Identification of REAMP markers related to morpho-phisiological and agronomic traits in oilseed sunflower (Helianthus annuus L.) under normal and limited irrigation conditions

Akbari, Nasrin; Darvishzadeh, Reza

doi:10.22034/csrar.2024.382749.1316

Identification of REAMP markers related to morpho-phisiological and agronomic traits in oilseed sunflower (Helianthus annuus L.) under normal and limited irrigation conditions

Document Type : Original Article

Authors

Nasrin Akbari ¹

Reza Darvishzadeh ²

¹ PhD Student in Plant Breeding-Molecular Genetics and Genetic Engineering, 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

10.22034/csrar.2024.382749.1316

Abstract

Introduction: Sunflower (Helianthus annuus L.) is one of the most valuable agricultural products, mainly cultivated for edible oil. As an oilseed plant, the sunflower has the fifth place in the world after soybeans, rapeseed, cotton and peanut. Due to climatic changes, reduction of water received from rains and incorrect management of water consumption, the crop production experienced severe drought stress during the growth period which causes the fluctuation and decrease of the product. Considering the importance of studying and selecting cultivars tolerant to abiotic stresses such as drought stress, investigating the genetic diversity for adaptive responses in sunflower cultivars and genotypes, as well as identifying QTLs are necessary for breeding programs. The aim of this study is to investigate genetic diversity and identify markers related to drought tolerance in the inbred lines population of oilseed sunflower using the REAMP (retrotransposon microsatellite amplified polymorphism) markers.
Materials and Methods: In the present research, the genetic diversity of an oilseed sunflower population including 100 inbred lines was evaluated in terms of morpho-physiological traits using a 10x10 simple lattice design with two replications under two normal and limited irrigation conditions during two consecutive years. Different morpho-physiological traits were measured under both conditions. DNA extraction was done from 78 lines out of 100 investigated genotypes by CTAB method. Then, in order to evaluate the quality and quantity of extracted DNA, 1% agarose gel electrophoresis and spectrophotometry were used. The molecular profile of genotypes was prepared using 7 REAMP primers combinations. Based on molecular data and Neighbor Joining algorithm in DARWin 6.0.21 software the studied genotypes were grouped in three groups. Cluster and population structure analyses as well as analysis of molecular variance were performed with GenAlEx and Structure 2.3.3. The number of possible sub-populations (optimal K) was determined based on the delta K (ΔK) method. For optimal K, the Qst matrix was calculated. Using mixed linear model (MLM) related to Q + K matrices (matrix of population structure coefficients + matrix of kinship relations) molecular markers associated with studied morpho-physiological traits were identified.
Results and Discussion: Based on the results of cluster analysis, the genotypes were grouped into 3 groups. Each group included genotypes from different geographical areas. The results of principal component analysis showed that the first three components explain 71.32% of the total changes. Principal coordinate analysis was not able to completely separate the genotypes into separate groups. Molecular analysis of variance showed that 91% of the variation are within group and the rest 9% are between the groups, which indicates high genetic diversity within the groups. According to the mixed linear model, totally 20 molecular markers showed a significant relationship (P≤0.01) with the studied morpho-physiological traits under both normal and limited irrigation conditions. “6181810” marker with oil content and "cf8267" marker with two leaf length and leaf width traits showed a significant relationship (P≤0.01) under limited irrigation conditions. “658268” marker showed a significant relationship with yield and leaf width traits under both normal and limited irrigation conditions. Observing the relationship between one marker and several traits can be derived from the effects of pleiotropy or the linkage of genomic regions involved in the control of these traits.
Conclusion: The results obtained from this study present valuable information on the genetic basis of studied traits that can be used for breeding and developing high performance varieties in sunflower. In this research some common markers were identified. Identification of markers that showed linkage with several traits in both conditions, such as “658268” and “cf8267” markers, are more important in the breeding program due to the possibility of simultaneous breeding of several traits

Keywords

Association analysis

Drought stress

Retrotransposon based molecular markers

Abid, M., Ali, S., Qi, L.K., Zahoor, R., Tian, Z., Jiang, D., Snider, J.L. and Dai, T., 2018. Physiological and biochemical changes during drought and recovery periods at tillering and jointing stages in wheat (Triticum aestivum L.). Scientific Reports, 8(4615), pp.1-15. doi: 10.1038.s41598-018-21441-7

Ahmadpour, S., Sofalian, O., Darvishzadeh, R. and Abbaspour, N., 2018. Preliminary evidence of the associations between DNA markers and morphological characters in sunflower under natural and salt stress conditions. Zemdirbyste-Agriculture, 105(3), pp.279-286. doi: 10.13080/z-a.2018.105.036

Al-Maskri, A.H., Sajjad, M. and Khan, S.H., 2012. Association mapping: a step forward to discovering new alleles for crop improvement. International Journal of Agriculture and Biology, 14(1), pp.153-160.

