Diversity arrays technology (DArT) markers in apple for ... - CiteSeerX

Mol Breeding (2012) 29:645–660 DOI 10.1007/s11032-011-9579-5

Diversity arrays technology (DArT) markers in apple for genetic linkage maps Henk J. Schouten • W. Eric van de Weg • Jason Carling • Sabaz Ali Khan • Steven J. McKay • Martijn P. W. van Kaauwen • Alexander H. J. Wittenberg • Herma J. J. Koehorst-van Putten • Yolanda Noordijk • Zhongshan Gao • D. Jasper G. Rees • Maria M. Van Dyk • Damian Jaccoud • Michael J. Considine Andrzej Kilian

•

Received: 6 September 2010 / Accepted: 9 April 2011 / Published online: 15 May 2011 Ó The Author(s) 2011. This article is published with open access at Springerlink.com

Abstract Diversity Arrays Technology (DArT) provides a high-throughput whole-genome genotyping platform for the detection and scoring of hundreds of polymorphic loci without any need for prior

Henk J. Schouten, W. Eric van de Weg these authors contributed equally to this work.

Electronic supplementary material The online version of this article (doi:10.1007/s11032-011-9579-5) contains supplementary material, which is available to authorized users. H. J. Schouten (&)  W. E. van de Weg  S. A. Khan  M. P. W. van Kaauwen  H. J. J. Koehorst-van Putten  Y. Noordijk Wageningen University and Research Centre, P.O. Box 16, 6700 AA Wageningen, The Netherlands e-mail: [email protected] J. Carling  D. Jaccoud  A. Kilian Diversity Arrays Technology, PO Box 7141, Yarralumla, ACT 2600, Australia S. J. McKay Department of Horticultural Science, University of Minnesota, Alderman Hall, 1970 Folwell Ave, St. Paul, MN 55108, USA Z. Gao Department of Horticulture, Zhejiang University, Hangzhou 310029, China

sequence information. The work presented here details the development and performance of a DArT genotyping array for apple. This is the first paper on DArT in horticultural trees. Genetic mapping of DArT markers in two mapping populations and their integration with other marker types showed that DArT is a powerful high-throughput method for obtaining accurate and reproducible marker data, despite the low cost per data point. This method appears to be suitable for aligning the genetic maps of different segregating populations. The standard M. J. Considine Department of Agriculture and Food Western Australia, South Perth, WA 6151, Australia A. H. J. Wittenberg KeyGene bv, P.O. Box 216, 6700 AE Wageningen, The Netherlands D. J. G. Rees ARC: Biotechnology Platform, Agricultural Research Council, Private Bag X5, Onderstepoort, Pretoria 0110, South Africa e-mail: [email protected] M. M. Van Dyk Department of Genetics, Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, Private Bag X20, Hatfield, Pretoria 0028, South Africa e-mail: [email protected]

M. J. Considine School of Plant Biology, and the Institute of Agriculture, University of Western Australia, M084, Crawley, WA 6009, Australia

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complexity reduction method, based on the methylation-sensitive PstI restriction enzyme, resulted in a high frequency of markers, although there was 52–54% redundancy due to the repeated sampling of highly similar sequences. Sequencing of the marker clones showed that they are significantly enriched for lowcopy, genic regions. The genome coverage using the standard method was 55–76%. For improved genome coverage, an alternative complexity reduction method was examined, which resulted in less redundancy and additional segregating markers. The DArT markers proved to be of high quality and were very suitable for genetic mapping at low cost for the apple, providing moderate genome coverage. Keywords Apple  DArT  Genetic mapping  Molecular markers  Diversity

Introduction The genus Malus has been a focus of molecular studies since the mid-1980s, when Weeden and Lamb (1985) used isozymes as a means of discriminating between different apple cultivars. As new marker types have been developed, scientists have readily adopted them into their studies of apple genetics, progressing from rapid amplification of polymorphic DNA (RAPD) (Koller et al. 1993), through restriction fragment length polymorphism (RFLP) and isozymes (Maliepaard et al. 1998), to amplified fragment length polymorphism (AFLP) and simple sequence repeat (SSR) (Guilford et al. 1997, Hokanson et al. 1998) and on to targeted markers (e.g., Broothaerts 2003; Calenge et al. 2005; Chagne´ et al. 2007) and single nucleotide polymorphism (SNP) arrays (Micheletti et al. 2011). The progress in the molecular characterisation of the Malus genome has recently gained further momentum with whole-genome sequencing (Velasco et al. 2010). Molecular markers have been applied widely to evolutionary and pedigree studies in apple, including both wild Malus species (Richards et al. 2009) and domestic cultivars (e.g., Cabe et al. 2005; Evans et al. 2010). Additionally, markers developed for the apple have been applied fairly widely to other pome species, and vice versa, notably pear (Pyrus spp., Yamamoto et al. 2001; Hemmat et al. 2003; Dondini et al. 2004), quince (Cydonia oblonga; Yamamoto

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et al. 2004) and loquat (Eriobotrya japonica; Gisbert et al. 2009; He et al. 2010). Simultaneously, linkage maps have been constructed for a number of domestic cultivars, and several map alignments have been reported (Maliepaard et al. 1998; N’Diaye et al. 2008; Patocchi et al. 2009; Van Dyk et al. 2010). The construction of linkage maps has facilitated the identification of molecular markers associated with numerous phenotypic traits. Among the traits examined to date are resistance to apple scab caused by the fungus Venturia inaequalis (Koller et al. 1994; Calenge et al. 2004; Bus et al. 2005a, b; Soriano et al. 2009), fire blight caused by the bacterium Erwinia amylovora (Peil et al. 2007), columnar growth habit (Moriya et al. 2009), several fruit quality traits (e.g., King et al. 2001; Liebhard et al. 2003a; Costa et al. 2005, 2008, 2010; Kenis et al. 2008) and chilling requirement (Van Dyk et al. 2010). The identification of such molecular markers is essential for marker-assisted selection in apple breeding programs (Gianfranceschi et al. 1996; Liebhard et al. 2003b; Gardiner et al. 2007; Zhu and Barrett 2008). In recent years, genomic methods have been embraced by apple researchers. The enhanced ability to study gene expression has resulted in new understandings of developmental processes (Ban et al. 2007; Espley et al. 2009). Transcription analyses of apple fruit development using cDNA microarrays (Soglio et al. 2009) and plant physiological responses to pathogens (Norelli et al. 2009) has facilitated the development of new molecular markers (e.g., Chagne´ et al. 2008; Igarashi et al. 2008). The further development of highthroughput genetic technologies will continue to expand the ability of scientists to investigate the details of the genetics of apple and its relatives (Shulaev et al. 2008). Since the proof-of-concept paper (Jaccoud et al. 2001), Diversity Arrays Technology (DarT) has been developed as an inexpensive whole-genome profiling technique for many organisms, especially plants. The website www.diversityarrays.com has a current list of organisms for which arrays are available ([50). DArT, in its current implementation, is a hybridisation-based genome profiling technology that does not require sequence information and uses microarrays to identify and type DNA polymorphisms. As the DArT markers are typed in parallel, it is possible to identify hundreds or even thousands of polymorphic markers

