Marker-assisted selection strategy to pyramid two or more QTLs for quantitative trait-grain yield under drought

Background Marker-assisted breeding will move forward from introgressing single/multiple genes governing a single trait to multiple genes governing multiple traits to combat emerging biotic and abiotic stresses related to climate change and to enhance rice productivity. MAS will need to address concerns about the population size needed to introgress together more than two genes/QTLs. In the present study, grain yield and genotypic data from different generations (F3 to F8) for five marker-assisted breeding programs were analyzed to understand the effectiveness of synergistic effect of phenotyping and genotyping in early generations on selection of better progenies. Results Based on class analysis of the QTL combinations, the identified superior QTL classes in F3/BC1F3/BC2F3 generations with positive QTL x QTL and QTL x background interactions that were captured through phenotyping maintained its superiority in yield under non-stress (NS) and reproductive-stage drought stress (RS) across advanced generations in all five studies. The marker-assisted selection breeding strategy combining both genotyping and phenotyping in early generation significantly reduced the number of genotypes to be carried forward. The strategy presented in this study providing genotyping and phenotyping cost savings of 25–68% compared with the traditional marker-assisted selection approach. The QTL classes, Sub1 + qDTY1.1 + qDTY2.1 + qDTY3.1 and Sub1 + qDTY2.1 + qDTY3.1 in Swarna-Sub1, Sub1 + qDTY1.1 + qDTY1.2, Sub1 + qDTY1.1 + qDTY2.2 and Sub1 + qDTY2.2 + qDTY12.1 in IR64-Sub1, qDTY2.2 + qDTY4.1 in Samba Mahsuri, Sub1 + qDTY3.1 + qDTY6.1 + qDTY6.2 and Sub1 + qDTY6.1 + qDTY6.2 in TDK1-Sub1 and qDTY12.1 + qDTY3.1 and qDTY2.2 + qDTY3.1 in MR219 had shown better and consistent performance under NS and RS across generations over other QTL classes. Conclusion “Deployment of this procedure will save time and resources and will allow breeders to focus and advance only germplasm with high probability of improved performance. The identification of superior QTL classes and capture of positive QTL x QTL and QTL x background interactions in early generation and their consistent performance in subsequent generations across five backgrounds supports the efficacy of a combined MAS breeding strategy”. Electronic supplementary material The online version of this article (10.1186/s12284-018-0227-0) contains supplementary material, which is available to authorized users.


