Insertion site-based polymorphism markers open new
perspectives for genome saturation and marker-assisted
selection in wheat
Etienne Paux1,*,†, Se´bastien Faure1,†,‡ , Fre´de´ric Choulet1, Delphine Roger2, Vale´rie Gauthier2, Jean-Pierre Martinant2, Pierre Sourdille1, Franc¸ois Balfourier1, Marie-Christine Le Paslier3, Aure´lie Chauveau3, Mehmet Cakir4, Be´atrice Gandon2and Catherine Feuillet1
1INRA UBP UMR 1095, Genetics, Diversity & Ecophysiology of Cereals, Clermont Ferrand, France 2Limagrain Verneuil Holding, Riom cedex, France
3Unite´ INRA-EPGV UR1279, CEA, Institut de Ge´nomique, Centre National de Ge´notypage, Evry cedex, France 4
School of Biological Sciences & Biotechnology, Faculty of Sustainability, Environmental and Life Sciences, Murdoch University, Murdoch, WA, Australia
Received 17 July 2009; revised 15 October 2009; accepted 15 October 2009.
*Correspondence (fax +33 473 62 44 53; e-mail etienne.paux@clermont.inra.fr) †These authors contributed equally to this work.
‡Current address: Genetics & Genomics in Cereals, BIOGEMMA, 8 rue des Fre`res Lumie`re, 63028 Clermont Ferrand cedex 2, France.
Keywords: Insertion site-based polymorphism, molecular marker, polymorphism, mapping, marker-assisted selection, wheat.
Summary
In wheat, the deployment of marker-assisted selection has long been hampered by the lack of markers compatible with high-throughput cost-effective genotyping techniques. Recently, insertion site-based polymorphism (ISBP) markers have appeared as very powerful new tools for genomics and genetic studies in hexaploid wheat. To demonstrate their possible use in wheat breeding programmes, we assessed their potential to meet the five main requirements for utilization in MAS: flexible and high-throughput detection methods, low quantity and quality of DNA required, low cost per assay, tight link to target loci and high level of polymorphism in breeding material. Toward this aim, we developed a programme, IsbpFinder, for the automated design of ISBP markers and adapted three detection methods (melt-ing curve analysis, SNaPshotMultiplex System and Illumina BeadArray technology) for high throughput and flexible detection of ISBP or ISBP-derived SNP markers. We demonstrate that the high level of polymorphism of the ISBPs combined with cost-effective genotyping methods can be used to efficiently saturate genetic maps, dis-criminate between elite cultivars, and design tightly linked diagnostic markers for virtually all target loci in the wheat genome. All together, our results suggest that ISBP markers have the potential to lead to a breakthrough in wheat marker-assisted selection.
Introduction
Wheat is the staple food for 35% of the world popu-lation (http://www.idrc.ca/en/ev-31631-201-1-DO_TOPIC. html) and with 217 million hectares (16% of all crop area; FAOstat, 2008) it is the most widely grown crop in the world, before maize and rice. In the last 40 years, wheat yield has doubled from 1.4–2.8 t⁄ ha (FAOstat, 2008) mostly as a result of the Green Revolu-tion through the applicaRevolu-tion of technologies (pesticides, irrigation and synthetic nitrogen fertilizer) and the
devel-opment of science-based breeding methods to produce improved wheat varieties (Edgerton, 2009). However, in the past decade wheat yield and production have reached a plateau worldwide, while consumption contin-ued to increase and exceeded world production. In 2007, wheat stocks reached their lowest level since 1981, i.e. 60 days of consumption (http://www.fas.us-da.gov/grain/circular/2008/04-08/graintoc.asp#Wheat) trig-gering high price volatility and food insecurity while also threatening social peace in a number of developing countries. This trend is likely to increase in the next
years with a reduction of the wheat harvested area concomitantly an increasing competition between the production for food and non-food uses, such as biofuels within the cereal harvested areas.
A ‘Second Green Revolution’ is needed to ensure envi-ronmentally sustainable wheat production in sufficient amount and quality to meet the needs of the 21st cen-tury (Edgerton, 2009). To face this challenge, a break-through is needed in wheat breeding programmes to ensure better and faster selection of favourable alleles for agronomically relevant traits underlying yield, biotic and abiotic stress resistance and quality. Until recently, the difficulty of accessing the huge (17 Gb) and complex (hexaploid and repetitive) wheat genome has hampered the development of highly saturated genetic maps thereby limiting the deployment of cost-efficient marker-assisted selection (MAS) and association genetics. Very recently, the marshalling of international efforts to con-struct physical maps and sequence the wheat genome (Gill et al., 2004; Feuillet and Eversole, 2007; Paux et al., 2008) combined with the development of new popula-tions and methods (Reynolds et al., 2007) opened new perspectives for wheat breeding. In particular, a new generation of high-throughput molecular markers offers the potential to revolutionize breeding methodologies through enhanced germplasm characterization and cost-effective MAS (for reviews, see Edwards and McCouch, 2007; Gupta et al., 1999; Koebner, 2004; Landjeva et al., 2007; Varshney et al., 2007).
The benefit of genome saturation with molecular mark-ers in enhancing breeding programmes efficiency is clearly exemplified in maize. Through the 1980’s, maize and wheat yield had similar growth rates but in the last 10 years maize yield has increased by 17% whereas wheat yield has increased by only 4% (FAOstat, 2008).The divergence between the growth curves coin-cides with the application of DNA markers in private maize breeding programmes and the development of high-throughput genotyping methods (Ragot and Lee, 2007). Even though the use of MAS in wheat began about the same time as maize its deployment in breeding programmes remained limited. One of the main reasons is that in contrast to maize, wheat is a naturally inbreeding species and there are no hybrid seeds on the market (Koebner, 2004) thereby reducing the possibility for breeding companies to recover investments in wheat research. A dramatic reduction in genotyping costs is thus needed to promote the wide-scale use of MAS in wheat. This can be achieved with increased marker availability as
well as high-throughput, high-multiplexing genotyping strategies.
