Crop Biotechnology

In collaboration with Payame Noor University and Iranian Biotechnology Society

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

Journal Metrics

News

Estimating breeding value of the morphological traits in oilseed sunflower genotypes under normal and drought stress conditions with microsatellite and retrotransposon based markers

Document Type : Research Paper

Authors

1 Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran.

2 Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran

Abstract

Knowledge on genes effect and action (additive/dominance) is one of the necessities to achieve cultivars with high performance and quality. Estimating the breeding value (additive effect) can be done thanks to molecular markers through best linear unbiased prediction (BLUP). In the present study, 100 oilseed sunflower genotypes were evaluated based on the 10×10 lattice design during two crop years of 1392-1393 under normal and drought stress (irrigation limitation) conditions. The breeding value of 13 traits in 78 genotypes out of 100 was estimated due to having genotyping data with SSR and Retrotransposon based markers in each one of normal and drought stress (irrigation limitation) conditions through BLUP. For this purpose, the kinship matrix was calculated by SSR and Retrotransposon based markers data. According to total ranks of breeding values of all studied traits estimated by molecular data of both markers, in normal conditions, genotypes 47, 11, 8 and 35 and under drought stress (irrigation limitation) conditions, genotypes 8, 11 and 35 showed the highest breeding value. Based on SSR markers data in normal conditions; genotypes 76, 36, 34 and 41 and based on Retrotransposon based markers data; genotypes 61, 78, 72 and 52, and in drought stress (irrigation limitation) conditions based on SSR markers data; genotypes 76, 38, 34, 29 and 70 and based on Retrotransposon based markers data; genotypes 16, 71, 78 and 61 showed the lowest breeding value. Considering both studied conditions and all studied traits and both molecular markers information, genotypes 8, 11 and 35 with high breeding value are introduced as desirable parents for breeding programs.

