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Field Crops Research 144 (2013) 281–287

Contents lists available at SciVerse ScienceDirect

Field Crops Research

jou rn al h om epage: www.elsev ier .com/ locate / fc r

QTL for high grain yield under lowland drought in the background of popularice variety Sabitri from Nepal

am Baran Yadawa,b,c, Shalabh Dixita, Anitha Ramana, Krishna Kumar Mishrab, Prashant Vikrama,.P. Mallikarjuna Swamya, Ma. Teresa Sta Cruza, Paul T. Maturana, Madhav Pandeyc, Arvind Kumara,∗

International Rice Research Institute (IRRI), DAPO Box 7777, Metro Manila, PhilippinesNepal Agricultural Research Council (NARC), Kathmandu, NepalInstitute of Agriculture & Animal Science, Rampur, Chitawan, Nepal

r t i c l e i n f o

rticle history:eceived 24 September 2012eceived in revised form 27 January 2013ccepted 28 January 2013

eywords:iceroughtieldTLAS

a b s t r a c t

Drought is the major cause of the yield reduction in rainfed rice areas. High GXE interaction in multi-location trials is a major challenge for breeders in developing breeding lines with a stable performanceover diverse environments. In a BC1-derived mapping population developed from the cross IR77298-5-6-18/2*Sabitri, a large-effect QTL, qDTY3.2, was identified on the short arm of chromosome 3 near RM231.This QTL explained 23.4% of the phenotypic variance for grain yield under severe lowland drought andhad a consistent effect across varying water stress severities in the Philippines and Nepal in DS2011,WS2011 and WS2012. Although this QTL co-localizes with the HD9 locus related to flowering time, aneffect of this QTL was seen on the grain yield of QTL-positive lines under severe and moderate droughtstress conditions regardless of the flowering time in this population. The gene content analysis of thislocus showed the presence of a wide range of genes related to stress tolerance along with those related

to flowering time. The study used bulk segregant analysis (BSA) coupled with screening at more than onelocation to simultaneously identify and test the effect of qDTY3.2 in the target environment. These resultsindicate that managed dry-season screening coupled with phenotyping of mapping populations at thetarget site and genotyping approaches such as BSA allow the identification and validation of the effect ofQTLs on grain yield under drought that will lead to the rapid development of drought-tolerant versions

gh m

of popular varieties throu

. Introduction

Rice is one of the most widely grown cereals in the world. Inhe year 2011, rice was grown on an area of 160.2 million ha with aroduction of 693.2 million tons (www.ricestat.irri.org). Rice is cul-ivated across diverse regions and ecosystems; of these, rainfed ricecosystems occupy around 38% of the total rice area (Haefele andijmans, 2007). Rice in these areas is grown by small landholdersith limited inputs, leading to low productivity.

Water stress is the biggest challenge for rice production inainfed rice ecosystems (Dixit et al., 2012a). In Asia alone, nearly

4 million ha of rainfed lowland rice and 8 million ha of upland ricere prone to frequent drought (Huke and Huke, 1997). Farmers inhese areas rely solely on rain water for field operations and are

∗ Corresponding author. Tel.: +63 2 580 5600.E-mail addresses: rbaran 9@yahoo.com (R.B. Yadaw), s.dixit@irri.org

S. Dixit), a.raman@irri.org (A. Raman), k mishra1966@yahoo.comK.K. Mishra), p.vikram@irri.org (P. Vikram), m.swamy@irri.orgB.P.M. Swamy), m.stacruz@irri.org (Ma.T.S. Cruz), pmaturan@irri.orgP.T. Maturan), mppandey.pb@gmail.com (M. Pandey), a.kumar@irri.org (A. Kumar).

378-4290/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.fcr.2013.01.019

arker-assisted breeding of the identified QTLs.© 2013 Elsevier B.V. All rights reserved.

strongly affected by uneven rainfall. Even in years of normal rain-fall, the crop can be exposed to drought stress of varying severitiesat any stage during the growing season because of the uneven dis-tribution of rainfall. However, drought stress at the reproductivephase of the crop is particularly damaging (Hsiao, 1982; O’Toole,1982; Lanceras et al., 2004; Venuprasad et al., 2009). In Nepal, 46%of the rice area is rainfed. This includes rainfed lowland areas in theTerai (plains and foothills) and dry upland areas in the Terai as wellas hill areas (CBS, 2002). In the years of less rainfall or uneven distri-bution of rainfall, these areas are significantly affected by drought,leading to a significant reduction in rice production in Nepal.