Amiteye, S., 2021. Basic concepts and methodologies of DNA marker systems in plant molecular breeding. Heliyon, 7(10), e08093. doi: 10.1016/j.heliyon.2021.e08093

Andleeb, T., Shah, T., Nawaz, R., Munir, I., Munsif, F. and Jalal, A., 2020. QTL mapping for drought stress tolerance in plants. Signaling and Communication in Plants, pp.383-403. doi: 10.1007/978-3-030-40277-8_16

Bankole, F., Menkir, A., Olaoye, G., Crossa, J., Hearne, S., Unachukwu, N. and Gedil, M., 2017. Genetic gains in yield and yield related traits under drought stress and favorable environments in a maize population improved using marker assisted recurrent selection. Frontiers in Plant Science, 8, pp.808. doi: 10.3389/fpls.2017.00808

Bhatt, R.M. and Rao, S.N.K., 2005. Influence of pod load on response of okra to water stress. Indian Journal of Plant Physiology, 10(1), pp.54-59.

Bhutta, A.R. 1998. Biological studies on some fungi associated with sunflower in Pakistan. Doctoral dissertation, Sindh Agriculture University TandoJam.

Bradbury, P.J., Zhang, Z., Kroon, D.E., Casstevens, T.M., Ramdoss, Y. and Buckler, E., 2007. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics, 23(19), pp.2633–2635. doi: 10.1093/bioinformatics/btm308

Ching, A., Caldwell, K.S., Jung, M., Dolan, M., Smith, O.S(H), Tingey, S., Morgante, M. and Rafalski, A.J., 2002. SNP frequency, haplotype structure and linkage disequilibrium in elite maize inbred lines. BMC Genetics, 3(19), pp.1-14. doi: 10.1186/1471-2156-3-19

Darbani, S.P., Mehrabi, A.A., Pordad, S.S., Maleki, A. and Farshadfar, M., 2020. Effect of drought stress on agromorphological traits in sunflower (Helianthus annuus L.) genotypes and identification of informative ISSR markers. Plant Genetic Resources: Characterization and Utilization, 18(2), pp.49-62. doi: 10.1017/s1479262120000040

Dehmer, K.J. and Friedt, W., 1998. Development of molecular markers for high oleic acid content in sunflower (Helianthus annuus L.). Industrial Crops and Products, 7(2-3), pp.311–315. doi: 10.1016/s0926-6690(97)00063-0

Doyle, J.J. and Doyle, J.L., 1990. Isolation of plant DNA from fresh tissue. Focus, 12, pp.13-15.

Du, W.J., Wang, M., Fu, S.X. and Yu, D.Y., 2009. Mapping QTL for seed yield and drought susceptibility index in soybean (Glycine max L.) across different environments. Journal of Genetics and Genomics, 36(12), pp.721–731. doi: 10.1016/s1673-8527(08)60165-4

Evanno, G., Regnaut, S. and Goudet, J., 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology, 14(8), pp.2611-2620, doi: 10.1111/j.1365-294X.2005.02553.x

Fernandez, O., Urrutia, M., Berton, T., Bernillon, S., Deborde, C., Jacob, D., Maucourt, M., Maury, P., Durufé, H., Gibon1, Y., Langlade, N.B. and Moing, A., 2019. Metabolomic characterization of sunfower leaf allows discriminating genotype groups ors tress levels with a minimal set of metabolic markers. Metabolomics, 15(4), pp.1-15. doi: 10.1007/s1130 6-019-1515-4

Gbadegesin, M.A. and 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), pp.639-650. doi: 10.4238/vol9-2gmr713

