Hostname: page-component-54dcc4c588-m259h Total loading time: 0 Render date: 2025-09-25T07:02:53.863Z Has data issue: false hasContentIssue false

Decoding biochemical trait inheritance through generation mean analysis in pearl millet [Pennisetum glaucum (L.) R. Br.]

Published online by Cambridge University Press:  10 September 2025

Sunaina Yadav
Affiliation:
ICAR-Indian Agricultural Research Institute, New Delhi, India
Sumerpal Singh*
Affiliation:
ICAR-Indian Agricultural Research Institute, New Delhi, India
Tripti Singhal
Affiliation:
ICAR-Indian Agricultural Research Institute, New Delhi, India
Rakesh Bhardwaj
Affiliation:
ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
Sonu Shekhwat
Affiliation:
ICAR-Indian Agricultural Research Institute, New Delhi, India
Aavula Naveen
Affiliation:
ICAR-Indian Agricultural Research Institute, New Delhi, India
Hemanth S
Affiliation:
ICAR-Indian Agricultural Research Institute, New Delhi, India
*
Corresponding author: Sumerpal Singh; Email: sumerpalsingh@yahoo.com

Abstract

Pearl millet is a vital nutri-cereal that serves as a staple food and a significant source of calories for millions of people in arid and semi-arid tropical regions. The present work aims to conduct various genetic interpretations using six generations of six crosses, which were evaluated during the South-west monsoon season 2021 and 2022 at IARI, New Delhi, India for different biochemical traits. Amylose content (AC) among all the genotypes varied from 21.3–26.4g/100g, starch content (SC) from 56.1–71.0g/100g, oil content from 5.37–13.2g/100g, total protein content from 5.7–14.7g/100g, phytic acid content from 0.86–1.01g/100g and total phenolic content (TPhC) from 0.06–0.19g/100g. Seed yield per spike (SYS) showed positive correlation with thousand seed weight (TSW) and SC while there was negative correlation with protein content. Significant variation was observed for almost all the traits except in a few cases. AC and SC were governed by both additive and dominant gene actions. However, phytic acid, TPhC, TSW and SYS exhibited a stronger bias towards dominant gene action, therefore selection can be delayed to later generations to achieve greater homozygosity and trait stability. In contrast, oil and protein content were primarily controlled by additive effects, indicating that early-generation selection may prove beneficial for identifying superior breeding lines.

