Chapter 20
Marker Assisted Breeding
Michael J. Thomson, Abdelbagi M. Ismail*, Susan R. McCouch,
and David J. Mackill
MJT, AMI, DJM: International Rice Research Institute (IRRI),
DAPO Box 7777, Metro Manila,
Philippines
SRM: Department of Plant Breeding and Genetics, Cornell University,
162 Emerson Hall, Ithaca, NY 14853-1901, USA
Summary...............................................................................................................................................................
I Introduction....................................................................................................................................................
II Molecular Markers as Tools for Dissecting Quantitative Traits.......................................................................
A Dissecting Complex Traits Using QTL Mapping.......................................................................................
B Gene Discovery: Genomics and Positional Cloning.................................................................................
C Strategies for Marker-Assisted Selection................................................................................................
III Case Studies from a Model Crop: MAS for Abiotic Stress Tolerance in Rice.................................................
A Flooding...................................................................................................................................................
B Salinity.....................................................................................................................................................
C Phosphorus Deficiency............................................................................................................................
D Drought....................................................................................................................................................
IV Future Perspectives........................................................................................................................................
A Association Mapping for Abiotic Stress Tolerance...................................................................................
B Variety Development and Gene Deployment...........................................................................................
C Bioinformatics Supporting Molecular Breeding........................................................................................
1 Integrating Marker Genotype and Plant Phenotype Data...................................................................
2 Using a Gene and Plant Ontology......................................................................................................
3 Databases for the Next Generation of Plant Breeders.......................................................................
V Conclusions....................................................................................................................................................
Acknowledgements...............................................................................................................................................
References............................................................................................................................................................
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* Author for Correspondence, e-mail: abdelbagi.ismail@cgiar.org
A. Pareek, S.K. Sopory, H.J. Bohnert and Govindjee (eds.), Abiotic Stress Adaptation in Plants: Physiological, Molecular
and Genomic Foundation, pp.451–469.
DOI 10.1007/978-90-481-3112-9_20, © Springer Science + Business Media B.V. 2010.
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Michael J. Thomson et al.
Summary
Recent advances in understanding molecular and physiological mechanisms of abiotic stress responses,
along with breakthroughs in molecular marker technologies, have enabled the dissection of the complex
traits underlying stress tolerance in crop plants. Quantitative trait loci (QTLs) controlling different abiotic stress traits form the basis for a precise marker-assisted backcrossing (MABC) strategy to rapidly
transfer tolerance loci into high-yielding, but stress-sensitive varieties. Case studies are presented to
demonstrate the progress and potential for MABC programs to develop rice varieties with increased
tolerance to flooding, salinity, phosphorus deficiency and drought, amongst others. Future opportunities
exist for employing association genetics for more efficient allele mining for abiotic stress tolerance from
germplasm collections, as well as leveraging the power of bioinformatics and genomics data for more
efficient trait dissection and use in breeding. Plant breeders now have a wealth of information and tools
available to tackle these serious constraints posed by abiotic stresses, with the promise of delivering
stable, high yielding varieties, able to thrive in the increasingly degrading soils and the ominously
changing environment.
Keywords
I
abiotic stresses • association mapping • Oryza sativa L. • QTLs • rice
Introduction
As reviewed in previous chapters, abiotic stress
responses consist of complex and dynamic systems which allow plants to deal with the manifold
stresses encountered in variable environments.
The pattern of plant diversity across the various
ecosystems around the world represents the
consequences of adaptation of each plant species
interacting with its environment over a period
of time. Out of this assortment of diversity, few
Abbreviations: AGI – Arabidopsis Genome Initiative; BAC
– bacterial artificial chromosome; CSSLs – chromosomal
segment substitution lines; EPSO – European Plant Science
Organization; ERF – ethylene responsive factors; FNP – functional nucleotide polymorphism; HKT – transporters highaffinity K+ transporter; IRGSP – International Rice Genome
Sequencing Project; IRIS – International Rice Information
System; LD – linkage disequlibrium; LOD – scores logarithm of the odds ratio; MABC – marker-assisted backcrossing; MAS – marker-assisted selection; NILs – near-isogenic
lines; OsHKT8 – Oryza sativa cation transporter HKT8;
PUP1 – phosphorus uptake 1; QTLs – quantitative trait loci;
RFLPs – restriction fragment length polymorphism; RILs –
recombinant inbred lines; ROS – reactive oxygen species;
SOS – salt overly sensitive; SSR – simple sequence repeat;
SNP – single nucleotide polymorphism; SKC1 – shoot
potassium content 1; SUB1 – submergence 1
selected species have undergone the process of
domestication, whereby intense selection pressure
was imposed for traits important to humans. While
domestication brought about larger fruits, nonshattering grains, and higher harvest index, each
species was constrained by the innate responses
for dealing with various abiotic stresses. However
natural and artificial selection still played a notable role during this domestication, particularly for
adaptation to various environmental perturbations.
At the same time, the production environment
was altered to allow more stable crop production
through land leveling, tillage, irrigation, weeding, soil amendments and mitigation strategies,
designed to limit stress-induced yield losses. The fact
that landraces of most crops are relatively tolerant
to a wide array of abiotic stresses demonstrates
the success of early plant selectors; however, it is
due to recent advances in genetics and molecular
biology technology that plant breeders have begun
to develop new strategies for developing tolerant,
high yielding varieties of various crops.
The ability of crop plants to succeed in stressprone environments is becoming increasingly
important with the need to
•• Produce more food from marginal resources as a
response to increasing human population pressure
•• Allow crops to adjust to the adverse global climate
changes
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Marker Assisted Breeding
•• Improve the lives of the poorest farmers who depend
on stable yields in marginal environments
•• Produce higher yields on shrinking resources such
as water and agricultural lands, which are rapidly
degrading
Fortunately, within major crop species, potential
donors exist in the available germplasm pool for
tolerance to most abiotic stresses, although with
varying degrees of accessibility. Many traditional landraces have higher levels of adaptation
to stresses as compared to modern high yielding varieties, but they also have many undesirable traits and lower yields. Wild crop relatives
may even have higher tolerance, but also pose
greater problems in their use for breeding. Plant
breeders have faced difficulties in transferring
abiotic stress tolerance from exotic germplasm
because of negative linkage drag and also due to
the polygenic nature of many abiotic stress tolerance traits. Furthermore, breeding programs need
to take into account that crops often face multiple
abiotic stresses over the course of a growing season, such as an early drought followed by flooding later in the season, and salinity stress followed
by drought as in many coastal and inland areas.
