DNA marker applications to molecular genetics and genomics in tomato

Abstract

Tomato is an important crop and regarded as an experimental model of the Solanaceae family and of fruiting plants in general. To enhance breeding efficiency and advance the field of genetics, tomato has been subjected to DNA marker studies as one of the earliest targets in plants. The developed DNA markers have been applied to the construction of genetic linkage maps and the resultant maps have contributed to quantitative trait locus (QTL) and gene mappings for agronomically important traits, as well as to comparative genomics of Solanaceae. The recently released whole genome sequences of tomato enable us to develop large numbers of DNA markers comparatively easily, and even promote new genotyping methods without DNA markers. In addition, databases for genomes, DNA markers, genetic linkage maps and other omics data, e.g., transcriptome, proteome, metabolome and phenome information, will provide useful information for molecular breeding in tomatoes. The use of DNA marker technologies in conjunction with new breeding techniques will promise to advance tomato breeding.

Introduction

DNA markers have promoted genetics, genomics and breeding in a wide range of plant species, including tomato, through their use in the construction of high-density linkage maps, which are a useful tool for marker-assisted selection, association analysis and QTL analysis. However, the development of sufficient numbers of DNA markers to saturate the linkage maps has proven costly in terms of time, labor and financial resources, since the DNA markers have mainly been developed from randomly selected clones of genomic and cDNA libraries or PCR with random primers. Moreover, the construction of large-insert genomic libraries, the use of so-called chromosome walking to cover the candidate genomic regions and the sequencing of the selected clones have also required the use of map-based cloning.

The genome sequences of Arabidopsis thaliana (The Arabidopsis Genome Initiative 2000) and rice (International Rice Genome Sequencing Project 2005) have greatly assisted in the production of a large number of DNA markers for marker-assisted selection in the breeding of vegetable and cereal crops (Varshney et al. 2005). Because the nucleotide sequences as well as the gene order in the genomes are generally conserved between the model plants and crops (Tang et al. 2008), genomes of the model plants could allow us to estimate the number and the variation of genes in particular plant species. Recently, the genome sequences of various crops have been analyzed using a next-generation sequencers (NGS), a GS FLX+ System (Roche, Basel, Switzerland), HiSeq2000 (Illumina, San Diego, USA), or a SOLiD 5500xl system (Life Technologies, USA), which can produce up to 700 Mb, 600 Gb and 180 Gb of sequence data in a single experiment, respectively: apple (Velasco et al. 2010), banana (D’Hont et al. 2012), cacao (Argout et al. 2011), Chinese cabbage (The Brassica rapa Genome Sequencing Project Consortium 2011), cucumber (Huang et al. 2009), grape (The French–Italian Public Consortium for Grapevine Genome Characterization 2007), maize (Schnable et al. 2009), melon (Garcia-Mas et al. 2012), papaya (Ming et al. 2008), pigeonpea (Varshney et al. 2012), potato (The Potato Genome Sequencing Consortium 2011), sorghum (Paterson et al. 2009), soybean (Schmutz et al. 2010) and strawberry (Shulaev et al. 2011). Tomato is one of the major vegetable crops and regarded as a model for fruiting plant and Solanaceae relatives, and the genome sequencing has recently been completed using both NGSs and a fluorescent capillary sequencer with the Sanger method (The Tomato Genome Consortium 2012).

Genome-wide DNA polymorphism information can be obtained relatively easily by using NGSs for genome-scale genetic analyses such as genome-based breeding and genome-wide association studies. In this review, we summarize the studies of DNA markers developed for the genetics and molecular breeding in tomato and their applications, e.g., genetic linkage map, QTL and gene mappings, comparative genomics and functional annotations of DNA polymorphism. In addition, we introduce the databases for tomato genomics and genetics, and finally describe future perspectives of tomato breeding using the advanced DNA markers and genotyping technologies.

