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Field Crops Research 131 (2012) 88–96

Contents lists available at SciVerse ScienceDirect

Field Crops Research

jou rna l h om epage: www.elsev ier .com/ locate / fc r

dentification and mapping of a QTL (qDTY1.1) with a consistent effect on grainield under drought

rishna Hari Ghimirea,1, Lenie A. Quiatchona,b, Prashant Vikramb, B.P. Mallikarjuna Swamyb,halabh Dixitb, Helaluddin Ahmedb, Jose E. Hernandeza, Teresita H. Borromeoa, Arvind Kumarc,b,∗

University of the Philippines Los Banos, College, Laguna 4031, PhilippinesInternational Rice Research Institute, DAPO Box 7777, Metro Manila, PhilippinesPlant Breeder (Drought and Aerobic Rice), Plant Breeding, Genetics, and Biotechnology Division, Philippines

r t i c l e i n f o

rticle history:eceived 3 November 2011eceived in revised form 25 February 2012ccepted 25 February 2012

eywords:roughtega-varieties

ulk segregant analysis

a b s t r a c t

The identification and introgression of QTLs for grain yield under drought is a preferred breeding strategyto improve the drought tolerance of popular elite rice varieties. Swarna and IR64 are two high-yieldingrice varieties widely grown in rainfed areas of South and Southeast Asia but they are highly sensitive todrought. Dhagaddeshi, a traditional drought-tolerant donor, was crossed with Swarna and IR64 to developtwo recombinant inbred line populations. The two populations were phenotyped for grain yield underreproductive-stage drought stress and non-stress during DS2010 and DS2011. Bulk segregant analysiswas followed to identify the loci linked to grain yield under drought. A major-effect QTL, qDTY1.1, forgrain yield under drought was identified on chromosome 1 between the marker intervals RM431 and

uantitative trait loci RM12091 in both populations. The Dhagaddeshi allele at qDTY1.1 contributed to increased yield underdrought and explained 32.0% and 9.3% of the phenotypic variance and 24.9% and 8.6% additive effectof the trial mean yield in Dhagaddeshi × Swarna and Dhagaddeshi × IR64 populations, respectively. Aconsistent effect of qDTY1.1 has also been reported earlier. The presence of qDTY1.1 in several traditionaldrought-tolerant donors and its consistent effect across the genetic backgrounds makes it a suitable QTL

d bree

for use in marker-assiste

. Introduction

Rice is the major staple food crop for more than half of theorld’s population. The stagnating yields of rice varieties and

limate change-related risks are major concerns for world foodecurity. It is estimated that the world needs to produce 40% moreice to feed the population by 2025 (FAO, 2002). A large portionf this predicted increase has to come from rainfed lowland andpland rice areas, which occupy nearly 38% of the total cropped area

nd contribute only 21% to total rice production (Khush, 1997). Thencrease in frequency and severity of drought occurrence duringhe cropping season is being felt worldwide. Drought is the most

Abbreviations: DNA, deoxyribonucleic acid; QTL, quantitative trait loci; LOD,ogarithm of odds; MAB, marker-assisted breeding.∗ Corresponding author. Tel.: +63 2 580 5600x2586; fax: +63 2 580 5699.

E-mail addresses: ghimirekh@gmail.com (K.H. Ghimire), p.vikram@irri.orgP. Vikram), m.swamy@irri.org (B.P.M. Swamy), s.dixit@cgiar.org (S. Dixit),elaluddinahmed@hotmail.com (H. Ahmed), jehernandez@uplb.edu.phJ.E. Hernandez), thborromeo@yahoo.com (T.H. Borromeo), akumar@cgiar.orgA. Kumar).

1 Present address: Nepal Agricultural Research Council, Post Box 1, Pokhara,epal.

378-4290/$ – see front matter © 2012 Elsevier B.V. All rights reserved.oi:10.1016/j.fcr.2012.02.028

ding to improve the grain yield of drought-susceptible rice varieties.© 2012 Elsevier B.V. All rights reserved.

severe climate-related risk for rice production in rainfed areas ofmore than 20 million ha in South and Southeast Asia (Pandey andBhandari, 2007). Nearly 17 million hectares of rice area in easternIndia and adjoining areas of Nepal are highly drought-prone (Hukeand Huke, 1997). However, most of the popular high-yielding ricevarieties, such as Swarna, IR64, and MTU1010, grown in these areasare highly sensitive to drought and suffer high yield losses in yearswith drought (Verulkar et al., 2010; Vikram et al., 2011).

Crop genetic improvement for environmental stresses such asdrought is challenging due to the complexity of the trait and poorunderstanding of the physiological and molecular mechanismsassociated with drought (Sinclair, 2011). Efforts made to improvethe drought tolerance of rice varieties using secondary traits suchas root architecture, leaf water potential, panicle water potential,osmotic adjustment, and relative water content as selection criteria(Fukai et al., 1999; Price and Courtois, 1999; Jongdee et al., 2002;Pantuwan et al., 2002) did not provide the results expected. Moder-ate to high heritability of grain yield (GY) under an irrigated controland drought has been reported recently (Venuprasad et al., 2007;

Kumar et al., 2008), indicating the suitability of GY as an appropriateselection criterion to improve yield under drought.

The identification and introgression of quantitative trait loci(QTLs) governing GY under drought have been advocated to be an

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fficient approach to improve the drought tolerance of rice varietiesVikram et al., 2012). Several QTLs for GY under reproductive-stagerought stress (RS) have been reported for upland as well as low-

and rice (Bernier et al., 2007; Venuprasad et al., 2009; Vikram et al.,011). Bernier et al. (2007) reported qDTY12.1, a large-effect QTL forigh GY under RS in upland situations. qDTY3.1 in Apo/2* SwarnaVenuprasad et al., 2009) and qDTY1.1 in N22/Swarna (Vikram et al.,011) are two large-effect QTLs for GY under lowland RS reported

n recent years. Marker-assisted introgressions of qDTY12.1 in Van-ana and qDTY2.2 and qDTY4.1 in IR64 have successfully improvedhe GY of Vandana and IR64, respectively, under drought (IRRI,npublished).