Gupta, P.K., Rustgi, S. and Kulwal, P.L., 2005. Linkage disequilibrium and association studies in higher plants: Present status and future prospects. Plant Molecular Biology, 57(4), pp.461-485. doi: 10.1007/s11103-005-0257-z

Hossain, M.I., Khatun, A., Talukder, M.S.A., Dewan, M.M.R. and Uddin, M.S., 2010. Effect of drought on physiology and yield contributing characters of sunflower. Bangladesh Journal of Agricultural Research, 35(1), pp.113-124. doi: 10.3329/bjar.v35i1.5872

Hongtrakul, V., Huestis, G.M. and Knapp, S.J., 1997. Amplified fragment length polymorphisms as a tool for DNA fingerprinting sunflower germplasm: genetic diversity among oilseed inbred lines. Theoretical and Applied Genetics, 95, pp.400–407. doi: 10.1007/s001220050576

Jannink, J.L. and Walsh, B., 2002. Association mapping in plant populations. In: Kang, M.S. (ed.), Quantitative Genetics. Genomics and Plant Breeding, CAB International, pp.59-68.

Kalendar, R., Flavell, A.J., Ellis, T.H.N., Sjakste, T., Moisy, C. and Schulman, A.H., 2011. Analysis of plant diversity with retrotransposon-based molecular markers. Heredity, (Edinb) 106(4), pp.520-530. doi: 10.1038/hdy.2010.93

Kebriyaee, D., Kordrostami, M., Rezadoost, M.H. and Lahijl, H.S., 2012. QTL analysis of agronomic traits in rice using SSR and AFLP markers. Notulae Scientia Biologicae, 4(2), pp.116–123. doi: 10.15835/nsb427501

Liu, X., Du, J., Khan, A.M.D., Cheng, J., Wei, C., Mei, Z., Chen, H., He, T. and Fu, J., 2020. Analysis of genetic diversity and similarities between different Lycium varieties based on ISSR analysis and RAMP-PCR markers. World Academy of Sciences Journal, 2, pp.83–90. doi: 10.3892/wasj.2020.39

Mora, F., Quitral, Y.A., Matus, I., Russell, J., Waugh, R. and del Pozo, A., 2016. SNP-based QTL mapping of 15 complex traits in barley under rain-fed and well-watered conditions by a mixed modeling approach. Frontiers in Plant Science, 7, pp.1-11. doi: 10.3389/fpls.2016.00909

Nezhadahmadi, A., Prodhan, Z.H., and Faruq, G., 2013. Drought tolerance in wheat. The Scientific World Journal, 11, pp.1–12. doi: 10.1155/2013/610721

Noryan, M., Hervan, I.M., Hossein Sabouri, H., Kojouri, F.D. and Mastinu, A., 2021. Drought Resistance Loci in Recombinant Lines of Iranian Oryza sativa L. in Germination Stage BioTech, 10(4), 26. doi: 10.3390/biotech10040026

Peakall, R. and Smouse, P.E., 2012. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics, 28(19), pp.2537-2539. doi: 10.1093/bioinformatics/bts460

Perrier, X. and Jacquamoud-collect, J.P., 2006. DARWin software. htt://Darwin.cirad.fr/

Pekcan, V., Evci, G., Yilmaz, M.I., Nalcaiyi, A.S.B., Erdal, Ş.Ç., Cicek, N., Ekmekci, Y. and Kaya, Y., 2016. Effects of drought on morphological traits of some sunflower lines. Ekin Journal of Crop Breeding, 2(2), pp.54-68.

Pritchard, J.K., Stephens, M. and Donnelly, P., 2000. Inference of population structure using multilocus genotype data. Genetics, 155(2), pp.945-959. doi: 10.1093/genetics/155.2.945

Oraguzie, N.C., Wilcox, P.L., Rikkerink, E.H.A. and de Silva, H.N., 2007. Linkage disequilibrium, Association Mapping in Plants. Springer. New York. NY. pp.11-39.

