Triticeae Genomics and Genetics 2025, Vol.16 http://cropscipublisher.com/index.php/tgg © 2025 CropSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved.
Triticeae Genomics and Genetics 2025, Vol.16 http://cropscipublisher.com/index.php/tgg © 2025 CropSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. CropSci Publisher is an international Open Access publishing specializing in Triticeae genome, trait-controlling, Triticeae gene expression and regulation at the publishing platform that is operated by Sophia Publishing Group (SPG), founded in British Columbia of Canada Publisher CropSci Publisher Editedby Editorial Team of Triticeae Genomics and Genetics Email: edit@tgg.cropscipublisher.com Website: http://cropscipublisher.com/index.php/tgg Address: 11388 Stevenston Hwy, PO Box 96016, Richmond, V7A 5J5, British Columbia Canada Triticeae Genomics and Genetics (ISSN 1925-203X) is an open access, peer reviewed journal published online by CropSci Publisher. The journal publishes original papers involving in all aspects of Triticeae sciences. Subject areas covered comprise classical genetics analysis, structural and functional analysis of Triticeae genome, gene expression and regulation, efficient breeding of improved varieties, as well as transgenic varieties. It is positioned to meet the needs of breeders, geneticists, molecular biologists, and anyone, worldwide, engaged in the field of Triticeae research. All the articles published in Triticeae Genomics and Genetics are Open Access, and are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. CropSci Publisher uses CrossCheck service to identify academic plagiarism through the world’s leading plagiarism prevention tool, iParadigms, and to protect the original authors’ copyrights.
Triticeae Genomics and Genetics (online), 2025, Vol. 16, No.6 ISSN 1925-203X http://cropscipublisher.com/index.php/tgg © 2025 CropSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. Latest Content Chromosome Rearrangements and Genome Evolution in Hexaploid Wheat Yali Wang, Chunxiang Ma Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 237-244 High-Throughput Phenotyping Coupled with GWAS for Fusarium Head Blight Resistance in Wheat Bing Wang, Xiuhua Liu, Jie Zhang Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 245-253 Marker-Assisted Selection for Lodging Resistance in Rye Breeding Programs Jin Zhou, Shujuan Wang Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 254-261 Co-expression Network Analysis Reveals Modules Linked to Spike Development in Wheat Zhengqi Ma, Wei Wang Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 262-268 Transgenic Improvement of Nitrogen Use Efficiency in Wheat Using Root-Specific Promoters Xingzhu Feng Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 269-277
Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 237-244 http://cropscipublisher.com/index.php/tgg 245 Review Article Open Access Chromosome Rearrangements and Genome Evolution in Hexaploid Wheat Yali Wang, Chunxiang Ma Modern Agricultural Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China Corresponding email: chunxiang.ma@cuixi.org Triticeae Genomics and Genetics, 2025, Vol.16, No.6 doi: 10.5376/tgg.2025.16.0026 Received: 06 Sep., 2025 Accepted: 23 Oct., 2025 Published: 06 Nov., 2025 Copyright © 2025 Wang and Ma, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Wang Y.L., and Ma C.X., 2025, Chromosome rearrangements and genome evolution in hexaploid wheat, Triticeae Genomics and Genetics, 16(6): 237-244 (doi: 10.5376/tgg.2025.16.0026) Abstract Triticum aestivum(AABBDD) is an important staple food crop worldwide. Its evolutionary history is complex, having undergone multiple polyploidy events and extensive chromosomal rearrangements. This study provides an overview of the structural characteristics of the hexaploid wheat genome and the functional differentiation among the A, B, and D subgenomes. It conducts a detailed analysis of the types of chromosomal rearrangements and their driving mechanisms, with a focus on the roles of homologous recombination and transposition elements. Meanwhile, this study also emphasizes the impact of chromosomal structural variations on gene expression regulation, adaptive evolution, and trait diversification, especially the significance driven by both natural selection and artificial breeding. Through case analysis, it demonstrates the practical application value of chromosomal rearrangement, such as the fusion process of the A, B, and D genomes. And the wide application of wheat-rye translocation lines such as 1BL/1RS in disease-resistant breeding. By revealing the relationship between chromosomal structure and functional genomes, this study is expected to promote molecular design breeding of high-yield and stress-resistant wheat varieties. Keywords Hexaploid wheat; Chromosomal rearrangement; Genomic evolution; Structural variation; Polyploidization 1 Introduction Hexaploid wheat (Triticum aestivumL.) did not emerge suddenly from A single mutation. Instead, it underwent a series of complex natural hybridization and polyploidization processes before finally developing the current AABBDD structure containing three subgenomes A, B, and D (Zhang et al., 2021). During this process, A and B originated from tetraploid wheat (Triticum turgidum, AABB), while the D genome was later introduced through hybridization with Aegilops tauschii (DD). This long and repetitive evolutionary path has enabled hexaploid wheat to accumulate a rich genetic background and has also made it occupy an extremely important position among the major crops worldwide (Liu et al., 2025). However, the genomic structure of wheat is not static. In addition to polyploidy, chromosomal translocations, inversions, centromeric relocations and other seemingly "chaotic" changes have also been pushing it forward (Zhao et al., 2023). Sometimes, these structural changes can cause fluctuations in gene expression; Sometimes, they may affect the recombination frequency and even agronomic traits, such as disease resistance or adaptability (Huo et al., 2018; Lv et al., 2023). Of course, not all variations bring benefits, but it is precisely these constantly adjusting processes that have shaped the form of wheat that we see today. To truly understand the significance of these structural changes, especially those that "subtly" influence the direction of breeding, delving into the mechanisms behind chromosomal rearrangements is an indispensable and crucial step. This study will systematically explore the types, mechanisms and evolutionary consequences of chromosomal rearrangements in hexaploid wheat, integrate the latest progress in fields such as genome assembly, comparative genomics and multi-omics analysis, summarize the polyploid origin of wheat, and describe in detail the changes in chromosomal structure and their impact on genomic function and breeding potential. By clarifying the role of chromosomal rearrangement in the evolution of wheat genomes, this study provides strong support for the genetic improvement and sustainable production of wheat in the future.
Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 237-244 http://cropscipublisher.com/index.php/tgg 246 2 Structural Characteristics of the Hexaploid Wheat Genome 2.1 Composition and functional divergence of the A, B, and D genomes The three subgenomes - A, B, and D - each undertake different "tasks" in hexaploid wheat. They come from different ancestors, and this background itself determines that they will not be exactly the same. Not all gene families can find "common points" with each other in wheat germplasm. In fact, the truly universally shared part accounts for only about 23% (Cheng et al., 2025). The D genome is particularly special. It shows more genetic variations during the formation of hexaploids, which is quite crucial in breeding. Furthermore, the distribution of transposition elements and the phenomenon of gene duplication were not evenly distributed among the three subgenomes A, B, and D. These differences ultimately contributed to the functional complexity and plasticity of the wheat genome (Liu et al., 2025). 2.2 Relationships and regulatory mechanisms of homologous chromosomes and genes In hexaploid wheat, homologous chromosomes from different subgenomes do not live independently of each other. There are indeed interactions among them, but the ways are rather complex. The interactions between these chromosomes largely depend on sequence similarity and some transposable elements specific to subgenomes. In other words, although homologous genes may seem similar, their expression level is often limited by the chromatin environment of the subgenome where they are located (Wang et al., 2025). For instance, if the genes are from wild relatives, the situation is even more different: the introduced genes may have reduced expression due to regulatory disorders, and the original homologous copies may not be able to "fill in" (Coombes et al., 2021; Jia et al., 2021). In addition, three-dimensional structures like topological associative domains (Tads) also play a regulatory and stabilizing role behind the scenes. 2.3 Features of large-scale genome duplication, deletion, and expansion The hexaploid wheat genome itself is not "calm". It has experienced many fluctuations after multiplexing, including large-scale repetition, fragment deletion and amplification. This state of "constant change" has actually shaped its complexity today. The insertion of transposition elements and the repetition of fragments not only bring about new genes but also rewrite functions. Structural variations such as presence/absence variations and copy number variations have now been found in the wheat genome with more than 1.9 million non-redundant events, especially concentrated around the centromere (De Oliveira et al., 2020; Cheng et al., 2025). Sometimes, large fragment deletions may also be the result of human manipulation, such as gamma-ray induction or gene infiltration breeding. Such variations sometimes directly affect agronomic traits (Komura et al., 2022). So, to some extent, the "turmoil" of structure is also one of the sources of the diversity and adaptability of wheat. 3 Major Types and Mechanisms of Chromosome Rearrangements 3.1 Structural variations such as inversions, translocations, duplications, and deletions It is actually not a rare thing for the structure of chromosomes to change. Once a double-strand break occurs in DNA, if the subsequent repair is not handled properly, problems are very likely to arise. Either an extra section was inserted or the position was connected wrongly. Thus, the common types of rearrangement such as inversion, transposition, repetition and absence were thus formed. At certain times, rearrangement occurs very intensely, such as large-scale chromosome breakage and recombination. Within just one cell cycle, the structural appearance may change significantly (Pellestor, 2019; Pellestor et al., 2021; Krupina et al., 2023). Inversion duplication is sometimes not complicated. It is that the DNA at the breakpoint turns back and "self-initiates" synthesis, resulting in a wrong connection again. This pattern is also common (al-Zain et al., 2023). To figure out where these rearrangements come from, high-resolution breakpoint analysis is needed; otherwise, the ins and outs won't be clear. 3.2 Roles of homologous and non-homologous recombination in chromosomal rearrangements When it comes to the "behind-the-scenes drivers" of rearrangement, homologous recombination (HR) and non-homologous end join (NHEJ) are basically the "main forces". HR is supposed to be a fine-tuning tool that precisely repairs by similar sequences. However, unfortunately, it sometimes "makes mistakes", and multiple intrusions may cause structural troubles such as translocation (Kot et al., 2021). NHEJ is more straightforward.
Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 237-244 http://cropscipublisher.com/index.php/tgg 247 Even without sequence control, it can still force both ends together, but problems such as messy insertion points and base loss are hard to avoid. In addition, copy-related mechanisms such as FoSTeS or MMBIR often get involved. When the copy fork stalls or templates switch, inversions or complex concatenation follow (Burssed et al., 2022). There are many types of rearrangements, and to a large extent, it depends on which repair mechanism takes the lead. 3.3 Transposons and repetitive sequences driving genome structural dynamics Sometimes, chromosomal rearrangement is not even caused by "correction errors", but rather that certain sequences themselves are "too noisy". Transposons and repetitive DNA fragments are like this. They not only keep jumping in the genome but also tend to cluster together. This kind of accumulation often provides ready-made "anchor points" for rearrangement. Especially for repetitive units like LINE and satellite sequences, if non-allelic recombination (NAHR) occurs, large fragment structural changes are almost inevitable (Luo, 2025). For instance, retrotransposons particularly tend to aggregate around centromeres. This behavior intensifies regional duplication and instability, providing a "testing ground" for the structural evolution of the genome (Gozashti et al., 2025). In other words, they not only participate in structural changes but may also profoundly influence gene functions and even species adaptation. 4 Roles of Chromosome Rearrangements in Hexaploid Wheat Evolution 4.1 Genome conflicts and structural stabilization after polyploidization The step of doubling did not immediately stabilize the hexaploid wheat. At the very beginning, there were many contradictions among the genomes and their structures were also very unstable. Especially among the three subgenomes A, B and D, integration is not an easy task. Chromosomal translocations such as 4A, 5A, and 7B, as well as the rearrangement of centromeric positions, are actually gradually explored during the process of genomic "self-repair" (Zhao et al., 2023; Liu et al., 2025). And the accumulation of those specific centromere repeat sequences is not a useless decoration. It plays a significant role in cell division, especially in the later stage of hybridization. With them present, chromosomes are more likely to separate correctly and the genome is more complete. 4.2 Effects of chromosome rearrangements on gene expression and trait variation Sometimes, gene expression can be "dragged down" by structures. Once chromosomal rearrangement alters the position of regulatory elements or the openness of chromatin, the expression of genes also fluctuates accordingly. Especially for those genes involved in translocation, they evolve at a faster rate and the recombination frequency may also change, making it easy for new phenotypes to "emerge". However, it doesn't always bring good things. In some cases, structural changes may instead disrupt the original balance. However, in breeding, this kind of interference sometimes becomes a means instead. For example, by using the infiltration of rye chromosome fragments, disease resistance was successfully introduced (Figure 1) (Wang et al., 2023). In addition, the activity of transposers and fragment duplication are often involved, which alter the regulatory logic of the genome and make wheat more flexible at the expression level. 4.3 Contributions of rearrangements to adaptive evolution and environmental stress responses Not every adaptation to the environment relies on sudden changes. In fact, changes in the structure of chromosomes themselves can also help species adapt. For hexaploid wheat, chromosomal rearrangement is like a "quick adjustment" tool. When the external environment undergoes drastic changes or the pressure of breeding selection increases, it can rapidly bring about genetic variations. For instance, genetic segments from wild species, combined with some complex rearrangements, not only enhance disease resistance but also affect yield and quality. These seemingly chaotic changes actually have certain directionality behind them (Zhao et al., 2023; Liu et al., 2025). Moreover, the transposon in the wheat genome is also very active. Coupled with the fact that the centromeric region itself is prone to "movement", the flexibility of the overall structure is also enhanced accordingly. It can be said that throughout the evolution of wheat, these "constantly adjusting" mechanisms have never ceased.
Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 237-244 http://cropscipublisher.com/index.php/tgg 248 Figure 1 Plants (a), spikes (b), and seeds (c) images of Yukuri, MY11and six wheat-rye addition lines (Adopted from Wang et al., 2023) 5 Detection Technologies and Analytical Methods for Chromosome Rearrangements 5.1 Application of optical mapping, Hi-C, and long-read sequencing in structural variation detection How to detect structural variations has always been an unavoidable problem in the study of genomic rearrangement. Especially for crops like wheat, which have a large and complex genome, relying solely on a single technology often leads to neglecting one aspect for another. For instance, optical spectra can reveal which regions are broken, how they are rearranged, and also indicate the direction, distinguishing between equilibrium and non-equilibrium rearrangements. However, this needs to be combined with other methods, and there are blind spots when used alone (Qu et al., 2023). Hic is not only capable of creating three-dimensional structure diagrams. It can also identify rearrangement types such as translocation and inversion, even see the breakpoints, and extract copy number information from the same dataset (Burden et al., 2025; Galbraith et al., 2025). Long-read sequencing, such as PacBio and Oxford Nanopore, has more advantages when dealing with repetitive and disordered fragments. It can accurately locate breakpoints and has become a standard feature in many high-standard national-level testing processes. 5.2 Visualization value of cytogenetic techniques such as FISH and GISH When it comes to which method is the most intuitive, the visualization technology at the cellular level is still irreplaceable. Methods like FISH and GISH, although "old-fashioned", can really directly identify where the problem lies on the chromosome. The resolution of FISH can reach the level of thousands of bases, and it is effortless to locate structural problems such as translocation and inversion (Qu et al., 2023; Xia, 2025). GISH is particularly useful for polyploid species. In a subgenomic mixed crop like wheat, the components of each genome can be clearly distinguished, and even which fragments are introduced from exogenous sources, such as wild species transplanting fragments (Hu et al., 2020). Although they are not as information-rich as high-throughput technologies, they have irreplaceable intuitive value in the judgment of spatial position and chromosomal background.
Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 237-244 http://cropscipublisher.com/index.php/tgg 249 5.3 SV, CNV, and translocation identification based on reference genome analysis pipelines The rearrangement recognition processes that rely on algorithms are also indispensable to the reference genome. High-throughput sequencing data, such as double-end sequencing or long-read assembly, can quickly identify large fragment variations, but only if there is a control reference to facilitate the comparison of which variations are specific and which may be common but harmless (Mitsuhashi et al., 2020; Jilani and Haspel, 2021; Eisfeldt et al., 2024). These processes can not only locate the breakpoints but also restore the direction and connection sequence of the variant fragments, which is particularly important when it is necessary to distinguish between pathogenicity and natural diversity. Of course, it is difficult to cover all aspects by relying solely on such processes. Only by integrating them with optical, Hi-C, FISH and other data can the accuracy of detection and the analytical ability for complex rearrangements be improved, especially in genomes with intense structural dynamics like those of hexaploid wheat. 6 Case Studies: Chromosome Rearrangements in Wheat Origin and Breeding 6.1 Evidence of chromosomal rearrangements during A, B, and D genome fusion inTriticum aestivumorigin The evolutionary process of wheat is not as simple as just putting together three sets of genes. Before the fusion of the A, B and D genomes, each had distinct chromosomal structures. However, after the fusion, these differences triggered a series of rearrangements. Translocation and inversion among 4A, 5A, and 7B are not isolated phenomena but structural features that are widely present in hexaploid wheat and its wild relatives (Shi et al., 2022). Evidence from techniques such as FISH and chromosome staining also indicates that these rearrangements do not only occur in modern wheat; they began as early as the stage when subgenomic D was introduced (Figure 2). These changes not only helped stabilize the newly formed wheat genome but also accelerated the integration among the three subgenomes. Figure 2 Characterization of chromosomal translocations 4AS∙4AL-1DS and 1DL∙1DS-4AL derived from wheat cultivar Bima 4 (Adopted from Shi et al., 2022) 6.2 Application of wheat–rye translocations in disease resistance breeding Not all chromosomal rearrangements occur naturally; they can also be "artificially" created in breeding work. For instance, chromosomal translocations like 1BL/1RS in rye and wheat have long been widely used for disease resistance improvement. This translocation brought the resistance gene from rye into wheat and incidentally improved some yield-related traits (Jiao et al., 2024). What's more interesting is that the repetitive sequence of chromosome 1RS is particularly active, with many deletions and variations, indicating that it is not a static structure but is constantly evolving. However, these changes did not affect its status as a "darling" in breeding; instead, due to its significant effects, it was retained for a long time. 6.3 Structural variations associated with yield and stress tolerance in modern cultivated wheat Structural variations are almost everywhere in the wheat varieties grown today. Not only the inversions or translocations that are very "conspicuous" at first glance, but even the increase or decrease in copy number is often found to be linked to yield and stress resistance. Through pan-genome comparison, many variations have
Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 237-244 http://cropscipublisher.com/index.php/tgg 250 been traced back to the breeding starting materials, and some are directly related to adaptation to specific environments (Salina et al., 2022). Sometimes, the presence of these variations can also inhibit unnecessary recombination, thereby stably maintaining the combination of superior traits. Whether naturally formed or preserved during the breeding process, these chromosomal rearrangements are constantly shaping the expressiveness and adaptability of modern wheat. 7 Conclusion and Future Perspectives The chromosomal structure of hexaploid wheat (Triticum aestivum) is not always stable as before. From the moment it was formed by the combination of the A, B and D genomes, various structural changes have never ceased. Over the past two decades, researchers' attention to this type of chromosomal rearrangement phenomenon has continued to rise, not only because it leaves traces in genomic evolution, but also because it can actually affect the formation of agronomic traits. Transposition, inversion, repetition, absence... These seemingly "chaotic" changes have actually played a considerable role in regulating gene expression, mitigating the interference of redundant genes, and enhancing adaptability to adverse circumstances. Isn't a classic case like the 1BL/1RS translocation the best proof that structural variations can be transformed into high-yield and disease-resistant advantages? Sometimes, it is precisely these "reassembled pieces" on the genome that endow wheat with stronger environmental adaptability and breeding potential. However, it is not that easy to figure out these structural variations. How large is the genome of wheat? 17Gb. And repetitive sequences are everywhere, like a maze. This places extremely high demands on the identification and splicing of structural variations. Although we already have some new technologies at hand, such as long-read sequencing and Hi-C, which theoretically can figure out these complex structures, in practice, the resource investment is not small. In addition, to precisely distinguish homologous sequences from the A, B, and D subgenomes, sometimes the conclusions drawn by different tools are not exactly the same, and there are always some ambiguous areas in the results. Not only that, balanced structural variations, such as inversions, remain one of the most challenging aspects to overcome in current bioinformatics detection. To clarify the specific impact of these variations on traits, it is necessary to analyze them in combination with other data such as the transcriptome and epigenome, which adds a lot of difficulty to the research. The future direction seems to be quite clear: we should not only apply these research achievements to breeding, but also prevent them from remaining just "visible but intangible". The first step is to make the detection of structural variations faster, more accurate and more stable. At the same time, the detection scope should be expanded to cover more wheat germplasm backgrounds and establish a clear "structural variation map". Next, it is not only about identification but also about verifying the function. For instance, through CRISPR/Cas splicing experiments, RNA sequencing or ATAC-seq and other means, it is further clarified which variations can truly affect the phenotype. Take it a step further and incorporate these structural variation information into the genomic selection model to truly integrate them into the breeding process. In addition, do not overlook their interaction with the environment - certain variations may only show their effects in specific climates or adverse conditions. Structural variation is not a solo battle. There are still many unsolved links in its relationship with the environment, gene expression, and phenotypic changes. In conclusion, to turn chromosomal rearrangement into a "powerful tool" in breeding, the collaboration of genomics, bioinformatics technology and breeding strategies is indispensable. Acknowledgments We appreciate Dr Wu from the Hainan Institution of Biotechnology for his assistance in references collection and discussion for this work completion. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.
Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 237-244 http://cropscipublisher.com/index.php/tgg 251 References Al-Zain A., Nester M., Ahmed I., and Symington L., 2023, Double-strand breaks induce inverted duplication chromosome rearrangements by a DNA polymerase δ-dependent mechanism, Nature Communications, 14: 5546. https://doi.org/10.1038/s41467-023-42640-5 Burden F., Rathje C., Ellis P., Holl J., Lewis C., and Farré M., 2025, Detecting chromosomal rearrangements in boars using Hi‐C, Animal Genetics, 56(2): 227-234. https://doi.org/10.1111/age.70009 Burssed B., Zamariolli M., Bellucco F., and Melaragno M., 2022, Mechanisms of structural chromosomal rearrangement formation, Molecular Cytogenetics, 15: 15-26. https://doi.org/10.1186/s13039-022-00600-6 Cheng H., Kong L., Zhu K., Zhao H., Li X., Zhang Y., Ning W., Jiang M., Song B., and Cheng S., 2025, Structural variation-based and gene-based pangenome construction reveals untapped diversity of hexaploid wheat, Journal of Genetics and Genomics, 52(6): 774-785. https://doi.org/10.1016/j.jgg.2025.03.015 Coombes B., Fellers J., Grewal S., Rusholme-Pilcher R., Hubbart-Edwards S., Yang C., Joynson R., King I., King J., and Hall A., 2021, Whole‐genome sequencing uncovers the structural and transcriptomic landscape of hexaploid wheat/Ambylopyrum muticum introgression lines, Plant Biotechnology Journal, 21: 482-496. https://doi.org/10.1101/2021.11.16.468825 De Oliveira R., Rimbert H., Balfourier F., Kitt J., Dynomant E., Vrána J., Doležel J., Cattonaro F., Paux E., and Choulet F., 2020, Structural variations affecting genes and transposable elements of chromosome 3B in wheats, Frontiers in Genetics, 11: 891 https://doi.org/10.3389/fgene.2020.00891 Eisfeldt J., Ameur A., Lenner F., De Boer E., Ek M., Wincent J., Vaz R., Ottosson J., Jonson T., Ivarsson S., Thunström S., Topa A., Stenberg S., Rohlin A., Sandestig A., Nordling M., Palmebäck P., Burstedt M., Nordin F., Stattin E., Sobol M., Baliakas P., Bondeson M., Höijer I., Saether K., Lovmar L., Ehrencrona H., Melin M., Feuk L., and Lindstrand A., 2024, A national long-read sequencing study on chromosomal rearrangements uncovers hidden complexities, Genome Research, 34: 1774-1784. https://doi.org/10.1101/gr.279510.124 Galbraith K., Wu J., Sikkink K., Mohamed H., Reid D., Perez-Arreola M., Belton J., Nomikou S., Melnyk S., Yang Y., Liechty B., Jour G., Tsirigos A., Hermel D., Beck A., Sigal D., Dahl N., Vibhakar R., Schmitt A., and Snuderl M., 2025, Detection of gene fusions and rearrangements in FFPE solid tumor specimens using Hi-C, The Journal of Molecular Diagnostics, 27(5): 346-359. https://doi.org/10.1016/j.jmoldx.2025.01.007 Gozashti L., Harringmeyer O., and Hoekstra H., 2025, How repeats rearrange chromosomes: the molecular basis of chromosomal inversions in deer mice, Cell Reports, 44(5): 115644. https://doi.org/10.1016/j.celrep.2025.115644 Hu Q., Maurais E., and Ly P., 2020, Cellular and genomic approaches for exploring structural chromosomal rearrangements, Chromosome Research, 28: 19-30. https://doi.org/10.1007/s10577-020-09626-1 Huo N., Zhang S., Zhu T., Dong L., Wang Y., Mohr T., Hu T., Liu Z., Dvorak J., Luo M., Wang D., Lee J., Altenbach S., and Gu Y., 2018, Gene duplication and evolution dynamics in the homeologous regions harboring multiple prolamin and resistance gene families in hexaploid wheat, Frontiers in Plant Science, 9: 673. https://doi.org/10.3389/fpls.2018.00673 Jia J., Xie Y., Cheng J., Kong C., Wang M., Gao L., Zhao F., Guo J., Wang K., Li G., Cui D., Hu T., Zhao G., Wang D., Ru Z., and Zhang Y., 2021, Homology-mediated inter-chromosomal interactions in hexaploid wheat lead to specific subgenome territories following polyploidization and introgression, Genome Biology, 22: 26. https://doi.org/10.1186/s13059-020-02225-7 Jiao C., Xie X., Hao C., Chen L., Xie Y., Garg V., Zhao L., Wang Z., Zhang Y., Li T., Fu J., Chitikineni A., Hou J., Liu H., Dwivedi G., Liu X., Jia J., Mao L., Wang X., Appels R., Varshney R., Guo W., and Zhang X., 2024, Pan-genome bridges wheat structural variations with habitat and breeding, Nature, 637(8045): 384-393. https://doi.org/10.1038/s41586-024-08277-0 Jilani M., and Haspel N., 2021, Computational methods for detecting large-scale structural rearrangements in chromosomes, Bioinformatics, 3: 45-60. https://doi.org/10.36255/exonpublications.bioinformatics.2021.ch3 Komura S., Jinno H., Sonoda T., Oono Y., Handa H., Takumi S., Yoshida K., and Kobayashi F., 2022, Genome sequencing-based coverage analyses facilitate high-resolution detection of deletions linked to phenotypes of gamma-irradiated wheat mutants, BMC Genomics, 23: 111. https://doi.org/10.1186/s12864-022-08344-8 Kot P., Yasuhara T., Shibata A., Hirakawa M., Abe Y., Yamauchi M., and Matsuda N., 2021, Mechanism of chromosome rearrangement arising from single-strand breaks., Biochemical and Biophysical Research Communications, 572: 191-196. https://doi.org/10.1016/j.bbrc.2021.08.001 Krupina K., Goginashvili A., and Cleveland D., 2023, Scrambling the genome in cancer: causes and consequences of complex chromosome rearrangements, Nature Reviews Genetics, 25: 196-210. https://doi.org/10.1038/s41576-023-00663-0
Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 237-244 http://cropscipublisher.com/index.php/tgg 252 Liu S., Li K., Dai X., Qin G., Lu D., Gao Z., Li X., Song B., Bian J., Ren D., Liu Y., Chen X., Xu Y., Liu W., Yang C., Liu X., Chen S., Li J., Li B., He H., and Deng X., 2025, A telomere-to-telomere genome assembly coupled with multi-omic data provides insights into the evolution of hexaploid bread wheat, Nature Genetics, 57: 1008-1020. https://doi.org/10.1038/s41588-025-02137-x Luo M.T., 2025, Phylogenetic analysis of sugarcane for sugar production: population structure and adaptive evolution based on whole-genome data, Journal of Energy Bioscience, 16(1): 13-20. https://doi.org/10.5376/jeb.2025.16.0002 Lv R., Gou X., Li N., Zhang Z., Wang C., Wang R., Wang B., Yang C., Gong L., Zhang H., and Liu B., 2023, Chromosome translocation affects multiple phenotypes causes genome-wide dysregulation of gene expression and remodels metabolome in hexaploid wheat, The Plant Journal, 106(4): 1059-1078. https://doi.org/10.1111/tpj.16338 Mitsuhashi S., Ohori S., Katoh K., Frith M., and Matsumoto N., 2020, A pipeline for complete characterization of complex germline