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. 2021 Jun 15;19(9):1852–1862. doi: 10.1111/pbi.13600

The pan‐genome of the cultivated soybean (PanSoy) reveals an extraordinarily conserved gene content

Davoud Torkamaneh 1,2,3, Marc‐André Lemay 1,2, François Belzile 1,2,✉
PMCID: PMC8428833  PMID: 33942475

Summary

Studies on structural variation in plants have revealed the inadequacy of a single reference genome for an entire species and suggest that it is necessary to build a species‐representative genome called a pan‐genome to better capture the extent of both structural and nucleotide variation. Here, we present a pan‐genome of cultivated soybean (Glycine max), termed PanSoy, constructed using the de novo genome assembly of 204 phylogenetically and geographically representative improved accessions selected from the larger GmHapMap collection. PanSoy uncovers 108 Mb (˜11%) of novel nonreference sequences encompassing 3621 protein‐coding genes (including 1659 novel genes) absent from the soybean ‘Williams 82’ reference genome. Nonetheless, the core genome represents an exceptionally large proportion of the genome, with >90.6% of genes being shared by >99% of the accessions. A majority of PAVs encompassing genes could be confirmed with long‐read sequencing on a subset of accessions. The PanSoy is a major step towards capturing the extent of genetic variation in cultivated soybean and provides a resource for soybean genomics research and breeding.

Keywords: pan‐genome, soybean, de novo assembly, GmHapMap, genic PAV, long‐read sequencing

Introduction

The availability of reference genomes for most crops has created a crucial foundation for identification of genetic variants and subsequent genotype‐phenotype association studies. Until recently, all genetic variation in plants was identified based on the comparison of individual genomes to a single reference genome (Bayer et al., 2020). Although this approach has allowed the efficient discovery of single‐nucleotide polymorphisms (SNPs) and large presence and absence variants (PAVs), DNA sequences that were not found in, or were highly diverged from, the reference genome are overlooked (Tao et al., 2019a,b). One of the key features of structural variants (SVs; e.g. PAVs) is that they encompass large numbers of nucleotides and can consequently functionally impact a larger portion of the genome as compared to SNPs (Danilevicz et al., 2020). Thus, this can result in substantial variation in the functional gene complement between individuals of the same species. The phenotypic relevance of SVs is becoming increasingly evident in plant genomes (Tao et al., 2019a,b; Tranchant‐Dubreuil et al., 2019).

To capture the variation in gene content among the individuals of the same species, the concept of the ‘pan‐genome’ was proposed (Tettelin et al., 2005). Broadly, a pan‐genome refers to the full complement of genes of a species and it is commonly partitioned into a set of core genes that are shared by all or most individuals and a set of variable genes that are partially shared (Tettelin et al., 2005; Golicz et al., 2016a,b). The variable genome represents diversity within the species; thus, its size is inherently linked with other genetic properties of the species, such as genome size, ploidy level, mode of reproduction, and bottlenecks during domestication. (Bayer et al., 2020; Khan et al., 2020; Tao et al., 2019a,b). In general, three main approaches have been used to assemble a pan‐genome: comparative de novo assembly (Zhao et al., 2018), iterative assembly (Hurgobin et al., 2018), and the ‘map‐to‐pan’ strategy (Hu et al., 2017; Wang et al., 2018).

A growing number of pan‐genomes have been reported in the literature and these often encompass both the domesticated species and their wild progenitors (e.g. rice (Oryza rufipogon vs Oryza sativa), barley (Hordeum spontaneum vs Hordeum vulgare), wheat (Triticum araraticum, Triticum turgidum ssp. dicoccoides and Triticum dicoccoides vs Triticum aestivum), soybean (Glycine soja vs G. max) (Gao et al., 2019; Golicz et al., 2016a,b; Gordon et al., 2017; Hirsch et al., 2014; Hurgobin et al., 2018; Li et al., 2014a,b; Lin et al., 2014; Liu et al., 2020; Montenegro et al., 2017; Wang et al., 2018). Integrating crop wild relatives to assemble a genus‐level pan‐genome can help uncover genetic diversity that has been lost during domestication and breeding (Khan et al., 2020). However, this can result in a larger pan‐genome with a higher proportion of variable genes than typically exist in the cultivated germplasm that forms the basis for a majority of plant breeding work.

Soybean, G. max (L.) Merr, is an ancient polyploid (paleopolyploid) and multiple evolutionary (two whole‐genome duplications) and co‐evolutionary forces (domestication and extensive selective breeding) have shaped its genome (Zhou et al., 2015). Soybean is one of the first legume species with a complete genome sequence, and this has led to an unprecedented understanding of the genome organization and evolution in the Fabaceae (Schmutz et al., 2010). Over the past few years, multiple new assemblies have been released for both cultivated [Zhonghuang 13 (Shen et al., 2018) and Lee (Valliyodan et al., 2019)] and wild (Li et al., 2014a,b; Xie et al., 2019) soybeans that have opened the door to understand soybean genome organization and to accelerate breeding.