Rice cultivation, particularly in rainfed lowland areas, faces aparadox with the varieties grown and the availability of water.Although most of these areas are cultivated under limited water andinput supplies, large proportions of these rainfed areas are coveredwith high-yielding varieties bred specifically for irrigated ecosys-tems. These varieties suffer heavy yield losses even under mild tomoderate stress conditions. On the other hand, exposure of the rice

crop to water stress due to centuries of rainfed rice cultivation inthese areas has also led to the development of valuable drought-tolerant landraces. However, these landraces are not widelyadapted due to low yield potential and low input responsiveness.

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82 R.B. Yadaw et al. / Field Cro

ecent studies have shown that direct selection for grain yieldn populations derived from crosses of such drought-tolerant lan-races and popular high-yielding varieties has proven beneficialor combining high yield potential and drought tolerance (Kumart al., 2009; Venuprasad et al., 2007, 2008). These findings haveed to the identification of several large-effect QTLs for high grainield under drought (Bernier et al., 2007; Venuprasad et al., 2009;ikram et al., 2011; Ghimire et al., 2012). Such QTLs with a directffect on grain yield under drought provide a unique opportunityn rice to improve widely adapted commercial varieties through

arker-assisted selection (MAS) without altering their morpholog-cal characters preferred by farmers and consumers. Nonetheless,or MAS to be effective, it is important that the QTL have a largend consistent effect across the target population of environmentsTPE) and shows less genotype by environment (GXE) interaction.he presence of GXE interaction plays a crucial role in determininghe performance of genetic materials tested at different locationsnd in different years, and influences the selection process (Beckernd Leon, 1988; Purchase et al., 2000).

Drought research at IRRI involves the development of largeapping populations/segregating generations of breeding lines by

rossing drought-tolerant donors to popular high-yielding vari-ties and screening them for grain yield in field conditions under

managed water stress environment in the dry season. The map-ing populations/breeding lines with higher performance are thencreened in the TPE to identify QTLs/breeding lines with a con-istent effect/performance across the TPE. A moderate to highorrelation between the yield performance under managed stressnd the TPE has been reported (Verulkar et al., 2010).

Sabitri is a popular high-yielding but drought-susceptible vari-ty of Nepal. Being preferred by farmers and consumers, it covers aarge area under rainfed and irrigated lowland conditions in Nepal.n our study, we combine QTL mapping through bulk segregantnalysis (BSA) and phenotyping at more than one location (IRRInd a target environment) to identify and test the consistency of

QTL (qDTY3.2) for grain yield under drought in the background ofariety Sabitri. The study also reports the results of an in silico geneontent analysis showing the diversity of genes within this region.

. Materials and methods

Our study presents results from five experiments conducted athe International Rice Research Institute (IRRI), Los Banos, Laguna,hilippines, and the National Rice Research Program (NRRP), Hardi-ath, Nepal. The experiments at IRRI were conducted during the dryeason (DS) of 2011 while those at NRRP were conducted during theet season (WS) of 2011 and 2012.

.1. Plant materials

A population of 294 BC1-derived lines developed from the crossR77298-5-6-18/2*Sabitri was tested for QTL mapping under severeeproductive-stage drought at IRRI in DS2011 and under naturallyccurring drought and non-stress conditions at NRRP (target envi-onment) in WS2011 and WS2012. Sabitri is a popular indica varietyf Nepal, which covers a large area under rainfed and irrigatedowland conditions. This variety has long duration and is high yield-ng but highly susceptible to drought. IR77298-5-6-18 (indica) is aear-isogenic line of IR64 with high yield and improved droughtolerance showing duration similar to that of Sabitri in Nepal.

.2. Experimental details

.2.1. Description of stress ecosystemsIn this study, the term “lowland” refers to trials conducted under

ooded, puddled, transplanted, and anaerobic conditions. Trials inhich drought stress was imposed at the reproductive stage are

earch 144 (2013) 281–287

termed stress trials. The stress trials were further classified intosevere, moderate, and mild stress trials based on the percentageyield reduction compared with their non-stress counterpart basedon the classification reported by Kumar et al. (2008). Experimentsconducted under well-irrigated conditions throughout the growingseason are referred to as non-stress trials.