Background
Rice breeding methodology followed in the past as well as the present ranges from conventional breeding (Singh et al. 1998;Xinglai et al. 2006;Baenziger et al. 2008;Obert et al. 2008;Brick et al. 2008;Kumar et al. 2014), hybrid breeding (Shull 1948;Reif et al. 2005), marker-assisted breeding (MAB; Price 2006;McNally et al. 2009;Breseghello and Sorrells 2006;Kumar et al. 2014), and transgenic breeding (Bhatnagar-Mathur et al. 2008;Yang et al. 2010) to genome-wide association studies and genomic selection (Brachi et al. 2012;Begum et al. 2015;Biscarini et al. 2016). Grain yield as well as resistance against existing as well as emerging biotic and abiotic stresses is not a straightforward result of understanding the physiological, biochemical, and molecular mechanisms of genetic loci. Three major interactions, i) interaction between genes for the same trait, ii) genes for different traits, and iii) interactions of genes with environments and genetic background restricting the use of QTLs in introgression programs Wang et al. 2012;Xue et al. 2009;Almeida et al. 2013;Elangovan et al. 2008;Cuthbert et al. 2008;Heidari et al. 2011;Bennett et al. 2012). Selection of an appropriate donor/recipient to create desirable variability (Mondal et al. 2016;) and precise selection under variable conditions, environments, and stress intensity levels is must. A large population size is generally required for selecting appropriate plants possessing the needed gene combinations, desired plant type, and higher yield. An integration of modern, novel, and affordable breeding strategies with knowledge of associated mechanisms, interactions, and associations among related or unrelated traits/factors is necessary in rice breeding improvement programs.
The conventional breeding approach involving a series of phenotyping and genotyping screening of a large population to obtain desired variability and a high frequency of favorable genes in combination was earlier followed by several drought breeding program . A conventional breeding approach involving sequential selection of large segregating populations for biotic (bacterial late blight, blast) and abiotic stresses (drought, submergence) across generations helped breeders to develop breeding lines combining tolerance of both stresses. Superior lines in terms of acceptable plant type, grain yield, and quality traits and stable performance under different environments are promoted for release Sandhu and Kumar 2017).
Modern molecular breeding strategies have been implemented to practice a more precise, quick and cost-effective breeding strategy compared to traditional conventional rice breeding improvement programs. Previously, many QTLs for grain yield under drought using different strategies such as selective/whole-genome genotyping, bulk segregant analysis (Vikram et al. 2011;Yadaw et al. 2013;Mishra et al. 2013;Sandhu et al. 2014;Ghimire et al. 2012) have been identified. The successful introgression and pyramiding of the identified genetic regions in different genetic backgrounds using marker-assisted backcrossing Mishra et al. 2013;Sandhu et al. 2014;Venuprasad et al. 2009;Sandhu et al. 2013;Sandhu et al. 2015) has been reported.
Accurate repetitive phenotyping in multi-locations and multi-environments under variable growing conditions is required to evaluate the performance and adaptability of the developed MAB products. There have been several examples of introgression of single genes for both biotic and abiotic stresses (gall midge - Das and Rao 2015;blast -Miah et al. 2016;brown plant hopper -Jairin et al. 2009;submergence -Septiningsih et al. 2009) in the background of popular high-yielding varieties as well as introgression of more than one gene for biotic stresses (xa5 + xa13 + Xa21 - Singh et al. 2001, Kottapalli et al. 2010Xa21 + xa13 -Singh et al. 2011) for oligogenic traits controlled by major genes.
Several major large-effect QTLs such as qDTY 1.1 (Vikram et al. 2011;Ghimire et al. 2012), qDTY 2.1 (Venuprasad et al. 2009), qDTY 2.2 (Venuprasad et al. 2007;Swamy et al. 2013), qDTY 3.1 (Venuprasad et al. 2009), qDTY 4.1 , qDTY 6.1 (Venuprasad et al. 2012), qDTY 10.1 , and qDTY 12.1 (Bernier et al. 2007) for grain yield under reproductive-stage (RS) drought stress have been identified. A total of 28 significant marker trait associations were detected for yield-related trait in genome wide association study of japonica rice under drought and non-stress conditions (Volante et al. 2017). Moreover, each of these identified QTLs has shown a yield advantage of 300-500 kg ha − 1 under RS drought stress depending upon the severity and timing of the drought occurrence. However, in order to provide farmers with an economic yield advantage under drought, it is necessary that two or more such QTLs be combined to obtain a targeted yield advantage of 1.0 t ha − 1 under severe RS drought stress (Sandhu and Kumar 2017;Kumar et al. 2014).
Polygenic traits governed by more than one gene within the identified QTLs do not follow the simple rule of single gene introgression. The positive/negative interactions of alleles within QTLs and with the genetic background (Dixit et al. 2012a, b), pleiotropic effect of genes and linkage drag (Xu and Crouch 2008;Vikram et al. 2015;Vikram et al. 2016;Bernier et al. 2007;Venuprasad et al. 2009;Vikram et al. 2011;Venuprasad et al. 2012) played an important role in determining the effect of introgressed loci. The reported linkage drag of the qDTY QTLs has been successfully broken and individual QTLs have been introgressed into improved genetic backgrounds (Vikram et al. 2015). To identify an appropriate number of plants with positive interactions and high phenotypic expression, MAB requires genotyping and phenotyping of large numbers of plants/progenies in each generation from F 2 onwards. In this case, MAB for more than two genes/QTLs is not a cost-effective approach. The population size to be genotyped and phenotyped for complex traits such as drought increases significantly as two or more QTLs are considered for introgression. To enhance breeding capacity to develop climate-resilient rice cultivars, there is a strong need to develop a novel, cost/ labor-effective, and high-throughput breeding strategy. The effective integration of molecular knowledge into breeding programs and making MAB cost-effective enough to be fully adapted by small-or moderate-sized breeding programs are still a challenge.
In the present study, we closely followed the marker-assisted introgression of two or more QTLs for RS drought stress in the background of rice varieties; Swarna-Sub1, IR64-Sub1, Samba Mahsuri, TDK1-Sub1, and MR219 from F 3 to F 6 /F 7 /F 8 generations. Class analysis for different combinations of QTLs for yield under RS drought stress as well as under irrigated control conditions was performed with the aim to understand the effectiveness of synergistic effect of phenotyping and genotyping in early generations on selection of better progenies. We hypothesized that a QTL class that has performed well in an early generation may maintain its performance across generations/years and seasons.