Bernier et al. (2009) have reported a consistent effect of qDTY12.1cross different environments; however, the lack of repeatability of

QTL effect across different genetic backgrounds has been one ofhe major factors limiting the use of QTLs in molecular breedingBernier et al., 2008; Vikram et al., 2011). With a few high-yieldingopular varieties occupying a large area in the drought-prone rain-ed ecosystem, identifying major QTLs that show a consistent effectgainst the backgrounds of different popular varieties and the usef such QTLs to improve drought-susceptible varieties could be anffective marker-assisted breeding strategy.

Recently, some studies have used the bulked segregant anal-sis (BSA) approach to identify large-effect drought GY QTLsVenuprasad et al., 2009; Vikram et al., 2011). BSA involves theulking of DNA of phenotypic extremes and genotyping of par-nts along with the bulks with markers polymorphic between thewo parents involved in the development of the population. In ourtudy, the BSA approach was applied to identify QTLs showing aonsistent effect in the background of two popular high-yieldingarieties, Swarna and IR64, grown on millions of hectares in Southnd Southeast Asia.

. Materials and methods

The study was conducted at the International Rice Researchnstitute (IRRI), Los Banos, Laguna, Philippines, in the dry seasonsf 2010 (DS2010) and 2011 (DS2011). IRRI is located at 14◦13′Natitude, 121◦15′E longitude, at an altitude of 21 m above mean seaevel. The soil type of the experimental field is a Mahaas clay loam,sohyperthermic mixed typic tropudalf.

.1. Development of mapping populations

Two F3:5 recombinant inbred line (RIL) populations were devel-ped from crosses involving traditional drought-tolerant donorhagaddeshi as a male parent and Swarna and IR64 as female par-nts. True F1 seeds from the two separate crosses were grown to get2 seeds. A single seed from each F2 plant was selected and single F3lants were grown and harvested individually. F3-derived F4 plantsere grown for seed increase and F4 plants were harvested in bulk

o get F3:4 RILs. The Dhagaddeshi × Swarna population with 259ILs and Dhagaddeshi × IR64 population with 251 RILs were phe-otyped for yield and yield-related traits under RS during DS2010nd DS2011 and under non-stress (NS) during DS2010.

.2. Phenotyping

RS and NS experiments were laid out in an alpha lattice designith two replications. Experiments were sown on 21 December

009 and 17 December 2010 for DS2010 and DS2011, respectively.eeds were sown in a nursery and 21-day-old seedlings were trans-

lanted in a puddled field with a single seedling per hill in a 5-mow plot with a spacing of 20 cm × 20 cm. Inorganic NPK fertilizersere applied at 90-30-30 kg ha−1. Bayluscide (niclosamide 0.25 kg

.i. ha−1) was applied immediately after transplanting to protect

esearch 131 (2012) 88–96 89

the seedlings from snail damage. Weeds were controlled by apply-ing post-emergence herbicide Sofit (pretilachlor 0.3 kg a.i. ha−1) 4days after transplanting and by hand weeding 25–30 days aftertransplanting. Furadan (carbofuran 1 kg a.i. ha−1) was applied 5days after transplanting and Cymbush (cypermethrin 1 kg a.i. ha−1)was applied 16 days after transplanting to control insect pests.

A 5-cm water level was maintained until maturity in NS experi-ments. In RS experiments, stress was imposed by draining water 30days after transplanting (DAT). Perforated PVC pipes were placedat 1-m soil depth at 6 different points of a field of 500 m2. The watertable was measured every day until maturity after draining thefield. When the water table reached below 100 cm and remainedso for more than 2 weeks, irrigation was provided and the field wasdrained after 24 h for the next stress cycle to continue. The neededirrigation was provided to save the plants from being exposed tovery severe stress that leads to a loss of all genetic variability forGY under RS in the mapping population (Kumar et al., 2007).

2.2.1. Data recording and analysisData for days to 50% flowering (DTF) were recorded as the total

number of days from the date of seeding to the date when 50%of the plants in a row exserted their panicles. Plant height (PHT)was recorded in cm from 5 random plants in the row, measuredfrom the base of the plant to the tip of the panicle. GY per plotwas recorded after harvesting, threshing, and drying to moisturecontent adjusted to 14% and converted to kg ha−1.

2.3. Genotyping

2.3.1. DNA extraction and polymerase chain reaction (PCR)amplification

Genotyping was conducted in the Molecular Markers Applica-tion Laboratory (MMAL), IRRI. Fresh leaf samples were collectedfrom both the RIL populations planted in DS2010. Leaf samplescollected from each row were bulked. DNA was extracted using amodified CTAB protocol reported by Murray and Thompson (1980).PCR amplification was carried out with a 20 �l reaction mixturecontaining 2 �l of 25 ng �l−1 DNA template, 2 �l of 10× PCR buffer,2 �l of 1 mM dNTPs, 0.6 �l of MgCl2, 1 �l each of 5 �M forward andreverse primer, 1 �l of 5 U/�l Taq DNA polymerase, and 10.4 �l ster-ile H2O. The PCR profile for SSR described by Thompson et al. (2006)was used: initial denaturation at 94 ◦C for 5 min; then 35 cycles ofdenaturation at 94 ◦C for 30 s, annealing at 55 ◦C for 30 s, and exten-sion at 72 ◦C for 30 s; and final extension at 72 ◦C for 5 min andstorage at 10 ◦C forever. PCR products were resolved using high-resolution 8% non-denaturing polyacrylamide gel electrophoresis(PAGE) as described by Sambrook and Russel (2001).

