Plant Science 217–218 (2014) 47–55

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SNP discovery and high-density genetic mapping in faba bean (Vicia faba L.) permits identification of QTLs for ascochyta blight resistance Sukhjiwan Kaur a , Rohan B.E. Kimber e , Noel O.I. Cogan a , Michael Materne c , John W. Forster a,d,∗ , Jeffrey G. Paull b a Department of Environment and Primary Industries, Biosciences Research Division, AgriBio, Centre for AgriBioscience, 5 Ring Road, La Trobe University Research and Development Park, Bundoora, Victoria 3083, Australia b School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, Glen Osmond, South Australia 5064, Australia c Department of Environment and Primary Industries, Biosciences Research Division, Grains Innovation Park, PMB 260, Horsham, Victoria 3401, Australia d La Trobe University, Bundoora, Victoria 3086, Australia e South Australian Research and Development Institute, GPO Box 397, Adelaide, South Australia 5001, Australia

a r t i c l e

i n f o

Article history: Received 18 September 2013 Received in revised form 19 November 2013 Accepted 25 November 2013 Available online 1 December 2013 Keywords: Grain legumes Comparative genetics Fungal disease Disease resistance Trait dissection Breeding

a b s t r a c t Ascochyta blight, caused by the fungus Ascochyta fabae Speg., is a common and destructive disease of faba bean (Vicia faba L.) on a global basis. Yield losses vary from typical values of 35–40% to 90% under specific environmental conditions. Several sources of resistance have been identified and used in breeding programs. However, introgression of the resistance gene determinants into commercial cultivars as a gene pyramiding approach is reliant on selection of closely linked genetic markers. A total of 14,552 base variants were identified from a faba bean expressed sequence tag (EST) database, and were further quality assessed to obtain a set of 822 high-quality single nucleotide polymorphisms (SNPs). Sub-sets of 336 ESTderived simple sequence repeats (SSRs) and 768 SNPs were further used for high-density genetic mapping of a biparental faba bean mapping population (Icarus × Ascot) that segregates for resistance to ascochyta blight. The linkage map spanned a total length of 1216.8 cM with 12 linkage groups (LGs) and an average marker interval distance of 2.3 cM. Comparison of map structure to the genomes of closely related legume species revealed a high degree of conserved macrosynteny, as well as some rearrangements. Based on glasshouse evaluation of ascochyta blight resistance performed over two years, four genomic regions controlling resistance were identified on Chr-II, Chr-VI and two regions on Chr-I.A. Of these, one (QTL-3) may be identical with quantitative trait loci (QTLs) identified in prior studies, while the others (QTL-1, QTL-2 and QTL-4) may be novel. Markers in close linkage to ascochyta blight resistance genes identified in this study can be further validated and effectively implemented in faba bean breeding programs. Crown Copyright © 2013 Published by Elsevier Ireland Ltd. All rights reserved.

1. Introduction Faba bean (Vicia faba L.) is a diploid (2n = 2x = 12) legume species with a facultative allogamous reproductive habit, such that rates of outcrossing differ between environments. It is one of the earliest legumes to have been domesticated, and ranks fourth in terms of cultivation area among the cool-season food legumes after pea, chickpea and lentil (FAOSTAT 2011). Faba bean plays an important role in management of soil fertility through crop rotation and nitrogen fixation, hence contributing to agricultural sustainability. The size of the nuclear genome of faba bean is substantial (c. 13 Gb) and hence poses a major challenge to effective genetic and genomics studies [1].

∗ Corresponding author. Tel.: +61 390327054. E-mail address: [email protected] (J.W. Forster).

World production of faba bean is dominated by China which produces c. 60% of the total crop, followed by Ethiopia. Egypt is also a significant producer and major importer [2], and the majority of faba beans produced in Australia have been exported to Egypt for human consumption as dry beans and to Saudi Arabia for processing and canning. Major priorities for faba bean breeding programs include resistance/tolerance to biotic and abiotic stresses, adaptation to target environment, and manipulation of plant growth and development habit, phenology and seed quality traits in order to enhance crop management [3,4]. The majority of these traits are known to be under the control of multiple genes, and are hence difficult to incorporate into commercial cultivars through use of traditional breeding methods. Implementation of markerassisted selection (MAS) methods provides a potential solution for this problem, through development and use of molecular genetic markers in close linkage to genes for agronomic traits, although complexities of genetic control require sophisticated use of MAS approaches.

0168-9452/$ – see front matter. Crown Copyright © 2013 Published by Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.plantsci.2013.11.014

