# Rice Molecular Breeding Laboratories in the Genomics Era: Current Status and Future Considerations

Authors: Bert C. Y. Collard; Casiana M. Vera Cruz; Kenneth L. McNally; Parminder S. Virk; David J. Mackill
Journal: International Journal of Plant Genomics
 (2008)

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## Abstract

Using DNA markers in plant breeding with marker-assisted selection (MAS) could greatly improve the precision and efficiency of selection, leading to the accelerated development of new crop varieties. The numerous examples of MAS in rice have prompted many breeding institutes to establish molecular breeding labs. The last decade has produced an enormous amount of genomics research in rice, including the identification of thousands of QTLs for agronomically important traits, the generation of large amounts of gene expression data, and cloning and characterization of new genes, including the detection of single nucleotide polymorphisms. The pinnacle of genomics research has been the completion and annotation of genome sequences forindica and japonica rice. This information—coupled with the development of new genotyping methodologies and platforms, and the development of bioinformatics databases and software tools—provides even more exciting opportunities for rice molecular breeding in the 21st century. However, the great challenge for molecular breeders is to apply genomics data in actual breeding programs. Here, we review the current status of MAS in rice, current genomics projects and promising new genotyping methodologies, and evaluate the probable impact of genomics research. We also identify critical research areas to “bridge the application gap” between QTL identification and applied breeding that need to be addressed to realize the full potential of MAS, and propose ideas and guidelines for establishing rice molecular breeding labs in the postgenome sequence era to integrate molecular breeding within the context of overall rice breeding and research programs.


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## Body

## 1. Introduction

Rice (Oryza sativa) is the well-known
holder of two important titles: the most important food crop in the world and a
model cereal species. Rice is the staple food in many parts of the world,
including many developing countries in Asia, Africa, and Latin
America. The projected increase in global population to 9 billion
by 2050 and predicted increases in water scarcity, decreases in arable land, the
constant battle against new emerging pathogens and pests, and possible adverse effects
from climate change will present great challenges for rice breeders and
agricultural scientists [1–4]. Because
of rice’s global importance, small genome size, and genetic relatedness to
other major cereals, efforts were undertaken to sequence the entire genomes of
the two subspecies of rice—indica and japonica. Genome sequence drafts
were completed for both subspecies in 2002 [5, 6] and a high-quality and annotated version of the japonica species was completed in 2005 [7], which represent landmark
achievements in biological research.One practical output from genomics
research was the development of DNA markers (or molecular markers) in the late
1980s and 1990s. Marker-assisted selection (MAS)—in which DNA markers are used to infer
phenotypic or genotypic data for breeding material—is widely accepted to have great potential to
improve the efficiency and precision of conventional plant breeding, which may
ultimately lead to the accelerated release of new crop varieties [8–13]. The potential advantages of molecular breeding demonstrated
by numerous examples of MAS in rice and other crops have prompted many rice
breeding and research institutes to establish biotechnology or DNA marker labs
within the institute.Genomics is the study of gene location, function, and
expression. Strictly speaking, the study of gene location might
be classified as molecular genetics research. However, for simplicity, we broadly define genomics as the study of
genes and genomes, which includes identifying the location of genes as well as
the study of gene function and regulation (expression). The beginning
of the 21st century has been considered the dawn of the genomics era due to the
enormous amount of genomics research in bacterial, plant, and animal species,
as well as the rapid development of high-throughput equipment for whole-genome genotyping,
gene expression, and genome characterization, and the establishment of advanced
bioinformatics tools and databases. These rapid developments have irreversibly
influenced and redefined plant breeding in the 21st century as “molecular plant
breeding” or “genomics-assisted breeding” [14].However, plant breeders and agricultural scientists face many challenges to integrate and exploit these new
molecular and genomics-related technologies for more rapid and efficient
variety development [15, 16]. In this article, we review the current global
rice molecular breeding lab with an emphasis on recent research and the impact
of rice genomics resources. We also review some current genomics research and
promising new genotyping methodologies with high potential for applied
outcomes. Finally, we consider the obstacles to the successful application of
molecular genetics and genomics research in rice breeding programs and propose
ideas on how some of these problems should be solved.


## 2. The Rice Molecular Breeding Lab

### 2.1. View of the Rice “Pregenome Sequence” Molecular Breeding Lab

We arbitrarily define the “pregenome sequence
molecular breeding lab” as before 2000. Although the first rice genome sequence drafts were published in 2002
and the complete genome sequence was published in 2005, sequence data were
available before these publication dates so it is very difficult to exactly
pinpoint the time when rice genome sequence data influenced applied rice
genetics and breeding. In the early to mid-1990s, restriction fragment length polymorphism (RFLP) and random
amplified polymorphic (RAPD) markers were commonly used for rice breeding
research [17–21]. In Japan, RFLPs continue to be a marker system of choice [22]. Often, RFLP and RAPD markers were converted into
second generation, polymerase chain reaction (PCR)-based markers called
sequence tagged site (STS) markers to improve technical simplicity and
reliability [23–25]. Simple sequence repeats (SSR; or
“microsatellites”) became the most widely used markers in cereals and rice is
no exception [26–28].
In earlier reports, the principles and techniques of
detecting SSR polymorphisms were called simple sequence length polymorphism
(SSLP) markers [28, 29]. SSRs are highly reliable (i.e., reproducible), codominant in inheritance, highly polymorphic
(compared to other markers), and generally transferable between mapping
populations. The only disadvantages of SSRs are that they typically require
polyacrylamide gel electrophoresis and generally give information only about a
single locus per assay.The first SSRs were reported in 1996 [30]. By 1997, there were 121 validated SSRs, which
were adequate for marker-assisted evaluation of germplasm and the construction
of framework linkage maps but had limited use for MAS, due to limited genome
coverage [29]. By
2001, there were a total of ∼500 SSRs that were developed from 57.8 Mb of
publicly available rice genome data [31], which further increased
the utility of these markers.


### 2.2. The Postgenome Sequence Rice Molecular Breeding Lab: Opening the “Treasure Chest” of New Rice Markers

#### 2.2.1. SSRs

Analysis of the completed rice genome sequence provided the identification of literally tens
of thousands of new targets for DNA markers, especially SSRs. Using publicly available BAC and PAC clones, more
than 2200 validated SSRs were released in 2002 [32]. This was soon followed by 18828 Class I (di-, tri-,
tetra-repeats) SSRs that were released after the completion of the Nipponbare
genome sequence in 2005 [7]. This number is by far the largest number of publicly available SSRs for
any crop species. The extremely high density of SSRs (approx. 51 SSRs per Mb)
will provide a considerable “tool kit” for map construction and MAS for
numerous applications. Given that many
labs are currently well equipped for SSR analysis, it is highly likely that SSRs
will continue to be the marker of choice for years to come.


#### 2.2.2. Single Nucleotide Polymorphisms (SNPs)

SNPs are the most abundant and ubiquitous type of polymorphisms in all organisms,
and many researchers propose that these markers will be the marker of choice in
the future [33]. In rice, SNPs can be readily identified by direct
comparisons of Nipponbare and 93-11 genomes, or by sequence alignment with one
or both reference sequences with available sequence data in public databases [34–36]. Recently, more SNP data have become available that have been generated
by comparing partial sequences from multiple genotypes [37–39]. In some cases, DNA sequencing of target regions in specific genotypes is
required. However, experimental validation of SNP-based markers is required
since inaccuracies in sequence data have been reported [34, 36]. The ease with which SNPs can be identified in silico and increase in publicly available
rice DNA sequence data will undoubtedly ensure that SNP-based markers will be
more commonly used in the future.It should be noted that lower levels of SNP marker polymorphism are usually detected in more closely related
genotypes, which are more representative of breeders’ elite germplasm (indica×indica or japonica×japonica-derived material), when
compared with the japonica-indica reference genotypes used to determine SNP frequency. The frequency of SNPs
between subspecies was reported to be from 0.68% to 0.70%, whereas it was 0.03%
to 0.05% between japonica cultivars and
0.49% between indic a cultivars [35]. Interestingly, SNPs were
not evenly distributed along chromosomes.


#### 2.2.3. Indels

Insertion/deletion (indel) mutations are abundant mutations that occur in coding and noncoding
regions. Indels can also be quickly identified in silico by direct comparions ofjaponica and indica genome sequences. The enormous number of indels between the two subspecies will
provide an indispensable resource of polymorphic markers for indica×japonica populations or populations with specific introgressions [34, 35]. Either of the two rice genome reference sequences
can be easily compared with other sequences for further indel identification,
as was done between Nipponbare and Kasalath, a commonly used indica accession [40]. Like SNP-based markers, indels also need to be
experimentally validated.Introns are noncoding regions within
genes and hence they “tolerate” insertion/deletion mutations compared with
exons. Consequently, many indels have been identified in introns and these size
polymorphisms have been exploited by the development of a new class of intron
length polymorphic (ILP) markers [41]. Experimental validation of these markers indicated that the
majority were reliable and codominant, and that although ILPs were designed
from indica/japonica comparisons,
they were also polymorphic between varieties within both subspecies although
the level of polymorphism was lower.


#### 2.2.4. “Custom-Made” Markers

The great resource for molecular breeders is the DNA sequence provided by the
genome sequences since it permits markers that are tightly linked to target
loci to be “custom-made” or “tailor-made” to suit the aims of MAS. The large
number of custom-made markers that have already been designed or the potential
for new ones to be designed is a unique feature of the rice molecular breeding lab.
The number of markers that can potentially be generated using the rice genome
sequence in silico is practically unlimited (Figure1). The markers might be derived directly from
the Nipponbare/93-11 sequences or used to identify corresponding EST or genomic
sequences available from databases (i.e., BAC or PAC clones containing target
genes that may not actually be present in reference genotypes) [42–44]. In principle, custom-made
markers can be any type although they most commonly include new SSRs, indels,
PCR-based SNPs, and cleaved amplified polymorphic site (CAPS) markers—which are the technically the simplest markers
to be used for marker genotyping [43, 45]. It should be noted that these markers
must be tested in wet-lab experiments.Figure 1
Process for developing custom-made
rice DNA markers.Candidate gene (CG) identification
can be integrated with customized marker design and development. The advantage
of CG-derived markers is that they are usually more tightly linked to the gene
or QTL controlling the trait. This approach has been successfully used for
identifying CGs associated with disease resistance, since cloned plant disease
resistance genes possess conserved domains [46, 47].


### 2.3. Protocols, Resources, and Laboratory Organization

Since marker genotyping methods were first developed in the 1980s, numerous protocols
and variations now exist. Many protocols have been refined and optimized
specifically for the lab in which marker genotyping is conducted and will
depend on budget, equipment, and personnel. One feature of rice molecular breeding
labs is their diversity. Molecular breeding labs require a large initial
capital investment and since many labs are based in developing countries, the
equipment and resources often differ markedly from those of well-funded labs in
developed countries. The cost of marker genotyping is, therefore, a critical
factor for the extent of MAS in rice, and this is likely to continue to be the
case for years to come given the unlikely dramatic decrease in costs.


#### 2.3.1. DNA Extraction Protocols

Many general DNA extraction methods that are used in diverse plant species have been
used in rice, from which it is relatively easy to extract DNA (see, e.g., [48–50]). Some methods have been specifically developed
for rice [51]. The DNA
extraction component is often the most time-consuming and laborious step of
marker genotyping. For this reason, high-throughput methods using 96-well PCR
plates have been developed [52]. The method by Xu et al. [52] does not require liquid nitrogen or
freeze drying for initial grinding of leaf tissue or the use of organic
solvents.Alternative “quick and dirty” methods for DNA
extractions in rice were evaluated and optimized at IRRI [53]. These methods were
selected from published papers in the literature based on the time and
resources required for using the protocols, as well as cost, and optimized for
routine use. Two methods were selected as being the best when considering
success of PCR amplification of SSRs, time, and cost [51, 54]. The modified method by Wang et al. [54] greatly reduced the time and cost for routine DNA extractions and was
adapted into a 96-well plate method.


#### 2.3.2. SSR Genotyping

SSR genotyping typically requires high-resolution electrophoresis, which is performed
using polyacrylamide gels or, in some cases, high-resolution agarose. The
majority of labs use standard gel electrophoresis equipment and stain gels with
DNA-binding stains such as ethidium bromide, safer analogs, or silver staining (for
acrylamide gels only). Multiplexing refers to the combination of primer pairs
in PCR (multiplex PCR) or samples during gel electrophoresis (multiplex gel loading)
[55]. This has considerable potential for increasing the efficiency of marker genotyping due
to savings in time and resources. Multiplex loading is simpler, since there are
fewer variables and it has been successfully demonstrated to greatly increase
genotyping efficiency [23, 28, 29].In some labs, capillary
electrophoresis systems have been
established. The accuracy of marker allele determination is one of the major
advantages of these platforms, since size differences of 1 bp can be discerned.
Multiplex loading can also be relatively easily performed using these genotyping
platforms, which use fluorescently-labeled primers in PCR [56, 57]. These platforms can also be used for DNA sequencing,
highlighting their versatility. Unfortunately, the cost of consumables, the initial
expense of capital equipment purchase, and possibly the reliable acquisition of
consumables and technical servicing may restrict their wider-scale adoption in
actual breeding stations.


#### 2.3.3. SNP Genotyping

The two simplest and most widely used methods for detecting SNP markers are PCR-based
SNPs (that target SNPs by primer design) and restriction digestion of PCR
amplicons, which are called cleaved amplified polymorphic site (CAPS) markers [43–45]. Komori
and Nitta also used a variant of the CAPS method called derived CAPS (dCAPS),
in which artificial restriction digestion sites are created in PCR amplicons.
All methods use standard lab equipment [58].Capillary electrophoresis platforms can
also be used for SNP detection, based on the principle of single nucleotide primer
extension (SNuPE; [35]). The high resolution of capillary
electrophoresis equipment also permits small indels (say, <3 bp that are too
small to be resolved on standard agarose or to be detected with acrylamide). A
codominant single nucleotide length polymorphism marker (i.e., 1 bp indel) was
developed from the intron region of the Pi-ta gene by Jiang et al. [59].


#### 2.3.4. Indel Genotyping

One attractive feature of many indels, including ILPs, is that standard agarose
electrophoresis or acrylamide gel electrophesis equipment and methods used for
SSR detection can be used [41]. Another attractive feature of indels that are located within genic
regions is that they are gene-specific markers, so the possibility of
recombination between marker and gene is eliminated.


#### 2.3.5. Data Management

It is important that molecular breeding labs have a system in place to store
marker data, since they are an extremely useful resource for future breeding
research. There is not a universal method for data storage—systems range
from in-house Excel files to sophisticated laboratory information management
systems (LIMS). We have found that
standard database software is adequate for marker data storage. The development
of template files and standard operating procedures for all researchers to use
is more important. This information can be exploited for future genotyping activities.
Careful data collation is essential to ensure that parental genotyping is not
unnecessarily repeated and to determine opportunities for multiplexing.For labs that generate large amounts of genotypic data, a more formal LIMS could be appropriated (see Figure2); some of these systems have been recently developed for general crop species  [60, 61].Figure 2
“Impact circle” overview of genomics
and molecular research in rice. Research
areas have been placed in either the inner or outer circle. Inner circle
research activities are considered to provide more direct applied benefits (in
terms of new markers, information for the development of new markers, or new
breeding lines) to the rice molecular breeding lab/breeding program, which is
located in the center. Research areas indicated in the outer circle are generally
considered to provide indirect benefits to the rice molecular breeding
lab/breeding program.


#### 2.3.6. Rice Molecular Breeding Internet Resources

Markers and maps
The Internet has become a vital and convenient repository for marker and map data, and the rice molecular
breeder must become familiar with these resources. There are excellent
resources for published rice DNA markers that are maintained at the Gramene website
[62, 63] http://www.gramene.org/ (these resources are the envy of other cereal
researchers!). These web resources can
be used for many applications, including obtaining SSR primer sequences, marker
allele size data, and the map position of markers. Gel photos on a reference set of rice
genotypes can also be obtained from this link. A large repository of published
linkage maps, genes, QTLs, mutants, and references can also be searched in
Gramene. The comparative map viewer (CMap) can be used to visually compare maps
side by side [64].
The integrated rice genome explorer (INE;http://rgp.dna.affrc.go.jp/giot/INE.html)
was developed to provide quick and simple correlations between genetic markers
and EST, and physical maps with the rice genome sequence [65] are another excellent
resource. These features can be viewed rapidly in the database.“Genome browsers”: the genome sequence  resource for searching
The completed rice genome
sequence map would be of limited use if it was not easy to search. For this purpose,
user-friendly “genome browsers” (Gbrowse) have been developed. The Institute
for Genomics Research (TIGR) Gbrowse resource (http://www.tigr.org/tdb/e2k1/osa1/) was
designed for scientists to data-mine the rice genome [66, 67]. The rice genome sequence has been
organized into “pseudomolecules” which are virtual contigs of the 12 rice
chromosomes. Each gene has been designated with a locus identifier that enables
specific points of reference to be identified within the pseudomolecule. This
resource consists of annotated genes, identified motifs/domains within the
predicted genes, a rice repeat database, identified related sequences in other
plant species, and identified syntenic sequences between rice and other
cereals. The TIGR Gbrowse enables structural and functional annotations to be
quickly viewed. The latest version of the rice genome browser supports “tracks,”
which allow users to view specific features such as markers and putative genes
within defined regions. Enhanced data access is available through web interfaces,
FTP downloads, and a data extractor tool [68].
More recently, a genome browser was
established within Gramene that enables the Nipponbare genome sequence to be
quickly searched. This sequence is linked to genetic linkage maps in the
Gramene database. Genome browsers are extremely user-friendly resources for
assisting with basic and applied research.


### 2.4. Marker-Assisted Selection (MAS) in Rice

MAS is the process of using DNA markers to assist in the selection of plant
breeding material [11, 12, 69, 70]. Collard and Mackill [8] described three fundamental advantages of MAS compared with
conventional phenotypic screening.(i)
It is generally simpler
than phenotypic screening, which could save time, effort, resources, and, for
some traits, money. Furthermore, MAS screening is nondestructive.(ii)
Selection can be
carried out at any growth stage. Therefore, breeding lines can be screened
as seedlings and undesirable plant genotypes can be quickly eliminated. This
may be useful for many traits but especially for the traits that are
expressed at specific developmental stages.(iii)
Single plants can be
selected and their precise genotype can be determined which permits early
generation selection in breeding schemes. For most traits, homozygous and
heterozygous plants cannot be identified by conventional phenotypic
screening. Using conventional screening methods for many traits, single-plant
selection is often unreliable due to environmental effects, which can be
variable. One of the most important ways in which these advantages can be exploited by breeding
programs is the more precise and efficient development of breeding lines during
frequently-used breeding methods such as backcrossing, bulk, and pedigree
methods [9, 13]. Target genotypes can
be more effectively selected, which may enable certain traits to be “fast-tracked,”
potentially, resulting in quicker variety release. Markers can also be used as
a replacement for phenotyping, which allows selection in off-season nurseries,
making it more cost effective to grow more generations per year or to reduce
the number of breeding lines that need to be tested, by the elimination of
undesirable lines at early generations [13]. MAS has
numerous applications in rice (Table 1). Some MAS applications represent
activities that are impossible using conventional breeding methods (e.g.,
marker-assisted backcrossing and pyramiding). Collard and Mackill [8]
emphasized the importance of exploiting the advantages of marker-assisted
breeding over conventional breeding in order to maximize the impact on crop
improvement.Table 1
Examples of marker-assisted selection in rice. na = not applicable.
ApplicationTraits or germplasmGene/QTLsMarkers usedReferenceEarly generation selectionBacterial blightXa21STS[78]Gene surveys in parental materialBlast disease, predom. Korean germplasmPi5(t)PCR/DNA gel blot[79]Gene surveys in parental materialBlast diseasePi-zSSR[42]Gene surveys in parental materialBlast diseasePi-taGene-specific marker[80]Genetic diversity assessmentJaponica varieties for hybrid combinationsnaSSR and RAPD[77]Genetic diversity assessmentIndian aromatic and quality ricenaSSR[74]Genetic diversity assessmentU.S.  varietiesnaSSR[76]Genetic diversity assessmentNepalese landracesnaSSRs[81]Genetic diversity assessmentRepresentative wild rice in China,Oryza rufipogon Griff.naSSR[82]Genetic diversity assessmentIndonesian varieties and landracesnaSSR[83]Genotype identity testingHybrid ricenaSTS and SSR[70, 72]MABCBacterial blightXa21STS and RFLP[84]MABCBacterial blightXa21STS and AFLP[85]MABCBacterial blightxa5STS[86]MABCDeep rootsQTLs on chromosomes 1, 2, 7, and 9RFLP and SSR[87]MABCBacterial blightxa5,
xa13, Xa21STS[88, 89]MABCBlastPi1SSR and ISSR[90]MABCQualityWaxyRFLP and AFLP[91]MABCBacterial
blight + qualityxa13,
Xa21STS, SSR, and AFLP[92]MABCSubmergence tolerance, disease resistance,
qualitySubchr9 QTL, Xa21, Bph and blast QTLs, and quality lociSSR and STS[93]MABCBlast diseasenaSSR[94]MABCRoot traits and aromaQTLs on chromosomes 2, 7, 8, 9, and 11RFLP and SSR[95, 96]MABCHeading dateQTLs for heading date (Hd1, Hd4, Hd5, or Hd6)RFLP, STS, SSR, CAPS, dCAPs[97]MABCSubmergence toleranceSub1 QTLSSR[98]MESIndirect selection for adaptationnaSSRs[99]PyramidingBacterial blightXa4,
xa5, Xa10RFLP and RAPD[100]PyramidingBacterial blightxa5,
xa13, Xa4, Xa21RFLP, STS[17]PyramidingBlast diseasePi1,
Piz-5, Pi2, PitaRFLP, STS[101]PyramidingBacterial blightxa5,
xa13, Xa21STS and CAPS[102]PyramidingBacterial blightxa5,
xa13, Xa21SSR and STS[103]PyramidingBacterial blight and waxy genesxa5,
xa13, Xa21, WxSSR, STS, and CAPS[104]PyramidingInsect resistance and bacterial blightXa21 and BtSTS[59]PyramidingBrown plant-hopperBph1 and Bph2STS[105]PyramidingThermosensitive genetic male sterility
(TGMS) genestms2, tgms,tms5SSR[106]PyramidingBacterial blightXa7 and Xa21STS[107]PyramidingBacterial blightXa4,
xa5, and Xa21STS[108]PyramidingBacterial blightXa4,
Xa7, and Xa21STS[109, 110]Pyramiding/transgene selectionBlast and bacterial blightPi-z
and Xa21STS[111]Pyramiding/transgene selectionBacterial blightxa5,
xa13, Xa21STS (check)[112]Pyramiding/transgene selectionBacterial blight, yellow stem borer,
sheath blightXa21, Bt RC7 chitinase gene, BtSTS[113]


#### 2.4.1. Genotype Identity Testing

DNA markers can be used to simply and quickly identify varieties—or confirm the identity of a varietal
impostor. For simpleF1 hybrids, codominant markers can be used to
determine whether putative hybrids are genuine. Multiple F1s can
also easily be screened and desirable genotypes can be selected.Seed purity or intra-variety
variation can easily be tested using markers. This can be more accurate than
phenotypic evaluation [71]. For the testing of hybrid rice lines, using STS and SSR markers was
considerably easier than using typical “grow-out tests” that involve growing
plants to maturity and evaluating purity based on morphological and floral
characteristics [70, 72]. SSRs from mitochondrial genes have been targeted for the development
of markers to study maternally inherited traits such as cytoplasmic male sterility
or the maternal origin of rice accessions [73]. It has often been determined that relatively few well-chosen
markers can provide sufficient data for varietal discrimination.


#### 2.4.2. Genetic Diversity Analysis of Breeding Material

There have been numerous research papers on the assessment of genetic diversity in specific
germplasm collections using different types of markers [74, 75]. However, in recent years, SSRs have become the
marker of choice for this application (see Table 1). An example was the use of
SSR markers to broaden the genetic base of U.S.
rice varieties [76]. DNA markers have also
been used in hybrid rice breeding in order to predict genotypes that combine to
give superior hybrid vigor [77].


#### 2.4.3. Gene Surveys in Parental Material

The accurate evaluation of genes in breeders' germplasm is of great importance for
the selection of parental lines and development of new breeding populations.
Having gene information for specific target loci (deduced from markers) can be
extremely useful for breeders to efficiently use germplasm. An example of this
was demonstrated by Wang et al. [80, 114], who used a set of dominant allele-specific markers for surveying markers to detect the presence of the Pi-ta resistance gene for rice blast in
a large germplasm collection (n=141).


