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  • Research article
  • Open Access

RNA-seq analysis reveals considerable genetic diversity and provides genetic markers saturating all chromosomes in the diploid wild wheat relative Aegilops umbellulata

BMC Plant Biology201818:271

https://doi.org/10.1186/s12870-018-1498-8

  • Received: 29 January 2018
  • Accepted: 25 October 2018
  • Published:

Abstract

Background

Aegilops umbellulata Zhuk. (2n = 14), a wild diploid wheat relative, has been the source of trait improvement in wheat breeding. Intraspecific genetic variation of Ae. umbellulata, however, has not been well studied and the genomic information in this species is limited.

Results

To develop novel genetic markers distributed over all chromosomes of Ae. umbellulata and to evaluate its genetic diversity, we performed RNA sequencing of 12 representative accessions and reconstructed transcripts by de novo assembly of reads for each accession. A large number of single nucleotide polymorphisms (SNPs) and insertions/deletions (indels) were obtained and anchored to the pseudomolecules of Ae. tauschii and barley (Hordeum vulgare L.), which were regarded as virtual chromosomes of Ae. umbellulata. Interestingly, genetic diversity in Ae. umbellulata was higher than in Ae. tauschii, despite the narrow habitat of Ae. umbellulata. Comparative analyses of nucleotide polymorphisms between Ae. umbellulata and Ae. tauschii revealed no clear lineage differentiation and existence of alleles with rarer frequencies predominantly in Ae. umbellulata, with patterns clearly distinct from those in Ae. tauschii.

Conclusions

The anchored SNPs, covering all chromosomes, provide sufficient genetic markers between Ae. umbellulata accessions. The alleles with rarer frequencies might be the main source of the high genetic diversity in Ae. umbellulata.

Keywords

  • Aegilops umbellulata
  • Aegilops tauschii
  • Barley
  • DNA markers
  • RNA sequencing
  • Synteny

Background

Aegilops umbellulata Zhuk. (2n = 14), a wild diploid wheat relative, is distributed in West Asia and is known as the U-genome donor of Ae. columnaris and Ae. triaristata [1, 2]. Ae. umbellulata (UU genome) has crossability with tetraploid wheat (T. turgidum L.; AABB genome), which allows generation of synthetic hexaploids (AABBUU genome) through ABU triploids. Some combinations of interspecific crosses between Ae. umbellulata accessions and tetraploid wheat result in hybrid incompatibility, such as severe growth abortion and grass-clump dwarfness [3]. This observation suggests the existence of unrevealed genetic polymorphisms in Ae. umbellulata that potentially vary phenotypic traits.

Ae. umbellulata have been used for breeding of bread wheat and is a considerable resource of disease resistance genes [410]. Leaf rust and stripe rust resistance genes [6, 8, 11] and high-molecular weight glutenin subunits [5, 12] have been introduced into bread wheat cultivars. Chhuneja et al. (2008) [6] and Bansal et al. (2017) [8] established introgression lines of leaf and stripe rust resistance genes derived from synthetic hexaploids (AABBUU). The cross of the synthetic hexaploids (AABBUU) with T. aestivum cv. Chinese Spring PhI, which carries an epistatic inhibitor of Ph1 gene, induced homologous pairing and resulted in transfer of the leaf and stripe rust resistance genes of Ae. umbellulata into the bread wheat T. aestivum. Although Ae. umbellulata provides valuable genetic resources for breeding of bread wheat, it has not been well studied and information on its genome is limited. Evaluation of intraspecific genetic diversity based on genome-wide polymorphisms in Ae. umbellulata would impart practical information for designing genetic markers, facilitating the efficient use of Ae. umbellulata for breeding.

Since the tribe Triticeae has a large genome, most of which is occupied by repetitive sequences, development of high-quality physical maps and whole genome sequencing are challenging. RNA sequencing (RNA-seq) is one of the solutions for detection of single nucleotide polymorphisms (SNPs) and evaluation of genetic diversity by avoiding these genome complexities of the Triticeae. RNA-seq approaches for identifying novel genetic markers have been applied to several Triticeae species such as T. monococcum [13] and Ae. tauschii [1416]. RNA-seq has the advantage of direct detection of SNPs linked to causal genes for targeted phenotypes. RNA-seq-based bulked segregant analysis narrowed down the genome location of a wheat yellow rust resistance gene, Yr15, and a wheat spot blotch resistance gene, Sb3, within 0.77 cM and 0.15 cM intervals, respectively [17, 18].