The advent of high‐throughput sequencing technologies and availability of a high‐quality reference genome has provided an exceptional opportunity to systematically detect the DNA sequence variation among soybeans (Chung et al., 2014; Fang et al., 2017; Lam et al., 2010; Maldonado dos Santos et al., 2016; Song et al., 2017; Torkamaneh et al., 2017; Torkamaneh et al., 2020; Valliyodan et al., 2016; Zhou et al., 2015). All these studies used a single reference genome (Williams 82 (Schmutz et al., 2010)) to align reads and call variants. However, any DNA sequences that were absent from the reference genome were necessarily ignored. To overcome this shortcoming, two pan‐genomes were developed for soybean. An initial pan‐genome of G. soja was constructed by de novo assembly of seven accessions and this revealed that the G. soja pan‐genome is 30.2 Mbp larger than the genome of a single accession and ˜80% of the genes were present in all seven accessions (core) (Li et al., 2014a,b). A much more exhaustive pan‐genome has been recently constructed by de novo assembly of 26 soybean accessions including 3 G. soja accessions, 9 G. max landraces, and 14 G. max cultivars. This broader work reached a very different conclusion, namely that ˜50% of genes in G. soja and G. max are dispensable (Liu et al., 2020).

Here, in contrast, we present a pan‐genome focused uniquely and specifically on cultivated soybean (PanSoy). It is constructed using the de novo genome assembly of 204 phylogenetically and geographically representative accessions of improved G. max selected from the larger GmHapMap collection (Torkamaneh et al., 2020). The objective of this study was to describe the entire gene repertoire in the cultivated soybean gene pool and thereby dispense with the added complexity of a non‐cultivated wild species.

Results

PanSoy assembly

On the basis of a cladogram of 1007 soybean accessions from the GmHapMap dataset (Torkamaneh et al., 2020), 204 clusters encompassing the diversity among this large set of improved G. max accessions were defined to guide the construction of a G. max pan‐genome (PanSoy). In soybean, genome coverage plateaus (at ˜95%) at sequencing depths ≥15× (Figure S1a). Therefore, from each cluster, a single accession with sequencing depth ≥15× and mapping depth ≥10× was selected (Table S1, Figure S1b,c). De novo assembly of short reads was performed for each accession producing a total of 71 Gb of contigs ≥500 base pairs (bp) in size, for an average N50 value of 1.5 kb (Table S2). To obtain an estimate of the size of PanSoy, a sequence‐based pan‐genome using the ‘map‐to‐pan’ strategy (Hu et al., 2017) was constructed by iteratively comparing each of the 204 sets of contigs to the soybean (cv. ‘Williams 82’) reference genome (Wm82.a4.v1) (Schmutz et al., 2010) to identify previously unknown sequences. A total of 1 485 025 unaligned contigs (ranging from 500 to 32 kb) was obtained (Figure S2a). This totalled 1.9 Gb of novel sequences (1.6 and 0.3 Gb, respectively, of fully and partially unaligned sequences) with <90% identity to the reference genome, for an average of 7.6 Mb of fully unaligned sequence (contigs ≥ 500 bp) per accession (Figure 1a). After removing redundant and putative contaminating sequences (e.g. organellar or non‐soybean DNA), 160 066 unique novel contigs (ranging from 500 to 5 kb), totalling 108 Mb, were retained and collectively represent an addition of ⁓11% to the soybean reference genome (978 Mb). The average contribution of each individual accession was ⁓680 kb (median of 504 kb) (Figure 1b). Incremental random subsets of 20 accessions contributed progressively fewer megabases of novel sequence (Figure 1c). The final random subset of 24 accessions (the two last steps in the figure) contributed only 5 Mb to the total, suggesting that the core set used here had succeeded in capturing a vast majority of novel sequences not present in the Wm82 reference genome. We did not observe any correlation between the proportion of novel sequences per accession and the depth of coverage (Pearson’s R2  = 0.005). On average, 629 K SNPs, 139 K short indels (≤5 bp), and 22 K long indels (>5 bp) per individual accession were detected relative to the Wm82 reference (Figure 1d). Finally, the extent of PanSoy sequence coverage was evaluated using a recently assembled reference genome ‘Lee’ (Valliyodan et al., 2019), a genetically distant accession compared with Wm82. Almost all Lee genome sequences (99.91%) could be mapped to the PanSoy, whereas only 92% could be mapped to Wm82. Together, these results suggest that PanSoy offers an exhaustive characterization of the G. max genome.

Figure 1.

Figure 1

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Composition of the PanSoy. (a) Distribution of fully and partially unaligned contigs among the 204 accessions of the soybean core set. (b) Contribution of each individual accession to the 108 Mb of novel sequences (average = ⁓680 kb; median = 504 kb). (c) Cumulative length of nonreference sequences based on incremental subsets (n = 20) of randomly selected accessions. Each bar represents the mean contribution of 100 random subsets. d Distribution of SNPs and indels (short ≤ 5 bp and long > 5 bp) among 204 soybean accessions of the core set compared with the Wm82 soybean reference genome.