#### 2.4.4. Marker-Evaluated Selection (MES)

This novel approach was used to identify genomic regions under selection (i.e.,
allelic shifts) of breeding populations using a modified bulk-population
breeding system in target environments [99]. This approach makes no prior assumptions about
traits for selection; however, selection is imposed in target environments.
High-density or whole-genome marker coverage is an important prerequisite for
MES. Theoretically, once specific alleles or genomic regions have been
identified to be under selection, they can be combined via MAS to develop new
breeding lines that are the “ideotypes” (i.e., ideal genotypes).


#### 2.4.5. Marker-Assisted Backcrossing (MABC)

MABC is the process of using markers to select for target loci, minimize the length
of the donor segment containing a target locus, and/or accelerate the recovery
of the recurrent parent genome during backcrossing [115, 116]. These three levels of selection have been referred to as foreground,
recombinant, and background selection, respectively [8]. Terms were described after Hospital and Charcosset
[116], who referred to foreground selection as the selection of a target locus
and background selection as the selection of the recurrent parent genome using
markers on noncarrier chromosomes and also on the carrier chromosome. MABC is
superior to conventional backcrossing in precision and efficiency. Background
selection can greatly accelerate a backcrossing program compared to using
conventional backcrossing [117]. Furthermore, recombinant selection can minimize the size of the donor
chromosome segment, thus reducing “linkage drag”—a “universal enemy” of the plant breeder [115]. This approach has been widely used and, due to the prevalence of several
rice “mega varieties,” it is likely to continue being a successful approach [118].For basic research applications, the MABC approach
can be used to develop near-isogenic lines (NILs) with far greater precision than
conventional backcrossing. Near-isogenic lines are valuable tools to characterize
individual genes or QTLs. However, in many situations, NILs produced, using
conventional backcrossing possess, many unknown donor introgressions on
noncarrier chromosomes (i.e., chromosomes without target genes) and large donor
chromosomal segments on the carrier chromosome. By using an MABC approach, NILs
could be developed to ensure that lines are not influenced by “background”
donor introgression and possess minimal donor segments flanking the target
locus. We propose that NILs developed using such approaches are referred to as “precision
introgression lines” (PILs). Ideally, markers with known map or physical
positions should be used for PIL development.


#### 2.4.6. Pyramiding

Pyramiding is the process of combining genes or QTLs in progeny usually arising from
different parents [101, 119]. Using
conventional methods, this is extremely difficult or impossible to do in early
generations (e.g., F2 or F3) because single plants need
to be screened for multiple diseases or pathogen races. Because of the
importance of blast and bacterial blight, many pyramiding efforts have been
directed toward breeding for resistance to these two diseases (Table 1). There
is strong evidence that combining resistance genes may provide broad-spectrum resistance
[88, 120–123].Although widely used for combining
disease resistance genes or QTLs, pyramiding can be used for other abiotic
stress tolerance and agronomic traits. An example of pyramiding agronomic genes
was the combination of three thermosensitive genetic male sterility genes [106].


#### 2.4.7. Using Transgenes

There has been much research in developing transgenic rice lines for basic and applied
research applications [124]. MAS is traditionally used to screen for transformants for the
transgene(s) [111].
However, with the availability of transgenics in rice for several useful traits
such as resistan ce to diseases (bacterial
blight, blast, sheath blight, yellow mottle virus), resistan ce
to insects (stem borer, leaffolders), resistan ce
to herbicide, tolerance of abiotic stress (drought, salt), nutritional traits
(iron and pro-vitamin A), and photosynthetic traits [125, 126]; there is a strong interest in using transgenes in breeding. Rice
breeders are excited to transfer them to successful mega varieties through
conventional backcrossing or MABC. For example, transgenic rice (southern U.S. japonica-type
varieties) with inherent ability to produce beta-carotene developed by
Syngenta is available at IRRI and in several other national programs. However,
these cultivars are not adapted to the tropical conditions in Asia,
where most consumers prefer indica-type rice varieties. Therefore, at IRRI, we
are introgressing the beta-carotene loci from japonica-type donor varieties
into popular indica-type Asian rice varieties, using MABC. Initially,
we used 3 GR1 events (GR1-146, GR1-309, and GR1-652) as donor parents, while 2
IRRI-bred mega varieties (IR64 and IR36) and a popular Bangladeshi variety (BR29)
were used as recurrent parents. Subsequently, we received 6 GR2 events (GR2-E,
GR2-G, GR2-L, GR2-R, GR2-T, and GR2-W). Four indica varieties, IR64, IR36, BR29,
and PSB Rc 82, were used as recurrent parents. Advanced backcross progenies are
available and some are ready for field testing [127–129].


## 2.1. View of the Rice “Pregenome Sequence” Molecular Breeding Lab

We arbitrarily define the “pregenome sequence
molecular breeding lab” as before 2000. Although the first rice genome sequence drafts were published in 2002
and the complete genome sequence was published in 2005, sequence data were
available before these publication dates so it is very difficult to exactly
pinpoint the time when rice genome sequence data influenced applied rice
genetics and breeding. In the early to mid-1990s, restriction fragment length polymorphism (RFLP) and random
amplified polymorphic (RAPD) markers were commonly used for rice breeding
research [17–21]. In Japan, RFLPs continue to be a marker system of choice [22]. Often, RFLP and RAPD markers were converted into
second generation, polymerase chain reaction (PCR)-based markers called
sequence tagged site (STS) markers to improve technical simplicity and
reliability [23–25]. Simple sequence repeats (SSR; or
“microsatellites”) became the most widely used markers in cereals and rice is
no exception [26–28].
In earlier reports, the principles and techniques of
detecting SSR polymorphisms were called simple sequence length polymorphism
(SSLP) markers [28, 29]. SSRs are highly reliable (i.e., reproducible), codominant in inheritance, highly polymorphic
(compared to other markers), and generally transferable between mapping
populations. The only disadvantages of SSRs are that they typically require
polyacrylamide gel electrophoresis and generally give information only about a
single locus per assay.The first SSRs were reported in 1996 [30]. By 1997, there were 121 validated SSRs, which
were adequate for marker-assisted evaluation of germplasm and the construction
of framework linkage maps but had limited use for MAS, due to limited genome
coverage [29]. By
2001, there were a total of ∼500 SSRs that were developed from 57.8 Mb of
publicly available rice genome data [31], which further increased
the utility of these markers.


## 2.2. The Postgenome Sequence Rice Molecular Breeding Lab: Opening the “Treasure Chest” of New Rice Markers

### 2.2.1. SSRs

Analysis of the completed rice genome sequence provided the identification of literally tens
of thousands of new targets for DNA markers, especially SSRs. Using publicly available BAC and PAC clones, more
than 2200 validated SSRs were released in 2002 [32]. This was soon followed by 18828 Class I (di-, tri-,
tetra-repeats) SSRs that were released after the completion of the Nipponbare
genome sequence in 2005 [7]. This number is by far the largest number of publicly available SSRs for
any crop species. The extremely high density of SSRs (approx. 51 SSRs per Mb)
will provide a considerable “tool kit” for map construction and MAS for
numerous applications. Given that many
labs are currently well equipped for SSR analysis, it is highly likely that SSRs
will continue to be the marker of choice for years to come.


### 2.2.2. Single Nucleotide Polymorphisms (SNPs)

SNPs are the most abundant and ubiquitous type of polymorphisms in all organisms,
and many researchers propose that these markers will be the marker of choice in
the future [33]. In rice, SNPs can be readily identified by direct
comparisons of Nipponbare and 93-11 genomes, or by sequence alignment with one
or both reference sequences with available sequence data in public databases [34–36]. Recently, more SNP data have become available that have been generated
by comparing partial sequences from multiple genotypes [37–39]. In some cases, DNA sequencing of target regions in specific genotypes is
required. However, experimental validation of SNP-based markers is required
since inaccuracies in sequence data have been reported [34, 36]. The ease with which SNPs can be identified in silico and increase in publicly available
rice DNA sequence data will undoubtedly ensure that SNP-based markers will be
more commonly used in the future.It should be noted that lower levels of SNP marker polymorphism are usually detected in more closely related
genotypes, which are more representative of breeders’ elite germplasm (indica×indica or japonica×japonica-derived material), when
compared with the japonica-indica reference genotypes used to determine SNP frequency. The frequency of SNPs
between subspecies was reported to be from 0.68% to 0.70%, whereas it was 0.03%
to 0.05% between japonica cultivars and
0.49% between indic a cultivars [35]. Interestingly, SNPs were
not evenly distributed along chromosomes.


### 2.2.3. Indels

Insertion/deletion (indel) mutations are abundant mutations that occur in coding and noncoding
regions. Indels can also be quickly identified in silico by direct comparions ofjaponica and indica genome sequences. The enormous number of indels between the two subspecies will
provide an indispensable resource of polymorphic markers for indica×japonica populations or populations with specific introgressions [34, 35]. Either of the two rice genome reference sequences
can be easily compared with other sequences for further indel identification,
as was done between Nipponbare and Kasalath, a commonly used indica accession [40]. Like SNP-based markers, indels also need to be
experimentally validated.Introns are noncoding regions within
genes and hence they “tolerate” insertion/deletion mutations compared with
exons. Consequently, many indels have been identified in introns and these size
polymorphisms have been exploited by the development of a new class of intron
length polymorphic (ILP) markers [41]. Experimental validation of these markers indicated that the
majority were reliable and codominant, and that although ILPs were designed
from indica/japonica comparisons,
they were also polymorphic between varieties within both subspecies although
the level of polymorphism was lower.


### 2.2.4. “Custom-Made” Markers

The great resource for molecular breeders is the DNA sequence provided by the
genome sequences since it permits markers that are tightly linked to target
loci to be “custom-made” or “tailor-made” to suit the aims of MAS. The large
number of custom-made markers that have already been designed or the potential
for new ones to be designed is a unique feature of the rice molecular breeding lab.
The number of markers that can potentially be generated using the rice genome
sequence in silico is practically unlimited (Figure1). The markers might be derived directly from
the Nipponbare/93-11 sequences or used to identify corresponding EST or genomic
sequences available from databases (i.e., BAC or PAC clones containing target
genes that may not actually be present in reference genotypes) [42–44]. In principle, custom-made
markers can be any type although they most commonly include new SSRs, indels,
PCR-based SNPs, and cleaved amplified polymorphic site (CAPS) markers—which are the technically the simplest markers
to be used for marker genotyping [43, 45]. It should be noted that these markers
must be tested in wet-lab experiments.Figure 1
Process for developing custom-made
rice DNA markers.Candidate gene (CG) identification
can be integrated with customized marker design and development. The advantage
of CG-derived markers is that they are usually more tightly linked to the gene
or QTL controlling the trait. This approach has been successfully used for
identifying CGs associated with disease resistance, since cloned plant disease
resistance genes possess conserved domains [46, 47].


## 2.2.1. SSRs

Analysis of the completed rice genome sequence provided the identification of literally tens
of thousands of new targets for DNA markers, especially SSRs. Using publicly available BAC and PAC clones, more
than 2200 validated SSRs were released in 2002 [32]. This was soon followed by 18828 Class I (di-, tri-,
tetra-repeats) SSRs that were released after the completion of the Nipponbare
genome sequence in 2005 [7]. This number is by far the largest number of publicly available SSRs for
any crop species. The extremely high density of SSRs (approx. 51 SSRs per Mb)
will provide a considerable “tool kit” for map construction and MAS for
numerous applications. Given that many
labs are currently well equipped for SSR analysis, it is highly likely that SSRs
will continue to be the marker of choice for years to come.


## 2.2.2. Single Nucleotide Polymorphisms (SNPs)

SNPs are the most abundant and ubiquitous type of polymorphisms in all organisms,
and many researchers propose that these markers will be the marker of choice in
the future [33]. In rice, SNPs can be readily identified by direct
comparisons of Nipponbare and 93-11 genomes, or by sequence alignment with one
or both reference sequences with available sequence data in public databases [34–36]. Recently, more SNP data have become available that have been generated
by comparing partial sequences from multiple genotypes [37–39]. In some cases, DNA sequencing of target regions in specific genotypes is
required. However, experimental validation of SNP-based markers is required
since inaccuracies in sequence data have been reported [34, 36]. The ease with which SNPs can be identified in silico and increase in publicly available
rice DNA sequence data will undoubtedly ensure that SNP-based markers will be
more commonly used in the future.It should be noted that lower levels of SNP marker polymorphism are usually detected in more closely related
genotypes, which are more representative of breeders’ elite germplasm (indica×indica or japonica×japonica-derived material), when
compared with the japonica-indica reference genotypes used to determine SNP frequency. The frequency of SNPs
between subspecies was reported to be from 0.68% to 0.70%, whereas it was 0.03%
to 0.05% between japonica cultivars and
0.49% between indic a cultivars [35]. Interestingly, SNPs were
not evenly distributed along chromosomes.


## 2.2.3. Indels

Insertion/deletion (indel) mutations are abundant mutations that occur in coding and noncoding
regions. Indels can also be quickly identified in silico by direct comparions ofjaponica and indica genome sequences. The enormous number of indels between the two subspecies will
provide an indispensable resource of polymorphic markers for indica×japonica populations or populations with specific introgressions [34, 35]. Either of the two rice genome reference sequences
can be easily compared with other sequences for further indel identification,
as was done between Nipponbare and Kasalath, a commonly used indica accession [40]. Like SNP-based markers, indels also need to be
experimentally validated.Introns are noncoding regions within
genes and hence they “tolerate” insertion/deletion mutations compared with
exons. Consequently, many indels have been identified in introns and these size
polymorphisms have been exploited by the development of a new class of intron
length polymorphic (ILP) markers [41]. Experimental validation of these markers indicated that the
majority were reliable and codominant, and that although ILPs were designed
from indica/japonica comparisons,
they were also polymorphic between varieties within both subspecies although
the level of polymorphism was lower.


## 2.2.4. “Custom-Made” Markers

The great resource for molecular breeders is the DNA sequence provided by the
genome sequences since it permits markers that are tightly linked to target
loci to be “custom-made” or “tailor-made” to suit the aims of MAS. The large
number of custom-made markers that have already been designed or the potential
for new ones to be designed is a unique feature of the rice molecular breeding lab.
The number of markers that can potentially be generated using the rice genome
sequence in silico is practically unlimited (Figure1). The markers might be derived directly from
the Nipponbare/93-11 sequences or used to identify corresponding EST or genomic
sequences available from databases (i.e., BAC or PAC clones containing target
genes that may not actually be present in reference genotypes) [42–44]. In principle, custom-made
markers can be any type although they most commonly include new SSRs, indels,
PCR-based SNPs, and cleaved amplified polymorphic site (CAPS) markers—which are the technically the simplest markers
to be used for marker genotyping [43, 45]. It should be noted that these markers
must be tested in wet-lab experiments.Figure 1
Process for developing custom-made
rice DNA markers.Candidate gene (CG) identification
can be integrated with customized marker design and development. The advantage
of CG-derived markers is that they are usually more tightly linked to the gene
or QTL controlling the trait. This approach has been successfully used for
identifying CGs associated with disease resistance, since cloned plant disease
resistance genes possess conserved domains [46, 47].


## 2.3. Protocols, Resources, and Laboratory Organization

Since marker genotyping methods were first developed in the 1980s, numerous protocols
and variations now exist. Many protocols have been refined and optimized
specifically for the lab in which marker genotyping is conducted and will
depend on budget, equipment, and personnel. One feature of rice molecular breeding
labs is their diversity. Molecular breeding labs require a large initial
capital investment and since many labs are based in developing countries, the
equipment and resources often differ markedly from those of well-funded labs in
developed countries. The cost of marker genotyping is, therefore, a critical
factor for the extent of MAS in rice, and this is likely to continue to be the
case for years to come given the unlikely dramatic decrease in costs.


### 2.3.1. DNA Extraction Protocols

Many general DNA extraction methods that are used in diverse plant species have been
used in rice, from which it is relatively easy to extract DNA (see, e.g., [48–50]). Some methods have been specifically developed
for rice [51]. The DNA
extraction component is often the most time-consuming and laborious step of
marker genotyping. For this reason, high-throughput methods using 96-well PCR
plates have been developed [52]. The method by Xu et al. [52] does not require liquid nitrogen or
freeze drying for initial grinding of leaf tissue or the use of organic
solvents.Alternative “quick and dirty” methods for DNA
extractions in rice were evaluated and optimized at IRRI [53]. These methods were
selected from published papers in the literature based on the time and
resources required for using the protocols, as well as cost, and optimized for
routine use. Two methods were selected as being the best when considering
success of PCR amplification of SSRs, time, and cost [51, 54]. The modified method by Wang et al. [54] greatly reduced the time and cost for routine DNA extractions and was
adapted into a 96-well plate method.


### 2.3.2. SSR Genotyping

SSR genotyping typically requires high-resolution electrophoresis, which is performed
using polyacrylamide gels or, in some cases, high-resolution agarose. The
majority of labs use standard gel electrophoresis equipment and stain gels with
DNA-binding stains such as ethidium bromide, safer analogs, or silver staining (for
acrylamide gels only). Multiplexing refers to the combination of primer pairs
in PCR (multiplex PCR) or samples during gel electrophoresis (multiplex gel loading)
[55]. This has considerable potential for increasing the efficiency of marker genotyping due
to savings in time and resources. Multiplex loading is simpler, since there are
fewer variables and it has been successfully demonstrated to greatly increase
genotyping efficiency [23, 28, 29].In some labs, capillary
electrophoresis systems have been
established. The accuracy of marker allele determination is one of the major
advantages of these platforms, since size differences of 1 bp can be discerned.
Multiplex loading can also be relatively easily performed using these genotyping
platforms, which use fluorescently-labeled primers in PCR [56, 57]. These platforms can also be used for DNA sequencing,
highlighting their versatility. Unfortunately, the cost of consumables, the initial
expense of capital equipment purchase, and possibly the reliable acquisition of
consumables and technical servicing may restrict their wider-scale adoption in
actual breeding stations.


### 2.3.3. SNP Genotyping

The two simplest and most widely used methods for detecting SNP markers are PCR-based
SNPs (that target SNPs by primer design) and restriction digestion of PCR
amplicons, which are called cleaved amplified polymorphic site (CAPS) markers [43–45]. Komori
and Nitta also used a variant of the CAPS method called derived CAPS (dCAPS),
in which artificial restriction digestion sites are created in PCR amplicons.
All methods use standard lab equipment [58].Capillary electrophoresis platforms can
also be used for SNP detection, based on the principle of single nucleotide primer
extension (SNuPE; [35]). The high resolution of capillary
electrophoresis equipment also permits small indels (say, <3 bp that are too
small to be resolved on standard agarose or to be detected with acrylamide). A
codominant single nucleotide length polymorphism marker (i.e., 1 bp indel) was
developed from the intron region of the Pi-ta gene by Jiang et al. [59].


### 2.3.4. Indel Genotyping

One attractive feature of many indels, including ILPs, is that standard agarose
electrophoresis or acrylamide gel electrophesis equipment and methods used for
SSR detection can be used [41]. Another attractive feature of indels that are located within genic
regions is that they are gene-specific markers, so the possibility of
recombination between marker and gene is eliminated.


### 2.3.5. Data Management

It is important that molecular breeding labs have a system in place to store
marker data, since they are an extremely useful resource for future breeding
research. There is not a universal method for data storage—systems range
from in-house Excel files to sophisticated laboratory information management
systems (LIMS). We have found that
standard database software is adequate for marker data storage. The development
of template files and standard operating procedures for all researchers to use
is more important. This information can be exploited for future genotyping activities.
Careful data collation is essential to ensure that parental genotyping is not
unnecessarily repeated and to determine opportunities for multiplexing.For labs that generate large amounts of genotypic data, a more formal LIMS could be appropriated (see Figure2); some of these systems have been recently developed for general crop species  [60, 61].Figure 2
“Impact circle” overview of genomics
and molecular research in rice. Research
areas have been placed in either the inner or outer circle. Inner circle
research activities are considered to provide more direct applied benefits (in
terms of new markers, information for the development of new markers, or new
breeding lines) to the rice molecular breeding lab/breeding program, which is
located in the center. Research areas indicated in the outer circle are generally
considered to provide indirect benefits to the rice molecular breeding
lab/breeding program.


### 2.3.6. Rice Molecular Breeding Internet Resources

Markers and maps
The Internet has become a vital and convenient repository for marker and map data, and the rice molecular
breeder must become familiar with these resources. There are excellent
resources for published rice DNA markers that are maintained at the Gramene website
[62, 63] http://www.gramene.org/ (these resources are the envy of other cereal
researchers!). These web resources can
be used for many applications, including obtaining SSR primer sequences, marker
allele size data, and the map position of markers. Gel photos on a reference set of rice
genotypes can also be obtained from this link. A large repository of published
linkage maps, genes, QTLs, mutants, and references can also be searched in
Gramene. The comparative map viewer (CMap) can be used to visually compare maps
side by side [64].
The integrated rice genome explorer (INE;http://rgp.dna.affrc.go.jp/giot/INE.html)
was developed to provide quick and simple correlations between genetic markers
and EST, and physical maps with the rice genome sequence [65] are another excellent
resource. These features can be viewed rapidly in the database.“Genome browsers”: the genome sequence  resource for searching
The completed rice genome
sequence map would be of limited use if it was not easy to search. For this purpose,
user-friendly “genome browsers” (Gbrowse) have been developed. The Institute
for Genomics Research (TIGR) Gbrowse resource (http://www.tigr.org/tdb/e2k1/osa1/) was
designed for scientists to data-mine the rice genome [66, 67]. The rice genome sequence has been
organized into “pseudomolecules” which are virtual contigs of the 12 rice
chromosomes. Each gene has been designated with a locus identifier that enables
specific points of reference to be identified within the pseudomolecule. This
resource consists of annotated genes, identified motifs/domains within the
predicted genes, a rice repeat database, identified related sequences in other
plant species, and identified syntenic sequences between rice and other
cereals. The TIGR Gbrowse enables structural and functional annotations to be
quickly viewed. The latest version of the rice genome browser supports “tracks,”
which allow users to view specific features such as markers and putative genes
within defined regions. Enhanced data access is available through web interfaces,
FTP downloads, and a data extractor tool [68].
More recently, a genome browser was
established within Gramene that enables the Nipponbare genome sequence to be
quickly searched. This sequence is linked to genetic linkage maps in the
Gramene database. Genome browsers are extremely user-friendly resources for
assisting with basic and applied research.


## 2.3.1. DNA Extraction Protocols

Many general DNA extraction methods that are used in diverse plant species have been
used in rice, from which it is relatively easy to extract DNA (see, e.g., [48–50]). Some methods have been specifically developed
for rice [51]. The DNA
extraction component is often the most time-consuming and laborious step of
marker genotyping. For this reason, high-throughput methods using 96-well PCR
plates have been developed [52]. The method by Xu et al. [52] does not require liquid nitrogen or
freeze drying for initial grinding of leaf tissue or the use of organic
solvents.Alternative “quick and dirty” methods for DNA
extractions in rice were evaluated and optimized at IRRI [53]. These methods were
selected from published papers in the literature based on the time and
resources required for using the protocols, as well as cost, and optimized for
routine use. Two methods were selected as being the best when considering
success of PCR amplification of SSRs, time, and cost [51, 54]. The modified method by Wang et al. [54] greatly reduced the time and cost for routine DNA extractions and was
adapted into a 96-well plate method.


## 2.3.2. SSR Genotyping

SSR genotyping typically requires high-resolution electrophoresis, which is performed
using polyacrylamide gels or, in some cases, high-resolution agarose. The
majority of labs use standard gel electrophoresis equipment and stain gels with
DNA-binding stains such as ethidium bromide, safer analogs, or silver staining (for
acrylamide gels only). Multiplexing refers to the combination of primer pairs
in PCR (multiplex PCR) or samples during gel electrophoresis (multiplex gel loading)
[55]. This has considerable potential for increasing the efficiency of marker genotyping due
to savings in time and resources. Multiplex loading is simpler, since there are
fewer variables and it has been successfully demonstrated to greatly increase
genotyping efficiency [23, 28, 29].In some labs, capillary
electrophoresis systems have been
established. The accuracy of marker allele determination is one of the major
advantages of these platforms, since size differences of 1 bp can be discerned.
Multiplex loading can also be relatively easily performed using these genotyping
platforms, which use fluorescently-labeled primers in PCR [56, 57]. These platforms can also be used for DNA sequencing,
highlighting their versatility. Unfortunately, the cost of consumables, the initial
expense of capital equipment purchase, and possibly the reliable acquisition of
consumables and technical servicing may restrict their wider-scale adoption in
actual breeding stations.