Information

Type
Crops and Soils Research Paper
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Anilkumar, C, Rao, AM, Ramesh, S, Bhavani, B and Pranesh, (2019) Genetics of fruit yield and its component traits under different fruiting habit backgrounds in chilli (Capsicum annuum L.). Journal of Genetics 98, 1–0. https://doi.org/10.1007/s12041-019-1133-y Google Scholar
Annor, GA, Tyl, C, Marcone, M, Ragaee, S and Marti, A (2017) Why do millets have slower starch and protein digestibility than other cereals?. Trends in Food Science and Technology 66, 7383. https://doi.org/10.1016/j.tifs.2017.05.012.Google Scholar
Bisen, P, Dadheech, A, Namrata, N, Kumar, A, Solanki, G and Dhakar, TR (2017) Combining ability analysis for yield and quality traits in single cross hybrids of quality protein maize (Zea mays L.) using diallel mating design. Journal of Applied and Natural Science 9, 1760–6. https://doi.org/10.31018/jans.v9i3.1435 Google Scholar
Chandra, S, Singh, SP, Satyavathi, CT, Sankar, SM, Singh, AM and Bhat, JS (2022) Genetics of fertility restoration for the A1 cytoplasmic genic male sterility system in pearl millet (Pennisetum glaucum (L.) R. Br.). Indian Journal of Genetics and plant breeding 82, 6572. https://doi.org/10.31742/IJGPB.82.1.9 Google Scholar
Chaudhari, GR, Patel, DA, Kalola, AD and Kumar, S (2022) Graphical and numerical analysis of the components of gene effect on the quality traits of bread wheat (Triticum aestivum L.) under varying environmental conditions. Agriculture 12, 2055. https://doi.org/10.3390/agriculture12122055 Google Scholar
Dias-Martins, AM, Pessanha, KL, Pacheco, S, Rodrigues, JA and Carvalho, CW (2018) Potential use of pearl millet (Pennisetum glaucum (L.) R. Br.) in Brazil: Food security, processing, health benefits and nutritional products. Food Research International 109, 175–86. https://doi.org/10.1016/j.foodres.2018.04.023 Google Scholar
Fonseca, A and Patterson, FL (1968) Hybrid vigour in a seven parent diallel cross in common winter wheat (T. aestivum L.). Crop Science 8, 8588. https://doi.org/10.2135/cropsci1968.0011183X000800010025x Google Scholar
Gaoh, BS, Gangashetty, PI, Mohammed, R, Dzidzienyo, DK and Tongoona, P (2020) Generation mean analysis of pearl millet [Pennisetum glaucum (L.) R. Br.] grain iron and zinc contents and agronomic traits in West Africa. Journal of Cereal Science 96, 103066. https://doi.org/10.3389/fpls.2021.693680 Google Scholar
Godasara, SB, Dangaria, CJ, Savaliya, JJ, Pansuriya, AG and Davada, BK (2010) Generation mean analysis in pearl millet [Penisetum glaucum (L.) R. Br.]. Agricultural Science Digest 30, 50–3. https://doi.org/10.20546/ijcmas.2016.501.047 Google Scholar
Hassan, ZM, Sebola, NA and Mabelebele, M (2021) The nutritional use of millet grain for food and feed: A review. Agricultural and food security 10, 14. https://doi.org/10.1186/s40066-020-00282-6 Google Scholar
Kaushik, G, Singhal, P and Chaturvedi, S (2018) Food processing for increasing consumption: The case of legumes. In Food Processing for Increased Quality and Consumption. New York: Academic Press, pp. 128. https://doi.org/10.1016/B978-0-12-811447-6.00001-1 Google Scholar
Kearsey, MJ and Pooni, HS (1996) The Genetical Analysis of Quantitative Traits. UK (London): Chapman and Hall Google Scholar
Kempthrone, O (1957) An Introduction to Genetic Statistics. New York: John Wiley and Sons, Inc.Google Scholar
Khatri, AB, Patel, PT, Patel, R, Patel, MS, Shah, SK, Patel, JS and Vaghela, PO (2023) Genetic analysis of grain biochemical parameters and yield in pearl millet [Pennisetum glaucum (L.) R. Br.]. Journal of Cereal Science 113, 103746. https://doi.org/10.1016/j.jcs.2023.103746 Google Scholar
Kumar, G, Singh, AK, Barfa, D, Karthik, D, Kumar, P and Kushwah, MK (2017) Generation mean analysis for grain yield and its contributing traits in pearl millet [Pennisetum glaucum (L.)]. International Journal of Current Microbiology and Applied Sciences 6(8), 355–60. https://doi.org/10.20546/ijcmas.2017.608.047 Google Scholar
Kumar, M, Patel, M and Rani, K (2022) Delineating genetic inheritance and nonallelic genic interactions for grain iron and zinc concentration, yield and its attributes by generation mean analysis in pearl millet [Pennisetum glaucum (L.) R. Br.]. Genetic Resources and Crop Evolution 69, 117–43. https://doi.org/10.1007/s10722-021-01208-2 Google Scholar
Lee, D-J, Durbán, M and Eilers, PHC (2013) Efficient two-dimensional smoothing with P-spline ANOVA mixed models and nested bases. Computational Statistics & Data Analysis 61, 2237. https://doi.org/10.1007/s00122-017-2894-4 Google Scholar
Mather, K (1949) Biometrical Genetics: The Study of Continuous Variation. London: Metuen and Co. Ltd. https://www.cabidigitallibrary.org/doi/full/10.5555/19490101201 Google Scholar
Mather, K and Jinks, JL (1971) Biometrical genetics. The Study of Continuous Variation. Springer New York, NY: Chapman and Hall Ltd. 1382https://doi.org/10.1007/978-1-4899-3404-8 Google Scholar
Pujar, M, Govindaraj, M, Gangaprasad, S, Kanatti, A and Shivade, H (2020) Genetic variation and diversity for grain iron, zinc, protein and agronomic traits in advanced breeding lines of pearl millet [Pennisetum glaucum (L.) R. Br.] for biofortification breeding. Genetic Resources and Crop Evolution 67, 2009–22. https://doi.org/10.1007/s10722-020-00956-x Google Scholar
Samtiya, M, Aluko, RE and Dhewa, T (2020) Plant food anti-nutritional factors and their reduction strategies: An overview. Food Production, Processing and Nutrition 2, 114. https://doi.org/10.1186/s43014-020-0020-5 Google Scholar
Sharma, P, Kamboj, MC, Singh, N, Kumar, R and Kumar, N (2022) Generation mean analysis in maize (Zea mays) for yields and yield attributing traits. Indian Journal of Agricultural Sciences 92, 110–7. https://doi.org/10.56093/ijas.v92i1.120854 Google Scholar
Sheoran, OP, Tonk, DS, Kaushik, LS, Hasija, RC and Pannu, RS (1998) Statistical Software Package for Agricultural Research Workers. Hisar: Department of Mathmetics Statistics, CCS HAU 139143.Google Scholar
Singh, M, Dubey, RB, Ameta, KD, Haritwal, S and Ola, B (2017) Combining ability analysis for yield contributing and quality traits in yellow seeded late maturing maize (Zea mays L.) hybrids using Line x Tester. Journal of Pharmacognosy and Phytochemistry 6, 112118.Google Scholar
Singh, RK and Choudhary, BD (1985) Bio∼trjcal Methods in, Quanti/alive Genetic An41ysis. p 54. Kalyani Publishers. Ludhiana.Google Scholar
Tomar, M, Bhardwaj, R, Kumar, M, Singh, SP, Krishnan, V, Kansal, R, Verma, R, Yadav, VK, Ahlawat, SP, Rana, JC and Bollinedi, H (2021) Nutritional composition patterns and application of multivariate analysis to evaluate indigenous Pearl millet (Pennisetum glaucum (L.) R. Br.) germplasm. Journal of Food Composition and Analysis 103, 104086. https://doi.org/10.1016/j.jfca.2021.104086 Google Scholar
Varshney, RK, Shi, C, Thudi, M, Mariac, C, Wallace, J, Qi, P, Zhang, H, Zhao, Y, Wang, X, Rathore, A, Srivastava, RK, Chitikineni, A, Fan, G, Bajaj, P, Punnuri, S, Gupta, SK, Wang, H, Jiang, Y, Couderc, M, Katta, MAVSK, Paudel, DR, Mungra, KD, Chen, W, Harris-Shultz, KR, Garg, V, Desai, N, Doddamani, D, Kane, NA, Conner, JA, Ghatak, A, Chaturvedi, P, Subramaniam, S, Yadav, OP, Berthouly-Salazar, C, Hamidou, F, Wang, J, Liang, X, Clotault, J, Upadhyaya, HD, Cubry, P, Rhoné, B, Gueye, MC, Sunkar, R, Dupuy, C, Sparvoli, F, Cheng, S, Mahala, RS, Singh, B, Yadav, RS, Lyons, E, Datta, SK, Hash, CT, Devos, KM, Buckler, E, Bennetzen, JL, Paterson, AH, Ozias-Akins, P, Grando, S, Wang, J, Mohapatra, T, Weckwerth, W, Reif, JC, Liu, X, Vigouroux, Y and Xu, X (2017) Pearl millet genome sequence provides a resource to improve agronomic traits in arid environments. Nature Biotechnology 35, 969976. https://doi.org/10.1038/nbt.3943 Google Scholar
Supplementary material: File