These stresses could even occur simultaneously,
like P- and Zn-deficiencies and Fe- and Al-toxicities often found in acid and alkaline soils (Ismail
et al. 2007). These challenges have forced breeders to search for innovative strategies to make
further progress on the seemingly intractable
problems that have continued to hinder conventional breeding efforts.
Recent advances in understanding the molecular mechanisms of abiotic stress responses, along
with the breakthroughs in molecular marker technologies, have now enabled the dissection of
complex traits underlying many types of stress
tolerance in crop plants. The process of genetic
linkage mapping as applied towards polygenic
traits has led to the identification of quantitative trait loci (QTLs) that control complex traits
in plants. Furthermore, by using natural genetic
variation to investigate the elaborate systems
plants have evolved to deal with a host of abiotic
stresses, geneticists can now identify superior
tolerance alleles and transfer these alleles into
high yielding, stress sensitive varieties. These
advances have paved the way towards a markerassisted breeding approach that employs the latest
technologies to further improve the performance
of varieties and elite breeding lines developed
through conventional crossing. While transgenic
technologies will ultimately play an important
role in developing abiotic stress tolerant plants,
a marker-assisted approach provides a useful
alternative when the required traits are available within the species gene pool, and especially
in situations where genetically modified organisms face difficulties in approval and are yet to be
widely accepted.
In this chapter, we will cover the use of molecular markers as tools for dissecting the complex
traits associated with tolerance of abiotic stresses
in major crop plants through QTL mapping, gene
discovery and marker-assisted selection (MAS).
We will then provide several case studies of how
these techniques are currently being employed in
the case of rice, to enhance tolerance towards abiotic stresses, including salinity, flooding, phosphorus deficiency, and drought. Lastly, we will
provide some thoughts on how future advances,
such as bioinformatics and association genetics,
might empower marker-assisted breeding techniques for developing high yielding, stress-tolerant varieties in a more efficient way.
II Molecular Markers as Tools
for Dissecting Quantitative Traits
Plant adaptation to variable environments is
reflected by an interrelated set of complex physiological and morphological traits, each with an
intricate regulatory system. By integrating physiological and genetic strategies, we can obtain a
deeper understanding of the underlying molecular mechanisms, which opens the way towards
a more targeted breeding approach for higher
stress tolerance in crop plants. The breakthrough
that has made this approach possible was the
introduction of easy-to-use DNA markers that
brought QTL mapping into the mainstream, making it possible to efficiently map the genetic loci
controlling complex traits. This was made possible through genetic linkage analysis, allowing
the construction of linkage maps, and the identification of QTLs controlling particular traits
based on statistical methods that help establish
the association between molecular markers and
phenotypic data.
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A Dissecting Complex Traits Using QTL
Mapping
Although the theoretical underpinnings of modern
QTL mapping were introduced earlier in the twentieth century, the method was limited in application due to the dependence on morphological
markers to tag genes (Sax 1923; Thoday 1961). It
was not until the introduction of molecular markers, starting with isozymes, that QTL mapping
could provide comprehensive coverage of the
genome in scanning the loci that control complex
traits (Tanksely 1993). Once DNA markers such
as RFLPs and SSRs became widely available for
most plant species, QTL mapping was quickly
adopted. To date, there are over 10,000 mapped
QTLs reported for rice and maize in the Gramene
database (www.gramene.org). One of the key
advantages of QTL mapping is the ability to map
genes underlying many different traits and trait
components using the same mapping population
and the same genetic linkage map. For abiotic
stress tolerance, it becomes possible to test different physiological components and compare the
QTL locations for these with the QTLs for tolerance or yield under stress to identify the causal
factors. Furthermore, use of permanent mapping
populations, such as recombinant inbred lines
(RILs) or chromosomal segment substitution
lines (CSSLs) enables testing stress tolerant traits
in replicated experiments across different environments, which can help differentiate the QTLs
based on their effectiveness at different stress
levels. Once important QTL targets are identified, i.e., large-effect QTLs from the donor that
provide increased stress tolerance, these can be
captured as single introgressions in a set of nearisogenic lines (NILs), which can help unravel the
complexity of different traits by limiting the variation between lines to focus only on the locus of
interest. The NILs then provide the foundation
for further physiological characterization, finemapping, and ultimately cloning of the QTL to
identify the causal gene.
B Gene Discovery: Genomics
and Positional Cloning
Not long after QTL mapping became commonplace, another breakthrough arrived that brought
genetic mapping to the DNA sequence level, i.e.,
Michael J. Thomson et al.
the first high-quality complete sequencing of a
plant, Arabidopsis (Arabidopsis Genome Initiative
2000), which was followed soon after by the complete sequencing of rice (International Rice Genome
Sequencing Project 2005). Having the complete
DNA sequence was an instant boon to genetic
mapping as it presented an opportunity to make a
universal consensus map that can bring together
genetic mapping data from disparate sources into
a single physical map based on the DNA sequence.
This eliminated problems with ambiguous marker
orders and variable map distances, and allowed
previously mapped genes, QTLs, and markers to
be integrated regardless of the original mapping
population. In addition, it provided many new
markers across the genome, which is essential for
fine-mapping. For example, the rice microsatellite
map grew from 500 simple sequence repeat (SSR)
markers using conventional techniques (Temnykh
et al. 2001); to 2,740 SSRs using limited sequence
data (McCouch et al. 2002); to 18,828 SSRs using
the complete rice genome sequence (International
Rice Genome Sequencing Project 2005). In the
future, single nucleotide polymorphism (SNP)
markers will further increase the number of available markers and will enable more cost-effective
high-throughput genotyping techniques (Box 20.1).
This wealth in the number of markers is also helpful when screening large numbers of background
markers to offset low polymorphism rates, when
dealing with closely related parents.
Box 20.1 How Will New Marker Technologies Impact Marker-Assisted Breeding?
Present Technology: Currently, SSRs are predominantly being used to map and introgress
agronomically important QTLs into popular
varieties using MABC. However, their use is
still limited by the lack of sufficient polymorphism particularly within related genotypes,
labor requirements and cost of application. In
addition, SSR markers have a low potential
for multiplexing and require lengthy periods
of time to genotype many markers (as during
the initial background selection in the markerassisted backcrossing protocol; see Fig. 1).
This results in a short window of time available to obtain the necessary data to select which
individuals to backcross.
20
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Marker Assisted Breeding
Box 20.1 (continued)
Future Technology: New marker technologies,
such as single nucleotide polymorphism (SNP)
markers, promise to greatly increase the number
of available markers and the speed of genotyping while lowering the cost per data point. For
example, Illumina SNP-bead arrays can be used
to simultaneously genotype 1,536 SNP markers
across the genome for each DNA sample.
