Open Access

Application of whole genome re-sequencing data in the development of diagnostic DNA markers tightly linked to a disease-resistance locus for marker-assisted selection in lupin (Lupinus angustifolius)

  • Huaan Yang1,
  • Jianbo Jian2,
  • Xuan Li2,
  • Daniel Renshaw1,
  • Jonathan Clements1,
  • Mark W. Sweetingham1,
  • Cong Tan3 and
  • Chengdao Li1, 3Email author
Contributed equally
BMC Genomics201516:660

https://doi.org/10.1186/s12864-015-1878-5

Received: 19 May 2015

Accepted: 24 August 2015

Published: 2 September 2015

Abstract

Background

Molecular marker-assisted breeding provides an efficient tool to develop improved crop varieties. A major challenge for the broad application of markers in marker-assisted selection is that the marker phenotypes must match plant phenotypes in a wide range of breeding germplasm. In this study, we used the legume crop species Lupinus angustifolius (lupin) to demonstrate the utility of whole genome sequencing and re-sequencing on the development of diagnostic markers for molecular plant breeding.

Results

Nine lupin cultivars released in Australia from 1973 to 2007 were subjected to whole genome re-sequencing. The re-sequencing data together with the reference genome sequence data were used in marker development, which revealed 180,596 to 795,735 SNP markers from pairwise comparisons among the cultivars. A total of 207,887 markers were anchored on the lupin genetic linkage map. Marker mining obtained an average of 387 SNP markers and 87 InDel markers for each of the 24 genome sequence assembly scaffolds bearing markers linked to 11 genes of agronomic interest. Using the R gene PhtjR conferring resistance to phomopsis stem blight disease as a test case, we discovered 17 candidate diagnostic markers by genotyping and selecting markers on a genetic linkage map. A further 243 candidate diagnostic markers were discovered by marker mining on a scaffold bearing non-diagnostic markers linked to the PhtjR gene. Nine out from the ten tested candidate diagnostic markers were confirmed as truly diagnostic on a broad range of commercial cultivars. Markers developed using these strategies meet the requirements for broad application in molecular plant breeding.

Conclusions

We demonstrated that low-cost genome sequencing and re-sequencing data were sufficient and very effective in the development of diagnostic markers for marker-assisted selection. The strategies used in this study may be applied to any trait or plant species. Whole genome sequencing and re-sequencing provides a powerful tool to overcome current limitations in molecular plant breeding, which will enable plant breeders to precisely pyramid favourable genes to develop super crop varieties to meet future food demands.

Keywords

Genome sequencing Re-sequencing Next-generation sequencing (NGS) Marker-assisted selection (MAS) Diagnostic markers Precision breeding

Background

Over thousands of years, the success of plant breeding and selection has relied on phenotypic measurements and breeder experience. The Green Revolution has greatly boosted the world grain production from the 1940s to 1960s. The advent of molecular biotechnology has progressively provided improved tools for precision plant breeding for genetic improvement. The concept of marker-assisted selection (MAS) in plant breeding was proposed in the 1980s [1], and has the potential to vastly enhance the efficiency of genetic improvement [2, 3]. In the last 30 years, molecular markers have been gradually applied to assist plant breeding of agricultural crops. A small number of commercial varieties obtained through marker-assisted breeding were released in rice, soybean, maize, barley, wheat and potato [3]. However, the gap between the expectations and actual impact of MAS is well recognised. Most of the thousands of publications with the terms “marker-assisted selection”, “quantitative trait loci (QTLs)” or “molecular markers” have failed to show any impact in plant breeding [35].

There are two major challenges in developing molecular markers for MAS. Firstly, markers must be closely linked to genes of agronomic traits of interest to enable the accurate prediction of desired plant phenotypes [3]. The most desirable markers for MAS are “co-segregating”, where marker genotypes are completely consistent with plant phenotypes in segregating breeding populations. Co-segregating markers offer maximum accuracy on MAS [6, 7]. Secondly, the genotypes of the markers should match plant phenotypes in a wide range of breeding germplasm, allowing broad application in a breeding program. Unfortunately, most of the molecular markers developed over the last 30 years through DNA fingerprinting and genetic mapping are not on target gene sequences; and some genetic distances exist between markers and genes. As a result, genetic recombination may occur in the region between the marker and the gene on the chromosome during evolution and in the plant breeding process. In MAS practice, it is a common problem that cultivars exhibiting desirable marker genotypes may not necessarily have the targeted genes and vice versa, which is known as “false positives” [8, 9]. When a cultivar containing a desirable gene is crossed with a breeding line with a false positive marker genotype, the F2 progeny plants will show the same marker allele, even though the gene of interest is segregating; therefore, the marker cannot be used for MAS. In order to deal with the prevalence of the false positives, molecular biologists have to undertake “marker validation” work to determine which markers fit which crosses in plant breeding programs [8, 9]. The marker validation step not only increases the overall cost, but also greatly slows down the pace of MAS [811]. The best solution for this plight is to develop “diagnostic markers” [12]; that is, markers which have marker genotypes consistent with plant trait phenotypes in all of the breeding germplasm in a breeding program. Diagnostic markers can be used in MAS without the marker validation step [12]. It is now well recognised that the development of diagnostic markers is the key for successful, large-scale and broad application of MAS in plant breeding [1012].

Functional markers designed on target gene sequences are diagnostic [12], but their development requires identifying, cloning and understanding the genes and their functions. Non-genic diagnostic markers can be developed on random sequences without knowledge of the causal genes by DNA fingerprinting and genetic mapping to select markers with genotypes matched to plant phenotypes in breeding germplasm [1315]. Traditional methods of developing functional markers and diagnostic markers are tedious and time consuming [16]. The advancements in next-generation sequencing (NGS) and whole genome sequencing have vastly improved the capacity for marker discovery in plants. For example, more than 55 million SNPs were discovered in maize by genome sequencing and re-sequencing [17, 18] and 18.9 million SNPs were obtained by re-sequencing a core collection of rice accessions [19]. Although genome sequencing has been increasingly applied to a wide range of plant species in recent years, there is no report on how to use whole genome sequencing and re-sequencing data to overcome the key challenges and to develop markers widely applicable for plant breeding programs.

Narrow-leafed lupin (Lupinus angustifolius L.) was fully domesticated by the early 1970s in Australia and is currently cultivated in Australia, Europe, America and Africa. Over the last 15 years, the DNA fingerprinting method microsatellite-anchored fragment length polymorphism (MFLP) [20] has been used to develop PCR-based markers linked to major genes of industry importance in lupin [16, 2130]. A genetic linkage map was published in 2005 based on a F8 recombinant inbred line (RIL) population originating from a wild × domesticated cross [31]. Three updated versions of the map from the same mapping population followed [3234]. Most of the markers on these maps were anonymous without sequence information. The application of NGS technology in the last four years has accelerated molecular research on this legume species. NGS has been used to end-sequence a small portion of a bacterial artificial chromosome (BAC) library [35] and in a transcriptome study [36]. NGS was applied as a DNA fingerprinting method to rapidly develop markers for MAS [37], and to construct a sequence-defined, dense genetic map in lupin [38]. More significantly, a draft genome sequence has been established, providing first insight into the lupin genome [38].

