Relevance

Drought is a natural climatic phenomenon, but with climate change and increasing global temperatures, the frequency and intensity of drought events are expected to rise. Drought tolerance is a crucial trait for plants, especially in agriculture, where water scarcity can have significant impacts on crop yields. Developing crops with enhanced drought tolerance is essential for ensuring food production under changing climate conditions. To accelerate drought tolerance breeding, it is vital to understand the physiological and genetic basis of crop responses to drought. However, drought tolerance is a complex quantitative trait controlled by multiple genetic loci and susceptible to environmental influences.

In the context of plant breeding, researchers and breeders aim to identify and utilize specific alleles within the germplasm to enhance desired traits in new varieties. Cotton germplasm resources contain beneficial alleles for breeding elite germplasm emerging environmental and climate conditions. However, accessions and lines have traditionally been characterized based on phenotypes, but phenotypic profiles are limited by the cost, time, and space required to make visual observations and measurements.

A genome-wide association study constitutes an efficient method used in genetics to identify associations between genetic variants and specific traits or phenotypes. When applied to the study of local varieties of crops GWAS can provide valuable insights into the genetic basis of important traits.

The scientific novelty of this project lies in its up-to-date approach, encompassing three key aspects. Firstly, it aims to screen and classify 288 cotton species in the Kazakhstan collection based on their drought tolerance, both in field and laboratory conditions. Secondly, it involves the study of genetic diversity, population structure, and phylogenetic analysis of cotton species in the Kazakhstan collection, employing high-throughput genotyping arrays. Lastly, the project aims to identify SNP markers that are significantly associated with traits related to drought stress tolerance.

Objective

• Selection of biological material and SNP markers for research.
• Obtaining phenotypic and bioinformatics analysis.
• Genotyping cotton accessions.
• Bioinformatics analysis of phenotyping data.
• The project will be accomplished with the design of scientific publications and with recommendation letter for improving the efficiency of genomic selection of drought-resistant cotton accessions in drought areas.

Expected results

Based on the objectives of the project, the main expected results are:

• Based on the result of the phenotyping data of tolerance traits, the accessions in Kazakhstan collection will be classified into drought -tolerance classification such as advanced, medium tolerant, sensitive and highly sensitive genotypes. The most drought-tolerant genotypes identified will be finally recommended for breeding use in the drought areas.
• Based on the result of the genotyping data of species using SNP markers, genetic diversity, the population structure, and phylogenetic relationship will be obtained and explained.
• Phenotyping and genotyping data from cotton species will be assessed for the identification of marker-trait associations by using GWAS.
• Scientific articles will be published based on the requirement of the project.

Principal investigator

Tussіpkan D., PhD, H-index 5. Scopus author ID: 57218999913; https://orcid.org/0000-0003-1337-2834.

Members of the research group

Tussipkan Dilnur — PhD in Crop genetics and Breeding, leading researcher, National Center for Biotechnology, Astana, Kazakhstan. Researcher ID: AAQ-6368-2021, ORCID ID: 0000-0003-1337-2834, Scopus ID: 5721899991

Manabayeva Shuga Askarovna — PhD in Biology Sciences, head of the Plant Genetic Engineering Laboratory, National Center for Biotechnology, Astana, Kazakhstan. Researcher ID: A-2529-2015, ORCID: 0000-0001-7884-1713, Scopus Author ID: 55615915000

Makhmadjanov Sabir Partovich — PhD in Agricultural Sciences, head of the Department Transfer and Adaptation of Crop Varieties of LLP “Agricultural Experimental Station of Cotton and Melon Growing”, Atakent village, Maktaaral district, Turkistan Region, Kazakhstan. ORCID: 0000-0001-5623-0591, Scopus author ID: 57809032800

Tokhetova Laura Anuarovna —  Doctor of Agricultural Sciences, principal researcher of LLP “Agricultural Experimental Station of Cotton and Melon Growing”, Atakent village, Maktaaral district, Turkistan Region, Kazakhstan. ResearcherID: ААС-6892-2021, ORCID: 0000-0003-2053-6956, Scopus author ID: 55601836700