Mol Breeding (2012) 29:645–660

in a single experiment (Wittenberg et al. 2009). This highly parallel assay results in a reduction of the price per data point to around US $0.01 in organisms with well-developed arrays. The DArT assay primarily detects dominant markers, mostly resulting from single nucleotide polymorphisms and indels in restriction sites and differences in methylation of restriction sites. When methylation-sensitive restriction enzymes (like PstI; see Gruenbaum et al. 1981) were used in the large genomes of cereals (Wenzl et al. 2004, 2006; Akbari et al. 2006), DArT markers were located preferentially at the gene-rich, subtelomeric regions of the chromosomes. The use of methylation-sensitive enzymes may provide also an insight into epigenetic variation (Wenzl et al. 2004). Interestingly, while DArT has performed well in over 50 crops, there are no reported applications of DArT in horticultural trees. As DArT has been applied successfully to complex amphidiploid genomes like wheat (Akbari et al. 2006), oat (Tinker et al. 2009) and sugarcane (Heller-Uszynska et al. 2010), application to the duplicated genomes of the pome fruits such as apple (Velasco et al. 2010), pear and loquat should be promising too. Here, we present the development and validation of DArT for apple using a complexity reduction method similar to the one used for the cereal genomes. We compare this with a second complexity reduction method that is similar to the method used for the fungus Mycosphaerella graminicola (Wittenberg et al. 2009), and we discuss the characteristics of the detected markers. We combined DArT markers with other marker types in genetic linkage maps, providing insight into the coverage of the DArT markers in the apple genome. We used as a starting point the progeny and genetic linkage map of Prima 9 Fiesta, which was the first genetic linkage map for apple covering all 17 chromosomes (Maliepaard et al. 1998). In addition, we used a more recent progeny of other parents for genetic mapping. Furthermore, we evaluated the performance of DArT in a genetic diversity analysis of 44 diverse apple accessions and a set of Australian breeding lines.

Materials and methods Plant material For the building of the DArT libraries, care was taken to represent a wide genetic diversity, including

647

several major founders in apple breeding worldwide, founders of more local breeding programs, modern cultivars and some very recent selections from ongoing breeding programs. Forty-four accessions of Malus were used for the library development (Online Resource 1a). For mapping, two populations were used. The first, Prima 9 Fiesta, was used to examine the reliability of the DArT data by evaluating the ease by which the DArT markers were integrated in an existing, wellestablished linkage map. This population consists of 156 individuals (Maliepaard et al. 1998), of which a subset of 121 individuals were DArT genotyped. The second population, 2000–2012 (Soriano et al. 2009), was used to demonstrate the ease by which DArT markers allow for the alignment between mapping populations. In addition, this population was used to compare two DArT complexity reduction methods with respect to the number of markers and genome coverage. It comprises 894 individuals, 399 of which were used in the current study. The parentages of both populations are presented in Fig. 1. DNA extraction For the development of the DArT libraries and genetic diversity studies, leaves were collected from grafted trees with a preference for younger, actively expanding leaf material. Genomic DNA was extracted using a modified cetyltrimethylammonium bromide (CTAB)/ chloroform/isoamylalcohol protocol based on the method of Doyle and Doyle (1987). The addition of 2% w/v polyvinylpyrrolidone (PVP-40, Sigma, K value: 29–32) (Aljanabi et al. 1999; Kim et al. 1997) appeared to be essential for preventing the inhibition of restriction endonuclease digestion in many of the apple leaf samples tested. For the mapping populations, DNA was extracted according to Maliepaard et al. (1998) and Soriano et al. (2009). Construction of DArT arrays A crucial step in the Diversity Arrays Technology is the complexity reduction of genomic representations. In this manuscript, complexity reduction refers to the reproducible selection of a subset of DNA fragments from a whole genome. These fragments, after being cloned into E. coli vectors (TOPO) and amplified with M13 primers, were printed onto slides as probes

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Fig. 1 Pedigree of the two mapping populations examined. Common parents are highlighted

for microarray hybridisations. The complexity reduction method used most often in DArT involves digestion with the methylation-sensitive restriction enzyme, PstI. In conjunction with digestion using this relatively rarely-cutting restriction enzyme (six bp recognition site plus methylation sensitivity; Gruenbaum et al. 1981), an enzyme with frequent cutting capabilities is used (Wenzl et al. 2004). In this study, the frequently-cutting enzymes AluI, BstNI, TaqI or MseI were used. PCR adapters were ligated to the PstI fragment ends, and the PCR-amplification was performed using primers complementary to the PstI adapters, according to Wenzl et al. (2004). Only those fragments with PstI adapters at both ends were amplified. Initial assessment of the enzyme combinations was performed by agarose gel electrophoresis, as described by Jaccoud et al. (2001). On this basis, the genomic representations produced by the digestion with PstI, in combination with either AluI or

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BstNI (PstI/AluI and PstI/BstNI), were considered to be the most suitable due to the absence of visible bands in the gel smear (Kilian et al. 2005). An initial library of 768 clones was prepared for PstI/AluI and a second initial library of the same size for PstI/BstNI, using DNA from 15 diverse heritage apple varieties (Online Resource 1a). The inserts of the 2 9 768 clones were amplified and printed on glass slides to provide small arrays for testing. The 15 cultivars listed in Table 1 were hybridised in duplicate to the arrays, according to Wenzl et al. (2004). The PstI/ AluI complexity reduction method was found to give a higher number of candidate polymorphic markers (16% of clones) than the PstI/BstNI method (10% of clones). The PstI/AluI library was therefore expanded by an additional 3,840 clones derived from the 15 heritage cultivars, and 9,984 clones from modern apple cultivars and breeding lines (Online Resource 1a). The total size of the expanded library for the PstI/AluI complexity reduction was 14,592 clones.