Phomopsis stem blight (PSB) caused by the fungal pathogen Diaporthe toxica is a major disease in lupin. It infects young stems, remaining as a latent subcuticular coralloid hyphal structure in green plants [39]. Upon plant senescence, the fungus colonizes the stems and develops large lesions. During saprophytic colonization, the fungus produces mycotoxins which can kill animals that graze on lupin stubble [40]. Selection for PSB disease resistance is a key objective in lupin breeding programs. Conventional methods of screening for PSB resistance are difficult and time consuming [41, 42]. Genetic analysis has indicated at least three major genes (Phr1, Phr1 and PhtjR) among Australian domesticated lupin lines, each independently conferring resistance to PSB [43, 44]. The R gene PhtjR is present in cultivar Tanjil, which has been extensively used as a parental line in the Australian lupin breeding program since its release in 1998. Seven sequence-specific, simple PCR-based markers were developed which flank the R gene PhtjR [44]; unfortunately, none have both the key characters of co-segregating and diagnostic desired for MAS. The R gene PhtjR has been integrated in the dense genetic map [38]. The objectives of this study were: (1) to undertake genome sequencing and re-sequencing on representative commercial lupin cultivars to discover molecular markers at the whole genome level, and (2) to examine the use of whole genome sequencing and re-sequencing to rapidly develop diagnostic markers closely linked to genes of agronomic interest for large scale application of MAS in molecular lupin breeding without the knowledge of functional genes.

Results

Whole genome re-sequencing in nine cultivars

The sequenced commercial cultivars were selected to represent a subset of the lupin breeding history released from 1973 to 2007. For each of the nine re-sequenced lupin cultivars, approximately 10 to 16 Gb of high quality clean sequencing data was obtained (Table 1), which represents 9-15X coverage of the lupin genome size at 1.1 Gb [38]. The sequence reads for each cultivar were assembled into scaffolds using the software program SOAPdenovo [45], and the N50 of assembled scaffolds for each cultivar ranged from 7,633 bp to 10,864 bp (Table 1). The total length of scaffold span for each cultivar ranged from 485 Mbp to 513 Mbp, approximately 90 % of the length of the reference genome assembly based on cultivar Tanjil [38]. The genome GC content of all re-sequenced cultivars was around 32 % (Table 1), which was consistent with the GC content of the reference genome [38].. The re-sequencing data of the nine lupin cultivars have been deposited at Genbank (NCBI accession number: “PRJNA290411”; website address: http://www.ncbi.nlm.nih.gov/bioproject/PRJNA290411).
Table 1

Statistics of denovo genome sequence assembly of re-sequenced nine cultivars of Lupinus angustifolius

 

Unicrop

Yorrel

Merrit

Kalya

Tallerack

Quilinock

Mandelup

Coromup

Jenabilup

Raw data (Mbp)

13,334

14,322

15,958

15,760

11,043

17,275

17,727

15,242

14,588

Clean data (Mbp)

12,714

13,642

15,275

15,069

10,524

16,471

16,936

14,605

14,003

Q20 base rate (%)

95.3

96.6

96.9

97.0

95.9

96.8

97.0

97.0

97.1

Number of scaffolds

208,181

277,622

309,904

371,733

256,387

279,705

383,911

268,036

363,979

Total scaffold span (Mbp)

485

497

501

513

488

500

512

504

498

Scaffold N50 (bp)

10,864

9,463

8,814

9,307

9,070

9,835

9,423

10,487

7,633

Average scaffold length (bp)

2,332

1,789

1,617

1,380

1,905

1,789

1,332

1,882

1,369

Longest scaffold (bp)

305,995

183,544

191,423

156,385

229,074

228,256

147,382

211,945

125,123

GC content (%)

32.96

32.70

32.87

32.87

32.62

32.72

32.90

32.65

32.89

Marker discovery by genome sequencing and re-sequencing

Pairwise comparison of whole genome sequencing data among the reference genome (cultivar Tanjil) and nine re-sequenced lupin cultivars revealed 180,596—795,735 SNP markers (Table 2). The number of insertion/deletion (InDel) markers between cultivars ranged from 33,094 to 122,513. In general, the number of InDels was positively correlated with the number of SNPs detected for each cultivar (Table 2).
Table 2

Numbers of SNP markers and InDel markers discovered by pairwise comparison of whole genome sequencing and re-sequencing data among 10 cultivars of Lupinus angustifolius*

Lupin cultivars

Unicrop

Yorrel

Merrit

Kalya

Tallerack

Quilinock

Mandelup

Coromup

Jenabillup

Yorrel

SNP

361,783

        

InDel

74,074

        

Merrit

SNP

387,619

379,884

       

InDel

42,670

53,825

       

Kalya

SNP

231,674

363,644

399,442

      

InDel

50,771

70,606

40,572

      

Tallerack

SNP

457,861

516,424

581,288

466,314

     

InDel

84,239

100,668

71,220

80,863

     

Quilinock

SNP

358,425

402,839

386,350

370,174

521,485

    

InDel

60,592

74,470

39,501

56,952

86,272

    

Mandelup

SNP

383,509

333,375

363,518

405,193

525,458

399,216

   

InDel

59,611

62,906

34,838

57,048

83,158

57,406

   

Coromup

SNP

358,729

318,466

338,840

377,613

509,809

365,480

210,394

  

InDel

59,469

61,381

35,666

57,069

84,167

55,839

39,077

  

Jenabillup

SNP

325,324

360,401

312,064

330,028

452,170

180,596

287,423

266,773

 

InDel

52,035

65,258

27,406

48,075

75,939

33,094

42,073

41,398

 

Tanjil (Reference)

SNP

644,901

510,722

432,717

564,221

795,735

609,359

601,497

543,048

467,465

InDel

93,730

105,235

59,780

90,986

122,513

93,675

88,261

88,910

79,623

* SNP markers are presented in black; InDel markers are in green

Sequence comparison between the reference genome sequence cultivar Tanjil and each of the nine re-sequenced cultivars revealed significant genetic diversity variation at the genome level and at chromosome level (Fig. 1). Cultivar Unicrop, which was the earliest fully domesticated cultivar in this species with most distant pedigree kinship from later released cultivars, showed the greatest level of diversity. In comparison, cultivar Merrit, which has the closest pedigree kinship which reference genome cultivar Tanjil [46], exhibited the least diversity among the nine sequenced cultivars (Fig. 1). At chromosome level, the sequences in sequence-defined linkage group [38] SLG-1, SLG-2, SLG-8 and SLG-11 were highly diverse; while SLG-3 was more conserved, particularly in the second half of this linkage group (Fig. 1).
Fig. 1

Genome-wide genetic diversity as measured by SNP abundance along each linkage group between reference cultivar Tanjil and nine re-sequenced cultivars of Lupinus angustifolius. Twenty linkage groups (SLG) were displayed in a circle. The inner number was SLG index and the outer was physical position (Mb). The circular histograms from circular 1 to 9 with different filling colour were SNP frequency distributions of nine cultivars in whole genome and the response relationship was given in the core area. Higher peaks indicated larger number of SNPs in the interval and lower troughs meant low abundance of SNP. The SNP frequency was counted in non-overlapping 100 kb intervals along each chromosome