## 2.3.3. SNP Genotyping

The two simplest and most widely used methods for detecting SNP markers are PCR-based
SNPs (that target SNPs by primer design) and restriction digestion of PCR
amplicons, which are called cleaved amplified polymorphic site (CAPS) markers [43–45]. Komori
and Nitta also used a variant of the CAPS method called derived CAPS (dCAPS),
in which artificial restriction digestion sites are created in PCR amplicons.
All methods use standard lab equipment [58].Capillary electrophoresis platforms can
also be used for SNP detection, based on the principle of single nucleotide primer
extension (SNuPE; [35]). The high resolution of capillary
electrophoresis equipment also permits small indels (say, <3 bp that are too
small to be resolved on standard agarose or to be detected with acrylamide). A
codominant single nucleotide length polymorphism marker (i.e., 1 bp indel) was
developed from the intron region of the Pi-ta gene by Jiang et al. [59].


## 2.3.4. Indel Genotyping

One attractive feature of many indels, including ILPs, is that standard agarose
electrophoresis or acrylamide gel electrophesis equipment and methods used for
SSR detection can be used [41]. Another attractive feature of indels that are located within genic
regions is that they are gene-specific markers, so the possibility of
recombination between marker and gene is eliminated.


## 2.3.5. Data Management

It is important that molecular breeding labs have a system in place to store
marker data, since they are an extremely useful resource for future breeding
research. There is not a universal method for data storage—systems range
from in-house Excel files to sophisticated laboratory information management
systems (LIMS). We have found that
standard database software is adequate for marker data storage. The development
of template files and standard operating procedures for all researchers to use
is more important. This information can be exploited for future genotyping activities.
Careful data collation is essential to ensure that parental genotyping is not
unnecessarily repeated and to determine opportunities for multiplexing.For labs that generate large amounts of genotypic data, a more formal LIMS could be appropriated (see Figure2); some of these systems have been recently developed for general crop species  [60, 61].Figure 2
“Impact circle” overview of genomics
and molecular research in rice. Research
areas have been placed in either the inner or outer circle. Inner circle
research activities are considered to provide more direct applied benefits (in
terms of new markers, information for the development of new markers, or new
breeding lines) to the rice molecular breeding lab/breeding program, which is
located in the center. Research areas indicated in the outer circle are generally
considered to provide indirect benefits to the rice molecular breeding
lab/breeding program.


## 2.3.6. Rice Molecular Breeding Internet Resources

Markers and maps
The Internet has become a vital and convenient repository for marker and map data, and the rice molecular
breeder must become familiar with these resources. There are excellent
resources for published rice DNA markers that are maintained at the Gramene website
[62, 63] http://www.gramene.org/ (these resources are the envy of other cereal
researchers!). These web resources can
be used for many applications, including obtaining SSR primer sequences, marker
allele size data, and the map position of markers. Gel photos on a reference set of rice
genotypes can also be obtained from this link. A large repository of published
linkage maps, genes, QTLs, mutants, and references can also be searched in
Gramene. The comparative map viewer (CMap) can be used to visually compare maps
side by side [64].
The integrated rice genome explorer (INE;http://rgp.dna.affrc.go.jp/giot/INE.html)
was developed to provide quick and simple correlations between genetic markers
and EST, and physical maps with the rice genome sequence [65] are another excellent
resource. These features can be viewed rapidly in the database.“Genome browsers”: the genome sequence  resource for searching
The completed rice genome
sequence map would be of limited use if it was not easy to search. For this purpose,
user-friendly “genome browsers” (Gbrowse) have been developed. The Institute
for Genomics Research (TIGR) Gbrowse resource (http://www.tigr.org/tdb/e2k1/osa1/) was
designed for scientists to data-mine the rice genome [66, 67]. The rice genome sequence has been
organized into “pseudomolecules” which are virtual contigs of the 12 rice
chromosomes. Each gene has been designated with a locus identifier that enables
specific points of reference to be identified within the pseudomolecule. This
resource consists of annotated genes, identified motifs/domains within the
predicted genes, a rice repeat database, identified related sequences in other
plant species, and identified syntenic sequences between rice and other
cereals. The TIGR Gbrowse enables structural and functional annotations to be
quickly viewed. The latest version of the rice genome browser supports “tracks,”
which allow users to view specific features such as markers and putative genes
within defined regions. Enhanced data access is available through web interfaces,
FTP downloads, and a data extractor tool [68].
More recently, a genome browser was
established within Gramene that enables the Nipponbare genome sequence to be
quickly searched. This sequence is linked to genetic linkage maps in the
Gramene database. Genome browsers are extremely user-friendly resources for
assisting with basic and applied research.


## 2.4. Marker-Assisted Selection (MAS) in Rice

MAS is the process of using DNA markers to assist in the selection of plant
breeding material [11, 12, 69, 70]. Collard and Mackill [8] described three fundamental advantages of MAS compared with
conventional phenotypic screening.(i)
It is generally simpler
than phenotypic screening, which could save time, effort, resources, and, for
some traits, money. Furthermore, MAS screening is nondestructive.(ii)
Selection can be
carried out at any growth stage. Therefore, breeding lines can be screened
as seedlings and undesirable plant genotypes can be quickly eliminated. This
may be useful for many traits but especially for the traits that are
expressed at specific developmental stages.(iii)
Single plants can be
selected and their precise genotype can be determined which permits early
generation selection in breeding schemes. For most traits, homozygous and
heterozygous plants cannot be identified by conventional phenotypic
screening. Using conventional screening methods for many traits, single-plant
selection is often unreliable due to environmental effects, which can be
variable. One of the most important ways in which these advantages can be exploited by breeding
programs is the more precise and efficient development of breeding lines during
frequently-used breeding methods such as backcrossing, bulk, and pedigree
methods [9, 13]. Target genotypes can
be more effectively selected, which may enable certain traits to be “fast-tracked,”
potentially, resulting in quicker variety release. Markers can also be used as
a replacement for phenotyping, which allows selection in off-season nurseries,
making it more cost effective to grow more generations per year or to reduce
the number of breeding lines that need to be tested, by the elimination of
undesirable lines at early generations [13]. MAS has
numerous applications in rice (Table 1). Some MAS applications represent
activities that are impossible using conventional breeding methods (e.g.,
marker-assisted backcrossing and pyramiding). Collard and Mackill [8]
emphasized the importance of exploiting the advantages of marker-assisted
breeding over conventional breeding in order to maximize the impact on crop
improvement.Table 1
Examples of marker-assisted selection in rice. na = not applicable.
ApplicationTraits or germplasmGene/QTLsMarkers usedReferenceEarly generation selectionBacterial blightXa21STS[78]Gene surveys in parental materialBlast disease, predom. Korean germplasmPi5(t)PCR/DNA gel blot[79]Gene surveys in parental materialBlast diseasePi-zSSR[42]Gene surveys in parental materialBlast diseasePi-taGene-specific marker[80]Genetic diversity assessmentJaponica varieties for hybrid combinationsnaSSR and RAPD[77]Genetic diversity assessmentIndian aromatic and quality ricenaSSR[74]Genetic diversity assessmentU.S.  varietiesnaSSR[76]Genetic diversity assessmentNepalese landracesnaSSRs[81]Genetic diversity assessmentRepresentative wild rice in China,Oryza rufipogon Griff.naSSR[82]Genetic diversity assessmentIndonesian varieties and landracesnaSSR[83]Genotype identity testingHybrid ricenaSTS and SSR[70, 72]MABCBacterial blightXa21STS and RFLP[84]MABCBacterial blightXa21STS and AFLP[85]MABCBacterial blightxa5STS[86]MABCDeep rootsQTLs on chromosomes 1, 2, 7, and 9RFLP and SSR[87]MABCBacterial blightxa5,
xa13, Xa21STS[88, 89]MABCBlastPi1SSR and ISSR[90]MABCQualityWaxyRFLP and AFLP[91]MABCBacterial
blight + qualityxa13,
Xa21STS, SSR, and AFLP[92]MABCSubmergence tolerance, disease resistance,
qualitySubchr9 QTL, Xa21, Bph and blast QTLs, and quality lociSSR and STS[93]MABCBlast diseasenaSSR[94]MABCRoot traits and aromaQTLs on chromosomes 2, 7, 8, 9, and 11RFLP and SSR[95, 96]MABCHeading dateQTLs for heading date (Hd1, Hd4, Hd5, or Hd6)RFLP, STS, SSR, CAPS, dCAPs[97]MABCSubmergence toleranceSub1 QTLSSR[98]MESIndirect selection for adaptationnaSSRs[99]PyramidingBacterial blightXa4,
xa5, Xa10RFLP and RAPD[100]PyramidingBacterial blightxa5,
xa13, Xa4, Xa21RFLP, STS[17]PyramidingBlast diseasePi1,
Piz-5, Pi2, PitaRFLP, STS[101]PyramidingBacterial blightxa5,
xa13, Xa21STS and CAPS[102]PyramidingBacterial blightxa5,
xa13, Xa21SSR and STS[103]PyramidingBacterial blight and waxy genesxa5,
xa13, Xa21, WxSSR, STS, and CAPS[104]PyramidingInsect resistance and bacterial blightXa21 and BtSTS[59]PyramidingBrown plant-hopperBph1 and Bph2STS[105]PyramidingThermosensitive genetic male sterility
(TGMS) genestms2, tgms,tms5SSR[106]PyramidingBacterial blightXa7 and Xa21STS[107]PyramidingBacterial blightXa4,
xa5, and Xa21STS[108]PyramidingBacterial blightXa4,
Xa7, and Xa21STS[109, 110]Pyramiding/transgene selectionBlast and bacterial blightPi-z
and Xa21STS[111]Pyramiding/transgene selectionBacterial blightxa5,
xa13, Xa21STS (check)[112]Pyramiding/transgene selectionBacterial blight, yellow stem borer,
sheath blightXa21, Bt RC7 chitinase gene, BtSTS[113]


### 2.4.1. Genotype Identity Testing

DNA markers can be used to simply and quickly identify varieties—or confirm the identity of a varietal
impostor. For simpleF1 hybrids, codominant markers can be used to
determine whether putative hybrids are genuine. Multiple F1s can
also easily be screened and desirable genotypes can be selected.Seed purity or intra-variety
variation can easily be tested using markers. This can be more accurate than
phenotypic evaluation [71]. For the testing of hybrid rice lines, using STS and SSR markers was
considerably easier than using typical “grow-out tests” that involve growing
plants to maturity and evaluating purity based on morphological and floral
characteristics [70, 72]. SSRs from mitochondrial genes have been targeted for the development
of markers to study maternally inherited traits such as cytoplasmic male sterility
or the maternal origin of rice accessions [73]. It has often been determined that relatively few well-chosen
markers can provide sufficient data for varietal discrimination.


### 2.4.2. Genetic Diversity Analysis of Breeding Material

There have been numerous research papers on the assessment of genetic diversity in specific
germplasm collections using different types of markers [74, 75]. However, in recent years, SSRs have become the
marker of choice for this application (see Table 1). An example was the use of
SSR markers to broaden the genetic base of U.S.
rice varieties [76]. DNA markers have also
been used in hybrid rice breeding in order to predict genotypes that combine to
give superior hybrid vigor [77].


### 2.4.3. Gene Surveys in Parental Material

The accurate evaluation of genes in breeders' germplasm is of great importance for
the selection of parental lines and development of new breeding populations.
Having gene information for specific target loci (deduced from markers) can be
extremely useful for breeders to efficiently use germplasm. An example of this
was demonstrated by Wang et al. [80, 114], who used a set of dominant allele-specific markers for surveying markers to detect the presence of the Pi-ta resistance gene for rice blast in
a large germplasm collection (n=141).


### 2.4.4. Marker-Evaluated Selection (MES)

This novel approach was used to identify genomic regions under selection (i.e.,
allelic shifts) of breeding populations using a modified bulk-population
breeding system in target environments [99]. This approach makes no prior assumptions about
traits for selection; however, selection is imposed in target environments.
High-density or whole-genome marker coverage is an important prerequisite for
MES. Theoretically, once specific alleles or genomic regions have been
identified to be under selection, they can be combined via MAS to develop new
breeding lines that are the “ideotypes” (i.e., ideal genotypes).


### 2.4.5. Marker-Assisted Backcrossing (MABC)

MABC is the process of using markers to select for target loci, minimize the length
of the donor segment containing a target locus, and/or accelerate the recovery
of the recurrent parent genome during backcrossing [115, 116]. These three levels of selection have been referred to as foreground,
recombinant, and background selection, respectively [8]. Terms were described after Hospital and Charcosset
[116], who referred to foreground selection as the selection of a target locus
and background selection as the selection of the recurrent parent genome using
markers on noncarrier chromosomes and also on the carrier chromosome. MABC is
superior to conventional backcrossing in precision and efficiency. Background
selection can greatly accelerate a backcrossing program compared to using
conventional backcrossing [117]. Furthermore, recombinant selection can minimize the size of the donor
chromosome segment, thus reducing “linkage drag”—a “universal enemy” of the plant breeder [115]. This approach has been widely used and, due to the prevalence of several
rice “mega varieties,” it is likely to continue being a successful approach [118].For basic research applications, the MABC approach
can be used to develop near-isogenic lines (NILs) with far greater precision than
conventional backcrossing. Near-isogenic lines are valuable tools to characterize
individual genes or QTLs. However, in many situations, NILs produced, using
conventional backcrossing possess, many unknown donor introgressions on
noncarrier chromosomes (i.e., chromosomes without target genes) and large donor
chromosomal segments on the carrier chromosome. By using an MABC approach, NILs
could be developed to ensure that lines are not influenced by “background”
donor introgression and possess minimal donor segments flanking the target
locus. We propose that NILs developed using such approaches are referred to as “precision
introgression lines” (PILs). Ideally, markers with known map or physical
positions should be used for PIL development.


### 2.4.6. Pyramiding

Pyramiding is the process of combining genes or QTLs in progeny usually arising from
different parents [101, 119]. Using
conventional methods, this is extremely difficult or impossible to do in early
generations (e.g., F2 or F3) because single plants need
to be screened for multiple diseases or pathogen races. Because of the
importance of blast and bacterial blight, many pyramiding efforts have been
directed toward breeding for resistance to these two diseases (Table 1). There
is strong evidence that combining resistance genes may provide broad-spectrum resistance
[88, 120–123].Although widely used for combining
disease resistance genes or QTLs, pyramiding can be used for other abiotic
stress tolerance and agronomic traits. An example of pyramiding agronomic genes
was the combination of three thermosensitive genetic male sterility genes [106].


### 2.4.7. Using Transgenes

There has been much research in developing transgenic rice lines for basic and applied
research applications [124]. MAS is traditionally used to screen for transformants for the
transgene(s) [111].
However, with the availability of transgenics in rice for several useful traits
such as resistan ce to diseases (bacterial
blight, blast, sheath blight, yellow mottle virus), resistan ce
to insects (stem borer, leaffolders), resistan ce
to herbicide, tolerance of abiotic stress (drought, salt), nutritional traits
(iron and pro-vitamin A), and photosynthetic traits [125, 126]; there is a strong interest in using transgenes in breeding. Rice
breeders are excited to transfer them to successful mega varieties through
conventional backcrossing or MABC. For example, transgenic rice (southern U.S. japonica-type
varieties) with inherent ability to produce beta-carotene developed by
Syngenta is available at IRRI and in several other national programs. However,
these cultivars are not adapted to the tropical conditions in Asia,
where most consumers prefer indica-type rice varieties. Therefore, at IRRI, we
are introgressing the beta-carotene loci from japonica-type donor varieties
into popular indica-type Asian rice varieties, using MABC. Initially,
we used 3 GR1 events (GR1-146, GR1-309, and GR1-652) as donor parents, while 2
IRRI-bred mega varieties (IR64 and IR36) and a popular Bangladeshi variety (BR29)
were used as recurrent parents. Subsequently, we received 6 GR2 events (GR2-E,
GR2-G, GR2-L, GR2-R, GR2-T, and GR2-W). Four indica varieties, IR64, IR36, BR29,
and PSB Rc 82, were used as recurrent parents. Advanced backcross progenies are
available and some are ready for field testing [127–129].


## 2.4.1. Genotype Identity Testing

DNA markers can be used to simply and quickly identify varieties—or confirm the identity of a varietal
impostor. For simpleF1 hybrids, codominant markers can be used to
determine whether putative hybrids are genuine. Multiple F1s can
also easily be screened and desirable genotypes can be selected.Seed purity or intra-variety
variation can easily be tested using markers. This can be more accurate than
phenotypic evaluation [71]. For the testing of hybrid rice lines, using STS and SSR markers was
considerably easier than using typical “grow-out tests” that involve growing
plants to maturity and evaluating purity based on morphological and floral
characteristics [70, 72]. SSRs from mitochondrial genes have been targeted for the development
of markers to study maternally inherited traits such as cytoplasmic male sterility
or the maternal origin of rice accessions [73]. It has often been determined that relatively few well-chosen
markers can provide sufficient data for varietal discrimination.


## 2.4.2. Genetic Diversity Analysis of Breeding Material

There have been numerous research papers on the assessment of genetic diversity in specific
germplasm collections using different types of markers [74, 75]. However, in recent years, SSRs have become the
marker of choice for this application (see Table 1). An example was the use of
SSR markers to broaden the genetic base of U.S.
rice varieties [76]. DNA markers have also
been used in hybrid rice breeding in order to predict genotypes that combine to
give superior hybrid vigor [77].


## 2.4.3. Gene Surveys in Parental Material

The accurate evaluation of genes in breeders' germplasm is of great importance for
the selection of parental lines and development of new breeding populations.
Having gene information for specific target loci (deduced from markers) can be
extremely useful for breeders to efficiently use germplasm. An example of this
was demonstrated by Wang et al. [80, 114], who used a set of dominant allele-specific markers for surveying markers to detect the presence of the Pi-ta resistance gene for rice blast in
a large germplasm collection (n=141).


## 2.4.4. Marker-Evaluated Selection (MES)

This novel approach was used to identify genomic regions under selection (i.e.,
allelic shifts) of breeding populations using a modified bulk-population
breeding system in target environments [99]. This approach makes no prior assumptions about
traits for selection; however, selection is imposed in target environments.
High-density or whole-genome marker coverage is an important prerequisite for
MES. Theoretically, once specific alleles or genomic regions have been
identified to be under selection, they can be combined via MAS to develop new
breeding lines that are the “ideotypes” (i.e., ideal genotypes).


## 2.4.5. Marker-Assisted Backcrossing (MABC)

MABC is the process of using markers to select for target loci, minimize the length
of the donor segment containing a target locus, and/or accelerate the recovery
of the recurrent parent genome during backcrossing [115, 116]. These three levels of selection have been referred to as foreground,
recombinant, and background selection, respectively [8]. Terms were described after Hospital and Charcosset
[116], who referred to foreground selection as the selection of a target locus
and background selection as the selection of the recurrent parent genome using
markers on noncarrier chromosomes and also on the carrier chromosome. MABC is
superior to conventional backcrossing in precision and efficiency. Background
selection can greatly accelerate a backcrossing program compared to using
conventional backcrossing [117]. Furthermore, recombinant selection can minimize the size of the donor
chromosome segment, thus reducing “linkage drag”—a “universal enemy” of the plant breeder [115]. This approach has been widely used and, due to the prevalence of several
rice “mega varieties,” it is likely to continue being a successful approach [118].For basic research applications, the MABC approach
can be used to develop near-isogenic lines (NILs) with far greater precision than
conventional backcrossing. Near-isogenic lines are valuable tools to characterize
individual genes or QTLs. However, in many situations, NILs produced, using
conventional backcrossing possess, many unknown donor introgressions on
noncarrier chromosomes (i.e., chromosomes without target genes) and large donor
chromosomal segments on the carrier chromosome. By using an MABC approach, NILs
could be developed to ensure that lines are not influenced by “background”
donor introgression and possess minimal donor segments flanking the target
locus. We propose that NILs developed using such approaches are referred to as “precision
introgression lines” (PILs). Ideally, markers with known map or physical
positions should be used for PIL development.


## 2.4.6. Pyramiding

Pyramiding is the process of combining genes or QTLs in progeny usually arising from
different parents [101, 119]. Using
conventional methods, this is extremely difficult or impossible to do in early
generations (e.g., F2 or F3) because single plants need
to be screened for multiple diseases or pathogen races. Because of the
importance of blast and bacterial blight, many pyramiding efforts have been
directed toward breeding for resistance to these two diseases (Table 1). There
is strong evidence that combining resistance genes may provide broad-spectrum resistance
[88, 120–123].Although widely used for combining
disease resistance genes or QTLs, pyramiding can be used for other abiotic
stress tolerance and agronomic traits. An example of pyramiding agronomic genes
was the combination of three thermosensitive genetic male sterility genes [106].


## 2.4.7. Using Transgenes

There has been much research in developing transgenic rice lines for basic and applied
research applications [124]. MAS is traditionally used to screen for transformants for the
transgene(s) [111].
However, with the availability of transgenics in rice for several useful traits
such as resistan ce to diseases (bacterial
blight, blast, sheath blight, yellow mottle virus), resistan ce
to insects (stem borer, leaffolders), resistan ce
to herbicide, tolerance of abiotic stress (drought, salt), nutritional traits
(iron and pro-vitamin A), and photosynthetic traits [125, 126]; there is a strong interest in using transgenes in breeding. Rice
breeders are excited to transfer them to successful mega varieties through
conventional backcrossing or MABC. For example, transgenic rice (southern U.S. japonica-type
varieties) with inherent ability to produce beta-carotene developed by
Syngenta is available at IRRI and in several other national programs. However,
these cultivars are not adapted to the tropical conditions in Asia,
where most consumers prefer indica-type rice varieties. Therefore, at IRRI, we
are introgressing the beta-carotene loci from japonica-type donor varieties
into popular indica-type Asian rice varieties, using MABC. Initially,
we used 3 GR1 events (GR1-146, GR1-309, and GR1-652) as donor parents, while 2
IRRI-bred mega varieties (IR64 and IR36) and a popular Bangladeshi variety (BR29)
were used as recurrent parents. Subsequently, we received 6 GR2 events (GR2-E,
GR2-G, GR2-L, GR2-R, GR2-T, and GR2-W). Four indica varieties, IR64, IR36, BR29,
and PSB Rc 82, were used as recurrent parents. Advanced backcross progenies are
available and some are ready for field testing [127–129].


## 3. Current Genomics Research and Promising New Genotyping Methods

Attendance at the most recent international rice genetics conference held in Manila,
Philippines (2005), indicated a mind-boggling amount of current research activities in rice
genetics and genomics. These developments have been outlined in general and
specific review articles (see, e.g., the excellent reviews [14, 121, 130–134]). In this section, we provide a brief overview of some
of these research areas, with a focus on selected current genomics research
projects that in our opinion are directed toward tangible applied molecular
breeding outcomes. We also review some potentially useful and recently developed
genotyping methods that could be used in breeding programs.


### 3.1. A Brief Overview of Recent Rice Functional Genomics Research and Annotation of the Rice Genome

Although the DNA sequences for Nipponbare and 93-11 are complete, rice genome sequence
resources are constantly being revised and updated in terms of gene annotation
[67]. There are two
levels of annotation: structural annotation which refers to gene identification
based on ESTs and full-length cDNA (FL-cDNA) sequences, and functional
annotation which refers to the determination of gene function [132, 135]. The
generation of EST libraries and FL-cDNA libraries has occurred simultaneously
with genome sequencing for both japonica and indica
subspecies [136, 137].Since the actual function of the
vast majority of genes remains unknown, functional annotation relies primarily
on bioinformatics evidence to assign gene function [138]. To systematically and efficiently annotate the rice
genome, an automated system and database called rice
genome automated annotation system (RiceGAAS) was developed. This system automatically searches for rice genome sequences from GenBank, and processes them  based on gene prediction and homology search programs for structural annotation. To facilitate the efficient management and retrieval
of data for rice genome annotation, annotation databases such as the rice
annotation project database (RAP-DB) [139] were developed.Research in plant functional
genomics provides useful data for functional annotation [140]. Reverse
genetics approaches (studying the effect of gene alterations on phenotype) such
as generating specific gene knockouts by RNA interference (RNAi), transfer-DNA
(T-DNA), and transposon-mediated (Ac, Ds, Ac/Ds, and Tos17), and chemical/irradiated mutants have been successfully used
to elucidate gene functions and determine tissue- or organ-specific gene
expression (by using reporter genes) [141–146]. There are literally hundreds of thousands of mutant lines, albeit only a
very small number of genotypes produced by basic research labs around the world
can be screened for specific genes. Data generated by reverse genetics studies are
publicly available and have been stored in curated databases such as the International
Rice Information System (IRIS) [147], OryzaGenesDB [148],
and EU-OSTID [149] for greater dissemination to the wider scientific
community.Microarrays have been widely adopted
by plant scientists to study gene function. In rice, microarrays have been used
to study processes related to yield (e.g., grain filling) and response to biotic
and abiotic stresses [150–154]. Many databases have been developed to store
gene expression data (reviewed in [135]). Most microarray
studies have used gene-specific probes to detect gene expression and, hence,
new “tiling microarrays” may study whole-genome expression, which is more
informative because it is less biased [155, 156].Although, to date, progress has been
limited in rice, proteomics research also offers great promise for determining
gene functions [157, 158]. In the
future, it is hoped that a complete integration with proteomics and
metabolomics will provide the ultimate data to elucidate not only individual
gene functions but also complex pathways [135].The generation of a deluge of
genomics data has been accompanied by several integrative bioinformatics tools
and databases. One notable example is called “Rice PIPELINE” which was
developed for the collection and compilation of genomics data, including genome
sequences, full-length cDNAs, gene expression profiles, mutant lines, andcis
elements from various databases [159]. Rice PIPELINE can
be searched by clone sequence, clone name, GenBank accession number, or
keyword. Another web-based database system, called “PlantQTL-GE,” was developed
to facilitate quantitative traits locus (QTL)-based candidate gene
identification and gene function analysis [160]. This database integrated marker data and gene
expression data generated from microarray experiments and ESTs from rice and Arabidopsis thaliana. Specific QTL
marker intervals or genomic regions can be targeted for candidate gene analysis,
which could be useful for identifying new candidate genes. Both databases are
publicly available.


