qtls affecting morph-physiological traits related to drought tolerance detected in rice

8/14/2019 QTLs Affecting Morph-physiological Traits Related to Drought Tolerance Detected In Rice

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obtain accurate data. This creates a major problem in phenotyping

a typical mapping population of$200 individuals or lines for these

physiological traits.

Three morphological traits, stomata frequency (SF), specific leaf

weight (SLW),and leafchlorophyll content (CC)are alsoknown to be

associated with Pn, gs and Tr and supposed to be less affected by the

sampling environments. SF is known to be able to influence gs and

thus Pn [1618]. Under drought, the negative effect of reducedgs on

plant growth can be partially compensated by increased SF [19,20].

The variation of leaf SF is an adaptive feature of plants to ensure an

optimum gs and assimilation rate to maintain the maximum CO2fixation, which is known as one of thegain and losscompensation

mechanics in crop plants [21]. SLW is reportedly another factor

influencing photosynthesis. Genotypes with higher SLW tend to

have greater photosynthesis [22], which may allow plants to better

adapt to water stress conditions. High SLW also enables greater

carbon gainby reducingtranspiration losses under drought [12].For

example, cotton cultivars with high SLW is reportedly associated

with increased yield in the dryland conditions [23]. Moreover, SLW

was highly correlated with WUE in cotton, which appeared to be

responsible for higher yield under water stress [24]. Thus, it is

suggested that selection for increased yield may be achieved by

indirect selection for high SLW [25,26]. CC is reportedly to becorrelated significantly with Pn and is one of the key components

involved in the photosynthetic process [9,27]. Down-regulation of

photosynthesisunderstress wasalso associated withdecreasein CC.

A low CC reduces light absorbance and thus the heating effect from

high light intensities, which is always accompanied by drought

stress. Moreover, lower CC in plants is associated witha higher lipid/

protein ratio which can increase lipid fluidity of the thylakoid

membrane, and thus increases DT [28]. Despite the known

influences of above-mentioned morph-physiological traits on DT

and photosynthesis in rice, little is known on the genetic bases of

these traits andtheirassociations withgrainyieldunder water stress

and non-stress conditions in rice.

Here, we report an effort to genetically dissect QTLs affecting

the above-mentioned morph-physiological traits, characterizetheir relationships with grain yield under drought stress and

non-stress conditions using a unique set of overlapping introgres-

sion lines (ILs).

2. Materials and methods

2.1. Plant materials

A large set of 254 advanced BC introgression lines (ILs) were

developed by crossing Teqing (the recurrent parent) with Lemont

(the donor) followed by 24 times of backcrossing and 35 times

of selfing [29,30]. Teqing is a high yielding Chinese indica rice

cultivar with a moderate level of DT, whereas Lemont is a japonica

variety from Southern US which has a higher photosyntheticcapacity [31] and is susceptible to drought [32]. The 254 ILs were

genotyped with 160 well-distributed polymorphic single

sequence repeat (SSR) markers and a complete linkage map

was constructed, as previously described [29]. From the 254 ILs,

weselecteda subsetof 55ILs as the materials for this study based

on the following criteria: (1)each IL has minimumdonor(Lemont)

genome; (2) each introgressed donor segment was partially

shared by at leasttwo ILssuch that the55 ILstogether contain the

whole donor genome in overlapping segments; (3) all ILs had

similar heading dateunderthe normal irrigated conditions so that

environmental influences in phenotyping physiological traits

from the variation in heading date of the ILs were minimized. The

parents were used as control in the experiments to monitor the

severity of drought stress.