Genotyping sequence-defined DNA markers on a genetic linkage map

The genome sequencing and re-sequencing data were successfully applied to genotype markers in the sequence-defined lupin genetic linkage map [38]. A total of 3,277 DNA markers from the 20 linkage groups were characterized for the 10 sequenced cultivars, including 2,902 SNP markers and 375 InDel markers (Additional file 1). By using the DNA sequences bearing the marker variation sites to Blast search of the genome sequencing data, the genotypes of these 3,277 markers on the reference cultivars Tanjil and on the nine re-sequenced cultivars were obtained and recorded (Additional file 1). For completeness, Additional file 1 contains all the 20 SLGs, the list of mapped SNP markers and InDel markers, the sequences bearing the marker sites, and the positions of nucleotides of the mapped markers in their corresponding scaffolds in the reference genome sequence assembly [38].

Enrichment of molecular markers for the lupin genetic map

Sequence alignments on the 4,214 scaffolds anchored on the sequence-defined lupin genetic linkage map between the two cultivars Tanjil and Unicrop, the two parental lines of the F8 RIL mapping population used to establish the dense genetic linkage map [38], identified 207,887 markers, which included 174,639 SNP markers and 33,248 InDel markers (Additional file 2). The average marker density of the enriched genetic linkage map was 127 markers per CentiMorgan. The distribution of these markers in each linkage group is summarized in Table 3. The average length of the 4,214 scaffolds anchored on the genetic linkage map was 17,035 bp. The average numbers of SNP markers and InDel markers per scaffold were 41.4 and 7.9, respectively. Detailed numbers of markers detected on each anchored scaffold, and their corresponding positions in the genetic linkage map are presented in Additional file 2.
Table 3

Summary of SNP markers and InDel markers integrated into the sequence-defined genetic linkage map through sequence comparison on scaffolds in Lupinus angustifolius a

Linkage groups

Genetic length (cM)

Number of anchored scaffoldsb

Number of SNP markers detected

Number of InDel markers detected

SLG-1

234.3

763

35,605

5,036

SLG-2

156.7

724

24,158

5,190

SLG-3

149

236

8,071

2,027

SLG-4

144.2

400

14,160

3,202

SLG-5

101.9

365

13,028

2,654

SLG-6

89

129

4,830

1,437

SLG-7

86.5

114

6,959

1,512

SLG-8

85

289

13,761

1,688

SLG-9

83.5

155

8,772

1,578

SLG-10

82.6

138

6,230

1,132

SLG-11

82.2

344

13,869

2,164

SLG-12

64.9

143

5,778

1,094

SLG-13

52.2

155

6,566

1,022

SLG-14

51.1

57

2,806

735

SLG-15

34.5

32

1,676

430

SLG-16

33.3

47

1,468

443

SLG-17

32.4

40

1,612

549

SLG-18

26.6

28

1,616

478

SLG-19

20.6

13

1,499

416

SLG-20

19.4

42

2,175

461

Sub total

1629.9

4,214

174,639

33,248

aThe sequence-defined genetic linkage map has been published previously [38]

bFull list of scaffolds anchored on the genetic linkage map, and the number of markers detected from each scaffold are presented in Additional file 2

Marker mining on scaffolds linked to genes of agronomic traits of interest

The 24 previously-developed DNA markers linked to 11 genes of agronomic traits of interest were located on 23 scaffolds in the draft genome sequence assembly [38]. Marker MoA [23] and MoLI [30] were on the same scaffold. Each of the other 22 markers was on a separate specific scaffold (Table 4). The length of these 23 scaffolds ranged from 8,191 bp to 64,039 bp, and the average length was 27,687 bp (Table 4).
Table 4

Marker mining on 23 genome sequence assembly scaffolds bearing 24 markers linked to 11 key genes of agronomic traits of interest by sequence alignments among 10 sequenced cultivars of Lupinus angustifolius a

Agronomic traits

Name of markers

Distance between marker and target gene (cM)

Reference

Scaffold identified

Scaffold size (bp)

Number of SNP markers from scaffold sequence alignment

Number of InDel markers from scaffold sequence alignment

Disease resistance gene PhtjR

DAFWA6895

0

[38]

Scaffold84773

33,448

489

101

Disease resistance gene PhtjR

PhtjM1

1.3

[44]

scaffold70674

11,068

102

39

Disease resistance gene PhtjR

PhtjM4

1.1

[44]

scaffold16849

40,716

526

259

Disease resistance gene PhtjR

PhtjM6

1.9

[44]

scaffold2572

55,753

808

263

Disease resistance gene PhtjR

PhtjM7

1.1

[44]

scaffold57606

13,893

188

62

Disease resistance gene Lanr1

DAFWA5820

0

[38]

scaffold 31581

15,706

225

33

Disease resistance gene Lanr1

AntjM1

3.5

[22]

scaffold83350

11,407

74

35

Disease resistance gene Lanr1

AntjM2

2.3

[14]

scaffold2992

33,979

341

188

Disease resistance gene Lanr1

AnSeq3

0.9

[37]

Scaffold33942

64,039

716

138

Disease resistance gene Lanr1

AnSeq4

0.9

[37]

Scaffold31346

33,727

221

158

Seed coat colour

DAFWA6428

0

[38]

scaffold11676

22,481

588

154

Seed coat colour

DAFWA4544

0

[38]

scaffold13708

44,176

821

81

Disease resistance gene AnMan

AnManM1

5.0

[16]

scaffold36514

50,220

311

213

Disease resistance gene Phr1

Ph258M1

5.7

[21]

scaffold84752

21,471

292

94

Disease resistance gene Phr1

Ph258M2

2.1

[21]

scaffold16252

15,559

212

25

Resistance gene against lupin rust disease

RustM1

Unknown

Unpublished

scaffold15347

42,210

578

25

Early flowering gene Ku

KuH

0

[25]

scaffold21489

30,923

676

23

Soft-seed coat gene mollis

MoA, MoLi

0

[23, 30]

scaffold75616

14,783

63

16

Pod-non-shattering le

LeLi

6.0

[29]

scaffold87978

9,909

59

17

Pod-non-shattering gene le

LeM2

1.3

[24]

scaffold79908

20,738

103

22

Pod-non-shattering gene tardus

TaM1

2.1

[26]

scaffold15347

21,529

578

25

Pod-non-shattering gene tardus

TaLi

1.4

[27]

scaffold36274

8,191

62

4

Low alkaloid gene iucundus

IucLi

0.9

[28]

scaffold30160

20,677

667

22

Average scaffold size and marker numbers

27,687

378

87

a The list of 10 sequenced cultivars is presented in Tables 2 and 5

Sequence alignments on the 23 scaffolds among 10 sequenced cultivars discovered a total of 8,700 SNP markers and 1,997 InDel markers (Table 4). The average numbers of SNP and InDel markers for each scaffold were 378 and 87, respectively. Generally, scaffolds in longer length contained more markers than shorter scaffolds. For example, scaffold2572 (55,753 bp in length) contained 1,071 markers; while scaffold36247 (8,191 bp in length) had 66 markers (Table 4).