Cost effectiveness of the early generation selection
The genotyping cost for the whole population considering all QTL classes from F 3 to F 7 /F 8 ranged from USD 9225 to USD 21760 whereas the genotyping cost accounting for further advancement and screening (F 4 to F 7 /F 8 ) of only superior classes in F 3 varied from USD 5730 to USD 8978 (Table 6). A genotyping cost savings of USD 12443, 3720, 14,780, 2273, and 6225 was observed in Swarna-Sub1, IR64-Sub1, Samba Mahsuri, TDK1-Sub1, and MR219 backgrounds, respectively, with a range of savings of USD 2273 to USD 14780 in all five backgrounds.
The phenotyping cost for the whole population ranged from USD 29197 to USD 157455 whereas it was USD 20225 to USD 50507 in the case of selected classes (Table 7). A phenotyping cost savings of USD 60023, 8973, 10,963, 106,948, and 30,029 was observed in Swarna-Sub1, IR64-Sub1, Samba Mahsuri, TDK1-Sub1, and MR219 backgrounds, respectively, with phenotyping cost savings of USD 8973-106,948 in all five backgrounds. The genotyping and phenotyping cost and savings were high in Samba Mahsuri as the number of plant samples in the whole population set in the F 4 generation was more than in the QTL class selected in F 3 (DTY 2.2 + DTY 4.1 ) ( Table 6). The cost savings was inversely proportional to the number of QTL combination classes identified as providing superior performance in F 3 .

Interaction among QTLs and with background
In our study, qDTY 1.1 showed positive interactions with qDTY 2.1 , qDTY 2.2 , and qDTY 3.1 , whereas qDTY 2.2 showed positive interactions with qDTY 4.1 , qDTY 12.1 , and qDTY 3.1 . qDTY 3.1 showed positive interactions with qDTY 1.1 , qDTY 2.2 , qDTY 12.1 , qDTY 6.1 , and qDTY 6.2 at least in the genetic backgrounds that we studied in the present experiment. Such information will be helpful to breeders in selecting QTL combinations in their MAB programs.

Phenotypic evaluation of QTLs pyramided lines
The yield reduction in RS drought stress experiments was 45, 77, 79, and 97% in F 3 , F 5 , F 7 , and F 7 generations, respectively, in Swarna-Sub1 introgression lines as compared to the mean yield of the NS experiments. In IR64-Sub1, the yield reduction was 22, 96, 82, and 97% in F 3 , F 4 , F 6 , and F 7 generations, respectively. In the Samba Mahsuri background, the mean yield reduction was 66, 98, and 98% in F 3 , F 7 , and F 8 generations, respectively, in the RS drought stress experiment compared with NS experiments. A grain yield reduction of 68, 93, 98, and 96% was observed in F 4 , F 6 , F 7 , and F 8 generations, respectively, under RS drought stress compared with NS in TDK1-Sub1 introgressed lines. In MR219 introgressed lines, the yield reduction under RS drought stress compared with NS was 88, 93, and 93% in F 3 , F 5 , and F 7 generations, respectively. Accurate standardized phenotyping under RS drought stress assists breeders in rejecting inferior QTL classes in F 3 itself and is the basis of success of the combined MAS breeding approach. It is evident from the yield reduction as well as the water table depths (Fig. 2a-e) that the stress level in RS drought stress experiments ranged from moderate to severe drought stress intensity at the reproductive stage in most of the cases. DTF of majority of pyramided lines was less than that of recipient lines under RS but not under NS. Some of the selected progenies showed early DTF than recipient under NS and this may have resulted from linkages of the drought QTLs with earliness (Vikram et al. 2016). Most of the progenies showed similar PHT as that of recipient cultivars under NS but higher PHT under RS because of their increased ability to produce biomass under RS (data not presented).
(See figure on previous page.) Fig. 1 a Graph representing the generation (X axis) and mean grain yield (Y axis) of selected SwarnaSub1 pyramided lines under NS (control); b Graph representing the generation (X axis) and mean grain yield (Y axis) of selected SwarnaSub1 pyramided lines under RS drought stress; c Graph representing the generation (X axis) and mean grain yield (Y axis) of selected IR64Sub1 pyramided lines under NS (control); d Graph representing the generation (X axis) and mean grain yield (Y axis) of selected IR64Sub1 pyramided lines under RS drought stress; e Graph representing the generation (X axis) and mean grain yield (Y axis) of selected Samba Mahsuri pyramided lines under NS (control); f Graph representing the generation (X axis) and mean grain yield (Y axis) of selected Samba Mahsuri pyramided lines under RS drought stress; g Graph representing the generation (X axis) and mean grain yield (Y axis) of selected TDK1Sub1 pyramided lines under NS (control); h Graph representing the generation (X axis) and mean grain yield (Y axis) of selected TDK1Sub1 pyramided lines under RS drought stress; i Graph representing the generation (X axis) and mean grain yield (Y axis) of selected MR219 pyramided lines under NS (control); and (j) Graph representing the generation (X axis) and mean grain yield (Y axis) of selected MR219 pyramided lines under RS drought stress

