ANALYSIS OF SOME VALUABLE ECONOMIC TRAITS OF SOFT WHEAT VARIETIES TRITICUM AESTIVUM L. BASED ON MOLECULAR MARKERS

SCIENCE AND SCIENTIFIC RESEARCH IN THE MODERN WORLD Vol. 3 No. 5 (2025) 27 ANALYSIS OF SOME VALUABLE ECONOMIC TRAITS OF SOFT WHEAT VARIETIES TRITICUM AESTIVUM L. BASED ON MOLECULAR MARKERS Madina Sh. Alieva1, Marguba A. Togaeva2, Orzigul Z. Xo’janazarova3 1Master’s Student, University of Turon, Karshi 180100, Uzbekistan; E-mail: madinaaliy[email protected]om 2Department of General Methodological Sciences, Faculty of Digital Technologies, University of Economics and Pedagogy, Karshi 180100, Uzbekistan; [email protected] Undergraduate Student (Biology), 1st Year 3University of Economics and Pedagogy (NTM), Uzbekistan E-mail: [email protected] Abstract. The article presents the results of research work on the search and creation of new source materials for the successful implementation of modern programs for the cultivation of multi-purpose wheat, resistant to biotic and abiotic stresses, rich in macroand microelements. It is important to carry out an experimental project to predict the location of quantitative traits in ancient local soft wheat samples, as well as to determine the degree of polymorphism between the type of plant population being analyzed and the parental genomes. In the research work, wheat microsatellite (SSR) markers identified for PCR screening of samples were utilized, and the genetic diversity between them was investigated based on the polymorphism of the SSR markers. Key words: SSR markers, PCR screening, quantitative trait locus, QTL map, biotic and abiotic stress, macroand microelements, amphidiploid, tetraploid, diploid, biofortification, chromosome, homozygote, genome, MAS, selection. Introduction. Most common wheat varieties grown in fields around the world today are genetically similar and have a narrow genetic base compared to the local varieties SCIENCE AND SCIENTIFIC RESEARCH IN THE MODERN WORLD Vol. 3 No. 5 (2025) 28 that have been cultivated since ancient times. This similarity is primarily due to the fact that modern wheat varieties are selected mainly for their yield. In global agricultural practices, modern intensive commercial varieties are developed specifically with yield in mind. As a result, these varieties are gradually replacing ancient local varieties in our country's grain fields. In contrast, local varieties are often more resilient to biotic and abiotic stresses and are richer in macro and microelements. It is well understood that identifying and developing new source materials is crucial for the successful implementation of modern, complex wheat breeding programs. The value of new starting materials has increased, especially in recent years, due to the increasing complexity of the tasks that need to be solved by breeding in terms of increasing yield, immunity, product quality, and resistance to stress conditions. The second method for transferring beneficial genes from cereal plants into the common wheat genome involves creating amphidiploids through crosses between tetraploid and diploid wheat species, followed by hybridization with common wheat. Tetraploid wheat species with an AB genome act as intermediaries in both methods. Among monocotyledons, the largest family is Orchidaceae, while Poaceae (the grass family) ranks as the second largest in terms of the number of species. The Poaceae family is the most economically significant among cereal plants. Cereal crops grow on all continents and play a vital role in many ecosystems. This family includes important crops such as wheat (Triticum L.), rice (Oryza L.), corn (Zea L.), barley (Hordeum L.), and rye (Secale L.). Rice and corn are part of the tribe Oryzeae, while the genera Triticum L., Aegilops L., Secale L., and Hordeum L. belong to the tribe Triticeae. This tribe typically contains over 150 species at various stages of maturity, including important varieties like common wheat (Triticum aestivum L.), durum wheat (Triticum turgidum L.), emmer (Triticum monococcum L.), rye (Secale cereale L.), and barley (Hordeum vulgare L.) [1; 46-58-b]. SCIENCE AND SCIENTIFIC RESEARCH IN THE MODERN WORLD Vol. 3 No. 5 (2025) 29 Selecting