Keywords

Main Subjects

References
Abdi, N., Darvishzadeh, R., Jafari, M., Pirzad, A. & Haddadi, P. (2012). Genetic analysis and QTL mapping of agro-morphological traits in sunflower (Helianthus annuus L.) under two contrasting water treatment conditions. Plant Omics, 5(2), 149-158. https://hal.inrae.fr/hal-02642526. Afrouz, G., Darvishzadeh, R., Alipour, H., Marcelo Soriano Viana, J. & Razi, M. (2021). Estimating breeding value of agrobiologic traits in maize (Zea mays L.) under normal and salinity stress conditions based on single nucleotide polymorphism (SNP) marker. Cereal Research, 11(1), (55-75). . https:// doi:10.22124/cr.2021.19661.1669. Afrouz, G., Darvishzadeh, R., Alipour, H., Marcelo Soriano Viana, J. & Akbari, N. (2023). Estimation of breeding value of seed related morpho-physiological traits in maize (Zea mays L.) under normal and salinity stress conditions based on SNP marker. Iranian Journal of Field Crop Science, 54(1), (183-196). https:// doi: 10.22059/ijfcs.2022.345118.654921. Akbari, N. & Darvishzadeh, R. (2024). Identification of IRAP markers associated with agro-morphological traits in oil seed sunflower (Helianthus annuus L.) under normal and water limited conditions. Cellular and Molecular Research (Iranian Journal of Biology), 36(4), 343-357. (in Persian). https://cell.ijbio.ir › article_2166. Amiteye, S. (2021). Basic concepts and methodologies of DNA marker systems in plant molecular breeding. Heliyon, 30;7(10): e08093. https://doi.org/10.1016/j.heliyon.2021.e08093. Asif Shehzada, M., Nawaza, F., Ahmadb, F., Ahmadc, N. & Masood, S. (2020). Protective effect of potassium and chitosan supply on growth, physiological processes and antioxidative machinery in sunflower (Helianthus annuus L.) under drought stress. Ecotoxicology and Environmental Safety, 187:109841. https://doi.org/10.1016/j.ecoenv. Basirnia, A., Darvishzadeh, R. & Abdollahi Mandoulakani, B. (2016). Retrotransposon insertional polymorphism in sunflower (Helianthus annuus L.) lines revealed by IRAP and REMAP markers. Plant Biosystems, 150(4), 641-652. https://doi.org/10.1080/11263504.2014.970595. Bernardo, R. (2020). Reinventing quantitative genetics for plant breeding: something old, something new, something borrowed, something BLUE. Heredity, 125, 375-385 https://doi.org/10.1038/s41437-020-0312-1. Bernardo, R. & Yu, J. (2007). Prospects for genome wide selection for quantitative traits in maize. Crop Science, 47(3), 1082-1090. Darvishzadeh, R., Maleki, H.H. & Sarrafi, A. (2011). Path analysis of the relationships between yield and some related traits in diallel population of sunflower (Helianthus annuus L.) under well-watered and water-stressed conditions. Australian Journal of Crop Science, 5(6), 674-680. Doyle, J.J. & Doyle, J.L. (1990). Isolation of plant DNA from fresh tissue. Focus, 12(1), 13-15. Falconer, D.S. & Mackay, T.F.C. (1996). Introduction to quantitative genetics. 4th Edition. Addison Wesley Longman, Harlow, Essex, UK: Longmans Green, 3. Foulley, J.L. (2015). Mixed Model Methodology. Part I: Linear Mixed Models. Technical Report, e-print: https://doi: 10.13140/2.1.3072.0320. Ghaffari, M., Toorchi, M., Valizadeh, M. & Shakiba, M.R. (2012). Morphp-physiological screening of sunflower inbred lines under drought stress condition. Turkish Journal of Field Crops, 17(2), 185-190. Ghahramani, S. & Darvishzadeh, R. (2021). Estimating Breeding Value of Agro-biological Traits in Maize Using IRAP and REMAP Markers. Crop Biotechnology, 11(2), 33-48. DOI: 10.30473/CB.2022.62429.1865. https://doi. 10.30473/CB.2022.62429.1865. Gbadegesin, M.A. & Beeching, J.R. (2010). Enhancer/Suppressor mutator (En/Spm)-like transposable elements of cassava (Manihot esculenta) are transcriptionally inactive. Genetics and Molecular Research, 9(2), 639-650. https://doi.10.4238/vol9-2gmr713. Grnt, O.M. (2012). Understanding and exploiting the impact of drought stresson plant physiology. In: Abiotic stress Rosponses in plants. Springer, New York, 89-104. https://doi.10.1007/978-1-4614-0634-1_5. Henderson, C.R. (1990). Statistical methods in animal improvement: historical overview. In Advances in statistical methods for genetic improvement of livestock. Part of the Advanced Series in Agricultural Sciences book series.18, 2-14. Springer, Berlin, Heidelberg. Jannink, J.L., Lorenz, A.J. & Iwata, H. (2010). Genomic selection in plant breeding: from theory to practice. Briefings in Functional Genomics, 9(2), 166-177. https://doi.org/10.1093/bfgp/elq001. Kalendar, R., Flavell, A.J., Ellis, T.H.N., Sjakste, T., Moisy, C. & Schulman, A.H. (2011). Analysis of plant diversity with retrotransposon-based molecular markers. Heredity, (Edinb) 106, 520-530. https://doi.10.1038/hdy.2010.93. König, S, Simianer, H. & Willam, A (2009). Economic evaluation of genomic breeding programs. Journal of Dairy Science, 92, 382-391. https://doi.org/10.3168/jds.2008-1310. Meuwissen, T.H.E, Hayes, B.J. & Goddard, M.E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157, 1819-1829. https://doi. 10.1093/genetics/157.4.1819. Meuwissen, T. & Goddard, M. (2010). Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics, 185(2), 623-631. https://doi.10.1534/genetics.110.116590. Muhammad Samran Gul, R., Sajid. M., Rauf, S., Munir, H., Shehzad, M. & Waseem, H. (2021). Evaluation of drought-tolerant sunflower (Helianthus annuus L.) hybrids in autumn and spring planting under semi-arid rainfed conditions. 28:24. Oilseeds and fats, Crops and Lipids Published by EDP Sciences, https://doi.org/10.1051/ocl/2021012. Patterson, H.D. & Thompson, R. (1971). Recovery of inter-block information when block sizes are unequal. Biometrika, 58(3), 545-554. https://doi.org/10.2307/2334389. Piepho, H.P. (2009). Ridge regression and extensions for genomewide selection in maize. Crop Science, 49(4), 1165-1176. https://doi.org/10.2135/cropsci2008.10.0595. Rauf, S. (2019). Breeding strategies for sunflower (Helianthus annuus L.) Genetic improvement. In: Al-Khayri, J.M., Jain, S.M., and Johnson, D.V. (Eds.), Advances in Plant Breeding Strategies: Industrial and Food Crops, 637-673. Razi, M., Darvishzadeh, R., Doulati Baneh, H., Amiri, M. E., & Martinez-Gomez, P. (2020). Estimating the breeding value of some pomological traits in grape cultivars of West Azarbaijan using ISSR markers. Research in Pomology, 5(1), 126-138. Roudbari, Z., Mohammadi‐Nejad, G. & Shahsavand‐Hassani, H. (2017). Field screening of primary and secondary tritipyrum genotypes using selection indices based on blup under saline and normal conditions. Crop Science, 57(3), 1495-1503. https://doi. 10.2135/cropsci2016.09.0789. Saatchi, M., Miraei-Ashtiani, S.R., Nejati Javaremi, A., Moradi-Shahrebabak, M. & Mehrabani-Yeghaneh, H. (2010). The impact of information quantity and strength of relationship between training set and validation set on accuracy of genomic estimated breeding values. African Journal of Biotechnology, 9 (4): 438-442, http://www.academicjournals.org/AJB Sahranavard-Azartamar, F., Ghadimzadeh, M. & Darvishzadeh, R. (2016). Genetic diversity and structure analysis of oily sunflower (Helianthus annuus L.) based on microsatellit markers. Plant Genetic Researches, 2(2), 15-32. http://doi. 10.29252/pgr.2.2.15. Saint Pierr, C., Crossa, J.L., Bonnett, D., Yamaguchi-Shinozaki, whetK. & Reynolds, M.P. (2012). Phenotyping transgenic wheat for drought resistance. Journal of Expeimental Botany, 63(5), 1799-1808. http://doi. 10.1093/jxb/err385. Schaeffer, L.R. (2006). Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics, 123, 218-223. https://doi.10.1111/j.1439-0388.2006.00595.x. Shi, H., Wu, Y., Yi, L., Hu, H., Su, F., Wang, Y., Li, D. & Hou, J. (2023). Analysis of QTL mapping for germination and seedling response to drought stress in sunflower (Helianthus annuus L.). Peer J, 3.11: e15275, http://doi.org/10.7717/peerj.15275. Sinha, D., Maurya, A.K., Abdi, G., Majeed, M., Agarwal, R., Mukherjee, R., Ganguly, S., Aziz, R., Bhatia, M., Majgaonkar, A., Seal, S., Das, M., Banerjee, S., Chowdhury, S., Adeyemi, S.B. & Chen, J.T. (2023). Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals. Genes. 14(7), 1484, https://doi.org/10.3390/ genes14071484. Tahmasbali, M., Darvishzadeh, R. & Fayaz Moghaddam, A. (2020). Estimating Breeding Value of Agronomic Traits in Oriental Tobacco Genotypes under Broomrape Stress and Normal Conditions. Plant Genetic Researches, 7(1), 103-126. https://doi.org/10.52547/pgr.7.1.7. Tolk, J.A. & Howell, T.A. (2012). Sunflower water productivity in four Great Plains soils. Field Crops Research, 127, 120-128. https://doi.org/10.1016/j.fcr.2011.11.012. Yang, R.-C. 2010. Towards understanding and use of mixed-model analysis of agricultural experiments. Canadian Journal of Plant Science 90, 605-627.
Crop Biotechnology
Volume 13, Issue 45 - Serial Number 45
March 2024
Pages 41-61
Files
Share
How to cite
Statistics