Investigation of Genetic Relationships within Miscanthus using SNP Markers Identified using SLAF-Seq

doi:10.21203/rs.3.rs-152687/v1

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Investigation of Genetic Relationships within Miscanthus using SNP Markers Identified using SLAF-Seq

https://doi.org/10.21203/rs.3.rs-152687/v1

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Background: Miscanthus, which is a leading dedicated-energy grass in Europe and in parts of Asia, is expected to play a key role in the development of the future bioeconomy. However, due to its complex genetic background, it is difficult to investigate phylogenetic relationships and the evolution of gene function in this genus. Here, we investigated 50 Miscanthus germplasms: 1 female parent (M. lutarioriparius), 30 candidate male parents (M. lutarioriparius, M. sinensis, and M. sacchariflorus), and 19 offspring. We used high-throughput Specific-Locus Amplified Fragment sequencing (SLAF-seq) to identify informative single nucleotide polymorphisms (SNPs) in all germplasms.

Results: We identified 800,081 SLAF tags, of which 160,368 were polymorphic. Each tag was 264–364 bp long. The obtained SNPs were used to investigate genetic relationships within Miscanthus. We constructed a phylogenetic tree of the 50 germplasms using the obtained SNPs, and found that the germplasms fell into two clades: one clade of M. sinensis only and one clade that included the offspring, M. lutarioriparius, and M. sacchariflorus. Genetic cluster analysis indicated that M. lutarioriparius germplasm C3 was the most likely male parent of the offspring.

Conclusions: As a high-throughput sequencing method, SLAF-seq can be used to identify informative SNPs in Miscanthus germplasms and to rapidly characterize genetic relationships within this genus. Our results will support the development of breeding programs utilizing Miscanthus cultivars with elite biomass- or fiber-production potential.

Epigenetics & Genomics

Miscanthus

SLAF-seq

SNP

SLAF tags

high-throughput sequencing

identification of genetic relationship.

Miscanthus, which is a high-biomass-yielding perennial C4 grass, has emerged as a candidate second-generation energy crop with great potential utility [1–4]. Miscanthus is a heterogeneous gramineous plant that hybridizes interspecifically, generating a complex genetic background. Despite increasing interest, molecular research into Miscanthus has been scant, due to its large genome (approximately 2.65 Gb) [5], complex genetic background, and limited available sequence data. Because inter- and intraspecific hybridization is common in Miscanthus, this genus is characterized by rich genetic diversity, and hybrid offspring often have high biomass [3]. However, this high degree of genetic diversity also increases the complexity of interspecific relationships in the Miscanthus, and, consequently, the difficulty of genetic evolution analyses in this genus. Therefore, it is difficult to mine functional genes in the Miscanthus, which seriously affects the utility of Miscanthus species for energy production and conversion [6]. Molecular markers would be useful for further investigations of Miscanthus plants; such markers have been widely used in studies of genetics, molecular population genetics, species formation, evolutionary and phylogenetic relationships, and molecular taxonomy [7].

First generation molecular markers include restriction fragment length polymorphisms (RFLPs) [8, 9], random amplified polymorphic DNA (RAPD) [10, 11], and amplified fragment length polymorphisms (AFLPs) [12], while second generation molecular markers include simple sequence repeats (SSRs) [13] and inter-simple sequence repeats (ISSRs) [14]. However, these markers have several limitations: they are low throughput, inaccurate, time-consuming, labor-intensive, and costly [3]. These drawbacks have motivated the development of third-generation molecular markers. Third-generation molecular markers are SNPs. These markers are polymorphic and a generally widely distributed throughout the whole genome [15]. SNP markers are amenable to large-scale automated monitoring and have been instrumental in various techniques associated with crop breeding, such as the construction of genetic maps, the DNA fingerprinting of germplasm resources, the detection of molecular biodiversity, and the analysis of linkage disequilibrium [16]. This continuous development of molecular marker technology has accelerated functional gene identification and characterization in other crops, and has led to the development of varieties with improved functional traits [6]. Thus, these techniques might be useful for molecular genetic research in Miscanthus.

Although genotyping-by-sequencing (GBS) and restriction site-associated DNA sequencing (RAD-seq) have been used extensively in Miscanthus, there are still some difficulties and challenges associated with the application of these techniques, especially to non-model taxa like Miscanthus [17]. One obstacle to the widespread use of GBS is the difficulty of the associated bioinformatics analysis, which is typically hampered by a large number of erroneous SNP interferences that are not easy to diagnose or correct [18].

