The role of pan-genomics in understanding genetic diversity and structural variations for developing climate-resilient crops

Introduction

The genetic variation found in the gene pool of modern crop species is the outcome of a range of changes, including single-nucleotide polymorphisms (SNPs), indels, and large structural variants (SVs). This variation serves as the raw material upon which both human and natural selection can act. To maintain and enhance crop productivity in the face of an increasing world population and climate change, it is essential to understand the processes that cause this variation, assess its extent, and utilize it effectively (Brozynska et al., 2016) [5] . With the availability of reference genomes for most modern crops, genome-wide surveys of SNPs and subsequent marker-trait association studies can now link genetic variation to phenotypic variation. However, our understanding of genetic variation was previously limited due to our reliance on resequencing approaches based on a single reference genome. While some methods may have limited capabilities in identifying structural variations (SVs), mounting evidence suggests that SVs, including copy number variants (CNVs) and presence/absence variants (PAVs), are prevalent in crops and play crucial roles in determining agronomical traits (Springer et al., 2009; Hirsch et al., 2014; Li et al., 2014; Zhao et al., 2014 and Lu et al., 2015) [42,17,55] . Genetic variation, particularly structural variation, can result in substantial differences in functional gene complement and gene content between individuals of the same species. To better capture this variation, the concept of the "pan-genome" has been proposed, which involves investigating the entire gene repertoire of a species by sequencing multiple individuals of the species (Tettelin et al., 2005) [47] . Recent advances in sequencing technology and analysis tools have made it feasible to sequence multiple accessions of crop species (Golicz et al., 2016a) [14] . A wave of pan-genomic studies on major crops and related species, including Brachypodium distachyon (Gordon et al., 2017) [16] , Brassica napus (Hurgobin et al., 2018) [19] , Brassica oleracea (Golicz et al., 2016b) [14] , Brassica rapa (Lin et al., 2014), maize (Hirsch et al., 2014) [17] , rice (Schatz et al., 2014; Zhao et al., 2015 and Wang et al., 2018) [39,54,44] , soybean (Li et al., 2014) and wheat (Montenegro et al., 2017) [34] has revolutionized our understanding of genetic diversity within species, revealing a more comprehensive picture of gene content variation. This method has been particularly impactful in crop research, unveiling previously hidden genetic resources that could be valuable for crop improvement and adaptation to changing environments. The growing body of pan-genomic studies across various crop species has provided insights into the genetic basis of important agronomic traits and the evolutionary history of domesticated plants. This pan-genomics has shed new light on the landscape of crop diversity and improvement. In this review, we highlight the progress made in understanding crop pan-genomics, discuss the biological processes that cause SVs, review important agronomic traits affected by SVs, and present our perspective on the application of pangenomics in crop improvement.

Pan-genome

Pan-genomes were first introduced by Tettelin to describe gene diversity in Streptococcus agalactiae. Pan-genomes can be constructed through the sequencing of individual genomes or survey of genetic variations within a species to describe the extensive repertoire of variation. The use of pangenomes removes the sample bias caused by using single reference genome assemblies, allowing the identification of structural variations (SVs) to assess the diversity within species. This diversity can encompass PAVs and non-genic regions (Tettelin et al., 2005) [47] .

Classification of genes in pan-genome

The pan-genome represents the full complement of genes within a species, which can be partitioned into a set of Core genome that are shared by all individuals, a set of dispensable genome/Accessory /Variable genome that are partially shared and Unique genome is individual specific (Tettelin et al., 2005) [47] .

Types of pan-genome 1. Open pan-genome: Open pan-genome approach refers to the condition when there is no fixed quantity of the genes in a species and whenever a new organism is included in the pan-genome analysis, there is a probability of identification of the novel genes.

Closed pan-genome:

In this approach the gene pool is restricted and the pan-genome analyzed based on the definite number of individuals (or samples, strains) would not be extended in terms of gene numbers with the genome of additional individuals analyzed.

Pan-genome assembly methods

Pangenome can be constructed based on genome sequencing data. Genome sequencing reads for genomes A, B and C are shown in the image, each colour represents a gene in the genome (Figure 1). The genome sequencing reads may be assembled into a pangenome using de novo, iterative and graph-based assemblies which may influence the positioning of the assembled genes. The *indicates that genome A is used as reference genome in iterative assembly method. Pangenomes are mostly constructed in one of three ways.