2.2.2. Management of field trialsFive trials for the identification of QTLs in a BC1-derived

mapping population were conducted under lowland stress andnon-stress conditions during DS2011 at IRRI and in WS2011 andWS2012 at NRRP (Table 1). Field trials for QTL mapping using theBC1-derived population were conducted in an �-lattice design withtwo replications. For all trials, a row-to-row and plant-to-plantdistance of 0.2 m and a row length of 5 m were maintained. Seedswere sown in a raised-bed nursery and 21-day-old seedlings weretransplanted in the main field. Approximately 5 cm of standingwater was maintained from transplanting up to stress initiation at30 days after transplanting (DAT) for stress trials. Stress trials wereallowed to dry after this and life-saving irrigation was providedonly when the water table in the field was below 100 cm and morethan 75% of the lines showed severe leaf rolling and leaf drying. Thedata on water table depth and season rainfall at IRRI in DS2011 arepresented in additional files 1 and 2 to indicate the severity of stressduring this season. In the trial conducted at IRRI, basal applicationequivalent to 40 kg ha−1 phosphorus (P) and potash (K) was pro-vided at the time of transplanting in the form of superphosphateand potassium chloride while 120 kg ha−1 nitrogen (N) was appliedin the form of ammonium sulfate in three splits of 30, 45, and45 kg ha−1 at 10, 20, and 45 DAT, respectively. However, in trialsconducted at NRRP, fertilizers were applied at 60:30:30 kg ha−1 N,P2O5, K2O. Half the N and all P2O5 and K2O were applied basal,and the rest of the N was applied top dressed at 25 days afterseeding (DAS). Weeds were controlled by the application of post-emergence herbicide Sofit (pretilachlor ± safener, 0.3 kg a.i. ha−1)at 4 DAT while hand weeding was carried out in the later stagesat IRRI. Hand weeding was done at 25 DAT to control weedsat NRRP. For insect control, furadan (carbofuran, 1 kg a.i. ha−1)was applied at 5 DAT, followed by cymbush (cypermethrin,1 l ha−1) ± dimotrin (cartap hydrochloride, 0.25 kg a.i. ha−1) at16 DAT. bayluscide (niclosamide, 0.25 kg a.i. ha−1), a molluscicide,was applied to control snails at IRRI, whereas, at NRRP, endosulfan(6,7,8,9,10,10-hexachloro-1,5,5a,6,9,9a-hexahydro-6,9-methano-2,4,3-benzodioxathiopin-3-oxide) (50% EC) at 2 ml l−1 of waterwas applied for insect control.

2.3. Data collection

In all trials, data on days to flowering (DTF), plant height (PH),and grain yield (GY) were recorded. DTF was recorded as the num-ber of days from sowing up to the day when 50% of the plants fromeach plot had flowering tillers. PH (cm) was measured from the soilsurface to the tip of the tallest panicle at physiological maturityfrom three random plants and then their average was calculated.Plots were harvested at maturity from ground level and dried to12% moisture content. The harvest from each plot was weighed forestimation of GY per plot.

2.4. Statistical analysis

Data from all experiments for computation of means andstandard error of difference (SED) were analyzed with CROP-STAT version 7.2.3. The following mixed model (Eq. (1)) was used

R.B. Yadaw et al. / Field Crops Research 144 (2013) 281–287 283

Table 1Mean ± SED and P values for a mapping population and parents under LSS, LMS and LNS conditions.

Season Location Ecosystem Particulars GY DTF PH

M P R M P R M P R

DS2011 IRRI LSS Population 1.0 ± 0.4 **** 0–3.9 93 ± 9 ** 79–112 72 ± 9 ** 54–98Sabitri 0 98 67IR77298-5-6-18 0.2 97 74

WS2011 NRRP LMS Population 2.5 ± 1 NS 1.1–4.1 97 ± 4 ** 87–114 86 ± 13 **** 58–110Sabitri 2.4 100 83IR77298-5-6-18 2.7 100 103

WS2012 NRRP LMS Population 2.1 ± 1 **** 1.1–4.0 89 ± 4 **** 79–100 90 ± 6 **** 71–113Sabitri 1.9 92 82IR77298-5-6-18 2.7 92 97

WS2011 NRRP LNS Population 7.2 ± 2 NS 2.9–9.8 97 ± 4 ** 87–114 106 ± 5 NS 97–115Sabitri 6.9 100 112IR77298-5-6-18 7.2 100 107

WS2012 NRRP LNS Population 3.6 ± 1 **** 1.1–4.1 – – – 112 ± 5 **** 71–113Sabitri 4.1 – – – 118IR77298-5-6-18 4.4 – – – 116

LSS: lowland severe stress; LMS: lowland moderate stress; LNS: lowland non-stress; GY: grain yield (t ha−1); DTF: days to 50% flowering; PH: plant height; M ± SED:mP on-si

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ean ± standard error of difference; R: rangerobability of difference (P): ** = 1% and **** = 0.01% significance, respectively; NS: n

herein the genotype effects were considered fixed and the repli-ates and block effects were considered random:

ikl = � + gi + rk + blk + eikl (1)

here yijk is the measurement recorded on a plot, � is the overallean, gi is the effect of the ith genotype, rk is the effect of the kth

eplicate, blk is the effect of the lth block within the kth replicate,nd eikl is the error.

