Early impact of genomics on PGR

The advent of agriculture made possible by domestication greatly affected the diversity of crops (Gepts, Reference Gepts2006). The voyages of Christopher Columbus marked the earliest recorded acquisition of new plant resources, and, ever since, collected plants have been conserved in botanical gardens and herbaria (Short, Reference Short2003). The rediscovery of Mendel's law in the early 20th century helped the dramatic increase in agricultural productivity, although the overall genetic diversity decreased as a result of modern agricultural practices. Fearing genetic erosion, the world community increased the effort to better evaluate PGR in genebanks (Hoisington et al., Reference Hoisington, Khairallah, Reeves, Ribaut, Skovmand, Taba and Warburton1999).

The characterization of PGR by comparisons of plant morphology, such as yield, colour, texture, taste, etc., is the simplest and easiest approach (Gilbert et al., Reference Gilbert, Lewis, Wilkinson and Caligari1999; Hoisington et al., Reference Hoisington, Khairallah, Reeves, Ribaut, Skovmand, Taba and Warburton1999). In addition to these qualitative/quantitative phenotypic traits, pedigree analysis and geographical distribution are also helpful for measuring genetic diversity (Hammer, Reference Hammer2003). A renewed impetus towards PGR characterization was made possible by the development of modern molecular techniques.

Current status of plant genomics

The genetic diversity of major crops has been declining through domestication and the introduction of modern plant breeding (Tanksley and McCouch, Reference Tanksley and McCouch1997; Hyten et al., Reference Hyten, Smith, Frederick, Tucker, Song and Cregan2009). To prevent the genetic vulnerability of crops and to preserve valuable genetic resources, it needs to collect, preserve, examine and utilize germplasm effectively. The concept of the core set was proposed to minimize replicates and ensure the representation of the maximum genetic diversity of the entire germplasm collection (Frankel, Reference Frankel, Arber, Llimensee, Peacock and Starlinger1984; Brown, Reference Brown, Brown, Frankel, Marshall and Williams1989; van Hintum, Reference van Hintum, Johnsons and Hodgkin1999). Phenotyping was the traditional criteria for germplasm evaluation; however, currently, these evaluations are changed to genotyping by molecular markers (Tanksley and McCouch, Reference Tanksley and McCouch1997).

Genetic markers are powerful tools for genetic mapping, and molecular markers are highly polymorphic, easily detected and unaffected by the environment (Andersen and Lubberstedt, Reference Andersen and Lubberstedt2003). Various molecular markers have been developed, such as restriction fragment length polymorphisms, randomly amplified polymorphic DNA, simple sequence repeats, amplified fragment length polymorphisms and single nucleotide polymorphisms (SNPs) (Gupta et al., Reference Gupta, Roy and Prasad2001), which are used for the construction of genetic and physical maps. These markers are applied in plant breeding for quantitative trait loci (QTLs) mapping, map-based cloning, marker-assisted selection, etc. (Moose and Mumm, Reference Moose and Mumm2008).

Previously, genome-sequencing projects depended on Sanger sequencing methods. Recently, introduction of next-generation sequencing (NGS) technologies into plant breeding programmes has enabled the acquisition of high-throughput sequence data inexpensively in a short time (Morozova and Marra, Reference Morozova and Marra2008). However, the de novo assembly of plant genomes using NGS with short-read length is not yet adequate because most plant genomes are large and harbour long repeat sequences (Varshney et al., Reference Varshney, Nayak, May and Jackson2009). Thus, NGS technologies are applied for the resequencing of species for which a complete reference genome sequence exists and are actively used for high-throughput genotyping of up to a million SNP markers in Arabidopsis and several polyploidy crops (Rostoks et al., Reference Rostoks, Ramsay, MacKenzie, Cardle, Bhat, Roose, Svensson, Stein, Varshney, Marshall, Graner, Close and Waugh2006; Weber et al., Reference Weber, Weber, Carr, Wilkerson and Ohlrogge2007; Hyten et al., Reference Hyten, Song, Choi, Yoon, Specht, Matukumalli, Nelson, Shoemaker, Young and Cregan2008; Akhunov et al., Reference Akhunov, Nicolet and Dvorak2009; Yan et al., Reference Yan, Yang, Shah, Sanchez-Villeda, Li, Warburton, Zhou, Crouch and Xu2010). Genome-wide SNP genotyping is a powerful tool for association mapping and evolutionary studies (Akhunov et al., Reference Akhunov, Nicolet and Dvorak2009). Furthermore, SNP markers can be used more effectively when combined with genotypes and haplotypes (Hamblin et al., Reference Hamblin, Warburton and Buckler2007; Yan et al., Reference Yan, Yang, Shah, Sanchez-Villeda, Li, Warburton, Zhou, Crouch and Xu2010). This multiplexed genotyping technology facilitates the effective examination and selection of germplasms by unravelling novel and potentially agronomically useful alleles (Tanksley and McCouch, Reference Tanksley and McCouch1997). The QTL mapping of soybean rust was successfully conducted by SNP genotyping using the GoldenGate assay (Hyten et al., Reference Hyten, Smith, Frederick, Tucker, Song and Cregan2009). These NGS technologies and the massively developed genome-wide markers are also applied for the construction of high-density maps and genetic diversity analysis (Gupta et al., Reference Gupta, Rustgi and Mir2008).