2.3.2. Parental polymorphism survey and bulk segregant analysis(BSA)

A parental polymorphism survey was carried out with 800simple sequence repeat (SSR) markers from available rice geneticmaps (Temnykh et al., 2001; McCouch et al., 2002; IRGSP, 2005).The marker order was taken from published rice genetic maps(Temnykh et al., 2001). These 800 SSR markers were used for aparental polymorphism survey among Dhagaddeshi, Swarna, andIR64 genotypes. From each population, the 7.5% highest and 7.5%lowest yielding lines were selected based on GY data from a stresstrial of DS2010. The DNA of the 7.5% highest-yielding lines wasmixed in equal quantity to prepare the high bulk and that of

the lowest-yielding lines was mixed to prepare the low bulk foreach population. For BSA, a total of 294 and 269 polymorphic SSRmarkers distributed on all 12 chromosomes were used to identifymarkers showing a significant banding pattern for high and low

90 K.H. Ghimire et al. / Field Crops Research 131 (2012) 88–96

011.

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Fig. 1. Depleting water table in RS trials, during DS2010 and DS2

ulks in Dhagaddeshi × Swarna and Dhagaddeshi × IR64 popula-ions, respectively.

.3.3. Whole-population genotypingPolymorphic markers showing a distinct banding pattern

etween high and low bulks were selected. Markers for which tol-rant parent allele bands correspond to the high bulk under RS andusceptible parent allele bands correspond to the low bulk underS were run on the whole population. Single-marker regressionnalysis was carried out to identify the significant markers asso-iated with GY under RS using Qgene (Nelson, 1997). Additionalolymorphic markers on both sides of the significant markers iden-ified in single-marker analysis were run on the whole populationo determine the QTL flanks.

.4. Statistical analysis

Analysis of variance was performed using the linear mixedodel analysis program of CROPSTAT v7.2.3 (Archive.irri.org,

009). For estimation of mean within a year, replications and blocksithin replicates were taken as random, while entries were taken asxed. Season effects were also taken as random during estimationf the entry means across years.

All the sources of variation were considered as random whenstimating variance components. Broad-sense heritability (H) wasalculated for all the traits in both NS and RS using the formula

= Vg

Vg + Ve/r

here Vg is genotypic variance, Ve is error variance, and r is theumber of replications. All the sources of variation were considereds random when estimating variance components.

Each value plotted in the graph is the average of 6 observations.

2.5. QTL analysis

In our study, single-marker analysis was performed using Qgene(Nelson, 1997). Composite interval mapping (CIM) through QTLNetwork v2.1 was carried out to determine QTL flanks and theeffects of identified QTLs on GY and GY-related traits (Yang et al.,2008). One thousand permutations were used to calculate the F-value to control the genome-wise type I error. An experiment-wisesignificance of P ≤ 0.01 was maintained for QTL detection as wellas for QTL effects. For the genome scan, the window size and walkspeed were fixed as 1 and 0.1 cM, respectively. Significant markerintervals with QTLs for all the traits were detected. The effect ofQTL by marker intervals is presented as percentage additive effectof the respective population mean. The percentage additive effectwas estimated using the formula

AE% = AE × 100PM

where AE% is percentage additive effect, AE is additive effect, andPM is population mean. AE and PM were taken from the reportof QTL network software. For the estimation of genetic distancesbetween markers for QTL mapping, one million bases on a rice chro-mosome were assumed to be equivalent to approximately 4 cM toestimate the genetic distances (IRGSP, 2005).

3. Results

The rainfall pattern, water table status (Fig. 1), and reduction inmean GY under RS compared with NS clearly indicate the severity

of stress imposed in RS experiments during DS2010 and DS2011.Severity of drought stress was higher in DS2010 than in DS2011(Fig. 1). In DS2010, the water table decreased continuously afterimposition of stress and reached 100-cm depth on March 4 and

rops Research 131 (2012) 88–96 91

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K.H. Ghimire et al. / Field C

emained so up to March 24. Life-saving irrigation on March 25,3 days after stress imposition, caused an increase in water tableepth to 60 cm. Water table depths again decreased and reached00 cm on April 1. In DS2011, the water table reached 100 cmelow ground level only during the last 2 weeks of the stress periodMarch 20–April 1) but the level remained almost constant below0 cm throughout the stress period from March 10 onward. Theigher severity of drought stress in DS2010 is reflected in a highereduction in mean GY and PHT in comparison to DS2011. Detailedeather data including rainfall, temperature, and vapor pressureepression (VPD) are presented in Supplementary Figures 1–4.

.1. Phenotypic variation across the population

.1.1. Days to 50% flowering (DTF)The tolerant parent Dhagaddeshi showed early flowering under

S conditions during DS2010 and DS2011 and NS conditions in theS2010 (Table 1). Drought stress delayed mean DTF in the Dha-addeshi × Swarna population under RS by 5 days in DS2010, withean DTF being 89 under RS and 84 under NS conditions. However,

he mean DTF under RS in DS2011 was 80. Heritability estimatesor DTF in this population ranged from 69.8% under RS in DS2010o 76.7% under NS in DS2010. The heritability over two seasons ofS was 59.6% (Table 1). In the Dhagaddeshi × IR64 population, theean DTF remained constant at 79 under all conditions as well as

n the combined analysis over two seasons. The heritability esti-ates ranged from 64.3% under RS in DS2010 to 74.9% under NS

n DS2010. The combined heritability over two seasons of RS was6.7% (Table 1).