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Of breeders’ target traits, ascochyta blight, caused by Ascochyta fabae Speg., is a serious and destructive disease that is prevalent in the Middle East, Europe, Canada, New Zealand, and Australia. Symptoms of infection include reduction in photosynthetic area, pod and seed infection, seed staining and seed abortion leading to yield reduction and reduced market value. Typical yield losses of 35–40% occur, which may increase to 90% under conditions favourable to spread of the disease, such as cool and wet weather [5,6]. A common means by which disease is introduced into new growing regions is by seed-borne infection, but the spread of inoculum in faba bean-growing districts is through wind- or rain-dispersed ascospores or conidia, respectively [7]. The disease can be managed effectively by application of chemicals and crop rotation, but at an economic cost to growers and at risk of failure if disease pressure is high. The most effective and sustainable means of control is the development of resistant cultivars. Several sources of resistance have been identified from faba bean breeding programs, but little information is available on the genetic basis of disease resistance [6,8–11]. A number of studies have identified QTLs for ascochyta blight resistance in faba bean [6,12,13]. However, no MAS systems have yet been established [14]. The major reason for this shortfall is the low marker density of previously generated genetic maps, such that genetic markers have not been defined in sufficiently close linkage to target QTLs to permit effective implementation in breeding programs [2,4]. In the absence of numerous species-specific markers, comparative genetics approaches have been used to transfer genetic markers developed in closely related legume species such as Medicago truncatula Gaertn. and soybean (Glycine max [L.] Merr.) for linkage mapping in faba bean. Such gene-based orthologous markers allow assessment of the degree of extent of macrosynteny between species to be characterised and enable targeted identification of markers for crop breeding. Several faba bean linkage maps have been constructed using intron targeted amplified polymorphisms (ITAPs) [15,16] developed from a comparative study between M. truncatula, G. max and Lupinus albus L. [17]. However, advances in sequencing technologies now provide opportunities to develop large numbers of gene-based markers such as EST-derived SSRs and SNPs from under-resourced crop species, and hence to address problems with limited map resolution in faba bean. For other cool-season grain legumes, large numbers of SNPs and SSRs have been developed from lentil [18,19], chickpea [20–24] and field pea [25,26]. Several recent studies have reported the development of ESTand genomic DNA-derived SSRs from faba bean [26–28], and a linkage map based on a limited number (128) of genomic DNA-derived and EST-SSR markers has been recently described [29]. However, no faba bean-specific SNP marker resources have been described to date. SNP markers, being robust, bi-allelic and co-dominant in nature, highly abundant and amenable to highthroughput genotyping, provide the ideal system for high-density genetic mapping. In addition, SNP discovery from transcribed regions of the genome provides the basis to establish a direct link between sequence polymorphism and putative functional variation. The objectives of the current study were: development of SNP markers based on analysis of transcriptome data generated from two distinct faba bean genotypes; construction of a genetic linkage map for the Icarus × Ascot mapping population, which varies for ascochyta blight resistance; comparative genomics analysis between faba bean and other closely related legume species; and identification of QTLs associated with ascochyta blight resistance in order to identify closely linked markers that could be used in future breeding programs.

2. Materials and methods 2.1. Plant materials A recombinant inbred (RIL) population was generated from an intra-specific cross between single genotypes from the faba bean cultivars Icarus (ascochyta blight susceptible) and Ascot (ascochyta blight resistant). Single seed descent was undertaken from F2 progeny-derived genotypes for three generations under controlled environment conditions to generate an F5:6 mapping population consisting of 95 RILs. Leaf material (constituting a 15–20 mm leaf punch) was harvested from young plants, and genomic DNA was extracted using the QIAGEN DNeasy 96 Plant Kit according to the manufacturer’s instructions. DNA was eluted into 50 ␮L of sterile water and stored at −20 ◦ C until required. 2.2. SNP discovery and assay design Quality-trimmed transcriptome sequencing data generated from both the Icarus and Ascot parents was reference-aligned to the already published database EST database [26] in order to identify base variants using the NextGENe software v1.96 (Softgenetics, State College, PA, USA). Base variants were further filtered to obtain a sub-set of high quality SNPs. All insertion–deletion mutants (indels) and any base variants that were heterozygous within a genotype were excluded. The data set was further filtered on the basis of sequencing depth (>6 reads), and presence of other base variants within 20 bp regions on either side of the targeted SNP. Following identification of predicted exon–intron boundaries using putative M. truncatula orthologues as a reference, a final set of potential SNPs was generated for genotyping purposes. Regions extending 100 bp 5 - and 3 - from the target sequence variant were selected for a sub-set of SNPs that were sent to Illumina for assay design, and the sub-set of finalised SNPs for mapping purposes was selected based on designability score (>0.6) (Supplementary 1). 2.3. SSR and SNP genotyping EST-derived SSR marker assays [26] were screened on the mapping family parents for detection of polymorphisms. Primer synthesis and PCR amplifications were performed following methods using M13-tailed assays [30]. PCR products were combined with the ABI GeneScan LIZ500 size standard and analysed using an ABI3730xl (Life Technologies Australia Pty Ltd., Victoria, Australia) capillary electrophoresis platform according to the manufacturer’s instructions. Allele sizes were scored using GeneMapper® 3.7 software package (Life Technologies Australia Pty Ltd.). SNP genotyping was performed using a highly multiplexed SNPOPA pool according to the manufacturer’s instructions, with 250 ng of template genomic DNA from each RIL and parental genotype. Genotyping assays were processed by the Illumina iScan reader, and assignment of genotypes was performed using the GenomeStudio software v2011.1 (Illumina). 2.4. Genetic linkage mapping and linkage group nomenclature All genotyping data was tested for conformance to the expected Mendelian segregation ratio (1:1) using 2 test. Markers with a 2 score >10 (P < 0.001) were excluded from further analysis. The genetic linkage map was generated using Map Manager Software v20 [31]. Map distances were calculated using the Kosambi mapping function [32] at a threshold LOD score of 4. LGs were assigned, when possible, on the basis of commonality with already published linkage maps [15,16]. To achieve this outcome, sequences underpinning EST-derived markers on the Icarus × Ascot linkage map were used for BLAST analysis against available sequence

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Table 1 Total number of markers (SSRs and SNPs) analysed, tested for polymorphism and assigned to genetic map locations. Marker type

Total number of markers

EST-SSRs EST-SNPs Total markers

No. of polymorphic markers

No. of mapped markers

336 768

71 480

57 465

1104

551

522

data corresponding to mapped ITAP markers, as well as the M. truncatula genome.

