSR markers. Genes. 2022;13(8):1410. https://doi.org/10.3390/genes13081410
Rohlf FJ. NTSYS-pc: numerical taxonomy and multivariate analysis system. Exeter Publishing Ltd., New York; 1988.
Sneath Peter HA, Sokal RR. Numerical taxonomy. The principles and practice of numerical classification. Freeman and Company, San Francisco;1973.
Jaccard P. Nouvelles recherches sur la distribution florale. Bull Soc Vaud Sci Nat. 1908;44:223?70. https://doi.org/10.5169/seals-268384
Lush JL. Intra-sire correlations or regressions of offspring on dam as a method of estimating heritability of characteristics. J Anim Sci. 1940;1940(1):293?301. https://doi.org/10.2527/jas1940.19401293x
Dubey S, Rangaiah S. Broad sense and narrow sense heritability in F4 and F5 generations of finger millet, Eleusine coracana (L.) Gaertn. Electron J Plant Breed. 2019;10(1):66?75. https://doi.org/10.5958/0975-928X.2019.00008.5
Smith JD, Kinman ML. The use of parent-off spring regression as an estimator of heritability. Crop Sci. 1965;5:595?96. https://doi.org/10.2135/cropsci1965.0011183X000500060035x
Zhu Y, Ma T, Lin Y, et al. SSR molecular marker developments and genetic diversity analysis of Zanthoxylum nitidum (Roxb.) DC. Sci Rep. 2023;13:20767. https://doi.org/10.1038/s41598-023-48022-7
Vieira ML, Santini L, Diniz AL, Munhoz CD. Microsatellite markers: what they mean and why they are so useful. Genet Mol Biol. 2016;39(3):312?28. https://doi.org/10.1590/1678-4685-GMB-2016-0027
Ding Y, Zhang J, Lu Y, Lou L, Tong Z. Development of EST-SSR markers and analysis of genetic diversity in natural populations of endemic and endangered plant Phoebe chekiangensis. Biochem Systemat Ecol. 2015; 63:183?89. https://doi.org/10.1016/j.bse.2015.10.008
Manimekalai M, Dhasarathan M, Karthikeyan A, Murukarthick J, Renganathan VG, Thangaraj K, et al. Genetic diversity in the barnyard millet (Echinochola frumentacea) germplasms revealed by morphological traits and simple sequence repeat markers. Curr Plant Biol. 2018;14:71?78. https://doi.org/10.1016/j.cpb.2018.09.006
Roy P, Sogir SB, Basak T. On the Polymorphism Information Content (PIC)- A practical application for the DNA sequencing data. Eur J Med Health Res. 2023;1(1):21?29. https://doi.org/10.59324/ejmhr.2023.1(1).04
Gimode D, Odeny DA, de Villiers EP, Wanyonyi S, Dida MM, Mneney EE, et al. Identification of SNP and SSR markers in finger millet using next generation sequencing technologies. PLoS One. 2016;11(7):e0159437. https://doi.org/10.1371/journal.pone.0159437
Prabhu R. Genetics studies in barnyard millet [Echinochloa frumentaceae (Roxb.) Link] for earliness. Ph.D [Thesis]. Agricultural College and Research Institute, Tamil Nadu Agricultural University, Madurai; 2020.
Sallam A, Alqudah AM, Baenziger PS, Rasheed A. Editorial: Genetic validation and its role in crop improvement. Front Genet. 2023;4(13):1078246. https://doi.org/10.3389/fgene.2022.1078246
Viswabharathy S, Thulasinathan T, Subburaj S, Seeli FD, Ayyenar B, Kambale R, et al. Harnessing parent-offspring regression analysis to develop high-yielding submergence tolerant lines of Oryza sativa L. Electron J Plant Breed. 2023;14(2):675?86. https://doi.org/10.37992/2023.1402.080
Kahani F, Hittalmani S. Identification of F2 and F3 segregants of fifteen rice crosses suitable for cultivation under aerobic situation. SABRAO J Breed Genet. 2016;48(2):219?29.
Seeli FD, Manonmani S, Pushpam R, Raveendran M. Parent progeny regression analysis in segregating generations of drought QTLs pyramided rice lines (Oryza sativa L.). Electron J Plant Breed. 2021;12(4):1178?88. https://doi.org/10.37992/2021.1204.162
Viswabharathy S, Kalaimagal T, Manonmani S, Jeyakumar P, Raveendran M. Estimation of narrow sense heritability in early segregating generations of rice introgressed with Sub1 QTL. Electron J Plant Breed. 2023;14(3):912?22. https://doi.org/10.37992/2023.1403.103
Kumar S, Kumar P, Arya VK, Kumar R, Kerkhi SA. Estimates of genetic components and regression analysis for grain yield and various morphological traits in bread wheat (Triticum aestivum L.). J Appl Nat Sci. 2018;10(1):6?11. https://doi.org/10.31018/jans.v10i1.1569
Jayakodi M, Madheswaran M, Adhimoolam K, Perumal S, Manickam D, Kandasamy T, et al. Transcriptomes of Indian barnyard millet and barnyardgrass reveal putative genes involved in drought adaptation and micronutrient accumulation. Acta Physiol Plant. 2019;41:66. https://doi.org/10.1007/s11738-019-2855-4
Qi X, Lindup S, Pittaway TS, Allouis S, Gale MD, Devos KM. Development of simple sequence repeat markers from bacterial artificial chromosomes without subcloning. Biotechniques. 2001;31(2):355?62. https://doi.org/10.2144/01312st08
Supari N, Javed MA, Jahan N, Khalili E, Khan S. Screening of previously reported microsatellite markers, associated with panicle characteristics, for maker assisted selection in Malaysian rice (Oryza sativa L.). The J Anim Plant Sci. 2016;26(4):1117?23.

Downloads
Published
Versions
- 26-04-2025 (2)
- 15-04-2025 (1)
How to Cite
Issue
Section
License
Copyright (c) 2025 S Monika, C Vanniarajan, R Chandirakala, R Renuka

This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright and Licence details of published articles
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Open Access Policy
Plant Science Today is an open access journal. There is no registration required to read any article. All published articles are distributed under the terms of the Creative Commons Attribution License (CC Attribution 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited (https://creativecommons.org/licenses/by/4.0/). Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).