Recently, the highest-quality genome sequences have been developed in the diploid Triticeae species barley (Hordeum vulgare L.) [19, 20] and Ae. tauschii [21, 22]. By utilizing highly conserved chromosomal synteny across Triticeae species [23, 24], the pseudomolecules of barley and Ae. tauschii can be regarded as virtual chromosomes of other Triticeae species. By combining RNA-seq with positional information from this synteny, a large number of SNPs and indels can be anchored to the chromosomes, facilitating design of genome-wide genetic markers [16]. The RNA-seq-based approach for marker development is considered applicable to other wild wheat species when enough genomic information is lacking.

Here, to evaluate genetic polymorphisms and capture genetic markers in Ae. umbellulata, transcripts of 12 representative accessions of Ae. umbellulata were first reconstructed by de novo assembly of reads from RNA-seq on the Illumina MiSeq platform. Using the deduced transcript sequences, a large number of SNPs and indels between the Ae. umbellulata accessions were detected and anchored to the barley and Ae. tauschii pseudomolecules. Comparative analysis of DNA polymorphisms between Ae. umbellulata and Ae. tauschii revealed relatively high genetic diversity in Ae. umbellulata.

Methods

Plant materials, library construction and RNA sequencing

Twelve accessions of Ae. umbellulata were chosen from the wheat genetic resources database of the National BioResource Project-Wheat (Japan, https://shigen.nig.ac.jp/wheat/komugi/top/top.jsp) to represent the diversity of this species (Fig. 1; Table 1). T. urartu KU-199-5 was used as the outgroup species for the comparative analysis between Ae. umbellulata and Ae. tauschii. Total RNA was extracted from leaves of Ae. umbellulata and T. urartu at the seedling stage using a Sepasol-RNA I Super G solution (Nacalai Tesque, Kyoto, Japan). The total RNA was treated with DNase I at 37 °C for 20 min to remove contaminating DNA. A total of 6 to 10 μg of RNA was used for constructing paired-end libraries. The libraries were constructed with TruSeq RNA Library Preparation Kit v2 (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions, and were sequenced with 300-bp paired-end reads on an Illumina MiSeq sequencer.
Fig. 1
Fig. 1

The geographic distribution of the 12 tested accessions of Ae. umbellulata on the map of the northwestern part of the Middle East

Table 1

List of Ae. umbellulata accessions used in this study

Accession number

Origins

Locality

KU-4017

Iraq

18.8 km NNE from Sulaymaniyah to Chuarta

KU-4026

Iraq

25.9 km S from Kirkuk to Baghdad

KU-4035

Iraq

5.5 km ENE from Koi Sanjak to Ranya

KU-4043

Iraq

SSW of Rowanduz

KU-4052

Iraq

4.4 km NW from Amadiyah Mazorka Gorge

KU-4103

Turkey

North of Van

KU-5934

Turkey

Suburbs of Kayseri

KU-5954

Turkey

Suburbs of Kutahia

KU-8-7

Turkey

Suburbs of Burdur (D)

KU-12180

Greece

5.1 km W from Platania to Laerma Rhodes

KU-12198

Greece

5.4 km E from Mithymna to Madamados Lesbos

KU-8-5

Syria

6 km W of Qatana (Damascus - Mt. Hermon)

De novo assembly of reads from RNA-seq

Low-quality bases (average quality score per 4 bp < 30), adapter sequences, and reads < 100 bp were removed using the Trimmomatic version 0.33 tool [25]. The paired-end reads were assembled with Trinity version 2.0.6 software to reconstruct transcripts for each accession [26, 27]. If a gene had multiple isoforms, the first transcript sequence designated by Trinity was chosen as a unigene. A set of unigenes was made for each accession according to our previous report [16], and was used as a reference transcript dataset. Paired-end reads from each accession were aligned to the reference transcripts using the Bowtie 2 [28]. SAMtools and Coval software were used for SNP and indel calling [29, 30]. SNPs and indels were called when over 95% of the aligned sequences were different from those of the reference transcripts at positions with read depth > 10. Sequence data have been deposited to DDBJ Sequence Read Archive DRA006404.

Mapping the assembled transcripts, SNPs and indels to barley and Ae. tauschii genome sequences

The transcripts were mapped to the barley (Hordeum vulgare L.) reference genome “ASM32608v1 masked” [19] from the Ensembl Plants database [31] and to the Ae. tauschii genome “PRJNA341983” from the NCBI database [21] using Gmap software version 2014-12-31 [32] and bedtools [33]. Based on the transcripts mapping to the pseudomolecules of Ae. tauschii and barley, SNPs and indels were anchored to the chromosomes. The distribution of SNPs and indels on barley and Ae. tauschii chromosomes were visualized using CIRCOS software [34] (Krzywinski et al. 2009).

