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germplasm outside of India and Bangladesh and
for additional physiological mechanisms such as
ROS scavenging, compartmentation in older tis-
sue, and tissue tolerance (Ismail et al. 2007).
tolerance, since tolerance at the two stages is
weakly associated (Moradi et al. 2003).
Cloning of QTLs Associated with
Salinity Tolerance in Rice
Marker-assisted Backcrossing to Use
Salt Tolerance QTLs for Breeding
Despite many advances in molecular biology,
in both technical and theoretical understanding,
only one salinity tolerance QTL from rice has
been cloned so far (
qSKC1
As more QTLs are identified, they present
opportunities for breeding programs to improve
the salinity tolerance of rice varieties by pre-
cisely transferring QTLs using marker-assisted
backcrossing (MABC). Although conventional
backcrossing can transfer desired genes into
recipient varieties, the use of markers can accel-
erate the process by speeding up background
selection for recurrent parent alleles and reduc-
ing the size of the target introgression, thus
reducing the chances for negative linkage drag
(Young and Tanksley 1989; Collard and Mackill
2008; Ismail and Thomson 2011). An excel-
lent example is the MABC transfer of the
SUB1
QTL for submergence tolerance into numerous
popular rice varieties, leading to a substantial
increase in yield after recovery from variable
durations of submergence, ranging from a few
days to more than 2 weeks (Septiningsih et al.
2009; Mackill et al. 2012). A MABC program
has also begun for improving the salinity toler-
ance of rice, starting with the
Saltol
QTL being
transferred into popular varieties such as IR64,
BR11, BRRI dhan28 (BR28), and BRRI dhan29
(BR29), using FL478 as the donor of the tol-
erant Pokkali allele (Thomson et al. 2010). In
contrast to the
SUB1
story, however, the
Saltol
locus provides an intermediate increase in tol-
erance on its own - evidently multiple salinity
tolerance QTLs will need to be pyramided in
the same genetic background to provide ade-
quate tolerance for stress-prone environments
(our unpublished data). Thus, the development
of NILs for different QTLs and the combining of
multiple tolerance QTLs will need to be pursued
to obtain the desired tolerance for target envi-
ronments. This pyramiding of QTLs is required
for
OsHKT1.5
;Ren
et al. 2005). This in large part reflects the diffi-
culty of QTL cloning, and it is instructive that
in this case the cloning was carried out essen-
tially via ultra-fine-mapping of a single major
QTL controlling almost 50% of the total pheno-
typic variance, from a simple F
2
mapping pop-
ulation. Other mapping population types (RIL,
doubled-haploid, etc.) provide significant advan-
tages in terms of power and repeatability, but
make fine-mapping much harder. The majority
of QTL regions are very poorly mapped, the QTL
often being described by just 2 to 3 marker posi-
tions and often spanning 10 Mb or more of the
Nipponbare reference genome. Thus, as might
be expected, hundreds of candidate genes are
typically found within any given QTL interval.
In addition, most salinity tolerance QTLs explain
much smaller proportions of the total phenotypic
variance, which limits the power of QTL detec-
tion and makes definition of QTL limits more
difficult.
To some degree, comparison of QTLs identi-
fied between different mapping populations can
help to narrow QTL limits. For example, on
the long arm of chromosome 1 (Figure 3.1a),
a QTL explaining a significant proportion of the
phenotypic variance is observed in many pop-
ulations, with a position varying from 29.8 Mb
through to the chromosome end (approx. 43.5
Mb). However, comparison of QTLs identified
between mapping populations suggests that the
QTL is probably located in the region 37-42
Mb, and including a QTL from the Nipponbare
×
=
Kasalath population (for which Nipponbare
was the donor; Takehisa et al. 2004) helps pin
a peak at around 39-40 Mb. However, Pokkali
both
seedling-
and
reproductive-stage
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