The developments and prospects of plant super-pangenomes: Demands, approaches, and applications

Abstract

By integrating genomes from different accessions, pangenomes provide a more comprehensive and reference-bias-free representation of genetic information within a population compared to a single reference genome. With the rapid accumulation of genomic sequencing data and the expanding scope of plant research, plant pangenomics has gradually evolved from single-species to multi-species studies. This shift has given rise to the concept of a super-pangenome that covers all genomic sequences within a genus-level taxonomic group. By incorporating both cultivated and wild species, the super-pangenome has greatly enhanced the resolution of research in various areas such as plant genetic diversity, evolution, domestication, and molecular breeding. In this review, we present a comprehensive overview of the plant super-pangenome, emphasizing its development requirements, construction strategies, potential applications, and notable achievements. We also highlight the distinctive advantages and promising prospects of super-pangenomes while addressing current challenges and future directions.

Introduction

Hailed as the "moon landing project" of life sciences, the Human Genome Project was launched in 1990 with the goal of completely sequencing and analyzing the human genome. Following this, the release of the first reference genomes for the model plants Arabidopsis (Arabidopsis Genome Initiative, 2000) and rice (International Rice Genome Sequencing Project, 2005) ushered in the era of genomics in plant research. After decades of development, plant genome assembly has progressed from the chromosome level to telomere-to-telomere (T2T) assemblies (Naish et al., 2021; Song et al., 2021). Most recently, researchers have achieved complete genome-level T2T assemblies for all chromosomes (Chen et al., 2023a; Shang et al., 2023). This progress has resulted in an abundance of genome assemblies across hundreds of species (Sun et al., 2022), substantially contributing to our understanding of the molecular basis of plant diversity and enabling genetic improvements. However, with the extensive application of genomics at the population-level and in interspecies research, the limitations of using a single reference genome have become increasingly evident. For instance, it is challenging to use a single reference genome for capturing rare and complex genomic regions within a population, which often serve as important sources of plant genetic diversity (Wang et al., 2023c). To address these limitations, pangenomes that represent all genomic information of an entire population or taxa have gained prominence over the past decade (Shi et al., 2023). By expanding the scope of genomics to the genus level, this concept has further evolved into super-pangenomes (Khan et al., 2020). Super-pangenomes have established a crucial foundation for the protection and utilization of plant germplasm resources on a larger scale, particularly through the incorporation of wild relatives and molecular breeding for cultivated plants, emerging as a key research frontier in genomics. Here, we explore the conceptual evolution, construction strategies, and research achievements of plant super-pangenomes, providing a panoramic perspective and valuable insight for the effective use and better understanding of this powerful tool.

Development of the concepts of the pangenome and super-pangenome in plants

High-quality genome resources form the essential foundation for research areas such as germplasm conservation, utilization, and innovation in plants (Garg et al., 2024). As more high-quality reference genomes become available, the genomes of representative or key samples across diverse plant species have been analyzed and employed to discover valuable genetic variations and underlying molecular mechanisms. When comparing genomic differences among accessions within a population, it is evident that numerous complex structural variations exist, which cannot be fully captured by a single reference genome (Cheung et al., 2009). Therefore, to achieve a more comprehensive understanding of population diversity and to reduce the influence of reference genome bias on the detection of genetic variations, the pangenome concept, derived from bacterial research (Tettelin et al., 2005), has gained widespread attention. A pangenome represents the sum of all genomic information within a species. This concept has been successfully applied in human (Li et al., 2010) and Arabidopsis (Cao et al., 2011) populations and has been further improved and expanded through long-term studies of pangenomes in numerous animal and plant species (Eisenstein, 2023). A widely recognized pangenome usually consists of three components: (1) the core genome, present within all individuals of that population, essential for critical life processes; (2) the dispensable genome, found in two or more individuals, often associated with environmental adaptability; and (3) the private genome, present only in a single individual, often related to rare traits or phenotypes in the population and constitutes a significant rare gene pool.