2.2. Phenotyping experiments and data collection

The ILswere evaluatedin tworeplicated experiments under the

water stress (rainfed upland) and fully irrigated lowland (non-

stress) conditions in the experimental farm of the International

Rice Research Institute (IRRI) during the 2002 wet season (June

November). In the upland field, seeds were directly sown into two-

row plots on the raised beds with a spacing of 25 cm 20 cm in a

randomized block design with an incomplete block arrangement in

three replications. Water was supplied by the sprinkler irrigation

for the initial 60 days of the season to maintain the normal growth

of rice plants. Irrigation was then completely stopped until the

maturity. During this period of time, a total of 726 mm of rainfall

was recorded and plants suffered severe yield loss because the

uplands could not hold the water. In the irrigated lowland control,

seeds of the ILs were sown in the seedbed and 20-day seedlings

were transplanted into three-row plots (36 plants per plot) at a

spacing of 25 cm 20 cm. The field was managed according to the

standard procedures, with a basal fertilization rate of 30 kg ha1

for each of N, P, and K, and two additional 30 kg ha1 N

applications made at the 44 and 66 days after sowing. Weeds in

both the lowland and upland fields were controlled by a

combination of chemical and manual methods. Insects (particu-larly stem-borers) were controlled chemically.

In theuplandfield,the middle parts of three fullyexpandedflag

leaves on themain culms of three middle plants in each plot were

measured at the flowering time for Pn, gs, Tr and Ci from 9:00 a.m.

to 11:30 a.m. under the sunny condition ($1500 mol m2 s1 of

photosynthetic active radiation, $30 8C of the leaf chamber

temperature, and $350 cm3 m3 of CO2 concentration) using a

portable Li-COR6400 Photosynthesis System. CC were measured

onthe three last fully expandedflag leavesof themainstems from

three different plants in each plot at the booting stage in both the

lowland(non-stress) andupland(waterstress)experimentsusing

a SPAD-502 chlorophyll meter (Spectrum Technologies Inc.). CC

was presented as theSPADmeter reading. SF was measured using

the imprint method [33] in which stomata numbers on themiddleparts($0.5 0.5 = 0.25 cm2 onthe rightsideof theleafmain vein)

of three sampled flag leaves from each IL were counted in two

vision fields under the plan-NEOFLUAR 40*/0.75 lenses of the

microscope (Axioskop, IEISS, WestGermany). SF was expressed as

the average stomata number per vision field. In addition, three

fully expanded flag leaves of the main stems of three different

plants in each plot was sampled and measured for leaf area (LA)

using a LI-3000 leaf area meter (LI-COR Inc., Lincoln, NE, USA).

After that, the sampled leaves were dried in an oven at 70 8C for

72 h and the dry weight of the leaves from each IL was recorded.

Specific leaf weight was then calculated as the dry leaf weight (in

mg) per cm2 of LA. Because of the damages of some ILs from the

tongro disease,the data of fourphotosynthesistraits (Pn, Tr,gs,and

Ci) and grain yield (GY) in the non-stress lowland experiment in2002 were inaccurate and thus were excluded from the data

analyses.

The 55 ILs were again evaluated in two replicated experiments

under fully irrigated (non-stress) and water stress conditions in

2003 dryseason to measure GY,as previously described [29]. Seeds

of the ILs were sown in the seedbed and 30-day seedlings were

transplantedinto three-rowplots (36plants perplot or entry) with

a spacing of 25 cm 20 cm in a randomized block design with an

incomplete block arrangement in three replications in 2003. The

field was managed in the same way as the experiments conducted

in 2002. For the stress treatment, the field was drained at 60 days

after transplanting and no further irrigation was applied. This

treatment resulted in leaf rolling by 15 days after the field was

drained.By thedate of heading in the control plots,soil moisture in

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the stress plots reached 100 kPaat 15-cm depth. Grain yield (g/m2)

was recorded by harvesting all plants in each plot [29].

2.3. Data analyses and QTL identification

Analysis of variance was performed to evaluate differences

among the ILs, between the parents and the conditions (water

irrigationand waterstress) using theSAS PROC GLM[34]. Correlation

between the measured traits in each of the conditions and betweenlines for the same traits across the conditions was determined using

theSAS PROC CORR[34]. Theaverage values of themeasured traits of

the ILsobtained from the non-stressand stress conditions wereused

as input data to identify QTL affecting the target traits by one-way

ANOVA using SAS PROC GLM [34]. A probability level of P 0.005

was used for claiminga significant QTL. When a QTL was detected by

two or more linked markers, the one with the highest Fvalue was

presented. Since the use of a single arbitrary threshold in QTL

mapping could easily detect a QTL in one environment but not in

another [29], putative QTLs detected by the minimum threshold of

P 0.05 in one environment will also be reported as long as they

reached the selected threshold of P 0.005 in another.