Denovo assembly

The first, de novo sequencing and comparison, involves the sequencing, assembly, and comparison of multiple genomes to identify core and variable genes and genomic regions. This approach reveals the physical position of genes and other genomic elements. However, errors in assembly and annotation may lead to the false calling of variation. Furthermore, this approach is costly and requires highquality data with high sequencing coverage, limiting the application to relatively few individuals (Lischer & Shimizu, 2017) [30] .

Iterative mapping

This is the second method for pangenome construction.

Iterative mapping, and assembly, uses a single reference genome as a base for the pangenome. Whole genome sequence data for multiple individuals is aligned to the reference genome and any non-aligning sequence reads are assembled and added to the reference to build a pangenome. This approach is less expensive than de novo assembly and comparison as it requires much less data, and so permits the assessment of large numbers of individuals with relatively low sequencing coverage. After pangenome construction, gene PAVs can be called by realigning the sequencing data from each individual back to the final assembled pangenome. This approach usually only calls PAV within genes and requires further analysis to accurately place the nonreference contigs within a genomic context. However, a combination of de novo assembly for a small number of representative individuals together with iterative assembly using large numbers of low coverage individuals provides both genomic context and PAV at a population scale, permitting in depth diversity analysis (Fernandez, 2022 and Hu et al., 2020) [46] .

Graph based assembly

This method of assembly uses graph-based approaches, including sequence and variation graphs (VGs) and practical haplotype graphs. Pan genome graphs can be constructed from whole genome assemblies or by de novo graph genome assembly. Instead of a single representative sequence, the graph represents genomic variants as multiple paths. Sequence regions shared between individuals are collapsed into a single path and SVs are added to the graph as a node at the genomic location of their discovery. In, doing so, variant information for dispensable regions are stored as unique paths through the graph, displaying genomic diversity and sequence conservation. Graph-based pangenomes can provide gene position information, bu are computationally intensive to assemble, and graph quality correlates directly with the quality of the input data. However, with further advancements in DNA sequencing and data processing, particularly the expansion of high quality long read data, they become standard approach for pangenome construction ( [6,7,11,13] .

Super pan-genome

Super pan-genome is a pan-genome constructed from the genomes of different species within a genus. Useful to transfer genes from the species belonging to distantly related gene pools. Unifying genomic features by the super pangenome enables functional and evolutionary studies of genes across different species or populations Khan et al.

How a pangenome is generated?

A pangenome is generated by sequencing and analyzing the complete set of genes present across multiple individuals of a species, capturing both the core genome, which is shared by all individuals, and the accessory genome, which includes genes that are present in some but not all individuals. This comprehensive approach allows for the identification of genetic diversity and structural variants, such as insertions, deletions, inversions, and duplications, that contribute to phenotypic variation and adaptation. By studying these structural variants within the pangenome, researchers can gain insights into the evolutionary dynamics and adaptive potential of species, which is crucial for breeding programs aimed at improving crop resilience and productivity.

Structural variants

Structural variation was defined as the genomic alterations which includes insertions, deletions, duplications, inversions and translocations covering at least 1kb.Later adjusted to include small variants Della Coletta et al. (2021) [10] .

Utilizing pan-genomics for crop improvement 1. Pan-genomics in utilizing crop wild relatives (CWRs): Pan genomic studies characterize full genomic content of CWRs uncovering genetic diversity including SVs and SNPs, that defines uniqueness of individual CWRs laying the foundation for utilization of this untapped resource.

Domestication of new and existing species:

Utilization of pan genome information in identifying causal variants underlying domestication traits. Variation for fruit weight QTL fw3.2 caused by tandem duplication of the cytochrome P450 gene elucidated with the help of tomato pangenome Alonge et al.

Easing Mapping and GWAS: A major concern in

QTL mapping and GWAS based on SNPs from a single reference genome is reference bias. Maize gene conferring resistance to sugarcane mosaic virus is identified by GWAS using markers based on B73 but not present in PH207 genome assembly. In, pangenome perspective, the contribution of SVs to trait variation is clear. In Brassica napus, GWAS was performed with PAVs identified from eight whole-genome assemblies, and causal associations between SVs and silique length, seed weight, and flowering time were discovered that were not captured by SNP-GWAS.GWAS conducted using rice pan-genome genotyping array (RPGA) includes 80K genome-wide SNPs genotyping data of a rice diversity panel detected total of 42 loci, regulating grain size/weight traits in rice. (Daware et al., 2022) [8] .

Employing

Genomic prediction in crop improvement: Genomic selection is an alternative approach for complex traits controlled by QTLs with small effects. Uses SNPs as predictors for important agronomic traits predicted with fairly high accuracy and it is biased with the use of single reference genome. New approaches for identifying markers within a pangenome frame work are needed to improve prediction accuracy and one such approach is practical haplotype graph approach (PHG).

