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was identified through this strategy, in which
12% of the phenotypic extremes were used for
SG (Bernier et al. 2007). Navabi and colleagues
(2009) were able to detect this QTL by geno-
typing as few as 20 selected lines (4.5% of the
population). The success of both BSA and SG
depends on the precision of phenotyping and the
identification strategy of phenotypic extremes.
These methods can reliably detect large-effect
QTLs with minimum genotyping and thus allow
screening of larger numbers of mapping popula-
tions, identifying useful QTLs that are effective
across genetic backgrounds, or multiple QTLs
from different donors that are effective in the
same genetic background.
other traits such as DTF and plant height. BSA
was successfully used for the first time to iden-
tify such large-effect QTLs for grain yield under
drought (Venuprasad et al. 2009). Another large-
effect QTL for grain yield under favorable aero-
bic and irrigated lowland conditions,
qDTY
6.1
,
was identified in this population (Venuprasad
et al. 2012a). This QTL explained an R
2
value
under upland and lowland non-stress conditions
of up to 66% and 39%, respectively.
A series of experiments also was also
begun on F
3
-derived populations developed from
the cross of drought-tolerant donor N22 with
high-yielding mega-varieties Swarna, IR64, and
MTU1010, that resulted in the identification of
qDTY
1.1
,
a large-effect QTL having an effect on
grain yield under severe lowland drought across
these three populations. This QTL showed an
R
2
value of 13.4%, 16.9%, and 12.6% across
two seasons of screening under severe lowland
drought in N22/Swarna, N22/IR64, and N22/
MTU1010 populations, respectively (Vikram
et al. 2011). QTLs for grain yield under drought
at this locus have also been reported in other
populations derived from crosses of CT9993-5-
10-1-M/IR62266-42-6-2 and Apo/IR64 (Kumar
et al. 2007; Venuprasad et al. 2012b).
Major Rice QTLs Reported for Grain
Yield under Drought
The mapping strategy described above has
enabled the identification of a number of large-
effect QTLs affecting grain yield under drought
in both upland and lowland ecosystems. Table
4.1 presents a summary of such QTLs reported
in rice. The first reported large-effect QTL
for grain yield under drought was
qDTY
12.1
(Bernier et al. 2007). This QTL was identi-
fied in a population of 436 random F
3
-derived
lines from a cross between the upland rice
cultivars Vandana and Way Rarem. Located
between RM28048 and RM28166, this QTL
explained an R
2
of 33% under severe upland
drought conditions. Apart from this, the locus
showed its effect on many traits that affect grain
yield under drought, such as days to flower-
ing (DTF), plant height, biomass, harvest index
(HI), drought-response index (DRI), and panicle
number m
−
2
.
Two large-effect QTLs affecting grain yield
under lowland drought,
qDTY
2.1
and
qDTY
3.1
,
were identified in a BIL population derived from
a cross of the high-yielding lowland rice variety
Swarna and the upland rice variety Apo. Both
QTLs showed a very high effect under severe
lowland drought (R
2
values 16.3% and 30.7%).
The effect of both these QTLs was also seen on
QTL x Environment and QTL x
Genotype Interactions
For marker-assisted selection (MAS) to be
worthwhile, it is important that the identified
QTL show large and consistent effects under
varying environmental conditions and across a
wide range of genetic backgrounds (Bernier et al.
2009; Vikram et al. 2011). The high specificity
of the QTLs toward environmental conditions
has been one reason that, out of a large num-
ber of QTLs identified for drought tolerance,
only a few could be used in MAS. This situ-
ation becomes even more challenging if QTLs
are to be identified for complex traits such as
grain yield under drought. A variety of factors
apart from drought, such as soil, nutrients, solar
radiation, biotic stresses, and so forth, affect
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