3. Results

3.1. Genetic constitution of the 55 ILs

The 55 ILs together carry a total of 695 homozygous segments

and 52 heterozygous introgressed segments from Lemont with a

total length of 15569.6 cM, or 9.3 times as much as the Lemont

genome-equivalent. The proportions of the Lemont genome in the

55 ILs were normally distributed with an average 15.2% per IL,

ranging from 3.5 to 32.1% (Fig. 1). The average number and length

of homozygous and heterozygous donor segments per line were

13.6 and 37.4 cM, respectively. The overlapping introgressed

segments cover the whole Lemont genome (Fig. 2). The flowering

time of the 55 ILs under the lowland conditions was very uniform,

ranging from 86 to 89 days.

3.2. Trait variation and correlations

Table 1 shows the summary statistics of the parents and ILs for

the measured traits. Water stress caused significant reductions for

all measured traits in the parents, particularly for GY, which

decreased by 93.8% for Lemont and 89.9% for Teqing, respectively.

The parents differed significantly for Pn, Tr, SLW, CC and GY under

water stress, but only for CC and GY under the non-stress

condition. Under water stress, Lemont had significantly higher Pnand Tr, and significantly lower SLW, CC, and GY than Teqing. Under

thenon-stress condition, Teqinghad significantlyhigher CC andGYthan Lemont. The 55 ILs showed transgressive segregations

towards both directions for all measured traits under stress and

non-stress conditions. ANOVA based on the data from the stress

conditions indicatedthat thevariation among ILsaccounted forthe

most of the phenotypic variation for Pn (R2 = 82.0%),gs (R

2 = 77.7%),

Tr (R2 = 79.1%), and Ci (R

2 = 82.9%). ANOVA results indicated that

highly significant differences were detected between the stress

conditions forSF (R2 = 10.6%), SLW(R2 = 17.2%) and CC(R2 = 63.5%),

among the ILs for SF (R2 = 50.8%), SLW (R2 = 48.9%) and CC

(R2 = 18.2%), and the stress by IL interaction for SF (R2 = 29.5%),

SLW (R2 = 26.8%), CC (R2 = 7.9%), and GY (R2 = 10.8%). As compared

to the normal condition, SF, SLW, CC and GY of the ILs decreased

significantly under drought, but this reduction, though significant,

was much more pronounced for GY than for the morph-physiological traits measured. The average reduction in the 55

ILs was similar to that in the parents for all measured traits except

for SF and SLW, which were affected little by drought, and showed

a very high level of genotype by environment interaction.

Fig. 1. Distribution of the Lemont genome in the 55 and 254 Teqing introgression

lines (ILs).

Table 1

Phenotypic performancefor flag leafnet photosynthetic rate(Pn,mmol CO2 m2 s1), stomatalconductance (gs, mol H2O m

2 s1), transpiration rate(Tr, mmol H2O m2 s1),

intercellular CO2 concentration (Ci, mmol CO2 mol1), stomata frequency (stomata number per vision field), SLW (mg cm2) of leaf area, CC (SPAD reading) and grain yield

(g m2) of the 55 introgression lines (ILs) evaluated under the irrigated lowland (control) and/or rainfed upland (stress) conditions