A total of 3621 protein‐coding genes were predicted in the nonreference contigs. Of these, 1659 genes were identified as novel (<95% nucleotide identity to genes in the reference genome) and were expressed in one or more of the 101 tissues or conditions at ≥1 read per kilobase (kb) per million mapped reads (RPKM) in publicly available RNA‐sequencing (RNA‐Seq) data on 70 different G. max accessions (Table S3). In general, novel genes were shorter, owing to genes being predicted from relatively short contigs compared with the reference genome (Figure S2b). This could therefore lead to an underrepresentation of novel genes. PanSoy, including reference (Wm82; 978 Mb) and nonreference sequences (108 Mb), had a total size of 1086 Mb and 54 531 protein‐coding genes, 3% more than the reference Wm82 genome (52 872). On average, 93.2% of the 1440 single‐copy Embryophyta genes were completely assembled in the PanSoy based on the universal single‐copy orthologs (BUSCO) evaluation, showing high completeness of the gene annotation.

PAV discovery and functional characterization

A ‘map‐to‐pan’ strategy (Hu et al., 2017) was used to determine gene presence or absence (PAV) after mapping all raw DNA sequences of the 204 accessions to the PanSoy. Then, genes with gene‐body coverage of ≥0.75 and CDS coverage ≥0.95 were considered as present in the genome. A total of 54 531 genes were detected among the 204 accessions, accounting for 100% of genes in the PanSoy. A relatively finite number of genes was observed in the PanSoy (Figure 2a). The extent of the pan‐genome size was estimated by iterative (n = 100) random sampling of accessions (n from 1 to 204). The resulting graph suggests a closed pan‐genome in which the vast majority of genic PAVs has been captured in this selected subset of accessions. According to the frequencies of presence of a gene among accessions used to build the PanSoy, we found 49 431 (90.6%) hardcore genes, that is present in >99% of the 204 accessions, as well as 1401 (2.6%) ‘softcore’ (95–99%), 3402 (6.2%) ‘shell’ (5‐95%) and 297 (0.5%) ‘cloud’ genes (<5%) (Figure 2b, Table S4). The most fascinating feature of PanSoy is its extremely high core‐gene content (>93%) (Figure 2c). As a result, each accession contains between 92.8% and 98.5% of these core genes and at most 7.2% of the variable (‘shell’ and ‘cloud’) genes.

Figure 2.

Figure 2

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Genic composition of the PanSoy. (a) Capture of the pan‐ and core‐genome based on 100 randomized subsamples (with size of subsample varying from n = 1–204) of soybean accessions. The pan‐ and core‐genome curves were fitted using data points from all random subsamples and indicated by solid black lines. Upper and lower edges of the coloured areas (purple and green) correspond to the maximum and minimum numbers of genes present within a given subset of all accessions, respectively. (b) Composition of the PanSoy in terms of the subset of accessions within which a gene is found, thus defining the ‘core’ (≥95% of accessions) and ‘variable’ (<95%) sets of genes. (c) Landscape of genic PAVs. Genes are sorted by their occurrence with the highest frequency of occurrence on the left and the lowest on the right. (d) Frequency of duplicated vs. singleton genes in core vs. variable genome. e Functional classification of variable‐gene categories based on Gene Ontology (GO) term enrichment. f Proportion of TE‐related and methylated genes by gene category. mCG: genes methylated only in CG context; mC: genes methylated in all three contexts (CG/CHG/CHH).

Soybean has a highly duplicated genome (⁓75% of the genes present in multiple copies) as a result of two whole‐genome duplications (WGD) (Schmutz et al., 2010). As described by Sankoff et al. (2010), paralog reduction (e.g. deletion of duplicated genes) followed WGD. Therefore, the expectation was to observe a higher proportion of duplicated genes in the variable genome. Contrary to this expectation, the PanSoy core genome was found to be enriched in duplicated genes compared with the variable portion of the genome (P < 0.01 (Tukey's HSD test)) (Figure 2d). However, a significantly higher (P < 1.8e−13, Welch two‐sample t‐test) ratio of non‐synonymous/synonymous (dN/dS) substitutions was observed in variable genes (dN/dS = 1.63) compared with core genes (dN/dS = 1.29), suggesting that variable genes are evolving faster with a lower functional constraint.

The core genome contained highly conserved genes that are more often annotated as ensuring essential functions (using Gene Ontology, GO) (e.g. regulation of transcription), whereas this category was significantly underrepresented (adjusted P value = 3.8e−3) in the variable genome. Compared with all genes captured in PanSoy, genes belonging to the variable genome were significantly enriched (adjusted P value < 0.01) in genes annotated as being involved in biological processes such as regulation of immune and defence responses, signalling, and plant development (Figure 2e, Table S5). Therefore, it could be hypothesized that, despite their relatively small number, these variable genes could still make important contributions to phenotypic variation for agronomic traits related to defence, signalling, and development. We also found that variable genes were clustered in certain genomic regions (e.g. 33–36 Mb on chromosome 16) (Figure S3) where resistance genes are significantly enriched (GO term enrichment; corrected P = 3.3e−31). Finally, we found that the core genome is enriched in both transposable element (TE)‐related and methylated genes (pairwise proportion test, P ≤ 1.7e−8 and P ≤ 2.6e−11, respectively) (Figure 2f) which might be attributed to the higher level of duplicated genes in the core genome.