Selection of superior QTLs class in early generation
In a marker-assisted QTL introgression/pyramiding program, it would be very valuable to explore QTL combinations with high performance in early generations. The F 2 generation is highly heterogeneous; therefore, screening of a large population size is essential to maximize the exploitation of genetic variation (Kahani and Hittalmani 2015). Sometimes, based on the availability of resources, fields for phenotyping, as well as capacity of breeding programs, breeders have to reduce the population size, which may lead to a loss of existing positive genetic variability in the population . In the present study, the screening of a large-sized F 3 population was carried out under control (NS) and RS drought stress conditions. The classification of the population in different classes based on QTL combinations in  The letter display are QTL class labels ordered by mean grain yield of QTL class. Means followed by the same letter (within a column) are not significantly different, DS dry season, WS wet season, NS non-stress, RS reproductive-stage drought stress, X recipient parent (no QTL)    The letter display are QTL class labels ordered by mean grain yield of QTL class. Means followed by the same letter (within a column) are not significantly different, DS dry season, WS wet season, NS non-stress, RS reproductive-stage drought stress, X recipient parent (no QTL) each generation (F 3 to F 7 /F 8 ) followed by class analysis to see the performance of each QTL class across generation advancement proved to be an effective approach in identifying best-bet QTL combination classes across five high-yielding genetic backgrounds. The performance of the genotypes in a particular QTL class was consistent from F 3 to F 7 /F 8 generations in all five studied background in the present study. The advancement of the classes with high mean grain yield performance in the F 3 generation in addition to the MAB approach involving stepwise phenotyping and genotyping screening suggested this as being a cost/labor-and resource-effective breeding strategy. The lesser number of genotypes in advanced generations can be screened more precisely in a large plot size with more replications. The current cost-effective high-throughput phenotyping platform (Comar et al. 2012;Andrade-Sanchez et al. 2014;Sharma and Ritchie 2015;Bai et al. 2016) can be used for precise breeding and physiological studies considering the small population size. Even at the F 3 level, some heterozygosity will be observed when more genes are involved in the introgression program. However, in our study, we did not observe any change in performance of QTL classes found superior in F 3 , indicating the F 3 generation to be suitable to conduct class analysis and reject inferior classes. The genotyping cost was calculated considering five markers per QTL (one peak/near the peak, two right-hand-side flanking markers, and two left-hand-side flanking markers) and USD 0.50 per data point

Population size and validation of combined breeding strategy
In addition to the modern next-generation genotyping strategies (Barba et al. 2014;Rius et al. 2015;Dhanapal and Govindaraj 2015) and agricultural system models (Antle et al. 2016), several breeding strategies involving correlated traits as selection criteria in early generations (Senapati et al. 2009), grain yield , secondary traits (Mhike et al. 2012), genetic variance, heritability (Almeida et al. 2013), path coefficient analysis, selection tolerance index (Dao et al. 2017), and yield index (Raman et al. 2012) have been suggested for use in breeding programs. The consistent performance of pyramided lines with specific QTL combinations across generations (F 3 to F 7 /F 8 ) in five backgrounds in the present study validates the potential of the suggested combined MAS breeding approach presented in the current study. The integration of accurate phenotyping and the selection of the best class representing the genetic variability of the whole population in early generations are critical steps for the practical implementation of this ultimate novel breeding strategy. Keeping a large F 3 population size depending upon the number of genes/QTLs being introgressed and precise phenotyping to exploit the hidden potential of each genotype in each QTL class could maximize the potential output of each class in early generations. The most logical QTL-class performance-derived novel breeding strategy could be The phenotyping cost of USD 36.18 per entry was calculated considering two replications and screening under NS and RS drought stress with plot size of 1.54 m 2 (IRRI Standard drought screening costing) adopted to optimize the breeding efficiency of small-to moderate-sized breeding programs in rice breeding improvement programs. Further, the strategy could be equally useful to other crops in which major genes/ QTLs determine the expression of traits and QTL x QTL or QTL x genetic background interactions have been identified.
We were able to understand the effectiveness of early generation selection in the marker-assisted introgression program for drought because the breeding program maintained systematic data for both genotyping and phenotyping conducted over the past six or more years. It was only after we successfully identified the best lines coming from each introgression program after successful multi-location evaluation that we realized that, as the breeding program will need to bring in more and more genes for multiple traits to address each of the new emerging climate-related challenges, modifications that allow plant breeders to make large-scale rejections in the early generation will become necessary. The effectiveness of the combined MAS strategy is evident from the result that, in none of the five cases were the superior QTL class combinations identified in F 3 outperformed by inferior classes identified in F 3 in any advanced generation under both NS and variable intensities of RS drought stress in different seasons/years across generations from F 4 to F 6 /F 7 /F 8 .