qualitative traits for wheat biofortification is generally easier to implement than selecting quantitative traits using traditional genetic methods. In this context, traditional selection methods, particularly backcross selection that involves a diverse range of varieties (known as Participatory Varietal Selection – PVS), play a significant role. To effectively utilize wheat genomic resources, it is crucial to incorporate functional genomics and to receive state support for the preservation of biological diversity, particularly by safeguarding important varieties [2; pp. 1-9, 3; pp. 198–206]. The wheat genome is challenging to study because of its large number of repetitive sequences, heterozygosity, and polyploidy [4; pp. 71-91]. Recent advancements in bioinformatics and sequencing technologies have enabled the exploration of diverse living genome resources [5; pp. 17-53, 6; pp. 95–109]. Comparative genomics data for plants are proving to be effective in identifying new genes for the biofortification of modern wheat [7; pp. 1-18]. The use of various genomic technologies, in particular, mapping of quantitative trait loci (QTL), marker-assisted selection (MAS), and genomic selection (GS) methods, is widely used in wheat biofortification. In practice, it is said that there are several methods for mapping QTL [8; pp. 1-6]. Scientists have stated that QTL-based isolation and identification of loci within single genes is a somewhat complex process, even for model plants such as Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa L.) [9; p. 17]. It has been stated that experimental design is important for predicting quantitative trait loci, as well as determining the level of polymorphism between the type of plant population being analyzed and the parental genomes. Statistical methods for QTL detection require a large number of molecular markers with highly accurate genetic maps [6; pp. 95–109. 10; p. 347.]. QTL mapping is one of the main genomics methods aimed at studying and distinguishing complex phenotypes [11; pp. 34–48]. SCIENCE AND SCIENTIFIC RESEARCH IN THE MODERN WORLD Vol. 3 No. 5 (2025) 30 Several QTL mapping studies have identified many stable and consistent QTLs in the genetic basis of biofortification traits, which have enabled the use of these data in research. To map QTLs for biofortification traits, mapping studies have been conducted in different populations, such as doubled haploid lines (DH), recombinant inbred lines (RIL) or single-origin lines (SSD), F2-derived F3 (F2:3) or F4 (F2:4), BC3F2:3 populations, and BC5F2:F6 families. Each population has its own advantages and disadvantages. In studies conducted by scientists, the polyploidy level of common wheat allows researchers to study some homozygous populations, such as recombinant inbred chromosome lines (RICL) and recombinant replacement lines (RSL). To generate disomic intervarietal chromosomes (DIC), RICL lines with chromosomes 6B and 5B, derived from LDN Triticum turgidum (L.), were used. Each line obtained was homozygous for its specific recombined or unrecombined chromosome and was isogenic for the LDN state for all other chromosomes. It is known from the literature that these populations serve as a good genetic tool for high-resolution mapping of GPC genes/QTLs [12; 1586–1589]. The Gpc-6B1 locus, one of the important loci in the wheat genome, was mapped with high accuracy to the markers Xcdo365 (1.5 cm apart) and Xucw67 (1.2 cm apart) using recombinant lines derived from the LDN(DIC-6B) × LDN combination [13; pp. 1243–1251]. The mapped QTL/genes can be used to select elite lines using MAS. The closer the marker region is to the QTL/gene, the more accurate and easier the prediction will be. Scientists have performed wheat biofortification using several biofortification markers using the MAS method [14; pp. 136, 15; pp. 1243–1251]. To apply MAS selection for any desired trait in a crop, first, QTL mapping or marker-trait association (MTA) association mapping must be performed, identified, and then markers associated with these identified QTLs used for trait selection. However, there are several limitations to the methods used to identify MTAs, which