.1.2. Plant height (PHT)Mean plant height in the Dhagaddeshi × Swarna population

nder RS (83 cm) decreased by 14 cm in DS2010 compared withS (97 cm) while it was 97 cm under RS in DS2011. In the Dha-addeshi × IR64 population, mean plant height decreased by 19 cmnder RS (82 cm) in DS2010 compared with NS (101 cm) in DS2010.ean PHT was 93 cm under RS in DS2011 in the Dhagaddeshi × IR64

opulation. Moderate to high heritability estimates ranging from0.2% to 76.1% were recorded for PHT in the Dhagaddeshi × Swarnaopulation. The combined heritability estimate over two seasonsf RS trials was 68.6%. In the Dhagaddeshi × IR64 population, heri-ability estimates ranged from 52.1% to 70.1%, while the combinederitability for RS trials was 52.4%.

.1.3. Grain yieldTable 1 presents the mean GY, heritability (%), range, and LSD0.05

n three parents and two populations in DS2010 and DS2011. Theolerant parent Dhagaddeshi out yielded Swarna and IR64 under RSonditions during DS2010 and DS2011. The mean GY of the Dha-addeshi × Swarna population was 898 kg ha−1 and 2546 kg ha−1

nder RS and NS conditions, respectively, in DS2010. The mean GYnder RS in DS2011 was 1521 kg ha−1 and the combined mean overwo years under RS was 1217 kg ha−1 (Table 1). The heritabilitystimates for GY ranged from 49.3% under NS conditions to 53.0%nder DS2011 RS conditions. The combined heritability over twoears under RS conditions was 55.4%.

The mean GY in the Dhagaddeshi × IR64 population was68 kg ha−1 and 2547 kg ha−1 under RS and NS conditions, respec-ively, during DS2010, and 1281 kg ha−1 during RS conditions ofS2011. Heritability estimates ranged from 36.0% under RS inS2011 to 45.1% under NS in DS2010, while the combined heri-

ability estimate over two seasons under RS was 32.6% (Table 1).

.1.4. Phenotypic correlationPhenotypic correlations between GY and other traits (DTF

nd PHT) are presented in Table 2. In the Dagaddeshi × Swarna Tab

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92 K.H. Ghimire et al. / Field Crops R

Table 2Correlation of grain yield under reproductive-stage drought stress (RS) with DTFand PHT under RS and non-stress (NS) conditions.

Population Ecosystem DTF PHT

Dhagaddeshi × Swarna NS 2010 0.17** 0.24**

RS 2010 −0.22** 0.59**

RS 2011 −0.28** 0.39**

RS combined −0.43** 0.48**

Dhagaddeshi × IR64 NS 2010 0.14** 0.17**

RS 2010 −0.41** 0.12**

RS 2011 −0.34** 0.21**

RS combined −0.50** 0.18**

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ditional drought-tolerant donors such as N22 and Nootripathu

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** Significant at p = 0.01.

opulation, GY was negatively correlated with DTF under RS in bothears (−0.22 in DS2010 and −0.28 in DS2011) and combined overears (−0.43), but the correlation was positive (0.17) under NS con-itions. Similarly, in the Dhagaddeshi × IR64 population, GY wasegatively correlated with DTF under RS in both years (−0.41 in theS2010 and −0.34 in DS2011) and combined over years (−0.50). InS, GY was positively correlated with DTF (0.14). GY was positivelyorrelated with PHT in both populations under all conditions.

.2. QTL analysis

RM104, RM419, and RM210 were the markers identi-ed as significant in BSA in Dhagaddeshi × IR64 whereasM11943, RM431, RM18, and RM419 were the significant mark-rs in BSA in the Dhagaddeshi × Swarna population. In theingle-marker analysis, markers RM11943 and RM431 wereound significant for GY under RS in Dhagaddeshi × Swarnand RM104 was found significant in Dhagaddeshi × IR64. Sixarkers (RM212, RM315, RM104, RM529, RM2182, and RM2227)

n Dhagaddeshi × Swarna and nine markers (RM212, RM3825,M315, RM11943, RM431, RM12091, RM529, RM12146, andM12233) in Dhagaddeshi × IR64 flanking the significant markersere added and QTL analysis (using CIM) was performed. In bothopulations, a major-effect QTL for GY under RS was identified onhromosome 1 between the marker intervals RM431 and RM12091Table 3). This QTL was named qDTY1.1 and it showed a similar effectn both populations under RS conditions of DS2010 and DS2011

rials. Association of qDTY1.1 with DTF and PHT was observed inoth populations under RS of DS2010 and DS2011 as well as NS ofS2010 (Table 3).

able 3TLs associated with GY and related traits under reproductive-stage drought stress (RS) d

Population Trait Ecosystem QTL

Dhagaddeshi × Swarna GY RS2010 qDTY1.1

RS2011 qDTY1.1

RS combined qDTY1.1

DTF RS2010 qDTF1.1

NS 2010 qDTF1.1

PHT RS2010 qDTH1.1

RS2011 qDTH1.1

RS combined qDTH1.1

NS2010 qDTH1.1

Dhagaddeshi × IR64 GY RS2010 qDTY1.1

RS2011 qDTY1.1

RS combined qDTY1.1

DTF RS2010 qDTF1.1

NS2010 qDTF1.1

PHT RS2010 qDTH1.1

RS2011 qDTH1.1

RS combined qDTH1.1

NS2010 qDTH1.1

2: (%) phenotypic variance explained by the region, AE: (%) additive effects of the region

esearch 131 (2012) 88–96

3.2.1. Dhagaddeshi × Swarna populationA total of three QTLs (qDTY1.1, qDTF1.1, and qDTH1.1) were

identified for GY, DTF, and PHT under RS situations in theDhagaddeshi × Swarna population. All these were co-located onchromosome 1 between marker intervals RM431 to RM12091.qDTY1.1 showed an additive effect of 24.9% of the population meanwith phenotypic variance (R2) of 32.0% and qDTH1.1 showed anadditive effect of 16.5% and R2 of 52.6%. qDTF1.1, which co-locatedwith qDTY1.1 and qDTH1.1, was not consistent in DS2010 and DS2011under RS situations (Table 3).