2.5. Assessment of ascochyta blight incidence and QTL analysis Two phenotyping screening experiments for ascochyta blight were performed over two seasons (2011 and 2012) in controlled conditions within a glasshouse at the Waite Campus of the University of Adelaide, Glen Osmond, South Australia [33]. Both experiments were conducted in the same manner, balanced and performed under similar environmental conditions. Both trials used a single isolate of A. fabae, 88/10, collected in 2010 from a commercial faba bean crop in Tarlee, South Australia. Each trial was conducted as a randomised complete block design with four replicates and three plants per replicate. Plants were grown in trays containing 12 punnets per tray (0.55 L each) with one plant per punnet filled with potting mix and maintained outside for 4 weeks (to the stage of c. 4 leaf nodes) before being moved into the glasshouse. Plants were then inoculated with a pycniodiospore suspension of A. fabae using a spray applicator until run-off, at a density of 6 × 105 spores/ml in 2011 and 1 × 106 spores/ml in 2012, and maintained in high humidity at c. 16–18 ◦ C. Disease assessments were made on individual plants 21 days after inoculation (DAI) using a 1–9 scale (1 = no symptoms; 9 = severe disease) as well as percentage necrosis of stem and leaf in the second experiment (2012). Phenotypic evaluation data was analysed to calculate means after adjustments for any spatial patterns within the trial. Averages for plant symptom score were calculated from individual plant assessments and used to estimate genotype-specific average values for symptom score. Models were fitted using residual maximum likelihood (REML) as implemented in GenStat (GenStat Committee, 2002 and previous releases). Best linear unbiased predictions (BLUP) analysis was used to calculate narrow-sense heritability. Means of symptom rating from each individual of the mapping population were used to construct frequency distribution histograms in order to determine the mode of inheritance for the trait. QTL detection was performed using marker regression and interval mapping (IM) in QTL Cartographer v2.5 [34]. Simple interval mapping (SIM) and composite interval mapping (CIM) methods were used to identify QTLs for ascochyta blight resistance.

Significance levels for log-of-odds (LOD) thresholds were determined using 1000 permutations. 2.6. Comparative genomics All sequences underpinning EST-derived markers assigned to the linkage were BLAST analysed against the genomes of well-characterised closely related species including M. truncatula (version 3.5), Lotus japonicus L. (version 2.5), soybean (version 1) and chickpea (Cicer arietinum L.) [35] to detect significant matches at a threshold E value > 10−10 and determine the presence and extent of macrosynteny. 3. Results 3.1. Marker discovery and genetic linkage mapping From comparison of transcript reads obtained from both the Icarus and Ascot parents to the EST reference dataset, a total of 14,552 base variants were identified and a frequency of 3.72 SNPs per kb was observed. After application of quality filters, a set of 1905 predicted SNPs was generated. This set was further filtered on the basis of read coverage and probable proximity to exon–intron boundaries in order to obtain a sub-set of 822 candidate SNPs, from which a final multiplexed set of 768 SNPs was used in the OPA assay for genotyping and map construction. Of 768 SNPs screened on the Icarus × Ascot RIL population, 551 (72%) were found to be polymorphic (Table 1). During SNP analysis, three clusters were identified for each variant, corresponding to homozygous (AA), heterozygous (AB) and alternate homozygous (BB) classes. The majority of SNP markers produced two major clusters representing the two homozygous genotypic classes, but a small additional cluster corresponding to the heterozygous class was also occasionally observed. As the mapping population was genotyped at the F5 level, the frequency of such events was found to be relatively low (c. 10–20%). In addition to SNPs, a total of 336 EST-SSRs were also used to screen the mapping parents for polymorphism detection, of which 71 (21%) detected polymorphism (Table 1). Finally, a combined set of 622 markers (551 SNPs and 71 SSRs) was used for linkage mapping analysis. The 2 test (P < 0.05) identified 9.6% of SNPs and 8.1% of EST-SSRs that failed to segregate in accordance with the expected Mendelian inheritance and were

Table 2 Linkage map statistics for the Icarus × Ascot map. LG

Predicted Mt chromosome

Total no. of markers (SSRs and SNPs)

Length of LG (cM)

Average marker density (cM)

Chr-I.A Chr-I.A/V Chr-I.A/III/V Chr-I.B/VI Chr-I.B.2 Chr-I.B.3 Chr-II Chr-III.1 Chr-III.2 Chr-V.1 Chr-V.2 Chr-VI

Mt-5 Mt-2 Mt-2/6 Mt-4/8 Mt-8 Mt-8 Mt-3 Mt-1 Mt-6 Mt-7 Mt-7 Mt-4/8

59 34 31 69 9 9 72 62 11 67 30 69

139.2 45.4 105.3 99.9 19.5 22.7 239.4 171.8 44.0 127.3 62.2 140.1

2.4 1.3 3.4 1.4 2.2 2.5 3.3 2.8 4.0 1.9 2.1 2.0

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hence excluded from further analysis. A total of 522 of 524 input markers (99.6%), including 57 SSRs and 465 SNPs, were attributed to 12 LGs covering 1216.8 cM with an average marker density of 1 locus per 2.3 cM (Supplementary 2). the remaining two markers remaining unlinked. The genetic map spanned 9 well-supported LGs and 3 satellites consisting of 9–11 markers per LG (Table 2 and Fig. 1A and 1B). The total of LGs identified from the current study differs from the fundamental chromosome number for faba bean (x = 6). If possible, sequence-tagged markers were used to attribute LGs to those from previous studies, but the limited number of possible comparisons rendered this task challenging. 3.2. Phenotypic assessment and QTL identification Resistance to ascochyta blight was measured as symptom score of whole plant on a 1–9 scale (years 2011 and 2012) as well as percentage necrosis of stem and leaf (year 2012), and significant differences (P < 0.001) were observed among different RILs. On the basis of the frequency distribution, the presence of multiple genes responsible for ascochyta blight was deduced (Supplementary 3). The estimated narrow sense heritability (h2 ) was calculated to be moderately high (0.5–0.6), based on different measurements.