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Table 1 Number of DArT markers from the standard complexity reduction method, during successive mapping stages in the Prima 9 Fiesta population Mapping stages

Prima

Fiesta’

Both parents

Total

No. of events

No. No. of cumulative events

No. No. of cumulative events

No. cumu- No. of lative events

257

234

285

% No. events cumulative

Preparation data Polymorphic

776

Unclear parentage

11

246

14

220

0

285

25

3.2

751

Polymorphic Information Content \ 0.10

47

199

49

171

3

282

99

12.8

652

Redundant, completely identical

1

198

40

131

73

209

114

14.7

538

Remaining ungrouped

0

198

4

127

0

209

4

0.5

534

Insufficient linkage for phase determination

1

197

4

123

32

177

37

4.8

497

2 112

196 83

4 42

119 77

1 96

176 80

7 250

0.8 32.2

490 240

83

1

76

0

80

1

0.1

Mapping

Irregular segregation pattern Redundant, differences only for missing values Double recombinant

0

Unique mapped

83

Markers with solved parentages

3

Total uniquely mapped

86

86

76 3 79

Hybridisation to the expanded array To gain insight into the applicability of DArT in mapping, two segregating populations were hybridised to the expanded array. All genotypes were hybridised with two to four replicate arrays per genotype, using both Cy-3 and Cy-5 fluorescent labelling. Hybridisation, subsequent processing and data analysis were performed according to Wenzl et al. (2004). Integration of existing genetic markers into genetic linkage maps The genetic linkage map of Prima 9 Fiesta was the first for apple covering all 17 chromosomes (Maliepaard et al. 1998). It consisted of 138 dominant markers (mainly RAPDs and some AFLPs) and 152 essentially co-dominant markers (mainly RFLP, some isozymes and SSRs). Since then, 313 new markers have been added through successive European projects, of which 180 are dominant (mainly AFLP) and 133 are co-dominant, mainly consisting of SSR markers from the gDNA-based CH and Hi series

79

80 2 82

82

239 239

8

1.0

247

247

(Liebhard et al. 2003b, Silfverberg-Dilworth et al. 2006) as gathered from expressed sequence tag (EST) sequences (Soglio et al. 2009). Some of the new markers were specifically designed for fruit-quality genes (Costa et al. 2005, 2008, 2010) and allergy genes (Gao et al. 2005a, b, c). The quality of the map was thoroughly validated and improved, using JoinMapÒ (Van Ooijen and Voorrips 2008). This newly enriched, evaluated and extended map of Prima 9 Fiesta covers approximately 90% of the apple genome, and was used as a starting point for the mapping of DArT markers. An alternative method for complexity reduction We evaluated an alternative complexity reduction method that was applied by Wittenberg et al. (2005) to microbial genomes. This approach involved digestion with two six-base cutters, PstI and EcoRI. A standard adapter was ligated to the PstI ends of the restriction fragments and a long, asymmetric adapter with a 30 -amino (NH2) group on the short strand was ligated to the EcoRI ends. The amino group,

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combined with PCR suppression (Siebert et al. 1995; Broude et al. 2000), was used to prevent amplification of the EcoRI–EcoRI fragments. Only the PstI–PstI and PstI–EcoRI fragments were amplified. To further reduce the complexity of the genomic representations, a third endonuclease, the four-basepair cutter MboI, was used. No adapters were ligated to the MboI sites. Consequently, the fragments cut by MboI were not amplified (Wittenberg et al. 2005). For this alternative complexity reduction method, the DNA of the apple selection 1980-015-025, a parent of population 2000–2012 (Fig. 1), was used to construct a library of 6,144 fragments, which were printed onto slides (Wittenberg et al. 2005). Target DNA from this selection’s progeny, population 2000–2012, was assayed with this array, using the same alternative complexity reduction method. The adapters ligated to the target DNA of this progeny differed from those of the parental fragments printed on the slides, in order to prevent hybridisation of adapters to one another (Wittenberg et al. 2005). The alternative array was used for the genotyping of 244 progenies of population 2000–2012, all of which had also been genotyped with the standard method too. The maternal map of the heterozygous parent 1980-015-025 was constructed using DArT markers from both complexity reduction methods. In addition, several SSR markers were used as references on the linkage map; they were generated according to Patocchi et al. (2009).

Results Mapping of DArT markers in Prima 9 Fiesta The Prima 9 Fiesta progeny were hybridised to the expanded DArT array for the standard complexity reduction method, which provided 776 polymorphic markers. The call rate for the parental genotypes was 99.2%. The call rate is the percentage of targets that could reliably be assigned a score of ‘0’ or ‘1’ for a given candidate marker. The average call rate for the entire Prima 9 Fiesta mapping population was 96.7%. Of the aforementioned 776 Prima 9 Fiesta DArT markers, 247 (32%) were mapped to a unique position (Fig. 2). The other 68% were eliminated during the mapping process for several reasons (Table 1). Only 4.6% of the markers were discarded

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due to possible scoring problems. Of these, 3.5% were due to incomplete data on the mapping parents, leaving only 1.1% of the markers being possibly discarded for inadequate scoring, remaining ungrouped or showing irregular segregation patterns. The two latter phenomena could also be due to reasons other than scoring difficulties, such as a lack of marker coverage of the genomic regions or the presence of duplicated loci. Online Resource 1b documents the fact that adding DArT markers did not affect the previous high overall map quality, as measured by the average v2 value. A considerable number of clones exhibited identical segregation patterns in Prima 9 Fiesta compared to other clones, and therefore did not provide a higher genetic resolution in the map. As a result, a total of 364 (54%) of the 677 clones (=776–99; Table 1) were classified as redundant. Genome coverage The current DArT array provided moderate genome coverage, as illustrated in Fig. 2. Many markers clustered, producing several short genomic segments containing multiple markers, such as a segment of 4 cM at the top of linkage group (LG) 7 that contained eight unique Prima-specific DArT markers and one marker for both parental cultivars. However, several extended regions had no or very few markers, such as the entire LG1 of Prima, which contained only one Prima-specific and one common marker. Genome coverage was estimated using the integrated map of Fig. 2 as a reference and the following thresholds: (1) only parent-specific markers were considered, as markers common to both parents carry little genetic information, and (2) a single marker covers 10 cM surrounding its position. In these calculations, the current DArT array offered sufficient coverage for performing classical quantitative trait loci (QTL) mapping studies on around 55% of the Prima and 60% of the Fiesta genome. If a single marker were to sufficiently cover a larger window of 30 cM, then the genome coverage for Prima and Fiesta would be 76 and 74%, respectively. Suitability of DArT markers for map alignment To examine the power of DArT markers for aligning maps, the second mapping population, 2000–2012,

Mol Breeding (2012) 29:645–660 PF1 + + + +

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# 182512 Conf-1-CAPS Ch05g08

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186132 526906 182512 # 442410 518923 553494 460910 526944 10J16 441548 11E23_D1

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36

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461169 461279 461508

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526764 442210 443367 MC109c E34M59-251 E34M55-85 OPAD-02-0400 GD103 E35M50-206 MC245 MC018 OPS-10-1100_B OPAG-15-1250 E35M47-111 MC024b E33M52-177 E33M51-202 MC204b OPAC-03-0510 OPL-01-0900 E35M47-158 E36M51-243 E34M59-97

8 9 11

184650

MC097b MC227b

++ +++-

32

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7N16 519616 553843 183668 # 11G4 182773 # 185646 184384 # Hi04a08

