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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 (F to F ) for five marker-assisted breeding programs were analyzed to understand the effectiveness of 3 8 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 F /BC F /BC F 3 1 3 2 3 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 + qDTY +qDTY + qDTY and Sub1 + qDTY + qDTY in 1.1 2.1 3.1 2.1 3.1 Swarna-Sub1, Sub1 + qDTY + qDTY , Sub1 + qDTY + qDTY and Sub1 + qDTY + qDTY in IR64-Sub1, qDTY + 1.1 1.2 1.1 2.2 2.2 12.1 2.2 qDTY in Samba Mahsuri, Sub1 + qDTY + qDTY + qDTY and Sub1 + qDTY + qDTY in TDK1-Sub1 and qDTY + 4.1 3.1 6.1 6.2 6.1 6.2 12.1 qDTY and qDTY + qDTY in MR219 had shown better and consistent performance under NS and RS across 3.1 2.2 3.1 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”. Keywords: Drought, Drought yield QTLs, Marker-assisted selection breeding strategy, Pyramiding, Rice Background marker-assisted breeding (MAB; Price 2006; McNally et Rice breeding methodology followed in the past as well al. 2009; Breseghello and Sorrells 2006; Kumar et al. as the present ranges from conventional breeding (Singh 2014), and transgenic breeding (Bhatnagar-Mathur et al. et al. 1998; Xinglai et al. 2006; Baenziger et al. 2008; 2008; Yang et al. 2010) to genome-wide association stud- Obert et al. 2008; Brick et al. 2008; Kumar et al. 2014), ies and genomic selection (Brachi et al. 2012; Huang et hybrid breeding (Shull 1948; Reif et al. 2005), al. 2010; 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 straightfor- * Correspondence: firstname.lastname@example.org ward result of understanding the physiological, biochem- International Rice Research Institute, DAPO Box 7777, Metro Manila, ical, and molecular mechanisms of genetic loci. Three Philippines Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Kumar et al. Rice (2018) 11:35 Page 2 of 16 major interactions, i) interaction between genes for the Septiningsih et al. 2009) in the background of popular same trait, ii) genes for different traits, and iii) interac- high-yielding varieties as well as introgression of more tions of genes with environments and genetic back- than one gene for biotic stresses (xa5 + xa13 + Xa21 - ground restricting the use of QTLs in introgression Singh et al. 2001, Kottapalli et al. 2010; Xa21 + xa13 - programs (Kumar et al. 2014; Wang et al. 2012; Xue et Singh et al. 2011) for oligogenic traits controlled by al. 2009; Almeida et al. 2013; Elangovan et al. 2008; major genes. Cuthbert et al. 2008; Heidari et al. 2011; Bennett et al. Several major large-effect QTLs such as qDTY 1.1 2012). Selection of an appropriate donor/recipient to (Vikram et al. 2011; Ghimire et al. 2012), qDTY 2.1 create desirable variability (Mondal et al. 2016; Dixit et (Venuprasad et al. 2009), qDTY (Venuprasad et al. 2.2 al. 2014) and precise selection under variable conditions, 2007; Swamy et al. 2013), qDTY (Venuprasad et al. 3.1 environments, and stress intensity levels is must. A large 2009), qDTY (Swamy et al. 2013), qDTY (Venuprasad 4.1 6.1 population size is generally required for selecting appropri- et al. 2012), qDTY (Swamy et al. 2013), and qDTY 10.1 12.1 ate plants possessing the needed gene combinations, desired (Bernier et al. 2007) for grain yield under plant type, and higher yield. An integration of modern, reproductive-stage (RS) drought stress have been identi- novel, and affordable breeding strategies with knowledge of fied. A total of 28 significant marker trait associations associated mechanisms, interactions, and associations were detected for