Development of diagnostic markers linked to the R gene PhtjR by genotyping markers from the genetic linkage map

The R gene PhtjR conferring resistance to PSB disease was mapped in the SLG-11 of the sequence-defined genetic linkage map of lupin (Additional file 1). Of the 3,277 genotyped markers, 343 were on SLG-11 (Additional file 1). Thirty-three genotyped markers were distributed within 5 centiMorgans (cM) of the R gene PhtjR (highlighted in green in Additional file 1; also presented in Table 5). The comparison between the PhtjR gene phenotypes and the marker genotypes among the 10 sequenced cultivars identified 17 markers where the marker genotypes completely matched the PSB disease phenotypes (Table 5); these 17 markers were considered “candidate diagnostic markers” for the PhtjR gene. The other 18 markers showed the R-allele marker genotype on one or more cultivars without the R gene, which is the linkage disequilibrium decay [47], and is also called “false positive” [11, 48, 49] (Table 5).
Table 5

Identification of candidate diagnostic markers through genotyping sequence-defined markers with whole genome sequencing data from 10 cultivars on genetic linkage map flanking the R gene PhtjR conferring resistance to phomopsis in Lupinus angustifolius

aMarkers showing genotypes completely consistent with PSB disease phenotypes on all 10 cultivars are considered candidate diagnostic markers and are highlighted in green

bTwo nucleotides separated by a stroke line in brackets are SNP markers; nucleotides in brackets without a stroke line are InDel markers

cMarker positions are the nucleotide positions on the reference genome sequence assembly from cultivar Tanjil (Genbank BioProject number PRJNA179231)

dMarkers showing R-allele genotype on cultivars without the R gene Phtj (false positives) are highlighted in red

eMarker sequences missing in genome re-sequencing were recorded as missing data “-”

fGenotypes of R gene PhtjR on sequenced cultivars presented in blue: R = presence of PhtjR gene; S = absence of PhtjR gene [44]

Five candidate diagnostic markers, together with five non-diagnostic markers as controls, were converted into sequence-specific simple PCR markers by designing a pair of sequence-specific primers flanking each SNP site (Table 6). Validation tests confirmed that the five candidate diagnostic markers, DAFWA926, DAFWA2836, DAFWA3794, DAFWA6277 and DAFWA8077, were truly diagnostic on the 27 historical and current commercial cultivars released in Australia (Table 7). The three SNP markers most closely linked to the R gene (co-segregating), DAFWA3123, DAFWA4020 and DAFWA6895, had six to eight false positives (Table 7). SNP markers DAFWA2747 and DAFWA4021 have seven and eight false positives, respectively (Table 7). The genotypes of SNP markers were easily differentiated by high resolution melting (HRM) on LightScanner (Fig. 2).
Table 6

Conversion of SNP markers identified from genotyping markers on genetic linkage map flanking the R gene PhtjR into sequence-specific PCR markers suitable for genotyping by high resolution melting (HRM) with LightScanner

Marker

Primers

Primer sequence (5′-3′)

DAFWA926

DAFWA926F

GGTTGGGTTAACTTTTATGTCTAAAATC

DAFWA926R

GGTAAGTTTATTTTTCTAAAGTTGAAC

DAFWA2836

DAFWA2836F

CACATAAGAATATGGAAATGGAGA

DAFWA2836R

CTGTAAACTGAAGGTGGGCATT

DAFWA3794

DAFWA3794F

GAAAGGAGAAAACTAATCAACATAAG

DAFWA3794R

ATTAGGGTTTGAGATAGAGTAACAT

DAFWA2747

DAFWA2747F

CCTAACTTCCGATCCAGTAAGC

DAFWA2747R

CTTTGATCGCTTGGGTTTC

DAFWA6277

DAFWA6277F

TTCGGGAATTTGTATGAGCT

DAFWA6277R

GGATGGATTCAAAGGTTCAAG

DAFWA8077

DAFWA8077F

GAGATTATTTTCACAAGCTTCCTC

DAFWA8077R

CCTTTTAGCTTATTCAATTAGCTTG

DAFWA6895

DAFWA6895F

TGAAGGTCCAATACCAGCAAG

DAFWA6895R

CAACTTCCCTGGAGCAAAA

DAFWA4020

DAFWA4020F

CTAGATAGTTTCGTTTTATCATAC

DAFWA4020R

GACATAAAGCTTATATATTTGCA

DAFWA3123

DAFWA3123F

CCCTGGACTCTCTCCCTGTATT

DAFWA3123R

GAATGAAAGTTTGATATGCATAATAA

DAFWA4021

DAFWA4021F

GCTCAGAAACGGTGTCGTT

DAFWA4021R

GAAGACCTCCAAAACCAAAGC

Table 7

Validation of sequence-specific SNP markers identified from genotyping markers on a genetic linkage map flanking the R gene PhtjR conferring resistance to phomopsis stem blight disease on all historical and current commercial cultivars of Lupinus angustifolius released in Australia

aGenotypes of R gene PhtjR on commercial cultivars are presented as: R = presence of PhtjR gene; S = absence of PhtjR gene [44]

bGenetic distance of the marker to the R gene PhtjR in centiMorgans (cM) was adapted from the mapping studies [38]

cMarkers showing R-allele genotype on cultivars without the R gene (false positives) are in highlighted in red

dSNP markers showing marker genotypes completely consistent with the PhtjR gene phenotypes in all 27 commercial cultivars (no false positive) are diagnostic markers, and are highlighted in green

Fig. 2

Validation of simple PCR-based SNP markers linked to the R gene PhtjR conferring phomopsis stem blight disease resistance on all 27 historical and current cultivars of Lupinus angustifolius released in Australia by high resolution melting (HRM) on LightScanner. SNP marker DAFWA6277 (left) was confirmed as diagnostic for the PhtjR gene, as the three cultivars (Wonga, Tanjil and Barlock) showed the resistance marker allele (melting curves in blue), while all the other 23 cultivar not possessing the R gene has the susceptible marker allele (melting curves in red). In contrast, SNP marker DAFWA3123 (right) was confirmed as non-diagnostic, since six cultivars (Table 7) without the R gene had the resistance marker allele (melting curves in blue). Detailed records of genotypes for 27 cultivars of these two markers are presented in Table 7

Development of diagnostic markers linked to the R gene PhtjR by marker mining on a genome sequence assembly scaffold

The three SNP markers most-tightly linked to the R gene PhtjR (co-segregating, genetic distance 0 cM) on the genetic linkage map were DAFWA3132, DAFWA4020 and DAFWA6895 (Additional file 1), which were confirmed as non-diagnostic (Table 7). These three SNP markers on the same scaffold84773 in the lupin genome sequence assembly (Additional file 1). Scaffold84773 was used as a test case to investigate the feasibility of developing diagnostic markers by marker mining on genome sequencing assembly scaffolds.