### 3.2. Current Applied Genomics Research Highlights

#### 3.2.1. Association of Candidate Defense Genes with Quantitative Resistance to Rice Blast: A Case Study

The candidate gene approach has been used to integrate the molecular analysis of
host-pathogen interactions, gene mapping, and disease resistance in rice.
Candidate genes are similar to known genes or conserved motifs that make it
possible to infer their biological functions [161]. Through their association with disease resistance,
they become candidate defense response (DR) genes [122, 162, 163]. Advanced backcross lines of Vandana × Moroberekan, a japonica cultivar
from Africa exhibiting durable quantitative resistance to blast in Asia, were used to demonstrate this approach for blast
resistance. To accumulate different genes with quantitative resistance to
blast, 15 BC3F5 lines of Vandana × Moroberekan showing
partial resistance at IRRI and Cavinti, Philippines, and carrying DR candidate alleles were selected and crossed in all pairwise
combinations. Plant selections based on blast resistance and agronomic
acceptability were made in F2 and F3 populations, and the
top 60 F5 selections were evaluated in multilocation environments.To identify DR candidate genes in the progenies,
molecular analyses of rice genes involved in quantitative resistance were done in
selectedF4 lines, using STS markers derived from rice candidate
gene sequences and SSR markers located in the region of each candidate gene BAC
clone showing polymorphisms between Vandana, Moroberekan, and their progenies. A
total of 11 candidate genes were identified based on converging evidence (i.e.,
mapping, phenotyping, selection, microarray analysis) and used in this study. These
candidate genes with known biological functions were oxalate oxidase/germin-like
proteins, aspartyl protease (Esi-18), 14-3-3 proteins, PR-1, PBZ (PR10A), rice
peroxidase (POX 22.3), heat shock protein (HSP90), putative
2-dehydro-3-deoxyphosphoheptonate aldolase, thaumatin-like pathogenesis-related
protein, glyoxylase 1 (Oryza sativa),
and S-adenosyl L-homocystein hydrolase. DR candidate genes were examined using in silico analysis of their sequences
retrieved from the Rice Genome Program database. For genes occurring in gene
families such as oxalate oxidase belonging to germin-like proteins,
phylogenetic trees using the retrieved sequences were constructed to determine
their relatedness and groups. The conserved promoter motifs were also compared
and cis-elements in the 1000-bp upstream
regions were identified. For each gene, there was variation in the copy number
of cis-elements related to biotic
stress responses, such as W box, WNPR1, and WRKY. This study suggested that
these genes have potential associations with the response of rice to pathogen
infection such as the blast fungus Magnaporthe oryzae.


#### 3.2.2. Identification of SNP by Eco-TILLING at Specific Candidate Genes

TILLING or “targeting induced local lesions in genomes” is a reverse genetics technique
developed to identify variation inArabidopsis mutant libraries obtained from chemical mutagenesis with EMS [164, 165]. The approach involves
creating pools of mutant lines followed by amplification with differentially labeled,
locus-specific primers on these pools. If a pool contains a mutant variant,
then denaturation/renaturation of the PCR products will allow heteroduplex
mismatch molecules to be formed. Treatment of the products with the single-strand-specific
endonuclease CEL1 will cleave a mismatch site and generate fragments that on
separation and visualization by fluorescence will indicate the position of the
mutation in the amplicon. Eco-TILLING is the application of this technique to
discover allelic variation in natural populations. TILLING is accomplished
using pools of mutant library lines having a majority of the wild-type allele at
a given locus while Eco-TILLING contrasts a reference line, such as the source
of the sequence with a single diverse germplasm accession. The main requirement
for both TILLING and Eco-TILLING is sufficient sequence information for the
design of locus-specific primers. Hence, SNP discovery and genotyping can
proceed without the need for de novo sequencing, a requirement of other SNP
genotyping tools prior to assay design.At IRRI, we have designed
locus-specific primers for a range of candidate genes putatively involved in
drought, general stress response, and grain quality is leveraging the high-quality
sequence information for the japonica-type Nipponbare [7]. Candidate genes were
identified using convergent information taking into account genome annotation,
involvement of the ortholog in another species, expression data, and
colocalization with QTLs. Candidate genes for drought include DREB2a, ERF3,
sucrose synthase, actin depolymerizing factor, and trehalose-6-phosphate
phosphatase, among others. We have conducted Eco-TILLING at these candidate
genes using a diverse collection of 1536 O. sativa accessions from the international Genebank collection contrasted to
both japonica-type Nippponbare and indica-type IR64. Depending on the contrast,
from 4 to 9 haplotypes have been discovered in about 1 kb at the candidate gene
locus. Representative types for the haplotype mismatch patterns have been
sequenced, and association tests with phenotypic data for vegetative-stage
drought characters are under way. We have also optimized a procedure that
allows TILLING/Eco-TILLING products to be detected on agarose gels, thus eliminating
the need for fluorescent labeling and the use of an automated genotyper, with
savings in both time and costs [166]. This simplified procedure is now
our method of choice and its application to breeding will be described later.


#### 3.2.3. Genome-Wide SNP Discovery in Diverse Rice Germplasm

The availability of the high-quality sequence of Nipponbare provides the unprecedented
opportunity for genome-wide SNP discovery and improving our knowledge about
allelic diversity in rice. IRRI along with partners in the International Rice
Functional Genomics Consortium has undertaken a project to identify genome-wide
SNP in a diverse collection of 20 varieties [167] with funding from IRRI, the Generation Challenge
Program, and USDA-CSREES. The diverse varieties include representatives from
all variety groups—temperate and tropical japonica,
aromatic, aus, deep-water, and indica types—with Nipponbare included as a control. The
technology being used for SNP discovery is hybridization to very high-density
oligomer arrays pioneered by Perlegen Sciences, Inc. (Mountain View, Ca 94043, USA). On these arrays, four 25-mer oligomer features are tiled for each of the strands, where
the middle base is present as A, T, C, or G for the four features with a single base offset occurring before the next
set of features. Hence, 8 oligomer features interrogate each base of the
sequence of the target genome during hybridization. Application of Perlegen’s
technology has led to the identification of large sets of SNPs for human [168],
mouse [169], and Arabidopsis [170].Funding was available for SNP
discovery in 100 Mb of the rice genomes. Consequently, only the nonrepetitive
regions of the Nipponbare genome were selected for tiling onto high-density
oligomer arrays. However, the nonrepetitive regions span the entire genome with
the majority of 100 kb windows containing several or more tiled regions.
Following hybridization of the query genomes to arrays, about 260000
nonredundant SNPs were identified by Perlegen’s model-based algorithms. Efforts
are ongoing to extend this collection by applying the machine-learning-based
techniques developed for the analysis of the Arabidopsis project [170].The set of Perlegen model-based SNPs provides about
93% genome coverage by the criterion that at least 1 SNP occurs per 100 kb of
the genome. Since existing estimates of linkage disequilibrium (LD) in rice
indicate that LD extends to 100 kb or longer [171, 172], then the SNP dataset should be sufficient for identifying a collection
of tag SNPs that define haplotype blocks across the rice genome. This set of
tag SNPs can then be used to undertake whole-genome scans in a wider collection
of rice varieties, with the resulting genotypic data applied to association
studies with detailed phenotypes for traits of interest.


#### 3.2.4. Exploiting Wild Species

Landraces and wild species of rice (genusOryza) possess an underused
source of novel alleles that have great potential for crop improvement of
cultivated rice species (O. sativa and O. glaberrima), since they
possess new genes that could be exploited for yield increases and for developing
resistance to biotic stresses and tolerance of abiotic stresses [173, 174]. Consequently, many experiments have attempted to use
wild sources to develop new breeding material and also characterize genes and
QTLs from these sources. The advanced backcross QTL analysis (AB-QTL) approach—which is a method for integrating QTL mapping
with simultaneous line development—has been widely used to introgress wild genes and QTLs into adapted
varieties with great success for agronomic traits and yield (reviewed in [174]).Introgression lines (ILs) are derived by generating backcross lines
using MAS with relatively large, different donor chromosomal segments from wild
or exotic genotypes [119, 175]. ILs are useful for many applications in genetic
analysis (e.g., high-resolution mapping of QTL regions), since phenotypic
evaluation can be performed over multiple years and environments. In a study
analyzing ILs developed from Oryza
rufipogon in an indica background
(Teqing), many putative QTLs for yield and yield components were detected [176].Genome sequence research using wild species is well under way. TheOryza Map
Alignment Project (OMAP) was initiated to construct physical maps (derived
from BAC clones) of 11 wild and 1 cultivated species (O. glaberrima) and align them to the Nipponbare reference genome
sequence [177, 178]. Advanced backcross
populations (BC4F2) of 3 OMAP wild accessions are also
being generated for mapping important traits. Apart from providing insights
into evolution of the Oryza genus,
other expected outcomes are the identification of new genes and QTLs that could
be subsequently incorporated into adapted rice varieties.


#### 3.2.5. Association Mapping

Despite the widespread use and success of QTL mapping for identifying QTLs that control
traits, the method has inherent limitations [179, 180]. In practice, mapping populations are derived from bi-parental crosses that
represent only a small fraction of the total allelic variation, and QTL mapping experiments may require a large investment
in resources. Association mapping—based on linkage disequilibrium—may bypass these limitations of QTL mapping
because a greater number of alleles are analyzed and historic phenotypic data
for multiple traits can be readily used without the need for a specific
evaluation of populations generated solely for the purposes of QTL mapping
[181, 182]. Furthermore, association
mapping can offer improvements in resolution because analysis is based on the
accumulation of all meioses events throughout the breeding history.Linkage disequilibrium has been estimated in rice to
be approximately 100 to 250 kbp based on the characterization of two genes,xa5 (chromosome 5) and Waxy (chromosome 6) [171, 172]. A
more recent study indicated that the extent of LD was much larger: 20–30 cM [183]. The former
estimate suggests that high-density whole-genome scans are required for
efficient association mapping in rice. An alternative approach would be to focus
on regions previously delimited by QTL analysis or regions in combination with
candidate gene analysis.Several recent studies have investigated “population
structure” in rice, which is important for controlling the false discovery rate
[83, 184, 185]. Various methods of data analysis
have been evaluated. An example was the use of discriminant analysis involving
markers associated with previous QTLs [186]. Discriminant analysis results were consistent with previous
QTL results, although additional markers, not identified by QTL mapping methods,
were detected which may indicate new loci associated with specific traits.The “foundation” of previously identified QTLs for
numerous traits, the availability of candidate genes from genomics research,
and further improvements in statistical methodology [184] are likely to ensure that more rice researchers use
association mapping approaches in the future.


### 3.3. Recent and New Marker Genotyping Methods

#### 3.3.1. Optimizing and Refining Current Protocols

One very important point we would like to emphasize before reviewing new technology
is that there are great opportunities for further optimization of currently
used protocols, especially in terms of cost and throughput. Furthermore, many
innovations on standard methods are possible (see, e.g., [187]). This is important because many labs have already made a considerable
investment in lab equipment and have the technical expertise to use specific
protocols using specific markers.As discussed earlier, multiplexing
has considerable potential for increasing the efficiency of marker genotyping although
this has not been extensively explored in rice. Multiplex PCR could be
complicated since numerous variables (primer combination, annealing time and
temperature, extension time and temperature, and concentrations of primers and
magnesium chloride) are involved [188, 189]. However, in many cases, the investment in time and resources may be
justified. Coburn et al. [57] reported 80% successful PCR amplification for duplex PCR.
Multiplex loading is simpler and in our opinion could be applied on a much wider scale. At IRRI,
loading of two or even three markers (A. Das, pers. comm.) is frequently
possible, which saves time and resources. Of course, information regarding
marker allele sizes is a prerequisite for multiplex loading.


#### 3.3.2. Considering the Adoption of New Genotyping Methods

Many new promising genotyping methods could improve efficiency in terms of time and
potential cost [190]. Most of these methods are targeted
toward SNPs but most of them could be adapted for other marker types. Interestingly,
there are many high-throughput SNP genotyping platforms (that have often been
developed for medical applications), yet there has been no universally adopted
system [191, 192].In the context of plant breeding,
there are several important considerations. Cost is critical due to the large
number of samples breeders evaluate. Furthermore, 3 to 6 target traits usually segregate
in a single population so the frequency of lines with all the desirable gene
combinations is very low. This could undermine the suitability of some high-throughput
whole-genome profiling programs, although there could be numerous applications
in basic research.Obviously, some genotyping methods will
be more suitable for specific labs than others. For this reason, we have
classified these methods into two groups: regional hub labs and remote breeding
stations. A regional hub lab is defined as a research institute with a critical
mass of scientists who receive sufficient funding for long-term, broad
objective breeding research that includes genomics research (e.g., CGIAR
centers and national breeding institutes). We refer to a remote breeding station
as a “smaller” lab that has more limited capacity for marker genotyping in
terms of funding and resources.


#### 3.3.3. Remote Breeding Station Lab 1: Gel-Based Methods

PCR-based SNP methods
PCR-based SNP detection methods that use standard
agarose or acrylamide electrophoresis are obviously attractive because they are
technically simple and no further investment in equipment is required. The
simplest form of PCR-based SNP marker is based on designing PCR primers such
that a forward or reverse primer has a specific dNTP at the3′ end; PCR amplification is successful for the
appropriate primer-template combination and fails when the specific 3′ base in the primer is not complementary to the
template [43]. Reliability
has been an important issue with designing PCR-based SNP markers; hence,
several studies, exploring methods to improve reliability including the use of
additional primers, have been conducted [193–195]. Hayashi et al. [43]
introduced an artificial mismatch at the 3rd base from the 3′ end—in addition to the last 3′ base—which was found to increase specificity; a 67%
success rate was found for 49 target SNPs. This method can be used to develop
codominant allele-specific markers. Overall, these methods are useful to
complement the arsenal of CAPS markers for whichtarget SNP-containing sites are not available.Heteroduplex cleavage SNP detection methods
TILLING and Eco-TILLING methods (discussed previously) are reverse genetics methods used
to identify SNPs in target genes in mutants and germplasm collections,
respectively. However, simplified TILLING/Eco-TILLING methods, using standard
polyacrylamide or agarose gel electrophoresis detection methods, could be
applied for MAS and would be especially useful in situations, where it is difficult
to find other types of polymorphic markers [166, 196]. This method relies on the principle that CEL I cleaves heteroduplexes at
the position of SNPs.
In brief, the method involves the
following steps.(i)
PCR
amplification of the region of interest in parental lines (A and B)
(homozygous).(ii)
The
PCR products are combined in equal concentration and subjected to CEL I
digestion (TILLING/Eco-TILLING) in an agarose procedure to test for
polymorphism.(iii)
DNA
is extracted from each member of the breeding population (RIL-homozygous) and quantified.(iv)
DNA
extracted from either of the parental lines (e.g., parent A) is combined with
DNA from each of the RILs in a 1 : 1 ratio.(v)
The
mix is subjected to CEL I digestion. If an SNP is detected, this indicates that
the allele carried by the RIL is unlike that of the parent used to create the
mix (in this case, parent A). One possible limitation of this procedure is that it would be ideally done on
homozygous lines. If there is doubt, the assay should be conducted with just
the DNA from each of the RILs; no SNPs should be detected.PCR-RF-SSCP
polymerase chain reaction- (PCR-)  restriction fragment- (RF-)
single-strand conformation polymorphism (SSCP)—abbreviated to PRS—is essentially based on a combination of the
CAPS technique (i.e., restriction digestion of gene-specific PCR products) with
SSCP, which on its own can be used for SNP detection of small PCR amplicons
(100–400 bp) using
polyacrylamide gel electrophoresis (PAGE) [197–199]. This method has
been successfully used to detect SNPs in rice and other crops. One of the
advantages of this method is that much longer PCR amplicons (>2000 bp) can
be scanned for SNPs, and it may be well suited for labs with technical
expertise in polyacrylamide gel electrophoresis and/or silver staining.


#### 3.3.4. Remote Breeding Station Lab 2:  Non-Gel-Based Methods

Dot blots
Dot blots have been used for genotyping of rice breeding material [200]. The main advantages of
this methodare that gel
electrophoresis and even PCR in some cases are not required. This method used
cultivar-specific sequences that were previously identified by AFLP, STS, or
PRS. Genomic DNA from rice samples was spotted on membranes and short
oligonucelotide (28–45 bp) or digoxigenin
(DIG)-labeled PCR products (102–466 bp) were used
as probes. DIG labeling methods avoid the use of radioisotopes, which is
preferable in most labs and very important for remote breeding stations due to
delivery, storage, and disposal. Relatively high DNA yields were required for
this method (3.5–5 μg).
The dot blot genotyping method was
later extended to a robust SNP detection [201]. In this method, two nucleotide probes (17 nt) were used:
one allele-specific probe was DIG-labeled (at the 5′ end) and the other allele probe was unlabeled,
following the principles of competitive allele-specific short oligonucleotide
hybridization, which improves specificity. The probe targets were PCR products
that contained the SNP regions. This method has potential for high-throughput
capacity since 864 samples were blotted on a single membrane. Dot blot
genotyping has been used for high-throughput, large-scale MAS in commercial
companies [202].
Dot-blot assay was used in advanced Basmati-derived lines
that have reached the replicated yield trial at IRRI’s breeding program
(Reveche et al., unpublished data). This method, however, is not yet in
routine use but offers great potential for MAS in breeding program.


#### 3.3.5. Regional Hub Lab

Capillary electrophoresis platforms for SSR genotyping
To maximize the efficiency of multiplexing using capillary electrophoresis
platforms, marker “panels” can be assembled, which consist of markers with no
overlapping allele size ranges or the same fluorescent dyes [56, 203]. In general, panels of any
size and for any traits can be designed based on available primer resources and
previously determined allele sizes. Coburn et al. [57] reported assembling panels consisting of 6 to 11 SSRs that were
evenly spaced along all 12 chromosomes; most panels were designed such that
they are chromosome-specific. A greater flexibility of panel design was
demonstrated in maize, in which primers were redesigned for specific SSR loci
from sequence data [114]. This permitted a tenplex level of multiplexing (i.e., scoring of 10
individual SSR marker alleles in a single gel lane). Although these panels from
these two examples were designed for whole-genome scans, they have wider potential
in routine MAS. Furthermore, generic fluorescently labeling primer methods,
which greatly reduce costs, are other innovative methods by which the cost
efficiency of capillary electrophoresis methods can be improved [204, 205]. In our opinion, it would also be feasible to adopt capillary
electrophoresis systems in some remote breeding stations.SNuPE
Many SNP detection methods are based on the commonly used principle of single
nucleotide primer extension (also called single base extension, SBE). Briefly, this method works by using a
genotyping primer that immediately precedes an SNP at the3′ end in the template. This genotyping primer is
extended with a specific fluorescently labeled dideoxy nucleotide (ddNTP) that is
detected, which permits genotyping at a target locus. SNuPE can be performed
using capillary electrophoresis systems,
which could be very convenient if these platforms have been set up in labs for
SSR genotyping. Capillary electrophoresis platforms have a very high throughput
capacity: a pilot study in maize indicated that 1200 genotypes could be analyzed
per day [206].FRET-based genotyping
SNPs have become prominent in rice functional
genomics research because of their advantage of being prevalent in the genome. For
example, a recent study has reported an average occurrence of one SNP for every
40 kb in target regions in chromosomes 6 and 11 (S. McCouch, pers. comm.). If
these SNPs are informative and exist in alternate alleles of a gene for
resistance and susceptibility, for example, theXa21 gene for bacterial blight resistance, they would become useful
candidates for marker development. At IRRI, we have adopted a method for SNP
detection that uses the system known as fluorescence resonance energy transfer (FRET).
FRET is a radiation-less transmission of energy
from a donor molecule to an acceptor molecule when they are in close proximity
to one another (typically 10–100 Å). It has been mostly used in biomedical research and drug discovery to
detect SNPs in the human genome [207, 208] and in
homogeneous DNA diagnostics [209] as well as for other applications in protein interaction analysis [210]. In the conventional FRET
reported by Takatsu et al. [211],
the detection method requires special fluorescence-labeled probes, which are
expensive and difficult to optimize. Later in the same year, Takatsu et al. [212] developed a method based
on single base extension and applied SYBR Green I (bound to double-stranded
DNA) as an energy donor and fluorescence-labeled ddNTP as an energy acceptor. This
method avoids difficult probe design and allows a significant reduction in
detection cost.
We have adapted the method for large-scale MAS in rice and further reduced
the cost by optimization of expensive reagents (e.g., enzymes) during
purification steps of single-stranded DNA prior to SBE. We employed the method as
an SNP genotyping technique with the advantage of being high-throughput and
non-gel-based. Here, the amplified genomic DNA containing the polymorphic site is
incubated with a primer (designed to anneal immediately next to the polymorphic
site) in the presence of DNA polymerase, SYBR Green I, and ddNTP labeled with a
fluorophore (ROX or Cy5). The primer binds to the complementary site and is
extended with a single ddNTP. When SYBR Green I is excited at its excitation
wavelength of 495 nm, it will transfer the energy to the ddNTP at the
polymorphic site next to it. High fluorescence intensity will be measured at
each emission wavelength for SYBR Green I and the respective fluorophores for a
resistant and susceptible allele, so SNP can be discriminated after the SBE
reaction.


##### Microarray-Based Genotyping (MBG)

(A) SNP genotyping of alternate alleles
DNA microarray technology provides a snapshot of gene expression levels of all genes in an
organism in a single experiment. Depending on the objective of the experiment,
it allows the identification of genes that are expressed in different cell
types to learn how their expression levels change in different developmental
stages or disease states and to identify the cellular processes in which they
participate. This technology platform has also been used in genotyping studies,
such as the tagged microarray marker (TAM) approach and the high-throughput system
that makes genotyping efficient and low cost [213]. An alternative and simpler microarray technique was
described by Ji et al. [214]. MBG is
based on simple hybridization with fluorescence-labeled probes, which anneal
with specific alleles in PCR products. MBG for MAS of specific genes needs printing
of PCR products derived from breeding materials on glass. The alternate probes
of the gene (e.g., xa5 gene for
bacterial blight resistance) are labeled with fluorophores, such as Alexa-Fluor
546 (or Cy3) for the R allele and Alexa-Fluor 647 (or Cy5) for the S allele. MBG
is useful when the number of samples increases, thus decreasing the cost per data
point. In designing an experiment for marker-assisted breeding, we can save
time, space, and labor by establishing computer-aided data acquisition. MBG is
one of the most advanced techniques for automated data processing.
Although the use of some expensive equipment, including the arrayer and scanner, may make users
think twice, the cost per sample will be remarkably lower by using less
expensive supplies and reagents that are commercially available.(B) Single-feature polymorphism (SFP)
Microarray-based genotyping that used indel
polymorphisms or SFP provides the means to simultaneously screen hundreds to thousands
of markers per individual. This technology is particularly suited to
applications requiring whole-genome coverage, and the relatively low cost of
this assay allows a genotyping strategy using large populations. Along with
foreground selection for the target traits, high-resolution whole-genome
selection will provide a greater capacity for background selection to retain
the positive attributes of popular varieties in backcrossing programs.
Obtaining graphical genotypes of individuals will facilitate the pyramiding of
desirable alleles at multiple loci and will shorten the time needed for developing
new varieties.
SFP assays are done by labeling genomic DNA (target)
and hybridizing it to arrayed oligonucleotide probes that are complementary to
indel loci. The SFPs can be discovered through sequence alignments or by
hybridization of genomic DNA with whole-genome microarrays. Each SFP is scored
by the presence or absence of a hybridization signal with its corresponding
oligonucleotide probe on the array. Both spotted oligonucleotides [215] and
Affymetrix-type arrays [216] have
been used in these assays. For genotyping large populations, the cost per
individual is more critical than the cost per data point. Spotted
oligonucleotide microarrays have the potential to provide low-cost genotyping
platforms [217]. The availability of genomic sequences
from multiple accessions presents opportunities for the design of spotted long
oligonucleotide microarrays for low-cost/high-density genotyping of rice.
The SFP genotyping slide for rice has been
developed in the laboratory of D.  Galbraith, University of Arizona, Ariz, USA
[218]. Using the publicly available genomic sequences of rice
cultivars Nipponbare and 93-11 representing the japonica and indica subspecies,
respectively, they made alignment of these sequences and identified 1264 SFPs
suitable for probe design. With a median distance between markers of 128 kb, the
SFPs are evenly distributed over the whole genome. An early result using these
probes showed conservatively 30–50% polymorphism
between a pair of rice lines (the lowest between japonica types). Thus,
a single contrast produces around 400 well-spaced, polymorphic gene-based
markers for any pair of unrelated parental lines. One advantage of the DNA hybridization-based
genotyping procedure is that it can be used for quantitative genotyping of
pooled samples.
Both of these microarray-based genotyping platforms can
be combined for foreground (e.g., SNP genotyping of alternate alleles) and
background selection (e.g., SFPs) in breeding programs.MALDI TOF MS
Matrix-assisted
laser desorption/ionization time-of-flight mass spectrometry (MALDI TOF MS) has
been used for SNP genotyping in other crops such as barley and oilseed rape [219, 220]. The principle of mass spectroscopy is based on mass-to-charge
ratio rather than electrophoretic mobility. SNP genotypes can be discriminated after
SNuPE and then determining the molecular weight differences for the
incorporated ddNTPs. This system has potential for high-throughput genotyping
in regional hub labs because of the capacity to screen large numbers, speed of
genotyping (seconds compared with hours for gel-based systems), amenability to automation, and low-cost
potential.