SIM analysis detected four QTLs on Chr-1.A, Chr-II and Chr-VI using different phenotypic measurements performed over two seasons. These QTLs were further confirmed using CIM, and collectively accounted for a moderate proportion of phenotypic variance (Vp ) of 42–58%, although the effect of each individual locus was relatively small (Table 3 and Fig. 1A and B). Among the identified four QTLs, QTL-1 and QTL-2 were detected using three of the four measurements (plant score 2011; plant score 2012; stem necrosis 2012), while QTL-3 and QTL-4 were detected using two of the four measurements (plant score 2011; leaf necrosis 2012). All QTLs were also compared to the previously published QTL data for ascochyta blight resistance in faba bean [6,12,13]. QTL-3 on Chr-II potentially corresponds to a previously described locus (Af2), but identity was difficult to confirm due to a lack of common markers between studies.

3.3. Comparative genetics analysis Sequences underpinning mapped markers were compared to other legume genomes to detect conserved macrosynteny (Fig. 2A–D; Supplementary 4). Of the 522 sequences that showed

A Chr-I.A 0.0 4.0 8.7 9.8 12.6 14.8 19.5 20.0 22.2 28.3 30.5

2.8 3.3 4.4 7.3 12.6 27.8 32.5 37.2 38.3 44.3 45.4

SNP_50001828 SNP_50000538 SNP_50001826 SNP_50001827 SNP_50001606 SNP_50001918 SNP_50001605 SNP_50001917 SNP_50001916 SNP_50001919 SNP_50000236 SNP_50002102 SNP_50000117 SNP_50001385 SNP_50001747 SNP_50002293 SNP_50000307 SNP_50000314 SNP_50000315 SNP_50000316 SNP_50000317 SNP_50001871 SNP_50001357 SNP_50001872 SNP_50001793 SNP_50001290 SNP_50000379 SNP_50000290 SNP_50000169 SNP_50000883 SNP_50001562 SNP_50000886 SNP_50001561 SNP_50002045

QTL-2

99.9

SNP_50002158 PBA_VF_0344 PBA_VF_0343 SNP_50000034 SNP_50000744 SNP_50000745 SNP_50002319 SNP_50002316 SNP_50000268 SNP_50001017 SNP_50001020 SNP_50001016 SNP_50000243 SNP_50000244 SNP_50001018 SNP_50002318 SNP_50001799 SNP_50002447 SNP_50002412 SNP_50002448 PBA_VF_0216 SNP_50000808 SNP_50000809 SNP_50000054 SNP_50000052 SNP_50001337 SNP_50002108 SNP_50000963 SNP_50001526 SNP_50001317 SNP_50002306 SNP_50000212 SNP_50000214 SNP_50000325 SNP_50000670 SNP_50002128 SNP_50001347 SNP_50001349 SNP_50000661 SNP_50002004 SNP_50001533 SNP_50000922 SNP_50001346 SNP_50001350 SNP_50001737 SNP_50000536 SNP_50001348 SNP_50002127 PBA_VF_0341 PBA_VF_0169 SNP_50001182 SNP_50000535 SNP_50002456 SNP_50002460 SNP_50002454 PBA_VF_0242 SNP_50000134 SNP_50000137 SNP_50000533 SNP_50000532 SNP_50001904 SNP_50000133 PBA_VF_0346 SNP_50000202 SNP_50000201 SNP_50001845 SNP_50000403 SNP_50000024 SNP_50000404

0.0 3.5 4.6 12.1 13.2 14.9 18.4 19.5

SNP_50002378 SNP_50001992 SNP_50001498 SNP_50000208 SNP_50001475 SNP_50001889 SNP_50002156 SNP_50002155 PBA_VF_0377

0.0 2.9 3.5 4.0 5.6 6.7 8.3 9.4 11.0 12.7 26.6 30.0 31.7 33.4 38.2 44.6 45.7 46.2 49.6 50.7

Chr-I.A/III/V 0.0 1.7 19.6 21.9 26.7 32.8 42.7 46.7 52.1 53.2 58.6 62.0 64.8 68.3 90.4 92.8 99.2 101.5 102.0 103.1 104.2 105.3

SNP_50002450 PBA_VF_0442 SNP_50001332 SNP_50001333 PBA_VF_0292 SNP_50000131 SNP_50000685 SNP_50000440 SNP_50000444 SNP_50001169 SNP_50001170 SNP_50001210 SNP_50001023 SNP_50001022 SNP_50001281 SNP_50001287 SNP_50002030 SNP_50000159 SNP_50000161 PBA_VF_0303 PBA_VF_0045 SNP_50000618 PBA_VF_0043 PBA_VF_0075 SNP_50000285 SNP_50001395 SNP_50000308 SNP_50000083 SNP_50000085 SNP_50000011 SNP_50000084

59.8

63.3

63.9 64.4 66.6 67.7 76.3 92.8 95.1

Chr-1.B.2

Chr-I.B.3 0.0 2.3 5.7 17.2 19.4 21.6 22.7

SNP_50000436 SNP_50000127 SNP_50000129 PBA_VF_0380 SNP_50002190 PBA_VF_0330 SNP_50000750 SNP_50001725 SNP_50000022

Chr-II 0.0 1.1 11.0 12.1 22.8 25.0 30.3 35.0 35.5 37.7 40.4 42.6 43.7 46.6 48.3 49.9 54.8 66.3 69.1 75.8 80.5 88.5 95.9 98.8 99.3 110.8 116.1 134.5 136.9 142.7 146.9 150.3 151.9 157.3 160.7 161.8 165.8 171.1 174.5 177.3 179.0 182.4 184.1 185.7 194.5 196.2 201.6 203.8 210.6 211.1 212.7 223.2 229.3 234.0 239.4