21 34

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37 39 41 43 45 46 47 48

30 31 33 35 37 38 41 47

56 57 59 60 61

53 55 59 60

1.00E+17 10N23 460976 11A15 10K2 CH05e06

5J19 CH04g09z

186614 525645 182520 # 9.00E+21 6B6 183517 # 5M22 442210 9I22

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# 182991 CN493139z MC003

442050 # 183073 442706 # 182463 E35M51-98

186257 553289

441647 460970

# 183313 # 183742 519399 183703

28

OPE-04-1400 E36M59-315

185017 OPD-10-2000 E34M48-205 MC019a Mald1.05 MC023a MC034b

45 53

185846

33 34 35 36 37

29 30 31 33

42

+-

41 46 47 48 52

71 73

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+/-

79

7K9 7H3 5I22

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82 85

75 88 76

89 91

CH02b12

186344 185935 526844 183675 # 6H16 8D18_D1 2N15

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184305 # 1A21 8L10

66

CH05e03b CH03d10

68 69

183742 # 10I23

74

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57 CH03d01

78

+

58 59 60

182415 #

85

+

61 62 63 66 67

UBC220-1250 -

442761

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OPE-19-1550 OPD-01-2600 E36M59-152 MC228d MH602-D

525579 186485 554833

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186429

94

+

MC012b E34M55-272

442766 7BC7a E31M52-218

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++ ++ -

CH03d12 MC040b

7B9

442310 E34M55-84 OPAB-12-0680

+++-

CH05a05 MC204a MC011-

552845 187001 442219 460847 6P19 461800 186469 441630 443230 519400 442848

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183445 # 7D13 186485 7O24

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AU223657a

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184471 461256 185218 442825 526535

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461092

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60 63 64 68 71 73 75

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526018 526572 461502 # 182559 186532

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Mald3.02

462170 186208

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182651

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3O5 461699 186787 1M10 184740

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20 22 24 30

26 27

31

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CH02h11

460809 441674 525869 461271 186532 182559 #

32

32

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35 36 41

36 38

+++++++++ +

46 47 48 49 51 52 54 57 60

AT000420

62 66 68 71 77

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CH01b12z

91

SOD-3 MC105a

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553934

186634 518939

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87 89 90

68

012-6 0 3 6

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CH03d07?

PF7 +

7 10 11 13 14 15 16

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461879

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554574 461879

CH03g07

18 19

43 45

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012-4 0 4 6

69 71 72 73

37 39 40 42 44 48 49

40 43 44

CH02b10?

6P2 10H11

+ + +

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GD103

86

+

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63

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40

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70

69

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63 66 69

80

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519280

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MC003 OPD-08-1000

186908 441940 525950 186257 186838 183604 # 186063 518937 182463 # 184600

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13 14

34

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518995

+++186937 OPAF-13-1800 + + OPC-06-1300_B +MC096b + E35M51-158 + E35M51-110 E35M51-252 +NZ23g04

+-

OPA-09-2000

183577 # 460705 553952 526067 461392 185887 552850 441860

443206 NZ05g08 +MC013 Conf-4-CAPS + +CH04e02 + NZ05g08 +185348 MC036a + + OPE-18-0550 + E34M59-291 MC069b + E32M52-175 + E33M51-207 + 526087 + CN491231-SSR + MC034a + MC023c E35M52-292 + E34M59-167 +MC127a + E33M51-225 E33M52-169 + + LY27b CH02h11a ++ OPT-18-1600 +MC009 +MC019b - OPAC-03-0560 Conf-5+ +# 184104 + MH134 + Conf-6-SSR + 519662

+-

CH03e03

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183126 184693 555018

1 3 4 9

461798 # 184120

185353 525950 E36M57-243 UBC225-1500

+

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# 184119 OPD-05-2000 OPT-06-0550

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CH02f06

PRX-3 PRX-2 CH03e03

+# 183577 +CH03g07 + E35M51-154 + E35M50-215-5 + OPAC-06-1700 MC004a +-

CH05e03 E34M59-192 MC116b pAP79 MC096a

-

2 6

-

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++ + ++++-

0 7 10

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Mald4.02

+ + OPAG-05-1600 +184544 - OPAL-07-0780 CH03d01 E33M52-71 + CH03d01 +MC029a - OPT-02-0900 +# 182415 MC064b

pAP4a-X CN493139x

1 3 6 10

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184966 442431

0

17

Hi03e04x E34M59-390 Conf-2-CAPS E31M52-280 MDMADS2 CH04e05y CH02a04y CH03d10

+-

0

461303 460938

+ ++ +++++-

+

++

012-2

PF2

012-1 0

MH876 E35M52-82 OPAD-18-1130 UBC249-2000 MC014a OPC-08-1100

442943

651

74

3.00E+19 CH05a05

+ +/-

+ + + + ++-

+ + + + + +

+-

++-

++-

+

77

# 182694 526661 184957 184765 186359 # 183619 186460

+186378 + OPA-09-0700_BD + # 183153 + E35M48-131 + E34M48-252 + E35M47-275-2 +CH04e05z + 441985 + OPAM-19-0800 +443137 +MC014b +186703

+-

3 4 6

183165 # 11D9 11B5 527002 186990

+ +++-

+518983 +LY21 + OPAB-14-0600 # 182536 -

+-

+

4

MC057a

MC116a E36M51-206 OPAB-13-2600 OPC-08-1700 E35M47-220 OPT-09-1200

+-

3

0

MC064a

442697 461103

# 182859

+-

PF8

012-7 0 2

527002

OPAE-19-1600

+-

+-

MC056 MC228b 2B12a MC029b Sd1 OPT-14-0800

182820 OPA-10-1000 MS06c09

460767 553337 461607 185711 553964 525517 186911 E33M51-64 E34M59-83 Conf-8-SSR

22

Hi03a10p

30

186378 2D12 441985 183153 # 442371 460884 443137 186703 6N4 184379 # 182820 12H8 10O2

+/-

5 6 35 7 36 10 11 13 15

37 38 39 40

16

43 44

18 21

47

CH04e05

22 54 23 60 62 24 66 25 27 31 33 35 39 41

72 75 76

184661 443057 6I7 10C5 184289 # 184686 184313 # 185711 518887 553337 553964

+ ++ ++++++ + + + +/+ + +++++ + -

519609 443379 186096 461686 186784

+ + E33M51-68 AAT-2 + OPT-06-0800 + + CH01e12 + E34M48-323 MC206 +Mald4.03 LY16 185289 + E31M52-230 E31M52-465 +MC022 + OPAF-12-1600 + OPD-15-1300 + LAP-2_C + E33M51-280 +CH01f09 + Mald4.03 +# 182916 + E35M52-124 + E34M59-213 + E35M48-326 CH05a02y +MC027 441714 - OPAF-13-0800 + 461663 + CH01h10 E35M48-330 E31M52-198 + E36M57-142 + 186111 + OPAC-11-0780 - OPT-19-2200 MC104a E35M50-226 525634