A QTL class analysis of grain yield was conducted using theeak marker (RM231) for three different maturity classes, earlyDTF ≤ 90 days), medium (DTF from 90 to 99 days), and lateDTF ≤ 100 days), to determine the effect of DTF on grain yield dueo the marker. The analysis was conducted using the PROC GLM

odule of SAS V.9.1 (SAS Institute and Inc., 2004) using the modelresented in Eq. (2):

i = � + mi (2)

here yi is the phenotype value, � is the overall mean and mi is theffect of the marker.

.5. Genotyping, bulk segregant analysis (BSA), and QTL analysis

.5.1. DNA extraction and amplification of PCR productsFresh leaves from each line were collected and freeze–dried,

ut into small pieces in 2 ml tubes, and ground in a GENO grinder.NA extraction from ground samples was done using a modifiedTAB (cetyltrimethyl ammonium bromide) method. DNA samplesere quantified with 0.8% agarose gel and diluted to a concentra-

ion of approximately 25 ng �L−1. Polymerase chain reaction (PCR)as done with SSR markers in a 96-well polycarbonate plate with

he method described by Panaud et al. (1996). Polyacrylamide gellectrophoresis (Sambrook et al., 1989) was then used for size sep-ration of the amplified DNA. DNA fragments were then stainedith green SYBR safe DNA gel stain (invtrogen cat. no. S33102) for

5–20 min and visualized through a UV transilluminator with theelp of Alpha–Imager gel documentation software.

.5.2. Bulk segregant analysis (BSA), whole-populationenotyping, and QTL analysis

A total of 600 rice simple sequence repeat (SSR) markers wereested for polymorphism between the two parents, Sabitri and

gnificant.

IR77298-5-6-18. One hundred twenty-four SSR markers showedpolymorphism across the two parents and 96 of these polymorphicmarkers spaced equally across the genome were used to conduct aBSA. All markers were taken from the published rice genome maps(International Rice Genome Sequenceing Project, 2005) and theirphysical position (Mb) on the Nipponbare genome was used for anapproximate estimation of cM distances by multiplying by a factorof 3.92.

For conducting BSA, the 20 highest yielding lines (approx. 4%of the total lines) and the 20 lowest yielding lines were identi-fied based on grain yield data (Venuprasad et al., 2009) from thelowland severe stress trial conducted in DS2011. Equal amountsof DNA with similar concentration from each selected entry werebulked to generate the high- and low-yielding bulks. Both bulksalong with the parents were genotyped with the 96 selected SSRmarkers. Markers showing a clear difference in the form of band-ing patterns coinciding with those of the parents and clearly visibleband intensity between the high and low tail bulks were identifiedand used to genotype the full population of 294 lines.

2.5.3. QTL and gene content analysisComposite interval mapping was done using QTL Cartogra-

pher 2.5.005 (Wang et al., 2011). The minimal LOD value wasobtained empirically from 500 permutation tests to declare a QTL(Churchill and Doerge, 1994). The thresholds obtained correspondto an experiment-wise type 1 error of 0.05. Bayesian interval map-ping was performed using the software Q Gene 4.3.10 (Joehanesand Nelson, 2008) based on the methods outlined by Sillanpääand Arjas (1998) to confirm the location of the QTL peak andto determine the probability of the presence of more than oneQTL peak within the region. Gene content within the QTL regionwas obtained by in silico data mining through the biomart tool ofwww.gramene.org.

3. Results

3.1. Means and variance of the mapping population

Means of the population and the parents (IR77298-5-6-18 andSabitri) under drought stress and non-stress during the dry season(DS) of 2011 at IRRI and in the wet season (WS) of 2011 and 2012

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WS2012, the QTL explained 23.4% of the phenotypic variance forgrain yield under LSS conditions at IRRI with an LOD score of 10.1(Table 2; Fig. 2A). In LMS trials conducted at NRRP in WS2011 andWS2012, the QTL explained 2.8% of the phenotypic variance with