DNA markers and genotyping platforms in tomato

Molecular genetics based on DNA markers in tomato plants began with 57 restriction fragment length polymorphisms (RFLPs) (Bernatzky and Tanksley 1986). Since then the number of RFLP markers has increased to approximately 1000 for use in tomato genetics (Tanksley et al. 1992). However, a large amount of DNA as well as much expenditure of time and labor are required in the RFLP analysis based on the Southern-blotting method. Therefore, the RFLP markers have been replaced by PCR-based cleaved amplified polymorphic sequence (CAPS) markers (http://solgenomics.net), which are more convenient to handle than RFLP markers because their use requires less DNA and simpler laboratory experiments. DNA fingerprinting techniques, e.g., random amplified polymorphic DNA (RAPD) and amplified fragment length polymorphism (AFLP), are also conducted to develop DNA markers in tomato, because no sequence information is required, and because of the high polymorphism ratio due to multi-locus detection by single marker analysis (Saliba-Colombani et al. 2000).

Along with advances in genomic studies in plants, large amounts of sequence information, e.g., >200,000 expressed sequence tags (ESTs) and approximately 90,000 bacterial artificial chromosome (BAC)-ends, have been released for tomato species. Simple sequence repeat (SSR) markers can be rapidly and easily developed by using the sequence information derived from computational SSR-motif searches and primer designs for their flanking sequences (Fukuoka et al. 2005). SSR markers have advantages over the RFLP, CAPS, RAPD and AFLP markers due to multi-allelic detection, high-transferability across species, available tagged sequences and flexibility with various laboratory systems such as gel and capillary electrophoreses. In particular, capillary electrophoresis with a fluorescent fragment analyzer has major advantages for automatic analysis (allowing >2,000 samples/day in one analyzer) and for high-resolution analysis (distinguishing differences of only 1-bp length). Therefore, more than 20,000 SSR markers have been developed from EST and BAC-end sequences and used as genetic and genomic tools in tomato species (Ohyama et al. 2009, Shirasawa et al. 2010a).

Because the tomato genome sequence has been released (The Tomato Genome Consortium 2012), single nucleotide polymorphism (SNP), which is the most abundant polymorphism in genome in general, has been discovered by a re-sequencing strategy. In the re-sequencing strategy, the sequences obtained from the whole-genome, from complexity-reduced genomes such as the restriction site associated DNA (RAD), or from transcribed sequences are remapped onto the reference genome or unigenes with mapping software, e.g., Bowtie2 (Langmead et al. 2012) or BWA (Li and Durbin 2009) for the HiSeq2000 (Illumina Inc., San Diego, USA) and the GS reference mapper (Roche Applied Science, Mannheim, Germany) or MIRA (Chevreux et al. 2004) for the 454 GS FLX+ system (Roche Applied Science). By comparing the cDNA sequences, between 2,000 and 63,000 SNP candidates have been found in several studies (Hamilton et al. 2012, Jimenez-Gomez and Maloof 2009, Labate and Baldo 2005, Shirasawa et al. 2010b, Yamamoto et al. 2005, Yang et al. 2004). However, these candidates found by computational approaches frequently contain false positives due to the errors in the processes of sequencing or mapping, and thus it is necessary to select an accurate SNP site deeply covered with high-quality fragments on both strands to eliminate the false positives.

On the other hand, several SNP genotyping methodologies have been developed for application to various objectives. Depending on the purpose and degree of the throughput of the SNP for analysis, genotyping methods can be selected as follows. A huge number of SNPs in a small number of samples can be detected by the re-sequencing strategy using NGSs. Conversely, a small number of SNPs in a large number of samples, e.g., marker-assisted selection and cultivar identification, can be detected by TaqMan assay (Life Technologies), dot-blot SNP analysis (Shiokai et al. 2010, Shirasawa et al. 2006), the T_m-shift genotyping method (Fukuoka et al. 2008) and high-resolution melting analysis (Shirasawa et al. 2010a), because none of these method require electrophoresis. In addition, a high-throughput SNP analysis in a large number of samples can be performed effectively by array-based assays as genotyping platforms, GoldenGate and Infinium (Illumina Inc., San Diego, USA) and applied to the construction of high-density genetic linkage maps and performance of genome-wide association studies (Hamilton et al. 2012, Hirakawa et al. 2013, Shirasawa et al. 2010b, Sim et al. 2012). The diversity arrays technology (DArT) platform, which is one of the other array-based methods, has been applied to develop bin-mapped polymorphic markers across the introgression lines (ILs) population of tomatoes (Van Schalkwyk et al. 2012).