Treatment Traits Lemont Teqing Teqing ILs

Mean S.D. CV (%) Range

Stress Pn 24.4** 20.2 19.7 3.7 18.9 10.46.7

gs 0.17 0.14 0.15 0.04 23.7 0.060.21

Tr 5.15* 4.46 4.94 1.05 21.2 2.236.65Ci 112.0 102.6 140.7 41.1 29.2 55.2232.0

SF 61.5 59.9 70.0 12.3 16.1 47.5103.5

SLW 4.57 5.84* 5.58 0.73 13.2 3.537.24

CC 32.4 38.0* 37.4 3.1 8.3 29.844.5

GY 43.4 83.4*** 70.9 56.6 79.9 0.0236.3

Control SF 72.6 70.1 68.0 13.5 19.8 47.0111.0

SLW 5.82 6.27 6.48 1.08 16.7 4.1410.09

CC 39.4 45.9* 46.4 2.5 5.5 38.551.7

GY 705.3 822.4* 776.3 280.5 36.1 188.11342.4

Differencea SF 11.1*** 10.2*** 1.8 14.7 36.526.0

SLW 1.25*** 0.43*** 0.89 1.12 5.082.39

CC 7.0** 7.9** 9.2 3.1 16.72.1

GY 661.9*** 739.0*** 719.5 290.9 1298.8175.9

*, ** and *** represent significantly different levels at P< 0.05, 0.01 and 0.001, respectively.a

Difference = Stress control for all measured traits of the individual IL.

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Under water stress, there was a very high and positive

correlation (r= 0.92, P< 0.0001) between gs and Tr in the 55 ILs

(Table 2). Pn was moderately and positively correlated with gs, Trand SLW (r= 0.65, 0.62 and 0.35, P< 0.0001). Ci was moderately

and positively correlated with gs and Tr (r= 0.63 and 0.66,

P< 0.0001). GY was weakly and positively correlated with CC,

but not with any other traits. Interestingly, there was weak and

positive correlation between the trait values of CC (r= 0.40,

P< 0.001), SF (r= 0.29, P< 0.01) and SLW (r= 0.26, P< 0.05)

measured under the stress and non-stress, indicating that the ILs

behaved similarly for the three traits under stress and non-stress

conditions.

3.3. QTL affecting morph-physiological traits

A total of 14 QTLs affecting Pn,gs, Tr and Ci were identified under

the stress condition (Table 3, Fig. 2). These included five QTL (qPn2,

qPn5, qPn10, qPn11 and qPn12) for Pn, which were mapped to

chromosomes 2, 5, 10, 11 and 12 and collectively explained 39.9%

of the total phenotypic variation of the trait. The Lemont alleles at

qPn2 and qPn11 increased Pn, but decreased Pn at the other three

loci. Two QTL (qGs1 and qGs11) for gs were mapped to

chromosomes 1 and 11, which collectively explained 14.9% of

the total trait variation. The Lemont allele at qGs1 decreased gs but

increased gs at qGs11. Three QTL (qTr1, qTr5 and qTr11) affecting Tr

Fig. 2. Overlapping introgressed segments (the thick black lines on the right of each chromosome) in the 55 Teqing introgression lines and QTL for grain yield (GY), net

photosynthetic rate (Pn), stomatal conductance (gs), transpiration rate (Tr), intercellular CO2 concentration (Ci), stomatal frequency (SF), specific leaf weight and chlorophyll

content (CC) detected under irrigated and/or water stress conditions.



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were identified on chromosomes 1, 5 and 11, which together

explained 25.7% of the total phenotypic variation. The Lemont

alleles at the two loci (qTr1, qTr5) were associated with decreasedTr while associated with increased Tr at qTr11. FourQTLfor Ci (qCi3,

qCi7, qCi8 and qCi9) were mapped to chromosomes 3, 7, 8 and 9,

which collectively explained 52.8% of the total trait variation. The

Lemont alleles at all these loci increased Ci.

Eight QTLs affecting SF were identified and mapped to

chromosomes 3, 4, 7, 10 and 11, including three QTLs (qSf4, qSf7b

and qSf11) that expressed under both stress and non-stress

conditions, four QTLs (qSf3a, qSf3b, qSf7a, and qSf10a) that

expressed specifically under the control condition, and a single

QTL (qSf10b) which expressed specifically under water stress.