Impact: SNP markers will enable genomewide screens of thousands of markers simultaneously, which will allow all donor introgressions
to be quickly identified during the background
selection steps in the marker-assisted backcrossing protocol (see Fig. 1). This information will
allow more selective genotyping in subsequent
steps, since only the known introgressions need
to be tracked. Likewise, the high resolution
SNP genome scans can be used for the final
step to confirm the background conversion to
the recurrent parent.
Outlook: As new marker technologies bring
down the cost per data point, it makes more
sense to integrate high resolution genome scans
into marker-assisted breeding programs to
increase the overall efficiency by reducing the
time spent on marker genotyping and providing more robust data with fewer errors and more
confidence in the location of the donor introgressions. These improvements will likely result in
more effective use of markers in breeding for
abiotic stress tolerance in major crop plants.
These advances have improved the efficiency
of fine-mapping and have made cloning a QTL,
to isolate the casual gene, much easier in Arabidopsis (Lukowitz et al. 2000; Jander et al.
2002) and in rice (Ashikari and Matsuoka 2006),
in spite of the challenges still existing (Salvi
and Tuberosa 2005). Most of the QTLs cloned
to date were associated with morphological
attributes such as plant height, fruit characteristics or flowering time (Paran and Zamir 2003),
and similar approaches can be used successfully
to identify genes controlling key steps for other
physiological traits. Several major QTLs associated with tolerance of abiotic stresses have been
identified and fine-mapped in rice, with the first
examples of map-based cloning being the salinity
tolerance QTL SKC1 (Ren et al. 2005) and the
submergence tolerance QTL SUB1 (Xu et al.
2006). The fine-mapping and cloning of QTLs
has also given more confidence in the results of
the primary QTL studies, which have proven to
be highly accurate upon retrospect (Price 2006).
Once a QTL is cloned, knowledge of the underlying sequence allows further probing into allelic
variation at the causal gene level, which can help
identify the functional nucleotide polymorphism
(FNP) that controls the change in phenotype. The
FNP can then be used to develop a functional or
perfect marker that directly assesses the desired
phenotype at the molecular level. For example,
the cloning of the fragrance gene led to a perfect
marker for aroma in rice at an 8 bp deletion (Bradbury et al. 2005), and the cloning of the gene for
red pericarp in rice led to the identification of a 14
bp deletion that has been developed into a marker
for red rice (Sweeney et al. 2006). These functional markers can have several advantages in
marker-assisted breeding, especially as they will
always co-segregate with the desired phenotype,
eliminating the danger of recombination between
the linked markers and the target gene. This also
allows for rapid diagnosis of the allele state at
that gene across diverse germplasm accessions
(Andersen and Lubberstedt 2003; Mackill and
McNally 2005). In a number of cases, however,
the FNP may be elusive, especially if multiple
sequence changes can result in the same phenotype. In this case, the FNP marker may work well
with a particular source of the gene, but may not
work across all germplasm accessions that have
the trait.
C
Strategies for Marker-Assisted Selection
The recent advances in genomics have paved the
way for clear and reliable methods for MAS in
plants: from QTL identification, NIL development and fine-mapping to transferring the QTL
into popular varieties using a precise markerassisted backcrossing (MABC) strategy (Mackill
2006; Collard et al. 2005; Collard and Mackill
2008; Collard et al. 2008). MABC involves the
manipulation of genomic regions involved in the
expression of particular traits of interest through
DNA markers, and combines the power of a conventional backcrossing program with the ability
to differentiate parental chromosomal segments.
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Michael J. Thomson et al.
Fig. 1. Example of the marker-assisted backcrossing scheme used to transfer the SUB1 QTL for submergence tolerance into six
mega-varieties, showing recommended numbers of plants and markers for each step to develop a Sub1-converted near-isogenic
line (NIL), at either the BC2F2 or BC3F2 generation, depending on the number of background introgressions remaining at the
BC2F1 generation and the size of the target introgression desired (Neeraja et al. 2007; E. Septiningsih, unpublished).
The efficiency of a MABC program depends on a
number of factors, including the size and reliability of the target QTL effect, the precision of the
target gene/QTL fine-map, the rate of polymorphism
when identifying background markers, as well as
the cost, speed, and failure rate of the markers
employed in each customized MABC system. For
each set of parents and for each target QTL, a cus-
tomized MABC package needs to be developed
with optimized foreground markers to select the
QTL target, recombinant markers flanking the
locus of interest to reduce linkage drag and an
evenly-spaced set of polymorphic background
markers across the genome to select the recurrent parent background (Neeraja et al. 2007; Collard and Mackill 2008). QTL mapping has been
20
Marker Assisted Breeding
progressing at an accelerating pace over the past
decade, but few products using this technology
have been released to farmers. Constraints to the
use of marker-assisted selection for quantitative
traits include:
•• Poor resolution of QTLs on the genetic map
•• Small effects of many QTLs
•• Interaction of QTLs with environment or genetic
background
•• Poor selection of appropriate parents for mapping
populations
•• The expense of genotyping, limiting the number of
samples that can be processed
If these constraints are carefully addressed, breeders would be much more likely to use MABC to
develop stress-tolerant varieties.
The proper selection of QTL targets, combined with the development of an optimized
MABC package consisting of tested markers and appropriate donors, is essential for the
successful implementation of MABC for any
breeding targets. The selection of the QTL target and appraisal for its usefulness in a MABC
program needs to weigh the benefits of a markerassisted program versus conventional selection.
For example, often trait components can be used
directly for selection in plant breeding depending
on their degree of association with plant adaptability or yield under specific environments, the cost
and precision of their assessment and their interaction with the environment. The genetic control of
these traits can be affected by factors such as the
number of genes involved, extent of association
with undesirable pleiotropic effects, or adverse
genetic linkage. Traits such as yield, nutrient
acquisition and tolerance of abiotic stresses consist of several underlying components that need
to be combined to achieve higher performance.
At present, inadequate progress has been made
in using these physiological criteria for the largescale breeding needed to combine multiple traits
of importance, and their use has been essentially
limited to the identification of parental lines. The
value of using markers as a surrogate for direct
selection of stress tolerance components or yield
under stress will often depend on how difficult
are the phenotyping techniques and the amount of
replicated trials required versus how reliable are
the linked markers in predicting the phenotype
after its transfer to a recipient variety. Practically,
457
it is advisable to introgress QTLs of important
agronomic or adaptive values into varieties that
are well known to farmers and are covering large
areas. This will ensure that the new varieties will
be used immediately by farmers, millers and marketing channels, who are usually cautious when
taking on new varieties (Mackill 2006). Provided that popular varieties normally have limited lifespan, MABC also needs to be integrated
with conventional breeding to incorporate useful
QTLs into elite breeding lines.