012-8 0 1 2 3

0

182536 #

5

6I3

+-

461789 186784 461737

+

518577

+-

CH01c06

+/+-

+

4 11

11

12

15

13

20

+ +

18 21 24 34 26

37 39

27 28

41

29

42

30

43 45 49

9O24 5M2 5B10 184826 185283 526028 442481 183036 # 185289 CH02g09

+++ + + + + + +/+

32

50

34 37 38 39 43 44 45 46 48 52

51

9M2 2G7 441714

63

CH01h10

+

76

441618 526690

+

55

77

++-

+

58 59 60 68

45 46

47

526070 PGM-2

53

525837 E36M59-171 CH02b12y LY29b MC123b

78

185871

79

CH04e03 E34M48-154 E34M55-59 MC244a MC202

80 81 84 88

Fig. 2 Alignment of the 17 linkage groups of the mapping populations Prima 9 Fiesta (PF) and 2000–2012 (012). DArT markers are displayed in bold, those segregating in both parents are in italics and those generated with the alternative complexity

reduction method are underlined. The ? and - symbols next to a marker name indicate that the DArT marker was polymorphic in the mother and father, respectively. The # symbols indicate the DArT markers that have been sequenced

123

652

Mol Breeding (2012) 29:645–660 PF9

++++

012-9

Conf-12-CAPS E36M51-198

# 184321 442368 # 184050 554103 441965

+ +

012-10 183555 #

+

7

6

552809 184025 # 460957 554953

+-

7

10

0

16 21

2

23

8

24 26 29 31 34

184700 185101 # 182598

36

E34M55-181

39 43 47 52 54 55 56

38

# 184328

- CX023634-SSR Conf-9-SSR - OPAE-01-1190 E34M48-83 E35M50-284 E36M51-480 + E36M57-114 Mald4.01 + OPAE-01-1210 + Conf-10-STS Conf-13-SSR + Conf-11-SSR + OPAE-08-1000 + E33M52-96 + E32M52-242 + OPD-01-0600 + E35M50-281-2 + MC224a +CH01h02 + MC038a +462205 + MC117 +Mald2.01B +Mald2.01A + 185333 + MC115b +

6

13 14 19 21 22 23

442368 184050 #

+ +

9D20 554103

+

526197 519109

+

CH01f03

442542 441884 553238 460707

+-

+ +/+ + + -

+

38

184328 # 182598 #

42

443058

+

35

CH01h02

55

CN444542a +

60

185333

0

pADH32a MC228a

7 9 17 18 19 20

519383 OPAF-07-0890

186012

OPE-12-1600 461634 MC024a +MC049 +CH02b07 + E36M51-195 + OPAG-04-1000 + 460957 - OPAD-14-1020 Conf-15 +LY29a +461747 + -

+/-

59

-

61 +

64 65 66 68 70 71 76

+++++ + + + + + ++++++ +-

9 11 17 21

PF13

012-13 0

# 182625

0

10

CH05h05 E33M51-355

2

11

184627

4

553807

12 14

+ + + + + + +++-

E34M48-286 E34M59-255 LY05-Hi MC001a E36M57-117 MC039 MC130 GD147

+-

441485

+ DIA_5 +NH009b + OPAC-15-2050 + OPAC-15-2000 + Mald1.01 MH128 +Hi03e04z + E35M47-240-3 185125 -

MC221b MC041a E34M55-380 - OPAC-11-0460 +CH03h03 +CH03a08 +CH05f04 + Hi04g05 +CH01b12y - OPT-06-1200 + E35M51-75 + +