sometimes limit the effectiveness of MAS selection for wheat grain improvement or wheat biofortification. One of the challenges is the importance of SCIENCE AND SCIENTIFIC RESEARCH IN THE MODERN WORLD Vol. 3 No. 5 (2025) 31 validating markers with low efficiency and avoiding these QTLs. In addition to QTL mapping and association mapping, a new method, now known as genomewide selection (GS) or genome-wide selection (GWS), was proposed in 2001 to address the above-mentioned problems, which predicts the genetic value of a selected candidate or individual based on genomic estimated breeding values (GEBV) derived from markers that are highly distributed throughout the genome. Compared to MAS, GEBVs capture a greater proportion of the genetic variation for a given selection trait, including all lowand high-performance markers [16; pp. 109–147]. The particular importance of such molecular studies in wheat biofortification has been highlighted by many researchers. Research methodology. One of the most popular and widely used methods for genotyping plants, especially wheat, is the analysis of SSR (simple sequence repeat) markers. This method is a type of marker based on the use of PCR with specific primers (artificial oligonucleotide) sequences designed for the region where microsatellite repeats with base pairs from 2 to 6 are located. The identified wheat microsatellite (SSR) markers were used for PCR screening of the research samples, and the genetic diversity between them was studied based on the polymorphism of the SSR markers. Analysis and results. For molecular studies, a total of 129 SSR markers were selected from the GrainGenes (https://wheat.pw.usda.gov) wheat marker database, including BARC, GPW, WMC, and WMS (Appendix 2). Microsatellite genotyping was performed according to the method of Reddy et al. (2001). That is, the sizes of SSR allele fragments were visually assessed (relative to the weight of molecular markers with a gap of 25 base pairs (bp) between each fragment), and molecular differences between genotypes were determined (see Table 4.1). According to the amplification results, 81 of the 129 SSR primers involved in genotypic analyses between the study samples were found to be monomorphic, i.e., they were expressed by only one allele in all the studied samples (Figures 1-2). The remaining 48 primers were observed to be polymorphic, meaning that each SCIENCE AND SCIENTIFIC RESEARCH IN THE MODERN WORLD Vol. 3 No. 5 (2025) 32 sample analyzed at these loci differed from other varieties by 2 to 7 alleles (Figures 1-2). Figure 1. SSR marker polymorphism among study samples For molecular genetic characterization of soft wheat varieties, it is recommended to use microsatellite markers, with approximately one marker per chromosome (at least 20 polymorphic). Figure 2. PCR analysis of research samples using the primer Xbarc2886. Mmolecular weight marker; 1) Yaksart, 2) Krasnodar-99, 3) Hosildor (Sanzar-8), 4) Polymorph 37% Monomorphic 63% SSR marker polymorphism SCIENCE AND SCIENTIFIC RESEARCH IN THE MODERN WORLD Vol. 3 No. 5 (2025) 33 Sanzar-4, 5) Oq Bugdoy, 6) Qizil Bugdoy, 7) Durdona, 8) Gozgan, 9) Qizil Sharq, 10) Shalola, 11) Grom, 12) Chillaki Figure 3. PCR analysis of research samples using the Xwms501 primer. Mmolecular weight marker; 1) Yaksart, 2) Krasnodar-99, 3) Khosildor (Sanzar-8), 4) Sanzar-4, 5) Ak bugdoy, 6) Kizil bugdoy, 7) Durdona, 8) Gozgan, 9) Kizil sharq, 10) Shalola, 11) Grom, 12) Chillaki. Therefore, these samples can be characterized even with a single polymorphic marker. However, due to the limited number of alleles found at each microsatellite marker, the use of multiple microsatellite marker sets is required to evaluate larger samples. Conclusions and recommendations. When analyzing the level of polymorphism of primers across sets, polymorphism was observed in 12 out of 25 primer sets of Xbarc, 14 out of 45 primer sets of Xwmc, only 1 out of 5 primer sets of Xgpw, and 21 out of 54 primer sets of Xwms. The group of primers that showed the highest polymorphism (48%) among the study samples belonged to the Xwms set. At the same time, it was found that the set of markers with the lowest level of polymorphism (20%) among the studied samples belonged to the Xgpw primer set. List of used literature 1. Rodomiro Ortiz, Kenneth DS, Bram G, Raj G, Subbarao GV, Tomohiro B, David H, John MD, Iván Ortiz-Monasterio J, Matthew R (2008) Climate change: Can wheat beat the heat? Volume 126, Issues 1–2, June 2008, Pages 46-58 2. Khan S.; Nabi, G.; Ullah, M.W.; Yousaf, M.; Manan, S.; Siddique, R.; Hou, H. Overview on the Role of Advanced Genomics in Conservation Biology of Endangered Species. Int. J. Genom. 