So far, the greatest success in MABC for
improving tolerance of biotic and abiotic stresses
has been achieved with QTLs proven to provide
high levels of tolerance in many different genetic
backgrounds and environments (Collard and
Mackill 2008; Collard et al. 2008). A good example in cereals is the introgression of SUB1, the
major QTL for submergence tolerance, into several popular rice varieties (Xu et al. 2006; Neeraja
et al. 2007). Future breeding objectives, however,
may require more complex situations, such as the
pyramiding of multiple QTLs having more subtle
effects that are effective during different developmental stages, or the combining of QTLs for different abiotic stress tolerance into the same genetic
background. Up to now, MABC has been successful in transferring traits whose expression is controlled by a single gene or by a gene that controls
most of the phenotypic variance of the trait. However, the effective use of MABC in combining several genomic regions which control a single trait
or a few independent traits required for a desired
phenotype still awaits further development of
more efficient technologies and innovative strategies. An example of a successful approach used in
the transfer of a single QTL into several popular
rice varieties is presented in Fig. 1.
III Case Studies from a Model Crop:
MAS for Abiotic Stress Tolerance in Rice
Abiotic stresses seem to offer unique opportunities
for the application of markers because of the
identification of major QTLs coupled with the
progress made in understanding the biology of
tolerance to these stresses and the availability
of efficient phenotyping systems, which hold
promise for tackling such challenging traits. The
most common stresses adversely affecting rice
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Michael J. Thomson et al.
production are excess or deficiency of water,
extremes of temperature, and mineral deficiencies
or toxicities. We will briefly review the progress
made in several key stresses that limit rice
production worldwide.
A
Flooding
Excess water stress is a serious problem for rice
in flood-prone areas, with damage resulting from
water logging during germination, and partial or
even complete submergence for varying durations
during the growing season. Various tolerance
traits are necessary for high and stable productivity in these areas. Direct seeding of rice is increasingly being practiced in both rain-fed and irrigated
areas because of labor shortage for transplanting.
However, poor crop establishment remains a
major obstacle facing its large-scale adoption in
areas where flooding is anticipated due to rain or
uneven leveling. Using a backcross population
developed from a tolerant landrace “Khao Hlan
On”, and a sensitive variety IR64, six QTLs were
detected, two each on chromosomes 1, 7 and 9,
explaining 7–31% of the phenotypic variation,
and with LOD scores in the range of about 5–20
(Angaji et al. 2009). Current efforts focus on finemapping a few of these QTLs for exploitation
through MABC (E. Septiningsih, unpublished).
Complete submergence affects more than
10 million hectares of rice lands in Asia. This
stress received considerable attention in the past
few decades, and a few tolerant landraces were
identified that can withstand inundation for up
to 2 weeks. The physiological bases of tolerance were also extensively studied, and among
the traits identified (as critical for tolerance) are
high energy reserves, limited underwater growth
and retention of chlorophyll (Jackson and Ram
2003; Ella et al. 2003; Sarkar et al. 2006). The
Indian cultivar FR13A is the most widely used
source of submergence tolerance, and a major
QTL, designated SUB1, was identified that controls most of the submergence tolerance of this
genotype (Xu and Mackill 1996). FR13A also has
additional QTLs that contribute to its tolerance
(Nandi et al. 1997; Toojinda et al. 2003). SUB1
was subsequently fine-mapped and cloned, and
three genes encoding putative ethylene responsive factors (ERF), SUB1A, SUB1B, and SUB1C,
were identified. SUB1A was recognized as the
primary contributor to submergence tolerance
(Xu et al. 2006). Cloning of SUB1 provided an
excellent opportunity to gain a better understanding
of the molecular mechanisms and to unravel the
pathways underlying submergence tolerance, and
it also helped in designing precise gene-based
markers for more accurate genotyping. SUB1 has
been successfully introgressed through MABC
into a popular high-yielding variety, Swarna,
within a 2-year time frame (Neeraja et al. 2007).
“Swarna-Sub1”, the first example of a submergence tolerant mega variety, is being evaluated
in submergence-prone areas of India and Bangladesh. In the absence of submergence, there were
no significant differences in agronomic performance, grain yield or quality between Swarna and
Swarna-Sub1 (Sarkar et al. 2006; Neeraja et al.
2007), but a substantial enhancement of survival
(Fig. 2a) and two to three-fold increase in yield
over the intolerant parent was observed after submergence for 12–17 day period during vegetative
stage in the field (Singh et al. 2009). Due to its
large effect in providing tolerance, this QTL is an
excellent candidate for the application of MAS,
and progress has been made to successfully convert several popular rice varieties to submergence
tolerant varieties using marker-assisted backcrossing with SUB1 (Septiningsih et al. 2009).
The SUB1 QTL provides a marked improvement of submergence tolerance in all genetic
backgrounds and environments tested so far. Yet,
the level of tolerance is still below that of the
original donor FR13A. There is a need to identify
and combine additional genes from FR13A and
probably from additional donors, including those
that confer rapid recovery after submergence,
along with SUB1. Varieties that combine tolerance to submergence during germination as well
as the vegetative stage, together with tolerance to
partial long-term stagnant flooding, would have
a major advantage for achieving higher and more
stable productivity in flood-prone areas. Molecular markers specific to SUB1 and to QTLs associated with tolerance of anaerobic conditions
during germination are currently being used to
select new lines combining both traits.
B
Salinity
Salt stress negatively affects growth and productivity of most crop plants and recent research
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459
Fig. 2. (a) Performance of Sub1 introgression lines under field conditions. Fourteen day old seedlings were transplanted in the
field and completely submerged 14 days later, for 17 days. Photo was taken about 2 months after desubmergence. (1) IR64,
(2) IR64-Sub1, (3) Samba Mahshuri, (4) Samba Mahshuri-Sub1, (5) IR42 (sensitive check) and (6) IR49830 (tolerant, used
as SUB1 donor) (Photo courtesy of IRRI); (b) a rice farmer and his wife showing the performance of their local variety (right)
and an improved salt tolerant breeding line (left) in a highly alkaline soil in Faizabad district, Uttar Pradesh, India (Photo by A.