Using pangenomes to dissect agronomic traits:

Dispensable genome is enriched with genes involved in environmental responses. Pangenomes are being increasingly used in the detection of sequences associated with agronomically relevant traits, such as yield and stress resistance. This lead to the transition from so-called genomic-assisted to pan genomicassisted breeding strategies like: a. Resistance to biotic stress/Disease resistance:

Deletions in the Pi21 gene result in quantitative and durable resistance against blast diseases in rice. Fukuoka et al. (2009) [12] . In rapeseed 70% of the predicted resistance genes found in variable proportion of pangenome (53 accessions), which is absent from 'Darmor-bzh' reference Hurgobin et al. (2018) [19] . The CNV at the Rhg1 locus is resistant to cystic nematodes in soybean. The sunflower (Helianthus annuus L.) pangenome 1.5% of genes were derived from introgression with wild relatives, including two candidate genes associated with resistance to downy mildew: a syntaxin (SYP132) [bacterial resistance] and PR-1 protein secretion, and a GDSL-motif lipase involved in plant resistance to fungal infection. Among the dispensable sequences in the Brachypodium distachyon (L.) Beauv. pangenome assembly Brdisv1Bd1 11011965m was identified as a dispensable gene absent from the reference line (Bd21) and induced during infection of wheat stem rust in Bd1-1, a line resistant to Puccinia graminis f. sp. tritici b. Vernalization and flowering time: The Brachypodium distachyon reference (Bd21) has the shortest flowering time recorded in the species, which will be useful for identifying PAVs involved in flower variation. This dispensable gene, Brdisv1ABR21022861m, is present in delayed-and extremely delayed-flowering species. This gene encodes an NF-Y subunit transcription factor, which is a class of transcription factors known to regulate flowering in Arabidopsis, wheat and rice. In Brassica napus, pangenome-based comparative analysis and PAV GWAS identified variations causing several agronomically relevant traits including silique length, seed weight and flowering time (Song et al., 2020) [40] . c. Fruit, grain, yield and seed quality: The Assembled pepper (Capsicum spp.) pangenome (383 cultivars) was used as a reference for PAV GWAS analysis to detect deletions in genes involved in carotenoid and capsaicinoid biosynthetic pathways (Ou et al., 2018). A 2.5-kb deletion in the predicted Pungent gene 1 (Pun1, pan02g021380) was uncovered in 50 cultivars with known low capsaicin content. In tomato, the shorter, non-reference allele in the Tom LoxC promoter, which was abundant in wild relatives, was neglected/negatively selected in domestication, which is responsible for the increase in apocarotenoid levels in the varieties it is identified. In rice, a 1212-bp deletion 5 kb downstream of GW5 causes variation in grain width and weight. (Liu et al. 2017) [31] d. Abiotic stress tolerance and resistance to abiotic stress: A sorghum pangenome was used to identify drought-responsive genes in transcriptomic data generated from both resistant and susceptible genotypes (Abdel-Ghany et al., 2020; Ruperao et al., 2021; Varoquaux et al., 2019) [49] . A total of 79 genes absent from the reference genome were confirmed to be differentially expressed during drought stress, including two resistance-specific genes, Sobic.005G069800 and Sobic.006G127800, which were co-mapped with plant height and leaf pigment traits. Analysis of structural variations in a soybean pangenome identified a gene encoding an Fe 2+ /Zn 2+ -regulated transporter associated with iron deficiency chlorosis (Liu et al., 2020) [32] . The PAVs in Sub1A gene encoding ERF like genessubmergence tolerance&2 ERF genes SNORKEL 1&SNORKEL 2 -deep water response in rice (Xu et al. 2006). e. Plant architecture: In wheat, a extra copy of Rht-D1b resulting from a duplication of a >1Mb region causes >70%reduction in plant height (Li et al. 2022). In rice, a deletion in qPE9-1, resulted in loss of function of this gene, results in erect panicles.