Development of markers and genotyping

Indel markers were designed using indels longer than 3 bp that were anchored to the barley chromosomes. Primer sets were constructed with Primer3plus software [35]. To validate marker alleles, we genotyped F1 hybrid from a cross between Ae. umbellulata accessions KU-4017 and KU-4043. Total DNA was extracted from leaves of F1 plants and their parents. PCR was conducted using Quick Taq HD DyeMix (TOYOBO, Osaka, Japan). PCR products were resolved in 17% acrylamide gels, and the products were visualized under UV light after staining by ethidium bromide.

Comparison of genetic diversity between Ae. umbellulata and Ae. tauschii

The RNA-seq reads from the 10 Ae. tauschii accessions from the Transcriptome Shotgun Assembly division of DDBJ BioProject PRJDB4683 [16] were used for comparative analyses. We used the transcript sequences of Ae. tauschii KU-2075, which were constructed in our previous report [16], and Ae. umbellulata KU-4017 as reference transcripts. Quality control for the reads of Ae. tauschii and T. urartu was performed using Trimmomatic version 0.33 [25] in the same way as for Ae. umbellulata. The reads were aligned to the reference transcripts of Ae. tauschii KU-2075 and Ae. umbellulata KU-4017 using Bowtie 2 [28]. SNP calling was performed with SAMtools and Coval [29, 30] using the same criteria described above. SNPs that were assured of read depth > 10 and no ambiguous nucleotides in any accessions were selected as high-confidence SNPs and used for analyzing intra- and interspecific variation. The number of segregating sites, Tajima’s D statistic [36], and fixed nucleotide differences between species were estimated with DnaSP v5 software [37]. A neighbor-joining tree and a maximum likelihood tree were constructed based on the high-confidence SNPs. Bootstrap probability was calculated for 1000 replications.

Estimation of orthologous transcripts of Ae. umbellulata and Ae. tauschii

Orthologous pairs of the reference transcripts of Ae. tauschii KU-2075 and Ae. umbellulata KU-4017 were estimated according to reciprocal best hits of BLAST analysis. A BLASTN search was performed using transcripts of Ae. tauschii KU-2075 as the queries against transcripts of Ae. umbellulata KU-4017, and vice versa. When the same best hit was detected and query coverage was over 80% in both BLAST analyses, the transcripts from Ae. umbellulata KU-4017 and Ae. tauschii KU-2075 were judged an orthologous pair.

Gene expression analysis

The mapped reads that were concordantly aligned to the reference transcripts were chosen from the alignment file with SAMtools [29]. Fragments per kilobase per million mapped reads (FPKM) values were calculated based on the concordantly mapped reads [38].

Results

RNA sequencing of 12 Ae. umbellulata accessions

To evaluate genetic diversity based on a large number of DNA polymorphisms in the U-genome species Ae. umbellulata, RNA-seq was performed on the 12 representative accessions, generating 3.5–6.1 million paired-end reads per one accession (Table 2). These reads were analyzed according to the workflow shown in Additional file 1: Figure S1. After filtering out reads with low quality, 2.2–3.9 million paired-end reads (56.2–74.1%) were obtained. Due to the absence of a reference genome for Ae. umbellulata, transcript sequences for each of the 12 accessions were constructed by de novo assembly of the filtered reads. For each accession, 20,996 to 59,253 transcripts with N50 values of 899 to 1365 bp were deduced. One isoform was chosen as a unigene if a transcript had multiple isoforms. Finally, 12 sets of unigenes composed of 20,675 to 55,831 representative isoforms were obtained (Table 2) and used as reference transcript datasets for pairwise alignments between the accessions.
Table 2

Summary of RNA sequencing for 12 accessions of Ae. umbellulata

Accession

Read

pairs

Filtered

read pairs (%a)

(unigenes)

N50

(bp)

Median contig length

(bp)

Total assembled bases

(Mbp)

KU-4017

3,738,403

2,515,683 (67.29%)

39,359

(37640)

1238

658

36

KU-4026

4,935,634

3,429,252 (69.48%)

20,996

(20675)

900

578.5

15.6

KU-4035

6,077,268

3,890,911 (64.02%)

57,029

(52216)

1350

654

52.9

KU-4043

4,090,151

2,392,708 (58.50%)

50,985

(48590)

1095

518

38.9

KU-4052

3,829,992

2,152,503 (56.20%)

47,320

(44869)

1179

577

39.4

KU-4103

3,875,477

2,506,545 (64.68%)