DNA markers for genetics, genomics and molecular breeding in tomato

Genetic linkage maps

In tomato, the first genetic linkage map was constructed with mainly RFLP markers for an interspecific population derived from a cross between S. lycopersium and S. pennellii (Bernatzky and Tanksley 1986). This map consists of 112 RFLP and isozyme loci and covers ca. 760 cM (Table 1). Then, several interspecific genetic linkage maps were generated with RFLPs incorporating CAPS, SSR and SNP markers (Bernacchi and Tanksley 1997, Doganlar et al. 2002a, Fulton et al. 2002, Gonzalo and van der Knaap 2008, Grandillo and Tanksley 1996, Jimenez-Gomez et al. 2007, Shirasawa et al. 2010a, Sim et al. 2012, Tanksley et al. 1992, 1996, van der Knaap and Tanksley 2001, 2003) as summarized in the SOL Genomics Network (SGN) (Mueller et al. 2005, http://solgenomics.net). The numbers of mapped loci ranged from 93 to 4,491 and covered 887 to 1,670 cM (Table 1). An intraspecific map, which is considered more useful for breeding than interspecific maps, has also been constructed with SSR and SNP markers using a population derived from a cross between the tomato cultivars “MicroTom” and either “Ailsa Craig” or “M82” (Table 1; Shirasawa et al. 2010b). In addition, a total of 7054 non-redundant SNPs between Micro-Tom and other cultivars have been genotyped by the array technologies (Hamilton et al. 2012, Shirasawa et al. 2010b) and these SNPs were mapped onto the tomato genome (Hirakawa et al. 2013). Currently, sequencing of the whole genome of Micro-Tom is underway (Aoki et al. 2011). These maps, SNPs and genome sequences will provide opportunities for map-based cloning of genes responsible for Micro-Tom-derived mutant lines provided from the National BioResource Project (NBRP) for the tomato (Saito et al. 2011: http://tomatoma.nbrp.jp).