These QTLs collectively explained 66.4% and 40.0% of total

phenotypic variation for SF under stress and non-stress conditions,

respectively (Table 3, Fig. 2). The Lemont alleles at all loci except

qSf7a and qSf10b increased SF.Six QTLs affecting SLW were identified, including two QTLs

(qSlw7and qSlw8) that expressed under both stress and non-stress

conditions, two QTLs (qSlw3 and qSlw12) that expressed specifi-

cally under the irrigated condition, and two QTLs under (qSlw5 and

qSlw10) that were induced specifically under water stress.

Together, these QTLs explained 48.8% and 36.6% of total trait

variation under stress and non-stress conditions, respectively

(Table 3, Fig. 2). The Lemont allele at qSlw12 increased SLW while

the Teqing alleles at the remaining five QTLs increased SLW.

Five QTLs affecting CC were identified, including twoQTLs (qCc8

and qCc12) that expressed specifically under the non-stress

condition and three QTLs (qCc3, qCc6 and qCc11) that were

induced by water stress (Table 3, Fig. 2). These QTLs collectively

explained only 14.6% and 28.6% of the total trait variation underthe control and stress conditions, respectively. The Lemont alleles

at all loci except qCc12 were associated with decreased CC.

3.4. QTLs affecting GY

A total of seven QTLs affecting GY were detected and mapped to

rice chromosomes3, 5,7, 8,9, 10, and 12,includingthree QTLs(qGy5,

qGy8 and qGy10) that expressed under both stress and non-stress

conditions,threeQTLs (qGy3, qGy9 and qGy12)thatexpressedonlyin

the control condition and 1 QTL (qGy7) that expressed specifically

under the stress. Collectively, these QTLs explained 57.0% and 39.2%

of the trait variation under the non-stress and stress conditions,

respectively. The Teqing alleles at qGy3, qGy5, qGy9, qGy10 and

qGy12 increased GY and the Lemont allele at qGy7 increased GY.

Interestingly, the Lemont allele at qGy8 increased GY under stress

but decreased GY under the control condition.

4. Discussion

In this study, we deliberately selected 55 ILs from a larger IL

population based on known marker and phenotypic information

such that the mapping population had a manageable size and

allowed more timely and accurate measurements of the studied

traits in the replicated experiments. The tremendous segregation

for the measured morph-physiological traits in the ILs indicated

that this subset of ILs had sufficient amounts of genetic variation

for mapping QTLs affecting the measured traits. The small

experimental errors from our ANOVA results clearly indicated

that we were successful in controlling the micro-environmental

noises on the measured morph-physiological traits in our field

experiments. The 55 overlapping ILs covers the whole donorgenome, which minimized the chance of the main-effect QTLs

escaping from incomplete coverage of the donor genome, which

was proven to be true according to ourmapping results fora highly

heritable trait, 1000-grain weight, in which a total of 7 grain

weight QTLs were identified in the same genomic regions under

water stress condition in both the whole population of 254 ILs and

the subset population of 55 ILs (Table 4). Similarly, 60% of the GY

QTLs detected in the 254 ILs [29] were also identified in the 55 ILs,

further demonstrating that the 55 ILs had sufficient power to

detect most QTLs segregating in the original population. We noted

that the statistic powers associated with the detected grain weight

QTLs were significantly lower in the small subpopulation than that

in the big population. However, our overlapping IL population

should be much more powerful in detecting QTLs than commonlyused overlapping chromosomal single-segment substitution lines

[3537], because this population had, on average, eight more

replications for any specific genomic regions across the genome

and can even detect some of digenic interactions of large effect.

Evidence forthis argument came from thefact that 11 of the19 QTL

regions (Fig. 2) affecting the morph-photosynthesis traits identi-

fied in this study matched closely to previously identified QTLs for

the same or related traits in different mapping populations[16,38

40], suggesting our mapping results were quite robust.