### 4. Critical Assessment of the Impact  of Genomics Research

#### 4.1. Benefits to Breeding

To date, the outcomes from genomics research have had three main benefits to
breeders: increased knowledge regarding important traits, the generation of new
breeding lines, and a vast array of DNA marker tools. Genomics research
outcomes will provide considerably more information on the biology of traits,
especially for complex quantitative traits for which information can be very
limited [14]. Improved
knowledge regarding complex traits can be extremely useful for breeders. Currently,
there is an enormous amount of QTL and candidate gene data for these traits that
will be continually refined and validated until specific genes are
identified.In applied terms, one important tangible benefit has been the generation of new breeding lines arising from QTL
mapping experiments. These lines may include the “best” lines segregating for
the traits under study. Numerous introgression lines or chromosome segment
substitution lines (CSSL) and NILs developed for specific traits have
considerable potential for breeding programs [221, 222]. As discussed earlier, many new breeding lines with wild donor
introgression are the output from AB-QTL analysis experiments [174]. For breeding programs, ILs or AB-QTL analysis lines can be rapidly
converted into NILs via an MABC approach using only a small number of
backcrosses.From the molecular breeding
perspective, the most tangible benefit from genomics research is the wealth of
DNA markers associated with traits from previous research and the potential for
generating thousands of new markers from the two rice genome sequences [7, 130]. This has already had a
pronounced impact on plant breeding and thisimpact will undoubtedly continue in the future. In theory, the
lack of polymorphism for target markers in breeding material should no longer
be a problem as more and more “allele-specific marker kits” will be available
or be custom-made, where required for an increasing number of traits. Marker kits
will enable the precise selection of parental lines for the generation of new
breeding populations and reliable selection of segregating progeny. As more and
more genes are identified, the development of “functional markers” or “perfect”
markers will be more common [4]. Since functional markers are the site that
determines phenotype, they are thus the ultimate marker in a marker kit. Such
markers have been used for Xa21 with
great success [108–110]. Rice functional markers were recently developed for betaine
aldehyde dehydrogenase (BAD2; controlling fragrance) and xa5 was developed for bacterial blight
resistance [223, 224].


#### 4.2. Obstacles that Genomics Research Will not Solve

##### 4.2.1. Cost of Using DNA Markers

Despite the enormous potential for developing and using markers in rice, the cost of
genotyping is still a prohibitive barrier to the wider application of MAS. Even
with the global importance of rice, many developing countries have limited
research and development capability. Therefore, cost optimization of current
genotyping protocols and the development of new cost-effective protocols should
be a major priority for breeding research and especially the rice molecular
breeding lab. These improvements might involve simple optimizations of current laboratory
practices, adopting new more efficient methods, or developing new MAS
strategies and schemes.Collard and Mackill [8] stated
that preliminary cost analysis of MAS at IRRI indicated great potential for
reduction. They stated a cost of US $1.00 per marker data point achieved by a
post-doctoral research fellow or US $0.30 for a research technician, which we
have since revised to US $0.37. At first glance, this amount may not sound like
much, but when one considers that this indicates a cost of US $96 per plate,
and that literally thousands (or even tens of thousands) of breeding lines are
screened per annum in a typical rice breeding program, the importance of cost
becomes obvious.A detailed breakdown of cost components for the
marker genotyping of a single SSR marker using standard methods indicated some
interesting findings (Table2).Table 2
Cost breakdown of standard marker genotyping
and exploration of marker genotyping cost reduction opportunities.
SituationStepConsumables (US $)Labor (US $)Cost per marker (US $)Standard costDNA0.0510.4371.001PCR0.2110.076Gel0.0520.174Scenario 1—multiplex loadingDNA0.0510.4370.910PCR0.2110.076Gel0.0000.043Scenario 2—multiplex PCRDNA0.0510.4370.500PCR0.2110.076Gel0.0520.174Scenario 3—MAS pyramidingDNA0.0510.4370.676PCR 10.2110.076Gel 10.0520.174PCR 20.2110.076Gel 20.0520.174PCR 30.2110.076Gel 30.0520.174Scenario 4—DNA extraction performed by research technicianDNA0.0510.0400.604PCR0.2110.076Gel0.0520.174(i) Standard cost calculated based on the genotyping of 96
samples using a single SSR marker at IRRI from Collard and Mackill [8].(ii) Data in this section of the table are reported for the
second marker; hence, the gel cost for consumables is zero. The
calculation was performed using the data for a standard marker plus the
second marker (gel consumable cost = 0) and dividing by two.(iii) Multiplex loading by sequential loading of PCR samples,
assuming different DNA samples are run in all lanes. Labor would require
an extra 20 minutes for sequential loading, but gel preparation and
assembly are no longer required in this scenario.(iv) If direct pooling of PCR products is possible, only a
single loading is required for 96 samples (extra 5 minutes of labor). Gel
labor costs are reduced to US $0.011 and the total cost per marker is $0.893.(v) Multiplex PCR (i.e., duplex PCR) in which two markers
can be genotyped in the time and effort required for a single marker.(vi) For MAS pyramiding, the genotyping of three loci was
considered. For simplicity, it was assumed markers could not be
multiloaded, but obviously, if this was possible, it would indicate a
further cost reduction per marker screened.(vii) The DNA extraction step is the most costly from our
data analysis. There are considerable savings in expense, if genotyping
efforts of a postdoctoral researcher are coordinated with those of a
research technician at IRRI.(i)
PCR costs the most in terms of consumables.(ii)
The DNA extraction
step costs the most in terms of labor.(iii)
Overall, the DNA
extraction step is the most expensive. This analysis also provided a simple framework to investigate opportunities for some
cost reduction (Table 2). In summary, scenarios 1 and 2 highlight that the
optimization of technical procedures could decrease costs, scenario 3
highlights that the MAS scheme used will also vary costs, and scenario 4 shows
that MAS lab planning and appropriate delegation of duties can also reduce
costs.Detailed cost-benefit analyses of using markers for
specific traits could be critical information to determine the most appropriate
and advantageous situations for using markers. For example, in maize, an
extremely detailed cost-benefit analysis indicated that using markers for
selection foropaque2 (the gene
associated with quality protein maize) was more economical than conventional
screening methods [225]. In such cases, there is a clear-cut advantage of using markers in
breeding.


##### 4.2.2. QTL Application Research: Bridging  the  “Application Gap”

Many research steps are required from QTL discovery to the practical application of
markers in a breeding program [69]. The three main research areas can be described as “QTL
confirmation,” “broad-range QTL testing,” and “marker validation,” which we
collectively refer to as “QTL application research.” These research areas have been loosely defined as
QTL or marker validation activities—especially in wheat and barley. See references cited in [8]. However, in this paper, we have specifically defined the overall
research area as QTL application research and have defined three components. QTL
confirmation is desirable because factors such as small population sizes and
insufficient replication of trait data, and experimental errors can cause
inaccuracies in determining QTL positions and effects. Broad-range QTL testing
refers to verification of QTLs in different populations by using previously
reported markers in order to evaluate the effectiveness of the markers in
predicting phenotype. This is required because of the effects of genetic
background, possible epistatic interactions, and environmental effects that
could ultimately reveal that QTLs may not be relevant in a specific breeding
program. Marker validation activities are also required to evaluate the
reliability of the markers and to identify polymorphism in relevant breeding
lines. The latter two steps are also highly desirable for confirming marker-trait
linkages identified by association mapping.In practice, these research steps are often not performed and they represent an important obstacle for MAS to
have an impact on crop improvement; this was referred to as the “application
gap” by Collard and Mackill [8]. Although
there are encouraging examples of marker validation research, there are
relatively few published reports of QTL confirmation or broad-range application
research in rice. A notable exception was the confirmation of QTLs for sheath
blight resistance [226].


##### 4.2.3. “Phenotype Gap”

This term was used to refer to the increasing ratio of genomic sequence data to
known gene phenotype [227]—the term “phenotype gap” was originally coined by
mammalian researchers. As
mentioned earlier, this limits the ability to functionally annotate the
constantly growing amount of rice genome sequence data. In the next few
decades, the lack of knowledge of gene function will exist for the vast
majority of rice genes. For mutant studies, only a few selected genotypes have
been used, including only a single indica variety. Phenotypic analysis of mutant lines represents a considerable workload
[142]. Precision
phenotyping is also critical to the success of QTL or association mapping
experiments, but, unfortunately, the importance of refining and developing new
methods for precise phenotypic measurement is also often neglected in the
genomics era. Overcomingthe
phenotype gap represents the next great challenge for scientists involved in rice
genomics research.


## 3.1. A Brief Overview of Recent Rice Functional Genomics Research and Annotation of the Rice Genome

Although the DNA sequences for Nipponbare and 93-11 are complete, rice genome sequence
resources are constantly being revised and updated in terms of gene annotation
[67]. There are two
levels of annotation: structural annotation which refers to gene identification
based on ESTs and full-length cDNA (FL-cDNA) sequences, and functional
annotation which refers to the determination of gene function [132, 135]. The
generation of EST libraries and FL-cDNA libraries has occurred simultaneously
with genome sequencing for both japonica and indica
subspecies [136, 137].Since the actual function of the
vast majority of genes remains unknown, functional annotation relies primarily
on bioinformatics evidence to assign gene function [138]. To systematically and efficiently annotate the rice
genome, an automated system and database called rice
genome automated annotation system (RiceGAAS) was developed. This system automatically searches for rice genome sequences from GenBank, and processes them  based on gene prediction and homology search programs for structural annotation. To facilitate the efficient management and retrieval
of data for rice genome annotation, annotation databases such as the rice
annotation project database (RAP-DB) [139] were developed.Research in plant functional
genomics provides useful data for functional annotation [140]. Reverse
genetics approaches (studying the effect of gene alterations on phenotype) such
as generating specific gene knockouts by RNA interference (RNAi), transfer-DNA
(T-DNA), and transposon-mediated (Ac, Ds, Ac/Ds, and Tos17), and chemical/irradiated mutants have been successfully used
to elucidate gene functions and determine tissue- or organ-specific gene
expression (by using reporter genes) [141–146]. There are literally hundreds of thousands of mutant lines, albeit only a
very small number of genotypes produced by basic research labs around the world
can be screened for specific genes. Data generated by reverse genetics studies are
publicly available and have been stored in curated databases such as the International
Rice Information System (IRIS) [147], OryzaGenesDB [148],
and EU-OSTID [149] for greater dissemination to the wider scientific
community.Microarrays have been widely adopted
by plant scientists to study gene function. In rice, microarrays have been used
to study processes related to yield (e.g., grain filling) and response to biotic
and abiotic stresses [150–154]. Many databases have been developed to store
gene expression data (reviewed in [135]). Most microarray
studies have used gene-specific probes to detect gene expression and, hence,
new “tiling microarrays” may study whole-genome expression, which is more
informative because it is less biased [155, 156].Although, to date, progress has been
limited in rice, proteomics research also offers great promise for determining
gene functions [157, 158]. In the
future, it is hoped that a complete integration with proteomics and
metabolomics will provide the ultimate data to elucidate not only individual
gene functions but also complex pathways [135].The generation of a deluge of
genomics data has been accompanied by several integrative bioinformatics tools
and databases. One notable example is called “Rice PIPELINE” which was
developed for the collection and compilation of genomics data, including genome
sequences, full-length cDNAs, gene expression profiles, mutant lines, andcis
elements from various databases [159]. Rice PIPELINE can
be searched by clone sequence, clone name, GenBank accession number, or
keyword. Another web-based database system, called “PlantQTL-GE,” was developed
to facilitate quantitative traits locus (QTL)-based candidate gene
identification and gene function analysis [160]. This database integrated marker data and gene
expression data generated from microarray experiments and ESTs from rice and Arabidopsis thaliana. Specific QTL
marker intervals or genomic regions can be targeted for candidate gene analysis,
which could be useful for identifying new candidate genes. Both databases are
publicly available.


## 3.2. Current Applied Genomics Research Highlights

### 3.2.1. Association of Candidate Defense Genes with Quantitative Resistance to Rice Blast: A Case Study

The candidate gene approach has been used to integrate the molecular analysis of
host-pathogen interactions, gene mapping, and disease resistance in rice.
Candidate genes are similar to known genes or conserved motifs that make it
possible to infer their biological functions [161]. Through their association with disease resistance,
they become candidate defense response (DR) genes [122, 162, 163]. Advanced backcross lines of Vandana × Moroberekan, a japonica cultivar
from Africa exhibiting durable quantitative resistance to blast in Asia, were used to demonstrate this approach for blast
resistance. To accumulate different genes with quantitative resistance to
blast, 15 BC3F5 lines of Vandana × Moroberekan showing
partial resistance at IRRI and Cavinti, Philippines, and carrying DR candidate alleles were selected and crossed in all pairwise
combinations. Plant selections based on blast resistance and agronomic
acceptability were made in F2 and F3 populations, and the
top 60 F5 selections were evaluated in multilocation environments.To identify DR candidate genes in the progenies,
molecular analyses of rice genes involved in quantitative resistance were done in
selectedF4 lines, using STS markers derived from rice candidate
gene sequences and SSR markers located in the region of each candidate gene BAC
clone showing polymorphisms between Vandana, Moroberekan, and their progenies. A
total of 11 candidate genes were identified based on converging evidence (i.e.,
mapping, phenotyping, selection, microarray analysis) and used in this study. These
candidate genes with known biological functions were oxalate oxidase/germin-like
proteins, aspartyl protease (Esi-18), 14-3-3 proteins, PR-1, PBZ (PR10A), rice
peroxidase (POX 22.3), heat shock protein (HSP90), putative
2-dehydro-3-deoxyphosphoheptonate aldolase, thaumatin-like pathogenesis-related
protein, glyoxylase 1 (Oryza sativa),
and S-adenosyl L-homocystein hydrolase. DR candidate genes were examined using in silico analysis of their sequences
retrieved from the Rice Genome Program database. For genes occurring in gene
families such as oxalate oxidase belonging to germin-like proteins,
phylogenetic trees using the retrieved sequences were constructed to determine
their relatedness and groups. The conserved promoter motifs were also compared
and cis-elements in the 1000-bp upstream
regions were identified. For each gene, there was variation in the copy number
of cis-elements related to biotic
stress responses, such as W box, WNPR1, and WRKY. This study suggested that
these genes have potential associations with the response of rice to pathogen
infection such as the blast fungus Magnaporthe oryzae.


### 3.2.2. Identification of SNP by Eco-TILLING at Specific Candidate Genes

TILLING or “targeting induced local lesions in genomes” is a reverse genetics technique
developed to identify variation inArabidopsis mutant libraries obtained from chemical mutagenesis with EMS [164, 165]. The approach involves
creating pools of mutant lines followed by amplification with differentially labeled,
locus-specific primers on these pools. If a pool contains a mutant variant,
then denaturation/renaturation of the PCR products will allow heteroduplex
mismatch molecules to be formed. Treatment of the products with the single-strand-specific
endonuclease CEL1 will cleave a mismatch site and generate fragments that on
separation and visualization by fluorescence will indicate the position of the
mutation in the amplicon. Eco-TILLING is the application of this technique to
discover allelic variation in natural populations. TILLING is accomplished
using pools of mutant library lines having a majority of the wild-type allele at
a given locus while Eco-TILLING contrasts a reference line, such as the source
of the sequence with a single diverse germplasm accession. The main requirement
for both TILLING and Eco-TILLING is sufficient sequence information for the
design of locus-specific primers. Hence, SNP discovery and genotyping can
proceed without the need for de novo sequencing, a requirement of other SNP
genotyping tools prior to assay design.At IRRI, we have designed
locus-specific primers for a range of candidate genes putatively involved in
drought, general stress response, and grain quality is leveraging the high-quality
sequence information for the japonica-type Nipponbare [7]. Candidate genes were
identified using convergent information taking into account genome annotation,
involvement of the ortholog in another species, expression data, and
colocalization with QTLs. Candidate genes for drought include DREB2a, ERF3,
sucrose synthase, actin depolymerizing factor, and trehalose-6-phosphate
phosphatase, among others. We have conducted Eco-TILLING at these candidate
genes using a diverse collection of 1536 O. sativa accessions from the international Genebank collection contrasted to
both japonica-type Nippponbare and indica-type IR64. Depending on the contrast,
from 4 to 9 haplotypes have been discovered in about 1 kb at the candidate gene
locus. Representative types for the haplotype mismatch patterns have been
sequenced, and association tests with phenotypic data for vegetative-stage
drought characters are under way. We have also optimized a procedure that
allows TILLING/Eco-TILLING products to be detected on agarose gels, thus eliminating
the need for fluorescent labeling and the use of an automated genotyper, with
savings in both time and costs [166]. This simplified procedure is now
our method of choice and its application to breeding will be described later.


### 3.2.3. Genome-Wide SNP Discovery in Diverse Rice Germplasm

The availability of the high-quality sequence of Nipponbare provides the unprecedented
opportunity for genome-wide SNP discovery and improving our knowledge about
allelic diversity in rice. IRRI along with partners in the International Rice
Functional Genomics Consortium has undertaken a project to identify genome-wide
SNP in a diverse collection of 20 varieties [167] with funding from IRRI, the Generation Challenge
Program, and USDA-CSREES. The diverse varieties include representatives from
all variety groups—temperate and tropical japonica,
aromatic, aus, deep-water, and indica types—with Nipponbare included as a control. The
technology being used for SNP discovery is hybridization to very high-density
oligomer arrays pioneered by Perlegen Sciences, Inc. (Mountain View, Ca 94043, USA). On these arrays, four 25-mer oligomer features are tiled for each of the strands, where
the middle base is present as A, T, C, or G for the four features with a single base offset occurring before the next
set of features. Hence, 8 oligomer features interrogate each base of the
sequence of the target genome during hybridization. Application of Perlegen’s
technology has led to the identification of large sets of SNPs for human [168],
mouse [169], and Arabidopsis [170].Funding was available for SNP
discovery in 100 Mb of the rice genomes. Consequently, only the nonrepetitive
regions of the Nipponbare genome were selected for tiling onto high-density
oligomer arrays. However, the nonrepetitive regions span the entire genome with
the majority of 100 kb windows containing several or more tiled regions.
Following hybridization of the query genomes to arrays, about 260000
nonredundant SNPs were identified by Perlegen’s model-based algorithms. Efforts
are ongoing to extend this collection by applying the machine-learning-based
techniques developed for the analysis of the Arabidopsis project [170].The set of Perlegen model-based SNPs provides about
93% genome coverage by the criterion that at least 1 SNP occurs per 100 kb of
the genome. Since existing estimates of linkage disequilibrium (LD) in rice
indicate that LD extends to 100 kb or longer [171, 172], then the SNP dataset should be sufficient for identifying a collection
of tag SNPs that define haplotype blocks across the rice genome. This set of
tag SNPs can then be used to undertake whole-genome scans in a wider collection
of rice varieties, with the resulting genotypic data applied to association
studies with detailed phenotypes for traits of interest.


### 3.2.4. Exploiting Wild Species

Landraces and wild species of rice (genusOryza) possess an underused
source of novel alleles that have great potential for crop improvement of
cultivated rice species (O. sativa and O. glaberrima), since they
possess new genes that could be exploited for yield increases and for developing
resistance to biotic stresses and tolerance of abiotic stresses [173, 174]. Consequently, many experiments have attempted to use
wild sources to develop new breeding material and also characterize genes and
QTLs from these sources. The advanced backcross QTL analysis (AB-QTL) approach—which is a method for integrating QTL mapping
with simultaneous line development—has been widely used to introgress wild genes and QTLs into adapted
varieties with great success for agronomic traits and yield (reviewed in [174]).Introgression lines (ILs) are derived by generating backcross lines
using MAS with relatively large, different donor chromosomal segments from wild
or exotic genotypes [119, 175]. ILs are useful for many applications in genetic
analysis (e.g., high-resolution mapping of QTL regions), since phenotypic
evaluation can be performed over multiple years and environments. In a study
analyzing ILs developed from Oryza
rufipogon in an indica background
(Teqing), many putative QTLs for yield and yield components were detected [176].Genome sequence research using wild species is well under way. TheOryza Map
Alignment Project (OMAP) was initiated to construct physical maps (derived
from BAC clones) of 11 wild and 1 cultivated species (O. glaberrima) and align them to the Nipponbare reference genome
sequence [177, 178]. Advanced backcross
populations (BC4F2) of 3 OMAP wild accessions are also
being generated for mapping important traits. Apart from providing insights
into evolution of the Oryza genus,
other expected outcomes are the identification of new genes and QTLs that could
be subsequently incorporated into adapted rice varieties.


### 3.2.5. Association Mapping

Despite the widespread use and success of QTL mapping for identifying QTLs that control
traits, the method has inherent limitations [179, 180]. In practice, mapping populations are derived from bi-parental crosses that
represent only a small fraction of the total allelic variation, and QTL mapping experiments may require a large investment
in resources. Association mapping—based on linkage disequilibrium—may bypass these limitations of QTL mapping
because a greater number of alleles are analyzed and historic phenotypic data
for multiple traits can be readily used without the need for a specific
evaluation of populations generated solely for the purposes of QTL mapping
[181, 182]. Furthermore, association
mapping can offer improvements in resolution because analysis is based on the
accumulation of all meioses events throughout the breeding history.Linkage disequilibrium has been estimated in rice to
be approximately 100 to 250 kbp based on the characterization of two genes,xa5 (chromosome 5) and Waxy (chromosome 6) [171, 172]. A
more recent study indicated that the extent of LD was much larger: 20–30 cM [183]. The former
estimate suggests that high-density whole-genome scans are required for
efficient association mapping in rice. An alternative approach would be to focus
on regions previously delimited by QTL analysis or regions in combination with
candidate gene analysis.Several recent studies have investigated “population
structure” in rice, which is important for controlling the false discovery rate
[83, 184, 185]. Various methods of data analysis
have been evaluated. An example was the use of discriminant analysis involving
markers associated with previous QTLs [186]. Discriminant analysis results were consistent with previous
QTL results, although additional markers, not identified by QTL mapping methods,
were detected which may indicate new loci associated with specific traits.The “foundation” of previously identified QTLs for
numerous traits, the availability of candidate genes from genomics research,
and further improvements in statistical methodology [184] are likely to ensure that more rice researchers use
association mapping approaches in the future.