84 R.B. Yadaw et al. / Field Cro

t NRRP are presented in Table 1. In the lowland severe stress (LSS)rial, the population mean yield decreased to 1.0 t ha−1, the toler-nt parent IR77298-5-6-18 yielded 0.2 t ha−1, while the susceptiblearent Sabitri failed to produce grains. Under LMS conditions, meanrain yield of 2.5 and 2.1 t ha−1 was observed in WS2011 andS2012 at NRRP, respectively. In all three conditions, the tolerant

arent IR77298-5-6-18 out yielded the susceptible parent Sabitri.nder LNS conditions, the trials had a mean grain yield of 7.2 and.6 t ha−1, respectively, in WS2011 and WS2012 at NRRP. Both par-nts showed similar DTF in all trials conducted at the two sitesTable 1). However, both parents and the population flowered ear-ier at IRRI than at NRRP. Mean DTF of 93, 97, and 89 days wasbserved in the LSS trial at IRRI in DS2011 and in the LMS trialt NRRP in WS2011 and WS2012, whereas, under LNS conditions,ean DTF of 97 days was observed at NRRP in WS 2011. A reduc-

ion in plant height was seen in all stress trials compared with theon-stress trials. A mean PH of 73, 86, and 90 cm was recorded forhe population in the LSS trial at IRRI in DS2011 and in the LMS trialt NRRP in WS2011 and WS2012, respectively, whereas, under LNSonditions, a mean PH of 106 and 112 cm was recorded at NRRP in

S 2011 and WS2012, respectively.

.2. Bulk segregant analysis

A marker, RM231 on chromosome 3, was identified showinglear polymorphism between the two parents. The high and low

ail bulk bands coincided clearly with the parents. The clear bandingatterns of high and low tails for RM231 implied that the high and

ow tails had extreme differences in allelic frequency at this locusFig. 1). The results of BSA indicated that this marker could be linked

ig. 1. BSA with RM231 for high- and low-yielding bulks identified from screeningnder LSS. BSA: bulk segregant analysis, LSS: lowland severe stress (DS2011).

earch 144 (2013) 281–287

to a QTL for high grain yield under drought in DS2011. The high-yielding tail coincided with the IR77298-5-6-18 allele while thelow-yielding locus coincided with the Sabitri allele, indicating thatIR77298-5-6-18 could contribute the allele for the positive effecton GY.

3.3. Mapping QTLs for grain yield under drought stress andnon-stress

The phenotypic data from DS2011 were used to map QTLs forhigh grain yield under severe lowland drought linked to RM231.Composite interval mapping (CIM) analysis showed the presenceof a QTL, qDTY3.2, whose LOD peak was detected near RM231(flanked by RM231 and RM517). Over three seasons of testing atIRRI, Philippines, in DS2011, and at NRRP, Nepal, in WS2011 and

Fig. 2. QTL likelihood (A) and BIM posterior (B) curves for qDTY3.2 under varyingdrought stress conditions. (A) Genetic distance between markers is indicated on theX axis and LOD scores showing the significance of qDTY3.2 are indicated on the Y axis.Horizontal lines (color coded) correspond to LOD threshold value under respectivestress conditions. (B) BIM posterior (Y axis) curves showing QTL peak position undervarying degrees of drought stress. Marker loci are indicated on the X axis.

R.B. Yadaw et al. / Field Crops Research 144 (2013) 281–287 285

Table 2QTLs identified on chromosome 3 under LSS and LMS for GY (grain yield (t ha−1) and DTF (days to 50% flowering) and their combined effect over two seasons of testingthrough composite interval mapping (CIM).

QTL Season Location Marker Interval Peak position (cM)a BIM posterior LOD A R2

qDTY3.2 DS2011 LSS RM231 RM569–RM517 11.5 0.88 10.1 48.6 23.4qDTY3.2 WS2011 LMS RM231 RM569–RM517 11.5 0.43 3.0 4.7 2.8qDTY3.2 WS2011 LMS RM231 RM569–RM517 9.5 0.72 2.5 4.7 2.8qDTF3.1 DS2011 LSS RM517 RM517–RM411 25.5 0.71 4.8 -3.1 9.1

L A: additive effect as percentage of population mean, R2: phenotypic variance explained..

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DL

SS: lowland severe stress; LMS: lowland moderate stress; LOD: likelihood of odds;a Peak positions of the QTLs is based on bayesian interval mapping (BIM) analysis

OD scores of 3.0 and 2.5, respectively. The QTL had an additiveffect of 48.6% of the population mean under LSS conditions andt showed an additive effect of 4.7% of the population mean underMS conditions at NRRP in both WS2011 and WS2012 (Table 2). Theayesian interval mapping (BIM) analysis conducted to determinehe precise location of the QTL showed the presence of the QTL peakt 11.5 cM with RM231 as the marker closest to the peak and a BIMosterior value of 0.88 under LSS conditions (Table 2, Fig. 2B).

Another QTL, qDTF3.1, affecting DTF under LSS conditions wasetected with its LOD peak at 25.5 cM (position determined throughIM analysis with BIM posterior value of 0.79) flanked by RM517nd RM411, with RM517 as the marker closest to the LOD peakTable 2). This QTL explained 9.1% of the phenotypic variance underSS conditions. The QTL caused earliness with an additive effectf 3.1 days under LSS conditions while it had no effect on DTFn LMS and LNS trials conducted at NRRP in both WS2011 and

S2012.