Ismail taken on Oct. 7, 2007) [See Color Plate 12, Fig. 19].
has started to unravel the complexities of the
traits involved in its tolerance, such as control
of sodium transport, Na+ and K+ ion homeostasis,
and salt response signaling (Zhu 2003; Horie and
Schroeder 2004). The fundamental knowledge
of salt response mechanisms in plants forms
the basis for developing strategies to improve
salt tolerance in crop species such as rice (Sahi
et al. 2006; Ismail et al. 2007). Although rice is
relatively salt-sensitive, it is the only cereal that
can grow on many salt-affected soils because it can
survive recurrent floods in coastal areas and can
thrive in standing water that can help leach salts
from top soils in inlands. Tolerance of salt stress
in rice is complex and varies with the stage of
development, being relatively tolerant during
germination, active tillering and towards matu-
rity, but sensitive during the early vegetative and
reproductive stages (also see Chapter 18). Salinity tolerance at these two sensitive stages is only
weakly associated (Moradi et al. 2003). Hence,
discovering and combining suitable tolerance
traits at both stages is essential for developing
resilient salt-tolerant cultivars. Despite this complexity of traits associated with salinity tolerance
in rice, substantial progress has been made in
developing salt tolerant breeding lines that are
being evaluated and selected in farmers fields
(Fig. 2b), some of which have been also released
as varieties.
Tolerance during the early vegetative stage
involves a number of contributing traits, including
salt exclusion and control of ion homeostasis,
higher tissue tolerance by compartmentalizing
460
salt into vacuoles, responsive stomata that close
faster upon exposure to salt stress, up-regulation
of antioxidant systems for protection against
reactive oxygen species (ROS) generated during
stress, and vigorous growth to dilute salt concentration in plant tissue, amongst others (Yeo and
Flowers 1986; Moradi and Ismail 2007). During
the reproductive stage, tolerant genotypes tend
to exclude salt from flag leaves and developing
panicles (Yeo and Flowers 1986; Moradi et al.
2003). Developing tolerant varieties will entail
combining these various component traits into a
high yielding genetic background, which is hard
to achieve through conventional methods. The
recent advances in understanding the physiological and molecular bases of tolerance are providing
better tools to overcome these hurdles and can
substantially enhance progress by enabling more
precise genetic manipulation.
Recently, sodium transporters have been shown
to play key roles in maintaining ion homeostasis
in plants under salt stress, through several mechanisms that remove sodium from the cytoplasm
by either compartmentalizing it into vacuoles or
extruding it out of the cell (Horie and Schroeder
2004). The salt overly sensitive (SOS) pathway
is well characterized in Arabidopsis as being
involved in signal perception and ion homeostasis
under salt stress (Zhu 2003). Recently, the role of
this system in controlling salt stress in case of rice
has been further elucidated (Martinez-Atienza
et al. 2007). SOS pathway genes have also been
identified in Brassica (Kumar et al. 2009). In
addition, the HKT family of transporters has been
shown to play important roles in sodium and potassium uptake as well as homeostasis in a number
of plant species including rice (Horie et al. 2001;
Golldack et al. 2002). Recently, cloning of the rice
QTL SKC1, originally detected by its effect on K+
concentration, identified the causal gene as the
sodium transporter OsHKT8 (Ren et al. 2005).
Several mapping studies identified QTLs associated with salinity tolerance in rice. For example,
a study employing the tolerant Indica landrace
Nona Bokra with the susceptible japonica Koshihikari, identified several large-effect QTLs,
including the SKC1 QTL and a QTL for shoot
Na+ concentration on chromosome 7 (Lin et al.
2004). Similarly, a RIL population between the
highly tolerant landrace Pokkali and sensitive
IR29 identified a major QTL, designated Saltol,
on chromosome 1, which accounts for about 45%
Michael J. Thomson et al.
of the variation for seedling and shoot Na+/K+
ratio (Bonilla et al. 2002). While the salt-tolerant
landraces Pokkali and Nona Bokra were routinely
used in the past for breeding, the level of tolerance attained by new lines is always below that of
the traditional donors (Gregorio et al. 2002), and
the existing tolerant varieties seem to be superior
in only a few of the traits known to be associated
with tolerance. Combining superior alleles underlying these traits could potentially result in higher
levels of tolerance, a task difficult to achieve
through conventional methods. More recently, the
application of QTL mapping provided the means
to genetically dissect tolerance traits into discrete
QTLs that can then be pyramided into high-yielding
rice varieties using DNA markers. By integrating
physiological trait dissection with these genetic
and genomic tools, a more complete picture of
the complex mechanisms of salt tolerance in rice
is beginning to emerge. These advances in turn
provide the foundation for efficient deployment
of tolerance QTLs through MAS.
Near-isogenic lines have been developed for
Saltol, which is the major QTL on chromosome
1. The locus is currently being fine-mapped and
annotated for further candidate gene analysis and
more precise gene-specific markers are being
developed. A MABC strategy for Saltol was developed and is currently being used to incorporate
the Pokkali allele into popular salt stress-sensitive
varieties. Furthermore, other QTLs were identified on chromosomes 3, 4, 10 and 12 for salinity
tolerance at the seedling stage. Genetic stocks of
RILs and backcross populations were developed
to allow further analysis of these QTLs to evaluate
their usefulness in breeding. Mapping populations
are being developed to identify QTLs associated
with tolerance during the reproductive stage, to
ultimately combine tolerance at both stages for
more stable performance in salt affected areas.
After identifying a number of QTLs controlling
different mechanisms and providing tolerance at
different stages, MABC can be used to develop
rice varieties adapted to any specific target conditions based on the extent and time of stress, during the season when stress is anticipated.
C
Phosphorus Deficiency
After nitrogen, phosphorus is the most important
inorganic plant nutrient but the least available
in most soils because of its tendency for tight
20
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Marker Assisted Breeding
binding. As a consequence, phosphorus deficiency is widespread in many rice-growing areas,
particularly where farmers do not have access to
phosphate fertilizers and, in most cases, because
these soils have high P-fixing capacity. Breeding
efficient cultivars capable of effectively mining
the large pool of P already fixed in most soils will
help increase and sustain yields in low-input agricultural systems, particularly for cereal crops.