For decades, extensive pangenomic research has been conducted to investigate the population diversity of different plant species, particularly the diversity of cultivated germplasms accumulated over the centuries (Schreiber et al., 2024). With the inclusion of more wild relatives into the pangenome panel, the super-pangenome has become a more comprehensive framework to capture richer genomic and phenotypic diversity at the genus level in plants (Figure 1A). Originally designed as a pangenome of multiple pangenomes from different populations or species (Khan et al., 2020), most super-pangenomes are in practice directly constructed from the genomes of accessions representing different species. This has led to some ambiguity in distinguishing between pangenomes and super-pangenomes. Since the initial release of the Arabidopsis thaliana pangenome (Cao et al., 2011), over 110 pangenomes or super-pangenomes have been assembled for plant species, with at least 53 having been constructed using two or more species. However, only nine were explicitly declared as super-pangenomes (Figure 1B). Nevertheless, compared to a typical species-level pangenome, the widely recognized super-pangenome generally encompasses a broader range of genetic resources, including all genes from multiple representative species sampled at the genus level (super-pangenes), usually comprising genus-conserved (core genome), genus-variable (dispensable genome), and species-specific (private genome) gene sets (Figure 1C). Therefore, the plant super-pangenome represents an expansion of the pangenome, covering the genomic information of all concerned species, particularly the abundant genomic variations found in wild species. The super-pangenome possesses significant potential for practical application. In crops, super-pangenome research typically integrates cultivated varieties and their wild relatives for the exploitation and utilization of wild alleles in crop improvement.

Discrimination between the plant pangenome and super-pangenome

Although there is currently a lack of a clear distinction between pangenomes and super-pangenomes, discussing the differences between the two is conducive to a better understanding of the super-pangenome concept. First, the sampling scopes differ. Typical pangenome research covers different populations within a single species, while super-pangenome research can sample at the genus level or even higher taxonomic levels, such as the family level or above, thereby encompassing multiple species. Second, the dataset compositions differ. A pangenome dataset contains information on sequences, genes, and variants from all genomes within a species, whereas a super-pangenome dataset includes genomic information on sequences, genes, and variants from multiple species. When the sampling scope is large enough, the super-pangenome can also include the pangenomes of all the sampled species, effectively functioning as a super-collection of multiple pangenomes derived from different species (Khan et al., 2020). Finally, their implications differ. A pangenome represents the total genomic information of a single species, reflecting its genetic diversity. In contrast, a super-pangenome represents the genomic information of multiple species, reflecting the diversity of plant genes and functional variations at the genus level or higher. To provide a comprehensive review of the plant super-pangenome and its applications, we classify any pangenome research covering two or more species (regardless of prior declaration as a super-pangenome) as super-pangenome research.

Strategies for building the plant super-pangenome

Based on variations in sampling scope and dataset composition, current pipelines for building plant super-pangenomes can be classified into three types (Figure 2A). (1) The simple super-pangenome, which is sampled at the species level, where only one accession is taken for each species and is constructed using conventional pangenome methods. While the simple super-pangenome can reflect the genomic diversity of plant species at the genus or higher taxonomic levels, it is not a collection of multiple pangenomes from those species. (2) The intermediate super-pangenome, which is sampled at the accession level, where some species include multiple accessions, and is constructed by conventional pangenome methods. Although the intermediate super-pangenome could theoretically include pangenome information on multiple species, it does not include specifically defined datasets as seen in the original definition of the super-pangenome (Khan et al., 2020), and thus we term it the intermediate super pangenome. (3) The complete super-pangenome, where each species’ pangenomes are first constructed, and then a multi-species super-pangenome is generated by integrating them. This process is relatively complex but simultaneously incorporates the genomic information of the target taxa and the pangenomes of sampled species. To date, published plant super-pangenomes mainly consist of simple and intermediate, with only a few complete super-pangenomes constructed (Table 1).

Constructing a super-pangenome based on individual genomes or pangenomes generally follows the same bioinformatics strategy as conventional pangenome construction. Thus, their construction methods are discussed together here. To date, there are three primary strategies for the construction of plant super-pangenomes: map to pan, whole-genome alignment, and graph-based pangenome (Figures 2B–2D).