However, even though large efforts have produced thousands of microsatellites (or SSRs) and Single Nucleo-tide Polymorphisms (SNPs), their number remains too lim-ited to develop genome-wide selection in wheat. A potential source of high-density molecular markers lies in the 60%–70% of the wheat genome that is covered by transposable elements (TEs) (Li et al., 2004; Paux et al., 2006). Transposable elements are ubiquitous, found in high copy numbers, present in both hetero- and euchro-matin, and they show insertional polymorphism both within and between species (Kumar et al., 1997; Paux et al., 2006). These features allowed the development of several TE-based molecular markers, such as SSAP, IRAP, REMAP and RBIP (Kumar and Hirochika, 2001; Schulman et al., 2004) that have been used successfully to establish phylogenies, study biodiversity, generate genetic linkage maps, and identify markers linked to important agronomi-cal traits in several species such as barley, pea, rice and tobacco (Kalendar et al., 1999; Kenward et al., 1999; Schulman et al., 2004). Their potential use in crop breed-ing has been suggested (Kalendar and Schulman, 2006; Branco et al., 2007) but to our knowledge, they have never been applied to large-scale breeding programmes. Recently, we demonstrated the potential of small genomic sequences such as BAC-end sequences from hexaploid wheat to develop Insertion Site-Based Polymorphism (ISBP) markers that exploit knowledge of the sequence flanking a TE to design one primer in the transposable element and the other in the flanking DNA sequence (Paux et al., 2006). A similar approach has been undertaken by Devos et al. (2005) and very recently by Wanjugi et al. (2009) to develop Repeat Junction Markers (RJMs) from the D-gen-ome of Aegilops tauschii. As TEs are nested in the wheat genome where they display unique insertion sites that are highly polymorphic, the resulting amplicon represents a putative unique genome-specific molecular marker with a potential to access its sequence easily. To date, we have developed 711 ISBP markers that are evenly distributed along chromosome 3B of bread wheat and are represen-tative of all kind of junctions (various TE families in both repetitive and low copy DNA, either coding or non-cod-ing). ISBP markers have already proven to be extremely valuable for genetic, physical and radiation hybrid map-ping (Paux et al., 2008) as well as for evolutionary studies (Gao et al., unpublised data). In this work, we demon-strate their potential for MAS by showing that ISBPs meet the five main requirements for utilization in MAS (Collard
and Mackill, 2008): flexible and high-throughput detec-tion methods, low quantity and quality of DNA required, low cost per assay, tight link to target loci and a high level of polymorphism in breeding material.
Results and discussion
ISBPs can be detected with simple, high-throughput cost-efficient detection techniques
Insertion site-based polymorphism markers were imple-mented initially on agarose gel electrophoresis (Paux et al., 2006). While this simple technique allows for the detec-tion of presence⁄ absence polymorphism and to some extent size polymorphism, it cannot detect sequence poly-morphisms such as SNPs and it is not compatible with breeding programmes because of low throughput. SNPs represent 90% of the genetic variation in organisms and they have emerged recently as the markers of choice in breeding programmes as a result of their abundance and high-throughput detection capacities (Collins et al., 1998; Koebner and Summers, 2003). To evaluate the level of sequence polymorphism found in ISBP markers, 288 ISBPs were amplified from eight hexaploid wheat lines compris-ing five varieties (Chinese Sprcompris-ing, Renan, Courtot, Opata 85 and Synthetic W7984) that correspond to parents of genetic mapping populations available in our laboratory and three accessions (Sumai 3, Balkan and Dinka) that were selected to maximize diversity. Out of the 288 mark-ers, 157 showing amplification in most of the eight lines were selected for amplicon sequencing (Table S1).
Out of 157 sequenced fragments, 105 (67%) displayed sequence polymorphism between at least two wheat lines. The polymorphism consisted mainly in SNPs (401), with a few insertion–deletion polymorphisms (IDPs) (19). Consid-ering an average length of 253 bp per amplicon, the SNP frequency is one every 99 bases (Figure 1). This is
signifi-cantly higher than the frequency observed previously in coding regions of wheat with ranges from one SNP per 203 bp to one SNP per 540 bp (Somers et al., 2003; Ravel et al., 2006, 2007). As expected, SNPs were biased towards transition (60%) because of the high methylation level in TEs that leads to an increase in mutation frequency at deaminated sites (Clark et al., 2005; Rabinowicz et al., 2005). In addition, sequence composition of ISBPs showing 0–4 SNPs revealed a strong correlation (R2= 0.71) between the GC content and the SNP frequency, probably due to the CpG effects on higher mutation rates (Zavolan and Kepler, 2001; DeRose-Wilson and Gaut, 2007). Inser-tion–deletion polymorphisms corresponded to small (1–4 nt) deletions as well as to polymorphism in micro-satellite motifs. Indeed, mining ISBP sequences for SSRs showed that roughly 1.6% of the ISBP markers contained microsatellites. Thus, our results indicate that ISBPs are a rich source of polymorphism in wheat, both as pres-ence⁄ absence and at the sequence level. However, their widespread application in breeding programmes can be achieved only with high-throughput, cost-efficient assays that allow genotyping of one or a few individuals for hun-dreds of thousands of markers, or permit genotyping of thousands of individuals for one or few markers. To date, there are more than 30 different genotyping methods (Khlestkina and Salina, 2006; Kim and Misra, 2007). Among them, we have selected three with the throughput and flexibility to achieve the full benefit from the high level of polymorphism of the ISBPs while also meeting the requirements of modern breeding programmes (Bagge and Lu¨bberstedt, 2008): melting curve analysis, SNaPshot Multiplex System and Illumina BeadArray technology. Melting curve analysis
Each double-stranded DNA fragment has its own specific melting temperature (Tm) that is determined by DNA length and GC content. Plotting the fluorescence as a
(a)
(b)
Figure 1 Sequence comparisons of the ISBP markers cfb5029 (a) and cfb5008 (b) amplified from Chinese Spring (CS), Renan, Courtot, Synthetic W7984 (Synthetic), Opata, Sumai3, Dinka and Balkan. SNPs and IDPs are highlighted in grey.