The length of scaffold84773 on the reference genome sequence assembly based on cultivar Tanjil (Genbank accession number “gi 448398638”, AOCW01145302) was 33,448 bp. DNA sequence alignment of the 10 sequenced cultivars on scaffold84773 revealed 489 SNP markers and 101 InDel markers (Additional file 3). Of the 489 SNP markers, 187 had marker genotypes completely matching with PhtjR gene phenotypes on all 10 lupin cultivars, and were considered candidate diagnostic markers (highlighted in green in Additional file 3). The other 302 SNP markers were non-diagnostic, evidenced by one or more false positives in the 10 sequenced cultivars. Similarly, 56 InDel markers were identified as candidate diagnostic markers (highlighted in blue in Additional file 3); the other 45 InDel markers were non-diagnostic (Additional file 3).

A small subset of 10 SNP markers and four InDel markers arising from sequence alignment on scaffold84773 were selected for further investigation (Table 8). These 14 markers exhibited a wide range of variation in marker genotypes among 10 sequenced lupin cultivars. Markers SNP20, SNP25, SNP263, SNP271, InDel2 and InDel10 showed marker genotypes consistent with R gene PhtjR phenotypes of all 10 sequenced cultivars, and were identified as candidate diagnostic markers. On the 10 sequenced cultivars, false positives were discovered in InDel28 (1), SNP250, SNP268 and InDel66 (2), SNP264 (7), and SNP267 and SNP272 (8) (Table 8). Six SNP markers and four InDel markers were converted to sequence-specific PCR markers by designing a pair of sequence-specific primers flanking the marker variation sites (Table 9). Validation tests on the 27 Australian historical and commercial cultivars confirmed three SNP markers, SNP20, SNP25 and SNP263, had genotypes consistent with PSB phenotypes, and were diagnostic for the R gene PhtjR (Table 10). On these 27 cultivars, false positives were discovered on SNP271 (1), SNP250 (6) and SNP264 (17) (Table 10). Two InDel markers, InDel2 and InDel10, were diagnostic on all 27 cultivars, while InDel28 and InDel66 had four and eight false positives, respectively (Fig. 3).
Table 8

List of a small portion of SNP markers and InDel markers discovered by marker mining on scaffold84773 (Genbank accession # AOCW01145302) showing large variation in marker genotypes among 10 sequenced cultivars and identification of candidate diagnostic markers for the R gene PhtjR of Lupinus angustifolius a

aThe full lists of the 489 SNP markers and 101InDel markers discovered from sequence alignment on scaffold84773 are markers in Additional file 3. Names of identified markers are consistent with the names labelled numerically in Additional file 3

bMarkers showing R-allele genotypes on cultivars without the R gene PhtjR (false positives) are in highlighted in red

cMarkers showing genotypes consistent with disease resistance phenotypes on all 10 sequenced cultivars are considered as candidate diagnostic markers, and are highlighted in green

Table 9

Conversion of SNP markers and InDel markers arising from marker mining on scaffold84773 into sequence-specific PCR markers in Lupinus angustifolius

Marker name

Primers

Primer sequence (5′-3′)

SNP 20

SNP20F

GTCCCTGCCATTATTAATAGTTACT

SNP20R

CATCATGAGTCAATTTACCACTTA

SNP 25

SNP25F

GTCACTAATTTTATCTTTGCAAGA

SNP25R

GATCATAAGAATAATAATAATAATTTGGT

SNP 250

SNP250F

GACTTAGTAATGTGCAACAAGAG

SNP250R

CTGACACTACAGGTTCGCCT

SNP 263

SNP263F

GGAACATTGTGATTCAGTCACC

SNP263R

GATAGGTTTGTTGCAATAAGCG

SNP264

SNP264F

GTTTCTTAGTTGCATAGTTGCAA

SNP264R

CAAAACATTCATAAGTAACAAGG

SNP271

SNP271F

CGACACCATCTGATATATGAAAATAA

SNP271R

ACCGGAAATCTGTGTTTTTC

InDel2

InDel2F

GATAAAGTATATCTAAATTATGTTTGC

InDel2R

CTATATTTTGTATCAATTATAACAAATT

InDel10

InDel10F

GTTAAGTGGTAAATTGACTCATG

InDel10R

GTTTTRCATTCTTGCAAAGATAAAATTAG

InDel28

InDel28F

CTACAATAGCCACACAAATAG

InDel28R

GTTTAGATGGCCMTGTGC

InDel66

InDel66F

CTTCTGAGTTGGACCATAAAC

InDel66R

ACTCACATTTACAGAACTTTAACT

Table 10

Validation of sequence-specific SNP and InDel markers arising from marker mining on scaffold84773 linked the R gene PhtjR conferring resistance to PSB disease on all historical and current commercial cultivars of Lupinus angustifolius released in Australia

aGenotypes of R gene PhtjR on commercial cultivars: R = presence of PhtjR gene; S = absence of PhtjR gene [44]

bMarkers showing R-allele genotype on cultivars without the R gene (false positives) are highlighted in red

cMarkers showing genotypes completely consistent with PhtjR gene phenotypes in all 27 commercial cultivars are diagnostic markers, and are highlighted in green

Fig. 3

Validation of InDel markers arising from marker mining on genome sequence assembly scaffold84773 linked to the R gene PhtjR conferring phomopsis stem blight disease resistance on all 27 historical and current cultivars of Lupinus angustifolius by polyacrylamide electrophoresis gels. The 27 cultivars are: Uniwhite (Lane 1), Uniharvest (Lane 2), Unicrop (Lane 3), Marri (Lane 4), Illyarrie (Lane 5), Yandee (Lane 6), Chittick (Lane 7), Danja (Lane 8), Geebung (Lane 9), Gungurru (Lane 10), Yorrel (Lane 11), Warrah (Lane 12), Merrit (Lane 13), Myallie (Lane 14), Kalya (Lane 15), Wonga (Lane 16), Belara (Lane 17), Tallerack (Lane 18), Tanjil (Lane 19), Moonah (Lane 20), Quilinock (Lane 21), Jindalee (Lane 22), Mandelup (Lane 23), Coromup (Lane 24), Jenabillup (Lane 25), Gunyidi (Lane 26) and Barlock (Lane 27). Disease phenotypes of the cultivars are presented as “S” (susceptible) or “R” (resistant) in blue letters. Marker “InDel10” was confirmed as diagnostic for the PhtjR gene, since it showed the marker genotypes consistent with PSB phenotypes on all cultivars. In comparison, marker “InDel 66” was confirmed non-diagnostic, since eight cultivars (arrowed in red) without the R gene had the resistance marker allele (“false positives”)

Linkage confirmation, validation, and application of established markers

The two sequence-specific, PCR-based SNP markers developed from genotyping markers from the genetic linkage map, DAFWA6277 and DAFWA8077, were successfully genotyped on the F8 population containing 186 RILs segregating for the R gene PhtjR [44]. Linkage analysis using the software program MapManager [50] based on marker genotypes and PSB disease phenotypes confirmed that these two markers are linked to the R gene PhtjR with a genetic distance of 1.1 cM, which would be approximately 99 % accurate for selecting lupin progeny with the R gene for MAS.