Table 1 Genetic linkage maps developed in tomato and its relatives

Cross combinations	Population types	No. of marker loci	Marker types	Map length (cM)	References
Interspecies
S. lycopersiucm ‘LA1500’ × S. pennellii ‘LA716’	F2 (n = 46)	112	RFLP, Isozyme	760	Bernatzky and Tanksley (1986)
S. lycopersiucm ‘VF36-Tm2a’ × S. pennellii ‘LA716’	F2 (n = 67)	1030	RFLP, Isozyme	1276	Tanksley et al. (1992)
S. lycopersiucm ‘E6203’ × S. hirsutum ‘LA1777’	BC2 (n = 149)	135	RFLP	1356	Bernacchi and Tanksley (1997)
S. lycopersiucm ‘Sun 1642’ × S. pimpinellifolium ‘LA1589’	F2 (n = 100)	108	RFLP	1174	van der Knaap and Tanksley (2001)
S. lycopersiucm ‘E6203’ × S. pimpinellifolium ‘LA1589’	BC2F6 (n = 170)	127	RFLP	1282	Doganlar et al. (2002a)
S. lycopersiucm ‘Yellow Stuffer’ × S. pimpinellifolium ‘LA1589’	F2 (n = 200)	93	RFLP	1076	van der Knaap and Tanksley (2003)
S. lycopersiucm ‘LE777’ × S. chmielewskii ‘CH6047’	F2 (n = 149)	255	AFLP, CAPS, SCAR, SSR	887	Jimenez-Gomez et al. (2007)
S. lycopersiucm ‘Rio Grande’ × S. pimpinellifolium ‘LA1589’	F2 (n = 94)	97	CAPS, RFLP, SSR	1174	Gonzalo and van der Knaap (2008)
S. lycopersiucm ‘Sausage’ × S. pimpinellifolium ‘LA1589’	F2 (n = 106)	96	CAPS, RFLP, SSR	1072	Gonzalo and van der Knaap (2008)
S. lycopersiucm ‘LA925’ × S. pennellii ‘LA716’	F2 (n = 83)	2116	SSR, SNP	1503	Shirasawa et al. (2010a)
S. lycopersiucm ‘LA925’ × S. pennellii ‘LA716’	F2 (n = 79)	3503	SNP	1670	Sim et al. (2012)
S. lycopersiucm ‘Moneymaker’ × S. pennellii ‘LA716’	F2 (n = 160)	3687	SNP	1155	Sim et al. (2012)
S. lycopersiucm ‘Moneymaker’ × S. pimpinellifolium ‘LA121’	F2 (n = 183)	4491	SNP	1049	Sim et al. (2012)
Intraspecies
S. lycopersiucm ‘Levovil’ × S. lycopersiucm var. cerasiforme ‘Cervil’	F7 (n = 153)	377	AFLP, RAPD, RFLP	965	Saliba-Colombani et al. (2000)
S. lycopersiucm ‘Ailsa Craig’ × S. lycopersiucm ‘Micro-Tom’	F2 (n = 120)	989	SNP, SSR	1468	Shirasawa et al. (2010b)
S. lycopersiucm ‘M82’ × S. lycopersiucm ‘Micro-Tom’	F2 (n = 135)	637	SNP	1423	Shirasawa et al. (2010b)

Mapping of QTLs and genes for agronomical traits

In the map-based cloning strategy, to carry out subsequent regional fine mapping following the genome-wide linkage mapping, the introgression lines (ILs) composed of 76 lines, which together cover the entire genome of the donor parent, S. pennellii “LA716,” in the background of the recurrent parent, S. lycopersicum “M82,” have been developed by a method of marker-assisted backcrossings (Eshed and Zamir 1994). The ILs are available from the Tomato Genetics Resource Center (TGRC: http://tgrc.ucdavis.edu) and the NBRP for tomato (Saito et al. 2011, http://tomatoma.nbrp.jp). The developed genetic resources, e.g., genetic linkage maps and introgression lines, have been used for identification of agronomically important genes for disease resistance (Cf-2: Dixon et al. 1996, Cf-9: Jones et al. 1994, I2: Ori et al. 1997, Mi: Milligan et al. 1998, Pto: Martin et al. 1993, Sw-5: Brommonschenkel et al. 2000, Tm-1: Meshi et al. 1988, Tm-2: Meshi et al. 1989, Ve: Kawchuk et al. 2001, and reviewed in Flooad and Panthee 2012), fruit characteristics (Brix9-2-5: Fridman et al. 2000, FAS: Cong et al. 2008, FW2.2: Frary et al. 2000, LC: Munos et al. 2011, OVATE: Liu et al. 2002, SUN: Xiao et al. 2008, U: Powell et al. 2012), hybrid vigor (SFT: Krieger et al. 2010), plant architectures (D: Bishop et al. 1996, SP: Pnueli et al. 1998) and several traits summarized in The Tomato Genome Consortium (2012). Of course, DNA markers as well as the cloned genes themselves could be used for marker-assisted selection in breeding (Labate et al. 2007). Furthermore, associations between genotypes and phenotypes have been revealed: the allele distribution of FAS and LC for fruit locule number and flat shape and OVATE and SUN for elongated shape in tomato cultivars is strongly associated with fruit shape diversity (Rodríguez et al. 2011); and mutant alleles of D for dwarfism and SP for determinate plant height are observed in dwarf tomato and processing cultivars (Fig. 1).