rearrangements from long DNA reads, Genome Medicine, 12: 28. https://doi.org/10.1186/s13073-020-00762-1 Pellestor F., 2019, Chromoanagenesis: cataclysms behind complex chromosomal rearrangements, Molecular Cytogenetics, 115: 4-15. https://doi.org/10.1186/s13039-019-0415-7 Pellestor F., Gaillard J., Schneider A., Puechberty J., and Gatinois V., 2021, Chromoanagenesis the mechanisms of a genomic chaos, Seminars in Cell and Developmental Biology, 115: 4-15. https://doi.org/10.1016/j.semcdb.2021.01.004 Qu J., Li S., and Yu D., 2023, Detection of complex chromosome rearrangements using optical genome mapping, Gene, 897: 147688. https://doi.org/10.1016/j.gene.2023.147688 Salina E., Muterko A., Kiseleva A., Liu Z., and Korol A., 2022, Dissection of structural reorganization of wheat 5B chromosome associated with interspecies recombination suppression, Frontiers in Plant Science, 13: 884632. https://doi.org/10.3389/fpls.2022.884632 Shi P., Sun H., Liu G., Zhang X., Zhou J., Song R., Xiao J., Yuan C., Sun L., Wang Z., Lou Q., Jiang J., Wang X., and Wang H., 2022, Chromosome painting reveals inter-chromosomal rearrangements and evolution of subgenome D of wheat, The Plant Journal, 112(1): 55-67. https://doi.org/10.1111/tpj.15926 Wang J., Zhao X., and Gao F.M., 2025, Molecular breeding strategies for pyramiding disease resistance in wheat, Triticeae Genomics and Genetics, 16(4): 184-194. https://doi.org/10.5376/tgg.2025.16.0020 Wang T., Li G., Jiang C., Zhou Y., Yang E., Li J., Zhang P., Dundas I., and Yang Z., 2023, Development of a set of wheat-rye derivative lines fromHexaploid triticale with complex chromosomal rearrangements to improve disease resistance agronomic and quality traits of wheat, Plants, 12(22): 3885. https://doi.org/10.3390/plants12223885 Xia Y., 2025, Exploration and genetic counseling of using multiple genetic techniques to detect derived chromosomes in prenatal diagnosis, Journal of Advances in Medicine Science, 8(1): 12-17. https://doi.org/10.26549/jams.v8i1.24436 Zhang S., Du P., Lu X., Fang J., Wang J., Chen X., Chen J., Wu H., Yang Y., Tsujimoto H., Chu C., and Qi Z., 2021, Frequent numerical and structural chromosome changes in early generations of synthetic hexaploid wheat, Genome, 64(4): 205-217. https://doi.org/10.1139/gen-2021-0074 Zhao J., Xie Y., Kong C., Lu Z., Jia H., Ma Z., Zhang Y., Cui D., Ru Z., Wang Y., Appels R., Jia J., and Zhang X., 2023, Centromere repositioning and shifts in wheat evolution, Plant Communications, 4(9): 100556. https://doi.org/10.1016/j.xplc.2023.100556
Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 245-253 http://cropscipublisher.com/index.php/tgg 245 Research Insight Open Access High-Throughput Phenotyping Coupled with GWAS for Fusarium Head Blight Resistance in Wheat Bing Wang, Xiuhua Liu, Jie Zhang Tropical Microbial Resources Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China Corresponding email: jie.zhang@cuixi.org Triticeae Genomics and Genetics, 2025, Vol.16, No.6 doi: 10.5376/tgg.2025.16.0027 Received: 20 Sep., 2025 Accepted: 30 Oct., 2025 Published: 20 Nov., 2025 Copyright © 2025 Wang et al., This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Wang B., Liu X.H., and Zhang J., 2025, High-throughput phenotyping coupled with GWAS for fusarium head blight resistance in wheat, Triticeae Genomics and Genetics, 16(6): 245-253 (doi: 10.5376/tgg.2025.16.0027) Abstract Fusarium head blight (FHB) of wheat is a fungal disease that seriously affects global wheat yield and food safety. Its resistance breeding has always been limited by inaccurate phenotypic evaluation and low efficiency of resistance gene localization. This study introduced the prevalence mechanism of FHB and the resistance types of wheat, and analyzed the biological basis of its main phenotypic indicators. By constructing a GWAS model based on SNP chips and resequencing data, and conducting joint analysis in combination with multi-dimensional phenotypic information, multiple stably expressed resistance QTLS and candidate genes were identified. It further revealed the regulatory pathways related to the infection, spread and toxin accumulation of scab. In the practical case section, this study reviewed the resistance research practices of representative wheat groups in the United States, China and other places, and verified the application value of combined phenotypic-genotype analysis in the discovery of new resistance resources. This study demonstrates the significant role of high-throughput phenotypes and GWAS integration strategies in enhancing the efficiency of resistance gene mining and phenotypic accuracy, and is expected to achieve new breakthroughs in precise breeding of FHB resistance, providing technical support for ensuring global wheat production security. Keywords Wheat scab; High-throughput phenotype; Genome-wide association analysis (GWAS); Resistant QTL; Candidate gene 1 Introduction Wheat scab (FHB) has long been a headache for breeders, not only because of the yield loss it causes, but also because of the food safety risks it triggers. Fusarium graminearumis the "culprit" of this disease. It causes grains to accumulate a large amount of mycotoxins, such as trichothecene compounds. Contaminated grains may be inedible at all and even affect the health of humans and animals (Song et al., 2025; Wang et al., 2025). If it were merely a decrease in output, the problem might still be manageable, but unfortunately, this disease is very likely to break out. The climate has become increasingly unstable, and coupled with some changes in agricultural management methods, wheat scab has become more frequent and severe in many areas in recent years. Many people think that as long as they find disease-resistant genes, the problem can be solved. But the actual situation is much more complicated. Wheat resistance to scab is a typical quantitative trait regulated by multiple genes, and the contribution of each locus is usually not significant (Syed et al., 2025). What is even more difficult is that there are not many truly stable resistant germplasm resources. Even the resistance genes that have been cloned, such as Fhb1 and Fhb7, cannot be used at will in actual breeding projects. Most commercial varieties still lack resistance. Furthermore, the expression of resistance traits is also complex influenced by the environment, host and pathogenic bacteria, making phenotypic assessment difficult to standardize. This makes large-scale and precise screening extremely challenging (Buerstmayr et al., 2020). Of course, there are still breakthroughs. In recent years, the development of molecular marker technology and the wide application of GWAS have indeed made the identification of resistance sites much more efficient (Jiang et al., 2025). The problem lies in that whether the technological achievements in the laboratory can truly be implemented in the fields still needs to overcome a complete set of adaptation thresholds for the breeding system. Whether it is high-throughput assessment or genomic tools, if they cannot be integrated into the existing processes, they will be difficult to play a substantive role.
Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 245-253 http://cropscipublisher.com/index.php/tgg 246 This study reviews the current threats and challenges faced by scab, elaborates on the methods and results of high-throughput phenotypic analysis and genome-wide association analysis, and finally explores its impact on future wheat breeding strategies and food security. This study aims to accelerate the identification and application of wheat scab resistance gene loci by combining high-throughput phenotypic analysis with genome-wide association analysis (GWAS), thereby enhancing breeding efficiency and food security. By integrating advanced phenotypic analysis techniques with genomic tools, this study aims to overcome the bottlenecks of traditional resistance evaluation and gene mapping, providing new ideas and resources for resistance breeding. 2 Pathogenesis and Resistance Mechanisms of Fusarium Head Blight in Wheat 2.1 Pathogenic characteristics and epidemic conditions of Fusarium graminearum Once wheat encounters high humidity and high temperature weather during the flowering period, it is very likely to be affected by scab. The "culprit" behind it - Fusarium graminearum- is no new face. This pathogen not only leads to reduced yields but also leaves toxins such as deoxynivalenol (DON) in grains, which are not very friendly to human and animal health. Its spread is not entirely a natural accident. Climate change has contributed to it, and some farming methods have also unintentionally provided it with a "breeding ground", such as no ploughing and corn-to-wheat rotation (Fernando et al., 2020). Once it invades, it gradually breaks through the defense line of wheat by secreting enzymes and effectors. The production of DON further accelerates the progression of the disease. 2.2 Types of resistance in wheat Not all wheat gives up without a fight. Some varieties can resist pathogenic bacteria when they first come into contact, which is called type I resistance. While others, even if infected, can limit the spread of the pathogen within the spike, which belongs to type II resistance (Wu et al., 2022). Of course, there is also the accumulation resistance to toxins such as DON, which is particularly crucial for ensuring food safety. It is worth noting that a single resistance is often insufficient. The varieties that truly stand out are mostly the "result" of the superposition of several resistances. 2.3 Biological basis of resistance traits and phenotypic indicators Ultimately, the resistance display of wheat is still the "collaborative result" of genes and molecules. Some key signaling pathways are activated at the early stage of pathogen invasion. For example, genes involved in pathogen recognition, cell wall reinforcement or toxin detoxification are up-regulated in expression (Dong et al., 2023; Wang et al., 2025; Yang et al., 2025). At the same time, mechanisms such as phenylalanine metabolism, the glutathione cycle, and those related to reactive oxygen species will also be involved (Figure 1). We can observe these resistance manifestations in various ways. Besides the traditional disease grade scoring, molecual-level and metabolomics data also provide a considerable amount of quantifiable evidence. In terms of resistance, "appearance" and "core" are not two separate levels, but rather a whole that reflects each other. 3 High-Throughput Phenotyping Technologies and Their Application in FHB Evaluation 3.1 High-throughput platforms: image analysis, near-infrared spectroscopy (NIRS), and thermal imaging Nowadays, relying on manual visual inspection to assess the resistance to Fusarium head blight (FHB) can no longer keep up with the pace. Image analysis (such as RGB, multispectral, hyperspectral), near-infrared spectroscopy (NIRS), and thermal imaging techniques have gradually become mainstream tools for tracking plant health and identifying disease manifestations (Leiva-Sandoval, 2023). Whether in greenhouses or directly in fields, these systems can be deployed and put into use. Some even incorporate devices like Phenocave, which are low-cost and not complicated to operate, making it easier for breeders and research teams to access these methods that were originally only available in high-end laboratories (Yang et al., 2020). 3.2 Automated phenotypic data collection and disease severity detection algorithms In the past, collecting lesion data was time-consuming and relied on human eye judgment. Now, with the use of automatic acquisition systems and image recognition algorithms, the process has become fast and accurate (Xu and Li, 2022; Jin et al., 2025). Deep learning has been able to identify the subtle changes in grain morphology, and the size of the lesion area can also be quantified very clearly. The most crucial point is that the difference
Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 245-253 http://cropscipublisher.com/index.php/tgg 247 between the output result and the manual scoring by experts is not significant, but it saves a lot of manpower. It is precisely for this reason that the acquisition of phenotypic data can finally be more standardized and consistent. Figure 1 Fusarium head blight resistance in Xinong 511 and Aikang 58 (Adopted from Yang et al., 2025) 3.3 Multi-timepoint, multi-trait dynamic monitoring and data standardization Fusarium head blight does not end as soon as it appears. It will continue to develop throughout the entire growth period. Therefore, data at a single time point is inevitably one-sided. The advantage of the new generation phenotypic platform lies in its ability to monitor multiple traits in real time and continuously, including various detailed reactions during the disease development process. Although data collection has become easier, standardization processing is still necessary in the later stage to ensure that data from different batches and locations match. This step still cannot be ignored. Especially when it comes to linking phenotypic results with GWAS, the accuracy of data integration becomes even more important (Yang et al., 2020; Leiva-Sandoval, 2023). Judging from the current trend, these technologies are gradually shortening the path between genotype screening and resistance breeding. 4 Overview of Genome-Wide Association Study (GWAS) Methods 4.1 Principles and application processes of GWAS in plants To identify the genes behind complex traits like fusarium head blight resistance, GWAS has become a mainstream tool. This type of analysis usually focuses on SNPS (Single nucleotide polymorphisms), but in fact, the entire process is more complicated than imagined. From the very beginning of designing the experiment, to obtaining phenotypic and genotypic data, and then to the later statistical analysis, no step can be taken lightly. For plant research, the appeal of GWAS lies in its ability to identify the connections between traits and genetic variations in natural populations, thereby helping to identify potential breeding candidate genes (Uffelmann et al., 2021). However, the reliability of the result depends on whether each link is solid, especially the rigor of the statistical part and the consistency of the data.
Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 245-253 http://cropscipublisher.com/index.php/tgg 248 4.2 SNP array and resequencing data selection and processing for GWAS Not all genetic markers are suitable for GWAS, especially when the data quality is substandard. At present, there are two commonly used methods: one is SNP chips, and the other is high-throughput resequencing. The former is cheap and efficient, and is suitable for large samples. Although the latter is a bit more expensive, it has more comprehensive information and can also capture rare mutations (Yin et al., 2020). No matter which way it is, data cleaning is the first step: low-quality markers need to be removed and missing genotypes need to be filled. These may seem like technical details, but in fact, they directly affect whether reliable conclusions can be drawn in the subsequent analysis. 4.3 Common statistical models and control strategies in FHB resistance GWAS Once the phenotypic and genotypic data are in place, the next challenge lies in modeling. The initial GWAS method only measured each SNP one by one, but in plant research, the problem of too many false positives often troubles researchers. Thus, the mixed linear model (MLM) has become the mainstream choice because it can simultaneously consider the group structure and the kinship among individuals (Huang et al., 2025). In some studies with large sample sizes and complex backgrounds, more advanced methods are also employed, such as MLMM (Multilocus Mixed Model) or meta-analysis. Controlling confounding effects, conducting multiple tests and corrections reasonably, and combining them with clear result visualization all determine whether GWAS results are truly useful, especially when it comes to the breeding of Fusarium head blight resistance, which cannot be taken lightly. 5 Integrated Analysis of High-Throughput Phenotyping and GWAS 5.1 Enhancing mapping accuracy with multidimensional phenotypic data To precisely identify the genes related to FHB resistance, relying solely on traditional methods is often insufficient. Nowadays, many studies tend to combine high-throughput phenotypes with GWAS. Because this method can obtain various types of data - such as morphological, physiological and biochemical traits, and can also dynamically track plant changes (Merida-Garcia et al., 2024). Compared with traditional measurements, this type of phenotypic data contains much more information and can also capture some response details that would otherwise be easily overlooked. With these more "abundant" data, the accuracy of detecting marker-trait associations naturally improves, especially when studying complex traits like FHB that are controlled by multiple genes, the advantages are more obvious. 5.2 Principal component extraction and mixed model construction in joint analysis The "abundance" of multi-dimensional data does not mean that analysis is easy. Too much data and too many variables often lead researchers into an "information overload". In order to reduce interference and highlight key features, principal component analysis (PCA) is generally used for a round of dimensionality reduction first to extract the main variations (Zhang et al., 2020). These principal components are then incorporated into the mixed linear model to correct background noise and enhance the stability of the analysis. In this way, even if the research involves multiple traits and multiple time points, some gene loci with less obvious effects can be screened out (Wu et al., 2021). Especially for genes with pleiotropy or temporal dynamic effects, this method can capture signals more effectively. 5.3 Stability assessment of QTLs through multi-environment and temporal-spatial data integration No matter how well a QTL performs at a certain experimental point, if it becomes ineffective in a different year or environment, it is clearly not suitable for breeding. This situation is not uncommon. Therefore, before evaluating whether the QTLS related to FHB resistance are "reliable", multi-point validation across environments and years is very necessary. With the help of high-throughput phenotypic platforms, researchers can now repeatedly collect data under different field conditions, with more standardized operations and smaller errors. By combining these phenotypic data with GWAS results for analysis, it is possible to more clearly see which QTLS can be stably expressed in various environments (Xiao et al., 2021; Merida-Garcia et al., 2024). The ultimately selected "stable performance" genetic markers are more suitable for practical breeding applications and can also help us understand exactly how genes and the environment interact with each other.
RkJQdWJsaXNoZXIy MjQ4ODYzNA==