3.2.2. Dhagaddeshi × IR64 populationThe three QTLs on chromosome 1 identified in the Dha-

gaddeshi × Swarna population were also significant in Dhagad-deshi × IR64. qDTY1.1 showed an additive effect of 8.6% of thepopulation mean with R2 of 9.3%. The QTL qDTH1.1 contributed aphenotypic variance of 18.5% with an additive effect of 7.4% ofthe population mean. qDTF1.1, which was not consistent in theDhagaddeshi × Swarna population, was also not consistent acrossDS2010 and DS2011 in this population (Table 3). The marker inter-val for qDTY1.1 and qDTF1.1 co-located between markers RM104 andRM12091. The marker interval for qDTH1.1 was RM3825-RM315(Table 3).

4. Discussion

The high-yielding rice mega-varieties developed for irrigatedecosystems are highly popular among farmers in the rainfed areasof South and Southeast Asia because of their high yield potentialand good grain quality. However, these varieties are highly suscep-tible to drought, resulting in high yield losses in years with drought(Verulkar et al., 2010). The development of drought-tolerant ricevarieties in elite genetic backgrounds is a high-priority area ofrice research for sustaining rice production and productivity inrainfed environments (Fischer et al., 2003). Marker-assisted intro-gression of major-effect drought GY QTLs in well-adapted ricemega-varieties can help prevent a reduction in yield and providefarmers with better opportunities to obtain good yield in years withdrought stress.

Wild progenitor species and landraces possess valuablegenes/QTLs for drought (Kamoshita et al., 2008). Earlier also, tra-

were used to identify major-effect QTLs for drought-tolerance traits(Gomez et al., 2010). In our study, an Indian traditional donor,Dhagaddeshi, was used as a donor parent to identify favorable QTL

etected in Dhagaddeshi × Swarna and Dhagaddeshi × IR64 populations.

Marker interval F-Value R2 (%) AE (%)

RM431-RM104 63.13 22.6 26.0RM431-RM104 78.97 22.8 25.1RM431-RM104 117.45 32.0 24.9RM431-RM104 27.56 10.5 3.2RM431-RM104 21.31 7.1 2.6RM11943-RM431 166.66 41.8 15.6RM11943-RM431 240.65 49.1 17.4RM11943-RM431 270.98 52.6 16.5RM11943-RM431 177.65 43.1 15.2RM104-RM12091 9.00 9.2 13.8RM104-RM12091 10.09 6.2 3.7RM104-RM12091 18.90 9.3 8.6RM104-RM12091 11.43 6.8 1.4RM104-RM12091 10.16 7.9 1.5RM3825-RM315 58.59 18.1 8.4RM3825-RM315 30.13 12.8 3.9RM3825-RM315 45.32 18.5 7.4RM315-RM11943 42.31 16.3 8.4

expressed as percentage of trial mean.

K.H. Ghimire et al. / Field Crops Research 131 (2012) 88–96 93

F ined oa -axis.

D e as ‘S

av

riemt6gtaieaPentyepc2

ig. 2. F statistic curve denoting a QTL for grain yield (qDTY1.1) under stress (combnd peak markers are mentioned on the X-axis and F values are indicated on the Yhagaddeshi, Swarna and IR64. Tolerant allele presented as ‘T’ and susceptible allel

lleles for GY under RS, which are worthy of MAS to improve mega-arieties such as Swarna and IR64.

The expression of genetic variability for GY under droughtequires a mapping population to be exposed to adequate sever-ty of drought stress that appears most frequently in the targetcosystem. A GY reduction of 30% under mild stress, 31–65% foroderate stress, and 65–85% for severe stress has been reported

o be appropriate (Kumar et al., 2007). A reduction of 64.7% and5.9% in mean GY was observed in Dhagaddeshi × Swarna and Dha-addeshi × IR64 populations, respectively, in DS2010 comparedo mean GY under NS conditions of DS2010 (Table 1). Moder-te to high heritability was seen for GY under RS conditions,ndicating the suitability of GY as a selection criterion (Kumart al., 2007; Venuprasad et al., 2007). GY under RS was neg-tively correlated with DTF and was positively correlated withHT in DS2010 and DS2011. Bernier et al. (2007) and Vikramt al. (2011) have also reported such correlation patterns. Theegative correlation of DTF with GY under RS compared withhe positive correlation in NS is mainly because of the lowerield of lines with higher DTF resulting from a delay in flow-

ring due to drought. Slower floral development and reducedanicle elongation rate have been reported to be the mainauses for delayed flowering (Lafitte et al., 2004; Pantuwan et al.,002).

ver two years) in (A) Dhagaddeshi × Swarna and (B) Dhagaddeshi × IR64. FlankingFigure also presents the comparison of marker alleles at qDTY1.1 among Apo, N22,’.

The main advantage of using BSA is its cost efficiency for geno-typing over other genotyping methods for the identification ofmajor QTLs (Venuprasad et al., 2009; Vikram et al., 2011). However,BSA has the limitation of not detecting small-effect QTLs. Further,through BSA, interaction between major-effect and minor-effectloci or between two minor-effect loci cannot be detected because,in order to reduce genotyping costs, all markers showing parentalpolymorphism are not run on the whole population.