Rakhimzhanova Aizhan Oserbayevna — Master in technics and technology, Researcher National Center for Biotechnology, Astana, Kazakhstan. Researcher ID: ABI-1883-2020, ORCID: 0000-0002-4799-436X Scopus author ID: 57217680399

Ramazanova Malika Baglanovna — Master in Natural Sciences, junior researcher, National Center for Biotechnology, Astana, Kazakhstan. Researcher ID: JJC-3385-2023, ORCID: 0000-0003-4994-2202, Scopus Author ID: 57226155889

Zhumabay Nurbek Bolatuly — Master student in Natural Sciences, laboratory Assistant, National Center for Biotechnology, Astana, Kazakhstan.

Orken Aisulu — Master student in Biology Sciences, laboratory Assistant, National Center for Biotechnology, Astana, Kazakhstan.

Publications and security documents of the project manager and members of the research group

1. Dilnur Tussipkan, Vladislav Shevtsov, Malika Ramazanova, Aizhan Rakhimzhanova, Alexandr Shevtsov, and Shuga Manabayeva. Kazakhstan Tulips: Comparative Analysis of Complete Chloroplast Genomes of Four Local and Endangered Species of the Genus Tulipa L. Frontiers in Plant Science. DOI: 10.3389/fpls.2024.1433253, Volume 15 – 2024. Percentile in Scopus: 88%, 61/516-Plant Science; H-index: 216, Quartiles in Quartiles in Web of Science: Q1, Impact Factor in 2024: 4.1
2. GUBAIDULLIN, N., KALI, B., TUSSIPKAN, D., & MANABAYEVA, S. A. (2024). A promising strategy for conservation of endemic plant Euonymus koopmannii. Biodiversitas Journal of Biological Diversity, 25(7). Percentile in Scopus: 64; H-index: 27, Quartiles in Quartiles in Web of Science: Q3, Impact Factor in 2024: 1; Page 3114-3120.
3. Kali, B.; Bekkuzhina, S.; Tussipkan, D., Manabayeva, S. A First Approach for the In Vitro Cultivation, Storage, and DNA Barcoding of the Endangered Endemic Species Euonymus koopmannii. Plants 2024, 13, 2174. https://doi.org/10.3390/plants13162174, Percentile in Scopus: 86%, 96/721; H-index: 92, Quartiles in Quartiles in Web of Science: Q1, Impact Factor in 2024: 4, Page 1-15.
4. Sutula, Maxim, Ayan Kakanay, Dilnur Tussipkan, Samatulla Dzhumanov, and Shuga Manabayeva. 2024. “Phylogenetic Analysis of Rare and Endangered Tulipa Species (Liliaceae) of Kazakhstan Based on Universal Barcoding Markers” Biology 13, no. 6: 365. https://doi.org/10.3390/biology13060365; Percentile in Scopus: 85%, 33/221; H-index: 75, Quartiles in Quartiles in Web of Science: Q2, Impact Factor in 2024: 3.6, Page 1-17.
5. Abeuova L., Kali B., Tussipkan D., Akhmetollayeva A., Ramankulov Y., Manabayeva S. A. CRISPR/Cas9-mediated multiple guide RNA-targeted mutagenesis in the potato. Transgenic Research, 2023-06-18, journal-article, DOI: 10.1007/s11248-023-00356-8, Part of ISSN: 0962-8819, Percentile in Scopus: 94%, 27/456 , H-index: 89, Quartiles in Web of Science: Q3, Impact Factor in 2023: 3.145. Page 1-15.
6. Amirgazin A., Shevtsov V., Tussipkan D., Lutsay V., Ramankulov Y., Shevtsov A., and Manabayeva S. A, 2023, Characterization of the complete mitochondrial genome of the Indian crested porcupine (Hystrix indica). Animal Gene, 2023-03, journal-article, DOI:10.1016/j.angen. 2022.200144, Part of ISSN: 2352-4065, Percentile in Scopus: not applicable, H-index: 2, Quartiles in the DJR: Q4, Impact Factor in 2023: 1.21.
7. Tussipkan D. and Manabayeva S. A., 2022, Alfalfa (Medicago Sativa L.): Genotypic Diversity and Transgenic Alfalfa for Phytoremediation. Frontiers in Environmental Science, journal- article, Vol. 10, article number 828257, DOI: 10.3389/fenvs.2022.828257, Part of ISSN: 2296-665X, Percentile in Scopus: 78 (2022), Quartiles in Web of Science: Q2, H-index: 67, Impact Factor: 5.411. Scimago Journal & Country Rank: Q1.