To overcome these challenges, we aimed to develop and identify SNP markers for Miscanthus using SLAF-seq techniques. That is, we aimed to reduce the genomic complexity using specific digestion, develop markers via the high-throughput sequencing of representative libraries, and determine phylogenetic relationships using genotyping.

SLAF-seq uses bioinformatics methods to systematically analyze known genome sequences, genome sequences of related species, bacterial artificial chromosome (BAC) sequences, or Fosmid sequences [19–23]. SLAF-seq techniques differ in several ways from GBS or RAD-seq techniques. First, there are many more SLAF tags, with SLAF-seq identifying one tag about every 10 K; second, SLAF tags are uniformly distributed, so important chromosome segments are not missed; and third, SLAF-seq methods avoid repetitive sequences, thus improving sequencing cost-effectiveness. As such, SLAF-seq utilizes deep sequencing to ensure genotyping accuracy; a reduced representation strategy to reduce sequencing costs; a pre-designed representation scheme to optimize marker efficiency; and a double-barcode system for large populations [24].

SLAF-seq has been widely used for the development of specific molecular markers and genetic maps [25]. For example, Sun et al. used 50,530 SLAFs with 13,291 SNPs to genotype the F1 population of the common carp [24]. Due to its efficient identification of SNP markers, SLAF-seq has been used in a wide variety of crops [26–30]. SLAF-seq was also used to develop the first high-density genetic maps for several economically important species, including sesame [31], cucumber [23], the brown alga Undaria pinnatifida (Phaeophyceae) [32], wax gourd [33], watermelon (Citrullus lanatus L.) [34], and Salvia miltiorrhiza [25]. In addition, an increasing number of studies of the Gramineae have been performed using SLAF-seq [21, 22, 35, 36]. For example, SLAF-seq was used to develop the first 7E-chromosome-specific molecular markers for Thinopyrum elongatum [35], while 5,142 polymorphic SLAFs were analyzed to identify a new maize inflorescence meristem mutant [36]. Zhang et al. used 69,325 high-quality SLAFs, of which 26,248 were polymorphic, to develop sufficient markers for a segregating Agropyron F1 population [28]. Furthermore, a high-density genetic linkage map for orchard grass was developed using 2,467 SLAF markers and 43 SSR markers [37], and the semi-dwarf gene in barley was fine-mapped using molecular markers developed with SLAF-seq [21]. The successful application of SLAF-seq in other species provides reference materials for the development of SNPs in this study.

However, SLAF-seq has yet to be used to develop SNP markers in Miscanthus. Therefore, we aimed to use SLAF-seq to determine the genetic relationships among Miscanthus species, as well as to develop SNP markers. These results provide useful data for molecular marker-assisted Miscanthus breeding programs.

Evaluation of the digestive enzymes

Based on the S. bicolor reference genome, we predicted that EcoRV + ScaI was a suitable restriction enzyme combination for the digestion of the Miscanthus genome. The pair-end digestion efficiency of EcoRV + ScaI for the control genome (Nipponbare) was 90.87%, and 97.80% of control reads lacked a complete restriction endonuclease recognition sequence, indicating that this enzyme combination was suitable. After EcoRV + ScaI digestion, we predicted 52,287 SLAF tags in the sorghum genome.

Analysis of the SLAF-seq data

We generated 57.81 M clean sequence reads after SLAF library construction and high-throughput sequencing (Supplement Table 1). The average GC content across all sequences was 41.39%. Across all sequences, the average number of bases with a quality score ≥ 30 (Q30) was 93.66%, indicating that 93.66% of all bases had a 0.1% chance of error (i.e., 99.9% confidence).

We developed 469,509 SNPs and 800,081 SLAF tags; 160,368 of the SLAF tags were polymorphic (Supplement Table 2). The average sequencing depth per sample was 11.76x for the female parent (sample A12), 15.47x for the male parents (samples A1–A11, B1–B10, and C1–C9), and 7.85x for the progeny (samples D1–D19). Overall, we obtained 469,509 SNPs (Supplement Table 3).