15

24 25 26 27 28 30 31 35

E35M50-239 E34M59-410 EST-1 E34M59-314 MC109a E33M51-169 Hi03e04y OPAB-13-2200 OPAE-19-0250

36

37 38 44 45 46 49 51 53

37

58 59 60

518575 186714 462175 # 183247

184627 554693 CH05h05z

-

# 183529

72

MC127d MC028a OPA-11-1050 MC060 USASSR11 CH02b03b MS02A01

77 80 85

# 183090

91

+-

27

461022 182529 #

+

CH02g01

+

+

17 31 21 27

34 35 36

43 44 46 47 49 56 60

46

56

63 66

NH009b

5P13 183891 # 441485 6O5

+

+

+ +

62 63 65

96 97 98

186434 pAP4b

+-

68 81

CH05f04

+

518676 184790 OPD-01-1200 E34M48-181 CH01e01 PGD-1 E34M59-239

71

E34M48-182 E35M53-177 E35M53-314

462175 442053 461749 183714 # CH02c11

442552 183247 # CH03d11

186426 184838

185549

184742 # 184110 553959 552945 553118

E35M50-69 185879 +CH03g06 + 7B9a + E33M52-193 + E35M47-149-8 + E35M47-151-7

-

-

++

+/-

- UBC213-1200 E34M55-292 E33M52-322 E31M52-114 441813 +MH116 - OPAE-19-1800 + E34M55-91 + 184555

+

CH04g09y

-

-

90

9M8

+-

97

1L20-! 182835 # 183090 # 5H14 184374 # 6G15 2I8

+-

103 105 106 107 108 112

0

MS06g03

8C7 12L9 8F3

-

+

-

1

441808 442037

49

51

50 51

53

+

16

22

185082

+

21 26

7P5

+

24 31 32 33

441509 554678 185319

+++

25

26 30 31 35 36

+ +++++

75

0

11

443195

+

51

526150

+

53 54 55 87

552945

-

19 22 23 24 25 27

E32M52-165

31

+ + ++-

E31M52-110 pB610b CH01d08

554081 E35M51-230 E31M52-214

462056

525795 E32M52-114 + +MdACS1 + OPAC-08-0630 +CH02d11 +442008 +-

73

LY37a AAT-4 NZ02b01 LY26 CH05a02

++ -

-

62

18

184544

185550 185550

++ +-

OPA-16-1800

+++ + +-

CH03b10 E35M51-245 E31M52-164

+-

+ + + + ++ ++-

441762 442482

441511 526908 526299 # 182445 Conf-19--SSR CH02c09

# 183710

34 37 38 42 43

+

89

186331

+-

95

442695

-

99

526557 518909

-

100

555037

-

186490

-

5

CH05d04

+/-

13

10M16

+-

19

Ch04g04

+/-

33

461499 10J9 10B21

+

6 7 8 11 13 15

16 17 34 19 20 22 23 30 38

E35M50-113 +CH01f02 + 443308 + OPAM-19-1020 - OPAD-12-0800 +443077

43 46 49 53 54 56

185669

62 64

++ ++ + +++++

E34M48-175

# 183720

+ +

40

CH01g12

+/-

45

3L18

+++

CH04d02

49

3N14 9H3 525882 6H17 519342 183985 # 183186 # 183701

51 59 60 61

+ ++ ++ +-

65

Hi07f01

460973 OPA-01-0700

66 67

# 183485

69

73 75

MC105b

461822 185621 525670

183986 # 183720 #

72

70

81

71

82

75

86

Hi07f01

184963 183485 # 461822 525797 184694 185896

87

+ + +/++ + + ++-

110

CH04d10

+/-

115 116 117

185320 + CH04h02e +/443030 -

118

185357

124

9B21

-

+-

012-15

PF16

0

461263

+-

++-

6

NZ02b01

+/+

++++ ++++ +++-

9

31 34 36 39 41 42 45

5B20 1O3 184280 # 184477

+ + +-

184574

+-

CH01d08

++-

183149 # 184899 519690 184708 526669

++++-

52 56 61 62 66 70 71

62

462056

+-

73 74 71

526576

+-

75 76 82 84

78 81 82 84

85

526173 552762 182599 # 526885

++++-

88

461423

+-

97

182445 # 526167

+

86 90 92 95 99

E36M51-101

460791 441510 461923 460798 461175 553104

+

105

76

# 182590 443085

Conf-24-seq

0 0 1 4

73

# 182459 184477

+ + + +

+

CH04g07

66 67 71

443193 442863

526179 184899

10D23

63

CH03d02

012-12

50 +-

59

83

10

1M11

461081 184554

64

7

16

+-

63

+-

-

123

526057 525861 526003 441669 525825

Hi03g06

518573

+

62

PF15

-

6P21

61

+

2

59

42

54 55 57 60

+

3 8 9 15

52

41

52

1

37 41 44 47 48

28

+186490 +MC016 +518917 +MC032b +443179 +MC225 +443372 + OPA-06-1200 443303 +MC125a E34M55-202 + CN493139 OPC-08-0700 CH04g04 ++ 443032 E31M52-290 + 442826 +NZ28F4 - OPAB-12-2100 MC070 +E35M48-196 + + 184451 + MC127b + OPA-10-1100_B

48

7BC7b

E35M51-286

18 21 26 31 33 35 39

47

+ +/+

+-

Fig. 2 continued

17

+

-

+/-

12

-

+-

CH02d08

14

13 15

+

012-14

PF12 +/-

2 5 6

- OPAC-11-0600 +# 183719 E34M48-190 185124

+

CH04a12

0

-1 0

518989

+

012-11

-4

185136 525720 461081 186843

++/-

-

461773 # 182874

OPJ-06-1300 CH05d03 E33M51-300 OPT-02-0400

-

-

0

519152

+CH01g05 - OPAD-18-0680 E36M57-253 +# 183655 MS01a05 442170

-

67

69

+ + + +-

+-

76

OPC-08-0790 MS06g03

461548

+-

62

68

E36M51-360 MC032a MC125b E32M52-85

442891 186714

53 57

E35M48-159 OPA-09-0200

-

+

-

+-

39

64

MC023b E31M52-410 MC225b

-

41 44

443095

-

-

38

CH02c11 MC227a CH03d11 E35M52-224 MC030a MC025b

441808 442037

+-

+-

1H17

185131 184618

+ Conf-16-SSR CH04h02z ++CH04h02y - Conf-17--SSR + E35M53-150 + E34M59-139 + OPA-09-0600 MC004 461385 E35M53-187 + E34M55-410 - OPAD-02-0500 + # 183677 + MC045 CH02d12 +MC036b +CH04a12 + E35M51-301 + MC051 + UBC203-0400 E33M52-115 + E33M51-133 + 460857

22

PF14 -

+-

+ 24

16

441502

553408

-

23

442891

+

-

49

MC244b

+ + + +

57 58

-

113

+ + + +

PF11

0

8

- OPAC-06-0590 +519109 - OPAB-14-0800 460707 + OPAB-08-0940 + MC104b MC007 + CH05c07 +186510 +

PF10

0 5 6

100

101 108 109

99 103 106 107 108

11B12 Hi23g12

183710 # 9D2

-

+++

++ ++ + + + + + + + + + + + + + +

CH02a03

442923 441883 Ma CH05e04 MC001b MC044 LY05b MC050 MC242 E34M48-198 MC078 Hi04e04

526724 442132 Mald1.09 CH05a04 Mald1.02 Mald1.06 CH05a04 Mald1.03

185214 E35M51-67

462075 462051

MC191 OPJ-01-0950 MC221a OPAF-13-0900 OPAF-07-0640 MC041b CH03a08y P137-1250 + pAP260b + # 184079 E32M52-113 + E32M52-330 + +CH04f10 # 183165 + MC057b + E35M47-156-6 + 441862 4G11 +HB03AT + E36M59-177 +LY27a # 183930 + OPAG-15-0680 # 183421

0 2 3 12 13

012-16 0 1 2 5 6

18 21 25 26 28 29

7

30 36 37 38 39 41 43

8 16 30

CH05h05y

461640 Ch02a03

8K19 7A6 443159 461310 525633 185494 184045 # 442923 442679 441883 185094 186214 183169 # 3D6 Hi04e04

+ ++ ++ + + + + + ++++ + + ++/-

44 45 46

54

CH05a04

56

442727 184122 #

47 65 66 69 71 50 72 73

HB03AT Hi02c07p

78

184722 12P22 4J15

93

CH04f10

76 51

552929 184362 553235 442544 441862

+/++++ +++++++-

52 63 +/-

Mol Breeding (2012) 29:645–660 PF17 +++ +++ ++ + + + -

CH04c06y E33M52-333 CH04c10

0

442865

2 5 6

FDH-1

461519 553948 443198 519569 184490

1

012-17 0 4

5

8 9

6 7

CH01h01 MDMADS_1 MC098

10 11 13 16 21 24 26

461612

32

E35M48-245 4F11

# 182634

E35M52-305 MC228c

+ GE212858-SSR +MC121 MC012a + 460877 + E36M51-133 E33M51-265 + E31M52-238 441670 + E35M51-385 +MC224b 185405 + MC052b +S + E35M47-137-9 + E35M52-117 + E35M48-105 + Conf-23-seq - OPAD-12-0580 +AAT CH05d08 +MC115a + # 183185

8

9

12 33

48 49

653

184490 461367 184664 182816 # 461023 526235 184746 186513 553948 525628 183635 # 184756 186441 183853 # 182634 # 526188

+ +++++++++++++/+-

18

CH01h01

21

525524 184808

37

460877

+

44

8C17

+

60

183534 # 5N4 461202 461443 182778 # 10L5 525667

+-

52 53 59 64 69 70 72 73

62

74

63

75

65

76 77 80 82

66 68

+ +

Fig. 3 Frequency distribution of 384 DNA-sequenced DArT markers from the standard PstI/AluI method. If DNA sequences of one or more clones were highly homologous to each other, then these clones were clustered into a common bin. The 384 markers provided 324 bins

+ + + + +

Fig. 2 continued

was hybridised to the same DArT array used for Prima 9 Fiesta. Additionally, several previously mapped SSR markers were included to confirm the DArT marker alignment. A similar number of polymorphic DArT markers was obtained (774 in 2000–2012 vs. 776 in Prima 9 Fiesta), in addition to a similar call rate (97.3%) and a similar redundancy level, leading to a comparable number of unique single locus markers (260 vs. 247). The two mapping populations were shown to have 70 common polymorphic DArT markers that consistently aligned homologous linkage groups with regard to their identity and orientation for all 17 linkage groups of apple (Fig. 2). Several cases of minor differences in marker order were observed, depicted as the crossing lines in Fig. 2. The overall consistency in the identity and orientation of the linkage groups and the marker order show that the DArT markers support the alignment of the mapping populations.