Fig. 1

Distribution of DWARF and SELF-PRUNING genes in the tomato lines and S. pennellii revealed by CAPS analysis. Dominant and recessive alleles of DWARF and SELF-PRUNING genes are shown by capital (D, SP) and lowercase (d, sp) letters, respectively. See Shirasawa et al. (2010b) for the details of the plant materials and the experimental conditions.

Comparative genomics

Genetic linkage maps contribute to not only QTL and gene mapping but also comparative genomics, which has a significant impact on the fields of plant genetics and genomics. Because tomato is recognized as a representative experimental model of the Solanaceae family, comparative maps have been developed by connecting orthologous markers as anchors between the pairs of the plant species, such as tomato and potato (Tanksley et al. 1992), tomato and eggplant (Doganlar et al. 2002b, Fukuoka et al. 2012, Wu et al. 2009b), tomato and pepper (Livingstone et al. 1999, Prince et al. 1993, Tanksley et al. 1988, Wu et al. 2009a) and tomato and tobacco (Wu et al. 2010) and chromosome segments conserved in Solanaceae have been identified (Wu and Tanksley 2010). These results were confirmed by the comparative analysis of genome sequences between tomato and potato species (The Tomato Genome Consortium 2012). The syntenic relationship among Solanaceae family members will contribute to the transfer of knowledge obtained from studies of tomato to other Solanaceae crops with respect to genetics, genomics and molecular breeding.

Functional annotation of DNA polymorphisms

The functional marker that is responsible for gene of protein functions could be effectively applied to molecular breeding such as marker-assisted selection. SNPs can be used as functional markers, because they have the potential to link to the gene functions. The SNPs can be classified into the following groups according to the locations on the genome sequence: cSNPs (SNPs in coding sequences causing amino-acid substitutions), sSNP (SNPs in coding sequences not causing amino-acid substitutions), iSNPs (SNPs in in-tron regions), rSNPs (SNPs in regulatory regions), uSNPs (SNPs in untranslated regions) and gSNPs (SNPs in intergenic regions). The cSNPs may directly effect protein function, if they are located on a catalytic site, while the uSNPs and rSNPs may effect gene expressions. These SNPs probably effect alternations of gene functions and are used for functional SNPs. In the field of human genomics, the SNP variations have been studied by comparing individual genome sequences (Altshuler et al. 2000, International HapMap 3 Consortium 2010, Li et al. 2009). The HapMap project (International HapMap Consortium 2003) has provided a huge number of genome-wide SNPs collected from several populations through the Single Nucleotide Polymorphism database (dbSNP) at the National Center for Biotechnology Information (NCBI) and the F-SNP (Functional SNP) databases (Lee and Shatkay 2008). In the field of plant genomics, on the other hand, genome-wide functional SNPs have not been analyzed sufficiently.

To speculate the functional effects of the cSNPs in the tomato genome, the SNP locations in catalytic sites have been identified by using the three-dimensional structure of proteins constructed by homology modeling (Hirakawa et al. 2013). The binding clefts as catalytic site in proteins can be predicted by the FPocket (Guilloux et al. 2009) or MetaPocket programs (Zhang et al. 2011). The amino acid residues important for catalytic activity have been predicted by calculating the protein-substrate affinity using binding simulation with the Autodock (Morris et al. 1998) or ASEdock of MOE software package (Kumar et al. 2011). Together with the information on the catalytic sites, the genes having the functional SNPs have been annotated by similarity searches against the KOG (Tatusov et al. 2003), KEGG (Ogata et al. 1999), NR in NCBI (http://www.ncbi.nlm.nih.gov), TAIR10 (Garcia-Hernandez et al. 2002) and PDB (Berman et al. 2000; http://www.pdb.org) databases and domain searches against the Pfam database (Punta et al. 2012). According to these analyses, the genes with the functional SNPs would be more applicable for molecular breeding than DNA markers linking to target genes, because the target genes might be lost by crossing over between the loci for the marker and the target genes in conventional marker-assisted selection (Shirasawa et al. 2004).