function of the temperature as the thermal cycler heats through the dissociation temperature of a PCR product results in a DNA melting curve. The shape and position of the curve is generally sufficiently distinctive to reveal poly-morphism between products amplified from different genotypes (Ririe et al., 1997; Shepherd and Henry, 1998). This technique was investigated as an alternative to aga-rose gel electrophoresis to score for the presence⁄ absence of amplicons and to detect SNPs and IDPs with or without prior knowledge of the presence of a polymorphism. Inser-tion site-based polymorphism were amplified by PCR in the presence of SYBR Green I and subsequently
submit-ted to a melting curve analysis on an ABIPRISM 7900HT.
In the framework of the construction of the hexaploid wheat chromosome 3B physical map (Paux et al., 2008), 711 ISBP markers were assigned to deletion bins by scoring for the presence⁄ absence of the amplicon in chro-mosome 3B aneuploid lines (nullisomic-tetrasomic, ditelo-somic and deletion lines; Endo and Gill, 1996; Sears, 1954; Sears and Sears, 1978) and were evaluated for poly-morphism between the five parents of our mapping popu-lations using melting curve analysis. Out of these 711 markers, 65% showed polymorphism between the five varieties, with 90% of the polymorphisms corresponding to presence⁄ absence of the amplicon. This is consistent with the previous results obtained on agarose gel (Paux et al., 2006) and confirms that at least 50% of the ISBP markers display presence⁄ absence polymorphism. The 711 ISBPs included the 105 markers for which SNPs were iden-tified already by amplicon sequencing as described above. Only 27% (28) of them were found to be polymorphic by melting curve analysis (MCA) demonstrating the limited resolution of MCA in detecting SNPs and IDPs. One limita-tion of the technique is the dye redistribulimita-tion during melt-ing, which affects the shape and position of the curve. It has been shown that sequence polymorphism needs to be high enough to induce a significant change in melting temperature of the amplicon (>1C) that can then be detected with confidence by MCA (Herrmann et al., 2006). New generation ‘saturation dyes’ such as LCGreen (Idaho Technology Inc., Salt Lake City, UT,
USA), EvaGreenTM (Biotium Inc. Hayward, CA, USA) and
SYTO9 (Life Technologies, Carlsbad, CA, USA) may over-come this limitation (Wittwer et al., 2003; Krypuy et al., 2006; Jeffery et al., 2007). Finally, melting curve analysis also suffers from a lack of sequence specificity that can result in possible confusion during allele scoring (von Ah-sen et al., 2001). While MCA is not suitable for SNP geno-typing because of its lack of sensitivity, it does provide a
powerful tool to discriminate between wheat lines in the absence of any sequence information, since at least 50% of the polymorphism revealed by ISBPs corresponds to a presence⁄ absence polymorphism. Moreover, its low cost (< $0.2⁄ assay), together with a limited amount of required DNA (25 ng of genomic DNA) and intermediate through-put (10 000 data points per day) make it suitable for marker-assisted selection.
SNaPshotMultiplex System
The SNaPshotMultiplex System (Applied Biosystems, Fos-ter City, CA, USA) is a primer extension-based mini-sequencing procedure. In this technique, the primer tar-gets a sequence immediately upstream of the SNP site and is extended by a single base in the presence of all four fluorescently labelled dideoxynucleotides (Pati et al., 2004). Alleles are represented by peaks of different col-ours on capillary sequencers thereby allowing the easy detection of co-dominant polymorphisms. In addition, the method enables multiplexing by designing primers of dif-ferent lengths. The SNaPshot Multiplex System has been used already to detect SNPs efficiently in plants such as Arabidopsis (Pati et al., 2004), sugar beet (Mo¨hring et al., 2005) and oat (Chen et al., 2007). To assess its efficiency for ISBP genotyping, assays were designed from 42 sequences selected from the 105 sequenced ISBPs and were tested on the Chinese Spring and Renan parental lines as well as on a heterozygous F1 line resulting from a cross between the two varieties. Out of the 42 assays, 24 (57%) allowed a clear distinction between parental alleles in both homozygous and heterozygous lines. The prob-lems encountered for the remaining 18 assays included the presence of double peaks in one of the parents (six assays), presumably due to heterozygosity at the corre-sponding locus, discrepancies between sequencing and SNaPshotresults suggesting sequencing errors for one of
the parents (eight assays) and absence of amplification (four assays).