3460416. 2016. -P. 1-9. 3. Wambugu P.W.; Ndjiondjop, M.N.; Henry, R.J. Role of genomics in promoting the utilization of Plant Genetic Resources in genebanks. Brief. Funct. Genom. 2018. -№ 17.-P. 198–206. SCIENCE AND SCIENTIFIC RESEARCH IN THE MODERN WORLD Vol. 3 No. 5 (2025) 34 4. Appels, R.; Eversole, K.; Stein, N.; Feuillet, C.; Keller, B.; Rogers, J. Consortium (IWGSC), T.I.W.G.S.; Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science 2018.. 361.-P. 71-91. 5. Muir P., Li S., Lou S., Wang, D., Spakowicz, D.J., Salichos., L.; Zhang, J., Weinstock, G.M., Isaacs, F., Rozowsky, J., et al. The real cost of sequencing: Scaling computation to keep pace with data generation. Genome Biol. 2016. -P. 17-53. 6. Zhang J.; Chiodini, R.; Badr, A.; Zhang, G. The impact of nextgeneration sequencing on genomics. J. Genet. Genom. 2011. -№ 38. -P. 95–109. 7. Bhatta M.; Baenziger, P.S.; Waters, B.M.; Poudel, R.; Belamkar, V.; Poland, J.; Morgounov, A. 2018. Genome-Wide Association Study Reveals Novel Genomic Regions Associated with 10 Grain Minerals in Synthetic Hexaploid Wheat. Int. J. Mol. Sci. 2018. 19. 3237.-P 1-18. 8. Miles C.M.; Wayne, M. Quantitative trait locus (QTL) analysis. Nat. Educ. 2008. -№ 208. -P. 1-6. 9. Wang B.; Liu, H.; Liu, Z.; Dong, X.; Guo, J.; Li, W.; Chen, Jing, Gao, C.; Zhu, Y.; Zheng, X.; Chen, Z.; et al. Identification of minor effect QTLs for plant architecture-related traits using super high-density genotyping and a large recombinant inbred population in maize (Zea mays). BMC Plant Biol. 2018. -№ 18. -P. 17. 10. Ishikawa A. A Strategy for Identifying Quantitative Trait Genes Using Gene Expression Analysis and Causal Analysis. Genes 2017. -№ 8.-P. 347. 11. Civelek M.; Lusis, A.J. Systems genetics approaches to understand complex traits. Nat. Rev. Genet. 2014.№ 15.-P. 34–48. 12. Joppa L.R.; Du, C.; Hart, G.E.; Hareland, G.A. Mapping gene (s) for grain protein in tetraploid wheat (Triticum turgidum L.) using a population of recombinant inbred chromosome lines. Crop Sci. 1997. -№ 37.-P. 1586–1589. 13. Olmos S., Distelfeld A., Chicaiza O., Schlatter A.R., Fahima T., Echenique V., Dubcovsky J. Precise mapping of a locus affecting grain protein content in durum wheat. Theor. Appl. Genet. 2003. -№ 107. -P. 1243–1251. SCIENCE AND SCIENTIFIC RESEARCH IN THE MODERN WORLD Vol. 3 No. 5 (2025) 35 14. Patil R.; Oak, M.; Deshpande, A.; Tamhankar, S. Development of a robust marker for Psy-1 homoeologs and its application in improvement of yellow pigment content in durum wheat. Mol. Breed. 2018. -№ 38.-P. 136. 15. Olmos S., Distelfeld A., Chicaiza O., Schlatter A.R., Fahima T., Echenique V., Dubcovsky J. Precise mapping of a locus affecting grain protein content in durum wheat. Theor. Appl. Genet. 2003. -№ 107. -P. 1243–1251. 16. Gupta P.K. 2014;109–147 p.][ Gupta, P.K.; Kulwal, P.L.; Jaiswal, V. Association mapping in crop plants: Opportunities and challenges. Adv. Genet. 2014. -№ 85.-P. 109–147. 17. Baboeva, S. S., Matkarimov, F. I., Usmanov, R. M., Turaev, O. S., Togaeva, M. A., Baboev, S. K., & Kushanov, F. N. (2023). Climate change impact on chlorophyll content and grain yield of bread wheat (Triticum aestivum L.). SABRAO J. Breed. Genet, 55(6), 1930-1940. 18. Kushanov et al. “Genetic Diversity, QTL Mapping, and MarkerAssisted Selection Technology in Cotton (Gossypium spp.)”. Front. Plant Sci. (2021): 12:779386. https://doi.org/10.3389/fpls.2021.779386 19. Kushanov, F.N.; Komilov, D.J.; Turaev, O.S.; Ernazarova, D.K.; Amanboyeva, R.S.; Gapparov, B.M.; Yu, J.Z. Genetic Analysis of Mutagenesis That Induces the Photoperiod Insensitivity of Wild Cotton Gossypium hirsutum Subsp. purpurascens. Plants 2022, 11, 3012. https://doi.org/10.3390/ plants11223012