Complexity of polyploid genomes

High-quality genome assembly of polyploid species has been difficult to achieve because of the inclusion of multiple closely related sub genomes and the associated challenges in discriminating homoeologous loci and creating non-mosaic sub-genome scaffolds. Many researchers have resorted to sequencing diploid progenitors or closely related species. However, closely related diploids fail to capture lineagespecific SNPs, SVs, and other forms of variation that have accumulated post-polyploidization. The degree of structural variation within polyploid species is greater than that within diploid species, and SVs may be particularly useful markers for genomic approaches to polyploid crop improvement. Allopolyploidy (the result of interspecific or intergeneric hybridization and chromosome doubling) and auto polyploidy (the result of whole-genome duplication) are particularly common in plant species (Kumar, [22,9,37,41,48] . All angiosperms have undergone at least two rounds of polyploidy in their evolutionary history. Many have returned to a diploid state, bearing remnants of this evolutionary history in their genome. As a natural mechanism, polyploidization can increase allelic diversity, expand gene complements, generate novel phenotypic variations, and aid in adaptation to new environments. Taking advantage of this, plant breeders have also generated artificial polyploids, resulting in increased grain yield, fruit size, and seedless fruits.

Although polyploid crops are vitally important for sustaining human life, genomic studies of these species have traditionally been very challenging for a number of reasons. Beyond these difficulties in genome assembly, genomic approaches to polyploid crop improvement face further complications: (1) dissection of the genetic architecture of complex traits can be confounded when variants are not mapped to the correct sub genome, which is a technical limitation, and (2) biologically, the more extensive epistatic interactions in polyploids and regulatory feedback between sub genomes can complicate the accurate prediction of phenotypes based on genotype. Advances in sequencing technologies and assembly algorithms have addressed the technical challenges in crop genomic research on polyploids. Long-read sequencing with low error rates (e.g., PacBio HiFi reads) has produced high-quality polyploids.

Editing regulatory elements for crop improvement

CREs are attractive genome editing targets. In tomatoes, inspired by the natural variation observed between wild and domesticated relatives, researchers used CRISPR-Cas9 mutagenesis to generate novel alleles of the promoter of SlCLV3, a gene affecting fruit size, and engineered a continuum of phenotypic variation. In maize, it was possible to engineer a spectrum of variation for yield-related traits by targeting homologues of CLV3 (Zhu et al., 2021) [57] .

Current developments in pan

Studies on pan-genomics

Bayer et al. (2019) [3] performed comparative analysis of RGA candidates in the pangenome of Brassica oleracea. In total, 59 RGA candidates were linked to Sclerotinia, clubroot and Fusarium wilt resistance QTL, and these findings have implications for crop breeding in B. oleracea, which may also be applicable to other crop species.

Normally sequenced reference cultivars do not contain all B. oleracea genes because of gene presence/absence variation between individuals. Qin et al. (2021) [36] had built a genebased pan-genome of 32 rice accessions of Oryza sativa and 1 accession of Oryza glaberrima as inference with Nipponbare genes as the base set and obtained a pangenome of cultivated rice comprising 66,636 protein-coding genes. Distribution analysis showed that 20,374 genes were categorized as ''core genes'' 46,262 genes were categorized as ''variable genes'' which included 14,609 accessionprivate genes. Varshney et al. (2021) [50] constructed the chickpea pan genome (592.58 Mb) using the iterative mapping and assembly approach. They constructed this pangenome to determine species divergence, migration of the cultigen (C. arietinum), rare allele burden, and fitness loss in chickpea. The predicted performance for 100 seed weight increased by up to 23% and 12% with the use of OCS-and haplotype based genomic approaches. Wang et al. (2022) [51] had developed the graph pangenomes to overcome the limits of current pangenome identification which will not provide information about the position information of the novel sequences. PAV-based GWAS analysis identified causal structural variations in rice grain weight and plant height, whereas SNP-based GWAS only identified approximate genomic locations. They also identified a new locus (qPH8-1) that was found to be associated with plant height on chromosome 8, which could not be detected using SNPbased GWAS. The pan genomic studies can provide plant geneticists and breeders a comprehensive genome resource. New tools are required to support variation graph assembly, pan-genome construction, and visualization. There is a need to develop an integrated pangenome browser capable of representing SNPs and SVs in multireference coordinate systems for genome analysis. Expanding the pangenome beyond species will increase the use of wild gene sequence diversity in crop improvement.

Future prospects

Pan genomic studies can provide plant geneticists and breeders with a comprehensive genome resource. New tools are required to support variation graph assembly, pangenome construction, and visualization. An integrated pangenome browser should be developed that is capable of representing SNPs and SVs in a multi-reference coordinate system for genome analysis. Expanding the pangenome beyond species will increase the use of wild gene sequence diversity for crop improvement.

Conclusions

A dispensable genome (structural variants) is not essential for survival. Pangenomes can be used to map dispensable genomes, highlighting the varieties of host genes that provide agronomic benefits. The application of pangenomewide SNPs, as well as the inclusion of PAV in association analysis, increases the power of genomic associations and the identification of causal variants for agronomic traits.

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