The possibility to discriminate heterozygous from homo-zygous lines, combined with multiplexing capacities (three markers were successfully genotyped in one assay; data not shown), intermediate throughput (10 000 data points per day; Bagge and Lu¨bberstedt, 2008) and a limited quantity of starting DNA (25 ng of genomic DNA) required for the assay, makes the SNaPshot technique well adapted for fine-mapping of small regions where recombi-nants are known already or for marker-assisted backcross-ing with ISBPs. However, because of its relatively high cost ($1 per data point), it is not suitable for large sets of
markers or genotypes and thus cannot be used for associa-tion mapping or characterizaassocia-tion of breeding materials. Illumina BeadArray technology
The recent development of highly parallel SNP detection assays has opened new perspectives in genome-wide pro-filing. Among different assays, the highly flexible Illumina BeadArray technology represents a very powerful tool for high-throughput cost-effective genotyping. Using allele-specific extension and ligation, the Illumina GoldenGate system can assess 96 to 1536 SNPs simultaneously on more than 480 individuals. This technique has proven already to be extremely efficient for gene-derived SNP genotyping in other crops such as barley and maize (Gup-ta et al., 2008). In hexaploid wheat, one major limi(Gup-tation is the presence of homoeologous loci that can lead to problems of cluster discrimination and subsequent allele calling (Ganal et al., 2009). As a consequence, even if a significant number of SNPs are available in wheat already (http://wheat.pw.usda.gov/SNP/new/index.shtml), very few results have been published so far on SNP mapping in wheat using Illumina platforms (Akhunov et al., 2009; Luo et al., 2009). Because of their genome-specificity, ISBPs potentially can overcome the limitation due to the pres-ence of homoeologous loci in the wheat genome. How-ever, so far, Illumina platforms have not been used to genotype repeat-derived SNPs and one of the challenges
has been to design primers that would ensure sufficient specificity, i.e. avoid binding with all copies of the corre-sponding transposable elements present in the genome, in an assay in which primer design is driven by the posi-tion of SNPs in the sequence. To evaluate the potential use of Illumina genotyping for ISBPs, a first set of 53 markers containing SNPs was selected. The selection was done to cover a range of different SNP ‘context sequences’ (i.e. the sequence surrounding the SNP): 21 contained a SNP but did not include the TE junction (Fig-ure 2a), ten contained a SNP in the vicinity of the TE junc-tion (Figure 2b), 11 had addijunc-tional polymorphisms near the targeted SNP without TE junctions (Figure 2c) and 15 had both additional SNPs and the junction in the vicinity of the targeted locus (Figure 2d). The ‘context sequences’ around the different SNPs were submitted to Illumina for assay design. All except two of the 53 markers had a design score above 0.6 compatible with their use in the GoldenGate assay. SNP scores ranged from 0.53 to 0.99 with an average of 0.86 (Table S2). In addition, as a signif-icant part of ISBP polymorphism originates from the pres-ence⁄ absence of a junction, we wanted to evaluate the efficiency of Illumina GoldenGate to genotype null allele polymorphisms. To this aim, 25 additional ISBPs showing no SNPs between the sequenced wheat lines also were subjected to primer design. To ensure that primers would be located on each side of the junction, the SNP was
(a)
(b)
(c)
(d)
Figure 2 Schematic representation of the different types of SNP context sequences used for the Illumina assay design. (a) The context sequence contains only the query SNP. (b) The context sequence contains the query SNP in the vicinity of a TE junction. (c) The context sequence contains the query SNP as well as an interfering SNP. (d) The context sequence contains the query SNP as well as an interfering SNP in the vicinity of a TE junction.
defined as the last base upstream the junction and the first three bases immediately downstream the junction were turned into N. The resulting ‘context sequences’ were submitted to Illumina for assay design and led to SNP scores ranging from 0.62 to 0.79 with an average of 0.7 (Table S2). The resulting Oligo Pool Assay (OPA) was tested on a set of 96 hexaploid wheat varieties chosen from the worldwide bread wheat core collection (Balfouri-er et al., 2007), 96 individuals from a Chinese Spring· Renan F2 population and aneuploid lines (nulli-somic-tetrasomic, ditelosomic and deletion lines) used as controls (Figure 3).
Out of the 53 ISBP-derived SNPs, 33 (62%) were mapped to the same chromosome and deletion bin loca-tions as those obtained by PCR amplification and were therefore considered ‘scorable’. The remaining 20 markers (38%) were not specific (absence of signal in other nulli-somic lines than that of chromosome 3B) and were dis-carded. The results showed that the success rate of markers containing additional polymorphism than the tar-get SNP was lower (43%) than for markers with a single SNP (73%). Similarly, for ISBPs containing no additional SNPs, the success rate was higher (91%) when the TE junction was in the vicinity of the targeted SNP as com-pared with ISBPs without junction (67%).
Out of the 33 ‘scorable’ ISBPs, 23 (70%) were polymor-phic in the core collection and 14 (42%) revealed poly-morphism between Chinese Spring and Renan. Eleven of them were mapped genetically in the CS· Re F2 popula-tion. Similar results were observed with the 25 ISBPs showing a presence⁄ absence polymorphism: 14 of them (56%) were mapped to their correct chromosomal loca-tion and 11 (44%) were discarded. Out of the 14 mapped markers, 10 (71%) displayed polymorphism among the 96 lines of the core collection. For these markers, no correla-tion was found between ‘context sequence’ and success rate, or with the polymorphism level.