Three of the sequence-specific, PCR-based markers arising from marker mining on scaffold87443 developed this study—SNP20, SNP25 and InDel10—were genotyped on the F8 RIL population derived from the Unicrop × Tanjil cross which was segregating for the PhtjR gene [44]. All three markers had marker genotypes completely consistent with PSB disease phenotypes on all 186 RILs (co-segregating). Further validation identified marker genotypes consistent with PSB disease phenotypes on all 69 advanced breeding lines and 163 parental lines used for crossing in the Australian lupin breeding program.

The genetic linkage analysis and validation tests confirmed that markers developed through the two different approaches in this study were all superior to previously developed markers [44] both in accuracy and in wide applicability. The two SNP markers, SNP20 and SNP25, which fit well with the cost-effective, high-throughput SNP genotyping platform LightScanner, have been applied for MAS in the Australian lupin breeding program.

Discussion

Genome sequence is a fundamental knowledge in understanding the genomics, genetic and biology in plants. Thanks to the advancements in parallel sequencing technologies in recent years, tens of thousands of genomes are in the process of being sequenced [51]. At current time, “close-to-complete genome sequences” have only been achieved on a few model plant species, such as Arabidopsis, rice, Brachypodium, and Medicago [51, 52] where DNA sequences are available almost continuously from the beginning to the end of each chromosome in the genomes. The lengths of sequence span of “complete” genome sequences are equal to the plant genome sizes. However, the majority of other published plant genomes are still at “draft” stage, where genome sequences are presented as large pieces of scaffold sequences. The scaffolds sequences can be aligned into each chromosome through the help of dense genetic linkage maps [53, 54], but many gaps exist between scaffolds on each chromosome. The sequence spans of “draft” genome sequences are smaller than the genome sizes. For examples, the length span of recently released high-depth (358X) genome sequence (1.34Gb) reached to 89.3 % coverage of the oak tree genome size (1.5Gb) [55]; the length of the genome sequence reported on Setaria (396.7 Mbp) was 77.8 % of the genome size (510 Mbp) [54]; the length of the cucumber genome sequence published (243.5 Mbp) was approximately 66 % of the genome size (367 Mbp) [56]. The two major challenges for obtaining complete genome sequences in plant genome sequencing projects are the large genome sizes and the repetitive sequences [52]. The lupin draft genome sequence has a relatively low genome coverage at 51.9 % [38], which was duo to three factors: the lupin genome size is pretty large (at 1.153 Gb) [38]; the genome is rich in repetitive sequences [34]; and the draft sequence was generated from a low costing sequencing project (equivalent to US$5,000) originated from two sequencing libraries with sequencing depth only at 27X [38]. In this study, the genome sequencing and re-sequencing data were used in the identification and selection of candidate diagnostic markers linked to a gene conferring disease resistance. The final selected candidate markers then went through the genetic linkage confirmation step and validation step in the same way as in other standard marker development methods [16, 21, 37]. The linkage confirmation and validation steps ensured that the final markers recommended for MAS were single copy in the genome, were closely linked to gene of interest, were applicable to wide range of breeding germplasm, and were desirable for marker-assisted plant breeding. There are lively discussions among plant scientists about what more can be gained from an in-depth, time-consuming and costly effort to generate high-quality complete sequences than from low coverage draft genome sequences [52]. The results in this study have demonstrated that low coverage genome sequencing and re-sequencing data were sufficient and very effective on marker development in molecular plant breeding. The same low coverage lupin genome sequence was also very successful in the discovery of a candidate gene based diagnostic markers linked to anthracnose disease resistance [38], and in the conversion of previously established gel-based InDel markers into SNP markers to suit modern SNP genotyping platforms for marker implementation in lupin breeding [51].

This study was the first attempt at whole genome re-sequencing of the legume crop species L. angustifolius following a 2013 report on its draft genome sequence [38]. Comparing the genome sequences of 10 sequenced cultivars identified 0.3 to 0.6 million molecular markers, which demonstrated the power of whole genome sequencing and re-sequencing for marker discovery. These markers provide lupin breeders and molecular geneticists with a broader suite of options for a wide range of breeding and research purposes. Lupin is a relatively new agricultural crop, domesticated in the early 1970s from its wild relatives. The abundance of SNP and InDel markers among commercial cultivars reflects the rich genetic diversity of the wild parental lines used in the domestication and breeding efforts over the last 40 years. It is evident that the selection pressure for certain desirable agronomic traits of interest in the lupin breeding program had a major impact on genetic diversity at chromosome level. For example, anthracnose disease caused a serious epidemic in Australia in 1996. A major R gene, Lanr1, had been exclusively utilized by the lupin breeding program to combat the disease since 1996 [22]; which resulted in the lower genetic diversity in SLG-1 where the Lanr1 gene was mapped among the recently released commercial cultivars. In contrast, there are at least three major R genes each independently conferring resistance to phomopsis stem blight disease applied in the Australian lupin breeding program [44]; the lack of selection pressure for PhtjR gene has helped to preserve the genetic diversity in SLG-11 where the PhtjR gene was mapped.

Genetic mapping is a commonly-used approach for marker-trait association discovery in plant molecular studies. In the last three decades, genetic linkage maps have been constructed for most cultivated grain crops. The application of NGS and genome sequencing in recent years has enhanced the power of plant genetic mapping. For example, a genotyping by sequencing (GBS) study discovered and mapped 416,856 markers in wheat [57]; a whole genome sequencing study on a F8 RIL population in rice mapped 1,226,791 SNP markers [58]; and sequencing and physical mapping identified 1,013,161–2,053,580 SNP markers in each of four mapping populations in barley [59]. In this study, we anchored 207,887 markers on the lupin genetic linkage map. In theory, all markers with known DNA sequences on genetic linkage maps can be genotyped by whole genome sequencing and re-sequencing data. With so many markers available on genetic linkage maps, the genes of interest to breeders are usually flanked by a large number of markers, which provides ample choice for identifying diagnostic markers desirable for MAS. Yet with traditional methods, identifying diagnostic markers through conversion and validation tests on a large number of markers is tedious and time consuming. Whole genome sequencing and re-sequencing has been demonstrated in this study to be a powerful approached to select diagnostic markers from genetic maps. The 10 lupin cultivars used in the genome sequencing and re-sequencing in this study were carefully selected based on their pedigree kinship to represent genetic diversity in commercial cultivars released in Australia. Therefore, most of the candidate diagnostic markers identified from genotyping these cultivars were validated as truly diagnostic on a wide range of historical and current commercial cultivars. Two of the sequence-specific, simple PCR-based SNP markers developed in this study, DAFWA6277 and DAFWA8077, meet the two key requirements for MAS of being “diagnostic” and “closely linked (1.1 cM) to the target gene of interest”.