A maximum likelihood phylogenetic tree, principal component analysis, and model‐based (Bayesian) clustering based on PAVs split PanSoy accessions into two highly supported clades and clusters (Figure 3a,b, Figure S4). Although the PanSoy accessions are listed as having diverse geographical origins, no clear correlation was observed between geographical origin or maturity groups and phylogenetic topology. However, we noticed that almost all accessions from countries where soybean cultivation is relatively recent (e.g. Canada and Brazil) were grouped in clade II, while accessions from long‐established soybean‐growing countries (e.g. China, Korea, Japan and the US) were found in both clades. This grouping is largely consistent with a tree constructed using SNPs. A significant (P < 0.01) correlation was observed between distance matrices constructed using either PAVs or SNPs (Mantel test = 0.49) (Figure S4). Furthermore, the second clade, on average, contained a significantly (P < 3.1e−5, Welch two‐sample t‐test) lower gene loss (1436) than the first clade (1729) and possessed higher gene content (Figure 3c) that could be at least partially attributed to the intense selection and introgression of genes and alleles for disease resistance and abiotic stress tolerance from ancestral accessions into modern elite cultivars, especially when establishing the crop in novel areas (e.g. tropical soybean in Brazil, very early maturity in Canada).

Figure 3.

Figure 3

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PanSoy population structure. (a) Maximum likelihood phylogenetic tree based on genic PAVs for 204 PanSoy accessions. The country of origin of accessions was differently coloured. (b) PCA plot of accession membership. (c) Scatter plot showing gene occurrence frequencies in Clade‐I and Clade‐II defined by the phylogenetic tree. (d) Short and long reads from Gm_H043 and Gm_H004 mapped to the Wm82 genome in a region of chromosome 3 where Gm_H004 contains a 6.8‐kb deletion that contains an annotated gene (Glyma.03G029300). The coloured dots in Gm_H004 short reads are mismatched nucleotides.

Long‐read sequencing and validation

Finally, to validate the PAVs that were called here based on the assembly of short reads, two accessions (Gm_H043 and Gm_H004) were selected for long‐read sequencing, thus enabling the direct detection of PAVs. This sequencing yielded 16.3 and 14.5 Gb of data, representing 17× and 15× depth of coverage and a read N50 of 7 and 22 kb for Gm_H043 and Gm_H004, respectively (Figure S5). After trimming, reads were aligned against the Wm82 reference genome and a comprehensive catalogue of structural variants, including deletions and insertions, was produced (Tables S6 and S7). To estimate the sensitivity and the precision of the PAVs, at first, the intervals spanned by deletions were compared with genic intervals to identify partial or complete overlaps (Figure 3d). This analysis revealed that 94.3% of the genic PAVs called on the basis of short reads were supported by the long‐read data. Secondly, on a broader level, unaligned short reads totalling 4.7 and 5.1 Mb of gDNA sequences from Gm_H043 and Gm_H004, respectively, were successfully mapped against the sequences of insertions identified via long reads. On average, 99% of the previously unaligned sequences could be mapped in this way, indicating the high quality of identification of novel nonreference sequences.

Discussion

Despite the fact that the soybean reference genome (Wm82) is among the most complete plant reference genomes, it does not capture fully the genetic variability of this species (Liu et al., 2020; Valliyodan et al., 2019) leading to interest in producing a pan‐genome for this important crop. In doing so, it is important to define how broadly one wants to sample diversity. In many species, researchers have often chosen to capture the broader diversity within both in a crop species together with its immediate ancestral species (Gao et al., 2019; Golicz et al., 2016a,b; Gordon et al., 2017; Hurgobin et al., 2018; Liu et al., 2020; Montenegro et al., 2017; Wang et al., 2018). Because such extended pan‐genomes typically capture a much larger number of variants than are present in the cultivated species alone, these can overestimate the diversity exploited by most breeders. In contrast, a pan‐genome consisting of cultivated accessions from a single species, such as PanSoy, aims to more closely reflect the diversity in cultivated soybean.

In soybean, both the wild progenitor (G. soja) and the cultivated species (G. max) belong to the same genus (Hymowitz and Newell, 1981). Recently, Liu et al. (2020) have developed what is likely the most exhaustive extended plant pan‐genome to date through re‐sequencing of close to 3000 accessions and de novo assembly of a representative subset of 26 selected wild, landrace, and cultivated soybean accessions based on a rich assortment of data (deep coverage with both short and long reads, optical mapping and Hi‐C data). In this recent work, more than twice the number of SNPs were identified compared with the number previously documented in G. max alone (31.9 vs ˜15 M SNPs; Fang et al., 2017; Torkamaneh et al., 2020; Zhou et al., 2015). Importantly, these authors reported that only 50% of soybean genes were present in a large majority (>90%) of the 26 accessions, suggesting that the other half is quite variable in its presence among accessions, and close to 20% of genes in individual cultivated accessions were labelled as dispensable. This is in stark contrast to the results obtained in this work, where >94% of genes are shared by >90% of G. max accessions. Based on the publicly available data from the study of Liu et al. (2020), who do not distinguish between G. soja and G. max accessions, it is difficult to directly compare these two contrasting assessments of the degree of consistency in gene content among G. max accessions. Nonetheless, based on their definition of ‘dispensable genes’ (present in a minimum of 2 and maximum of 24 accessions), it is conceivable that a large set of these ‘dispensable genes’ are labelled as such due to variability in their presence within the three G. soja accessions. Any gene shared among all G. max accessions but absent from at least one G. soja accession would be labelled ‘dispensable’. The relative richness of the three G. soja accessions for different forms of genetic polymorphism was amply documented in this study. Wild soybeans were found to be significantly enriched (3.3×) in private (i.e. sample‐specific) structural variants compared with cultivated soybeans (average of 22.2% vs. 6.7%, respectively) (Liu et al., 2020). Similarly, in examining the file summarizing SNP and indel variation, we find that over 50% of the indels observed within the 26 accessions used to construct the pan‐genome were found only within the subset of G. soja accessions. These data from Liu et al. (2020) suggest that the small set of three G. soja accessions were potentially the source of a disproportionate amount of the variation found and, conversely, that there is likely a greater amount of overlap in the gene sets among G. max accessions.