As expected, GY and its related morph-physiological traits

changed dramatically under water stress, reflecting important

aspects of plants to adapt to the stress. It is well known that GY in

rice tends to show considerable G E interactions [41,42]. Some

changes in morph-physiological traits caused by water stress are

Table 2

Correlation coefficients between specific leaf weight (SLW), chlorophyll content (CC), stomatal frequency (SF), net photosynthetic rate (Pn), stomatal conductance (gs),

transpiration rate (Tr) and intercellular CO2 concentration (Ci) evaluated at the booting/flowering stages under irrigated (control) and/or upland (stress) conditions in the 55

Teqing ILs

Stress Control

GY SLW CC SF Pn gs Tr Ci GY SLW CC

Stress

SLW 0.24CC 0.26* 0.25*

SF 0.13 0.03 0.04

Pn 0.21 0.35** 0.05 0.05

gs 0.02 0.21 0.23 0.04 0.65**

Tr 0.02 0.22 0.21 0.04 0.62** 0.92***

Ci 0.22 0.06 0.23 0.01 0.16 0.63*** 0.66***

Control

GY 0.18 0.02 0.33** 0.13 0.02 0.19 0.21 0.17

SLW 0.04 0.26* 0 0.11 0.09 0.05 0.05 0.27* 0.01

CC 0.19 0.31** 0.40*** 0.02 0.12 0.03 0.03 0.04 0.22 0.24

SF 0.07 0.18 0.06 0.29** 0.09 0.02 0.04 0.12 0.05 0.23 0.16

*, ** and *** represent the levels of significance at P< 0.05, 0.01 and 0.001, respectively.



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potentially able to contribute to GY, even though genetic evidence

for this association has been particularly lacking. Thus, traits

showing greater G E interactionsacross the stressand non-stressconditions are expected to contribute more to GY. In this study, all

measured morph-physiological traits were significantly sup-

pressed by drought, and SF and SLW showed considerable G E

interaction across the stress and non-stress conditions, suggestingthey were important components of GY. In contrast, the large

Table 3

Forty QTL affecting flag leaf net photosynthetic rate (Pn, mmol CO2 m2 s1), stomatal conductance (gs, mol H2O m

2 s1), transpiration rate (Tr, mmol H2O m2 s1),

intercellularCO2 concentration (Ci, mmol CO2 mol1), stomata frequency (mean stomata number per vision field), SLW (mg cm2), chlorophyll content (SPAD meter reading)

and grain yield (g m2) detected at the booting/flowering stages in 55 Teqing introgression lines under irrigated (control-C) and/or upland (stress-S) conditions

Trait QTL Ch. Marker interval Control Stress QTL and population reporteda

F Ab R2 (%) F A R2 (%)

Pn qPn2 2 RM324-RM145 11.18 1.93 11.2

qPn5 5 OSR35-RM159 10.37 1.53 9.3qPn10 10 RM258-RM228 10.47 1.71 8.8 GY

5 (C-)

qPn11 11 RM209-RM229 12.99 0.52 1.5 GY 5 (C-, S-)

qPn12 12 OSR20-RM277 10.51 1.53 9.1 CC4, GY5 (C-)

gs qGs1 1 OSR27-RM212 9.42 0.01 7.2 Tr1, SF3, GY5 (C-)

qGs11 11 RM209-RM229 8.57 0.01 7.7 GY 5 (C-, S-)

Tr qTr1 1 OSR27-RM212 10.64 0.38 7.7 Tr1, SF3, GY5 (C-)

qTr5 5 OSR35-RM159 6.68 0.40 8.5

qTr11 11 RM209-RM229 8.11 0.46 9.5 GY 5 (C-, S-)