31,418

(30873)

899

507

22.1

KU-5934

5,114,283

3,507,234 (68.58%)

57,466

(52751)

1332

630

52.7

KU-5954

3,686,807

2,694,916 (73.10%)

45,164

(41780)

1365

699

44.1

KU-12180

3,455,952

2,560,779 (74.10%)

46,323

(44000)

1229

631

41.2

KU-12198

3,623,492

2,666,981 (73.60%)

50,069

(46178)

1359

655

47,7

KU-8-5

3,669,766

2,699,452 (73.56%)

52,648

(48981)

1259

654

48.2

KU-8-7

3,798,153

2,524,378 (66.46%)

59,253

(55831)

1160

524

46.9

aPercentage of the number of filtered read pairs per the number of read pairs

Genome-wide identification of SNPs and indels in Ae. umbellulata

To detect SNPs and indels among the accessions, the filtered reads of each accession were aligned to the reference transcripts of all other accessions, and SNPs and indels were called according to the thresholds with read depth > 10. SNPs and indels identified from comparisons of the same accessions were regarded as artifacts. After filtering to remove these putative artifacts, 2925–44,751 SNPs and 77–1389 indels were obtained among the accessions (Table 3). The maximum numbers of SNPs and indels were obtained between KU-4035 and KU-12180 (44,751 SNPs and 1389 indels), with the minimum between KU-4017 and KU-4026 (2925 SNPs and 77 indels).
Table 3

The number of SNPs and indels detected in each transcript-read pairing of 12 Ae. umbellulata accessions

Transcript model

Read

total NR SNPs and indels

KU-

4017

KU-

4026

KU-

4035

KU-

4043

KU-

4052

KU-

4103

KU-

5934

KU-

5954

KU-

12,180

KU-

12,198

KU-

8–5

KU-

8–7

KU-

 

2925

16,611

11,171

12,091

5316

24,975

14,064

29,726

23,244

13,400

24,601

85,758

4017

 

77

524

348

381

169

854

401

869

648

443

846

3284

KU-

10,754

 

16,535

11,666

12,869

5428

19,463

11,889

23,915

17,452

11,478

20,185

63,233

4026

350

 

612

429

420

153

762

405

787

565

412

754

2645

KU-

9069

3639

 

11,969

13,141

5372

25,450

14,991

30,925

23,575

13,356

25,797

89,369

4035

274

94

 

426

423

167

889

387

901

651

396

847

3303

KU-

9748

2959

17,571

 

11,772

5325

24,871

15,017

30,191

22,942

13,557

26,117

85,656

4043

287

83

611

 

404

155

867

423

883

662

459

809

3398

KU-

10,355

3393

20,370

11,585

 

5172

25,332

14,713

30,020

23,340

14,824

26,197

88,716

4052

347

92

665

408

 

153

848

406

951

716

493

872

3525

KU-

11,611

3811

19,020

13,758

13,022

 

21,807

13,405

25,741

19,851

14,038

21,764

71,071

4103

348

99

672

462

429

 

827

417

786

605

474

732

2932

KU-

14,265

3465

25,574

16,432

16,766

6131

 

13,619

31,109

23,359

15,968

25,591

89,344

5934

443

97

854

582

527

187

 

385

932

663

540

855

3489

KU-

15,032

3585

28,423

18,003

18,391

6424

25,505

 

30,219

22,205

17,868

25,394

91,059

5954

433

89

909

572

560

174

859

 

942

650

563

872

3723

KU-

24,232

6057

44,751

27,569

29,176

9695

42,984

27,022

 

24,142

30,721

34,445

99,411

12,180

698

163

1389

901

896

296

1384

768

 

719

935

1140

4246

KU-

21,096

5282

40,683

24,658

25,784

9259

39,208

22,641

26,570

 

27,036

31,815

100,683

12,198

614

117

1311

777

805

246

1263

627

809

 

813

1022

4085

KU-

12,822

3444

23,775

15,520

16,626

5893

24,993

15,271

30,526

23,495

 

25,746

91,080

8–5

375

100

784

558

577

164

907

439

952

689

 

860

3635

KU-

17,577

5041

33,501

20,936

21,374

7235

32,035

18,925

30,559

24,589

21,646

 

94,187

8–7

530

128

1068

694

650

227

1057

557

924

716

670

 

3728

Upper and lower numbers at each comparison indicate SNPs and indels, respectively. NR non-redundant