In molecular plant breeding, it is common that markers identified from DNA fingerprinting and genetic mapping may not be diagnostic even though they are closely linked to genes of interest, which limited their application for MAS in plant breeding [811]. In this study, we demonstrated that whole genome sequencing and re-sequencing can be applied to develop diagnostic markers for MAS through marker mining on scaffolds bearing non-diagnostic markers. All of the 24 previously-established markers linked to the 11 genes of agronomic interest in lupin were successfully located on their specific scaffolds in the genome sequence assembly. Marker mining through scaffold sequence alignments obtained, on average, 378 SNP markers and 87 InDel markers for each of 23 scaffolds bearing markers linked to lupin genes of breeder interest. In the example of PSB disease resistance, none of the three SNP markers most-tightly linked (co-segregating, or 0 cM) to the R gene PhtjR on the genetic map were diagnostic. These three non-diagnostic markers were located on the same scaffold87443. Of the 590 DNA markers obtained from marker mining from scaffold87443, a staggering 243 markers showed a diagnostic nature in the 10 sequenced cultivars, which illustrates the effectiveness of this marker development strategy. Three markers developed by marker mining on the scaffold (two SNPs and one InDel marker) were confirmed as truly diagnostic on all of the commercial cultivars, breeding lines and parental lines, and co-segregated with the R gene which is highly desirable for MAS.

Development of diagnostic markers closely linked to genes of agronomic interest is the key to the successful broad application of MAS in routine plant breeding. Functional markers, also called genic markers, are clearly the best type of marker for MAS because there is no risk of genetic recombination to cause false positives. Functional markers have broad application for MAS in a breeding program without the need for a marker validation step. In major crops, functional markers have been successfully developed and applied in plant breeding, such as functional markers for the Pm3 gene conferring resistance against powdery mildew disease [60], the Cre3 gene conferring nematode resistance [12] in wheat, the fragrance gene in soybean [61] and the bacterial leaf blight disease resistance genes xa5 [62] and Xa21 [63] in rice. However, a plant genome may contain tens of thousands of genes [53, 64], and the development of functional markers requires identifying, cloning and determining the functions of target genes, all of which requires considerable research effort. The principle of the methods in developing non-genic diagnostic markers through whole genome sequencing and re-sequencing seen in this study is the same as that for DNA fingerprinting and genetic mapping in other crops, such as the SSR marker Xgwm382 for yellow rust disease resistance [13, 65] and a sequence-tagged microsatellite marker stem rust disease resistance gene Sr2 [66, 67] in wheat. The marker development strategies illustrated here do not require tedious gene cloning. In MAS, markers linked to target genes within 1 cM genetic distance provide >99 % accuracy for predicting and selecting desired genes, which satisfies the needs of most plant breeding applications. In lupin, 1 cM genetic distance is equivalent to approximately 0.6 Mbp in the lupin genome [38]. Such a large piece of DNA in a chromosome would cover thousands of closely-linked DNA markers, offering ample choice for identifying diagnostic markers for MAS through marker mining by genome sequencing and re-sequencing. The methods demonstrated in this study provide a solution to develop diagnostic markers for plant breeding. Further investigations such as sequencing the pathogen genome [68] and studying the plant-pathogen interactions [69] could lead to the identification of the R gene for the development of functional markers.

The lupin genome size is 1.1 Gb [38], which is slightly larger than the soybean genome at 950 Mbp [53]. Currently, the cost of re-sequencing the whole genomes of nine lupin cultivars to a depth of 10–15 X including bioinformatics analysis is approximately US$15,000 at the Beijing Genome Institute (BGI-Shenzhen). The cost of genome sequencing and re-sequencing in a breeding program is a one-off cost. Once the reference genome sequence and re-sequencing data are available, they can be used for genotyping and selecting diagnostic markers for any agronomic traits of interest within this species. Therefore, whole genome sequencing and re-sequencing provides a cost-effective approach for marker discovery and development for plant breeding programs. Once the marker development work is completed, it enters the marker implementation stage. Molecular markers have been applied to large-scale MAS in the Australian national lupin breeding program since 2002. Leaf samples were taken in breeder’s field plots commencing from three weeks after sowing early in June when plants were in the juvenile stage. Tens of thousands of breeding plants were screened and selected with molecular markers annually [51]. The MAS work was usually completed in the end of August at flowering. The application of MAS has made a major impact on lupin breeding. For example, MAS with markers linked to anthracnose disease resistance has replaced the tedious glasshouse and field disease screening trials, which not only saved the cost, but also increased the genetic improvement efficiency in lupin breeding [51]. The development of diagnostic markers reported in this study provides lupin breeders with new tools for MAS to select phomopsis stem blight resistance in lupin breeding.

Conclusions

Genome sequencing and re-sequencing revealed large genetic variations among commercial cultivars in Lupinus angustifolius. We demonstrated two approaches for rapid development of diagnostic markers for MAS by utilizing genome sequencing and re-sequencing data: (1) by genotyping and selecting markers from genetic linkage maps closely linked to genes of breeder interest, and (2) by marker mining from scaffolds bearing non-diagnostic markers. Whole genome sequencing and re-sequencing provides an efficient and cost-effective way to develop diagnostic markers which has broad application in marker-assisted selection. This approach does not require the gene identification and cloning that is needed to develop functional markers. The marker development strategies illustrated in this study may overcome the bottleneck in developing markers with wide applicability in molecular plant breeding. Whole genome sequencing and re-sequencing will facilitate diagnostic tests and selection without limitation of specific breeding parents or population structures. Plant breeders will be able to precisely pyramid favourable genes and alleles to develop super crop varieties to meet the future food demand.

Methods

Plant materials

Cultivars of L. angustifolius employed for genome re-sequencing and marker validation tests were grown from single-seed-descent derived self-pollinated lines to minimize heterogeneity. The marker population for genetic linkage analysis was the F8 RILs derived from a Unicrop (susceptible to PSB disease) × Tanjil (resistant) cross. Details on this F8 population have been described previously [44]. Advanced breeding lines and parental lines used for marker validation were from the Australian national lupin breeding program. All plant materials are kept at the Department of Agriculture and Food Western Australia, and are available for scientific research purpose on request.

Genome re-sequencing on nine cultivars

The nine re-sequenced cultivars were Unicrop (the first fully domesticated cultivar in this species which was release in 1973), Yorrel (released in 1989), Merrit (1991), Kalya (1996), Tallerack (1997), Quilinock (1999), Mandelup (1994), Coromup (2006), and Jenabillup (2007). Re-sequencing of the nine cultivars was performed by the whole genome shotgun (WGS) approach [70]. DNA was extracted from three-week-old seedlings grown in a glasshouse. DNA was randomly sheared by nebulization, end-repaired with T4 DNA polymerase, and size-selected by gel electrophoresis on 1 % low-melting-point agarose. A sequencing library of insert-size 500 bp was constructed for each cultivar according to the Illumina Inc. manufacturer instructions. Pair-end sequencing of the sequencing libraries was performed on NGS platform Hiseq2000 at Beijing Genome Institutes (BGI-Shenzhen). The sequencing data for each cultivar were assembled by SOAP de novo [71]. The assembled sequences were aligned into corresponding scaffolds based on the reference draft genome sequence of Tanjil by Short Oligonucleotide Alignment Program (SOAP 2.20) [72].