Cost-effectiveness of combined breeding strategy
Breeding practices are challenged by being laborious, time consuming, and non-economical, requiring large land space and a large population size (Sandhu and Kumar 2017), being imprecise, and having unreliable phenotyping screening (Bhat et al. 2016); hence, an economical, fast, accurate, and efficient breeding selection system is required to increase grain yield potential and productivity (Khan et al. 2015). The cost-benefit balance (Bhat et al. 2016) must be considered in increasing genetic gain in the new era of modern science. The use of the class analysis approach in the F 3 generation followed by advancing only higher performing classes reported a genotyping cost savings of 25-68% and phenotyping cost savings of 25-68% compared with the traditional molecular marker breeding approach (Table 6). Although the cost-benefit of the combined MAS breeding strategy will always be inversely proportional to the number of superior QTL class combinations identified for advancement in F 3 and subsequent generations, the cost savings will increase as the number of genes included in the introgression program increases because of the rejection of a larger proportion of the total population early in the F 3 generation. This procedure will save time, labor, resources, and space and will allow breeders to focus only on germplasm with higher value. This will reduce the population size for phenotypic and genotypic selection in advanced generations compared with earlier marker-assisted breeding strategies (Price 2006;McNally et al. 2009;Yadaw et al. 2013;Sandhu et al. 2014;Brachi et al. 2012;Begum et al. 2015). It will be practical and realistic only if the phenotyping, genotyping, and class analysis in early generations are accurate.

Interactions among QTLs and with background
The QTLs for grain yield under drought have shown QTL x QTL (Sandhu et al. 2018) as well as QTL x genetic background interactions (Dixit et al. 2012a, b;Sandhu et al. 2018). Many such interactions that may occur between QTL x QTL and QTL x genetic background are unknown. Such positive/negative interactions affecting grain yield under normal or RS situation can be captured through approach that combines selection based on phenotyping and genotyping in the early generations. The current study clearly demonstrated the success of selection based on combining phenotyping and genotyping in identifying better progenies in early generation thereby reducing the number of progenies to be advanced. Number of plants to be generated and evaluated in the early generations will depend upon the number of QTLs/genes to be introgressed together, size of introgressed QTLs region as well as availability of closely linked markers for each of the QTLs. The QTLs for grain yield under drought have shown undesirable linkages with low yield potential, very early maturity duration, tall plant height (Vikram et al. 2015). At IRRI, studies were undertaken to break the undesirable linkages of QTLs with tall plant height, very early maturity duration and low yield potential (Vikram et al. 2015). Such improved lines were used in the MAS introgression program. The drought tolerant donors N22, Dular, Apo, Way Rarem, Kali Aus, Aday Sel that are source of identified QTLs do not possess good grain quality. Even though, we did not study the linkage of qDTYs with grain quality, the introgressed lines released as varieties in IR64, Swarna backgrounds in India and Nepal did not reveal any adverse effect on grain quality. The yield superiority of lines with two or more QTLs under both NS and RS drought stress over the five high-yielding backgrounds clearly indicated that qDTY QTLs identified at IRRI are free from undesirable linkage drag and can be successfully used in MAB programs targeting yield improvement under RS drought stress. Further, in Swarna-Sub1, IR64-Sub1, and TDK-Sub1, the highest yielding classes identified were the classes possessing both Sub1 and combinations of the drought QTLs. The yield superiority of such classes across these three backgrounds over all the generations clearly indicated that tolerance of submergence and drought can be effectively combined even though they are governed by two different physiological mechanisms. In the QTL study undertaken at IRRI, qDTY 1.1 showed a significant mean yield advantage in MTU1010 and IR64 (Sandhu et al. 2015); qDTY 2.2 in Pusa Basmati 1460, MTU1010, and IR64 (Venuprasad et al. 2007;Swamy et al. 2013;Sandhu et al. 2013;Sandhu et al. 2014); qDTY 2.3 in Vandana and IR64 (Dixit et al. 2012b;Sandhu et al. 2014); qDTY 3.2 in Sabitri ); qDTY 6.1 in IR72 (Venuprasad et al. 2009); and qDTY 12.1 in Vandana (Bernier et al. 2007), Sabitri , Kalinga, and Anjali backgrounds. Similar interaction of qDTY 2.3 and qDTY 3.2 with qDTY 12.1 in a Vandana background (Dixit et al. 2012b); qDTY 2.2 and qDTY 3.1 with qDTY 12.1 in an MRQ74 background (Shamsudin et al. 2016); and qDTY 2.2 + qDTY 4.1 in an IR64 background ) was observed. The interaction of identified QTLs with other QTLs in more than two backgrounds supports the usefulness of such QTL classes in MAS. In all five of these cases, through genotyping and phenotyping we were able to identify QTL class combinations with positive interactions and higher yield. As more data are generated across different backgrounds and interactions are established, breeders will have the ability to identify and forward only selected classes without phenotyping from F 3 onward.