For efficient use of the identified SNPs and indels as genetic markers, their chromosomal locations must be known. Here, we used the Ae. tauschii and barley pseudomolecules as virtual chromosomes of Ae. umbellulata, and mapped the unigene sequences of the Ae. umbellulata reference transcript datasets to the Ae. tauschii and barley chromosomes. In the reference transcripts, 75.87–85.35% of the unigenes were mapped to Ae. tauschii and 52.08–67.69% to barley chromosomes (Additional file 1: Table S1). Based on the positional information of the mapped unigenes, SNPs and indels were anchored to the chromosomes of both species. In any pairwise comparison between Ae. umbellulata accessions, 81.83–89.50% of SNPs and 75.28–89.26% of indels were anchored to Ae. tauschii chromosomes, while 63.17–75.16% of SNPs and 59.04–77.78% of indels were anchored to barley chromosomes (Additional file 1: Tables S2, S3). The distribution of SNPs over each chromosome of Ae. tauschii and barley was visualized with CIRCOS [34] for the Ae. umbellulata accession pairs with the maximum or minimum number of SNPs. The SNPs covered all chromosomes (Fig. 2).
Fig. 2
Fig. 2

Distribution of transcripts and SNPs detected in pairwise comparisons of Ae. umbellulata accessions on the physical map of Ae. tauschii (a) and barley (b); scale in Mb. The three circles of the same color show the number of transcripts and SNPs from outer to inner circles. The red circles indicate the richest SNP pairs (KU-4035 read-mapped to KU-12180 transcript). The blue circles indicate the least rich pairs (KU-4026 read-mapped to KU-4017 transcript) in each combination of lineages. The green circles indicate the non-redundant SNPs mapped against KU-4043 transcripts. Arrowheads represent centromeric positions of each chromosome

Non-redundant SNPs and indels were estimated for each of the 12 sets of reference transcripts. A total of 63,233–100,683 non-redundant SNPs and 2645–4246 non-redundant indels were detected in the tested Ae. umbellulata accessions (Table 3). On average, 73,075 non-redundant SNPs (85.07%) were anchored to Ae. tauschii chromosomes, and 58,247 (70.40%) non-redundant SNPs to barley chromosomes (Additional file 1: Tables S4, S5). The smallest number of anchored non-redundant SNPs was observed on chromosomes 4D in Ae. tauschii and 4H in barley (Fig. 3). Each chromosome of Ae. tauschii and barley had an average of 10,439 and 8321 non-redundant SNPs, respectively. The anchored non-redundant SNPs were distributed over all seven chromosomes of Ae. tauschii and barley (Fig. 2).
Fig. 3
Fig. 3

Boxplots of non-redundant SNPs anchored to chromosomes of Ae. tauschii (gray) and barley (black). The number of non-redundant SNPs on the chromosomes for each reference transcript dataset is plotted. Ave.D and Ave.H respectively indicate average number of non-redundant SNPs per chromosome when genomes of Ae. tauschii and barley were used

We estimated the percentages of non-redundant SNPs anchored to the Ae. tauschii chromosomes overlapped those on barley chromosomes (Fig. 4). Venn diagrams showed that 69.18% of non-redundant SNPs were anchored to both Ae. tauschii and barley chromosomes. The percentage of non-redundant SNPs uniquely anchoring to Ae. tauschii chromosomes was 24.96%. Only 5.86% of non-redundant SNPs were uniquely anchored to barley chromosomes. After integration of these anchored non-redundant SNPs, 77,625 non-redundant SNPs were placed on the chromosomes.
Fig. 4
Fig. 4

Venn diagrams of non-redundant SNPs anchored to the Ae. tauschii and barley chromosomes. The numbers indicate mean values of non-redundant SNPs and indels derived from each of the reference transcript datasets

Application of indel markers to confirmation of F1 formation

To confirm usefulness of the identified polymorphisms as genetic markers, primer sets for 27 indels were designed. The indel markers were applied to genotype F1 hybrid from a cross between two Ae. umbellulata accessions, KU-4017 and KU-4043; nine markers enabled detection of the genetic differences between the accessions and confirmed their F1 formation (Additional file 1: Figure S2). The difference in amplicon size between the parents was observed in the five markers. Presence/absence of amplicons between the parents was detected in the two markers. In the other two markers, the parents were distinguished by an extra band.