PBA_VF_0091 SNP_50000310 SNP_50002112 SNP_50002113 SNP_50000864 SNP_50000863 PBA_VF_0094 SNP_50001697 SNP_50000038 SNP_50002393 SNP_50001061 SNP_50000288 SNP_50000066 PBA_VF_0008 SNP_50001770 SNP_50002137 SNP_50000410 SNP_50000171 SNP_50000217 SNP_50001575 PBA_VF_0306 SNP_50000752 SNP_50001464 SNP_50001392 SNP_50000702 SNP_50002037 SNP_50000363 SNP_50000971 SNP_50000676 PBA_VF_0379 SNP_50001667 SNP_50001687 PBA_VF_0450 SNP_50001451 SNP_50001772 SNP_50002261 SNP_50000787 SNP_50000785 SNP_50000789 SNP_50000788 SNP_50001773 SNP_50001857 SNP_50001461 SNP_50000667 SNP_50001683 SNP_50000666 SNP_50002297 SNP_50002299 SNP_50001684 SNP_50000086 SNP_50001777 SNP_50001622 SNP_50000739 SNP_50000737 SNP_50000332 SNP_50000660 SNP_50001566 PBA_VF_0384 SNP_50001691 SNP_50000565 SNP_50000564 SNP_50000562 SNP_50001851 SNP_50001447 SNP_50002264 SNP_50000547 SNP_50000356 SNP_50002041 SNP_50000018 SNP_50000506 SNP_50000505

QTL-3

105.2 107.4 109.0 115.6 116.7 119.6 127.0 131.1 139.2

SNP_50000965 SNP_50001536 SNP_50001537 SNP_50000013 SNP_50000012 SNP_50000836 SNP_50000837 SNP_50000831 SNP_50000600 SNP_50000602 SNP_50000273 SNP_50000985 SNP_50000878 SNP_50000386 SNP_50000997 SNP_50001563 SNP_50000387 SNP_50000347 PBA_VF_0022 SNP_50002062 SNP_50002120 SNP_50001540 SNP_50002210 SNP_50002121 SNP_50001174 SNP_50000978 SNP_50001774 SNP_50001180 SNP_50000451 SNP_50001040 SNP_50002129 SNP_50001423 PBA_VF_0287 SNP_50000924 PBA_VF_0198 SNP_50001280 SNP_50001270 SNP_50000741 SNP_50000740 SNP_50001004 SNP_50001246 SNP_50000130 SNP_50000090 SNP_50000089 SNP_50001956 SNP_50001955 SNP_50001954 SNP_50002198 PBA_VF_0389 SNP_50001200 SNP_50000489 SNP_50000490 SNP_50000121 SNP_50000123 SNP_50000122 SNP_50001613 SNP_50000541 SNP_50000754 SNP_50001146

QTL-1

36.3 39.2 40.3 40.8 43.1 49.0 49.5 53.5 57.6 58.1 59.7 61.4 65.0 68.6 69.2 72.1 74.3 77.2 80.0 95.0 97.2 97.7 98.8 103.6

Chr-I.B/VI

Chr-I.A/V 0.0

Fig. 1. Genetic linkage map constructed from genotyping of the Icarus × Ascot faba bean mapping population, depicting different QTL regions governing resistance to ascochyta blight. Marker loci are shown on the right of the linkage groups, and map distances between markers are indicated in cM on the left.

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B

Chr-III.1

63.8 70.5 77.2 78.3 79.4 81.6 86.3 89.2 96.9 99.2 100.3 106.3 111.1 112.7 113.8 116.6 121.3 122.4 128.9 129.4 131.6 132.7 133.2 138.0 151.1 157.2 157.7 171.8

0.0 2.8 6.3 14.2 15.9 17.5 22.2 25.6 44.0

0.0 5.0 6.1 6.6 7.7 13.1 13.6 21.0 21.5 24.3 24.8 33.1 34.2 36.6 37.1 43.7 60.7 63.6 66.4 68.6 70.8 84.9 88.4 98.9 99.4 99.9 102.2

104.5

106.7 107.8 110.0 110.5 111.0 113.3 119.9 122.8 125.6 127.3

SNP_50000557 SNP_50001249 SNP_50000734 SNP_50000731 SNP_50000365 SNP_50000368 SNP_50000732 SNP_50000729 SNP_50001564 SNP_50001565 SNP_50000823 SNP_50000434 SNP_50000728 SNP_50001251 SNP_50001250 SNP_50001252 SNP_50001247 SNP_50001248 SNP_50001769 SNP_50002277 SNP_50001708 SNP_50001705 SNP_50001706 SNP_50001704 SNP_50001710 SNP_50001711 SNP_50001707 SNP_50001709 SNP_50000604 SNP_50000328 SNP_50000326 SNP_50001983 SNP_50001982 SNP_50000487 SNP_50002360 SNP_50002361 SNP_50001894 SNP_50002175 SNP_50002410 SNP_50001804 SNP_50000422 SNP_50002020 SNP_50000421 SNP_50000548 PBA_VF_0224 SNP_50000396 SNP_50000395 SNP_50001194 PBA_VF_0103 PBA_VF_0102 SNP_50000760 SNP_50000761 SNP_50000758 PBA_VF_0109 SNP_50001325 SNP_50002330 SNP_50002206 SNP_50000774 SNP_50001304 SNP_50000390 PBA_VF_0357 SNP_50001088 SNP_50001523 SNP_50001228 SNP_50000873 PBA_VF_0073 SNP_50000197

0.0 2.2 3.9 4.4 6.7 16.4 17.5 18.6 33.8 35.4 37.0 42.3 49.1 62.2

Chr-VI 0.0

PBA_VF_0280 PBA_VF_0279 SNP_50001150 SNP_50001679 SNP_50000908 SNP_50000904 SNP_50000909 SNP_50000905 SNP_50000911 SNP_50001873 SNP_50000374 PBA_VF_0267 SNP_50002270 SNP_50000226 SNP_50000064 SNP_50000225 SNP_50000221 SNP_50000219 SNP_50000218 SNP_50000223 SNP_50000058 SNP_50000055 SNP_50000057 SNP_50000056 SNP_50000392 SNP_50002258 SNP_50002257 SNP_50000473 SNP_50002256 SNP_50000069