Map to pan is a simplified method designed to improve the efficiency of super-pangenome construction using an available reference genome. It sequentially maps the genomic sequencing reads of different samples to the reference genome, assembles the non-mapped reads into contigs representing a mixture of all non-reference sequences, and integrates these contigs with the reference genome to generate a linear super-pangenome with non-redundant sequences (Figure 2B). This strategy is cost-effective because each sample can be sequenced at a relatively low sequencing depth, which allows the pooling of hundreds or even thousands of samples. It is capable of gradually expanding the coverage of the genome, especially for species with complex genomes. However, its effectiveness heavily depends on the completeness and accuracy of the reference genome. As there is no assembly process, the map-to-pan method struggles with repeat-rich genomes and cannot detect complex or large structural variants (SVs) that cannot be covered by single short reads.

The whole-genome alignment strategy is the most straightforward way to construct a super-pangenome (Figure 2C). It involves assembling a high-quality individual genome using short and long sequencing reads, followed by comparative analyses to generate a super-pangenome that includes all non-redundant genes and/or genomic variants (Li et al., 2022). Currently, whole-genome alignment is the most widely used method, which does not rely on a reference genome and directly reflects the genetic variation among samples. However, assembling high-quality genomes for plant species with large, highly repetitive, heterozygous, and polyploid genomes remains challenging. Constructing a super-pangenome with large-scale samples is costly (Hurgobin and Edwards, 2017).

The graph-based strategy is the most promising approach for constructing a complete super-pangenome, as it stores all genomic sequences in a graph format that can be used as a reference for SNP and SV detection using both sequencing reads and assemblies (Wang et al., 2023b). It has recently emerged as a significant alternative to the traditional linear super-pangenome. It combines all genetic variants in a population, ensuring sequence continuity and accurately displaying the topology of SVs among different samples, offering advantages over conventional linear pangenomes. Currently, there are three approaches for generating a graph-based super-pangenome: the genetic variation graph, the reference-based graph, and the reference-free graph (Figure 2D).

To generate a genetic variation graph, genomic variants are identified for a population based on a reference genome, and then these variants are added to the reference genome. Tools such as the variation graph toolkit (Garrison et al., 2018) and Graphtyper2 (Eggertsson et al., 2017) are available to support this approach. A genomic variation graph can incorporate SNPs, insertions or deletions (indels), and SVs (>50 bp). Although this is a relatively simple approach, it is highly dependent on the quality of the reference genome and the variation calling process, making it insufficient for characterizing complex and large SVs (Wang et al., 2023b).

To generate a reference-based graph, the genomes of a population are sequentially aligned to a reference genome or the resulting pangenome graph, incrementally augmenting the graph with long query subsequences. This method, supported by tools like minigraph (Li et al., 2020) or minigraph-cactus (Hickey et al., 2024), is much faster than other methods and can effectively identify and integrate large SVs. However, it is less effective with SNPs and may introduce bias, potentially overlooking genetic variations exclusive to non-reference genomes. Additionally, the selection of different reference genomes and the order of alignment can result in varying pangenome graphs for the same population.

The reference-free graph, as suggested by its name, does not require a reference genome. To construct this graph, multiple genomes are aligned pairwise and divided into different aligned fragments, which are then clustered and converted into nodes on the graph. Supported by tools like PGGB (Garrison et al., 2024) and Cactus (Armstrong et al., 2020), this method ensures that the pangenome graph captures genomic diversity without reference bias by treating all genomes equally. However, it requires significant computational resources and complicates downstream analysis, particularly for large genomes with extensive repeat regions. Also, the optimal parameters for the PGGB tool vary across different species, typically requiring multiple computational experiments to fine-tune.