Two QTL mapping studies have been reported
in rice. Wissuwa et al. (1998) used a backcross
inbred population, with the recurrent parent Nipponbare (japonica, sensitive) and the landrace
Kasalath (indica, tolerant). They detected a major
QTL on chromosome 12 for P uptake, P-use efficiency, shoot dry weight, and tiller number. For
P uptake, this QTL had a LOD score of 10.7 and
explained about 28% of the phenotypic variation. Ni et al. (1998), using RILs from the cross
of IR20 (tolerant) with IR55178-3B-9-3 (sensitive), found a similarly strong QTL in the same
location. They measured P uptake efficiency as
relative tillering ability, relative shoot dry weight,
and relative root dry weight. Moreover, an intermediate QTL on chromosome 6 and several other
minor QTLs were mapped to several chromosomes. The QTL on chromosome 6 accounted
for 25–34% of the variance for the above traits
in the Ni et al. (1998) study, but has much less
effect (R2 = 9.8%) in the field study of Wissuwa
et al. (1998). Subsequent studies focused on the
major QTL for phosphorus uptake, located on
chromosome 12, designated “PUP1”. Wissuwa
and Ae (2001a) transferred this QTL by three
backcrosses into the japonica variety Nipponbare. The resulting lines containing the tolerant
allele showed a 170% increase in P uptake and
250% increase in yield when grown under low-P
conditions. The NILs with the PUP1 allele from
Kasalath had increased root growth under low-P
conditions, but the differences in root growth and
P uptake were not observed under anaerobic soil
conditions (Wissuwa and Ae 2001b). This QTL
explained close to 80% of the phenotypic variation
in a secondary mapping population (Wissuwa
et al. 2002). Additional cycles of fine mapping
further reduced the PUP1 interval to about 145 kb
(Heuer et al. 2009). Subsequent sequencing of the
corresponding chromosomal region in the donor
parent “Kasalath” showed that PUP1 locus in
Kasalath is much larger (278 kb), with large numbers of transposon- and retro-transposon-related
elements (Heuer et al. 2009). None of the genes
annotated in the PUP1 locus were found to be
related to previously known genes involved in P
uptake or metabolism, and detailed analyses of
the putative candidate genes are currently ongoing.
A marker assisted backcrossing system was
developed and is being used to transfer this QTL
into three popular upland rice varieties that were
sensitive to phosphorus deficiency, particularly
in acid soils (A. Ismail, M. Wissuwa, S. Heuer,
unpublished). The significance of this QTL in
enhancing P-uptake efficiency will further be validated after completing the development of these
near isogenic lines.
D
Drought
Drought is the most widespread and damaging of
abiotic stresses, but improving the drought tolerance of rice has been hindered by the low level
of genetic variability and the complex inheritance
of the trait. One of the most serious constraints
to improving drought tolerance is the difficulty
of accurately measuring the level of tolerance.
Stress symptoms such as leaf death and rolling
are the easiest to measure, but these traits are not
always related to yield under stress or to yield
in the target environment, which would include
yield under stress as well as yield potential without stress. Molecular approaches to drought tolerance have been widely applied to rice, beginning
with QTL analysis. Numerous QTLs were identified for secondary traits that are expected to be
associated with drought response, such as root
characteristics (depth, volume, thinness, penetration ability), leaf rolling and death, membrane
stability, and osmotic adjustment (Lafitte et al.
2006). However, very few studies have mapped
QTLs related to the actual objective of enhanced
yield under drought.
Babu et al. (2003) found important QTLs related
to grain yield under stress on chromosomes 4 and
12. More recently, two major QTLs for yield
under drought were mapped. Bernier et al. (2007)
identified a major QTL (qtl12.1) for drought tolerance in a Vandana × Way Rarem mapping population. This QTL improves yield under drought
by 47%, and explained more than 50% of the
genetic variance. Furthermore, this QTL co-localized with PUP1, the P uptake QTL on chromosome 12. Fine-mapping of this locus is currently
ongoing to establish whether the drought QTL is
462
Michael J. Thomson et al.
pleiotropic with PUP1 or whether the two are just
closely linked. Understanding this association is
important because the PUP1 locus was found
to enhance root growth and is effective only in
aerobic soils. A second major QTL was mapped
on chromosome 4 from an IR55419-04 (tolerant)
× Way Rarem mapping population (A. Kumar
et al., unpublished). This QTL is currently being
fine mapped at IRRI. Both QTLs hold greater
promise as targets for marker-assisted breeding
to enhance drought tolerance in rice.
IV
Future Perspectives
A Association Mapping for Abiotic Stress
Tolerance
Association or linkage disequlibrium (LD) mapping represents an alternative approach to identifying genes or genomic regions associated with
quantitative phenotypic variation (Buckler and
Thornsberry 2002; Gupta et al. 2005). Similar to
QTL mapping, association mapping exploits natural diversity and recombination within a population
to correlate polymorphisms with measurable phenotypic variations. However, in contrast to linkage
or QTL mapping, which depends on recombination events generated over a fixed number of
generations following a bi-parental cross, association mapping exploits larger number of historical
recombination events in a population or diverse set
of lines over the course of evolution.
Linkage disequilibrium mapping has several advantages over traditional QTL mapping
approaches (Thornsberry et al. 2001). First, it can
survey the range of allelic variation present in a
natural population, and is not restricted to a set
of alleles found in the two progenitors of a mapping population. Second, by relying on historical recombination, it is often possible to localize
QTLs in a genome to a higher degree of resolution
than is possible with the same number of individuals using traditional QTL linkage analysis.
Third, the technique can be used without developing new mapping populations. Thus, LD mapping can potentially achieve higher resolutions
with greater efficiency than linkage-based QTL
mapping techniques by taking advantage of both
the array of molecular diversity within a species
and the large amounts of historical recombina-
tion that has occurred within and between populations during evolution. It is these features that
have convinced geneticists and breeders to focus
attention on LD mapping as an efficient strategy
for identifying genes associated with traits of
interest, including abiotic stress resistance, based
on an exploration of the rich collection of rice
genetic resources.
To undertake an association mapping experiment, a collection of accessions is genotyped
for markers that span either the entire genome,
or a genomic region of interest (Wilson et al.
2004; Szalma et al. 2005). The markers are then
tested against a specific phenotype to determine
whether a statistical correlation exists between
marker genotypes and a particular trait. A significant association between marker(s) and trait may
arise either because the nucleotide polymorphism
causes the phenotypic difference, or because the
marker is in LD with the causal or functional polymorphism (Thornsberry et al. 2001).
The resolution of an association mapping
experiment depends on the extent of LD, which is
the correlation between polymorphic loci within
the test population (Flint-Garcia et al. 2003).
When a mutation arises in a population, it is automatically associated or comes in “disequilibrium”
with all the alleles present in the genome of the
individual that gave rise to the mutation. If the
mutation persists during evolution, associations
with other alleles are gradually eroded by segregation and recombination, so that over a period
of time, the mutation remains in LD only with
alleles which are closely-linked to it physically
(Hartl and Clark 1997).
The distance over which LD persists in a species or population determines the number and
density of markers required for association mapping, with large variations observed both within
and between genomes. The first study on LD
in rice reported an LD decay of ~70 to 100 kb
around the bacterial blight resistance locus, xa5,
in the aus sub-population (Garris et al. 2003).
More recent studies confirmed that LD generally
decays at the rate of ~50 to 100 kb in landraces of
indica, while it decays more slowly in japonica
(~150 kb in tropical japonica and >500 kb in
temperate japonica; Mather et al. 2007; Rakshit
et al. 2007). LD generally extends over significantly larger distances in elite varieties than in
landraces (Morrell et al. 2005; Remington et al.