1.1 2.2 8.3 14.3 17.7 18.8 20.4 20.9 22.0 27.4 28.6 34.4 36.3 37.9 44.0 45.1 46.2 46.7 50.1 60.5 64.0 68.2 69.8 82.2 82.7 83.3 86.4 102.9 106.2 109.7 110.8 118.1 126.8 130.8 134.8 140.1

Chr-III.2

SNP_50001647 SNP_50002189 PBA_VF_0083 SNP_50001516 SNP_50001532 SNP_50001531 SNP_50000763 SNP_50000764 SNP_50001880 SNP_50000993 PBA_VF_0340

PBA_VF_0316 SNP_50001365 SNP_50001360 SNP_50001364 SNP_50001363 SNP_50000206 PBA_VF_0452 SNP_50000647 SNP_50000233 SNP_50002031 SNP_50000914 SNP_50000858 SNP_50000643 SNP_50000644 SNP_50000648 SNP_50001230 PBA_VF_0187 PBA_VF_0202 SNP_50000432 SNP_50000856 SNP_50001909 SNP_50002192 SNP_50001976 SNP_50001507 SNP_50001977 SNP_50001974 SNP_50001978 SNP_50000805 SNP_50000803 SNP_50000806 SNP_50001409 SNP_50000804 SNP_50002207 SNP_50000004 SNP_50001549 SNP_50001548 SNP_50001550 SNP_50001156 SNP_50000409 SNP_50000407 SNP_50000406 SNP_50002325 SNP_50001436 SNP_50000115 SNP_50000113 SNP_50000114 SNP_50001300 SNP_50001421 PBA_VF_0078 PBA_VF_0079 SNP_50001987 SNP_50000586 PBA_0040 PBA_VF_0041 PBA_VF_0123 SNP_50000152 SNP_50000153 SNP_50000151 SNP_50000282 SNP_50000150 SNP_50001855 SNP_50001853 SNP_50002103 PBA_VF_0376 SNP_50001520 SNP_50000819 SNP_50000125

QTL-4

SNP_50000402 PBA_VF_0039 SNP_50001901 SNP_50000900 SNP_50001493 SNP_50002075 SNP_50002150 SNP_50002149 PBA_VF_0199 PBA_VF_0238 SNP_50001581 SNP_50002039 SNP_50000081 SNP_50001668 SNP_50001829 SNP_50001586 SNP_50001588 SNP_50000033 SNP_50000032 SNP_50000029 SNP_50000629 SNP_50000380 PBA_VF_0016 SNP_50000279 SNP_50001486 SNP_50000072 SNP_50001199 SNP_50001643 PBA_VF_0031 SNP_50000459 SNP_50000824 SNP_50000798 SNP_50001781 SNP_50002196 SNP_50000707 SNP_50000917 SNP_50002287 SNP_50002100 SNP_50000842 SNP_50000840 SNP_50000841 SNP_50000708 PBA_VF_0086 PBA_VF_0072 PBA_VF_0064 SNP_50000172 SNP_50000775 SNP_50000181 SNP_50000812 SNP_50001416 SNP_50001415 SNP_50002382 SNP_50000105 SNP_50000575 SNP_50002067 SNP_50000468 SNP_50000465 SNP_50000467 SNP_50000322 SNP_50002425 PBA_VF_0296 SNP_50000203

0.0 1.1 14.2 19.7 21.3 30.1 32.9 40.2 52.4 53.5 55.7 61.1 63.3

Chr-V.2

Chr-V.1

Fig. 1. (Continued ).

matches to at least one other species, 331 (63%) sequences displayed similarity to genomes of all four comparator species. Comparison of faba bean LGs to the chickpea genome revealed the highest number of matches (494: 95%). The syntenic relationships related faba bean LGs Chr-I.A to Ca1, 2 and 8, Chr-I.B to Ca7 and 8, Chr-II to Ca5, Chr-III to Ca4 and 8, Chr-V to Ca3 and Chr-VI to Ca6. Some faba bean LGs exhibited macrosynteny to more than one Ca group (Fig. 2A–D).

A total of 468 (90%) unique faba bean loci showed significant matches to different soybean chromosomal positions. As a palaeopolyploid with a larger fundamental chromosome number than that of faba bean, the majority of faba bean LGs exhibited matches to multiple soybean chromosomes (e.g. Chr-I.A to Gm01, 02, 13, 14, 15, 19; Chr-I.B to Gm08, 16, 17, Chr-II to Gm04, 06; ChrIII to Gm07, 10, 17; Chr-V to Gm01, 02, 16, 18, 19 and Chr-VI to Gm08, 12) (Fig. 2A–D).

Table 3 Identification of QTLs for ascochyta blight resistance in the Icarus × Ascot RIL population. QTL

Position (cM)

Flanking markers

Measurement

LOD threshold

Max LOD

Phenotypic variance (%)

QTL-1

Chr-I.A (54.4–60.0)

SNP 50001774 SNP 50001180 SNP 50000451

plant score 2011 plant score 2012 stem necrosis 2012

3.1 3.4 3.2

5.1 3.8 6.7

14 11 20

QTL-2

Chr-I.A (119.8–123.7)

SNP 50001613 SNP 50000541 SNP 50000754

plant score 2011 plant score 2012 stem necrosis 2012

3.1 3.1 3.2

3.5 3.5 3.9

9 10 11

QTL-3

Chr-II (75.3–79.3)

SNP 50001392 SNP 50000702

plant score 2011 leaf necrosis 2012

3.1 3.1

4.2 4.5

11 14

QTL-4

Chr-VI (44.0–48.0)

SNP 50001909 SNP 50002192 SNP 50001976

plant score 2011 leaf necrosis 2012

3.1 3.1

3.3 4.5

8 13

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S. Kaur et al. / Plant Science 217–218 (2014) 47–55

Fig. 2. Syntenic relationships of faba bean LGs with other genomes of other legume species. LGs or chromosomes are shaded in different colours for visualisation purposes. The details of colour codes are as follows, green – chickpea, violet – soybean, yellow – L. japonicus and red – M. truncatula. Coloured lines represent the corresponding positions of the orthologous sequences in faba bean.