Progress and achievements in plant super-pangenomic research

High-quality pangenomes are highly dependent on species/population diversity and the quality/quantity of accumulated genomic data. As a result, pangenome research has primarily focused on food and economically important crops, as these taxa have amassed extensive germplasm and genomic data resources over the past few decades (Sun et al., 2022; Shi et al., 2023), thereby providing more mature conditions for conducting pangenome research. In 2014, through third-generation sequencing technology, plant pangenomics entered a golden era, rapidly achieving widespread application across various plant species. Since 2016, pangenomics has evolved toward multi-species coverage and comparisons, culminating in the emergence of the super-pangenome (Figure 1B). To date, nearly all super-pangenomes (including pangenomes covering multiple species) are derived from crop species and their closely related wild species (Table 1). More than 25 crops have super-pangenomes for their respective genera (Table 1), significantly expanding pangenomic sequences and pangene resources across diverse germplasms, particularly from wild relatives. For example, the graph-based rice super-pangenome, constructed from 251 high-quality genomes of two cultivated rice species and their wild relatives, contained a total of 1.52 Gb non-redundant DNA sequences, which is approximately four times larger than an individual rice genome. It includes 1.15 Gb sequences and ∼12 000 genes that are absent from the Nipponbare reference genome (Kawahara et al., 2013), most of which were contributed by the wild rice species (Shang et al., 2022). In Citrullus, T2T assemblies of 27 genotypes from seven Citrullus species were integrated to generate a T2T super-pangenome, approximately 1.5 times larger than any individual genome, adding 399.2 Mb and 11 225 genes compared to the cultivated watermelon, with more than half derived from wild relatives (Zhang et al., 2024b). The Populus super-pangenome, covering 19 accessions from 18 species, contains 712 487 genes, of which an average of 51.3% per accession belong to the core gene set (Shi et al., 2024). These super-pangenome resources offer valuable genetic resources and powerful tools for critical research areas such as crop domestication, the exploration and utilization of favorable genes, molecular breeding, evolutionary adaptability, and polyploidization in plants.

To meet diverse research demands and leverage accumulated sequencing data, super-pangenome datasets constructed by different studies vary in taxonomic composition, content, and variation resources. To comprehensively explore genetic variations in thousands of chickpea (Cicer arietinum) accessions and identify wild sources of genetic superiority, the chickpea pangenome was constructed using a map-to-pan approach to maximize the integration of available data, including genomic sequencing data and de-novo-assembled sequences derived from up to 3171 cultivated (C. arietinum) and 195 wild (Cicer reticulatum) accessions (Varshney et al., 2021). This linear pangenome, which included 592.58 Mb genomic sequences and 29 870 genes (1582 novel gene models), was successfully used to identify domestication and migration signatures, superior haplotypes, and deleterious mutations for chickpea breeding. Many domesticated crops tend to have narrow genetic diversity due to long-term artificial selection, exhibiting a bottleneck in their genetic improvement. Utilizing wild relatives is key to overcoming such challenges. For example, a tomato super-pangenome was constructed from 11 wild and cultivated species in the Solanum section Lycopersicon (the tomato clade), incorporating 13 chromosome-level reference genomes and yielding 40 457 pangene families (Li et al., 2023). Of these, only 23 839 (54.0%) were conserved core genes, fewer than in a previously reported tomato pangenome of 586 accessions from two wild and cultivated species (Gao et al., 2019). A graph-based super-pangenome was also generated using these 13 genomes and an additional 100 tomato genomes to enable SV calling in a large-scale population and the SV-based genome-wide association studies (Li et al., 2023). To constructed a phylogenomic framework for examining the evolutionary history of strawberry (Fragaria spp.) species, alignments of the genomes from six Fragaria species were conducted to generate a pangenome for Fragaria, revealing more than 25 000 pangenes, 42.9% of which were core genes. This framework provides an important foundation for elucidating the evolutionary dynamics of gene families in Fragaria (Qiao et al., 2021).

To maximize the potential of these pangenome resources, many super-pangenome studies have established online databases to provide open and user-friendly access to the scientific community. Currently, at least 11 crop species, including rice (Zhao et al., 2018a; Shang et al., 2022), wheat (Chen et al., 2020), soybean (Bayer et al., 2022; Zhuang et al., 2022), rapeseed (Bayer et al., 2021), tomato (Li et al., 2023), watermelon (Zhang et al., 2024b), citrus (Liu et al., 2022), strawberry (Qiao et al., 2021), grape (Cochetel et al., 2023), and poplar (Shi et al., 2024), have online databases based on their super-pangenome datasets. These platforms feature tools for pangenome browsing, dataset querying, co-linearity and structural variation visualization, and multi-omics data analyses. For example, the Rice Super Pan-genome Information Resource Database (http://www.ricesuperpir.com/) allows for the visual exploration of genome-wide collinearity and variations among 251 genomes in four wild and cultivated rice species (Shang et al., 2022), providing high-quality assemblies and gene annotations. This is a highly efficient and powerful research tool for fully exploiting genomic diversity and conducting functional genomics studies in wild and cultivated rice. The SoyBase website (http://www.soybase.org/) provides sequence variation maps, pangene sets, BLAST services, and downloadable datasets for Glycine max and wild Glycine species (Zhuang et al., 2022). The Citrus Pan-genome to Breeding Database (http://citrus.hzau.edu.cn/) includes 23 genomes from 17 citrus species, featuring orthologous genes, functional annotations, and multi-omics datasets, such as gene expression and DNA methylome datasets for different wild species (Liu et al., 2022). These online resources greatly enhance the utility of super-pangenomes in studying the evolution, domestication, and breeding of crops and their wild relatives.