Comparison of genetic diversity in Ae. umbellulata and Ae. tauschii

Ae. tauschii is widely distributed over central Eurasia and has three divergent lineages, TauL1, TauL2 and TauL3 [39]. On the other hand, the habitat of Ae. umbellulata is limited to West Asia. To examine how differences in geographic distribution and evolutionary history of these species affected the extent of DNA polymorphisms and the distribution of allele frequency, genetic diversity in Ae. umbellulata and Ae. tauschii was evaluated with SNPs deduced using the same RNA-seq platform. To compare intraspecific diversity of the two Aegilops species, reads from RNA-seq of the 12 Ae. umbellulata accessions, the 10 Ae. tauschii accessions [16] and T. urartu KU-199-5 were aligned to the reference transcripts of Ae. umbellulata KU-4017. T. urartu KU-199-5 was used as an outgroup species. To elucidate the phylogenetic relationship of Ae. umbellulata and Ae. tauschii accessions, maximum likelihood and neighbor-joining trees were constructed based on the high-confidence SNPs (Fig. 5; Additional file 1: Figure S3a). The three species were clearly separated, with the Aegilops species more closely related than T. urartu, with fixed nucleotide differences between Ae. umbellulata and Ae. tauschii smaller than those between Ae. tauschii and T. urartu or between Ae. umbellulata and T. urartu (Additional file 1: Table S6). The external branches of Ae. umbellulata were longer than those of Ae. tauschii. Ae. umbellulata KU-12180 was isolated from the other accessions, supporting observations from the phylogenetic trees constructed based on nucleotide polymorphisms in a small number of genes [3]. However, the clear divergent lineages observed in Ae. tauschii were not found in the Ae. umbellulata accessions (Fig. 5). When the reference transcripts of Ae. tauschii KU-2075 was used for the alignments and SNP calling, similar results were obtained (Additional file 1: Table S6, Figures S3b, S4).
Fig. 5
Fig. 5

Phylogenetic relationship between the 12 Ae. umbellulata accessions, 10 Ae. tauschii accessions and one T. urartu accession based on SNPs estimated using the Ae. umbellulata KU-4017 reference transcript dataset. The tree was constructed by the maximum-likelihood method. The bootstrap values (1000 replicates) are shown on each branch. White and black circles of Ae. tauschii respectively correspond to the divergent lineages TauL1 and TauL2 [39]. T. urartu was used as the outgroup species

The number of segregating sites in Ae. umbellulata was larger than in Ae. tauschii (Table 4), indicating that Ae. umbellulata has relatively high genetic diversity. To test how differences in habitat and evolutionary history between Ae. umbellulata and Ae. tauschii affected allele frequency distribution in these two species, the derived allele frequency distribution for each species was estimated using T. urartu as an outgroup species (Fig. 6). At a polymorphic site, a nucleotide that is inconsistent with that of outgroup species is defined as a derived allele, because this allele is considered to be newly generated by a mutation in population of the tested species [40]. The derived allele frequency distributions of the two species showed distinct patterns. Alleles with intermediate frequency were predominantly detected in Ae. tauschii, while alleles with rarer frequency were more common in Ae. umbellulata. As expected from the difference in the allele frequency distributions, Tajima’s D statistic [36] for Ae. tauschii and Ae. umbellulata respectively gave positive and negative values (Table 4).
Table 4

Summary of nucleotide polymorphisms in Ae. umbellulata and Ae. tauschii

Reference

Ae. umbellulata KU-4017

Ae. tauschii KU-2075

SNPs

Ae. umbellulata

Ae. tauschii

Ae. umbellulata

Ae. tauschii

# of accession

12

10

12

10

# of site

31,677

31,677

29,702

29,702

# of segregating site

4751

4136

4301

3832

singleton

1992

1103

1759

1019

non-singleton

2759

3033

2542

2813

Tajima’s D

−0.24 NS

0.86 NS

−0.22 NS

0.89 NS

Fig. 6
Fig. 6

Derived allele frequency distribution in Ae. umbellulata (n = 12) (a) and Ae. tauschii (n = 10) (b), respectively. Ae. umbellulata KU-4017 transcripts were used as the reference. Derived alleles were estimated using the outgroup species T. urartu

Nucleotide diversity (θ) [41] in Ae. umbellulata and Ae. tauschii was estimated for each transcript. The θ value for each transcript of Ae. umbellulata was weakly correlated with that of Ae. tauschii (Figs. 7a, b: Kendall’s rank correlation τ = − 0.026 and 0.043). To avoid the possibility of bias due to differences in the accuracy and efficiency of short read alignments between intra- and interspecies, we compared θ for the 6062 orthologous pairs between Ae. umbellulata and Ae. tauschii. These pairs were retrieved by reciprocal best hits of BLAST analysis between the reference transcript datasets of Ae. umbellulata KU-4017 and Ae. tauschii KU-2075. This approach enables evaluation of genetic diversity using only the θ value based on SNPs derived from the intraspecies alignments of reads. Although gene expression of the orthologous pairs showed a relatively strong correlation (Fig. 7c: τ = 0.577), the values of θ between the pairs designated a weak correlation (Fig. 7d: τ = 0.049). Taken together, the reproducible observations from different approaches underpin the distinct extent of nucleotide polymorphisms between Ae. umbellulata and Ae. tauschii at the gene level.
Fig. 7
Fig. 7