.4. Characterization of QTL effect and gene content analysis

Because of the presence of a QTL affecting DTF (HD9) in closeicinity to qDTY3.2, a QTL class analysis was conducted with the peakarker (RM231) within three different maturity groups (divided

ased on DTF under non-stress in WS2011), early (DTF ≤ 90DAS),edium (DTF of 91-99 DAS), and late (DTF ≤ 100 DAS) with yield

ata of LSS conditions in which the QTL showed the highest effectFig. 3). The lines with the IR77298-5-6-18 allele at RM231 (+QTL)howed higher yield than those with the Sabitri allele at RM231–QTL) in all three maturity classes although the difference in grainield was not significant in the late maturity class. The +QTL linesad a mean grain yield of 2.1 t ha−1 in the early maturity group and.4 t ha−1 in the medium and late maturity groups compared to.5 t ha−1 foe early maturity group and 0.8 t ha−1 for medium and

ate maturity group for -QTL lines. Table 3 presents a list of lines

ith the IR77298-5-6-18 allele at RM231. Despite the varying DTF

similar to or fewer than Sabitri, LSD = 7.9 days), these lines showedn advantage over Sabitri under LSS conditions while they had sim-lar or higher yield under LMS and LNS conditions (see LSD values in

able 3ines with different DTF with IR77298-5-6-18 allele at RM231 showing higher yield than Sa

Line DTF NS PH NS Grain yield (t ha−1

LSS2 011

IR90252-B-434-1 99 104 1.2

IR90252-B-59-1 96 106 2.3

IR90252-B-346-1 93 104 1.7

IR90252-B-410-1 98 107 2.0

IR90252-B-12-2 93 107 1.8

IR90252-B-329-1 97 105 1.9

IR90252-B-502-2 103 108 4.0

IR90252-B-234-1 98 102 2.0

IR77298-5-6-18 100 108 0.2

Sabitri 100 111 0.0

TF: days to 50% flowering, PH: plant height, NS: non-stress.SD0.05 values: DTF: 7.9 days; PH: 9.9 cm; grain yield LSS2011: 0.8 t ha−1; LMS 2011: 2.0 t

early (DTF ≤ 90 days), medium (DTF of 91–99 days), and late (DTF ≤ 100 days). +QTLlines: those with IR77298-5-6-18 allele at RM231, –QTL lines: those with Sabitriallele at RM231, ***, ****: significant at 0.1 and 0.01% P levels, respectively.

Table 3 legend). These lines also had similar or reduced plant heightcompared with Sabitri (LSD = 9.9 cm). The additive effect of theIR77298-5-6-18 allele at RM231 was 48.6% of the population meanunder LSS conditions when the population mean was 1.0 t ha−1.However, under LMS conditions in DS2011 and DS2012 (popula-tion means = 2.5 t ha−1 and 2.1 t ha−1), the positive allele showed anadditive effect of 4.7% of the population mean (Table 2). A furtherdecline in additive effect was seen under LNS conditions in whichlines with the Sabitri allele at RM231 showed higher yield than lineswith the IR77298-5-6-18 allele at this locus. However, this differ-ence was non-significant. The gene content analysis of the regionshowed the presence of a variety of genes related to stress toler-ance along with genes related to flowering (Table 4). These included

genes such as heat shock proteins, membrane-related proteins,pollen sterility proteins, no apical meristem proteins, MADS boxproteins, cell division control proteins, and AP2 domain-containingproteins.

bitri under LSS conditions and similar or higher yield under LMS and LNS conditions.

)

LMS 2011 LMS 2012 LNS 2011 LNS 2012

1.8 2.3 7.6 4.22.1 2.1 8.3 4.32.5 3.1 7.6 4.32.2 2.1 8.3 4.33.5 3.0 7.3 4.52.4 2.3 7.9 4.52.7 1.7 8.7 4.73.0 1.7 8.0 4.82.7 2.7 7.2 4.42.4 1.9 6.9 4.1

ha−1, LMS 2012: 2.0 t ha−1; LNS 2011: 4.0 t ha−1 and LNS 2012: 2.0 t ha−1.

286 R.B. Yadaw et al. / Field Crops Research 144 (2013) 281–287

Table 4Genes related to stress tolerance within the qDTY3.2 region.