yield-related trait in genome wide asso- among related or unrelated traits/factors is necessary in rice ciation study of japonica rice under drought and breeding improvement programs. non-stress conditions (Volante et al. 2017). Moreover, The conventional breeding approach involving a series each of these identified QTLs has shown a yield advantage − 1 of phenotyping and genotyping screening of a large popu- of 300–500 kg ha under RS drought stress depending lation to obtain desired variability and a high frequency of upon the severity and timing of the drought occurrence. favorable genes in combination was earlier followed by However, in order to provide farmers with an economic several drought breeding program (Kumar et al. 2014). A yield advantage under drought, it is necessary that two or conventional breeding approach involving sequential se- more such QTLs be combined to obtain a targeted yield − 1 lection of large segregating populations for biotic (bacter- advantage of 1.0 t ha under severe RS drought stress ial late blight, blast) and abiotic stresses (drought, (Sandhu and Kumar 2017; Kumar et al. 2014). submergence) across generations helped breeders to de- Polygenic traits governed by more than one gene velop breeding lines combining tolerance of both stresses. within the identified QTLs do not follow the simple rule Superior lines in terms of acceptable plant type, grain of single gene introgression. The positive/negative inter- yield, and quality traits and stable performance under actions of alleles within QTLs and with the genetic back- different environments are promoted for release (Kumar ground (Dixit et al. 2012a, b), pleiotropic effect of genes et al. 2014;Sandhuand Kumar 2017). and linkage drag (Xu and Crouch 2008; Vikram et al. Modern molecular breeding strategies have been im- 2015; Vikram et al. 2016; Bernier et al. 2007; Venuprasad plemented to practice a more precise, quick and et al. 2009; Vikram et al. 2011; Venuprasad et al. 2012) cost-effective breeding strategy compared to traditional played an important role in determining the effect of conventional rice breeding improvement programs. Pre- introgressed loci. The reported linkage drag of the qDTY viously, many QTLs for grain yield under drought using QTLs has been successfully broken and individual QTLs different strategies such as selective/whole-genome have been introgressed into improved genetic back- genotyping, bulk segregant analysis (Vikram et al. 2011; grounds (Vikram et al. 2015). To identify an appropriate Yadaw et al. 2013; Mishra et al. 2013; Sandhu et al. 2014; number of plants with positive interactions and high Ghimire et al. 2012) have been identified. The successful phenotypic expression, MAB requires genotyping and introgression and pyramiding of the identified genetic phenotyping of large numbers of plants/progenies in each regions in different genetic backgrounds using generation from F onwards. In this case, MAB for more marker-assisted backcrossing (Yadaw et al. 2013; Mishra than two genes/QTLs is not a cost-effective approach. et al. 2013; Sandhu et al. 2014; Venuprasad et al. 2009; The population size to be genotyped and phenotyped for Sandhu et al. 2013; Sandhu et al. 2015) has been re- complex traits such as drought increases significantly as ported. Accurate repetitive phenotyping in two or more QTLs are considered for introgression. To multi-locations and multi-environments under variable enhance breeding capacity to develop climate-resilient rice growing conditions is required to evaluate the perform- cultivars, there is a strong need to