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Marker Assisted Breeding
2001; Tenaillon et al. 2001) due to inbreeding,
selection, bottlenecks and population admixtures
(Nordborg and Tavare 2002; Weir 1996).
The different rates of LD decay in different
sub-populations of rice offer opportunities to consciously move between low and high-resolution
mapping. In the first instance, the use of breeding
lines and elite varieties with extensive LD means
that a modest number of markers would be sufficient to identify region(s) of the genome containing
a gene or QTL (Agrama et al. 2007; Zhang et al.
2005). While the LD mapping resolution using
elite germplasm may not be significantly better
than QTL mapping, it does, nonetheless, provide
an opportunity to identify associations between
a phenotype and a larger set of alleles. In contrast, the use of wild species or landrace varieties that exhibit more rapid LD decay require a
larger number of markers to define the recombination profiles of the accessions and provide
significantly higher LD mapping resolution. In
cases where LD decay is very rapid it may not
be cost-effective to saturate the entire genome
with closely linked markers. In these situations,
markers are targeted to a candidate gene or QTL
region across the association mapping panel in an
effort to narrow down the interval containing the
target gene(s) (Garris et al. 2003; Kruglyak 1999;
Olsen et al. 2006; Sweeney et al. 2007).
Association mapping is most productive when
used in association with QTL mapping. Together,
these approaches provide rice researchers with
numerous possibilities for establishing meaningful associations between phenotypes and
genes. There are currently efforts underway to
develop a group of recombinant inbred populations for QTL analysis, along with an immortal
association mapping panel that will offer the
genetics and breeding community many opportunities to explore genetic diversity and the basis
of phenotypic variation in rice. The association
mapping panel will consist of several thousand
diverse, purified genetic stocks of wild, landrace
and elite rice accessions, and this collection can
be expanded at will. Purification of the lines is
required to ensure good quality and reproducibility of the genotyping and phenotyping effort that
provides the data for association analysis. Use
of genetically identical material will enable the
rice research community to leverage its collective
strengths to phenotype the lines in a distributed
manner, focusing on specific sub-populations and
traits that are most interesting or important to a
given group of researchers in different institutions
and environments. The lines will be genotyped
in a central facility and both the genotypic data
and the purified seed stocks will be made publicly available. Using this coordinated approach,
abiotic stress tolerance can be rigorously evaluated on a common set of materials over years
and environments, and data collected by different
groups of researchers can be analyzed together.
This would provide new opportunities to unravel
the relationship between genotype and phenotype,
and will deepen our understanding of the diverse
genetic mechanisms that allow plants to respond
to a wide range of environmental stresses.
Based on estimates of LD, it is suggested that
polymorphic markers will be needed approximately every 50 kb to cover the genome for association mapping in O. sativa. If this is true, then
~8,000 well-distributed polymorphic markers
would provide a good chance of performing
genome-wide association mapping in case of O.
sativa. However, to have a reasonable chance
of finding polymorphic markers across the different subpopulations, and to take advantage of
the more rapid LD decay in some regions of the
genome, it is recommended that a set of ~24,000
to 40,000 well-distributed markers be deve
loped for association mapping in rice. A fixed
genotyping array consisting of 44,100 SNP
markers is currently under development for rice
(www.ricediversity.org).
B Variety Development and Gene
Deployment
As plant breeders have achieved great success in
developing high-yielding varieties, it has become
increasingly difficult to significantly improve on
these varieties for the basic agronomic traits of
interest. Mega varieties that are widely grown
and liked by farmers have been ensconced in the
agricultural system and it is increasingly difficult to displace them due to their suite of desirable features. These varieties not only have high
grain yield, but often have improved quality traits
meaning that they can be easily sold and marketed, ensuring farmers of consistent demand.
Furthermore, the seed systems in rice-growing countries cannot easily cope with multiple varieties
464
and this means that only one or a few varieties
are easily available to the farmers. This situation
explains why the varietal upgrade path through
marker-assisted backcrossing is a reasonable one
that is likely to achieve more impact through
rapid adoption of improved varieties.
As discussed above, this approach is most
effective when major QTLs are available for the
traits of interest, and this has been the case for
several abiotic stresses. Nevertheless a similar
approach could be feasible for smaller QTLs.
Even a relatively small-effect QTL could have
a big impact if deployed over a large area. One
of the advantages of the MABC approach is that
linkage drag can be minimized through the use
of recombinant selection with flanking markers
(Collard and Mackill 2008). When multiple
QTLs are being transferred, this could make a
substantial difference in the ability to retain the
desirable features of the mega variety. A wider
use of this strategy by rice breeders underlines
the importance for fine-scale mapping and positional cloning of QTLs. The wide applicability of
this approach should not be seen as a substitute
for more conventional breeding approaches that
place emphasis on developing new mega varieties.
The MABC strategy is a relatively conservative
approach aimed at getting incremental improvements in the best varieties, while plant breeders
will continue to aim at the goal of new mega varieties through multiple approaches relying on conventional breeding and MAS.
C Bioinformatics Supporting Molecular
Breeding
1 Integrating Marker Genotype and Plant
Phenotype Data
Over 8,000 QTLs for a wide array of traits
have been reported in rice over the last 15 years.
Today, a coordinated body of information about
the genomic location of these QTLs, as well
as marker trait associations is available in the
Gramene database (www.gramene.org). This
information resource added value to individual
mapping studies by aligning all the QTLs to the
rice genome sequence using sequenced markers
as anchors. As a result, the rice research community can now readily access information about
linkage relationships among QTLs for different
Michael J. Thomson et al.
traits, derive hypotheses about the stability of
QTLs across genetic backgrounds and environments based on co-localization of QTLs across
studies, and rapidly identify numerous sources
of favorable alleles for diverse traits. The alignment of QTLs across multiple studies has also
facilitated the effort to fine-map and clone genes
underlying many of these QTLs, providing plant
breeders with “functional markers” for use in
MAS, and providing geneticists with new information about the genes and alleles that are critical
to agricultural performance.
As agronomically useful genes are identified,
they can be mapped to biochemical and regulatory pathways (Shimizu et al. 2007). Pathway
information helps researchers to predict how
variation at a particular locus may affect or be
affected by other genes in the same or different
pathways (European-Plant-Science-Organization
2005). It offers a framework for understanding
epistasis, and can help identify multiple genetic
factors that collectively determine the phenotype
of a plant. It also provides opportunities to implement a reverse genetics approach for finding new
genes associated with a trait of interest, and for
helping to identify parents that may contribute
useful variation to a breeding program.