Ensembl gene ID Gene start (bp) Gene end (bp) Description

LOC Os03g02830 1,101,445 1,105,644 Cell cycle control protein, putative, expressedLOC Os03g03100 1,297,068 1,299,382 OsMADS50–MADS-box family gene with MIKCc type-box, expressedLOC Os03g03540 1,527,965 1,530,620 No apical meristem protein, putative, expressedLOC Os03g04070 1,861,348 1,863,241 No apical meristem protein, putative, expressedLOC Os03g04400 2,017,672 2,021,076 Heat shock protein DnaJ, putative, expressedLOC Os03g05330 2,592,125 2,604,498 Heat repeat family protein, putative, expressedLOC Os03g05590 2,799,286 2,799,920 AP2 domain-containing protein, expressedLOC Os03g05730 2,851,289 2,855,678 Cell division control protein 48 homolog E, putative, expressedLOC Os03g06170 3,094,748 3,095,974 Hsp20/alpha crystallin family protein, putative, expressedLOC Os03g06630 3,341,250 3,343,544 Heat stress transcription factor, putative, expressedLOC Os03g07140 3,652,705 3,656,816 male sterility protein, putative, expressedLOC Os03g07830 3,983,896 3,984,852 AP2 domain-containing proteinLOC Os03g07940 4,055,775 4,060,463 AP2 domain-containing protein, expressedLOC Os03g08460 4,342,727 4,344,718 AP2 domain-containing protein, expressedLOC Os03g08470 4,347,672 4,349,543 AP2 domain-containing protein, expressedLOC Os03g08490 4,366,119 4,367,401 AP2 domain-containing protein, expressedLOC Os03g08754 4,518,407 4,524,780 OsMADS47–MADS-box family gene with MIKCc type-box, expressedLOC Os03g09900 4,922,848 4,925,169 Membrane protein, putative, expressedLOC Os03g10030 5,033,170 5,039,419 Membrane-associated DUF588 domain-containing protein, putative, expressedLOC Os03g10870 5,585,002 5,587,859 Membrane-associated DUF588 domain-containing protein, putative, expressedLOC Os03g11614 6,051,750 6,060,369 OsMADS1–MADS-box family gene with MIKCc type-box, expressed

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. Discussion

The development of high-yielding drought-tolerant rice vari-ties has been a major challenge for plant breeders. Over the pastew years, in the majority of rice-growing countries, a few varietiesave become popular among farmers and are grown on millionsf hectares because of their high yield potential and farmer andonsumer preferences. However, these varieties are highly sus-eptible to drought. As a result, farmers suffer high losses whenrought occurs. There is an urgent need to breed drought-tolerantice varieties with high yield potential to reduce such losses.

Recent studies have shown that selection for high grain yieldnder drought stress and non-stress in progenies developedrom crosses between drought-tolerant upland cultivars and high-ielding lowland cultivars has made it possible to develop varietiesith high yield potential, early vigor, and drought tolerance (Kumar

t al., 2009). Apart from this, a moderate to high correlationetween the yield of lines under managed dry-season screeningnd naturally occurring drought stress has been seen in the case ofreeding trials conducted at IRRI and in drought breeding trials inastern India (Verulkar et al., 2010).

The success of breeding for drought tolerance taking grain yields a selection criterion has led to the identification of several QTLselated to high grain yield under drought (Ghimire et al., 2012;ikram et al., 2011; Venuprasad et al., 2009; Bernier et al., 2007).hese studies used genotyping strategies such as BSA and selectiveenotyping using grain yield as a criterion to determine tolerantnd susceptible tails of the mapping population to reduce geno-yping efforts and time. BSA, in particular, has proven to be a quick,ost-effective, and efficient approach for identifying large-effectTLs with minimum genotyping. It not only reduces the genotyping

equirements for QTL mapping studies but also provides the lib-rty to work on multiple populations simultaneously (Vikram et al.,011; Ghimire et al., 2012). In addition, the technique eliminateshe possibility of detecting small-effect QTLs, which are difficult toandle in marker-assisted selection due to their inconsistent effect.owever, even for large effect QTLs a test of consistency in the

arget environment still stands as a major concern that needs to

e addressed before the use of these QTLs in MAS. In our study,e combine BSA and screening of the mapping populations in the