Databases for molecular genetics and genomics in tomato

The SGN (Mueller et al. 2005; http://solgenomics.net) is recognized as one of the databases for molecular genetics and genomics in Solanaceae, e.g., tomato, potato, pepper, eggplant, tobacco, petunia and so on. This database provides information about not only maps and markers but also mass data for genomes, sequences and expression patterns of genes, metabolite pathways, phenotypes and QTLs. Furthermore, the database provides information on the news, events and publications related to Solanaceae and links to the external databases providing Solanaceae genetic information, e.g., data on the genome, ESTs, markers, QTLs and mutants.

Among the tomato databases for DNA markers, the Solanaceae Coordinated Agricultural Project (SolCAP: http://solcap.msu.edu), which focuses on translating genomic advances to tomato and potato breeding, has provided 62,576 SNPs and experimentally validated data on 96 SNPs for 85 tomato cultivars (Hamilton et al. 2012). The Tomato Mapping Resource Database (http://www.tomatomap.net) releases genotyping data of 52 indel, 102 RFLP, 205 SNP and 94 SSR markers for 102 tomato lines including 9 wild species. In the Tomato SNPs database (http://www-plb.ucdavis.edu/labs/maloof/tomatosnp/), 12,568 and 5,004 SNPs detected by in silico analyses between S. lycopersicum and S. habrochaites and between S. lycopersicum and S. pennelliiare are available, of which 220 and 196 SNPs have been experimentally verified, respectively. The National Center of Biotechnology Information (NCBI) has established a database for SNPs known as dbSNP (http://www.ncbi.nlm.nih.gov/snp/), from which 376 tomato SNPs have been published. The VegMarks (http://vegmarks.nivot.affrc.go.jp) provides genetic linkage maps and genotyping data for 270 SNPs between S. lycopersicum “LA925” and S. pennellii “LA716” and 148 SSR markers for 10 lines. The MiBASE (http://www.pgb.kazusa.or.jp/mibase/), a database specific to Micro-Tom resources, also provides 1935 SNP candidates between Micro-Tom and either five lines and 409 EST-SSRs found by in silico analyses.

A portal website for tomato genomics, Kazusa Tomato Genomics Database (KaTomicsDB: http://www.kazusa.or.jp/tomato/) consisting of the following two databases, has been released. The first is the Tomato Marker Database (http://marker.kazusa.or.jp/tomato/), which mainly provides information on 8,297 SNP and 21,100 SSR markers, i.e., primer sequences and DNA fragments including marker loci, genetic linkage maps of the provided DNA markers and genotyping data of the SNPs for 42 lines (Hirakawa et al. 2013, Shirasawa et al. 2010a, 2010b). Moreover, most of the markers have been mapped on the tomato genome by similarity searches, and ordered with the predicted genes. The second is the Tomato Functional SNP Database (http://plant1.kazusa.or.jp/tomato/), which provides the genes with SNPs annotated by similarity searches against the databases of KOG (Tatusov et al. 2003), KEGG (Ogata et al. 1999), NR in NCBI (http://www.ncbi.nlm.nih.gov), TAIR10 (Garcia-Hernandez et al. 2002) and PDB (Berman et al. 2000; http://www.pdb.org). In addition, the web site allows visitors to browse the locations of SNPs on the three-dimensional structure built by homology modeling.

In addition to the databases described above, various databases for tomato genetics and genomics have been released (Table 2). In Table 2, these databases are roughly classified into seven categories, i.e., genome, DNA marker, ESTs, gene expression, metabolome, plant materials and portal sites. To integrate these databases, the Plant Genome DataBase Japan (PGDBj: http://pgdbj.jp) has been established through the National Bioscience Database Center (NBDC), Japan. In this database, over 50 plant species, including crops, fruits, trees and vegetables, have been registered. This database provides genome maps integrating the DNA markers, genetic linkage maps and QTLs collected from the databases and related articles.