Ci qCi3 3 RM148-RM227 16.87 38.4 19.9 Pn1, Rubisco/CC4

qCi7 7 RM478-RM234 7.81 26.1 9.9

qCi8 8 RM137-RM72 7.22 16.3 9.4 CC1, gs1, Photo capacity4

qCi9 9 RM242-RM278 11.94 22.0 13.6

SF qSf3a 3 RM168-OSR31 7.20 4.15 6.9 SF3, CC4

qSf3b 3 RM148-RM227 17.20 8.98 17.9 Pn1, Rubisco/CC4

qSf4 4 RM252-RM303 9.48 6.60 8.5 13.12 5.25 11.1 Pn1

qSf7a 7 RM481-RM436 7.77 6.00 4.4 CC1, Tr2

qSf7b 7 RM118-RM18 12.99 13.65 11.4 12.08 9.48 10.3

qSf10a 10 RM311-RM239 7.60 5.37 7.1 gs1, Ci

1

qSf10b 10 RM258-RM228 9.45 3.71 7.8 GY 5 (C-)

qSf11 11 RM209-RM229 11.25 5.71 10.2 12.76 4.31 10.8 GY 5 (C-, S-)

SLW qSlw3 3 RM148-RM227 12.32 0.53 12.7 Pn2, Rubisco/CC4

qSlw5 5 OSR35-RM159 11.65 0.35 11.7

qSlw7 7 RM118-RM18 9.75 0.80 8.2 9.07 0.31 7.8

qSlw8 8 RM126-RM483 7.51 0.43 7.8 10.24 0.31 8.9

qSlw10 10 RM258-RM228 9.76 0.26 8.2 GY 5 (C-)

qSlw12 12 OSR20-RM277 17.73 0.98 20.1 CC4, GY5 (C-)

CC qCc3 3 RM36-RM282 10.88 3.83 11.9 CC2

qCc6 6 RM3-RM141 7.28 1.22 7.3 Pn2

qCc8 8 RM126-RM483 10.44 1.14 5.7

qCc11 11 RM123-RM224 9.87 1.49 9.4

qCc12 12 OSR20-RM277 8.05 1.18 8.9 CC4, GY5 (C-)

GY qGy3 3 RM148-RM227 9.40 185.1 13.0 Pn2, Rubisco/CC4

qGy5 5 RM509-RM163 7.82 120.0 14.9 10.20 27.9 5.8 GY 5 (C-, S-)

qGy7 7 RM478-RM234 12.17 57.0 13.1 GY 5 (S+)

qGy8 8 RM137-RM72 13.24 145.1 9.1 10.18 31.6 10.7 CC1, gs1, Photo capacity4

qGy9 9 RM242-RM278 11.00 124.2 4.1 GY 5 (C-)

qGy10 10 RM258-RM228 8.78 108.9 8.3 8.79 29.2 9.6 GY 5 (C-)

qGy12 12 RM309-RM463 10.48 145.7 7.6

a 15 in superscript indicate cases where QTL were previously detected in the populations of Asominori IR24 CSSL (Hu et al., 2007), JYQ8 JX17 DH [39],

Nipponbare Kasalath BIL[16], Nipponbare Kasalath BIL[38] and 254 Teqing ILs [29], respectively. C and S represent the 254 Teqing ILs evaluated under the control and

stress conditions with + and representing the directions of the QTL additive effects identified at the Lemont allele.b QTL additive effects were associated with the Lemont allele.

Table 4

The comparison of QTLs for grain weight (GW) detected in 55 and 254 ILs from Lemont/Teqing under water stress condition in 2003 dry season

QTL Ch. Marker interval P55 P254

F a F a

qGw3 3 RM85-RM148 10.70 0.85 19.29 0.70

qGw4 4 RM551-RM417 14.28 0.78 15.65 0.47

qGw5 5 RM289-RM249 16.52 0.45 54.26 0.90

qGw6 6 RM30-RM439 8.96 0.70 89.78 1.69

qGw8 8 RM210-OSR7 14.25 1.05 28.96 0.89

qGw9 9 RM278-OSR28 14.64 0.79 43.60 1.03

qGw12 12 RM235-RM17 14.76 1.42 53.62 1.63



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change in CC caused by drought appeared to be more responsive

instead of adaptive to the stress because of this trait did not vary

much across the stress and non-stress conditions.