## 3.2.1. Association of Candidate Defense Genes with Quantitative Resistance to Rice Blast: A Case Study

The candidate gene approach has been used to integrate the molecular analysis of
host-pathogen interactions, gene mapping, and disease resistance in rice.
Candidate genes are similar to known genes or conserved motifs that make it
possible to infer their biological functions [161]. Through their association with disease resistance,
they become candidate defense response (DR) genes [122, 162, 163]. Advanced backcross lines of Vandana × Moroberekan, a japonica cultivar
from Africa exhibiting durable quantitative resistance to blast in Asia, were used to demonstrate this approach for blast
resistance. To accumulate different genes with quantitative resistance to
blast, 15 BC3F5 lines of Vandana × Moroberekan showing
partial resistance at IRRI and Cavinti, Philippines, and carrying DR candidate alleles were selected and crossed in all pairwise
combinations. Plant selections based on blast resistance and agronomic
acceptability were made in F2 and F3 populations, and the
top 60 F5 selections were evaluated in multilocation environments.To identify DR candidate genes in the progenies,
molecular analyses of rice genes involved in quantitative resistance were done in
selectedF4 lines, using STS markers derived from rice candidate
gene sequences and SSR markers located in the region of each candidate gene BAC
clone showing polymorphisms between Vandana, Moroberekan, and their progenies. A
total of 11 candidate genes were identified based on converging evidence (i.e.,
mapping, phenotyping, selection, microarray analysis) and used in this study. These
candidate genes with known biological functions were oxalate oxidase/germin-like
proteins, aspartyl protease (Esi-18), 14-3-3 proteins, PR-1, PBZ (PR10A), rice
peroxidase (POX 22.3), heat shock protein (HSP90), putative
2-dehydro-3-deoxyphosphoheptonate aldolase, thaumatin-like pathogenesis-related
protein, glyoxylase 1 (Oryza sativa),
and S-adenosyl L-homocystein hydrolase. DR candidate genes were examined using in silico analysis of their sequences
retrieved from the Rice Genome Program database. For genes occurring in gene
families such as oxalate oxidase belonging to germin-like proteins,
phylogenetic trees using the retrieved sequences were constructed to determine
their relatedness and groups. The conserved promoter motifs were also compared
and cis-elements in the 1000-bp upstream
regions were identified. For each gene, there was variation in the copy number
of cis-elements related to biotic
stress responses, such as W box, WNPR1, and WRKY. This study suggested that
these genes have potential associations with the response of rice to pathogen
infection such as the blast fungus Magnaporthe oryzae.


## 3.2.2. Identification of SNP by Eco-TILLING at Specific Candidate Genes

TILLING or “targeting induced local lesions in genomes” is a reverse genetics technique
developed to identify variation inArabidopsis mutant libraries obtained from chemical mutagenesis with EMS [164, 165]. The approach involves
creating pools of mutant lines followed by amplification with differentially labeled,
locus-specific primers on these pools. If a pool contains a mutant variant,
then denaturation/renaturation of the PCR products will allow heteroduplex
mismatch molecules to be formed. Treatment of the products with the single-strand-specific
endonuclease CEL1 will cleave a mismatch site and generate fragments that on
separation and visualization by fluorescence will indicate the position of the
mutation in the amplicon. Eco-TILLING is the application of this technique to
discover allelic variation in natural populations. TILLING is accomplished
using pools of mutant library lines having a majority of the wild-type allele at
a given locus while Eco-TILLING contrasts a reference line, such as the source
of the sequence with a single diverse germplasm accession. The main requirement
for both TILLING and Eco-TILLING is sufficient sequence information for the
design of locus-specific primers. Hence, SNP discovery and genotyping can
proceed without the need for de novo sequencing, a requirement of other SNP
genotyping tools prior to assay design.At IRRI, we have designed
locus-specific primers for a range of candidate genes putatively involved in
drought, general stress response, and grain quality is leveraging the high-quality
sequence information for the japonica-type Nipponbare [7]. Candidate genes were
identified using convergent information taking into account genome annotation,
involvement of the ortholog in another species, expression data, and
colocalization with QTLs. Candidate genes for drought include DREB2a, ERF3,
sucrose synthase, actin depolymerizing factor, and trehalose-6-phosphate
phosphatase, among others. We have conducted Eco-TILLING at these candidate
genes using a diverse collection of 1536 O. sativa accessions from the international Genebank collection contrasted to
both japonica-type Nippponbare and indica-type IR64. Depending on the contrast,
from 4 to 9 haplotypes have been discovered in about 1 kb at the candidate gene
locus. Representative types for the haplotype mismatch patterns have been
sequenced, and association tests with phenotypic data for vegetative-stage
drought characters are under way. We have also optimized a procedure that
allows TILLING/Eco-TILLING products to be detected on agarose gels, thus eliminating
the need for fluorescent labeling and the use of an automated genotyper, with
savings in both time and costs [166]. This simplified procedure is now
our method of choice and its application to breeding will be described later.


## 3.2.3. Genome-Wide SNP Discovery in Diverse Rice Germplasm

The availability of the high-quality sequence of Nipponbare provides the unprecedented
opportunity for genome-wide SNP discovery and improving our knowledge about
allelic diversity in rice. IRRI along with partners in the International Rice
Functional Genomics Consortium has undertaken a project to identify genome-wide
SNP in a diverse collection of 20 varieties [167] with funding from IRRI, the Generation Challenge
Program, and USDA-CSREES. The diverse varieties include representatives from
all variety groups—temperate and tropical japonica,
aromatic, aus, deep-water, and indica types—with Nipponbare included as a control. The
technology being used for SNP discovery is hybridization to very high-density
oligomer arrays pioneered by Perlegen Sciences, Inc. (Mountain View, Ca 94043, USA). On these arrays, four 25-mer oligomer features are tiled for each of the strands, where
the middle base is present as A, T, C, or G for the four features with a single base offset occurring before the next
set of features. Hence, 8 oligomer features interrogate each base of the
sequence of the target genome during hybridization. Application of Perlegen’s
technology has led to the identification of large sets of SNPs for human [168],
mouse [169], and Arabidopsis [170].Funding was available for SNP
discovery in 100 Mb of the rice genomes. Consequently, only the nonrepetitive
regions of the Nipponbare genome were selected for tiling onto high-density
oligomer arrays. However, the nonrepetitive regions span the entire genome with
the majority of 100 kb windows containing several or more tiled regions.
Following hybridization of the query genomes to arrays, about 260000
nonredundant SNPs were identified by Perlegen’s model-based algorithms. Efforts
are ongoing to extend this collection by applying the machine-learning-based
techniques developed for the analysis of the Arabidopsis project [170].The set of Perlegen model-based SNPs provides about
93% genome coverage by the criterion that at least 1 SNP occurs per 100 kb of
the genome. Since existing estimates of linkage disequilibrium (LD) in rice
indicate that LD extends to 100 kb or longer [171, 172], then the SNP dataset should be sufficient for identifying a collection
of tag SNPs that define haplotype blocks across the rice genome. This set of
tag SNPs can then be used to undertake whole-genome scans in a wider collection
of rice varieties, with the resulting genotypic data applied to association
studies with detailed phenotypes for traits of interest.


## 3.2.4. Exploiting Wild Species

Landraces and wild species of rice (genusOryza) possess an underused
source of novel alleles that have great potential for crop improvement of
cultivated rice species (O. sativa and O. glaberrima), since they
possess new genes that could be exploited for yield increases and for developing
resistance to biotic stresses and tolerance of abiotic stresses [173, 174]. Consequently, many experiments have attempted to use
wild sources to develop new breeding material and also characterize genes and
QTLs from these sources. The advanced backcross QTL analysis (AB-QTL) approach—which is a method for integrating QTL mapping
with simultaneous line development—has been widely used to introgress wild genes and QTLs into adapted
varieties with great success for agronomic traits and yield (reviewed in [174]).Introgression lines (ILs) are derived by generating backcross lines
using MAS with relatively large, different donor chromosomal segments from wild
or exotic genotypes [119, 175]. ILs are useful for many applications in genetic
analysis (e.g., high-resolution mapping of QTL regions), since phenotypic
evaluation can be performed over multiple years and environments. In a study
analyzing ILs developed from Oryza
rufipogon in an indica background
(Teqing), many putative QTLs for yield and yield components were detected [176].Genome sequence research using wild species is well under way. TheOryza Map
Alignment Project (OMAP) was initiated to construct physical maps (derived
from BAC clones) of 11 wild and 1 cultivated species (O. glaberrima) and align them to the Nipponbare reference genome
sequence [177, 178]. Advanced backcross
populations (BC4F2) of 3 OMAP wild accessions are also
being generated for mapping important traits. Apart from providing insights
into evolution of the Oryza genus,
other expected outcomes are the identification of new genes and QTLs that could
be subsequently incorporated into adapted rice varieties.


## 3.2.5. Association Mapping

Despite the widespread use and success of QTL mapping for identifying QTLs that control
traits, the method has inherent limitations [179, 180]. In practice, mapping populations are derived from bi-parental crosses that
represent only a small fraction of the total allelic variation, and QTL mapping experiments may require a large investment
in resources. Association mapping—based on linkage disequilibrium—may bypass these limitations of QTL mapping
because a greater number of alleles are analyzed and historic phenotypic data
for multiple traits can be readily used without the need for a specific
evaluation of populations generated solely for the purposes of QTL mapping
[181, 182]. Furthermore, association
mapping can offer improvements in resolution because analysis is based on the
accumulation of all meioses events throughout the breeding history.Linkage disequilibrium has been estimated in rice to
be approximately 100 to 250 kbp based on the characterization of two genes,xa5 (chromosome 5) and Waxy (chromosome 6) [171, 172]. A
more recent study indicated that the extent of LD was much larger: 20–30 cM [183]. The former
estimate suggests that high-density whole-genome scans are required for
efficient association mapping in rice. An alternative approach would be to focus
on regions previously delimited by QTL analysis or regions in combination with
candidate gene analysis.Several recent studies have investigated “population
structure” in rice, which is important for controlling the false discovery rate
[83, 184, 185]. Various methods of data analysis
have been evaluated. An example was the use of discriminant analysis involving
markers associated with previous QTLs [186]. Discriminant analysis results were consistent with previous
QTL results, although additional markers, not identified by QTL mapping methods,
were detected which may indicate new loci associated with specific traits.The “foundation” of previously identified QTLs for
numerous traits, the availability of candidate genes from genomics research,
and further improvements in statistical methodology [184] are likely to ensure that more rice researchers use
association mapping approaches in the future.


## 3.3. Recent and New Marker Genotyping Methods

### 3.3.1. Optimizing and Refining Current Protocols

One very important point we would like to emphasize before reviewing new technology
is that there are great opportunities for further optimization of currently
used protocols, especially in terms of cost and throughput. Furthermore, many
innovations on standard methods are possible (see, e.g., [187]). This is important because many labs have already made a considerable
investment in lab equipment and have the technical expertise to use specific
protocols using specific markers.As discussed earlier, multiplexing
has considerable potential for increasing the efficiency of marker genotyping although
this has not been extensively explored in rice. Multiplex PCR could be
complicated since numerous variables (primer combination, annealing time and
temperature, extension time and temperature, and concentrations of primers and
magnesium chloride) are involved [188, 189]. However, in many cases, the investment in time and resources may be
justified. Coburn et al. [57] reported 80% successful PCR amplification for duplex PCR.
Multiplex loading is simpler and in our opinion could be applied on a much wider scale. At IRRI,
loading of two or even three markers (A. Das, pers. comm.) is frequently
possible, which saves time and resources. Of course, information regarding
marker allele sizes is a prerequisite for multiplex loading.


### 3.3.2. Considering the Adoption of New Genotyping Methods

Many new promising genotyping methods could improve efficiency in terms of time and
potential cost [190]. Most of these methods are targeted
toward SNPs but most of them could be adapted for other marker types. Interestingly,
there are many high-throughput SNP genotyping platforms (that have often been
developed for medical applications), yet there has been no universally adopted
system [191, 192].In the context of plant breeding,
there are several important considerations. Cost is critical due to the large
number of samples breeders evaluate. Furthermore, 3 to 6 target traits usually segregate
in a single population so the frequency of lines with all the desirable gene
combinations is very low. This could undermine the suitability of some high-throughput
whole-genome profiling programs, although there could be numerous applications
in basic research.Obviously, some genotyping methods will
be more suitable for specific labs than others. For this reason, we have
classified these methods into two groups: regional hub labs and remote breeding
stations. A regional hub lab is defined as a research institute with a critical
mass of scientists who receive sufficient funding for long-term, broad
objective breeding research that includes genomics research (e.g., CGIAR
centers and national breeding institutes). We refer to a remote breeding station
as a “smaller” lab that has more limited capacity for marker genotyping in
terms of funding and resources.


### 3.3.3. Remote Breeding Station Lab 1: Gel-Based Methods

PCR-based SNP methods
PCR-based SNP detection methods that use standard
agarose or acrylamide electrophoresis are obviously attractive because they are
technically simple and no further investment in equipment is required. The
simplest form of PCR-based SNP marker is based on designing PCR primers such
that a forward or reverse primer has a specific dNTP at the3′ end; PCR amplification is successful for the
appropriate primer-template combination and fails when the specific 3′ base in the primer is not complementary to the
template [43]. Reliability
has been an important issue with designing PCR-based SNP markers; hence,
several studies, exploring methods to improve reliability including the use of
additional primers, have been conducted [193–195]. Hayashi et al. [43]
introduced an artificial mismatch at the 3rd base from the 3′ end—in addition to the last 3′ base—which was found to increase specificity; a 67%
success rate was found for 49 target SNPs. This method can be used to develop
codominant allele-specific markers. Overall, these methods are useful to
complement the arsenal of CAPS markers for whichtarget SNP-containing sites are not available.Heteroduplex cleavage SNP detection methods
TILLING and Eco-TILLING methods (discussed previously) are reverse genetics methods used
to identify SNPs in target genes in mutants and germplasm collections,
respectively. However, simplified TILLING/Eco-TILLING methods, using standard
polyacrylamide or agarose gel electrophoresis detection methods, could be
applied for MAS and would be especially useful in situations, where it is difficult
to find other types of polymorphic markers [166, 196]. This method relies on the principle that CEL I cleaves heteroduplexes at
the position of SNPs.
In brief, the method involves the
following steps.(i)
PCR
amplification of the region of interest in parental lines (A and B)
(homozygous).(ii)
The
PCR products are combined in equal concentration and subjected to CEL I
digestion (TILLING/Eco-TILLING) in an agarose procedure to test for
polymorphism.(iii)
DNA
is extracted from each member of the breeding population (RIL-homozygous) and quantified.(iv)
DNA
extracted from either of the parental lines (e.g., parent A) is combined with
DNA from each of the RILs in a 1 : 1 ratio.(v)
The
mix is subjected to CEL I digestion. If an SNP is detected, this indicates that
the allele carried by the RIL is unlike that of the parent used to create the
mix (in this case, parent A). One possible limitation of this procedure is that it would be ideally done on
homozygous lines. If there is doubt, the assay should be conducted with just
the DNA from each of the RILs; no SNPs should be detected.PCR-RF-SSCP
polymerase chain reaction- (PCR-)  restriction fragment- (RF-)
single-strand conformation polymorphism (SSCP)—abbreviated to PRS—is essentially based on a combination of the
CAPS technique (i.e., restriction digestion of gene-specific PCR products) with
SSCP, which on its own can be used for SNP detection of small PCR amplicons
(100–400 bp) using
polyacrylamide gel electrophoresis (PAGE) [197–199]. This method has
been successfully used to detect SNPs in rice and other crops. One of the
advantages of this method is that much longer PCR amplicons (>2000 bp) can
be scanned for SNPs, and it may be well suited for labs with technical
expertise in polyacrylamide gel electrophoresis and/or silver staining.


### 3.3.4. Remote Breeding Station Lab 2:  Non-Gel-Based Methods

Dot blots
Dot blots have been used for genotyping of rice breeding material [200]. The main advantages of
this methodare that gel
electrophoresis and even PCR in some cases are not required. This method used
cultivar-specific sequences that were previously identified by AFLP, STS, or
PRS. Genomic DNA from rice samples was spotted on membranes and short
oligonucelotide (28–45 bp) or digoxigenin
(DIG)-labeled PCR products (102–466 bp) were used
as probes. DIG labeling methods avoid the use of radioisotopes, which is
preferable in most labs and very important for remote breeding stations due to
delivery, storage, and disposal. Relatively high DNA yields were required for
this method (3.5–5 μg).
The dot blot genotyping method was
later extended to a robust SNP detection [201]. In this method, two nucleotide probes (17 nt) were used:
one allele-specific probe was DIG-labeled (at the 5′ end) and the other allele probe was unlabeled,
following the principles of competitive allele-specific short oligonucleotide
hybridization, which improves specificity. The probe targets were PCR products
that contained the SNP regions. This method has potential for high-throughput
capacity since 864 samples were blotted on a single membrane. Dot blot
genotyping has been used for high-throughput, large-scale MAS in commercial
companies [202].
Dot-blot assay was used in advanced Basmati-derived lines
that have reached the replicated yield trial at IRRI’s breeding program
(Reveche et al., unpublished data). This method, however, is not yet in
routine use but offers great potential for MAS in breeding program.


### 3.3.5. Regional Hub Lab

Capillary electrophoresis platforms for SSR genotyping
To maximize the efficiency of multiplexing using capillary electrophoresis
platforms, marker “panels” can be assembled, which consist of markers with no
overlapping allele size ranges or the same fluorescent dyes [56, 203]. In general, panels of any
size and for any traits can be designed based on available primer resources and
previously determined allele sizes. Coburn et al. [57] reported assembling panels consisting of 6 to 11 SSRs that were
evenly spaced along all 12 chromosomes; most panels were designed such that
they are chromosome-specific. A greater flexibility of panel design was
demonstrated in maize, in which primers were redesigned for specific SSR loci
from sequence data [114]. This permitted a tenplex level of multiplexing (i.e., scoring of 10
individual SSR marker alleles in a single gel lane). Although these panels from
these two examples were designed for whole-genome scans, they have wider potential
in routine MAS. Furthermore, generic fluorescently labeling primer methods,
which greatly reduce costs, are other innovative methods by which the cost
efficiency of capillary electrophoresis methods can be improved [204, 205]. In our opinion, it would also be feasible to adopt capillary
electrophoresis systems in some remote breeding stations.SNuPE
Many SNP detection methods are based on the commonly used principle of single
nucleotide primer extension (also called single base extension, SBE). Briefly, this method works by using a
genotyping primer that immediately precedes an SNP at the3′ end in the template. This genotyping primer is
extended with a specific fluorescently labeled dideoxy nucleotide (ddNTP) that is
detected, which permits genotyping at a target locus. SNuPE can be performed
using capillary electrophoresis systems,
which could be very convenient if these platforms have been set up in labs for
SSR genotyping. Capillary electrophoresis platforms have a very high throughput
capacity: a pilot study in maize indicated that 1200 genotypes could be analyzed
per day [206].FRET-based genotyping
SNPs have become prominent in rice functional
genomics research because of their advantage of being prevalent in the genome. For
example, a recent study has reported an average occurrence of one SNP for every
40 kb in target regions in chromosomes 6 and 11 (S. McCouch, pers. comm.). If
these SNPs are informative and exist in alternate alleles of a gene for
resistance and susceptibility, for example, theXa21 gene for bacterial blight resistance, they would become useful
candidates for marker development. At IRRI, we have adopted a method for SNP
detection that uses the system known as fluorescence resonance energy transfer (FRET).
FRET is a radiation-less transmission of energy
from a donor molecule to an acceptor molecule when they are in close proximity
to one another (typically 10–100 Å). It has been mostly used in biomedical research and drug discovery to
detect SNPs in the human genome [207, 208] and in
homogeneous DNA diagnostics [209] as well as for other applications in protein interaction analysis [210]. In the conventional FRET
reported by Takatsu et al. [211],
the detection method requires special fluorescence-labeled probes, which are
expensive and difficult to optimize. Later in the same year, Takatsu et al. [212] developed a method based
on single base extension and applied SYBR Green I (bound to double-stranded
DNA) as an energy donor and fluorescence-labeled ddNTP as an energy acceptor. This
method avoids difficult probe design and allows a significant reduction in
detection cost.
We have adapted the method for large-scale MAS in rice and further reduced
the cost by optimization of expensive reagents (e.g., enzymes) during
purification steps of single-stranded DNA prior to SBE. We employed the method as
an SNP genotyping technique with the advantage of being high-throughput and
non-gel-based. Here, the amplified genomic DNA containing the polymorphic site is
incubated with a primer (designed to anneal immediately next to the polymorphic
site) in the presence of DNA polymerase, SYBR Green I, and ddNTP labeled with a
fluorophore (ROX or Cy5). The primer binds to the complementary site and is
extended with a single ddNTP. When SYBR Green I is excited at its excitation
wavelength of 495 nm, it will transfer the energy to the ddNTP at the
polymorphic site next to it. High fluorescence intensity will be measured at
each emission wavelength for SYBR Green I and the respective fluorophores for a
resistant and susceptible allele, so SNP can be discriminated after the SBE
reaction.


#### Microarray-Based Genotyping (MBG)

(A) SNP genotyping of alternate alleles
DNA microarray technology provides a snapshot of gene expression levels of all genes in an
organism in a single experiment. Depending on the objective of the experiment,
it allows the identification of genes that are expressed in different cell
types to learn how their expression levels change in different developmental
stages or disease states and to identify the cellular processes in which they
participate. This technology platform has also been used in genotyping studies,
such as the tagged microarray marker (TAM) approach and the high-throughput system
that makes genotyping efficient and low cost [213]. An alternative and simpler microarray technique was
described by Ji et al. [214]. MBG is
based on simple hybridization with fluorescence-labeled probes, which anneal
with specific alleles in PCR products. MBG for MAS of specific genes needs printing
of PCR products derived from breeding materials on glass. The alternate probes
of the gene (e.g., xa5 gene for
bacterial blight resistance) are labeled with fluorophores, such as Alexa-Fluor
546 (or Cy3) for the R allele and Alexa-Fluor 647 (or Cy5) for the S allele. MBG
is useful when the number of samples increases, thus decreasing the cost per data
point. In designing an experiment for marker-assisted breeding, we can save
time, space, and labor by establishing computer-aided data acquisition. MBG is
one of the most advanced techniques for automated data processing.
Although the use of some expensive equipment, including the arrayer and scanner, may make users
think twice, the cost per sample will be remarkably lower by using less
expensive supplies and reagents that are commercially available.(B) Single-feature polymorphism (SFP)
Microarray-based genotyping that used indel
polymorphisms or SFP provides the means to simultaneously screen hundreds to thousands
of markers per individual. This technology is particularly suited to
applications requiring whole-genome coverage, and the relatively low cost of
this assay allows a genotyping strategy using large populations. Along with
foreground selection for the target traits, high-resolution whole-genome
selection will provide a greater capacity for background selection to retain
the positive attributes of popular varieties in backcrossing programs.
Obtaining graphical genotypes of individuals will facilitate the pyramiding of
desirable alleles at multiple loci and will shorten the time needed for developing
new varieties.
SFP assays are done by labeling genomic DNA (target)
and hybridizing it to arrayed oligonucleotide probes that are complementary to
indel loci. The SFPs can be discovered through sequence alignments or by
hybridization of genomic DNA with whole-genome microarrays. Each SFP is scored
by the presence or absence of a hybridization signal with its corresponding
oligonucleotide probe on the array. Both spotted oligonucleotides [215] and
Affymetrix-type arrays [216] have
been used in these assays. For genotyping large populations, the cost per
individual is more critical than the cost per data point. Spotted
oligonucleotide microarrays have the potential to provide low-cost genotyping
platforms [217]. The availability of genomic sequences
from multiple accessions presents opportunities for the design of spotted long
oligonucleotide microarrays for low-cost/high-density genotyping of rice.
The SFP genotyping slide for rice has been
developed in the laboratory of D.  Galbraith, University of Arizona, Ariz, USA
[218]. Using the publicly available genomic sequences of rice
cultivars Nipponbare and 93-11 representing the japonica and indica subspecies,
respectively, they made alignment of these sequences and identified 1264 SFPs
suitable for probe design. With a median distance between markers of 128 kb, the
SFPs are evenly distributed over the whole genome. An early result using these
probes showed conservatively 30–50% polymorphism
between a pair of rice lines (the lowest between japonica types). Thus,
a single contrast produces around 400 well-spaced, polymorphic gene-based
markers for any pair of unrelated parental lines. One advantage of the DNA hybridization-based
genotyping procedure is that it can be used for quantitative genotyping of
pooled samples.
Both of these microarray-based genotyping platforms can
be combined for foreground (e.g., SNP genotyping of alternate alleles) and
background selection (e.g., SFPs) in breeding programs.MALDI TOF MS
Matrix-assisted
laser desorption/ionization time-of-flight mass spectrometry (MALDI TOF MS) has
been used for SNP genotyping in other crops such as barley and oilseed rape [219, 220]. The principle of mass spectroscopy is based on mass-to-charge
ratio rather than electrophoretic mobility. SNP genotypes can be discriminated after
SNuPE and then determining the molecular weight differences for the
incorporated ddNTPs. This system has potential for high-throughput genotyping
in regional hub labs because of the capacity to screen large numbers, speed of
genotyping (seconds compared with hours for gel-based systems), amenability to automation, and low-cost
potential.