The primary goal of the present study was to accurately estimate the size of the core‐ and pan‐genome in a set of representative cultivated G. max accessions. Constructing a pan‐genome including wild relatives of the cultivated crops, commonly different species under the same genus, includes a sizeable genetic diversity that has been lost during domestication and selective breeding. These pan‐genomes that capture the complete gene repertoire at the genus level are called super pan‐genomes (Khan et al., 2020). They undoubtedly provide important insights into evolutionary aspects of the organization the plants’ genome but do not necessarily reflect the diversity used by breeding programmes, as these mostly rely on elite accessions. Wild relatives present challenges in cultivar development as it is difficult to harness desirable genes by genetic recombination, and moreover, the concomitant introgression of undesirable genes from the wild parent can result in an inferior phenotype (Shakiba and Eizenga, 2014). Therefore, investigating genetic variability within the elite germplasm has broader applicability and higher interest for plant breeders.

The exceptionally high level of core‐gene content found in PanSoy, compared with other plant pan‐genomes (e.g. Brassica oleracea (81%) (Golicz et al., 2016a,b); Solanum lycopersicum L. (74.2%) (Gao et al., 2019); Arabidopsis thaliana (70%) (Contreras‐Moreira et al., 2017); T. aestivum (64%) (Montenegro et al., 2017); Brassica napus (62%) (Hurgobin et al., 2018); O. sativa L. (54%) (Wang et al., 2018)), is a striking feature but might be due the fact that PanSoy was constructed from improved accessions of G. max alone, while the recent pan‐genome of soybean (Liu et al., 2020) and many other plants has often been constructed using mixed collections of wild, landrace and elite accessions (Gordon et al., 2017; Tao et al., 2019a,b), the former likely sharing fewer core genes with elite accessions. Interestingly, in tomato, a comparable species (e.g. genome size, rate of nucleotide variation, ploidy level, and outcrossing rate), the pan‐genome formed by domesticated elite accessions contained ˜90% of core genes (Gao et al., 2019), a result quite similar to our findings. In another major diploid crop species, rice, a lower core‐gene content (˜83%) was observed among cultivated elite accessions (Wang et al., 2018). This difference might be explained, however, by the fact that rice is divided into highly diverged groups of accessions (e.g. Xian/Indica and Geng/Japonica), reflecting a very different history of domestication and adaptation to broad geographic areas. In contrast, in many important soybean‐growing areas, modern‐day cultivars are known to have been derived from a relatively small number of founder lines (Hyten et al., 2006). Similar to the wild and cultivated soybean pan‐genome (Liu et al., 2020), a closed pan‐genome and an open core‐genome were observed in this study. This means that the pan‐genome size has reached a plateau, but that the core‐genome continues to decrease with every new genome included.

Gene Ontology (GO) enrichment analyses showed that the variable genes in PanSoy were significantly enriched in genes annotated as being involved in the regulation of immune and defence responses, signalling, and plant development. This is entirely consistent with previous findings in soybean (Li et al., 2014a,b; Liu et al., 2020) and other crops (Gao et al., 2019; Gordon et al., 2017; Wang et al., 2018). The enrichment of genes involved in specific pathways or processes, such as defence and signalling, and their high evolutionary rates are consistent with a scenario in which variable genes evolve rapidly and are more likely to play important roles in phenotypic variation of agronomic traits in cultivated soybeans.