Synteny between faba bean and M. truncatula was observed for 448 (86%) sequences. M. truncatula chromosomes Mt1, 3, 5, 7 and 8 exhibited high levels of macrosynteny and collinearity with faba bean chromosomes Chr-III.1, Chr-II, Chr-I.A, Chr-V and Chr-I.B, respectively (Fig. 2A–D). Conversely, Mt2, 4 and 6 revealed more complex relationships with faba bean chromosomes. The

information obtained from this comparison was also used to assist LG attribution. Among the four legume species used for comparative analysis, L. japonicus obtained the smallest number of hits (377: 72%) and segmental syntenic blocks were identified rather than whole chromosomal relationships. Multiple faba bean LGs showed matches

S. Kaur et al. / Plant Science 217–218 (2014) 47–55

to single Lj chromosomes. For example, Lj1 displayed similarity to Chr1.A, I.B, II and V. 4. Discussion 4.1. SNP markers for faba bean breeding The use of DNA-based markers allows enhancement of selection efficiency within a breeding program by substitution for laborious and time-consuming phenotypic assessment. Molecular genetic markers have proved to be valuable tools for various plant breeding applications, such as germplasm characterisation, early generation selection and determination of seed purity [36]. Until recently, SSRs were the commonly used marker system for plant genetic research and breeding applications. However, due to availability of whole genome or EST sequence information for many plant species, SNP markers have become increasingly important for high-throughput use in molecular breeding [37]. The present study reports the identification of a large collection of EST-derived SNPs for potential use in breeding programs, which have not previously been reported. The average SNP frequency (3.72 per kb) is higher than for other cool-season food legumes such as chickpea (0.043: [38]), lentil (0.21: [19]), field pea (1.85: unpublished data), possibly reflecting the influence of a partially outbreeding reproductive system [4,39], but still lower than for other cultivated species such as cereals (16.5 SNPs per kb in wheat and 4.2 SNPs per kb in rice: [40]) and perennial ryegrass (18.5 SNPs per kb, [41]). However, due to the elevated levels of genetic variability within faba bean populations, it is still possible to develop pathogen resistant lines through several cycles of single plant selection, using the capacity to self-pollinate. This method has been used extensively in the Australian faba bean breeding program to identify new sources of resistance [42]. Such conventional breeding methods will hence greatly benefit from MAS based on highly efficient markers such as SNPs, to increase time- and cost-efficiency in the selection procedures required for development of a homogeneous commercial cultivar. 4.2. Genetic linkage map structure Sub-sets of EST-derived SSRs [26] and SNPs were used for linkage analysis in order to generate the first comprehensive genetic map of faba bean. The only previous linkage maps to be based on sequence-tagged markers were constructed with ITAPs or SSRs [15,16,29], while other maps were mainly based on morphological markers, isoenzymes and RAPDs [6,13,43]. The SNP-based linkage map generated in the current study, which exceeds previous maps in marker density, will prove useful for applications such as traitdissection studies comparisons of QTL location between different studies, candidate gene selection and development of diagnostic markers for selection purposes. The total length of the map was 1216.8 cM, slightly shorter than in previous studies (1308 cM: [13]; 1685.8 cM: [15]; 2856.7 cM: [6]; 1875 cM: [16]; 1587 cM: [29]). These differences may be related to factors such as the number and type of markers, type of analysis software used for linkage mapping, number of progeny and genetic architecture of the parental genotypes. Moreover, the total number of LGs identified from the current study was similar to that reported by some authors [15] but lower than others (18 LGs: [13], 21 LGs: [6], 16 LGs: [15] and 15 LGs: [29]). However, in all instances the number of LGs identified has exceeded the fundamental chromosome number for the species. This effect may be due to the type of markers used and low density of linkage maps generated previously, leading to a proliferation of satellite LGs. Alternatively, the very large size of the faba bean genome may also be a factor, leading to a requirement for a larger number of recombination events in order to permit LG coalescence.