## 3.3.1. Optimizing and Refining Current Protocols

One very important point we would like to emphasize before reviewing new technology
is that there are great opportunities for further optimization of currently
used protocols, especially in terms of cost and throughput. Furthermore, many
innovations on standard methods are possible (see, e.g., [187]). This is important because many labs have already made a considerable
investment in lab equipment and have the technical expertise to use specific
protocols using specific markers.As discussed earlier, multiplexing
has considerable potential for increasing the efficiency of marker genotyping although
this has not been extensively explored in rice. Multiplex PCR could be
complicated since numerous variables (primer combination, annealing time and
temperature, extension time and temperature, and concentrations of primers and
magnesium chloride) are involved [188, 189]. However, in many cases, the investment in time and resources may be
justified. Coburn et al. [57] reported 80% successful PCR amplification for duplex PCR.
Multiplex loading is simpler and in our opinion could be applied on a much wider scale. At IRRI,
loading of two or even three markers (A. Das, pers. comm.) is frequently
possible, which saves time and resources. Of course, information regarding
marker allele sizes is a prerequisite for multiplex loading.


## 3.3.2. Considering the Adoption of New Genotyping Methods

Many new promising genotyping methods could improve efficiency in terms of time and
potential cost [190]. Most of these methods are targeted
toward SNPs but most of them could be adapted for other marker types. Interestingly,
there are many high-throughput SNP genotyping platforms (that have often been
developed for medical applications), yet there has been no universally adopted
system [191, 192].In the context of plant breeding,
there are several important considerations. Cost is critical due to the large
number of samples breeders evaluate. Furthermore, 3 to 6 target traits usually segregate
in a single population so the frequency of lines with all the desirable gene
combinations is very low. This could undermine the suitability of some high-throughput
whole-genome profiling programs, although there could be numerous applications
in basic research.Obviously, some genotyping methods will
be more suitable for specific labs than others. For this reason, we have
classified these methods into two groups: regional hub labs and remote breeding
stations. A regional hub lab is defined as a research institute with a critical
mass of scientists who receive sufficient funding for long-term, broad
objective breeding research that includes genomics research (e.g., CGIAR
centers and national breeding institutes). We refer to a remote breeding station
as a “smaller” lab that has more limited capacity for marker genotyping in
terms of funding and resources.


## 3.3.3. Remote Breeding Station Lab 1: Gel-Based Methods

PCR-based SNP methods
PCR-based SNP detection methods that use standard
agarose or acrylamide electrophoresis are obviously attractive because they are
technically simple and no further investment in equipment is required. The
simplest form of PCR-based SNP marker is based on designing PCR primers such
that a forward or reverse primer has a specific dNTP at the3′ end; PCR amplification is successful for the
appropriate primer-template combination and fails when the specific 3′ base in the primer is not complementary to the
template [43]. Reliability
has been an important issue with designing PCR-based SNP markers; hence,
several studies, exploring methods to improve reliability including the use of
additional primers, have been conducted [193–195]. Hayashi et al. [43]
introduced an artificial mismatch at the 3rd base from the 3′ end—in addition to the last 3′ base—which was found to increase specificity; a 67%
success rate was found for 49 target SNPs. This method can be used to develop
codominant allele-specific markers. Overall, these methods are useful to
complement the arsenal of CAPS markers for whichtarget SNP-containing sites are not available.Heteroduplex cleavage SNP detection methods
TILLING and Eco-TILLING methods (discussed previously) are reverse genetics methods used
to identify SNPs in target genes in mutants and germplasm collections,
respectively. However, simplified TILLING/Eco-TILLING methods, using standard
polyacrylamide or agarose gel electrophoresis detection methods, could be
applied for MAS and would be especially useful in situations, where it is difficult
to find other types of polymorphic markers [166, 196]. This method relies on the principle that CEL I cleaves heteroduplexes at
the position of SNPs.
In brief, the method involves the
following steps.(i)
PCR
amplification of the region of interest in parental lines (A and B)
(homozygous).(ii)
The
PCR products are combined in equal concentration and subjected to CEL I
digestion (TILLING/Eco-TILLING) in an agarose procedure to test for
polymorphism.(iii)
DNA
is extracted from each member of the breeding population (RIL-homozygous) and quantified.(iv)
DNA
extracted from either of the parental lines (e.g., parent A) is combined with
DNA from each of the RILs in a 1 : 1 ratio.(v)
The
mix is subjected to CEL I digestion. If an SNP is detected, this indicates that
the allele carried by the RIL is unlike that of the parent used to create the
mix (in this case, parent A). One possible limitation of this procedure is that it would be ideally done on
homozygous lines. If there is doubt, the assay should be conducted with just
the DNA from each of the RILs; no SNPs should be detected.PCR-RF-SSCP
polymerase chain reaction- (PCR-)  restriction fragment- (RF-)
single-strand conformation polymorphism (SSCP)—abbreviated to PRS—is essentially based on a combination of the
CAPS technique (i.e., restriction digestion of gene-specific PCR products) with
SSCP, which on its own can be used for SNP detection of small PCR amplicons
(100–400 bp) using
polyacrylamide gel electrophoresis (PAGE) [197–199]. This method has
been successfully used to detect SNPs in rice and other crops. One of the
advantages of this method is that much longer PCR amplicons (>2000 bp) can
be scanned for SNPs, and it may be well suited for labs with technical
expertise in polyacrylamide gel electrophoresis and/or silver staining.


## 3.3.4. Remote Breeding Station Lab 2:  Non-Gel-Based Methods

Dot blots
Dot blots have been used for genotyping of rice breeding material [200]. The main advantages of
this methodare that gel
electrophoresis and even PCR in some cases are not required. This method used
cultivar-specific sequences that were previously identified by AFLP, STS, or
PRS. Genomic DNA from rice samples was spotted on membranes and short
oligonucelotide (28–45 bp) or digoxigenin
(DIG)-labeled PCR products (102–466 bp) were used
as probes. DIG labeling methods avoid the use of radioisotopes, which is
preferable in most labs and very important for remote breeding stations due to
delivery, storage, and disposal. Relatively high DNA yields were required for
this method (3.5–5 μg).
The dot blot genotyping method was
later extended to a robust SNP detection [201]. In this method, two nucleotide probes (17 nt) were used:
one allele-specific probe was DIG-labeled (at the 5′ end) and the other allele probe was unlabeled,
following the principles of competitive allele-specific short oligonucleotide
hybridization, which improves specificity. The probe targets were PCR products
that contained the SNP regions. This method has potential for high-throughput
capacity since 864 samples were blotted on a single membrane. Dot blot
genotyping has been used for high-throughput, large-scale MAS in commercial
companies [202].
Dot-blot assay was used in advanced Basmati-derived lines
that have reached the replicated yield trial at IRRI’s breeding program
(Reveche et al., unpublished data). This method, however, is not yet in
routine use but offers great potential for MAS in breeding program.


## 3.3.5. Regional Hub Lab

Capillary electrophoresis platforms for SSR genotyping
To maximize the efficiency of multiplexing using capillary electrophoresis
platforms, marker “panels” can be assembled, which consist of markers with no
overlapping allele size ranges or the same fluorescent dyes [56, 203]. In general, panels of any
size and for any traits can be designed based on available primer resources and
previously determined allele sizes. Coburn et al. [57] reported assembling panels consisting of 6 to 11 SSRs that were
evenly spaced along all 12 chromosomes; most panels were designed such that
they are chromosome-specific. A greater flexibility of panel design was
demonstrated in maize, in which primers were redesigned for specific SSR loci
from sequence data [114]. This permitted a tenplex level of multiplexing (i.e., scoring of 10
individual SSR marker alleles in a single gel lane). Although these panels from
these two examples were designed for whole-genome scans, they have wider potential
in routine MAS. Furthermore, generic fluorescently labeling primer methods,
which greatly reduce costs, are other innovative methods by which the cost
efficiency of capillary electrophoresis methods can be improved [204, 205]. In our opinion, it would also be feasible to adopt capillary
electrophoresis systems in some remote breeding stations.SNuPE
Many SNP detection methods are based on the commonly used principle of single
nucleotide primer extension (also called single base extension, SBE). Briefly, this method works by using a
genotyping primer that immediately precedes an SNP at the3′ end in the template. This genotyping primer is
extended with a specific fluorescently labeled dideoxy nucleotide (ddNTP) that is
detected, which permits genotyping at a target locus. SNuPE can be performed
using capillary electrophoresis systems,
which could be very convenient if these platforms have been set up in labs for
SSR genotyping. Capillary electrophoresis platforms have a very high throughput
capacity: a pilot study in maize indicated that 1200 genotypes could be analyzed
per day [206].FRET-based genotyping
SNPs have become prominent in rice functional
genomics research because of their advantage of being prevalent in the genome. For
example, a recent study has reported an average occurrence of one SNP for every
40 kb in target regions in chromosomes 6 and 11 (S. McCouch, pers. comm.). If
these SNPs are informative and exist in alternate alleles of a gene for
resistance and susceptibility, for example, theXa21 gene for bacterial blight resistance, they would become useful
candidates for marker development. At IRRI, we have adopted a method for SNP
detection that uses the system known as fluorescence resonance energy transfer (FRET).
FRET is a radiation-less transmission of energy
from a donor molecule to an acceptor molecule when they are in close proximity
to one another (typically 10–100 Å). It has been mostly used in biomedical research and drug discovery to
detect SNPs in the human genome [207, 208] and in
homogeneous DNA diagnostics [209] as well as for other applications in protein interaction analysis [210]. In the conventional FRET
reported by Takatsu et al. [211],
the detection method requires special fluorescence-labeled probes, which are
expensive and difficult to optimize. Later in the same year, Takatsu et al. [212] developed a method based
on single base extension and applied SYBR Green I (bound to double-stranded
DNA) as an energy donor and fluorescence-labeled ddNTP as an energy acceptor. This
method avoids difficult probe design and allows a significant reduction in
detection cost.
We have adapted the method for large-scale MAS in rice and further reduced
the cost by optimization of expensive reagents (e.g., enzymes) during
purification steps of single-stranded DNA prior to SBE. We employed the method as
an SNP genotyping technique with the advantage of being high-throughput and
non-gel-based. Here, the amplified genomic DNA containing the polymorphic site is
incubated with a primer (designed to anneal immediately next to the polymorphic
site) in the presence of DNA polymerase, SYBR Green I, and ddNTP labeled with a
fluorophore (ROX or Cy5). The primer binds to the complementary site and is
extended with a single ddNTP. When SYBR Green I is excited at its excitation
wavelength of 495 nm, it will transfer the energy to the ddNTP at the
polymorphic site next to it. High fluorescence intensity will be measured at
each emission wavelength for SYBR Green I and the respective fluorophores for a
resistant and susceptible allele, so SNP can be discriminated after the SBE
reaction.


### Microarray-Based Genotyping (MBG)

(A) SNP genotyping of alternate alleles
DNA microarray technology provides a snapshot of gene expression levels of all genes in an
organism in a single experiment. Depending on the objective of the experiment,
it allows the identification of genes that are expressed in different cell
types to learn how their expression levels change in different developmental
stages or disease states and to identify the cellular processes in which they
participate. This technology platform has also been used in genotyping studies,
such as the tagged microarray marker (TAM) approach and the high-throughput system
that makes genotyping efficient and low cost [213]. An alternative and simpler microarray technique was
described by Ji et al. [214]. MBG is
based on simple hybridization with fluorescence-labeled probes, which anneal
with specific alleles in PCR products. MBG for MAS of specific genes needs printing
of PCR products derived from breeding materials on glass. The alternate probes
of the gene (e.g., xa5 gene for
bacterial blight resistance) are labeled with fluorophores, such as Alexa-Fluor
546 (or Cy3) for the R allele and Alexa-Fluor 647 (or Cy5) for the S allele. MBG
is useful when the number of samples increases, thus decreasing the cost per data
point. In designing an experiment for marker-assisted breeding, we can save
time, space, and labor by establishing computer-aided data acquisition. MBG is
one of the most advanced techniques for automated data processing.
Although the use of some expensive equipment, including the arrayer and scanner, may make users
think twice, the cost per sample will be remarkably lower by using less
expensive supplies and reagents that are commercially available.(B) Single-feature polymorphism (SFP)
Microarray-based genotyping that used indel
polymorphisms or SFP provides the means to simultaneously screen hundreds to thousands
of markers per individual. This technology is particularly suited to
applications requiring whole-genome coverage, and the relatively low cost of
this assay allows a genotyping strategy using large populations. Along with
foreground selection for the target traits, high-resolution whole-genome
selection will provide a greater capacity for background selection to retain
the positive attributes of popular varieties in backcrossing programs.
Obtaining graphical genotypes of individuals will facilitate the pyramiding of
desirable alleles at multiple loci and will shorten the time needed for developing
new varieties.
SFP assays are done by labeling genomic DNA (target)
and hybridizing it to arrayed oligonucleotide probes that are complementary to
indel loci. The SFPs can be discovered through sequence alignments or by
hybridization of genomic DNA with whole-genome microarrays. Each SFP is scored
by the presence or absence of a hybridization signal with its corresponding
oligonucleotide probe on the array. Both spotted oligonucleotides [215] and
Affymetrix-type arrays [216] have
been used in these assays. For genotyping large populations, the cost per
individual is more critical than the cost per data point. Spotted
oligonucleotide microarrays have the potential to provide low-cost genotyping
platforms [217]. The availability of genomic sequences
from multiple accessions presents opportunities for the design of spotted long
oligonucleotide microarrays for low-cost/high-density genotyping of rice.
The SFP genotyping slide for rice has been
developed in the laboratory of D.  Galbraith, University of Arizona, Ariz, USA
[218]. Using the publicly available genomic sequences of rice
cultivars Nipponbare and 93-11 representing the japonica and indica subspecies,
respectively, they made alignment of these sequences and identified 1264 SFPs
suitable for probe design. With a median distance between markers of 128 kb, the
SFPs are evenly distributed over the whole genome. An early result using these
probes showed conservatively 30–50% polymorphism
between a pair of rice lines (the lowest between japonica types). Thus,
a single contrast produces around 400 well-spaced, polymorphic gene-based
markers for any pair of unrelated parental lines. One advantage of the DNA hybridization-based
genotyping procedure is that it can be used for quantitative genotyping of
pooled samples.
Both of these microarray-based genotyping platforms can
be combined for foreground (e.g., SNP genotyping of alternate alleles) and
background selection (e.g., SFPs) in breeding programs.MALDI TOF MS
Matrix-assisted
laser desorption/ionization time-of-flight mass spectrometry (MALDI TOF MS) has
been used for SNP genotyping in other crops such as barley and oilseed rape [219, 220]. The principle of mass spectroscopy is based on mass-to-charge
ratio rather than electrophoretic mobility. SNP genotypes can be discriminated after
SNuPE and then determining the molecular weight differences for the
incorporated ddNTPs. This system has potential for high-throughput genotyping
in regional hub labs because of the capacity to screen large numbers, speed of
genotyping (seconds compared with hours for gel-based systems), amenability to automation, and low-cost
potential.


## Microarray-Based Genotyping (MBG)

(A) SNP genotyping of alternate alleles
DNA microarray technology provides a snapshot of gene expression levels of all genes in an
organism in a single experiment. Depending on the objective of the experiment,
it allows the identification of genes that are expressed in different cell
types to learn how their expression levels change in different developmental
stages or disease states and to identify the cellular processes in which they
participate. This technology platform has also been used in genotyping studies,
such as the tagged microarray marker (TAM) approach and the high-throughput system
that makes genotyping efficient and low cost [213]. An alternative and simpler microarray technique was
described by Ji et al. [214]. MBG is
based on simple hybridization with fluorescence-labeled probes, which anneal
with specific alleles in PCR products. MBG for MAS of specific genes needs printing
of PCR products derived from breeding materials on glass. The alternate probes
of the gene (e.g., xa5 gene for
bacterial blight resistance) are labeled with fluorophores, such as Alexa-Fluor
546 (or Cy3) for the R allele and Alexa-Fluor 647 (or Cy5) for the S allele. MBG
is useful when the number of samples increases, thus decreasing the cost per data
point. In designing an experiment for marker-assisted breeding, we can save
time, space, and labor by establishing computer-aided data acquisition. MBG is
one of the most advanced techniques for automated data processing.
Although the use of some expensive equipment, including the arrayer and scanner, may make users
think twice, the cost per sample will be remarkably lower by using less
expensive supplies and reagents that are commercially available.(B) Single-feature polymorphism (SFP)
Microarray-based genotyping that used indel
polymorphisms or SFP provides the means to simultaneously screen hundreds to thousands
of markers per individual. This technology is particularly suited to
applications requiring whole-genome coverage, and the relatively low cost of
this assay allows a genotyping strategy using large populations. Along with
foreground selection for the target traits, high-resolution whole-genome
selection will provide a greater capacity for background selection to retain
the positive attributes of popular varieties in backcrossing programs.
Obtaining graphical genotypes of individuals will facilitate the pyramiding of
desirable alleles at multiple loci and will shorten the time needed for developing
new varieties.
SFP assays are done by labeling genomic DNA (target)
and hybridizing it to arrayed oligonucleotide probes that are complementary to
indel loci. The SFPs can be discovered through sequence alignments or by
hybridization of genomic DNA with whole-genome microarrays. Each SFP is scored
by the presence or absence of a hybridization signal with its corresponding
oligonucleotide probe on the array. Both spotted oligonucleotides [215] and
Affymetrix-type arrays [216] have
been used in these assays. For genotyping large populations, the cost per
individual is more critical than the cost per data point. Spotted
oligonucleotide microarrays have the potential to provide low-cost genotyping
platforms [217]. The availability of genomic sequences
from multiple accessions presents opportunities for the design of spotted long
oligonucleotide microarrays for low-cost/high-density genotyping of rice.
The SFP genotyping slide for rice has been
developed in the laboratory of D.  Galbraith, University of Arizona, Ariz, USA
[218]. Using the publicly available genomic sequences of rice
cultivars Nipponbare and 93-11 representing the japonica and indica subspecies,
respectively, they made alignment of these sequences and identified 1264 SFPs
suitable for probe design. With a median distance between markers of 128 kb, the
SFPs are evenly distributed over the whole genome. An early result using these
probes showed conservatively 30–50% polymorphism
between a pair of rice lines (the lowest between japonica types). Thus,
a single contrast produces around 400 well-spaced, polymorphic gene-based
markers for any pair of unrelated parental lines. One advantage of the DNA hybridization-based
genotyping procedure is that it can be used for quantitative genotyping of
pooled samples.
Both of these microarray-based genotyping platforms can
be combined for foreground (e.g., SNP genotyping of alternate alleles) and
background selection (e.g., SFPs) in breeding programs.MALDI TOF MS
Matrix-assisted
laser desorption/ionization time-of-flight mass spectrometry (MALDI TOF MS) has
been used for SNP genotyping in other crops such as barley and oilseed rape [219, 220]. The principle of mass spectroscopy is based on mass-to-charge
ratio rather than electrophoretic mobility. SNP genotypes can be discriminated after
SNuPE and then determining the molecular weight differences for the
incorporated ddNTPs. This system has potential for high-throughput genotyping
in regional hub labs because of the capacity to screen large numbers, speed of
genotyping (seconds compared with hours for gel-based systems), amenability to automation, and low-cost
potential.


## 4. Critical Assessment of the Impact  of Genomics Research

### 4.1. Benefits to Breeding

To date, the outcomes from genomics research have had three main benefits to
breeders: increased knowledge regarding important traits, the generation of new
breeding lines, and a vast array of DNA marker tools. Genomics research
outcomes will provide considerably more information on the biology of traits,
especially for complex quantitative traits for which information can be very
limited [14]. Improved
knowledge regarding complex traits can be extremely useful for breeders. Currently,
there is an enormous amount of QTL and candidate gene data for these traits that
will be continually refined and validated until specific genes are
identified.In applied terms, one important tangible benefit has been the generation of new breeding lines arising from QTL
mapping experiments. These lines may include the “best” lines segregating for
the traits under study. Numerous introgression lines or chromosome segment
substitution lines (CSSL) and NILs developed for specific traits have
considerable potential for breeding programs [221, 222]. As discussed earlier, many new breeding lines with wild donor
introgression are the output from AB-QTL analysis experiments [174]. For breeding programs, ILs or AB-QTL analysis lines can be rapidly
converted into NILs via an MABC approach using only a small number of
backcrosses.From the molecular breeding
perspective, the most tangible benefit from genomics research is the wealth of
DNA markers associated with traits from previous research and the potential for
generating thousands of new markers from the two rice genome sequences [7, 130]. This has already had a
pronounced impact on plant breeding and thisimpact will undoubtedly continue in the future. In theory, the
lack of polymorphism for target markers in breeding material should no longer
be a problem as more and more “allele-specific marker kits” will be available
or be custom-made, where required for an increasing number of traits. Marker kits
will enable the precise selection of parental lines for the generation of new
breeding populations and reliable selection of segregating progeny. As more and
more genes are identified, the development of “functional markers” or “perfect”
markers will be more common [4]. Since functional markers are the site that
determines phenotype, they are thus the ultimate marker in a marker kit. Such
markers have been used for Xa21 with
great success [108–110]. Rice functional markers were recently developed for betaine
aldehyde dehydrogenase (BAD2; controlling fragrance) and xa5 was developed for bacterial blight
resistance [223, 224].


### 4.2. Obstacles that Genomics Research Will not Solve

#### 4.2.1. Cost of Using DNA Markers

Despite the enormous potential for developing and using markers in rice, the cost of
genotyping is still a prohibitive barrier to the wider application of MAS. Even
with the global importance of rice, many developing countries have limited
research and development capability. Therefore, cost optimization of current
genotyping protocols and the development of new cost-effective protocols should
be a major priority for breeding research and especially the rice molecular
breeding lab. These improvements might involve simple optimizations of current laboratory
practices, adopting new more efficient methods, or developing new MAS
strategies and schemes.Collard and Mackill [8] stated
that preliminary cost analysis of MAS at IRRI indicated great potential for
reduction. They stated a cost of US $1.00 per marker data point achieved by a
post-doctoral research fellow or US $0.30 for a research technician, which we
have since revised to US $0.37. At first glance, this amount may not sound like
much, but when one considers that this indicates a cost of US $96 per plate,
and that literally thousands (or even tens of thousands) of breeding lines are
screened per annum in a typical rice breeding program, the importance of cost
becomes obvious.A detailed breakdown of cost components for the
marker genotyping of a single SSR marker using standard methods indicated some
interesting findings (Table2).Table 2
Cost breakdown of standard marker genotyping
and exploration of marker genotyping cost reduction opportunities.
SituationStepConsumables (US $)Labor (US $)Cost per marker (US $)Standard costDNA0.0510.4371.001PCR0.2110.076Gel0.0520.174Scenario 1—multiplex loadingDNA0.0510.4370.910PCR0.2110.076Gel0.0000.043Scenario 2—multiplex PCRDNA0.0510.4370.500PCR0.2110.076Gel0.0520.174Scenario 3—MAS pyramidingDNA0.0510.4370.676PCR 10.2110.076Gel 10.0520.174PCR 20.2110.076Gel 20.0520.174PCR 30.2110.076Gel 30.0520.174Scenario 4—DNA extraction performed by research technicianDNA0.0510.0400.604PCR0.2110.076Gel0.0520.174(i) Standard cost calculated based on the genotyping of 96
samples using a single SSR marker at IRRI from Collard and Mackill [8].(ii) Data in this section of the table are reported for the
second marker; hence, the gel cost for consumables is zero. The
calculation was performed using the data for a standard marker plus the
second marker (gel consumable cost = 0) and dividing by two.(iii) Multiplex loading by sequential loading of PCR samples,
assuming different DNA samples are run in all lanes. Labor would require
an extra 20 minutes for sequential loading, but gel preparation and
assembly are no longer required in this scenario.(iv) If direct pooling of PCR products is possible, only a
single loading is required for 96 samples (extra 5 minutes of labor). Gel
labor costs are reduced to US $0.011 and the total cost per marker is $0.893.(v) Multiplex PCR (i.e., duplex PCR) in which two markers
can be genotyped in the time and effort required for a single marker.(vi) For MAS pyramiding, the genotyping of three loci was
considered. For simplicity, it was assumed markers could not be
multiloaded, but obviously, if this was possible, it would indicate a
further cost reduction per marker screened.(vii) The DNA extraction step is the most costly from our
data analysis. There are considerable savings in expense, if genotyping
efforts of a postdoctoral researcher are coordinated with those of a
research technician at IRRI.(i)
PCR costs the most in terms of consumables.(ii)
The DNA extraction
step costs the most in terms of labor.(iii)
Overall, the DNA
extraction step is the most expensive. This analysis also provided a simple framework to investigate opportunities for some
cost reduction (Table 2). In summary, scenarios 1 and 2 highlight that the
optimization of technical procedures could decrease costs, scenario 3
highlights that the MAS scheme used will also vary costs, and scenario 4 shows
that MAS lab planning and appropriate delegation of duties can also reduce
costs.Detailed cost-benefit analyses of using markers for
specific traits could be critical information to determine the most appropriate
and advantageous situations for using markers. For example, in maize, an
extremely detailed cost-benefit analysis indicated that using markers for
selection foropaque2 (the gene
associated with quality protein maize) was more economical than conventional
screening methods [225]. In such cases, there is a clear-cut advantage of using markers in
breeding.