Similar to whole‐genome sequencing analysis, the exhaustiveness of PAV discovery in pan‐genome analysis is largely affected by the joint effect of sample size and sequencing depth. Early pan‐genome studies were based on deep sequencing (˜100×) and de novo assembly of relatively small numbers of individuals, 3–50 (Gan et al., 2011; Golicz et al., 2016a,b; Gordon et al., 2017; Li et al., 2014a,b; Schatz et al., 2014; Song et al., 2020). In such cases, the accurate estimation of pan‐ and core‐genome size is challenging due to the small sample size and the effects of phylogenetic distribution and population structure (Gordon et al., 2017). These challenges can be overcome by increasing sample size but, despite the astounding reductions in the cost of DNA sequencing over the past decade, it can nonetheless be costly when large numbers of samples need to be deeply sequenced. More recently, pan‐genome studies using large numbers of samples with a medium sequencing depth (10–20×) have shown the statistical power commensurate to that of deep sequencing (Gao et al., 2019; Wang et al., 2018). Again, similar to what has been observed in whole‐genome sequencing analysis, the lower detection power in individual accessions can be compensated by increasing sample size (Wang et al., 2016). Powered by a large sample size (204), we find that the total length of unique nonreference sequence reached a relative plateau suggesting that diversity within cultivated soybean had been well captured. Furthermore, the PanSoy collection has been selected as a representative core set from the larger GmHapMap collection (Torkamaneh et al., 2020) to maximize the sampled genetic diversity among improved G. max. Lastly, to examine the extensiveness of the PanSoy, we assessed the extent of PanSoy sequence coverage using a recently assembled reference genome.

A final key feature of this work is the quality of the PanSoy. We first assessed the quality of PanSoy with conducting an extensive cross‐validation of PAVs via read mapping. We then evaluated the accuracy of PAVs via direct comparison with structural variants obtained from the long‐read sequencing. Together, these provide strong evidence that the PanSoy is of high quality. In conclusion, PanSoy sheds new light on the intraspecific variation in G. max and provides a platform to extensively explore, evaluate and characterize genetic diversity and evolution of cultivated soybean via investigation of its entire genomic repertoire.

Methods

Selection of the GmHapMap core collection

To extract a representative high‐quality core set among the 1007 accessions of GmHapMap (Torkamaneh et al., 2020), PLINK (Purcell et al., 2007) and the full set of nucleotide variants (⁓15 M) were used. Using the Clustering method (‐‐cluster), the complete collection was divided into 204 clusters (‐‐K 204). A single accession from each cluster was selected on the basis of sequencing depth (in all cases ≥15×; Table S1). The accessions within this core collection originate from 24 countries and five continents (Table S1). The raw DNA sequences of each accession were trimmed by Trimmomatic (v.0.36) (Bolger et al., 2014) with following parameters ‘ILLUMINACLIP:2:30:10 LEADING:20 TRAILING:20 SLIDINGWINDOW:4:20 MINLEN:36’ before being used for de novo genome assembly.

De novo assembly

To select the best k‐mer value, the genomes of a subset of soybean accessions (n = 5) were assembled using SOAPdenovo2 (v.240) (Luo et al., 2012) using different k‐mer values based on a linear model ‘K = 2 * int (0.38 * (sequencing depth) + 10) + 1’. Then the best k‐mer value (K = 33) was identified based on the N50 of the resulting genome assemblies. The de novo assembly of 204 genomes was performed in parallel on a Linux system with Slurm Workload Manager using following command line for SOAPdenovo2 SOAPdenovo‐63mer all ‐s configFile (with average insertion length of avg_ins=350) ‐o output ‐K 33 ‐R ‐F.

Construction of the PanSoy

The PanSoy was constructed using the EUPAN pipeline (Hu et al., 2017). In brief, the quality of each genome assembly was evaluated with QUAST (v. 2.357) (Gurevich et al., 2013) using the latest version of the Wm82 soybean reference genome (Wm82.a4.v1) (Schmutz et al., 2010). Then, unaligned contigs of at least 500 bp were extracted from the QUAST output and merged. This includes fully unaligned sequences (contigs with no alignment to the reference sequence) and partially unaligned sequences (contigs with at least one alignment and at least one unaligned fragment longer than 450 bp). Redundant sequences (>95% identity) were removed using CD‐HIT (v. 4.6.163) (Li and Godzik, 2006) with ‐c 0.9 ‐T 16 ‐M 50000 command line and a BLASTN ‘all‐versus‐all’ alignment approach. The unique nonreference contigs were mapped using BLASTN to the NT database (NCBI, November 2019) with parameters ‐evalue 1e‐5 ‐best_hit_overhang 0.25 ‐perc_identity 0.5 ‐max_target_seqs 10. Using NCBI taxa identifiers and based on E‐values, contigs with the best alignment to the organellar genomes (chloroplast and mitochondria), microorganisms (e.g. bacteria, fungi, etc.), and non‐Viridiplantae (e.g. human and animals) were removed as possible contaminants. Finally, the PanSoy sequence was constructed by combining sequences of the Wm82 reference genome and the novel non‐redundant sequences.

Annotation of the PanSoy

The GTF‐formatted gene and transcript annotation of the Wm82.a4.v1 reference genome was downloaded from Phythozome (https://phytozome.jgi.doe.gov/; Goodstein et al., 2012). A combination of three different approaches (ab initio predictions, expression evidence, and protein homologies) in MAKER (release 2.31.8) (Cantarel et al., 2008) was used to predict protein‐coding genes from novel nonreference sequences. Briefly, both RepeatMasker (http://www.repeatmasker.org/species/hg.html) and RepeatRunner (Duan et al., 2019) were used to mask the repeats. Then, SNAP (Johnson et al., 2008) and AUGUSTUS (Nachtweide and Stanke, 2019) were used for ab initio prediction with default parameters. Soybean expressed sequence tags (ESTs) and proteins were downloaded from GenBank and NCBI (January 2020) and aligned against novel non‐redundant nonreference sequences with BLASTN and BLASTX, respectively. Exonerate (Slater and Birney, 2005) and EVidenceModeler (Haas et al., 2008) were used to realign and combine ab initio predictions with RNA and protein evidence. Finally, to identify novel genes, redundant genes (≥95% similar to the genes in Wm82) were removed.