Radanović, A., Miladinović, D., Cvejić, S., Jocković, M. and Jocić, S., 2018. Sunflower genetics from ancestors to modern hybrids—A Review. Genes, 9(11), pp.528. doi: 10.3390/genes9110528

Rasoulzadeh Aghdam, M., Darvishzadeh, R., Sepehr, E. and Alipour, H., 2021. Association analysis of agronomic traits of oilseed sunflower (Helianthus annuus L.) lines with REMAP and IRAP markers under optimum and phosphorus deficit stress. Iranian Journal of Field Crop Science, 52(4), pp.179-196. [In Persian]. doi: 10.22059/ijfcs.2020.309464.654749

Rauf, S. and Sadaqat, H.A., 2008. Identification of physiological traits and genotypes combined to high achene yield in sunflower (Helianthus annuus L.) under contrasting water regimes. Australian Journal of Crop Science, 1(1), pp.23-30.

Ren, H., Han, J., Wang, X., Zhang, B., Yu, L., Gao, H., Hong, H., Sun, R., Tian, Y., Qi, X., Liu, Z., Wu, X. and Qiu, L.J., 2020. QTL mapping of drought tolerance traits in soybean with SLAF sequencing. The Crop Journal, 8(6), pp.977-989. doi: 10.1016/j.cj.2020.04.004

Robert, G.A., Rajasekar, M. and Manivannan, P., 2015. Triazole-induced drought stress amelioration on growth yield, and pigments composition of Helianthus annuus L. (sunflower). International Multidisciplinary Research Journal, 5, pp.6–15.

Salazar, J.A., Rasouli, M. and Martínez-Gomez, P., 2014. Random amplified microsatellite polymorphism (RAMP) application in prunus characterization and mapping. Acta Horticulturae, 5, pp.61–64. doi: 10.17660/actahortic.2014.1028.8

Seiler, G.J. and Guly, T.J., 2016. Sunflower: Overview, USDA-ARS, Northern Crop Science Laboratory, Fargo, ND, USA, Encyclopedia of Food Grains, Second Edition, pp.247-253.

Sinclair, T.R. and Ludlow, M.M., 1986. Influence of soil water supply on the plant water balance of four tropical grain legumes. Australian Journal of Plant Physiology, 13, pp.329–341. doi: 10.1071/pp9860329

Shirmohammadli, S., Sabouri, H., Ahangar, L., Ebadi, A.A. and Sajadi, S.J., 2018. Genetic diversity and association analysis of rice genotypes for grain physical quality using iPBS, IRAP, and ISSR markers. Journal of Genetic Resources, 4(2), pp.122-129. doi: 10.22080/jgr.2019.15415.1115

Sharfun-Nahar, M.M. and Hashmi, M.H., 2005. Seed-borne mycoflora of sunflower (Helianthus annuus L.). Pakistan Journal of Botany, 37(2), pp.451- 457.

Skirycz, A., Vandenbroucke, K., Clauw, P., Maleux, K., De Meyer, B., Dhondt, S., Pucci, A., Gonzalez, N., Hoeberichts, F., Tognetti, V.B., Galbiati, M., Tonelli, C., Van Breusegem, F., Vuylsteke, M. and Inzé, D., 2011. Survival and growth of Arabidopsis plants given limited water are not equal. Nature Biotechnology, 29, pp.212–214. doi: 10.1038/nbt.1800

Thirumarimurugan, M., Sivakumar, V.M., Xavier, A.M., Prabhakaran, D. and Kannadasan, T., 2012. Preparation of biodiesel from sunflower oil by transesterification. International Journal of Bioscience, Biochemistry and Bioinformatics, 2(6), pp.441-444.

Toum, L., Perez‑Borroto, L.S., Peña‑Malavera, A.N., Luque, C., Welin, B, Berenstein, A., Do Porto, D.F., Vojnov, A., Castagnaro, A.V. and Pard, E.M., 2020. Selecting putative drought‑tolerance markers in two contrasting soybeans. Scientifc Reports, 12(1), pp.10872. doi: 10.1038/s41598-022-14334-3

Yu, J., Pressoir, G., Briggs, W.H., Vroh Bi, I., Yamasaki, M., Doebley, J.F., McMullen, M.D., Gaut, B.S., Nielsen, D.M., Holland, J.B., Kresovich, S. and Buckler, E.S., 2006a. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics, 38(2), pp.203-208. doi: 10.1038/ng1702

Yu, J. and Buckler, E.S., 2006b. Genetic association mapping and genome organization of maize. Current Opinion in Biotechnology, 17(2), pp.155-160. doi: 10.1016/j.copbio.2006.02.003