Genetic distance analysis

In total, 11,244 highly consistent population SNPs with integrity > 0.8 and minor allele frequency (MAF) > 0.05 were identified across all samples. The clustering patterns of the 50 Miscanthus germplasms were optimal, and cross-validation error was lowest when the number of clusters (ΔK) was four: M. sinensis (A1–A11) fell into group 1; some offspring (D8, D9, D11–D13, D15, and D17–D19) and C3 (M. lutarioriparius) fell into group 2; M. lutarioriparius (C1, C2, and C4–C9) and M. sacchariflorus (B1–B10) fell into group 3; and the remaining offspring (D1–D7, D10, D14, and D16) and the female parent A12 (M. lutarioriparius) fell into group 4 (Table 1).

Table 1

The clusters associated with the 50 samples.
Sample ID	Group number	Sample ID	Group number
A2	1	C5	3
A3	1	C6	3
A4	1	C7	3
A5	1	C8	3
A6	1	C9	3
A7	1	D1	4
A8	1	D2	4
A9	1	D3	4
A10	1	D4	4
A11	1	D5	4
A12	4	D6	4
B1	3	D7	4
B3	3	D9	2
B4	3	D10	4
B5	3	D11	2
B6	3	D12	2
B7	3	D13	2
B8	3	D14	4
B9	3	D15	2
B10	3	D16	4
C1	3	D17	2
C2	3	D18	2
C3	2	D19	2
Sample ID: project sample number; Group Number: the group into which the sample was clustered.

PCA analysis indicated that the M. sinensis germplasms were distinct from all other groups, while the offspring, the female parent (M. lutarioriparius), and sample C3 (M. lutarioriparius) formed a loose cluster (Fig. 1). The remaining M. lutarioriparius and most of the M. sacchariflorus formed a cluster, as did two M. sacchariflorus (Fig. 1).

The phylogenetic analysis indicated that the samples fell into two large clades: one that included all samples for the candidate male parent M. sinensis; and one that included all other samples (female M. lutarioriparius, male M. sacchariflorus, male M. lutarioriparius, and all hybrids). In the second large clade, the hybrids were divided into two groups: one included D1–D7, D10, D14, and D16, while the other included D8, D9, D11–D13, D15, and D17–D19 (Fig. 2). Female parent A12 fell into the former group, while candidate male parent C3 fell into the latter. These results were consistent with our clustering analysis (Table 1), suggesting that the recovered genetic distances accurately reflected the genetic relationships among the four clusters.

Our combined phylogenetic analysis also indicated that C3 was the most likely male parent of the crossbred offspring, because this sample had the fewest abnormal offspring (Table 2).

Table 2

Phylogenetic relationships between candidate male parents and offspring.
Candidate male parent	Number of abnormal offspring	Candidate male parent	Number of abnormal offspring
B1	9	C1	11
B2	7	C2	6
B3	9	C3	0
B4	9	C4	11
B5	11	C5	10
B6	9	C6	9
B7	10	C7	11
B8	6	C8	9
B9	9	C9	14
B10	6

Analysis of the SLAF-seq data

Genetic improvements in biomass quality may be substantially accelerated by the development of genetic markers associated with quality traits [38]. SNPs, which are more abundant in the genome than any other molecular markers, are particularly useful for analyses of genetic diversity and population structure [39]. In this study, we used SLAF-seq to efficiently identify SNP markers. Compared with other methods, such as GBS and RAD-seq, SLAF-seq is more accurate, faster, and less expensive; SLAF-seq also reduces genome complexity [40]. Here, we obtained 800,081 SLAF tags and 469,509 SNPs. This is greater than the number of SNPs previously obtained in the Miscanthus genome using RAD-seq [41]. These polymorphic molecular markers are highly discriminatory, and can be used for genetic map construction and gene mapping in Miscanthus.

Genetic Relationships Analysis

The heterozygosity and polyploidy that have accumulated in Miscanthus genomes over their long evolutionary history challenge efforts to sequence complete genomes in this genus. Therefore, it is difficult to determine genetic relationships within Miscanthus.

Some studies have investigated phylogenetic relationships and species evolution using SNP markers [42]. These previous studies uncovered multiple instances of domestication behaviors or gene transfer between cultivated species and wild species [43]. To obtain comprehensive information about genetic variation, SNP markers must be developed for the whole genome. These developed SNP markers may clarify unresolved phylogenetic relationships, especially among closely related species [44]. Specifically, the SNP markers obtained in this study may help to resolve the phylogenetic relationships among similar species of Miscanthus, such as M. lutarioriparius and M. sacchariflorus (Fig. 1, Fig. 2).