Evaluation of the PanSoy

To assess the quality of the PanSoy sequence and annotation, BUSCO (Benchmarking Universal Single‐Copy Orthologues) (v.2.032) (Simao et al., 2015) was used to evaluate the newly assembled soybean reference genome ‘Lee’ and the PanSoy sequences. Then, RNA‐sequencing data sets from 101 different experiments on 70 different G. max accessions were downloaded from the NCBI SRA database (Table S3). All raw reads were combined in a single FASTQ file and aligned to the novel nonreference sequences using STAR (v.2.5.3a) (Dobin et al., 2013) with default parameters.

Determination of core and variable genes

Using a ‘map‐to‐pan’ strategy (Hu et al., 2017), presence and absence variants (PAVs) among genic regions were identified by alignment of all raw DNA sequences of 204 accessions to the PanSoy using BWA (Li and Durbin, 2009). Then, gene‐body coverage and CDS coverage were estimated using BEDtools (v. 2.17.075) (Quinlan, 2014). Genes with CDS coverage of ≥0.95 and gene‐body coverage of ≥0.75 were considered present. The frequency of PAVs was estimated and genes were classified into two major categories: ‘core’ (loss rate < 0.05) and ‘variable’ genes (loss rate ≥ 0.05), and four subcategories: ‘hardcore’, ‘softcore’, ‘shell’, and ‘cloud’ genes, that is present in >99%, >95–99%, 5‐95%, and <5% of the 204 accessions, respectively. The heatmap distribution of core and variable genes was plotted with the heatmap function in R. To estimate the size of the pan‐genome and core‐genome, accessions from PanSoy were randomly sampled (n = 1–204) with 100 iterations and plotted with ggplot2 package in R (R Core Team, 2019).

Functional analysis

The impact of variants was determined using SnpEff (Cingolani et al., 2012). The analysis of GO terms was performed using all PanSoy genes with the GO Term Enrichment Tool integrated in SoyBase (Grant et al., 2010) using Wm82 as the background. The list of duplicated genes, TE‐related genes, and methylated genes (mC and mCG) was extracted from Xu et al. (2018).

Population structure

The population structure analysis was performed using PAVs. Principal component analysis and kinship were computed in TASSEL (v.5.1) (Glaubitz et al., 2014) and visualized in R (R Core Team, 2019). Model‐based clustering of the 204 accessions was computed using a variational Bayesian inference implemented in fastSTRUCTURE (v1.0) (Raj et al., 2014). Five runs were performed for each number of populations (K) from 1 to 10. Then, the final matrix of admixture proportions (Q matrix) of the best‐fit K value (K = 2) was used to visualize population structure and admixture. Maximum Likelihood (ML) phylogenetic analysis was performed in MEGA (v.7) (Kumar et al., 2016) with bootstrap test (1000 replicates) to cluster taxa. Finally, the branches of the tree were aligned with the admixture classification.

Long‐read sequencing using Oxford Nanopore MinION

Two soybean accessions (Gm_H043 and Gm_H004) were selected for long‐read sequencing using the Oxford Nanopore MinION technology. The DNA extraction for each accession was performed using different experimental procedures. (i) The DNA of Gm_H043 was extracted from trifoliate leaves harvested 3 weeks after germination and growth in a greenhouse. Leaves were flash‐frozen in liquid nitrogen upon harvest and stored in a −80°C freezer until DNA extraction. DNA was extracted using the Qiagen Gentra Puregene kit following grinding of leaves frozen in liquid nitrogen in a mortar and pestle. The extracted DNA was used for Oxford Nanopore library preparation without further processing. (ii) The DNA of Gm_H004 was extracted from trifoliate leaves harvested 2 weeks after germination and growth in the lab. Leaves were flash‐frozen in liquid nitrogen upon harvest and stored in a −80°C freezer until DNA extraction. DNA was extracted using a CTAB‐based protocol following grinding of the leaves using a Qiagen TissueLyser instrument. The CTAB protocol consisted of a 45‐min incubation in a CTAB lysis buffer to which RNase A was added, a cleaning step using a 24:1 chloroform:isoamyl alcohol mix, a precipitation using isopropanol, and two 70% ethanol washing steps. Approximately 9.5 µg of the extracted DNA were size‐selected on a BluePippin High‐Pass Plus cassette using a 15‐kb threshold. The DNA recovered in two fractions (original fraction + 0.1% Tween 20 fraction) was purified using SparQ PureMag beads. Finally, a genomic DNA library for each accession was constructed using the SQK‐LSK109 library preparation kit following the manufacturer's instructions. A total of ˜250 ng (Gm_H043) and ˜120 ng (Gm_H004) of the constructed library were loaded each on a single flowcell and run in a MinION.