Yadav et al. supplementary material 1

Yadav et al. supplementary material
Download Yadav et al. supplementary material 1(File)
File 2.2 MB
Supplementary material: File

Yadav et al. supplementary material 2

Yadav et al. supplementary material
Download Yadav et al. supplementary material 2(File)
File 1.7 MB
Supplementary material: File

Yadav et al. supplementary material 3

Yadav et al. supplementary material
Download Yadav et al. supplementary material 3(File)
File 1.7 MB
Supplementary material: File

Yadav et al. supplementary material 4

Yadav et al. supplementary material
Download Yadav et al. supplementary material 4(File)
File 1.5 MB
Supplementary material: File

Yadav et al. supplementary material 5

Yadav et al. supplementary material
Download Yadav et al. supplementary material 5(File)
File 1.7 MB
Supplementary material: File

Yadav et al. supplementary material 6

Yadav et al. supplementary material
Download Yadav et al. supplementary material 6(File)
File 1.6 MB
Supplementary material: File

Yadav et al. supplementary material 7

Yadav et al. supplementary material
Download Yadav et al. supplementary material 7(File)
File 1.6 MB
Supplementary material: File

Yadav et al. supplementary material 8

Yadav et al. supplementary material
Download Yadav et al. supplementary material 8(File)
File 5.9 MB