53

4.3. Comparative genomics Comparative mapping and genome analysis can provide information on conservation and differences of gene content and order between different taxa [15,17]. Although the genome size of faba bean is c. 25-fold larger than that of M. truncatula, a close phylogenetic relationship exists between the two species within the Vicieae tribe of the Galegoid clade of the Papilionoideae sub-family of the legume family Fabaceae. Consequently, large-scale conservation of gene order may be anticipated, while the difference in genome size is likely to be predominantly due to differential content of dispersed repetitive DNA. Such relationships were confirmed in the present study, not only between faba bean and M. truncatula, but also with other Papilionoideae species such as L. japonicus, soybean and chickpea. Overall, direct and simple relationships were identified with the genomes of all comparator species, although due to the influence of evolutionary rearrangements, matches were in some instances obtained to multiple chromosomes. The present study failed to detect two or more faba bean chromosomes that showed multiple locus matches in similar order to the same model species chromosome. Therefore, the present study provided no evidence for palaeopolyploidisation or segmental duplication, or of significant rearrangements in the faba bean genome compared to those of other Galegoid genomes, even though the genome size is extremely large, supporting a hypothesis of large-scale retrotransposon expansion. 4.4. Phenotypic assessment and identification of QTLs The genetic basis for ascochyta blight resistance in faba bean is known to be complex. Several genetic studies on resistance to A. fabae reported both polygenic and major gene inheritance [8,12,13,39,44]. To date, 8 QTLs (Af1–Af8) controlling ascochyta blight resistance have been identified from different studies [6,12,13]. In the present study, Icarus × Ascot RILs were assessed for ascochyta blight based on stem necrosis, leaf necrosis and whole plant necrosis over two seasons. Both stem and leaf necrosis were measured separately, in addition to whole plant necrosis in the 2012 trial, as resistance on leaves and stems were suggested to be under different genetic control [9,44]. A total of four QTLs of low individual magnitude were identified on three LGs (Chr-I.A, Chr-II and Chr-VI). Two of the four QTLs, QTL-1 and QTL-2 were reported using whole plant scores for the 2011 and 2012 trials as well as stem necrosis for the 2012 trial. However, QTL-3 and QTL4 were detected from whole plant score for 2011 trial as well as from leaf necrosis symptoms from 2012 trial. These results support the hypothesis that resistance on stem and leaf could be under different genetic control, as suggested by other studies. The variation between the number of QTLs detected using the whole plant score data obtained from 2011 and 2012 trials might be due to the differences in severity of symptoms obtained between two years. A higher level of infection was detected for the 2011 trial (range of symptom scores from 2.7 to 9.0) compared to the 2012 trial (range of symptom score from 1.1 to 7.5), despite a similar spore count. Therefore, the variation in severity of symptoms could be due to a temperature difference of 2 ◦ C between the two trials (18 ◦ C for 2011 and 16 ◦ C for 2012), which may have affected the onset and severity of symptoms used for calculating whole plant score. The controlled environmental conditions were the same in each experiment, but the use of evaporative cooling to control elevated temperatures and glasshouse thermal control to maintain lower temperatures may cause a slight variation in minimum temperature, subject to external ambient conditions. All detected QTLs were compared to those from earlier published studies, but identity was difficult to determine due to the low level of marker commonality between the various genetic maps. As

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QTL-3 was identified on Chr-II, it may show identity to the previously identified Af2 QTL, which is located on same chromosome [6]. However, a dearth of common markers prevents definitive confirmation as this stage. QTLs identified from the current study could be entirely novel due to identification from Australian-bred germplasm with different genetic backgrounds to those used in prior studies. Multiple reports have been made of various sources of resistance to A. fabae from different backgrounds [8,11,39,45–47]. In addition, variation of environmental conditions could contribute to differences between trait-dissection studies, as implied by the variation of outcomes between the 2011 and 2012 phenotypic assessment experiments. 4.5. Marker assisted selection for ascochyta blight Ascochyta blight resistance is one of the highest priority traits for faba bean breeders both globally and in Australia. As resistance to the disease is known to be a complex trait, rapid and reliable screening methods, together with saturation of genomic regions associated with target regions and QTL validation in multiple environments and genetic backgrounds, are prerequisites for development of reliable marker-trait associations. Based on the present results, flanking SNP markers (SNP 50001180, SNP 50000541, SNP 50001392, SNP 50001909) may be used for introgression of QTLs for pathogen resistance from donor into recipient genetic backgrounds by back-crossing. As each QTL is of individually small effect, a gene pyramiding approach based on co-selection of multiple genomic regions may be appropriate. Introgression of selected QTLs into elite quality and agronomic backgrounds could significantly improve grain yield and quality of the crop. Acknowledgements This work was supported by funding from the Victorian Department of Environment and Primary Industries and the Grains Research and Development Council, Australia. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.plantsci. 2013.11.014. References [1] H. Wang, T. Yang, J. Guan, Y. Ma, X. Sun, X. Zong, Development and characterization of 20 novel polymorphic STS markers in Vicia faba (fava bean), Am. J. Bot. 98 (2011) 189–191. [2] A. Torres, C.M. Avila, N. Gutierrez, C. Palomino, M.T. Moreno, J.I. Cubero, Marker-assisted selection in faba bean (Vicia faba L.), Field Crop Res. 115 (2010) 243–252. [3] N. Rispail, P. Kalo, G.B. Kiss, T.H.N. Ellis, K. Gallardo, R.D. Thompson, E. Prats, E. Larrainzar, R. Ladrera, E.M. Gonzalez, C. Arrese-Igor, B.J. Ferguson, P.M. Gresshoff, D. Rubiales, Model legumes contribute to faba bean breeding, Field Crop Res. 115 (2010) 253–269. [4] A. Gnanasambandam, J. Paull, A. Torres, S. Kaur, T. Leonforte, H. Li, X. Zong, T. Yang, M. Materne, Impact of molecular technologies on faba bean (Vicia faba L.) breeding strategies, Agronomy 2 (2012) 132–166. [5] G. Jellis, G. Lockwood, R.G. Aubury, Further evaluation of chlorothalonil for control of Ascochyta fabae in faba beans. Test of agrochemicals and cultivars, Ann. Appl. Biol. 104 (1984) 58–59. [6] A. Diaz-Ruiz, Z. Satovic, C.M. Avila, C.M. Alfaro, M.V. Gutierrez, A.M. Torres, B. Roman, Confirmation of QTLs controlling Ascochyta fabae resistance in different generations of faba bean (Vicia faba L.), Crop Past. Sci. 60 (2009) 353–361. [7] W.J. Kaiser, Inter- and intranational spread of ascochyta pathogens of chickpea, faba bean and lentil, Can. J. Plant Pathol. 19 (1997) 215–224. [8] K. Rashid, C.C. Bernier, R.L. Conner, Evaluation of fava bean for resistance to Ascochyta fabae and development of host differential for race identification, Plant Dis. 75 (1991) 852–855. [9] K. Rashid, C.C. Bernier, R.L. Conner, Genetic of resistance in faba bean inbred lines to five isolates of Ascochyta fabae, Can. J. Plant Pathol. 13 (1991) 218–225.

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