Future directions

A wealth of genetic resources in Arabidopsis and other model species have promoted great advances in plant science. Furthermore, whole genome sequencing projects involving more than 20 plants will be completed in the near future (Gupta et al., Reference Gupta, Rustgi and Mir2008). With the improvement in sequencing techniques, more genetic resources, including the sequences, will be available in the future. Second (next) generation sequencers – Illumina's GA, Roche's 454 and Applied Biosystems' SOLiD – have generated large amounts of short DNA sequence reads. These have been updated to produce longer read lengths and greater amounts of sequence reads. Currently, several companies are attempting to introduce a new sequencing machine, which will be called third generation sequencing (Rusk, Reference Rusk2009). Helicos Biosciences developed a true single molecule sequencer that sequenced the virus M13 genome by an amplification-free method (Harris et al., Reference Harris, Buzby, Babcock, Beer, Bowers, Braslavsky, Causey, Colonell, DiMeo, Efcavitch, Giladi, Gill, Healy, Jarosz, Lapen, Moulton, Quake, Steinmann, Thayer, Tyurina, Ward, Weiss and Xie2008). Pacific Biosciences developed a single molecular real-time sequencing machine, based on an assessment of the temporal order of incorporation of fluorescently labelled nucleotides, which can produce reads longer than 1 kb (Eid et al., Reference Eid, Fehr, Gray, Luong, Lyle, Otto, Peluso, Rank, Baybayan, Bettman, Bibillo, Bjornson, Chaudhuri, Christians, Cicero, Clark, Dalal, deWinter, Dixon, Foquet, Gaertner, Hardenbol, Heiner, Hester, Holden, Kearns, Kong, Kuse, Lacroix, Lin, Lundquist, Ma, Marks, Maxham, Murphy, Park, Pham, Phillips, Roy, Sebra, Shen, Sorenson, Tomaney, Travers, Trulson, Vieceli, Wegener, Wu, Yang, Zaccarin, Zhao, Zhong, Korlach and Turner2009). Oxford Nanopore's sequencer is designed to avoid amplification or labelling by detecting a direct electrical signal (Clarke et al., Reference Clarke, Wu, Jayasinghe, Patel, Reid and Bayley2009). Despite dramatic improvements in sequencing speed and capacity, third generation sequencers will not completely replace the previous sequencing methods. Frequent use by researchers will likely reveal not only the benefits but also the limitations of these new techniques. Similar to the use of second generation sequencers together with ABI 3730, new sequencers will also be used with earlier technologies.

Until several years ago, whole genome plant sequencing projects were limited to model species. However, de novo sequencing and assembly are now easier due to longer reads and lower costs, which in the past few years has allowed for much greater sequencing depth. In addition to de novo genome sequencing, the whole genome sequence variations in 1001 accessions of Arabidopsis were analyzed in 2008 (Weigel and Mott, Reference Weigel and Mott2009). Furthermore, in rice, a high-throughput method for genotyping recombinant populations was developed (Huang et al., Reference Huang, Feng, Qian, Zhao, Wang, Wang, Guan, Fan, Weng, Huang, Dong, Sang and Han2009). Third generation sequencers could be also used for detection of sequence variation, associations between important agronomic traits and gene identification in regulatory networks by ChIP-chip and ChIP-seq protocols.

Presently, bioinformatics is the major bottleneck for a more complete exploitation of the information of genetic resources that is rapidly accumulating. The integration and organization of the available genomic resources to facilitate their use by researchers are therefore important. It could be a similar concept to that of an ‘omic space’ comprising a comprehensive omic planes (Toyoda and Wada, Reference Toyoda and Wada2004). Several integrated databases, such as the arabidopsis information resource (Arabidopsis), Gramene (rice) and SoyBase (soybean), provide genetic maps, genomic sequences, gene predictions, expressed sequence tags, marker data, QTLs, repetitive sequences, etc. One of the most significant contributions of the comprehensive genomic resources is that it provides a benefit to researchers who want to start new experiments or compare related information.