2
Using a Gene and Plant Ontology
The rapid accumulation of genetic and genomic
information today has led to a rapid change in our
understanding of the biological world. We must
scramble to identify and make use of relevant findings. Despite the abundance of new discoveries,
we are challenged to find an appropriate method
for screening through the mountain of data to
identify meaningful bits of information. Genome
databases are designed to address this problem by
providing a set of tools for data browsing and data
mining that are tailored according to the needs of
the biological community. While not specifically
designed for plant breeders, these databases are
indispensable to the modern plant breeding community because they provide information about
genes, alleles, germplasm, pathways, phenotypes
and environments (Bruskiewich et al. 2003;
Jaiswal et al. 2006b).
To be useful, data must be entered into the
database in a timely way, and it must be structured and organized so that disparate pieces of
20
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Marker Assisted Breeding
information can be retrieved computationally
to answer a relevant question. Underlying the
success of data mining activities is the use of
ontologies and controlled vocabularies (sets of
terms with defined relationships to each other),
that provide a structured set of hierarchical
relationships allowing independent pieces of
information to be associated with each other in
meaningful ways. In the context of genetics and
plant breeding, ontologies have been developed
for genes, phenotypes, traits, environments, etc.
(Clark et al. 2005; Ilic et al. 2007; Jaiswal et al.
2006a; Pujar et al. 2006). These ontologies make
it possible to compare data from diverse organisms and experiments within a data domain (i.e.,
genes), and they also make it possible for the
computer to identify meaningful associations
between data domains based on the relationships defined by the ontologies (i.e., genes and
phenotypes).
The development of ontologies and controlled
vocabularies is a rapidly evolving science. It represents a cross-road of expertise involving biologists, agriculturalists, computer scientists and
software engineers, whereby essential biological
relationships are examined and described in a
clear and logical manner. In the Gramene database, several ontologies are currently employed
to facilitate browsing and data mining activities,
including the gene ontology, the plant anatomy
ontology, the plant growth stage ontology, the trait
ontology and the environment ontology (Clark
et al. 2005; Ilic et al. 2007; Jaiswal et al. 2006b;
Pujar et al. 2006; www.gramene.org). Use of these
ontologies requires that data, or biological observations, be catalogued using structured as well
as controlled vocabularies. The use of controlled vocabularies means that scientists are offered
a menu of terms that can be used to describe or
annotate observations and they have to choose
from among these terms, rather than entering free
text at the time of entering data into a database.
The terms are defined in ways that allow a computer
to recognize and rapidly retrieve them when queried to do so (Bruskiewich et al. 2003; Jaiswal
et al. 2006b). The use of controlled vocabularies is
necessary so that new observations can be readily
placed into appropriate relationships with existing
data by a computer and it subsequently facilitates
retrieval of a meaningful set of related data points
when prompted by a query.
3 Databases for the Next Generation of Plant
Breeders
Because of their power and utility, genome databases have become part of the essential toolkit of
modern plant breeders and geneticists. Not only
do databases make it possible to identify candidate genes associated with phenotypes of interest, or to identify markers linked to a particular
QTL for use in MAS, but they efficiently leverage information from an experiment conducted
on one genome or in one environment as the basis
for predicting relationships between traits, genes
and pathways in another genome evaluated in a
different environment. The role of comparative
genomics and comparative biology as a paradigm
for addressing questions of basic biological significance has begun to impact the applied plant
breeding community as well. Questions that were
once debated with species-specific and regionally localized communities are now addressed
in a much broader, comparative context, and
the perspective of many plant breeders has been
transformed by the recent availability of relevant
genome data and the power of genome databases
and data mining tools to retrieve and assemble
meaningful information in real time.
Nonetheless, there is still a gap between our
ability to generate digital information about genes
and alleles and corresponding phenotypic variation
of interest, and a plant breeder’s ability to utilize
that information to develop a new variety. More
emphasis needs to be placed on characterizing
diverse genetic resources, both genotypically and
phenotypically, and high throughput phenotyping methodologies are needed to keep pace with
the flow of information from genome sequencing centers. Most importantly, we need greater
integration between databases that specialize in
germplasm information management and those
that specialize in the management of gene-based
knowledge. It is at the intersection of these two
worlds where the greatest gain for plant breeders
will lie, and where some of the most exciting scientific questions remain to be explored.
V
Conclusions
For rice, some of the best QTLs identified for
abiotic stress tolerance have significantly larger
466
effects than those identified for yield and related
agronomic traits (Mackill 2006). This suggests
that measurable progress in improving productivity under unfavorable conditions could
be achieved by transferring these loci into elite
genotypes. QTLs that have a large effect on the
phenotype and are relatively stable across genetic
backgrounds and environments are most desirable for applications in marker-assisted selection.
These QTLs could also aid in further dissection
of the physiological basis of tolerance of these
stresses. Isogenic lines that differ in the introgression of a particular QTL could be generated to
help in functional analysis and in evaluating the
effect of a particular QTL for yield improvement
or for general adaptability.
The past two decades witnessed substantial
progress in our understanding of plant functions
and plant adaptations to different environments.
This knowledge was aided by the integration of
new tools of molecular biology with the conventional phenotypic methods of plant physiology
and biochemistry. Most of the abiotic stress genes
in case of rice have been detected based on visual
symptoms and while these symptoms correspond
fairly well to the actual damage that is observed
under field conditions, more quantitative estimates of plant stress tolerance are needed. While
phenotyping methodologies have not yet been
automated, genotyping platforms are becoming
increasingly automated, efficient and cost effective. This makes it possible for plant physiologists to dissect complex phenotypes into distinct
factors that are associated with genetic loci or
QTLs. The phenotypic impact of each QTL can
subsequently be studied in specific genetic backgrounds by developing NILs for the locus under
investigation. The ability to map QTLs also provides information about the association between
physiological traits, particularly those that are
inter-dependent, as well as in understanding the
relationships of physiological and metabolic
processes with other developmental and morphological traits. Molecular marker technology has
greatly accelerated the progress in gene discovery through map-based cloning. The progress in
understanding gene function will further be aided
by expression analysis and evolutionary studies.
Complementing conventional methods of plant
breeding with MAS for favorable genes and QTL
has already enhanced our breeding efficiency.
Michael J. Thomson et al.
Indeed, it is an exciting era for plant breeders and
physiologists who can bring new knowledge and
technologies to bear on the development of more
productive, stress-tolerant varieties as we strive
to tackle the diverse challenges facing agriculture
in the future.
Acknowledgements
We gratefully thank E. Septiningsih and A. Kumar
for sharing unpublished data. Research activities
summarized here were supported in part, by the
German Federal Ministry for Economic Cooperation and Development (BMZ), the CGIAR Generation Challenge Program, the Bill and Melinda
Gates Foundation, and the International Rice
Research Institute.
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