sequence similarity or tight genetic linkage of the markers. To obtain an estimate of sequence-based redundancy, we sequenced 384 clones from the PstI/ AluI DArT array. We performed a local pairwise ‘‘blast’’ of all of these sequences and then clustered them into bins of highly similar sequences (e \ 1.0E-50), using a Perl script developed in-house (DArT PL unpublished). As Fig. 3 shows, the number of clones per bin varied from one (278 bins) to four (one bin only). There were 324 sequence bins identified; therefore, the percent of redundant clones in the initial sampling of the 384 clones was 15.6% when based on the sequences. Since these 384 sequenced clones represent only 2.6% of the 14,592 clones used for printing on the slides, the real redundancy was higher. Fitting the observed data from Fig. 3 to a Poisson distribution, we deduced that sequence similarity led to approximately 50% redundancy within the total set of clones. Comparison of these DNA-sequences to sequences in NCBI GenBank databases showed that close to 90% of BlastN and TblastX searches returned highly significant similarities to EST sequences (Online Resource 2). This indicates that the PstI/AluI DArT clones were derived mainly from genes or gene-like sequences.

DNA sequencing of DArT markers and redundancy estimation

Alternative complexity reduction method

In the mapping process, DArT markers were classified as redundant when they showed identical scores among the progeny, leading to clustering at one genetic position. Such clustering may be caused by high DNA

The high proportion of redundant markers hampers the statistical and genetic analyses of the data. Therefore, we wondered whether marker redundancy could be decreased by a less stringent complexity

123

654

Mol Breeding (2012) 29:645–660

Table 2 Number of DArT clones and resulting markers from two complexity reduction methods for the maternal map of population 2000–2012 Complexity Number of Number (and percentagea) Number of Number (and percentageb) Number (and percentagea) mapped of redundant markers of mapped markers after reduction spotted clones of segregating markers markers removal of the redundant ones method Standard

14,592

547 (3.7%)

542

282 (52%)

260 (1.8%)

Alternative

6,144

105 (1.7%)

104

24 (23%)

80 (1.3%)

a

as % of the number of spotted clones

b

as % of the number of mapped markers

reduction method and whether such an alternative approach could also be useful in increasing genome coverage. The standard complexity reduction method was based on PstI/AluI digestion. The alternative method that we examined here was based on PstI/ EcoRI/MboI digestion, which resulted in a larger number of fragments, so the chance that a clone was sampled multiple times diminished. The alternative array was used for the genotyping of 244 progenies of population 2000–2012, all of which had also been genotyped with the standard method. The maternal map of the heterozygous parent 1980-015-025 was constructed using DArT markers from both complexity reduction methods. Table 2 shows the number of spotted clones per complexity reduction method, the number of polymorphic maternal markers obtained, the number of maternal markers that could be mapped and the number of ‘unique’ markers. Figure 2 shows the resulting maps. Table 2 and Fig. 2 led to the following conclusions: 1. 2. 3.

4.

The degree of redundancy was lower for the alternative method; The two methods gave a similar genome coverage; There are no clear indications that the two methods differ in the genomic regions for which they raise polymorphic markers; Performance of both methods increased genome coverage compared to application of one method only.

Discussion Comparison of DArT with other marker technologies After being initially developed for rice, DArT markers have been developed for many additional

123

plant species (Jaccoud et al. 2001; Xie et al. 2006). These include barley (Wenzl et al. 2004), wheat (Akbari et al. 2006; White et al. 2008), cassava (Xia et al. 2005), Arabidopsis (Wittenberg et al. 2005), pigeon pea (Yang et al. (2006), oat (Tinker et al. 2009), sorghum (Mace et al. 2008) and many others (collated at www.diversityarrays.com/publications. html). The present study demonstrates the performance of DArT technology for low-cost, highthroughput genotyping in apple (Malus). Several lines of evidence support the utility of DArT for apple genomics studies. First, the call rate and reproducibility appear to be high. Non-DArT marker data required repeated examinations for identification of erroneous data, consuming many months of labour. DArT markers, however, did not require this laborious scrutinising; DArT genotyping was fully automated and more accurate than other marker systems. The nonDArT markers were gel-based systems (i.e. RFLP, RAPD, AFLP, SSR), and were scored manually. Second, the DArT markers integrated smoothly into the existing Prima 9 Fiesta map. Only one marker could not be placed (0.4%). This percentage is very low compared to that found in other marker systems, such as RFLP, RAPD, AFLP and SSRs. Also, DArT markers were robust among different mapping populations, allowing for map alignment. The standard DArT array gave similar numbers of non-redundant markers in the two mapping populations, indicating that it is robust over populations. Moreover, the majority of these markers were population-specific, indicating that the extensive pool of clones that are not polymorphic in one population are a vast reservoir of possible new markers in other populations. Thus, the DArT array is applicable over a wide range of mapping populations. The number of unique markers is therefore expected to increase further in more extensive studies.

Mol Breeding (2012) 29:645–660

DArT lends its general applicability for wide germplasm coverage to the hybridisation of PCR fragments that are hundreds of basepairs long. Polymorphisms are based on the presence or absence of restriction sites and are insensitive to SNPs and short indels in the hundreds of basepairs between the restriction sites (Wittenberg et al. 2005). This is reflected by the sequence data. Map-redundant DArT markers were often based on clones that differed in size and sequence but nevertheless showed identical segregation patterns in mapping. For instance, the three markers 183247, 183997 and 184057 showed identical segregation patterns and therefore mapped to the same position (LG10, Prima 9 Fiesta, LG4, 44 cM; Fig. 2, only 183247 is shown). The three DArT fragments varied in size (479, 469 and 502 bp, respectively) and exhibited some sequence polymorphism, as occurs for different alleles of a single gene. Their map-redundancy was confirmed at the sequence level, as ‘‘blasting’’ matched them to the same gene (Online Resource 2). This insensitivity to SNPs and short indels in the probe makes DArT robust for applications on genetically diverse germplasm, in contrast to the sensitivity of SNP arrays to similar polymorphisms. The proportion of informative markers from SNP arrays drops quickly when applied to germplasm that is genetically distinct from accessions that were used in the design of the SNP array. DArT appears to be less prone to this limitation. DArT and SNP platforms are both suitable for high-throughput genotyping, benefiting from automated scoring and data quality checks. The first small-scale SNP arrays for Malus were developed recently for Golden Delicious (Micheletti et al. 2011). Recently, initiatives have been undertaken for worldwide collaboration on the development of large-scale, multiple accession-based arrays. We expect that DArT will also play a longer term role because of its own specific advantages, including wide applicability, as discussed above, and low cost even when small numbers of accessions have to be genotyped.