qDTY1.1 identified in our study showed a consistent effect on GYunder RS in both Dhagaddeshi × Swarna and Dhagaddeshi × IR64populations (Fig. 2). The phenotypic variances explained by thisQTL were 32.0% and 9.3% in Dhagaddeshi × Swarna and Dhagad-deshi × IR64 populations, respectively. Additive effect as percent oftrial mean under RS of this QTL was 24.9% in Dhagaddeshi × Swarnaand 8.6% in Dhagaddeshi × IR64 populations (Table 3). A QTL inthe same region was also reported in a study with three popula-tions developed in the background of three drought-susceptiblevarieties, Swarna, IR64, and MTU1010 (N22 × Swarna, N22 × IR64,and N22 × MTU1010), by Vikram et al. (2011). Interestingly, in ourstudy as well as in an earlier study, qDTY1.1 has a smaller effect in

the IR64 background (Dhagaddeshi × IR64 population) than in theSwarna (Dhagaddeshi × Swarna population) (Vikram et al., 2011).Noticeably, Swarna was found to be more drought susceptible thanIR64 in both studies. Vikram et al. (2011) also reported that higher

94 K.H. Ghimire et al. / Field Crops Research 131 (2012) 88–96

e sim

sSQqitewmB2r2

ubsfvt

Fig. 3. Marker loci where drought-tolerant varieties Dhagaddeshi and N22 hav

usceptibility of Swarna led to a higher effect of qDTY1.1 in thewarna background compared to IR64. qDTY1.1 co-localized withTLs for drought-related agronomic traits DTF and PHT (Table 3).DTY1.1 did not show a significant effect on GY under NS conditionsn both populations (Table 3), indicating that this QTL contributeso a GY increase only under RS. qDTY1.1 is reported to have anffect on several drought-related traits such as root traits, relativeater content, biomass, basal root thickness, and osmotic adjust-ent (Gomez et al., 2010; Bernier et al., 2007; Zhang et al., 2001;

abu et al., 2003; Kanbar et al., 2003; Price et al., 2002; Robin et al.,003). Meta QTLs for maximum root length and GY have also beeneported in the qDTY1.1 region (Courtois et al., 2009; Swamy et al.,011).

qDTY1.1 is the only QTL reported for GY under RS in lowland sit-ations that has shown a consistent effect across different geneticackgrounds as revealed in our study as well as in a previous

tudy by Vikram et al. (2011). Recently, at the same locus, QTLsor GY under upland RS have been reported from drought-tolerantariety Apo (Venuprasad et al., 2011). Apo is an improved drought-olerant indica variety selected from a traditional variety, ‘Benong’

ilar alleles, different from the alleles in susceptible varieties Swarna and IR64.

from Indonesia. N22, earlier reported to contribute qDTY1.1, is anaus cultivar developed through selection from landrace Rajbhog(Vikram et al., 2011) whereas Dhagaddeshi, found to contributeqDTY1.1 in our study, is a traditional drought-tolerant donor fromIndia. The genetic closeness of N22 and Dhagaddeshi donors wastested using 600 SSR markers and they were found to be similar for67% of marker alleles (Fig. 3). The three drought-tolerant donors,N22, Dhagaddeshi, and Apo, along with susceptible checks IR64 andSwarna were screened with the peak marker of qDTY1.1, RM431.N22, Dhagaddeshi, and Apo possessed the same allele (T) that wasdifferent from the allele present in drought-susceptible (S) varietiesIR64 and Swarna (Fig. 2). Validation of drought GY QTLs on a panelof 90 drought-tolerant lines revealed that qDTY1.1 was present inmore than 50% of the lines, which included traditional donors suchas Dhagaddeshi, Aus Bak Tulsi, Aus 229, Duwai, Kali 9, Dular, KaliAus, and Moroberekkan (Swamy et al., 2011). Another large-effect

QTL, qDTY12.1, was identified in the Vandana × Way Rarem popu-lation. Vandana is a cross of Indian landrace Kalakeri, which alsobelongs to the aus group, whereas Way Rarem is an indica variety(Bernier et al., 2007). Vikram et al. (2011) reported recently that

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he genomic regions associated with drought tolerance are likelyo be accumulated in aus cultivars during the course of evolutionnd selection in drought-prone environments.

The consistent effects of qDTY1.1 across different genetic back-rounds indicate the presence of major genes involved in itsxpression. Differentially expressed genes 4, 5-DOPA dioxyge-ase extradiol, glycosyl transferase, amino acid transporters, theADS-box family gene, and serine/threonine protein kinase under

rought between N22 and IR64 have also been reported (Lenkat al., 2011). The role of amino acid transporters and kinases inbiotic stresses is reported and reviewed by several workers (Saijot al., 2000; Xiang et al., 2007; Markus et al., 2004; Vikram et al.,012). Identification and validation of a candidate gene effect underrought can be useful in identifying the genes and mechanism ofolerance associated with qDTY1.1.

. Conclusions

qDTY1.1 is the first QTL reported to show a consistent effect inhe background of popular high-yielding mega-varieties IR64 andwarna. Further, qDTY1.1 is the first QTL to have shown an effectn GY under RS in both lowland and upland situations, indicat-ng its suitability to improve yield under RS in very diverse soilypes and ecosystems. The consistent effect of qDTY1.1 across dif-erent genetic backgrounds and environments makes it a suitableandidate for use in marker-assisted breeding to improve modernigh-yielding but drought-susceptible varieties and to help farmersbtain sustainable yield under RS.

cknowledgments

The authors thank the Asian Development Bank for providingnancial support to conduct this study. Technical support received

rom Marilyn Del Valle, Joahna Ruth Ramirez, Paul Maturan, Maerresa Sta Cruz, Jocelyn Guevarra, and Ruth Erica Carpio is dulycknowledged.

ppendix A. Supplementary data

Supplementary data associated with this article can be found, inhe online version, at doi:10.1016/j.fcr.2012.02.028.

eferences

abu, R.C., Nguyen, B.D., Chamarerk, V., Shanmugasundaram, P., Chezhian, P.,Jeyaprakash, P., Ganesh, S.K., Palchamy, A., Sadasivam, S., Sarkarung, S., Wade,L.J., Nguyen, H.T., 2003. Genetic analysis of drought resistance in rice by molec-ular markers: association between secondary traits and field performance. CropSci. 43, 1457–1469.