Major applications of plant super-pangenomes

Covering multiple wild and cultivated species, super-pangenomes provide an important foundation for research areas such as genomic variation dynamics, population genetics, evolution, domestication, gene mining, and crop breeding in different plant species (Figure 3). Here, we summarize the achieved and potential applications of plant super-pangenomes to offer a comprehensive perspective and a better understanding of the developmental requirements and limitations of super-pangenomic research.

Complex genetic variations

Genomic variation is a crucial foundation for plant phenotypic diversity and heritability. Conventional variation detection methods typically identify small sequence variations in homologous regions by mapping genomic sequencing reads to a single reference genome. However, it is often challenging to detect variations in highly repetitive, structurally complex, and low-homology regions due to the low efficiency and accuracy of read mapping (Bayer et al., 2020). The super-pangenome, encompassing the complete genomic sequences and gene data of multiple species, facilitates direct comparisons of genomic sequences with various complex regions and repetitive elements. Moreover, graph-based pangenomes have demonstrated advantages in aligning short sequences to complex SV regions and achieving more accurate genotyping of SVs (Wang et al., 2023b). These improvements allow for comprehensive exploration of previously undetected, intricate genetic variations and aid in recovering the "missing heritability" in crop research (Zhou et al., 2022). By leveraging super-pangenome datasets, various complex SVs, such as inversions (Zhou et al., 2023; He et al., 2024b), tandem repeat sequences (He et al., 2024a), centromere region variations (Lv et al., 2024), transposon variations (Li et al., 2024c), and alternative splicing (Zhang et al., 2024a) of functional genes, have been effectively identified across different populations or species. These discoveries have significantly expanded the available genetic variation resources and provide a crucial foundation for critical research fields such as plant genetics, evolutionary biology, and crop breeding.

Population genetics and multi-omics

To optimize the use of available germplasm resources from closely related wild species for crops, an increasing number of population genetics studies have begun incorporating samples from both cultivated and wild species. This approach expands the diversity of the population and the potential beneficial gene pool (Schreiber et al., 2024). By covering dispensable and private genomic regions from different species, the super-pangenome circumvents the bias associated with a single reference genome and reveals more comprehensive genotype information, particularly multi-allelic genotypes, across large-scale populations comprised of multiple species (Raza et al., 2023). Consequently, the super-pangenome has emerged as an ideal tool for population genetics research. To date, most plant super-pangenomes have been directly or indirectly used to construct population-level genomic variation maps for population genetics analyses, such as SV-based genome-wide association studies, aimed at identifying significant functional genes related to economic traits and ecological adaptations. For example, a 1.4 kb deletion identified using the rice super-pangenome was found to be associated with grain yield, a variation that could not be detected through conventional SNP analyses based on a single linear reference genome (Shang et al., 2022). In recent years, the continuous accumulation of multi-omics data has further enriched the genotypic and phenotypic datasets of plant populations. This has enabled the super-pangenome to be widely applied in identifying superior variations at the transcriptional, epigenetic, and metabolic levels (Wei et al., 2024a; Ferrero-Serrano et al., 2024; Shi et al., 2024), significantly expanding the application of super-pangenomic datasets in population genetics and demonstrating their rich scalability and potential.