develop a novel, cost/ ance and adaptability of the developed MAB products. labor-effective, and high-throughput breeding strategy. There have been several examples of introgression of The effective integration of molecular knowledge into single genes for both biotic and abiotic stresses (gall breeding programs and making MAB cost-effective midge – Das and Rao 2015; blast – Miah et al. 2016; enough to be fully adapted by small- or moderate-sized brown plant hopper – Jairin et al. 2009; submergence – breeding programs are still a challenge. Kumar et al. Rice (2018) 11:35 Page 3 of 16 In the present study, we closely followed the respectively, under RS drought stress conditions in an marker-assisted introgression of two or more QTLs for MR219 background (Table 5). RS drought stress in the background of rice varieties; Swarna-Sub1, IR64-Sub1, Samba Mahsuri, TDK1-Sub1, Performance of pyramided lines in the F generation and MR219 from F to F /F /F generations. Class ana- Mean performances of QTL classes from F to F /F of 3 6 7 8 3 7 8 lysis for different combinations of QTLs for yield under Swarna-Sub1, IR64-Sub1, Samba Mahsuri, TDK1-Sub1, RS drought stress as well as under irrigated control con- and MR219 pyramided lines are shown in Tables 1, 2, 3, ditions was performed with the aim to understand the 4, and 5, respectively. effectiveness of synergistic effect of phenotyping and In a Swarna background, two classes (Sub1 + genotyping in early generations on selection of better qDTY +qDTY + qDTY and Sub1 + qDTY + 1.1 2.1 3.1 2.1 progenies. We hypothesized that a QTL class that has qDTY ) showed higher performance in F under both 3.1 3 performed well in an early generation may maintain its NS and RS drought stress (Table 1). In an IR64-Sub1 performance across generations/years and seasons. background, three classes (Sub1 + qDTY + qDTY , 1.1 1.2 Sub1 + qDTY + qDTY , Sub1 + qDTY + qDTY ) 1.1 2.2 2.2 12.1 Results showed higher performance under NS and RS drought Performance of lines introgressed with QTLs for grain stress both, whereas Sub1 + qDTY + qDTY + 3.2 2.3 yield under drought qDTY performed better under RS drought stress only 12.1 The pyramided lines with either a single gene or in com- in F (Table 2). In Samba Mahsuri background, the QTL bination of genetic loci associated with grain yield under class qDTY + qDTY showed a higher performance 2.2 4.1 drought produced a grain yield advantage over the re- than a single QTL under both NS and RS drought stress cipient parent across backgrounds and generations in F (Table 3). In a TDK1-Sub1 background, the classes (Fig. 1a to j). The pyramided lines with two or more consisting of pyramided lines with Sub1 + qDTY + 3.1 QTLs had shown a high grain yield advantage in qDTY + qDTY and Sub1 + qDTY + qDTY 6.1 6.2 6.1 6.2 Swarna-Sub1 (Table 1), IR64-Sub1 (Table 2), Samba showed a stable and high effect across variable growing Mahsuri (Table 3), TDK1-Sub1 (Table 4), and MR219 conditions in F (Table 4). In the MR219 background, (Table 5) backgrounds. In a Swarna-Sub1 background, a pyramided lines having qDTY + qDTY and qDTY 12.1 3.1 2.2 − 1 grain yield advantage of 76.2–2478.5 kg ha and 395.7– + qDTY showed significant yield advantage under both 3.1 − 1 2376.3 kg ha under non-stress (NS) in Sub1 + NS and RS drought stress (Table 5). qDTY +qDTY + qDTY and Sub1 + qDTY + 1.1 2.1 3.1 2.1 qDTY pyramided lines, respectively, was observed. Validation of MAB-selected class performance in 3.1 Under RS drought stress, a grain yield advantage of subsequent generations − 1 292.4–1117.8 and 284.2–2085.5 kg ha in Sub1 + The