Thus, our results indicate a success rate of 60% for the ISBP or ISBP-derived SNP markers (47⁄ 78) designed in this study. This is comparable to the rate for SNPs originating from genes that were genotyped at the same time (C. Ravel and J.P. Martinant, unpublished data). This demon-strates that the repetitive nature of the ISBPs does not impact the ability to detect SNPs originating from ISBP markers as well as presence⁄ absence polymorphism using the Illumina GoldenGate assay. Recently, Akhunov et al. (2009) reported the Illumina genotyping of gene-derived SNPs in hexaploid wheat with a 84.4% success rate, suggesting that the success rate can be increased
signifi-cantly through a careful selection or pre-screening of SNPs. The flexibility, high-throughput (up to 300 000 data points per day), low cost ($0.1 per assay for 384 SNPs on 480 genotypes) and limited amount of starting material (50 ng of genomic DNA) required for this assay provides a very competitive tool to wheat breeders with a variety of possible applications such as marker-assisted backcrossing, gene pyramiding and fingerprinting.
Finally, our results indicate that ISBPs are amenable to a large panel of polymorphism detection techniques. In addition to the three techniques presented above, we genotyped ISBPs successfully using allele-specific PCR, tem-perature gradient capillary electrophoresis and fluorescent capillary electrophoresis (data not shown). This flexibility makes ISBPs very attractive markers for breeding pro-grammes as detection techniques can be chosen based on the number of screening samples and the information (dominant or co-dominant) needed. While SSRs have been the most widely used markers in wheat breeding to date, it is likely that their limited throughput capabilities and rel-atively high costs ($0.60 per assay) will soon be replaced by the new generation of markers for wheat breeding programmes. SNPs are already the markers of choice in maize marker-assisted selection programmes (Ragot and Lee, 2007). In wheat where the intragenic polymorphism level is quite low, ISBPs and ISBP-derived SNPs can greatly complement gene-derived SNPs by providing additional high-throughput and cost-efficient markers for genome saturation.
ISBPs are highly polymorphic in breeding material and can discriminate between different genotypes Identifying sufficient polymorphic markers between donor and recurrent lines for a target region is another major limitation in the application of MAS in wheat breeding programmes. This problem is exacerbated when narrow gene pools are used. To evaluate the level of polymor-phism in wheat breeding material, 96 ISBP markers were used to study the genetic diversity among and between 46 Australian and 46 European elite lines (Table S3). These lines or the genes derived from these lines are widely used in Australian and European wheat breeding programmes as the sources of variety of desirable agronomic, biotic and abiotic traits such as grain yield, disease resistance, pre-harvest sprouting, flour colour and drought tolerance. Out of 96 ISBP markers, 35% showed melting curve poly-morphisms, with the number of alleles ranging from 2 to 6 (mean = 2.5) and a PIC value of 0.41. This is lower than
microsatellites that show PIC values of roughly 0.7 and average numbers of alleles of 6 to 7 in elite germplasm (Plaschke et al., 1995; Prasad et al., 2000; Roy et al.,
2006). This clearly reflects that a significant part of ISBPs are dominant markers and also demonstrates that both present and absent alleles are in almost equal proportion
Figure 3 Genotyping of wheat lines with the Illumina BeadArray technology. The graph is a normalized polar coordinate (R, h) plot. Theta (x-axis) is an angle of deviation of Cy3 and Cy5 fluorescence from pure Cy3 and pure Cy5 signal (0 and 1). The closer a point is to 0 or 1, the greater the proportion of Cy3 or Cy5 fluorescent signal, respectively, is present. R (y-axis) is the Manhattan distance of observed Cy3 and Cy5 fluorescence to the origin (pole), which is 0 fluorescence. The closer a point is to 0 on the y-axis, the weaker the total fluorescence. The darker coloured regions define genotype call areas for homozygous (red and blue dots) and heterozygous (purple dots) plants. The numbers of plants in each cluster are indicated below the x-axis. (a) M178_54 A⁄ G polymorphism. Yellow dots correspond to aneuploid lines harbouring the corresponding cfp3492 ISBP marker. Green dots correspond to aneuploid lines in which the genomic fragment harbouring cfp3492 has been deleted. (b) cfp60_106 pres-ence⁄ absence polymorphism. Red and black dots correspond to lines where cfp60 is present or absent, respectively. Yellow dots correspond to aneuploid lines harbouring the corresponding cfp60 ISBP marker. Green dots correspond to aneuploid lines in which the genomic fragment carry-ing cfp60 has been deleted.
in germplasm. Furthermore, sequencing of markers with no melting curve polymorphism revealed that 40% of the monomorphic markers displayed polymorphisms at the sequence level. Thus, an overall polymorphism rate of 60% was observed in this breeding panel.
A cluster analysis (Figure 4) resulted in two clearly sepa-rated groups that correspond to the European and Austra-lian wheat lines. Only four lines (Janz, Leichardt, Boston and Zhougke Nuomai) were not clustered in the ‘correct’ group. A detailed analysis of the pedigree of these lines would be necessary to determine the reasons for this clus-tering. The polymorphism was not restricted to the com-parison between Australian and European lines but also was found within the two groups with 33 polymorphic markers between European lines and 32 between Austra-lian lines (Figure 4) thereby demonstrating the potential of ISBPs to discriminate between wheat elite lines within a national selection programme. Recently, McNeil et al. (2008) demonstrated the usefulness of genomic sequences for the efficient development of markers tightly linked to a trait of interest. Among others, they derived an ISBP (X3B028F08) from a BAC contig spanning the region cor-responding to the Sr2 locus in Chinese Spring and used this marker to genotype a set of 51 unrelated wheat varie-ties differing for the presence⁄ absence of the stem rust resistance Sr2. Two alleles were detected in the set, show-ing a tight association between the presence⁄ absence of Sr2 for all but four of the lines. In addition, ISBP markers
have been successfully identified from sequencing the Sr2 locus (derived from the variety Hope) and indicate that ISBPs can provide diagnostic markers without functional characterization of the genes underlying the targeted traits (R. Appels, personal communication).