8. Tussipkan D. and Manabayeva S. A. 2021, Employing CRISPR/Cas Technology for the Improvement of Potato and Other Tuber Crops. Frontiers in Plant Science, 2021-10-26, journal-article, Vol.12, article number 747476, DOI: 10.3389/fpls.2021.747476, Part of ISSN: 1664-462X, Percentile in Scopus: 88%, 54/487, H-index: 187, Quartiles in Quartiles in Web of Science: Q1, Impact Factor in 2023: 6.627, Page 1-16.
9. Qiu C. S., Stybayev G., Wang Y.., Begalina A., Hua L., Aliya Baitelenova, Guo Yuan, Arystangulov S., Hua K., Kipshakpayeva G., Zhao X. and Tussipkan D. Flax Varieties Experimental Report in Kazakhstan in 2019. Journal of Natural Fibers, 2020, Vol. 19, issue 6, page: 2356-2365 DOI: 10.1080/15440478.2020.1813674, Part of ISSN: 1544046X 15440478, Percentile in Scopus: 68%, 48/150, H-index: 47, Quartiles in the Web of Science: Q1, Impact Factor in 2023: 3.507.
10. Tussipkan D. Peng Zh., Pan Zh., Palanga K. K., Jia Y., Gong W. and Du X. Association Analysis of Salt Tolerance in Asiatic cotton (Gossypium arboretum) with SNP Marker //International Journal of Molecular Sciences, 2019-05-01, journal-article, Vol. 20, issue 9, article number 2168, DOI: 10.3390/ijms20092168, Part of ISSN: 1422-0067, Percentile in Scopus: 87%, 10/78, H-index: 230, Quartiles in Web of Science Q1, Impact Factor in 2023: 6.208.
11. Palanga. K.K., Muhammad J., Harun O. R., Gong J.W., Li J.W., Muhammad S., Iqbal, Liu A., Shang H., Shi Y., Chen T., Ge Q., Zhang Zh., Tussipkan D., Li W., Li P., GongW., and Yuan Y. Quantitative Trait Locus Mapping for Verticillium wilt Resistance in an Upland Cotton Recombinant Inbred Line Using SNP-Based High Density Genetic Map.  Frontiers in Plant Science, 2017-04-05, journal-article, Vol.8, article number 382, DOI: 10.3389/fpls.2017.00382, Part of ISSN: 1664-462X, Percentile in Scopus: 88%, 54/487, H-index: 187, Quartiles in Web of Science: Q1, Impact Factor in 2023: 6.627.
12. Makhmadzhanov S. P., Daurenbek N.M., Tagayev А.М., Asabaev B.S., Kostak О. А. Technological properties of indoselected samples of cotton varieties M-4011, M-4017 // Scientific and practical journal “Science and education” periodical publication of the Zhangir Khan West Kazakhstan Agricultural-Technical University of the MA RK. – Uralsk. – 2022. – № 2-1 (67). – С.140-149.DOI 10.56339/2305-9397-2022-1-2-140-149 (In Russian)
13. Makhmadzhanov S. P., Daurenbek N.M., Asabaev B.S. Achievements of cotton selection in Kazakhstan //Science bulletin of S.Seifullin Kazakh Agrotechnical University.. – Nur-Sultan. – 2022. – № 2(113) 1часть. – С.136-144.DOI: 10.1007/s13593-015-0313-2 (In Russian)
14. Makhmadjanov, S.P., Tokhetova, L.A., Daurenbek, N.M., Tagaev, A.M., Kostakov, A.K. Cotton Advanced Lines Assessment in The Southern Region Of Kazakhstan (2023) Sabrao Journal of Breeding and Genetics, 55 (2), Page. 279-290.
15. A. Amalova, S. Abugalieva, V. Chudinov, G. Sereda, L. Tokhetova, A. Abdikhalyk and Y. Turuspekov. QTL mapping of agronomic traits in wheat using the UK Avalon × Cadenza reference mapping population grown in Kazakhstan. PeerJ, Vol.9. Page 1-25. doi: 10.7717/peerj.10733.
16. L. A. Tokhetova, S. I. Umirzakov, R. D. Nurymova, B. K. Baizhanova and G. B. Akhmedov. Analysis of Economic-Biological Traits of Hull-Less Barley and Creation of Source Material for Resistance to Environmental Stress Factors. International Journal of Agronomy, 2020, Vol.2020, DOI 10.1155/2020/8847753.
17. Karpova, O., Alexandrova A., Nargilova R., Ramazanova M., Kryldakov R., Iskakov B. ATDREB2A gene expression under control of the inducible promoter and virus 5’-untranslated regions improves tolerance to salinity in Nicotiana tabacum. International Journal of Biology and Biomedical Engineering, 2021-07-19, journal-article, DOI: 10.46300/91011.2021.15.32, Part of ISSN: 19984510, H-index:8, Quartiles in Scopus: Q4, Impact Factor in 2023: 0.14. Page 260–274.