ernier, J., Kumar, A., Venuprasad, R., Spaner, D., Verulkar, S., Mandal, N.P., Sinha,P.K., Peeraju, P., Dongre, P.R., Mahto, R.N., Atlin, G.N., 2009. Characterization ofthe effect of rice drought tolerance qtl12.1 over a range of environments in thePhilippines and eastern India. Euphytica 166, 207–217.

ernier, J., Atlin, G.N., Serraj, R., Kumar, A., Spaner, D., 2008. Breeding upland rice fordrought resistance. J. Sci. Food Agric. 88, 927–939.

ernier, J., Kumar, A., Venuprasad, R., Spaner, D., Atlin, G.N., 2007. A large-effect QTLfor grain yield under reproductive-stage drought stress in upland rice. Crop Sci.47, 507–516.

ourtois, B., Ahmadi, N., Khowaja, F., Price, A.H., Rami, J.F., Frouin, J., Hamelin, C.,Ruiz, M., 2009. Rice root genetic architecture: meta analysis from a drought QTLdatabase. Rice 2, 115–128.

AO, 2002. Crops and Drops – Making the Best Use of Water for Agriculture. Foodand Agriculture Organization of the United Nations, Rome.

ischer, K.S., Fukai, S., Laffite, R., McLaren, G., 2003. Know your target environment.In: Fischer, K.S., Fukai, S., Laffite, R., Atlin, G., Hardy, B. (Eds.), Breeding Rice forDrought-Prone Environments. International Rice Research Institute, Los Banos,

Philippines, pp. 5–22.

ukai, S., Pantuwan, G., Jongdee, B., Cooper, M., 1999. Screening for drought resis-tance in rainfed lowland rice. Field Crops Res. 64, 61–74.

omez, S.M., Bhoopati, N.M., Kumar, S.S., Ramasubramaniam, T., Chengsong, Z.,Jayaprakash, P., Senthil, A., Babu, R.C., 2010. Molecular mapping and location

esearch 131 (2012) 88–96 95

of QTLs for drought resistance traits in Indica rice (Oryza sativa L.) lines adaptedto target environments. Acta Physiol. Plant. 32, 355–364.

http://archive.irri.org/science/software/cropstat.asp, 2009.Huke, R.E., Huke, E.H., 1997. Rice Area by Type of Culture: South, Southeast, and East

Asia. IRRI, Los Banos, Philippines.IRGSP, 2005. The map-based sequence of the rice genome. Nature 436, 793–800.Jongdee, B., Fukai, S., Cooper, M., 2002. Leaf water potential and osmotic adjustment

as physiological traits to improve drought tolerance in rice. Field Crops Res. 76,153–163.

Kamoshita, A., Babu, R.C., Boopathi, N.M., Fukai, S., 2008. Phenotypic and genotypicanalysis of drought-resistance traits for development of rice cultivars adaptedto rain-fed environments. Field Crops Res. 109, 1–23.

Kanbar, A., Shashidhar, H.E., Hittalmani, S., 2003. Mapping QTL associated with rootand related traits in DH population of rice. Indian J. Genet. 62, 287–290.

Khush, G.S., 1997. Origin, dispersal, cultivation and variation of rice. Plant Mol. Biol.35, 25–34.

Kumar, A., Bernier, J., Verulkar, S., Lafitte, H.R., Atlin, G.N., 2008. Breeding for droughttolerance: direct selection for yield, response to selection and use of drought-tolerant donors in upland and lowland-adapted populations. Field Crops Res.107, 221–231.

Kumar, R., Venuprasad, R., Atlin, G.N., 2007. Genetic analysis of rainfed lowland ricedrought tolerance under naturally-occurring stress in eastern India: heritabilityand QTL effects. Field Crops Res. 103, 42–52.

Lafitte, H.R., Ismail, A., Bennett, J., 2004. Abiotic stress tolerance in rice for Asia:progress and the future. In: Fischer, T., et al. (Eds.), New Directions for a DiversePlanet: Proceedings of the 4th International Crop Science Congress. Brisbane,Australia.

Lenka, S.K., Katiyar, A., Chinnusamy, V., Bansal, K.C., 2011. Comparative analysisof drought responsive transcriptome in Indica rice genotypes with contrastingdrought tolerance. Plant Biotechnol. J. 9, 315–327.

Markus, K.G., Markus, S., Su, J.S., Kolukisaoglu, A., Louis, P., Sonia, C., Mark, D., Richter,A., Weder, B., Schulz, B., Martinoia, E., 2004. Disruption of AtMRP4, a guard cellplasma membrane ABCC-type ABC transporter, leads to deregulation of stomatalopening and increased drought susceptibility. Plant J. 39, 219–236.

McCouch, S.R., Teytelman, L., Xu, Y., Lobos, K.B., Clare, K., Walton, M., Fu, B., Maghi-rang, R., Li, Z., Xing, Y., Zhang, Q., Kono, I., Yano, M., Fjellstrom, R., Declerck, G.,Schneider, D., Cartinhour, S., Ware, D., Stein, L., 2002. Development and mappingof 2240 new SSR markers for rice (Oryza sativa L.). DNA Res. 9, 199–207.

Murray, M.G., Thompson, W.F., 1980. Rapid isolation of high molecular weight plantDNA. Nucleic Acids Res. 8, 4321–4325.

Nelson, J.C., 1997. QGENE software for marker-based genomic analysis and breeding.Mol. Breed. 3, 239–245.

Pandey, S., Bhandari, H., 2007. Introduction. In: Pandey, S., Bhandari, H., Hardy,B. (Eds.), Economic Costs of Drought and Rice Farmers’ Coping Mechanisms:A Cross-Country Comparative Analysis. International Rice Research Insti-tute/World Scientific Publishing, Los Banos, Philippines/Singapore, pp. 1–9.