Evolution and adaptation

Unlike typical species-level pangenomes, super-pangenomes are capable of identifying conserved, variable, and private genes at the genus level across multiple species (Figure 1C), providing crucial evidence for studying species divergence and its correlation with evolutionary drivers, such as environmental adaptability. For example, leveraging genomic data from 19 Populus species, a super-pangenome of the genus Populus was constructed, encompassing core (51.11%), semi-conserved (22.78%), dispensable (22.50%), and species-specific (3.61%) genes (Shi et al., 2024). Compared to other gene categories, species-specific genes demonstrated higher levels of methylation modification and lower levels of gene expression, and played key roles in species adaptation to diverse climates, such as temperate plateaus and tropical islands. Similarly, using high-quality genomes from diploid and allopolyploid species, a super-pangenome of the genus Glycine was constructed to explore the biased subgenomic fractionation during polyploidization. This analysis identified two genes associated with vegetative-reproductive phase transition and lateral shoot outgrowth, which may have contributed to the perenniality-annuality transition in this genus (Cochetel et al., 2023). These studies highlight the substantial advantages of super-pangenomes in addressing evolutionary biological questions at the genus level and above.

Selection and domestication

From wild ancestors to domesticated plants, the process of domestication involves extensive artificial selection of wild traits. Uncovering the genetic basis and molecular mechanisms behind crop domestication is an important approach to understanding the formation of important agronomic traits (Huang et al., 2022). Since most high-quality reference genomes are derived from cultivated species, genetic variation maps that rely on a single reference genome often have difficulty completely covering the regions involved in the domestication process. Super-pangenomes provide a powerful solution to this bottleneck by comprehensively evaluating the genetic diversity of populations across multiple species. For instance, using a pangenomic dataset of wild and cultivated accessions, 490 orthologous genes were identified as convergently selected in the domestications of rice and maize. Evolutionary and functional evidence supported the convergent selection of KRN2/OsKRN2 for grain number in both crops, with their complete loss-of-function alleles contributing to increased grain yield (Chen et al., 2022). Similarly, a pangenome-wide analysis of the LONELY GUY cytokinin biosynthesis gene family across the Solanum genus and other flowering plants revealed that LONELY GUY mutations were associated with convergent prickle losses in both cultivated and wild species, as well as outgroup species in flowering plants (Satterlee et al., 2024). Additionally, super-pangenome datasets generated from wild and cultivated populations enable the construction of whole-genome variation maps at the genus level. These maps provide landscape analyses of selective sweeps and domestication-related genes (Gui et al., 2022; Wu et al., 2023b), offering a macroscopic perspective on the genetic basis of crop domestication.

Gene mining

The efficient exploration and utilization of ecologically and economically favorable genes constitute an important basis for research on plant evolution and crop genetic improvement (Fernie and Yan, 2019). The plant super-pangenome captures the rich diversity of various species at the genus level, showcasing a wealth of gene resources from both wild and cultivated genotypes (Liu et al., 2020). This serves as an essential foundational platform and efficient tool for gene-mining research in plants. For example, by applying a graph-based pangenome constructed from 11 cotton accessions across five allotetraploid species, researchers identified genomic SVs responsible for speciation, domestication, and improvement in tetraploid cotton, including a 9-bp indel associated with the elimination of interspecific reproductive isolation between Gossypium hirsutum and Gossypium barbadense (Jin et al., 2023). Similarly, a super-pangenome analysis of the Solanum section Lycopersicon enabled the construction of a tomato-clade-wide gene repertoire, leading to the discovery of a wild tomato gene with the potential to increase the yield of modern cultivated tomatoes, as well as numerous quantitative trait loci associated with tomato-flavor-related traits and fruit metabolites (Li et al., 2023). Recently, a pangenome consisting of 16 founders for a permanent population of 18 421 (18 000) rice lines was used to identify 96 high-quality candidate genes and 170 masking epistasis quantitative trait locus pairs related to 16 traits (Wei et al., 2024b). By integrating more diverse germplasm from different species, super-pangenomes offer significant promise for further unlocking the potential to mine more favorable genes in the future.