performance of pyramided line classes identified as qDTY +qDTY + qDTY and Sub1 + qDTY + superior in the F generation was found to be consistent 1.1 2.1 3.1 2.1 3 qDTY pyramided lines, respectively, was observed and higher than other QTL classes throughout F ,F ,F , 3.1 4 5 6 (Table 1). In an IR64-Sub1 background, the pyramided F and F generations (except where the number of lines 7, 8 lines (Sub1 + qDTY + qDTY ) showed a grain yield per class was less) across all five studied backgrounds in 1.1 2.2 − 1 advantage ranging from 21.3 to 1571.4 kg ha and the present study. The high mean grain yield QTL clas- − 1 170.4 to 864.7 kg ha under NS and RS drought stress, ses in the F generation, Sub1 + qDTY +qDTY + 3 1.1 2.1 respectively. Under RS drought stress, the pyramided qDTY and Sub1 + qDTY + qDTY in a Swarna 3.1 2.1 3.1 lines (Sub1 + qDTY + qDTY + qDTY ) showed a background (Table 1), qDTY + qDTY in a Samba 3.2 2.3 12.1 2.2 4.1 − 1 grain yield advantage of 217.1 to 719.1 kg ha in an Mahsuri background (Table 3), and Sub1 + qDTY + 3.1 IR64-Sub1 background (Table 2). The grain yield advan- qDTY + qDTY and Sub1 + qDTY + qDTY in a 6.1 6.2 6.1 6.2 − 1 tage ranged from 48.0 to 2216.9 kg ha and 95.5 to TDK1-Sub1 background (Table 4) had maintained their − 1 1296.4 kg ha under NS and RS drought stress condi- high mean grain yield performance from the F to F 4 8 tions, respectively, in Samba Mahsuri introgressed with generations over other QTL classes. The low mean yield qDTY +qDTY (Table 3). In TDK1-Sub1 pyramided performers in the F generation, Sub1 + qDTY , Sub1 + 2.2 4.1 3 1.1 lines (Sub1 + qDTY + qDTY + qDTY ), the grain qDTY +qDTY in a Swarna-Sub1 background 3.1 6.1 6.2 1.1 3.1 − 1 yield advantage ranged from 65.2 to 792.0 kg ha and (Table 1),qDTY in a Samba Mahsuri background 2.2 − 1 155.9 to 2429.5 kg ha under NS and RS drought stress (Table 3), and qDTY +qDTY and Sub1 + qDTY + 6.1 3.1 6.2 conditions, respectively (Table 4). The pyramided lines DTY in a TDK1-Sub1 background (Table 4), were ob- 3.1 with qDTY + qDTY and qDTY + qDTY showed served to be lower yielders in each of the generations 12.1 3.1 2.2 3.1 − 1 a grain yield advantage of 735.1–1012.8 kg ha and from F to F . The significant high grain yield advantage 4 8 − 1 324.0–1240.9 kg ha , respectively, under NS and of Sub1 + qDTY + qDTY , Sub1 + qDTY + qDTY , 1.1 1.2 1.1 2.2 − 1 − 1 672.3–1059.5 kg ha and 571.4–1099.3 kg ha , Sub1 + qDTY + qDTY ,and Sub1 + qDTY + 2.2 12.1 3.2 Kumar et al. Rice (2018) 11:35 Page 4 of 16 Fig. 1 (See legend on next page.) Kumar et al. Rice (2018) 11:35 Page 5 of 16 (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 qDTY + qDTY in an IR64-Sub1 background Interaction among QTLs and with background 2.3 12.1 (Table 2) and of qDTY + qDTY and qDTY + In our study, qDTY showed positive interactions with 12.1 3.1 2.2 1.1 qDTY in an MR219 background (Table 5) was consist- qDTY ,qDTY , and qDTY , whereas qDTY showed 3.1 2.1 2.2 3.1 2.2 ent from the F to F generation. QTL classes Sub1 + positive interactions with qDTY ,qDTY , and qDTY . 4 7 4.1 12.1 3.1 qDTY + qDTY , Sub1 + qDTY + qDTY , and qDTY showed positive interactions with qDTY , 1.2 12.1 3.2 2.3 3.1 1.1 qDTY +qDTY +qDTY + Sub1 in an IR64-Sub1 qDTY ,qDTY ,qDTY , and qDTY at least in the 1.1 2.2 12.1 2.2 12.1 6.1 6.2 background showed lower yield from F to subsequent genetic backgrounds that we studied in the present experi- generations (Table 2). The low grain yield performance ment. Such information will be helpful to breeders in of qDTY +qDTY and qDTY +qDTY + selecting QTL combinations in their MAB programs. 