The polymorphism level, both in terms of percentage of polymorphic markers and number of alleles, indicated an overall increase from the centromere to the telomeres, consistent with previous studies that showed an effect of the chromosomal location on genetic diversity of microsat-ellites (Huang et al., 2002; Thuillet et al., 2004). Recombi-nation has been proposed to be a major factor affecting genetic diversity. In wheat, as crossing-over frequency increases gradually from the centromere to the telomeres (Lukaszewski and Curtis, 1993; Saintenac et al., 2009), we investigated the impact of the distance to centromere on the genetic diversity of ISBP markers. Interestingly, we found a strong correlation (R2= 0.76) between the
cross-over frequency estimated by Saintenac et al. (2009) and the average allele number in deletion bins based on MCA (data not shown). Therefore, the chromosomal location of ISBPs should be considered in the evaluation of genetic diversity and variety identification.
ISBPs provide a large and unbiased source of polymorphism in the wheat genome
One of the advantages of the ISBPs over other marker techniques lies in a straightforward design from short genomic sequences, as demonstrated previously by using BAC-end sequences (Paux et al., 2006). Until now, ISBPs have been designed in a semi-automated manner, since the amount of wheat genomic sequences available was quite limited. However, the recent development of the Roche 454 Genome Sequencer FLX Titanium (454 Life Sci-ences, Branford, CT, USA) technology opened new per-spectives for large scale sequencing of the wheat genome, with the possibility of generating more than 1 million reads in a single run. To benefit fully from this major breakthrough, we developed a software for the automated design of ISBP markers. This software, Isbp-Finder, uses annotation results generated by the
Repeat-Masker programme (Smit et al., 1996; http://
www.repeatmasker.org) to automatically detect junctions between TEs and design primers for the PCR amplification of the genomic fragment spanning a junction. It is adapted to marker design over short (e.g. BAC-end sequences) as well as large sequences such as BAC sequences. The automated detection of TE is based on
Figure 4 Neighbour-joining tree showing the relationships between the 92 elite wheat lines genotyped with the 96 ISBP markers. Austra-lian lines are in red, European lines are in green, Chinese Spring (CS) is in black.
sequence comparison with the TREP database that is dedi-cated to Triticeae (wheat, barley and rye) repeats (Wicker et al., 2002). To date, TREP comprises high-quality curated annotations for more than 1700 Triticeae TE sequences. Each individual entry contains the sequence of an element without any flanking sequences thereby making it easier to identify the borders of transposable elements in a sequence search against the database. Thus, using TREP for repeat masking in IsbpFinder ensures a very precise detection of transposable elements, and the efficient detection of junctions between them to automatically and rapidly design ISBP markers, that are associated with a ‘confidence’ score (high, medium or low), based on the unambiguous identification of a TE junction and the type of sequences on both sides of the junction (see Experi-mental Procedures section).
Two sequence datasets were used to assess the effi-ciency of IsbpFinder. The first dataset consisted of 37 421 public BAC-end sequences (BES) from chromosome 3AS-and chromosome 3B-specific BAC libraries (Paux et al., 2006; Gill et al., unpublished data). IsbpFinder identified ‘high confidence’ junctions, i.e. junctions for which at least one TE extremity was detected, in 9.72% of the BES and designed 3638 putative ISBP primer pairs. The second set contained 13 BAC contigs from 269 kb to 3110 kb repre-senting a total of 18.2 Mb of sequence from chromosome 3B (Choulet et al., unpublished data). Using IsbpFinder, 4847 putative ISBP markers were designed from ‘high con-fidence’ junctions, corresponding to an average of one marker per 3.8 kb. This high density of ISBP markers results from the highly fragmented organization of repeats in the wheat genome as a result of deletion and nested insertions within TEs (Kronmiller and Wise, 2008). The design was validated subsequently on a subset of 80 ISBP markers. PCR amplification of the ISBP markers was performed on genomic DNA from Chinese Spring and a nullisomic 3B– tetrasomic 3A (N3BT3A) line to control genome-specificity. All 80 markers amplified genomic DNA of Chinese Spring and 55 were specific of chromosome 3B as shown with the N3BT3A line, indicating that 70% of the predicted IS-BPs found by IsbpFinder correlative to unique genomic loci. A manually edited annotation of the 13 contigs revealed the presence of 3753 TE extremities, corresponding to 77.4% of the 4847 junctions predicted by IsbpFinder which was consistent with the 70% success rate.
Based on a density of one TE junction every 3.8 kb and a genome-specificity rate of 70%, we estimate a total number of 3 millions ISBPs with an average density of one ISBP per 5.4 kb in the hexaploid wheat genome. As a
comparison, 1396 SSRs were found in the non-repetitive fraction of the 18.2 Mb sequence sample, averaging 1 SSR every 13.1 kb and 1.3 million SSRs in the 17-Gb wheat genome (Choulet et al., unpublished data). Com-bining the estimated number of ISBPs with the average SNP density observed here suggests a potential of about 6 million ISBP-derived SNPs in the whole wheat genome, comparable to the estimated value in the human genome (Kruglyak and Nickerson, 2001). Thus, with their high number and density, ISBPs have the potential to saturate wheat genetic maps in the near future and become an attractive marker system for breeders. While few ISBPs or ISBP-derived SNPs are available at this time, a single run of Roche GSFLX Titanium should lead to the development of roughly 50 000 ISBPs and 100 000 ISBP-derived SNPs, with an approximate development cost of $60 per ISBP and $100 per ISBP-derived SNP, compared to the $250 per marker for SSR development.