Results achieved

2024

For the experiment, seeds of G. hirsutum 288 samples from the Kazakhstan collection were selected from “Agricultural Experimental Station of Cotton and Melon Growing” LLP and used in field and laboratory studies. The samples were genotyped using the Axiom Cotton Genotyping Array platform, with a total of 35,550 SNP markers selected. Among them, 30,264 were intraspecific markers, 7,392 were markers based on restriction site conservation, and 5,286 were markers derived from the specific set of G. hirsutum and G. barbadense. Additionally, key information such as Probe Set ID, Affy SNP ID, marker sequences, 3’→5′ or 5’→3′ directions, Allele A, Allele B, and cust_id was fully obtained.

According to the 2024 work plan, phenotypic data were collected under both laboratory and field conditions. In laboratory conditions, cotton samples were grown in polyethylene glycol 6000 (PEG 6000) under drought stress levels of 0% (control), 5%, 10%, 20%, 30%, and 40%. The study results determined that 20% and 30% PEG 6000 were the most suitable conditions. Phenotypic data were collected on seed germination percentage (GP), hypocotyl length and width (HL and HW), fresh weight (FW), plant height (PH), shoot dry mass (SDM), root dry mass (RDM), chlorophyll content (ChlC), malondialdehyde (MDA), and water content (WC).

The initial bioinformatics analysis showed that seed germination percentage under control conditions was 95±2.24%. The data obtained under stress conditions were compared with the control conditions. Under 5% PEG 6000, germination was 86.7±2.11%, showing an 8.3% reduction. Under 10% PEG 6000, germination was 78.3±7%, decreasing by 16.7%. Under 20% PEG 6000, germination remained at 86.7±2.11%, with an 8.3% reduction. Under 30% PEG 6000, germination was 76.7±3.3%, showing a 16.7% decrease. Under 40% PEG 6000, germination dropped to 61.7%, indicating a 33.3% reduction.

Under field conditions, phenotypic data were collected, and the initial bioinformatics analysis classified the 288 samples into four clusters. 40 samples showed a productivity range of 8.2–49.7%, classifying them as a highly sensitive group. 60 samples had a productivity range of 50.2–69.9%, falling into the sensitive group. 59 samples demonstrated 71.0–89.6% productivity, categorizing them as a moderately tolerant group. Finally, 129 samples exhibited 90.8–100.0% productivity and were classified as a highly tolerant group.