12.1 2.2 2.2 3.1 qDTY under RS drought stress in MR219 was main- 12.1 tained from the F to F generation (Table 5). None of Discussion 4 7 the inferior QTL classes identified in F outperformed the Phenotypic evaluation of QTLs pyramided lines identified superior QTL combination class or combination The yield reduction in RS drought stress experiments was classes in any advanced generation under NS as well as 45, 77, 79, and 97% in F ,F ,F ,and F generations, re- 3 5 7 7 under variable intensities of RS drought stress in different spectively, in Swarna-Sub1 introgression lines as compared seasons/years across generations from F to F /F . to the mean yield of the NS experiments. In IR64-Sub1, the 4 7 8 yield reduction was 22, 96, 82, and 97% in F ,F ,F ,and F 3 4 6 7 Cost effectiveness of the early generation selection generations, respectively. In the Samba Mahsuri back- The genotyping cost for the whole population considering ground, the mean yield reduction was 66, 98, and 98% in all QTL classes from F to F /F ranged from USD 9225 F ,F ,and F generations, respectively, in the RS drought 3 7 8 3 7 8 to USD 21760 whereas the genotyping cost accounting for stress experiment compared with NS experiments. A grain further advancement and screening (F to F /F )ofonly yield reduction of 68, 93, 98, and 96% was observed in F , 4 7 8 4 superior classes in F varied from USD 5730 to USD 8978 F ,F ,and F generations, respectively, under RS drought 3 6 7 8 (Table 6). A genotyping cost savings of USD 12443, 3720, stress compared with NS in TDK1-Sub1 introgressed lines. 14,780, 2273, and 6225 was observed in Swarna-Sub1, In MR219 introgressed lines, the yield reduction under RS IR64-Sub1, Samba Mahsuri, TDK1-Sub1, and MR219 drought stress compared with NS was 88, 93, and 93% in backgrounds, respectively, with a range of savings of USD F ,F ,and F generations, respectively. Accurate standard- 3 5 7 2273 to USD 14780 in all five backgrounds. ized phenotyping under RS drought stress assists breeders The phenotyping cost for the whole population ranged in rejecting inferior QTL classes in F itself and is the basis from USD 29197 to USD 157455 whereas it was USD of success of the combined MAS breeding approach. It is 20225 to USD 50507 in the case of selected classes evident from the yield reduction as well as the water table (Table 7). A phenotyping cost savings of USD 60023, 8973, depths (Fig. 2a-e)thatthe stress levelinRSdrought stress 10,963, 106,948, and 30,029 was observed in Swarna-Sub1, experiments ranged from moderate to severe drought stress IR64-Sub1, Samba Mahsuri, TDK1-Sub1, and MR219 back- intensity at the reproductive stage in most of the cases. grounds, respectively, with phenotyping cost savings of DTF of majority of pyramided lines was less than that of USD 8973–106,948 in all five backgrounds. The genotyping recipient lines under RS but not under NS. Some of the and phenotyping cost and savings were high in Samba selected progenies showed early DTF than recipient under Mahsuri as the number of plant samples in the whole NS and this may have resulted from linkages of the drought population set in the F generation was more than in the QTLs with earliness (Vikram et al. 2016). Most of the QTL class selected in F (DTY +DTY )(Table 6). The progenies showed similar PHT as that of recipient cultivars 3 2.2 4.1 cost savings was inversely proportional to the number of under NS but higher PHT under RS because of their QTL combination classes identified as providing superior increased ability to produce biomass under RS (data performance in F . not presented). 