PE to identify and test the effect of the QTL for grain yield underrought simultaneously. qDTY3.2, a large-effect QTL for grain yield

under drought, was identified on chromosome 3 using BSA. qDTY3.2has previously been reported to affect traits such as DTF, pani-cles m−2, biomass (BIO), PH, and harvest index (HI) under droughtstress and non-stress conditions (Bernier et al., 2007; Vikram et al.,2011). This QTL has also been reported to show interactions withother DTY QTLs, leading to their enhanced effect (Dixit et al., 2012b)under severe upland drought. Although this locus has been previ-ously reported for DTF as HD9 by Lin et al. (2002), it seems obviousthat this locus may lead to high yield under drought due to ear-liness leading to drought escape. The effect of this locus on DTFhas also been seen in previous studies conducted for the identifi-cation of QTLs for high yield under drought (Bernier et al., 2007;Vikram et al., 2011). In majority of the earlier studies, in which aneffect of this locus on DTF was observed, an early-flowering par-ent was used as a donor for a late-flowering susceptible parentto develop the mapping population. However, in this study, thetwo parents used to develop the mapping population had similarDTF under non-stress. This could be one reason that in the presentpopulation we do not see an effect of this locus on DTF. Single-marker analysis with peak QTL markers for lines within similarmaturity groups or after covariate adjustment of DTF to calculatemean grain yield has been used previously in studies in which QTLshave shown an effect on DTF along with grain yield to test the effectof QTLs on grain yield due to their effect on DTF (Venuprasad et al.,2009; Vikram et al., 2011). In our study, the class analysis conductedwith the three maturity classes under LSS showed no clear trendsof earliness leading to a yield advantage under drought (Fig. 3).The +QTL lines (with the IR77298-5-6-18 allele at RM231) showedhigher yield than the–QTL lines (with the Sabitri allele at RM231)in all three maturity groups (early, medium, and late). This locuslikely has an effect on other drought-related traits also leading toa yield advantage that may co-localize with the HD9 locus. In thisstudy, we were able to identify lines with varying DTF (similar toor earlier than Sabitri) with more stable yield across varying stressconditions, similar or higher yield under non-stress conditions, andsimilar or reduced plant height (Table 3). Late varieties such asSabitri are severely affected by late-season drought in the case ofearly withdrawal of monsoon. Earliness of 5-10 days in such vari-

eties ensures higher yield in such cases. QTLs such as qDTY3.2 arehighly likely to be desired by breeders to develop early versionsof late varieties such as Sabitri with more stable yield across vary-ing drought stress conditions without any yield penalty. However,

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reeding programs need to be careful in selection to make sure ear-iness does not lead to a yield penalty under non-stress conditions.

oreover, the imposition of drought well before the initiation ofhe reproductive stage (50 DAS under LSS conditions) in this studyliminated any chance of any line escaping drought. In order tourther understand this locus, we conducted an in silico gene con-ent analysis to determine the presence of other genes that mayffect yield under drought within this locus. This analysis showedhe presence of a wide range of genes that are well known to haven effect on stress tolerance of plants. These include genes relatedo heat shock proteins, male sterility proteins, and membrane andell cycle-related proteins (Table 4). qDTY3.2 showed an increas-ng effect with increasing severity of stress (Table 2). Such trendsf QTL effects have been seen previously in the case of qDTY12.1Bernier et al., 2009). Our study also shows that the procedure forhe identification of a QTL under managed dry-season screeninghrough BSA and conducting the second-season screening in thearget environment is not only beneficial for saving time and costut also effective for identifying consistent QTLs showing an effectnder varying degrees of stress across different environments.

. Conclusions

A large-effect QTL, qDTY3.2, explaining 23.4% of the phenotypicariance under severe lowland drought was detected in our study.his QTL showed a consistent effect at IRRI, Philippines, in DS2011n severe drought stress and a low effect at NRRP, Nepal, in WS2011nd WS2012 in moderate drought stress conditions, indicatingn increase in the effect of the QTL with an increase in stresseverity. Although this QTL co-localizes with HD9, a locus relatedo flowering time, the effect of qDTY3.2 was seen on grain yieldnder drought by using two parents with similar flowering time toevelop the mapping population. The gene content analysis of thisegion showed a wide variety of genes well known to be related totress tolerance in plants. The study shows qDTY3.2 as an importantocus with a wide variety of genes related to stress tolerance along

ith its effect on flowering. The study also shows the combina-ion of BSA and multi environment testing (MET) to be an efficientnd rapid approach to identify QTLs with a consistent effect onrain yield under drought. Marker-assisted selection (MAS) of theomplete span of qDTY3.2 will ensure introgression of all the coex-sting genes until the effect of the individual gene(s) present withinhis region is understood and major candidate gene(s) are identi-ed. The introgression of such QTLs opens an avenue for the rapid

mprovement of the drought tolerance of popular variety Sabitrind helping farmers obtain higher and more stable yield underrought in Nepal.

cknowledgements

The authors are grateful to the Bill and Melinda Gates Foun-ation for providing financial support for this study. The authorscknowledge S.N. Sah, Marilyn Del Valle, and Joanah Ruth Ramirezor their technical support in the completion of this study.

ppendix A. Supplementary data

Supplementary data associated with this article can be found, inhe online version, at http://dx.doi.org/10.1016/j.fcr.2013.01.019.

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