Molecular breeding

Wild species often contribute greater sequence diversity to crop super-pangenomes, especially wild alleles that have been lost in cultivated varieties due to genetic bottlenecks during the domestication process and could play a crucial role in regulating economic traits in crops (Qiao et al., 2021; Li et al., 2023; Zhang et al., 2024b). Conducting in-depth scans of genomes across large-scale taxa at the genus or family level, including wild species, allows for the identification of comprehensive genomic selective loci, including evolutionary constraints and deleterious mutations. This information can be efficiently applied to assess the breeding potential of germplasm and provide crucial guidance for addressing breeding challenges (Wu et al., 2023c). For example, by constructing a comprehensive map of quantitative trait nucleotides for different rice germplasm resources, a genome navigation system called RiceNavi was developed. This system facilitates the pyramiding of quantitative trait nucleotides and optimizes breeding routes for rice improvement (Wei et al., 2021). Furthermore, high-quality genomic data from large-scale populations can be applied to build genomic selection models based on advanced algorithms and prediction models for crop phenotypes, which hold promise for the development of smart crop breeding (Li et al., 2024a; Ferrero-Serrano et al., 2024; Liu et al., 2024). Such applications demonstrate great potential for linking super-pangenomic knowledge to diverse crop improvement needs.

Challenges and prospects

Super-pangenomes have revolutionized the study of plant genetics by providing insight into the genetic basis of key traits and facilitating the conservation and utilization of plant germplasm resources. These advances highlight the unique advantages and great potential for practical application. However, unlike plant genomics, which has extensively explored agricultural, medicinal, ornamental, and protected plant species across both angiosperms and gymnosperms (Sun et al., 2022), research on plant pangenomes and super-pangenomes remains predominantly concentrated on significant agricultural crops within angiosperms and their related species (Table 1). This is likely due to the long-term and systematic studies conducted on these crops, which have resulted in the accumulation of representative populations, high-quality genomic sequencing data, and genome assemblies, and corresponding phenotypic datasets, thereby fulfilling the essential requirements for super-pangenome research. As plant genomics continues to include more species, it will facilitate the widespread application of pangenomics and super-pangenomics research, potentially on a larger taxonomic scale (e.g., family level or above), providing powerful tools for addressing diverse challenges in downstream research fields.

High heterozygosity, repetition, and polyploidy are highly prevalent in plant genomes (Sun et al., 2022). Effectively integrating and handling large-scale complex sequence information to efficiently cover genomic data from different species remains a significant challenge in advancing plant super-pangenomes. The commonly used super-pangenome construction methods, which add non-reference sequences to a reference genome to form a linear sequence collection (Hu et al., 2020; Zhang et al., 2022b), cannot fully capture sequence deletions, inversions, translocations, and multi-allelic variations. While graph-based super-pangenomes are capable of effectively capturing genetic variations and, therefore, represent the future of reference genomes, storage and visualization remain major technical challenges (Hübner, 2022). For example, coordinate restoration for graph-based assemblies is complex, and it is currently difficult to accurately represent the original coordinates of target sequences in donor genomes. Most bioinformatics analysis tools are designed for linear reference genomes, and the supporting tools for graph-based reference genomes remain limited (Wang et al., 2023b). Additionally, the lack of a unified quality assessment standard is a common challenge for pangenome and super-pangenome studies. Addressing these problems will significantly enhance the development of super-pangenomes.

Future super-pangenome research could focus on building a scalable coordinate system and developing a more comprehensive suite of supporting analysis tools to better accommodate evolving application demands. These demands may include using gene expression profiles from diverse tissues under different environmental conditions to generate complete gene annotations; constructing more comprehensive genomic variation maps to explore phenotypic heritability; performing integrated analyses with multi-omics data, such as the epigenome, metabolome, and proteome, to uncover the full genetic basis of economically important traits; expanding the range of plant species studied to elucidate the evolutionary and ecological adaptation mechanisms across larger-scale plant taxa; and integrating intelligent algorithm frameworks, such as machine learning, to enhance gene mining and advanced crop breeding. In summary, the development of plant super-pangenomes not only deepens our theoretical understanding of plant genetic diversity and evolutionary processes but also provides practical tools for the conservation, utilization, and genetic improvement of plant germplasm resources.

Funding

This work was funded by the 10.13039/501100001809National Natural Science Foundation of China (32188102 and 32372148), the Innovation Program of Chinese Academy of Agricultural Sciences (CAAS-CSIAF-202303), and the 10.13039/501100012166National Key R&D Program of China (2022YFE0139400).

Acknowledgments

No conflict of interest declared.

Author contributions

W.H. and X.L. wrote the manuscript. L.S. and Q.Q. revised the manuscript.

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