Similar to many previous studies, most QTLs detected in this

study tended to be clusteredin certain genomic regions across the

rice genome (Fig. 2). In particular, QTLs affecting Pn, gs, Tr, and Citended to cluster in the same genomic regions, suggesting their

common genetic bases (pleiotropy) for the observed high and

positive phenotypic correlations between the physiologicaltraits.

In addition, the SLW QTLs were mapped three times with the SF

QTLs (qSf3, qSf7, and qSf10), twice with the CC QTLs (qCC8 and

qCC12) and the Pn QTLs (qPn5 and qPn10). The SF QTLs were

located twice in the Pn QTLs (qPn10 and qPn11). In these cases,the

QTL effects on the morphological traits and physiological traits

wereoften in different directions, suggesting tight linkage instead

of pleiotropy was more likely responsible for the observed

associations, even though the resolution of our genetic mapping

could not clearly distinguish one from the other. The genomic

correspondence and the inconsistent relationship in effect of the

QTL clusters provided a reasonable explanation for the low

correlation between the leaf morphological traits (SLW, SF and

CC) and photosynthesis traits (Pn, gs, Tr and Ci) observed in this

study.Our results provided direct evidence thatGY under stress were

genetically associated with all measured morph-physiological

traits except CC. This was reflected by the high genomic

correspondence between QTLs affecting the morph-physiological

traits and GY (Fig. 2). For example, five (qGy3, qGy7, qGy8, qGy9,

and qGy10) of the seven GY QTLs were mapped to the same

genomic locations as QTLs affecting one or more morpho-

photosynthesis traits. Of these, two QTL regions were particularly

interesting. The first one was flanked by RM148 and RM227 on

chromosome 3, where QTLs affecting Ci, SF, SLW and GY were

mapped together. The Lemont allele at this QTL region was

associated with increased Ci and SF, but decreased SLW and GY.

We further note that QTL for Pn, Rubisco content per chlorophyll

content were also mapped to the same region in different ricepopulations previously [38,39], the second one was flanked by

RM258 and RM228 on chromosome 10, where QTLs affecting Pn,

SF, SLW and GY were mapped together. The Lemont allele at this

region was associated with reduced Pn, SF, SLW and GY(Fig. 2 and

Table 3). In three remaining regions on chromosomes 7, 8 and 9,

QTLs for Ci andGY were mapped together. In the former two cases,

theLemontalleleswere associated withincreased Ci andGYunder

stress. However, in the latter case (chromosome 9), it increased Cibut reduced GY. This kind of inconsistence between GY QTLs and

component trait QTLs explained at least partially the observed

complex phenotypic relationships between GY and its component

traits. In two additional regions on chromosomes 11 (between

RM209 and RM229) and 12 (between OSR20 and RM277), clusters

of QTLaffecting multiplephotosynthesis traits weremappedwithGY QTL that were detected in the 254 ILs [29]. In both cases, the

Lemont alleles for increased photosynthesis traits were asso-

ciated with reduced GY under either stress and/or non-stress

conditions. Surprisingly, none of the CC QTLs were mapped with

the GY QTLs detected under stress even though there was a weak

phenotypic correlation between GY and CC. This conflict reflects

the complex genetics underlying GY because epistasis, whichis an

important genetic basis of GY in rice [43,44], could not be

adequately addressed in the 55 ILs. In conclusion, our results

indicate that improving DT of rice by selecting any single

secondary traits is not expected to be effective and identified

QTLs for GY and related morph-physiological traits should be

carefully confirmed before to be used for improving DT in rice by

MAS.

Acknowledgements

The authors are grateful to the financial support from Chinese

Ministry of Agriculture (948 # 2006-G51), the National 973 project

of Chinese Ministry of Science and Technology (grant no.

2003CB114308) and from the Rockefeller Foundation (#2005

FS029)to Z.K. Li.Q.X. Zhao wasalso supported by a Ph.D. fellowship

from the Rockefeller Foundation.

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