Scatter plot of nucleotide diversity (θ)in Ae. umbellulata (n = 12) and Ae. tauschii (n = 10) when 7666 transcripts derived from Ae. umbellulata KU-4017 were used as the references (a) and when 6622 transcripts derived from Ae. tauschii KU-2075 were used as the references (b). For both sets of transcripts, at least one of the species had polymorphisms. Scatter plot of gene expression (FPKM) in the 6062 orthologous pairs of Ae. umbellulata KU-4017 and Ae. tauschii KU-2075 retrieved by reciprocal best hits of BLAST analyses (c). Scatter plot of θ in the 3354 orthologous pairs of Ae. umbellulata KU-4017 and Ae. tauschii KU-2075 that had polymorphisms in one of the species (d). Kendall’s rank correlation (τ) for (a), (b), (c), and (d) was − 0.026 (p = 0.0033), 0.043 (p = 5.3e-6), 0.577 (p < 2.2e-16) and 0.049 (p = 0.0002), respectively

Discussion

RNA-seq is a powerful approach to identify novel genetic markers for Triticeae species

To identify genome wide polymorphisms (SNPs and indels) and to develop novel genetic markers, we conducted 300-bp paired-end RNA sequencing of leaf tissues from 12 representative Ae. umbellulata accessions using the Illumina MiSeq platform. By using Ae. tauschii and barley pseudomolecules as the virtual chromosomes of Ae. umbellulata due to the conserved synteny between Triticeae species [23, 24], an average of 73,075 and 58,247 non-redundant SNPs in Ae. umbellulata were successfully anchored to the chromosomes of Ae. tauschii and barley, respectively (Fig. 3; Additional file 1: Tables S4, S5). The application of reference-quality genome sequences of Ae. tauschii [21] dramatically improved the number of SNPs anchored to the chromosomes compared with a previous study [16], in which SNPs in Ae. tauschii were linked to the chromosomes by combining the draft genome sequences of Ae. tauschii [42] with its genetic linkage map [43]. Even when SNPs in Ae. tauschii were mapped to Ae. tauschii chromosomes, the number of anchored SNPs was slightly smaller than when the SNPs were mapped to the chromosomes of barley with reference-quality genome sequences [16]. The elaboration of SNP anchoring enabled capturing an average of 10,439 non-redundant SNPs per chromosome (Fig. 3; Additional file 1: Table S5), which were well distributed over each chromosome (Fig. 2). Since polymorphisms derived RNA-seq data were composed of only SNPs and indels in exons and untranslated regions of the expressed genes, the RNA-seq-based approach avoided the repetitiveness of intergenic regions and much of the genome complexity, resulting in identification of a large number of SNPs anchored to the virtual chromosomes. Recently, a high-density consensus linkage map including 3009 SNP markers derived from genotyping-by-sequencing was constructed in two biparental populations from four accessions of Ae. umbellulata [9]. The RNA-seq approach fills the gaps left by other genotyping methods such as genotyping-by-sequencing when developing genetic markers for Triticeae species without a genome sequence, such as Ae. umbellulata.

In our RNA-seq-based approach, the identified SNPs and indels were arranged on the Ae. umbellulata chromosomes in an order reflecting the conserved synteny with Ae. tauschii and barley (Fig. 2). When a genetic map is constructed using these anchored SNPs and indels, changes in the marker order should be considered carefully due to the existence of chromosomal rearrangements in Ae. umbellulata. Structural rearrangements have been observed for Ae. umbellulata chromosomes when the order of genetic markers was compared among Ae. umbellulata, Ae. tauschii and common wheat [9, 4446]. For example, chromosome 4 U has segmental homoeology to the group 6 chromosomes of common wheat [46]. Similarly, partial segments of chromosome 6 U have homoeology to hexaploid wheat group 4 and 5 chromosomes [9]. These observations support the occurrence of structural rearrangements such as translocation in Ae. umbellulata.

The power of indel detection with RNA-seq is not as high as that of SNPs, because indels in exons often have functionally deleterious effects on proteins and are purged from the genome by purifying selection. Notwithstanding this disadvantage, RNA-seq still provides useful indel markers for genetic mapping [47]. The indel markers were effective for validating detection of F1 alleles between Ae. umbellulata KU-4017 and KU-4043 (Additional file 1: Figure S2). These markers would allow rough map construction.