Pyramiding of multiple QTLs associated with multiple traits
With the identification of gene-based/closely linked markers for different biotic stresses (bacterial blight, blast, brown planthopper, gall midge) and abiotic stresses (submergence, drought, phosphorus deficiency, cold, anaerobic germination, high temperature), the MAB program is moving forward to introgress more genes/QTLs to develop climate-resilient and better rice varieties. For effective tolerance to develop a variety combining tolerance of biotic and abiotic stressesbacterial leaf blight (three genes -xa5, xa13, Xa21), blast (two -pi2, pi9), brown planthopper (two -BPH3, BPH17), gall midge (two -Gm4, Gm8), drought (three -qDTY 1.1 , qDTY 2.1 , qDTY 3.1 ), and submergence (Sub1)researchers will need introgression and the combination of 13-15 genes/QTLs in gene combinations mentioned here or in other combinations depending upon the prevalence of a pathotype/biotype in different regions. The number of genes to be introgressed is likely to increase as exposure of rice to high temperature at the reproductive stage will probably increase in most rice-growing regions. The introgression of 10-15 genes will not only require a larger initial population in F 2 and F 3 but will also lead to increased positive/negative interactions between genes/QTLs. With capacity development, as more and more breeding programs adopt marker-assisted introgression of more genes, the combined MAS strategy will be of great help to plant breeders in reducing the number of plants that they should handle in each generation and make their breeding program cost-effective.

Conclusions
The selection of QTL classes with a high mean yield performance and positive interactions among loci and with background in the early generation and consistent performance of QTL classes in subsequent generations across five backgrounds supports the effectiveness of a combined MAS breeding strategy. The challenge ahead is the appropriate estimation of the precise population size to be used for QTL class analysis in the early F 3 generation to maintain genetic variability as the number of genes/QTLs increases further. Integration of a cost-effective, efficient, designed, statistics-led early generation superior QTL class selection-based breeding strategy with new-era genomics such as genotyping by sequencing and genomic selection could be an important breakthrough to build up a scientific next-generation breeding program.

Methods
The study was conducted at the International Rice Research Institute (IRRI), Philippines, to introgress QTLs for grain yield under RS drought stress in the background of improved high-yielding widely grown but drought-susceptible varieties from India (Swarna, IR64, Samba Mahsuri), Lao PDR (TDK1), and Malaysia (MR219).
The screening of all five population sets was carried out under NS control and RS drought stress conditions. For the NS experiments, 5-cm water depth level was maintained throughout the rice growing season until physiological maturity. For the screening under RS drought stress, irrigation was stopped at 30 days after transplanting (DAT). The last irrigation was provided at 24 DAT and there was no standing water in the field when drought was initiated at 30 DAT. The stress cycle was continued until severe stress symptoms were observed. Monitoring of soil water potential was carried out by placing perforated PVC pipes at 100-cm soil depth in the field in a zig-zag manner. After the initiation of stress, the water table level was recorded daily. When approximately 70% of the lines exhibited severe leaf rolling or