Bioinformatic processing of Oxford Nanopore data

Base calling was performed on the raw FAST5 data using Guppy (v. 3.2.4) (Wick et al., 2019) on a GPU. Adapters were trimmed using Porechop (v. 0.2.4) (https://github.com/rrwick/Porechop). Reads were aligned against the Wm82 reference genome (including genomic, chloroplastic, and mitochondrial genomes) using NGMLR (v. 0.2.7) (Sedlazeck et al., 2018) with default settings. Calling of structural variation was performed with Sniffles (v. 1.0.11) (Sedlazeck et al., 2018) using default settings except for the minimum number of supporting reads and minimum length of reads which were set to 3 and 1 kb, respectively. Finally, an indexed FASTA file was created by the sequences of the insertions identified via long reads. Then, unaligned short reads from Gm_H043 and Gm_H004 were mapped against this FASTA file using BWA (Li et al., 2009). The number of mapped and unmapped reads was calculated using SAMtools (Li et al., 2009) from BAM files.

Conflicts of interest

The authors declare that they have no competing interests.

Author contributions

DT and FB conceived the project. DT carried out the development of PanSoy. MAL performed Oxford Nanopore long‐read sequencing and SV calling. DT and FB contributed to writing the manuscript.

Supporting information

Figure S1 Composition of the GmHapMap core collection.

Figure S2 Assembly of PanSoy.

Figure S3 Number of variable genes within 1‐Mb sliding windows across the soybean genome.

Figure S4 Population structure analysis of PanSoy.

Figure S5 Oxford Nanopore sequencing of Gm_H043 (left) and Gm_H004 (right).

Click here for additional data file. (1.4MB, docx)

Table S1 List of PanSoy accessions.

Table S2 Details of de novo assembly of PanSoy accessions.

Table S3 List of RNA‐seq data sets downloaded from NCBI and used in this study.

Table S4 List of gene PAVs (1 = present & 0 = absent) in PanSoy.

Table S5 Description of GO analysis results of variable genes.

Table S6 List of structural variants identified in Gm_H043 using Oxford Nanopre MinION sequencing data.

Table S7 List of structural variants identified in Gm_H004 using Oxford Nanopre MinION sequencing data.

Click here for additional data file. (42.5MB, xlsx)

Acknowledgements

This work was supported by the SoyaGen grant (www.soyagen.ca) funded by Genome Canada [#5801 to F.B.]. The authors wish to thank Génome Québec, Genome Canada, the government of Canada, the Ministère de l’Économie, Science et Innovation du Québec, Semences Prograin Inc., Syngenta Canada Inc., Sevita Genetics, Coop Fédérée, Grain Farmers of Ontario, Saskatchewan Pulse Growers, Manitoba Pulse & Soybean Growers, the Canadian Field Crop Research Alliance and Producteurs de grains du Québec. M‐A. Lemay is supported by scholarships from the NSERC Vanier program, the FRQNT B2X program, and the NSERC CREATE AgroPhytoSciences program. We also thank SoyBase team (Dr. Steven Cannon, Dr. Rex Nelson, and Dr. Anne Brwon) for integrating PanSoy data sets in SoyBase.

Torkamaneh, D., Lemay, M.‐A.and Belzile, F. (2021) The pan‐genome of the cultivated soybean (PanSoy) reveals an extraordinarily conserved gene content. Plant Biotechnol J, 10.1111/pbi.13600

Data Availability Statement

The PanSoy data sets including PanSoy sequences and annotation, novel sequences, and a matrix of genic PAVs are publicly available at SoyBase (https://soybase.org/projects/SoyBase.C2021.01.php). The PanSoy data have also been added to the SoyBase Blast engine search (https://soybase.org/sequenceserver/).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1 Composition of the GmHapMap core collection.

Figure S2 Assembly of PanSoy.

Figure S3 Number of variable genes within 1‐Mb sliding windows across the soybean genome.

Figure S4 Population structure analysis of PanSoy.

Figure S5 Oxford Nanopore sequencing of Gm_H043 (left) and Gm_H004 (right).

Click here for additional data file. (1.4MB, docx)

Table S1 List of PanSoy accessions.

Table S2 Details of de novo assembly of PanSoy accessions.

Table S3 List of RNA‐seq data sets downloaded from NCBI and used in this study.

Table S4 List of gene PAVs (1 = present & 0 = absent) in PanSoy.

Table S5 Description of GO analysis results of variable genes.

Table S6 List of structural variants identified in Gm_H043 using Oxford Nanopre MinION sequencing data.

Table S7 List of structural variants identified in Gm_H004 using Oxford Nanopre MinION sequencing data.

Click here for additional data file. (42.5MB, xlsx)

Data Availability Statement

The PanSoy data sets including PanSoy sequences and annotation, novel sequences, and a matrix of genic PAVs are publicly available at SoyBase (https://soybase.org/projects/SoyBase.C2021.01.php). The PanSoy data have also been added to the SoyBase Blast engine search (https://soybase.org/sequenceserver/).

Articles from Plant Biotechnology Journal are provided here courtesy of Wiley

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