Pantuwan, G., Fukai, S., Cooper, M., Rajatasereekul, S., O’Toole, J.C., 2002. Yieldresponse of rice (Oryza sativa L.) genotypes to drought under rainfed lowlands:2. Selection of drought resistant genotypes. Field Crops Res. 73, 169–180.

Price, A.H., Cairns, J.E., Horton, P., Jones, R.G.W., Griffiths, H., 2002. Linking drought-resistance mechanisms to drought avoidance in upland rice during a QTLapproach: progress and new opportunities to integrate stomatal and mesophyllresponses. J. Exp. Bot. 53, 989–1004.

Price, A.H., Courtois, B., 1999. Mapping QTLs associated with drought resistance inrice, problems and prospects. Plant Growth Regul. 29, 123–133.

Robin, S., Pathan, M.S., Courtious, B., Lafitte, R., Carandang, S., Lanceras, S., Amante,M., Nguyen, H.T., Li, Z.K., 2003. Mapping osmotic adjustment in an advancedbackcross inbred population of rice. Theor. Appl. Genet. 107, 1288–1296.

Saijo, Y., Hata, S., Kyozuka, J., Shimamoto, K., Izui, K., 2000. Overexpression of a singleCa2+-dependent protein kinase confers both cold and salt/drought tolerance onrice plants. Plant J. 23, 319–327.

Sambrook, J., Russel, D., 2001. Chapter 8: in vitro amplification of DNA by the poly-merase chain reaction. In: Molecular Cloning: A Laboratory Manual, 3rd ed. ColdSpring Harbor Laboratory Press, New York.

Sinclair, T.R., 2011. Challenges in breeding for yield increase for drought. TrendsPlant Sci. 16, 289–293.

Swamy, B.P.M., Vikram, P., Dixit, S., Ahmed, H.U., Kumar, A., 2011. Meta-analysis ofgrain yield QTL identified during agricultural drought in grasses showed con-sensus. BMC Genomics 12, 319.

Temnykh, S., Declerck, G., Lukashova, A., Lipovich, L., Cartinhour, S., McCouch, S.,2001. Computational and experimental analysis of microsatellites in rice (Oryzasativa L.): frequency, length variation, transposon associations and geneticmarker potential. Genome Res. 11, 1441–1452.

Thompson, M., Edwards, J., Septiningsih, E., Harrington, S., McCouch, S., 2006. Sub-stitution mapping of dth1.1 , a flowering time quantitative locus (QTL) associatedwith transgressive variation in rice, reveals multiple sub-QTL. Genetics 172,2501–2514.

Venuprasad, R., Bool, M.E., Quiatchon, L., Sta Cruz, M.T., Amante, M., Atlin, G.N.,2011. A large-effect QTL for rice grain yield under upland drought stress onchromosome 1. Mol. Breed., doi:10.1007/s11032-011-9642-2.

Venuprasad, R., Dalid, C.O., Del Valle, M., Zhao, D., Espiritu, M., Sta Cruz, M.T., Amante,

M., Kumar, A., Atlin, G.N., 2009. Identification and characterization of large-effectquantitative trait loci for grain yield under lowland drought stress in rice usingbulk-segregant analysis. Theor. Appl. Genet. 120, 177–190.

Venuprasad, R., Lafitte, H.R., Atlin, G.N., 2007. Response to direct selection for grainyield under drought stress in rice. Crop Sci. 47, 285–293.

9 rops R

V

V

V

6 K.H. Ghimire et al. / Field C

erulkar, S.B., Mandal, N.P., Dwivedi, J.L., Singh, B.N., Sinha, P.K., Mahato, R.N., Swain,P., Dongre, P., Payasi, D., Singh, O.N., Bose, L.K., Robin, S., Babu, R.C., Senthil,S., Jain, A., Shashidhar, H.E., Hittalmani, S., Vera Cruz, C., Paris, T., Hijmans, R.,Raman, A., Haefele, S., Serraj, R., Atlin, G., Kumar, A., 2010. Breeding resilientand productive rice genotypes adapted to drought-prone rainfed ecosystems ofIndia. Field Crops Res. 117, 197–208.

ikram, P., Kumar, A., Singh, A., Singh, N.K., 2012. Rice: genomics-assisted breeding

for drought tolerance. In: Tuteja, N., Gill, S.S., Tiburico, A.F., Tuteja, R. (Eds.),Improving Crop Resistance to Abiotic Stress. Wiley-VCH Verlag GmbH & Co.KGaA, Germany, pp. 713–729.

ikram, P., Swamy, B.P.M., Dixit, S., Sta Cruz, T., Ahmed, H.U., Singh, A.K., Kumar, A.,2011. qDTY1.1 , a major QTL for rice grain yield under reproductive-stage drought

esearch 131 (2012) 88–96

stress with a consistent effect in multiple elite genetic backgrounds. BMC Genet.12, 89.

Xiang, Y., Huang, Y., Xiong, L., 2007. Characterization of stress-responsive CIPK genesin rice for stress tolerance improvement. Plant Physiol. 144, 1416–1428.

Yang, J., Hu, C., Hu, H., Yu, R., Xia, Z., Ye, X., Zhu, J., 2008. QTL Network: mapping andvisualizing genetic architecture of complex traits in experimental populations.Bioinformatics 24, 721–723.

Zhang, J., Zheng, H.G., Aarti, A., Pantuwan, G., Nguyen, T.T., Tripathi, J.N., Sarial, A.K.,Robin, S., Babu, R.C., Nguyen, B.D., Sarkarung, S., Blum, A., Nguyen, H.T., 2001.Locating genomic regions associated with components of drought resistance inrice: comparative mapping within and across species. Theor. Appl. Genet. 103,19–29.