Contrasting patterns of nucleotide diversity between Ae. umbellulata and Ae. tauschii

Differences in the habitats, morphology, population structure and phenological traits between Ae. tauschii and Ae. umbellulata may result in differences in the pressures of natural selection and the effect of genetic drift on genes, shaping the extent of DNA polymorphisms and allele frequency distribution between the species. In spite of the limited habitats of Ae. umbellulata, the present study showed that Ae. umbellulata has higher genetic diversity than the more widely distributed species Ae. tauschii (Fig. 5; Table 4). This observation is consistent with a previous report [48], in which intra- and interspecific genetic variation in seven diploid Aegilops species was evaluated using amplified fragment length polymorphisms, also concluding that genetic diversity in Ae. umbellulata is higher than in Ae. tauschii. Our comparative analyses showed no clear lineage differentiation in Ae. umbellulata (Fig. 5; Additional file 1: Figures S3, S4) and the prevalence of alleles with rarer frequencies (Fig. 6; Additional file 1: Figure S5), implying that the alleles with rarer frequencies are the main source of the genetic diversity observed in Ae. umbellulata.

The longer external branches of the phylogenetic tree in Ae. umbellulata suggest higher genetic differentiation of each Ae. umbellulata accession than Ae. tauschii (Fig. 5; Additional file 1: Figures S3, S4). Generally self-pollination inhibits gene flow via pollen, increasing genetic differentiation among local populations [49]. Since Ae. umbellulata is a self-fertilizing plant, this general view could be applicable to the observed genetic differentiation between the accessions of Ae. umbellulata. Considering Ae. tauschii is also a self-fertilizing species, another factor may contribute to shaping the distinct patterns of nucleotide polymorphism in these two species. If the time of expansion and colonization into the modern habitats differed between species, neutral mutations are expected to have accumulated more within a local population of the species with the earlier expansion and colonization, generating genetic differentiation between local populations under the limited gene flow. If this hypothesis is accepted, the time of expansion and colonization into the modern habitat of Ae. umbellulata is presumed to be older than that of Ae. tauschii. These different evolutionary scenarios and habitats of Ae. tauschii and Ae. umbellulata are likely to have shaped distinct genetic diversity for each gene from their common ancestor. The scatter plots of nucleotide diversity in the transcripts of Ae. umbellulata and Ae. tauschii show weaker correlations between the orthologous pairs (Fig. 7), suggesting that genes of Ae. umbellulata were subjected to natural selection pressure and effects of genetic drift that were distinct from those of Ae. tauschii. Future larger-scale population genomic analyses in both species will disclose population dynamics with higher resolution and more powerfully detect footprints of natural selection in each gene.

Conclusion

The RNA-seq-based approach is efficient for development of a large number of molecular markers and for conducting population genetic analyses for a large number of genes in wheat wild relatives such as Ae. umbellulata lacking genomic information. In addition, Ae. umbellulata, harboring relatively high genetic diversity, has considerable potential as a genetic resource for breeding of common wheat.

Abbreviations

FPKM: 

Fragments per kilobase per million mapped reads

indels: 

Insertions and deletions

RNA-seq: 

RNA-sequencing

SNPs: 

Single nucleotide polymorphisms

Declarations

Acknowledgments

The Ae. umbellulata seeds used in this study were supplied by the National BioResource Project-Wheat, Japan (www.nbrp.jp). Computations for RNA sequence assembly and alignments of reads were performed on the NIG supercomputer at the ROIS National Institute of Genetics, Japan.

Funding

This work was supported by Grant-in-Aid for Scientific Research on Innovative Areas No. 17H05842, by Grant-in-Aid for Scientific Research (B) No. 16H04862 to ST from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan, and by MEXT as part of a Joint Research Program implemented at the Institute of Plant Science and Resources, Okayama University, Japan. KY was supported by JST, PRESTO (No. JPMJPR15QB).

Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

KY, KS and ST designed the whole project. MO, KY, KS and ST wrote the manuscript. MO, AM, and YM performed experiments. MO, RN, and KY conducted RNA-sequencing analyses. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare that they have no conflicts of interest.

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Authors’ Affiliations

(1)
Graduate School of Agricultural Science, Kobe University, Rokkodai 1-1, Nada-ku, Kobe 657-8501, Japan
(2)
Institute of Plant Science and Resources, Okayama University, Kurashiki, Japan

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© The Author(s). 2018

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