#### 4.2.2. QTL Application Research: Bridging  the  “Application Gap”

Many research steps are required from QTL discovery to the practical application of
markers in a breeding program [69]. The three main research areas can be described as “QTL
confirmation,” “broad-range QTL testing,” and “marker validation,” which we
collectively refer to as “QTL application research.” These research areas have been loosely defined as
QTL or marker validation activities—especially in wheat and barley. See references cited in [8]. However, in this paper, we have specifically defined the overall
research area as QTL application research and have defined three components. QTL
confirmation is desirable because factors such as small population sizes and
insufficient replication of trait data, and experimental errors can cause
inaccuracies in determining QTL positions and effects. Broad-range QTL testing
refers to verification of QTLs in different populations by using previously
reported markers in order to evaluate the effectiveness of the markers in
predicting phenotype. This is required because of the effects of genetic
background, possible epistatic interactions, and environmental effects that
could ultimately reveal that QTLs may not be relevant in a specific breeding
program. Marker validation activities are also required to evaluate the
reliability of the markers and to identify polymorphism in relevant breeding
lines. The latter two steps are also highly desirable for confirming marker-trait
linkages identified by association mapping.In practice, these research steps are often not performed and they represent an important obstacle for MAS to
have an impact on crop improvement; this was referred to as the “application
gap” by Collard and Mackill [8]. Although
there are encouraging examples of marker validation research, there are
relatively few published reports of QTL confirmation or broad-range application
research in rice. A notable exception was the confirmation of QTLs for sheath
blight resistance [226].


#### 4.2.3. “Phenotype Gap”

This term was used to refer to the increasing ratio of genomic sequence data to
known gene phenotype [227]—the term “phenotype gap” was originally coined by
mammalian researchers. As
mentioned earlier, this limits the ability to functionally annotate the
constantly growing amount of rice genome sequence data. In the next few
decades, the lack of knowledge of gene function will exist for the vast
majority of rice genes. For mutant studies, only a few selected genotypes have
been used, including only a single indica variety. Phenotypic analysis of mutant lines represents a considerable workload
[142]. Precision
phenotyping is also critical to the success of QTL or association mapping
experiments, but, unfortunately, the importance of refining and developing new
methods for precise phenotypic measurement is also often neglected in the
genomics era. Overcomingthe
phenotype gap represents the next great challenge for scientists involved in rice
genomics research.


## 4.1. Benefits to Breeding

To date, the outcomes from genomics research have had three main benefits to
breeders: increased knowledge regarding important traits, the generation of new
breeding lines, and a vast array of DNA marker tools. Genomics research
outcomes will provide considerably more information on the biology of traits,
especially for complex quantitative traits for which information can be very
limited [14]. Improved
knowledge regarding complex traits can be extremely useful for breeders. Currently,
there is an enormous amount of QTL and candidate gene data for these traits that
will be continually refined and validated until specific genes are
identified.In applied terms, one important tangible benefit has been the generation of new breeding lines arising from QTL
mapping experiments. These lines may include the “best” lines segregating for
the traits under study. Numerous introgression lines or chromosome segment
substitution lines (CSSL) and NILs developed for specific traits have
considerable potential for breeding programs [221, 222]. As discussed earlier, many new breeding lines with wild donor
introgression are the output from AB-QTL analysis experiments [174]. For breeding programs, ILs or AB-QTL analysis lines can be rapidly
converted into NILs via an MABC approach using only a small number of
backcrosses.From the molecular breeding
perspective, the most tangible benefit from genomics research is the wealth of
DNA markers associated with traits from previous research and the potential for
generating thousands of new markers from the two rice genome sequences [7, 130]. This has already had a
pronounced impact on plant breeding and thisimpact will undoubtedly continue in the future. In theory, the
lack of polymorphism for target markers in breeding material should no longer
be a problem as more and more “allele-specific marker kits” will be available
or be custom-made, where required for an increasing number of traits. Marker kits
will enable the precise selection of parental lines for the generation of new
breeding populations and reliable selection of segregating progeny. As more and
more genes are identified, the development of “functional markers” or “perfect”
markers will be more common [4]. Since functional markers are the site that
determines phenotype, they are thus the ultimate marker in a marker kit. Such
markers have been used for Xa21 with
great success [108–110]. Rice functional markers were recently developed for betaine
aldehyde dehydrogenase (BAD2; controlling fragrance) and xa5 was developed for bacterial blight
resistance [223, 224].


## 4.2. Obstacles that Genomics Research Will not Solve

### 4.2.1. Cost of Using DNA Markers

Despite the enormous potential for developing and using markers in rice, the cost of
genotyping is still a prohibitive barrier to the wider application of MAS. Even
with the global importance of rice, many developing countries have limited
research and development capability. Therefore, cost optimization of current
genotyping protocols and the development of new cost-effective protocols should
be a major priority for breeding research and especially the rice molecular
breeding lab. These improvements might involve simple optimizations of current laboratory
practices, adopting new more efficient methods, or developing new MAS
strategies and schemes.Collard and Mackill [8] stated
that preliminary cost analysis of MAS at IRRI indicated great potential for
reduction. They stated a cost of US $1.00 per marker data point achieved by a
post-doctoral research fellow or US $0.30 for a research technician, which we
have since revised to US $0.37. At first glance, this amount may not sound like
much, but when one considers that this indicates a cost of US $96 per plate,
and that literally thousands (or even tens of thousands) of breeding lines are
screened per annum in a typical rice breeding program, the importance of cost
becomes obvious.A detailed breakdown of cost components for the
marker genotyping of a single SSR marker using standard methods indicated some
interesting findings (Table2).Table 2
Cost breakdown of standard marker genotyping
and exploration of marker genotyping cost reduction opportunities.
SituationStepConsumables (US $)Labor (US $)Cost per marker (US $)Standard costDNA0.0510.4371.001PCR0.2110.076Gel0.0520.174Scenario 1—multiplex loadingDNA0.0510.4370.910PCR0.2110.076Gel0.0000.043Scenario 2—multiplex PCRDNA0.0510.4370.500PCR0.2110.076Gel0.0520.174Scenario 3—MAS pyramidingDNA0.0510.4370.676PCR 10.2110.076Gel 10.0520.174PCR 20.2110.076Gel 20.0520.174PCR 30.2110.076Gel 30.0520.174Scenario 4—DNA extraction performed by research technicianDNA0.0510.0400.604PCR0.2110.076Gel0.0520.174(i) Standard cost calculated based on the genotyping of 96
samples using a single SSR marker at IRRI from Collard and Mackill [8].(ii) Data in this section of the table are reported for the
second marker; hence, the gel cost for consumables is zero. The
calculation was performed using the data for a standard marker plus the
second marker (gel consumable cost = 0) and dividing by two.(iii) Multiplex loading by sequential loading of PCR samples,
assuming different DNA samples are run in all lanes. Labor would require
an extra 20 minutes for sequential loading, but gel preparation and
assembly are no longer required in this scenario.(iv) If direct pooling of PCR products is possible, only a
single loading is required for 96 samples (extra 5 minutes of labor). Gel
labor costs are reduced to US $0.011 and the total cost per marker is $0.893.(v) Multiplex PCR (i.e., duplex PCR) in which two markers
can be genotyped in the time and effort required for a single marker.(vi) For MAS pyramiding, the genotyping of three loci was
considered. For simplicity, it was assumed markers could not be
multiloaded, but obviously, if this was possible, it would indicate a
further cost reduction per marker screened.(vii) The DNA extraction step is the most costly from our
data analysis. There are considerable savings in expense, if genotyping
efforts of a postdoctoral researcher are coordinated with those of a
research technician at IRRI.(i)
PCR costs the most in terms of consumables.(ii)
The DNA extraction
step costs the most in terms of labor.(iii)
Overall, the DNA
extraction step is the most expensive. This analysis also provided a simple framework to investigate opportunities for some
cost reduction (Table 2). In summary, scenarios 1 and 2 highlight that the
optimization of technical procedures could decrease costs, scenario 3
highlights that the MAS scheme used will also vary costs, and scenario 4 shows
that MAS lab planning and appropriate delegation of duties can also reduce
costs.Detailed cost-benefit analyses of using markers for
specific traits could be critical information to determine the most appropriate
and advantageous situations for using markers. For example, in maize, an
extremely detailed cost-benefit analysis indicated that using markers for
selection foropaque2 (the gene
associated with quality protein maize) was more economical than conventional
screening methods [225]. In such cases, there is a clear-cut advantage of using markers in
breeding.


### 4.2.2. QTL Application Research: Bridging  the  “Application Gap”

Many research steps are required from QTL discovery to the practical application of
markers in a breeding program [69]. The three main research areas can be described as “QTL
confirmation,” “broad-range QTL testing,” and “marker validation,” which we
collectively refer to as “QTL application research.” These research areas have been loosely defined as
QTL or marker validation activities—especially in wheat and barley. See references cited in [8]. However, in this paper, we have specifically defined the overall
research area as QTL application research and have defined three components. QTL
confirmation is desirable because factors such as small population sizes and
insufficient replication of trait data, and experimental errors can cause
inaccuracies in determining QTL positions and effects. Broad-range QTL testing
refers to verification of QTLs in different populations by using previously
reported markers in order to evaluate the effectiveness of the markers in
predicting phenotype. This is required because of the effects of genetic
background, possible epistatic interactions, and environmental effects that
could ultimately reveal that QTLs may not be relevant in a specific breeding
program. Marker validation activities are also required to evaluate the
reliability of the markers and to identify polymorphism in relevant breeding
lines. The latter two steps are also highly desirable for confirming marker-trait
linkages identified by association mapping.In practice, these research steps are often not performed and they represent an important obstacle for MAS to
have an impact on crop improvement; this was referred to as the “application
gap” by Collard and Mackill [8]. Although
there are encouraging examples of marker validation research, there are
relatively few published reports of QTL confirmation or broad-range application
research in rice. A notable exception was the confirmation of QTLs for sheath
blight resistance [226].


### 4.2.3. “Phenotype Gap”

This term was used to refer to the increasing ratio of genomic sequence data to
known gene phenotype [227]—the term “phenotype gap” was originally coined by
mammalian researchers. As
mentioned earlier, this limits the ability to functionally annotate the
constantly growing amount of rice genome sequence data. In the next few
decades, the lack of knowledge of gene function will exist for the vast
majority of rice genes. For mutant studies, only a few selected genotypes have
been used, including only a single indica variety. Phenotypic analysis of mutant lines represents a considerable workload
[142]. Precision
phenotyping is also critical to the success of QTL or association mapping
experiments, but, unfortunately, the importance of refining and developing new
methods for precise phenotypic measurement is also often neglected in the
genomics era. Overcomingthe
phenotype gap represents the next great challenge for scientists involved in rice
genomics research.


## 4.2.1. Cost of Using DNA Markers

Despite the enormous potential for developing and using markers in rice, the cost of
genotyping is still a prohibitive barrier to the wider application of MAS. Even
with the global importance of rice, many developing countries have limited
research and development capability. Therefore, cost optimization of current
genotyping protocols and the development of new cost-effective protocols should
be a major priority for breeding research and especially the rice molecular
breeding lab. These improvements might involve simple optimizations of current laboratory
practices, adopting new more efficient methods, or developing new MAS
strategies and schemes.Collard and Mackill [8] stated
that preliminary cost analysis of MAS at IRRI indicated great potential for
reduction. They stated a cost of US $1.00 per marker data point achieved by a
post-doctoral research fellow or US $0.30 for a research technician, which we
have since revised to US $0.37. At first glance, this amount may not sound like
much, but when one considers that this indicates a cost of US $96 per plate,
and that literally thousands (or even tens of thousands) of breeding lines are
screened per annum in a typical rice breeding program, the importance of cost
becomes obvious.A detailed breakdown of cost components for the
marker genotyping of a single SSR marker using standard methods indicated some
interesting findings (Table2).Table 2
Cost breakdown of standard marker genotyping
and exploration of marker genotyping cost reduction opportunities.
SituationStepConsumables (US $)Labor (US $)Cost per marker (US $)Standard costDNA0.0510.4371.001PCR0.2110.076Gel0.0520.174Scenario 1—multiplex loadingDNA0.0510.4370.910PCR0.2110.076Gel0.0000.043Scenario 2—multiplex PCRDNA0.0510.4370.500PCR0.2110.076Gel0.0520.174Scenario 3—MAS pyramidingDNA0.0510.4370.676PCR 10.2110.076Gel 10.0520.174PCR 20.2110.076Gel 20.0520.174PCR 30.2110.076Gel 30.0520.174Scenario 4—DNA extraction performed by research technicianDNA0.0510.0400.604PCR0.2110.076Gel0.0520.174(i) Standard cost calculated based on the genotyping of 96
samples using a single SSR marker at IRRI from Collard and Mackill [8].(ii) Data in this section of the table are reported for the
second marker; hence, the gel cost for consumables is zero. The
calculation was performed using the data for a standard marker plus the
second marker (gel consumable cost = 0) and dividing by two.(iii) Multiplex loading by sequential loading of PCR samples,
assuming different DNA samples are run in all lanes. Labor would require
an extra 20 minutes for sequential loading, but gel preparation and
assembly are no longer required in this scenario.(iv) If direct pooling of PCR products is possible, only a
single loading is required for 96 samples (extra 5 minutes of labor). Gel
labor costs are reduced to US $0.011 and the total cost per marker is $0.893.(v) Multiplex PCR (i.e., duplex PCR) in which two markers
can be genotyped in the time and effort required for a single marker.(vi) For MAS pyramiding, the genotyping of three loci was
considered. For simplicity, it was assumed markers could not be
multiloaded, but obviously, if this was possible, it would indicate a
further cost reduction per marker screened.(vii) The DNA extraction step is the most costly from our
data analysis. There are considerable savings in expense, if genotyping
efforts of a postdoctoral researcher are coordinated with those of a
research technician at IRRI.(i)
PCR costs the most in terms of consumables.(ii)
The DNA extraction
step costs the most in terms of labor.(iii)
Overall, the DNA
extraction step is the most expensive. This analysis also provided a simple framework to investigate opportunities for some
cost reduction (Table 2). In summary, scenarios 1 and 2 highlight that the
optimization of technical procedures could decrease costs, scenario 3
highlights that the MAS scheme used will also vary costs, and scenario 4 shows
that MAS lab planning and appropriate delegation of duties can also reduce
costs.Detailed cost-benefit analyses of using markers for
specific traits could be critical information to determine the most appropriate
and advantageous situations for using markers. For example, in maize, an
extremely detailed cost-benefit analysis indicated that using markers for
selection foropaque2 (the gene
associated with quality protein maize) was more economical than conventional
screening methods [225]. In such cases, there is a clear-cut advantage of using markers in
breeding.


## 4.2.2. QTL Application Research: Bridging  the  “Application Gap”

Many research steps are required from QTL discovery to the practical application of
markers in a breeding program [69]. The three main research areas can be described as “QTL
confirmation,” “broad-range QTL testing,” and “marker validation,” which we
collectively refer to as “QTL application research.” These research areas have been loosely defined as
QTL or marker validation activities—especially in wheat and barley. See references cited in [8]. However, in this paper, we have specifically defined the overall
research area as QTL application research and have defined three components. QTL
confirmation is desirable because factors such as small population sizes and
insufficient replication of trait data, and experimental errors can cause
inaccuracies in determining QTL positions and effects. Broad-range QTL testing
refers to verification of QTLs in different populations by using previously
reported markers in order to evaluate the effectiveness of the markers in
predicting phenotype. This is required because of the effects of genetic
background, possible epistatic interactions, and environmental effects that
could ultimately reveal that QTLs may not be relevant in a specific breeding
program. Marker validation activities are also required to evaluate the
reliability of the markers and to identify polymorphism in relevant breeding
lines. The latter two steps are also highly desirable for confirming marker-trait
linkages identified by association mapping.In practice, these research steps are often not performed and they represent an important obstacle for MAS to
have an impact on crop improvement; this was referred to as the “application
gap” by Collard and Mackill [8]. Although
there are encouraging examples of marker validation research, there are
relatively few published reports of QTL confirmation or broad-range application
research in rice. A notable exception was the confirmation of QTLs for sheath
blight resistance [226].


## 4.2.3. “Phenotype Gap”

This term was used to refer to the increasing ratio of genomic sequence data to
known gene phenotype [227]—the term “phenotype gap” was originally coined by
mammalian researchers. As
mentioned earlier, this limits the ability to functionally annotate the
constantly growing amount of rice genome sequence data. In the next few
decades, the lack of knowledge of gene function will exist for the vast
majority of rice genes. For mutant studies, only a few selected genotypes have
been used, including only a single indica variety. Phenotypic analysis of mutant lines represents a considerable workload
[142]. Precision
phenotyping is also critical to the success of QTL or association mapping
experiments, but, unfortunately, the importance of refining and developing new
methods for precise phenotypic measurement is also often neglected in the
genomics era. Overcomingthe
phenotype gap represents the next great challenge for scientists involved in rice
genomics research.


## 5. Future Considerations for Integrating the Rice Molecular Breeding Laboratory  in the 21st Century

### 5.1. Molecular Breeding Lab Activities

QTL application research activities represent an extensive amount of time, effort,
and resources. In practice, it seems that molecular breeders will ultimately have to perform this research in
situations, in which important data for the application of MAS are not
available. From experience, it is clear that breeding programs that do not
undertake these activities risk wasting considerable time and resources. However,
in practice, QTL application research activities may be constrained by funding,
time, and resources; in some cases, these activities may be beyond the capacity
of many rice molecular breeding labs. Furthermore, a breeder may decide that,
based on the importance of the target trait, such QTL application research steps
do not worth the investment in time, resources, and money, since, at the end, the
markers may not turn out to be useful for selection in their own breeding
program.This poses a practical barrier to the application of
MAS in breeding programs for which there may not be any simple solutions. One
possible solution that might assist plant breeders and molecular breeders could
be the formation of molecular breeding networks in which practical information
and experiences are readily shared between labs regarding specific gene/QTL
targets and marker information. A web-based medium such as a “wiki” or
electronicRice Molecular Breeding Newsletter could be extremely useful. Greater integration with research objectives among
the research institutes involved in QTL mapping might also result in more
relevant data being generated for breeding programs.Many activities will occur in the future rice
molecular breeding lab. Obviously, the primary objectives will be to support
and assist the breeding program in the evaluation and selection of breeding
material. To fulfill this duty, organizational and maintenance activities such
as organizing protocols, marker data, supplies of consumables, equipment
maintenance, and LIMS will be critical. In-house data records for marker
optimization and parental screening will be critical; generally, the more detailed
the records, the better. This must include field and glasshouse leaf tissue collection
protocols, which cannot be neglected.It also seems certain that the development of custom-made markers will become more
commonplace, and so molecular breeders will need to be proficient in skills
such as PCR primer design, DNA sequence analysis, and using bioinformatics
databases and tools. Considerable in
silico applied genomics research will occur prior to wet-lab experiments
or before breeding populations are initiated. SNPs will be the inevitable polymorphism
target of choice arising from current and future genomics research, so rice
molecular breeders should consider this ahead of time. Molecular breeders will
also need to keep in touch with current bioinformatics tools and future
genomics advances.


### 5.2. Integration within Rice Breeding Programs

The advancements in the field of molecular breeding and genomics are proceeding at
such a rapid rate that it makes it difficult for molecular breeders, let alone
conventional plant breeders and other agricultural scientists, to keep abreast
of these new developments. Thus, when possible, plant breeding stations that
intend to adopt molecular breeding approaches should establish a molecular
breeding lab with designated molecular breeders and technical staff, in order
to maximize the likelihood of gaining benefits from molecular breeding. There
will be a critical need for molecular breeders—like conventional plant breeders—to be Jacks (or Jills) of all trades in order
to integrate the disciplines. In addition to a background in applied genomics,
the ideal molecular breeder should have a strong background in classical and
quantitative genetics and plant breeding. Molecular breeders will need to work
extremely closely with senior plant breeders for trait prioritization and
devising effective MAS strategies.Of course, establishing molecular breeding labs will
not be possible in many plant breeding stations, especially in developing
countries, because of limited funding and resources. However, collaboration with
national or international research institutions or universities could still
provide opportunities for such breeding programs to gain benefits from genomics
research.For genomics to be fully integrated
into the overall breeding program, we propose that molecular breeders be actively
engaged in “genomics extension activities” (analogous to “agricultural
extension”) to explain and disseminate information regarding markers and
advances in genomics. Appropriate activities may include training workshops and
developing practical manuals, booklets, and other educational material and
would address the knowledge gap between molecular biologists, plant breeders,
and other disciplines [8, 69]. Such activities might also encourage a greater
integration in situations, in which university research labs conducting basic
research are closely connected with actual breeding stations.


## 5.1. Molecular Breeding Lab Activities

QTL application research activities represent an extensive amount of time, effort,
and resources. In practice, it seems that molecular breeders will ultimately have to perform this research in
situations, in which important data for the application of MAS are not
available. From experience, it is clear that breeding programs that do not
undertake these activities risk wasting considerable time and resources. However,
in practice, QTL application research activities may be constrained by funding,
time, and resources; in some cases, these activities may be beyond the capacity
of many rice molecular breeding labs. Furthermore, a breeder may decide that,
based on the importance of the target trait, such QTL application research steps
do not worth the investment in time, resources, and money, since, at the end, the
markers may not turn out to be useful for selection in their own breeding
program.This poses a practical barrier to the application of
MAS in breeding programs for which there may not be any simple solutions. One
possible solution that might assist plant breeders and molecular breeders could
be the formation of molecular breeding networks in which practical information
and experiences are readily shared between labs regarding specific gene/QTL
targets and marker information. A web-based medium such as a “wiki” or
electronicRice Molecular Breeding Newsletter could be extremely useful. Greater integration with research objectives among
the research institutes involved in QTL mapping might also result in more
relevant data being generated for breeding programs.Many activities will occur in the future rice
molecular breeding lab. Obviously, the primary objectives will be to support
and assist the breeding program in the evaluation and selection of breeding
material. To fulfill this duty, organizational and maintenance activities such
as organizing protocols, marker data, supplies of consumables, equipment
maintenance, and LIMS will be critical. In-house data records for marker
optimization and parental screening will be critical; generally, the more detailed
the records, the better. This must include field and glasshouse leaf tissue collection
protocols, which cannot be neglected.It also seems certain that the development of custom-made markers will become more
commonplace, and so molecular breeders will need to be proficient in skills
such as PCR primer design, DNA sequence analysis, and using bioinformatics
databases and tools. Considerable in
silico applied genomics research will occur prior to wet-lab experiments
or before breeding populations are initiated. SNPs will be the inevitable polymorphism
target of choice arising from current and future genomics research, so rice
molecular breeders should consider this ahead of time. Molecular breeders will
also need to keep in touch with current bioinformatics tools and future
genomics advances.


## 5.2. Integration within Rice Breeding Programs

The advancements in the field of molecular breeding and genomics are proceeding at
such a rapid rate that it makes it difficult for molecular breeders, let alone
conventional plant breeders and other agricultural scientists, to keep abreast
of these new developments. Thus, when possible, plant breeding stations that
intend to adopt molecular breeding approaches should establish a molecular
breeding lab with designated molecular breeders and technical staff, in order
to maximize the likelihood of gaining benefits from molecular breeding. There
will be a critical need for molecular breeders—like conventional plant breeders—to be Jacks (or Jills) of all trades in order
to integrate the disciplines. In addition to a background in applied genomics,
the ideal molecular breeder should have a strong background in classical and
quantitative genetics and plant breeding. Molecular breeders will need to work
extremely closely with senior plant breeders for trait prioritization and
devising effective MAS strategies.Of course, establishing molecular breeding labs will
not be possible in many plant breeding stations, especially in developing
countries, because of limited funding and resources. However, collaboration with
national or international research institutions or universities could still
provide opportunities for such breeding programs to gain benefits from genomics
research.For genomics to be fully integrated
into the overall breeding program, we propose that molecular breeders be actively
engaged in “genomics extension activities” (analogous to “agricultural
extension”) to explain and disseminate information regarding markers and
advances in genomics. Appropriate activities may include training workshops and
developing practical manuals, booklets, and other educational material and
would address the knowledge gap between molecular biologists, plant breeders,
and other disciplines [8, 69]. Such activities might also encourage a greater
integration in situations, in which university research labs conducting basic
research are closely connected with actual breeding stations.


## 6. Concluding Remarks

Breeding research in rice is poised to gain many direct and indirect benefits from genomics
research. However, there are many challenges for rice scientists to fully
exploit and apply knowledge, resources, and tools in actual rice breeding
programs. There are great opportunities for more efficient rice breeding and
the faster development of new rice varieties in the future. We hope that some
of the ideas proposed in this article will encourage the rice scientific community
to collectively work toward converting rice from a model crop species into a
model species for marker-assisted breeding.



---

*Source: 524847-2008-05-28.xml*