Marker discovery among sequenced cultivars

Genome sequence data of the nine re-sequencing cultivars were mapped onto the reference sequences originated from cultivar Tanjil [38]. Based on the mapping result by SOAP 2.20, uniquely mapped single-end and paired-end results were used in the SNP calling. The genotypes of each individual at every genomic site were calculated by SOAPsnp [66]. Polymorphic loci against the reference sequence were selected and then filtered. SNP markers were recorded if they are supported by at least 3 reads with quality value greater than 20. The InDel markers (insertions and deletions shorter than 10 bp) were identified by gap allowed alignment (additional parameter of “-g 10” was used in SOAP2). InDels supported by at least three pair reads were detected by SOAPindel pipeline (http://soap.genomics.org.cn/) as described by Zheng et al [67]. Genomewide genetic diversity between reference cultivar Tanjil and the nine re-sequenced cultivars was based on the calculation of SNP abundance along each linkage group in the genetic map [38]. SNP numbers were counted in each non-overlapping 100 kb interval and displayed in a circular histogram using the software of circus (http://circos.ca/).

Genotyping sequence-defined DNA markers on a genetic linkage map

The sequence-defined lupin genetic linkage map and marker RAD sequence reads were reported previously [38]. The genome sequencing and re-sequencing data from each of the 10 sequenced cultivars were subjected to homology BLAST search with the RAD-seq sequence reads bearing the SNP markers and InDel markers from the genetic linkage map. The nucleotides from the SNP and InDel variation sites were recorded as marker genotypes for each cultivar. Marker sequences missing on the re-sequencing data were recorded as missing data. To maximize stringency, any RAD-seq sequences showing a sequence variation other than the target SNP/InDel site were discarded, and the corresponding genotype scored as “missing data”. Any markers with missing data on more than three of 10 sequenced cultivars were discarded.

Enrichment of molecular markers for the lupin genetic map

The genetic linkage map of L. angustifolius contained 20 SLGs with 8,244 sequence-defined markers, in which 4,214 scaffolds from the draft genome sequence assembly were anchored [38]. DNA sequences of these 4,214 scaffolds were aligned by sequence similarity and compared between cultivars Tanjil and Unicrop, being the two parental lines for the F8 RIL population based on which map was constructed [38]. The SNP markers and InDel markers discovered from sequence alignment on each scaffold were traced to each SLG through their respective SNP markers on the map.

Marker mining on scaffolds bearing markers linked to genes of agronomic traits of interest

In the last 15 years, 24 DNA markers have been established and linked to 11 genes of agronomic traits of interest by DNA fingerprinting methodologies at the Department of Agriculture and Food Western Australian [14, 16, 2130, 37, 38, 44]. The marker sequences were applied to the BLAST search of the reference genome sequence [38] to identify the specific scaffold for each marker (Table 4). For each scaffold, DNA sequences from 10 sequenced cultivars were aligned to identify the SNP markers and InDel markers for each scaffold, using the principle as demonstrated in Additional file 3.

Development of diagnostic markers through genotyping molecular markers from genetic linkage map flanking the R gene Phtj

The SNP markers and InDel markers with marker genotypes on 10 sequenced cultivars (Additional file 1) flanking the R gene PhtjR at genetic distance of 5 cM were investigated for development of diagnostic markers. The marker genotypes were compared with the PhtjR gene phenotypes. A marker is considered a “candidate diagnostic marker” for PhtjR gene if its genotypes match the PhtjR gene phenotypes on all 10 sequenced cultivars. To prove the concept of selection of diagnostic markers by this strategy, five candidate diagnostic markers together with five non-diagnostic markers as controls were selected for marker validation on all 27 historical and current commercial cultivars released in Australia to confirm their diagnostic nature. Each of these 10 selected SNP markers was converted into a sequence-specific, simple PCR-based marker by designing a pair of sequence-specific primers. Screening of these converted markers was conducted by HRM using LightScanner (Idaho Technology Inc., USA) according to the manufacturer’s instructions, except that EvaGreen Dye (Biotium, USA) replaced the LC Green Dye due to its lower cost and good performance.

Development of diagnostic markers linked to R gene PhtjR through marker mining from genome sequence assembly scaffold

The genome sequence assembly scaffold87443, which bears markers most-tightly linked to the R genes PhtjR (co-segregating) on the lupin genetic map (Additional file 2) was used as a test case for marker mining to identify diagnostic markers. Genome sequencing data on scaffold87443 from 10 sequenced cultivars were aligned; all SNP markers and InDel markers from the sequence alignment were recorded (Additional file 3). Markers showing genotypes consistent with PhtjR gene phenotypes on all 10 sequenced cultivars were regarded as candidate diagnostic markers (Additional file 3). In order to validate their diagnostic nature on a broader range of cultivars, six SNP markers and four InDel markers were converted into sequence-specific PCR-based markers by designing a pair of sequence-specific primers for each. The screening of converted SNP markers was through HRM on LightScanner. InDel markers were screened on 6 % acrylamide gel electrophoresis using the BIO-RAD Protean II electrophoresis unit at 80 volts for 6 h. The 10 converted markers were tested on the 27 historical and current commercial cultivars to examine the correlation of marker genotypes and PhtjR gene phenotypes.

Linkage confirmation and validation of established markers

The two diagnostic markers most closely linked to the PhtjR gene identified from genotyping markers from the lupin genetic linkage map (DAFWA6277 and DAFWA8077) and three diagnostic markers arising from marker mining from scaffold 84773 (SNP20, SNP25 and InDel10) were tested on a F8 population derived from the cross containing 186 RILs from a Unicrop (susceptible to PSB) × Tanjil (resistant) cross. The marker genotyping score data and PSB disease phenotyping data were merged and analysed using the software program MapManager QTX [45] to confirm the genetic linkage between these markers and the R gene PhtjR [44].

The two best SNP markers developed in this study (which were co-segregating with the R gene PhtjR and diagnostic on all released commercial cultivars), SNP20 and SNP25, were further validated on the 69 advanced breeding lines and on 163 parental lines used for crossing in the Australian lupin breeding program in 2014 to evaluate their applicability for MAS in lupin breeding.

Notes

Abbreviations

MAS: 

Marker-assisted selection

NGS: 

Next-generation sequencing

SNP: 

Single nucleotide polymorphism

InDel: 

Insertion/deletion

MFLP: 

Microsatellite-anchored fragment length polymorphism

RILs: 

Recombinant inbred lines

PSB: 

Phomopsis stem blight

PCR: 

Polymerase chain reaction

SLG: 

Eequence-defined linkage group

RAD-seq: 

Restriction-site associated DNA sequencing

HRM: 

High-resolution melting

Declarations

Acknowledgements

This research was funded by the Department of Agriculture and Food Western Australia (DAFWA) through the “Lupin Marker Strategy” project, and the Grains Research and Development Corporation (GRDC) of Australia through research project “DAW00238”.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Agriculture and Food Western Australia
(2)
Beijing Genome Institute – Shenzhen
(3)
State Agricultural Biotechnology Centre, Murdoch University

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