We developed 469,509 SNPs using SLAF-seq technology. Among these, 11,244 highly consistent population SNPs, with integrity > 0.8 and MAF > 0.05, were identified across all samples. We successfully identified the paternal parent and obtained an intraspecific hybrid polyploid population of M. lutarioriparius. Using the molecular markers obtained for this population in this study, high-density molecular marker linkage maps could be constructed, and QTL mapping analyses could be performed to investigate the genes controlling quantitative traits (such as biomass yield and cellulose content) in detail. These results support genetic improvements in biomass yield and fiber quality in Miscanthus species.

Plant materials

Fifty Miscanthus specimens: the maternal plant Miscanthus lutarioriparius (A12; tetraploid, 2n = 4x), the paternal candidate M. sinensis (A1–A11; diploid, 2n = 2x), the paternal candidate M. saccariflorus (B1–B10; diploid, 2n = 2x), the paternal candidate M. lutarioriparius (C1–C9; diploid, 2n = 2x), and 19 progeny (D1–D19; triploid, 2n = 3x). The 19 progeny were generated via open pollination from A12. A12 was bagged during flowering to test its self-pollination seed-setting rate. The measured self-pollinating seed-setting rate was 0, indicating that A12 was highly self-incompatible. Other Miscanthus genotypes that flowered simultaneously with A12 in the Germplasm Nursery were selected as paternal candidates. The maternal and paternal candidate materials were collected from across China (Fig. 3). All the plants were grown in the Miscanthus Germplasm Nursery at Hunan Agricultural University, Changsha, Hunan, China (latitude 28°11', longitude 113°04').

DNA extraction

We used fresh leaf material obtained from 50 Miscanthus individuals. Fresh leaves were frozen in liquid nitrogen and ground manually into a fine powder. Total genomic DNA was extracted following a modified cetyltrimethyl ammonium bromide (CTAB) method [45]. Because the leaves of Miscanthus are rich in phenols, the CTAB extraction solution was supplemented with 2% poly-N-vinylpyrrolidone (PVP) and 1% β-mercaptoethanol to purify the Miscanthus DNA. The concentration and quality of the extracted DNA were detected using 0.8% agarose gel electrophoresis and an ND-1000 spectrophotometer (Nano Drop), respectively.

Enzyme digestion design

To identify the most appropriate enzymes for genomic digestion, we selected the Sorghum bicolor genome as the reference genome (http://phytozome.jgi. doe.gov/pz/portal.html#!info?alias = Org_Sbicolor). This genome was selected because a recent study suggested that S. bicolor and Miscanthus were closely related [46]. In addition, the two genomes were of similar size and had similar GC levels [47, 48]. We then predicted suitable restriction enzyme combinations for the digestion of the Miscanthus genome, based on the S. bicolor genome, using DNassist [49]. To assess the efficiency of the predicted enzymes, the genome of Japanese rice (Oryza sativa L. subsp. japonica) was selected as a control.

SLAF library construction and high-throughput sequencing

Using the identified restriction enzyme combination (EcoRV + ScaI), the total genomic DNA of each sample was digested. After adding an A-tail to the 3' end of each digested fragment and ligating the fragment to the Dual-index sequencing adapter [50], each fragment was PCR amplified. The amplicons were purified, mixed, and cut. The digested fragments were chosen as target segments. The libraries were selected and sequenced using an Illumina HiSeq TM 2500 platform (Biomarker Technologies Corporation), with a read length of 2 ×100 bp. To assess the accuracy of SLAF library construction, the Japanese rice genome was again used as a control.

Development of SLAF tags and SNP markers

The Dual-index tags were used to classify the raw sequencing data by sample. Sequences read from the same locus were grouped using similarity clustering [51]. In general, only high-depth fragments were selected in each cluster group; low-depth segments were removed. Here, we first calculated the SLAF tags for each sample independently, and then all single-sample SLAF tags were clustered to derive population-wide SLAF tags. The SLAF tags with the greatest sequencing depths across all samples were selected as reference sequences for the development of SNP markers. Each sequence was aligned with the reference sequence using bwa [52]. We used GATK [53] and SAMtools [54] to identify SNPs. The SNPs identified by both methods were considered reliable. Of these reliable SNPs, those with integrity > 0.8 and MAF > 0.05 were considered highly consistent and were used for subsequent analyses.