Impact of pedigree structure Although the standard DArT array gave similar numbers of non-redundant markers for both mapping populations (247 for Prima 9 Fiesta vs. 260 for

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2000–2012), differences were observed in the distribution of these markers among the parents. Whereas the proportion of maternal, paternal and bi-parental markers were similar in Prima 9 Fiesta (35, 32 and 33%, respectively), unbalanced proportions were observed in 2000–2012, with relative under-representation of paternal markers and over-representation of bi-parental markers (35, 20 and 45%). These differences in representation reflect differences in the pedigree structures of these populations (Fig. 1). The father of population 2000–2012 arose from a cross between two sibs, resulting in an expected level of homozygosity of approximately 25%. This parent would yield no segregating markers in these homozygous regions. In addition, the parents of population 2000–2012 have common ancestors, Golden Delicious and Ingrid Marie, reducing levels of allelic diversity. This increases the number of bi-parental markers that segregate for both the father and the mother. Prima and Fiesta lack such recent common ancestors. The distribution of segregating DArT markers among the parents thus reflects the pedigree structures, further supporting the suitability of DArT for genetic studies.

Redundancy Markers that perfectly co-segregate do not provide additional genetic information, but slow down the mapping process and lead to the statistical overweighting of that specific position.Redundant markers were therefore removed. This reduced the number of polymorphic markers used in the mapping process by about half. Markers that segregated in both populations needed to be preferentially retained to facilitate linkage map alignment. Clustering of DArT markers can be caused by: (1) an absence of recombination between the markers due to tight genetic linkage; (2) sequence identity; or (3) high sequence similarity derived from different alleles from different apple accessions. The effective population size of Prima 9 Fiesta was 121, which can only partially explain the current high degree of redundancy in the DArT markers as a result of a lack of recombination between markers. Consequently, sequence identity and high sequence similarity were likely to be significant sources of redundancy. This is consistent with our estimation from Fig. 3 that

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sequence identity indeed led to approximately 50% redundancy. Based on the sequence information, we conclude that the clustering of PstI–AluI markers is mainly due to sequence similarity among the markers, in addition to genetic linkage. In classifying markers as ‘‘redundant’’ using a mapping process, more than just similar mapping positions were taken into account. For a proper classification, the parental origin and linkage phase have to be identical too. For example, JoinMap mapped the bi-parental DArT markers 183313 and 184063 within 0.1 cM from each other. Considering the size of the Prima 9 Fiesta population (n = 121 for the DArT markers) and the type of marker (bi-parental), both markers belonged to the same genetic bin; there was no evidence that they mapped to different positions. Their scores did not indicate any recombination. Although this suggests that they are identical, their linkage phase was different: they were in repulsion phase, indicating that these markers come from different genomic positions. Indeed, DNA sequencing confirmed a substantial sequence difference of the two clones (Online Resource 2). The positive side of redundancy is that the underlying markers can be regarded as repeats when present on the same slide and thus confirm validity of marker scores. The co-segregation of markers with highly similar sequences highlights the high reliability of the DArT markers.

DArT markers mainly derived from gene rich regions Our analysis of the sequences shows that 90% of the sequenced markers are highly homologous to mRNA sequences that are deposited in EST databases (Online Resource 2). This indicates that the set of DArT markers is highly enriched for genic regions. This phenomenon has been observed previously in other species like barley (Wenzl et al. 2006), wheat (Akbari et al. 2006) and sorghum (Mace et al. 2008). DArT markers are predominantly located in gene-rich islands in the subtelomeric regions of chromosomes. This is not surprising, as the PstI enzyme is methylation-sensitive (Gruenbaum et al. 1981), and targets hypomethylated, low-copy sequences, which occur primarily in the gene-rich regions (Feng et al. 2010).

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Comparison of complexity reduction methods The standard DArT array developed here provided moderate coverage of the apple genome (Fig. 2), allowing for a quick start in QTL mapping experiments. Coverage, however, was not uniform across the entire genome. Enhancing the array with more clones could increase genome coverage, but using the standard complexity reduction method to do so would not be efficient, due to sequence redundancy. Therefore, we tested an alternative complexity reduction method in order to develop more DArT markers and improve the genome coverage. Whereas the standard method only amplified PstI– PstI fragments, the alternative method amplified PstI–EcoRI fragments as well as PstI–PstI fragments, yielding higher complexity with the alternative method than with the standard method. We simulated the number of fragments that would be amplified using the published whole genome sequences of Arabidopsis thaliana, and found that the alternative would result in approximately 4.5 times more amplicons in Arabidopsis than the standard method (Mark Fiers, unpublished). The higher complexity of the alternative method and the printing of fewer of these amplicons resulted in less redundancy for population 2000–2012 (23%), compared to the standard method (52%). Despite the lower redundancy, the percentage of clones leading to unique single locus markers was low for the alternative method (1.3%) compared to the standard method (1.8%; Table 2). The most likely explanation is an unfavourable signal-to-noise ratio due to the higher degree of complexity. The analysis of raw image data, looking at the noise level, supports this explanation (data not presented). Another possible explanation is a reduction in polymorphism due to variation in methylation. Whereas the standard procedure only amplifies PstI–PstI segments, the alternative method also amplifies PstI–EcoRI segments. The EcoRI enzyme is methylation-insensitive, and thus possible polymorphism in methylation is not used. Wittenberg et al. (2005) ascribed up to 8% of the polymorphisms of the alternative method to differences in methylation in Arabidopsis thaliana. Importantly, a single standard DArT assay was clearly capable of providing reasonable genome coverage, as exemplified by the 17 linkage groups of the Prima 9 Fiesta map, with non-redundant

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markers distributed across all of the chromosomes, albeit unevenly (Fig. 2). Linked research The genetic maps shown here are the basis for several QTL studies that are currently underway, examining metabolomics (Khan et al., submitted), disease resistance, fruit quality traits and low allergenicity (Van de Weg et al., in prep.). Additionally, the standard DArT assay is being applied worldwide to a number of mapping populations and to the construction of consensus genetic maps (Van Dyk et al., in prep.). All the markers in this study are being sequenced and will be aligned with the apple genome sequence (Velasco et al. 2010). This will allow for the integration of the genetic positions of phenotypic traits with the whole-genome sequence of apple, thereby aiding in the search for underlying genes. Acknowledgments This study was carried out with financial support from the Ministry of Agriculture, Nature and Food Quality, The Netherlands. Unpublished data from the European projects DARE and ISAFRUIT were used, which were supported financially by the Commission of the European Communities under the Thematic Priority FAIR5 of the 4th Framework Programme of RTD (Contract no. FP4-FAIR-CT1997-3898), and 5–Food Quality and Safety of the 6th Framework Programme of RTD (Contract no. FP6-FOOD– CT-2006-016279), respectively. Also, unpublished data from a Voluntary Contribution project between the Australian National Apple Breeding Program, and Horticulture Australia Ltd, Australia (AP06009) were used. We are grateful to Mark Fiers (Plant and Food Research, New Zealand), for simulating the number of DArT restriction fragments in Arabidopsis, for different combinations of restriction enzymes. We acknowledge also James J. Luby and James M. Bradeen (University of Minnesota) for their contribution in selection of Malus genotypes for construction of libraries for the diversity arrays. Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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