Table 2 Databases for tomato genetics, genomics and breeding available from the World Wide Web

Categories	Database Names	URLs
Portal sites
	Kazusa Tomato Genomics Database (KaTomicsDB)	http://www.kazusa.or.jp/tomato/
	Plant Genome DataBase Japan (PGDBj)	http://pgdbj.jp
	SOL Genomics Network (SGN)	http://solgenomics.net/
	eusol (eusol)	http://www.eu-sol.net/
	Lat-SOL network (Lat-SOL)	http://cnia.inta.gov.ar/lat-sol/
	Plants Database	http://plants.usda.gov/
	PURDUE University	http://www.hort.purdue.edu/rhodcv/hort410/tomat/
	Solaneceae Genomics Resource	http://solanaceae.plantbiology.msu.edu/
Genome databases
	PlantGDB	http://www.plantgdb.org/SlGDB/
	A Tomato Integrated Database (TOMATOMICS)	http://bioinf.mind.meiji.ac.jp/tomatomics/
	International Solanaceae Genome Project	http://sol.kribb.re.kr/tomatogenome/
	Italian SOLAnaceae genomics resource (ISOL)	http://biosrv.cab.unina.it/isola/
	Tomato SBM Database	http://www.kazusa.or.jp/tomato_sbm/
DNA marker databases
	Tomato Marker Database	http://marker.kazusa.or.jp/tomato/
	A DNA marker database for vegetables (VegMarks)	http://vegmarks.nivot.affrc.go.jp/
	Tomato Mapping Resource Database	http://www.tomatomap.net/
	Solanaceae Coordinated Agricultural Project (SolCAP)	http://solcap.msu.edu/
	dbSNP (Short Genetic Variations) (dbSNP)	http://www.ncbi.nlm.nih.gov/snp/?term=Solanum+lycopersicum&SITE=NcbiHome&submit=Go
	Tomato SNP	http://www-plb.ucdavis.edu/labs/maloof/tomatosnp/
EST databases
	Micro-Tom Database (MiBASE)	http://www.kazusa.or.jp/jsol/microtom/
	Kazusa Full-length Tomato cDNA Database (KafTom)	http://www.pgb.kazusa.or.jp/kaftom/
	Solaneceae EST Database (SolEST)	http://biosrv.cab.unina.it/solestdb/
	A Comparative Omics Database for Plant Trichome (TrichOME)	http://www.planttrichome.org/trichomedb/estbyspecies_detail.jsp?species=Solanum%20lycopersicum
	DFCI Tomato Gene Index (DFCI)	http://compbio.dfci.harvard.edu/cgi-bin/tgi/gimain.pl?gudb=tomato
Gene expression databases
	PLEXdb (TomPLEX)	http://www.plexdb.org/plex.php?database=Tomato
	TIGR Solanaceae Genomics Resource (SGED)	http://www.jcvi.org/potato/
	Tomato Functional Genomics Database	http://ted.bti.cornell.edu/
	Plant Transcription Factor Database (PlantTFDB)	http://planttfdb.cbi.pku.edu.cn/index.php?sp=Sly
Metabolome databases
	Kazusa Plant Pathway Viewer (KaPPA-View4 SOL)	http://kpv.kazusa.or.jp/kpv4-sol/
	MassBase	http://webs2.kazusa.or.jp/massbase/
Plant material databases
	EU-SOL BreeDB database (BreeDB)	https://www.eu-sol.wur.nl/
	The ECPGR Tomato Database	http://documents.plant.wur.nl/cgn/pgr/tomato/
	Tomato Genetics Resource Center (TGRC)	http://tgrc.ucdavis.edu/
	TOMATOMA	http://tomatoma.nbrp.jp/
	Tomato Mutant Database	http://zamir.sgn.cornell.edu/mutants/
	Tomato Mutant DB (LycoTILL)	http://www.agrobios.it/tilling/
Others
	Tomato Functional SNP Database	http://plant1.kazusa.or.jp/tomato/
	GMO Detection method Database (GMDD)	http://gmdd.shgmo.org/event/view/113
	Solanaceae Source	http://www.nhm.ac.uk/research-curation/research/projects/solanaceaesource/
	The Tomato Genetics Cooperative	http://tgc.ifas.ufl.edu/
	JSOL	http://www.kazusa.or.jp/jsol/