Furthermore, with their even distribution and high den-sity, ISBPs can be mapped with a spacing far below 10 cM, thus providing several tightly linked flanking markers for virtually all target loci in the wheat genome. As an exam-ple, on chromosome 3B, a total of 81 ISBP markers were mapped by melting curve analysis in three populations (Chinese Spring· Renan, Courtot · Chinese Spring and Opata 85· Synthetic W7984) to construct a 3B neighbour map (Figure 5). This map covers 179.8 cM, which is consis-tent with the length of previously published genetic maps for this chromosome (Quarrie et al., 2005; Suenaga et al., 2005; Akbari et al., 2006; Paux et al., 2008; Saintenac et al., 2009). The ISBP markers covered the chromosome with an average of 1 marker per 2.19 cM. No obvious clus-tering was observed compared to other markers such as DArTs (Jaccoud et al., 2001) which appear to cluster often at the end of chromosomes (Semagn et al., 2006; Peleg et al., 2008). The largest gaps were found between mark-ers cfp45 and cfp3124 (12.5 cM) on the short arm, and between markers cfp3427 and cfp49 (14.8 cM) on the long arm of chromosome 3B. This unbiased distribution and high density of markers makes ISBPs useful for breed-ing programmes as flankbreed-ing markers linked to the target locus with a genetic distance of less than 5 cM are more than 99% reliable (Tanksley, 1983; Visscher et al., 1996; Collard and Mackill, 2008).
Conclusion
In wheat, the deployment of marker-assisted selection has long been hampered by the lack of markers
compati-ble with high-throughput cost-effective genotyping tech-niques. With the capability of detecting several million markers on high-throughput genotyping platforms and revealing polymorphism in the wheat elite gene pool, ISBPs have the potential to overcome these barriers and lead to a breakthrough in wheat marker-assisted selec-tion. To date, only few hundreds of ISBPs are available, mainly located on the chromosome 3B of hexaploid wheat (Paux et al., 2008). However, the development of
new sequencing technologies opens very exciting per-spectives with the development of several ten thousands of ISBPs or ISBP-derived SNPs from a single sequencing run that can be performed on individual sorted chromo-somes or on the whole genome. However, as SNP dis-covery relies on sequencing and comparing amplicons between different varieties, the next challenge will be to capture ISBPs in different lines without a first step of PCR amplification. We are currently exploring the poten-tial of genome partitioning techniques such as Roche Nimblegen Sequence Capture or Agilent SureSelectTM to reach this goal. Beside wheat, the potential of ISBP markers has already been demonstrated in other species such as barley (Dave Laurie, personal communication) and rye (Bartosˇ et al., 2008). Therefore, ISBPs represent a new potential for marker development in species facing both repetitive genome and low polymorphism level problems.
Experimental procedures
BAC-end sequences
The BAC-end sequences used for the design of ISBP markers were retrieved from the Genome Survey Sequences Database division of GenBank (accession numbers: DX363346––DX382744, EI666997––EI676076 and ER772249––ER781190).
Plant material and DNA extraction
Eight hexaploid wheat (T. aestivum) lines (Chinese Spring, Renan, Courtot, Synthetic W7984, Opata 85, Sumai 3, Balkan and Dinka) were used for sequencing while the nullisomic 3B-tetra-somic 3A line (Sears, 1954), two ditelo3B-tetra-somic 3B lines (Sears and Sears, 1978) and 14 deletion lines (3BS3, 3BS8, 3BS7, 3BS9, 3BS2, 3BS4, 3BS1, 3BS5, 3BL2, 3BL8, 3BL1, 3BL9, 3BL10 and 3BL7; Endo and Gill, 1996) were used for deletion mapping. Individuals from a Chinese Spring· Renan F2 population, a Courtot· Chinese Spring Double Haploid population and the ITMI population (Paux et al., 2008) were used for constructing the 3B neighbour genetic map and 96 varieties chosen among the worldwide bread wheat core collection (Balfourier et al., 2007), 46 European and 46 Australian wheat elite lines (Table S3) were evaluated with ISBP to analyse their potential for breeding. DNA extraction was performed as described by Graner et al. (1990).
PCR amplification
PCR reactions were carried out on standard 384-well thermocy-clers in a 10 lL final volume with 25 ng DNA, 1· buffer, 1M Be-tain, 0.4 mM dNTPS, 0.5 lM primers, 0.25 U Taq DNA
polymerase. The PCR programme was initial denaturation at Figure 5 Neighbour genetic map of wheat chromosome 3B.
95C for 5 min, then seven cycles of 95 C 30 s, 62 C ()1 C ⁄ cycle) 30 s, 72 C 30 s, 31 cycles of 95 C 30 s, 55 C 30 s, 72C 30 s, 11 cycles of 95 C 30 s, 56 C 30 s, 72 C 30 s and a final extension of 5 min at 72C.
Sequencing
PCR reactions were carried out as described above. PCR products were purified by ExonucleaseI and SAP treatment (5 lL DNA, 1 U ExoI, 1· SAP buffer, 1 U SAP in 9 lL, 37 C for 1 h). Purified fragments were sequenced on an ABIPRISM 3730XL machine
(Applied Biosystems) using the ABIPRISM BigDye terminator
v3.1 kit (Applied Biosystems). Sequences were analysed for SNP detection using the Genalys v2.8.3b software (http://soft-ware.cng.fr/docs/genalys.html).
Melting curve analysis
PCR reactions were carried out as described above except for the reaction mix where 0.16· SYBR Green I (Roche Diagnostics, Mannheim, Germany) was added. Melting curve analysis was per-formed on the ABIPRISM7900HT (Applied Biosystems), by using
the default ‘dissociation step’ to measure the fluorescence inten-sity of the PCR product in a linear denaturation ramp from 60– 95C. Dissociation curves were analysed using theSDS2.2.1
soft-ware (Applied Biosystems).
