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target QTL and similar genetic backgrounds. An
advanced backcross QTL (AB-QTL) approach
provides the opportunity to simultaneously iden-
tify and introgress QTLs in the recurrent parent,
and saves time involved in varietal development
(Tanksley et al. 1996). In doubled haploid (DH)
lines, an attempt is made to combine the advan-
tages of homozygosity with the speed at which an
early-generation population can be made (Peter-
son 2002). DH populations can be produced by
regenerating plants via the induction of chromo-
some doubling from pollen grains (Figure 4.1c);
however, the production of DH populations is
possible only in species that are amenable to
tissue culture (Collard et al. 2005). DH popula-
tions have been used previously for the identifi-
cation of QTLs for reproductive- and seedling-
stage drought tolerance in rice (Lanceras et al.
2004; Xu et al. 2011).
genome scanning (WGS) was used as a genotyp-
ing strategy in many studies (Figure 4.2a). WGS,
although costly, provides an opportunity for
identifying major- as well as minor-effect QTLs
and at the same time identifying the interaction
between different loci. Bulk segregant analysis
(BSA) is a DNA pooling technique that was first
proposed by Michelmore and colleagues (1991),
in which markers linked to disease resistance
genes were identified. It involves pooling of the
DNA of phenotypic extremes used for develop-
ing high and low bulks that are genotyped along
with the parents with all polymorphic markers
(Figure 4.2b). The markers with bulk bands cor-
responding clearly to the parents are considered
to be candidates, and a full population is geno-
typed with these markers for the identification
of QTLs. Apart from the fact that this strategy is
highly cost-effective and timesaving, it also elim-
inates the possibility of identifying any small-
effect QTLs. The only drawback of this strategy
is that it concentrates on just one segment of the
genome and hence the amount of information
it provides is limited. This technique has been
used to identify large-effect QTLs for grain yield
under drought (Venuprasad et al. 2009; Vikram
et al. 2011). Venuprasad and colleagues (2009)
used a subset of 4% of the total lines to con-
stitute the bulks, which led to the identification
of
qDTY
2.1
and
qDTY
3.1
, two large-effect QTLs
for grain yield under lowland drought. Another
trait-based genotypic analysis called “selective
genotyping” (SG) was suggested by Lebowitz
and colleagues (1987). In this approach, 10-15%
of the lines from the phenotypic extremes are
selected for genotyping (Figure 4.2c). This leads
to the generation of genotypic data for a sub-
set of the whole population, which in turn can
be used for developing a linkage map and in
mapping and interaction studies. The markers
found to be significant in the subset of the popu-
lation can be used to genotype the whole popu-
lation for a more precise estimation of the QTL
effect. This strategy is cost-effective, timesav-
ing, and provides information similar to that pro-
vided by WGS. One of the largest-effect QTLs
known for grain yield under drought,
qDTY
12.1,
MarkerGenotypingofMappingPopulations
Although uniform drought phenotyping is the
most important requirement of any QTL iden-
tification program (the IRRI drought-screening
phenotyping protocol has been described pre-
viously, e.g., Venuprasad et al. 2008; Serraj
et al. 2011), the appropriate use of molecu-
lar tools has become another important aspect
of QTL mapping. The challenge of identify-
ing a gene or QTL within a plant genome is
like finding the proverbial needle in a haystack
(Collard et al. 2005). However, genetic mark-
ers can be used to develop a systematic linkage
map of the chromosome, dividing the chromo-
some into smaller sections in which it is easier
to search for a putative QTL. Rice microsatel-
lite (SSR) markers provide a suitable base for
constructing a genetic linkage map. The ease of
polymerase chain reaction (PCR) amplification
and electrophoresis makes SSR markers suitable
for quick and easy genotyping of large mapping
populations. Moreover, the abundance of these
markers across the rice genome and their codom-
inant nature make them suitable for genotyping
a variety of mapping populations with elabo-
rate coverage of the genome. Earlier, whole-
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