Future perspectives of DNA markers in tomato genetics, genomics and breeding

The whole genome sequence of tomato has recently been released (Tomato Genome Consortium 2012). In this project, the genome sequences were linked to the 12 tomato chromosomes with two BAC-based physical maps and anchored/ oriented using a high-density genetic map, introgression line mapping and BAC fluorescence in situ hybridization (FISH). As the genetic map, an interspecific map, Tomato-EXPEN 2000 (Fulton et al. 2002), was selected because of the highest-density SSR, CAPS and RFLP marker loci (2,116 loci covering 1,503 cM) (Shirasawa et al. 2010a). Taking these results together, the consortium has released 12 pseudomolecules consisting of 760 Mb of the predicted genome size of 960 Mb, which will be used as a reference tomato genome for development of DNA markers.

The enhancing capacity of the NGSs will enable genotyping by sequencing (GBS), in which a huge number of SNPs can be genotyped by re-sequencing of multiple lines in a single experiment against reference genome sequences (Davey et al. 2011). In maize and rice, both of whose genome sequences have been determined (International Rice Genome Sequencing Project 2005, Schnable et al. 2009), the GBS strategy has been applied to construct the genetic linkage maps (Elshire et al. 2011, Xie et al. 2010). Even in plant species having larger and more complex genomes, the GBS has been carried out by sequencing of restriction site associated DNA to reduce the genome complexity (Rowe et al. 2011). In addition to the genetic mappings, the NGSs have also been applied to identify mutant genes directly. Sequencing of target regions with sequence capture technology is a straightforward strategy (Fu et al. 2010, Galvão et al. 2012). In Arabidopsis, rice and legumes with small genome sizes, whole genome re-sequencing with genetic segregation analysis has already identified mutant genes (Abe et al. 2012, Schneeberger et al. 2009, Uchida et al. 2011). In tomato, such re-sequencing methods will be available under the advances of the sequencing technology. Together with the advanced genotyping methods, high-throughput phenotyping systems and genetic statistics for hundreds of thousands of segregation data are also required for the progression of tomato molecular genetics.

In the studies on the functional SNPs taking account of the protein structural information, the amino-acid residues important for protein activities could be revealed by using computational approaches, e.g., calculation of the affinities between enzymes and substrates by binding simulation (Ishikawa et al. 2010). This strategy has been greatly advanced in medical sciences, i.e., pharmacogenomics and pharmacogenetics, in which medical molecules are designed to fit genotypes of patients (Evans and Relling 1999). In plants, on the other hand, the virtually predicted desirable genotypes can be selected from natural populations and artificial mutants by SNP analysis. The screening systems of mutant genes, e.g., targeting induced local lesions in genomes (TILLING) (Okabe et al. 2011, 2012) and deep-sequencing of target genes (Rigola et al. 2009), have already been developed in tomato. Moreover, in the future, desirable genotypes can be created via new breeding techniques, e.g., site-directed mutagenesis by gene targeting with nucleases or oligonucleotides (Lusser et al. 2012), whereas these technologies have been applied to only maize, tobacco, rice and oilseed rape, but not yet in tomato. Tomato molecular breeding will proceed through the SNP analysis and the genome manipulations.

Acknowledgments

We thank Satoshi Tabata, Sachiko Isobe and Shusei Sato (Kazusa DNA Research Institute) for encouragement and critical reading of the manuscript. This work was supported by the Kazusa DNA Research Institute Foundation, the KAKENHI Grant-in-Aid for Scientific Research (C) (24510286), JSPS, Japan, the Genomics for Agricultural Innovation Foundation (DD-4010/SGE-1001), MAFF, Japan and the Integrated Database Project Foundation, MEXT, Japan.

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