SNaPshotMultiplex System
Primers were designed manually. PCR reactions were carried out as described above. PCR products were purified by ExonucleaseI and SAP treatment (5 lL DNA, 1 U ExoI, 1· SAP buffer, 1 U SAP in 9 lL, 37C for 1 h). The SNaPshot
reac-tion was performed following the manufacturer’s instrucreac-tions (3 lL of purified fragment, 1 lL SNaPshot kit (Applied
Biosys-tems), 0.2 lM primers in 10 lL, 25 cycles of 96 for 10 s,
50C for 5 s and 60 C for 30 s) and were purified by adding 1 U SAP and incubating at 37C for 1 h. Fragments were run on an ABIPRISM 3130XL machine (Applied Biosystems) and
analysed using the GeneMapper v3.7 software (Applied Biosystems).
Illumina GoldenGate Assay
SNP genotyping data were generated with the GoldenGate Assay technology on a VeraCode⁄ BeadXpress platform (Illumina, Inc., San Diego, CA, USA). Data were produced according to the pro-tocol described by Lin et al. (2009). Allelic variants were captured as products of an extension–ligation reaction which are then labelled with respective fluorescent dyes and amplified at the same time in a universal polymerase chain reaction. The dye-labelled strand of the PCR product is hybridized to patented Illu-mina VeraCode microbeads. Analyses were performed using the BeadXpress Reader. VeraCode beads allow immediate association of allele-specific fluorescence signals to the SNP loci being interro-gated through the inscribed holographic barcodes.
Variability estimation and genetic relationships
Variability for each locus was measured using the Polymorphism Index Content (PIC) (Anderson et al., 1993):
PIC ¼ 1 X n i
p2 i
where piis the frequency of the ith allele.
Alleles were coded as numerical data in a binary matrix where 0 corresponds to the absence of amplification. Cluster analysis was performed by Neighbour-Joining tree based on a Sokal and Michener (=simple matching) dissimilarity matrix (Sokal and Michener, 1958).
Genetic mapping
A genetic map of chromosome 3B was constructed with the CsRe F2 population (Saintenac et al., 2009). Linkage estimation was based on the maximum likelihood method with Mapmaker (Lander et al., 1987) with LOD and h-values of 3 and 0.25, respectively, with the Kosambi mapping function to transform recombination fractions into centimorgans. The chromosome 3B neighbour genetic map was built following the strategy developed for the construction of the IBM map in maize (Cone et al., 2002), with segregating data from the following three mapping popula-tions: Chinese Spring· Renan as a framework, W7984 · Opata (the so-called ITMI reference population; Nelson et al., 1995) and the Courtot· Chinese Spring (Sourdille et al., 2003).
IsbpFinder
The IsbpFinder programme is written in Perl. It first detects transposable elements on query FASTA sequences by homology search against a repeat sequence library by using RepeatMasker (Smit et al., 1996; http://www.repeatmasker.org). The results of TE detection are parsed and then filtered to identify the posi-tions of the TE borders on the query sequences. For each junc-tion detected between two TEs or between a single TE and the flanking DNA, IsbpFinder extracts a subsequence of 1000 bps encompassing the target region. Then, it runs the Primer3 program (Rozen and Skaletsky, 2000) to design a couple of primers located on each side of the detected junction with the following parameters: expected amplicon size between 100 and 500 bps, primer Tm: 60C. In cases of detection of multiple junctions close to each other (<500 bps), IsbpFinder design primers so that only a single TE junction can be amplified. Finally, for each ISBP, IsbpFinder analyses the type of junction to be amplified and thus estimates a confidence score (high⁄ medium⁄ low): ‘high’ confidence corresponds to ISBPs designed over junctions for which at least one TE extremity was detected, ‘medium’ confidence to junctions with no TE extremity was detected and ‘low’ confidence to putative junctions between two similar TEs. Results are returned into an Excel-like table format containing the following data: the forward and reverse primers with Tm, the amplicon sequence with Tm and GC content, the junction type with abbreviated names of flanking
elements, the confidence score, the junction position and the detailed description on flanking elements as retrieved from the repeat database. It can also generate EMBL formatted outputs which allows a graphical representation of the primers and amplicon encompassing a TE junction. Thus, the RepeatMasker based-annotations and the ISBP distribution over the whole sequences can be visualized over a viewer software such as Artemis (Rutherford et al., 2000) for a user-friendly selection of markers. IsbpFinder requires RepeatMasker, Primer3 and the Bioperl libraries (version not older than 1.5; Stajich, 2007). Isbp-Finder is freely available upon request.
Acknowledgements
The authors would like to thank Karine Chevalier, Cyrille Saintenac, Karine Paux and Delphine Boyer for their tech-nical help. This work was supported by grants from the Agence Nationale de la Recherche––Direction Re´gionale de la Recherche et de la Technologie (06-CPER-054-01), the INRA and Fonds Unique Interministe´riel (Semences de Demain project).
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Supporting information
Additional Supporting Information may be found in the online version of this article:
Table S1 List of 157 sequenced ISBP markers, with sequence of origin and primers used for PCR amplification. Table S2 List of 25 ISBPs and 53 ISBP-derived SNPs geno-typed on Illumina platform. Each marker is associated with
the name of the corresponding ISBP, the type of polymor-phism (presence⁄ absence or SNP), the design score, the context sequence as well as the ASO A, ASO B and LSO primers.
Table S3 List of 46 European and 46 Australian wheat lines.
Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting materials sup-plied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.