Genetic Relationships Among Samples

We used admixture software [55] to determine the population structure of the 50 Miscanthus germplasms, assuming that these germplasms fell into 1–10 genetic clusters. We also performed principal components analysis (PCA) of the germplasms using cluster software [56]. PCAs perform linear transformations of variables to create orthogonal axes ordered by the proportion of variance explained [57]. An unrooted phylogenetic tree based on our SNP data for these 50 germplasms was constructed using the neighbor-joining (NJ) method [58] in MEGA 5.0 software [59]. SLAF-based paternity tests have two requirements: the sequencing depth of the parents in the SLAF tag must be ≥ 10, while the ratio of sequencing depth between parents and offspring in the SLAF tag must be ≥ 2; and the SLAF tag in the tag sequence must be present in one of the parents [44]. If both conditions are met, the SLAF tag is regarded as abnormal; if there are > 3% abnormal tags between the offspring and a given parent, there is unlikely to be a parental relationship [44].

AFLPs: amplified fragment length polymorphisms; BAC: bacterial artificial chromosome; CTAB: cetyltrimethyl ammonium bromide; GBS: genotyping by sequencing; ISSRs: inter-simple sequence repeats; MAF: minor allele frequency; NJ: Neighbor-joining; PCA: principal components analysis; PVP: poly-N- vinylpyrrolidone; RAD-seq: restriction site-associated DNA sequencing; RAPD: random amplified polymorphic DNA; RFLPs: restriction fragment length polymorphisms; SLAF-seq: specific-locus amplified fragment sequencing; SNPs: single nucleotide polymorphisms; SSRs: simple sequence repeats

Acknowledgements

We would like to thank LetPub (www.letpub.com) for providing linguistic assistance during the preparation of this manuscript.

Author Contributions

This research was designed by ZYC, HHH and ZLY; and was performed by YCH with assistance from YLS. ZYC and YCH wrote the paper with help from the other authors. All authors read and approved the final manuscript.

Authors’ information

At the time of this work, YCH and YLS were postgraduate students in the College of Bioscience and Biotechnology at Hunan Agricultural University, Hunan, China. ZYC, HHH, and YI are Assistant Professors, and ZLY is a Professor in the College of Bioscience and Biotechnology at Hunan Agricultural University. All authors are also members of Hunan Engineering Laboratory of Miscanthus Ecological Applications, Hunan, China.

Funding

This work was supported by The National Natural Science Foundation of China [31471557, 31871693].

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author details

a College of Bioscience & Biotechnology, Hunan Agricultural University, Changsha, 410128, PR China

b Hunan Engineering Laboratory of Miscanthus Ecological Applications, Hunan Agricultural University, Changsha, 410128, PR China

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Review #2 received at journal
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Editorial decision: Major revision
29 Apr, 2021
Review #1 received at journal
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Reviewer #2 agreed at journal
12 Apr, 2021
Reviewer #1 agreed at journal
06 Apr, 2021
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You are reading this latest preprint version

Investigation of Genetic Relationships within Miscanthus using SNP Markers Identified using SLAF-Seq

Status:

Version 1

Abstract

Figures

Background

Results

Evaluation of the digestive enzymes

Analysis of the SLAF-seq data

Genetic distance analysis

Discussion

Analysis of the SLAF-seq data

Genetic Relationships Analysis

Conclusions

Materials And Methods

Plant materials

DNA extraction

Enzyme digestion design

SLAF library construction and high-throughput sequencing

Development of SLAF tags and SNP markers

Genetic Relationships Among Samples

Abbreviations

Declarations

References

Supplementary Files

Status:

Version 1

Candidate male parent	Number of abnormal offspring	Candidate male parent	Number of abnormal offspring
B1	9	C1	11
B2	7	C2	6
B3	9	C3	0
B4	9	C4	11
B5	11	C5	10
B6	9	C6	9
B7	10	C7	11
B8	6	C8	9
B9	9	C9	14
B10	6

Candidate male parent	Number of abnormal offspring	Candidate male parent	Number of abnormal offspring
B1	9	C1	11
B2	7	C2	6
B3	9	C3	0
B4	9	C4	11
B5	11	C5	10
B6	9	C6	9
B7	10	C7	11
B8	6	C8	9
B9	9	C9	14
B10	6

Candidate male parent	Number of abnormal offspring	Candidate male parent	Number of abnormal offspring
B1	9	C1	11
B2	7	C2	6
B3	9	C3	0
B4	9	C4	11
B5	11	C5	10
B6	9	C6	9
B7	10	C7	11
B8	6	C8	9
B9	9	C9	14
B10	6