3 Kumar et al. Rice (2018) 11:35 Page 6 of 16 − 1 Table 1 Mean comparison of QTL classes of grain yield (kg ha ) across F to F generations under reproductive-stage drought 3 8 stress and irrigated non-stress control conditions in Swarna-Sub1 background at IRRI, Philippines QTL QTL 2012DS 2012DS 2012DS 2012DS 2012DS 2012WS 2013DS 2013DS 2014DS 2014DS 2015WS 2015WS 2016DS class NS_Med RS_Med RS_ Med NS_Late RS_Late NS NS RS NS RS NS RS RS F F F F F F F F F F F F F 3 3 3 3 3 4 5 5 7 7 8 8 8 Population size 663 366 304 91 84 754 432 432 432 432 52 52 52 A qDTY 4906 bc 2677 cde 2894 bcf 6766 gh 3674 c 3925 bc – – ––– – – 1.1 B Sub1+ 5431 efg 2228ab 2930 bg 4141 a 3652 bc 3536 bcd–– – – 5191 c 68.24 a 579 b qDTY 1.1 C DTY 4811cde 2828 efg 2962 abg 4265 ab 3719 bc 4176 abc– – ––– – – 2.1 D Sub1+ 5084 cf 2452 bcde 2776 abde 4649 ab 3554 bc 2729 a 4109 bc 793 ac ––– – – qDTY 2.1 E qDTY 5098 cdeg 3010 gh 3001 bg 4987 ac 2658 b – 4135 bc 973 ac 7941 ab 1868 cd–– – 3.1 F Sub1+ 4705 bc 3027 fh 2984 bg – 3315 bc 4663 ac 4107 cd 1097 cd 7934 b 1838 cd 4940 b 97.96 a 677 c qDTY 3.1 G Sub1 5430 cf 2642 bcefh 2334 ab 5338 bcd 3204 bc 3515 a 2948 abc 530 ac ––– – – H qDTY + 5394 df 2653 ce 3131 efg 6445 fg 3671 c 4308 ab – – ––– – – 1.1 qDTY 2.1 I Sub1 + 5444 ef 2428 ac 3133 efg 6642 fgh 3636 c 4460 ab 3710 bc 605 ab ––– – – qDTY + 1.1 qDTY 2.1 J qDTY + 4788 c 2693 de 2945 be 6395 fg 3481 bc 4288 ab – – ––– – – 1.1 qDTY 3.1 K Sub1 + 4989 cd 2832 efg 3003 ceg 6639 efh 3377 bc 5183 c 3456 b 677 ad –– 4676 a 159.19 b 566 b qDTY + 1.1 qDTY 3.1 L qDTY + 5265 bdf 2998 fh 2955 bg – 3620 bc 4623 ac 4116 cd 992 bcd 7932 ab 1672 bc–– – 2.1 qDTY 3.1 M qDTY + 5154 cf 3172 h 3162 efg 7380 hi 3714 bc – 4192 cd 1048 bcd 8194 b 1503 ab 5754 g 360.16 c 830 d 2.1 qDTY + 3.1 Sub1 N qDTY + 5055 cd 2845 df 3130 dg 7373 hi 3505 c 4807 bc 3912 bd 1073 c 8043 b 1854 d –– – 1.1 qDTY + 2.1 qDTY 3.1 O Sub1+ 5484 ef 3010 gh 3167 fg 6780 gh 3859 c 4838 bc 4141 c 1092 c 8297 b 1918 d 5434 e 356.81 c 931 d qDTY + 1.1 qDTY + 2.1 qDTY 3.1 X Parent 3818 a 2203 ab 2465 a 5827 cde 2828 ab 5146 c 2106 a 764 ac 5818 a 799 a 5358 f 64.45 a 398 a Trial mean 5077 2691 2937 6044 3474 4760 3615 838 7878 1652 5222 175 605 F- value 3.68 7.39 2.45 19.77 1.21 6.04 13.22 1.79 6.88 3.75 5.38 6.16 3.93 p-value 0.0168 <.0001 0.0018 0.0001 0.2838 <.0001 0.0003 0.0559 0.0003 0.0008 <.0001 0.2991 0.368 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, Med medium duration, Late late duration, X recipient parent (no QTL) Selection of superior QTLs class in early generation of resources, fields for phenotyping, as well as capacity of In a marker-assisted QTL introgression/pyramiding breeding programs, breeders have to reduce the popula- program, it would be very valuable to explore QTL com- tion size, which may lead to a loss of existing positive gen- binations with high performance in early generations. etic variability in the population (Govindaraj et al. 2015). The F generation is highly heterogeneous; therefore, In the present study, the screening of a large-sized F 2 3 screening of a large population size is essential to population was carried out under control (NS) and RS maximize the exploitation of genetic variation (Kahani drought stress conditions. The classification of the popula- and Hittalmani 2015). Sometimes, based on the availability tion in different classes based on QTL combinations in Kumar et al. Rice (2018) 11:35 Page 7 of 16 − 1 Table 2 Mean comparison of QTL classes of grain yield (kg ha ) across F to F generations under reproductive-stage drought 3 7 stress and irrigated non-stress control conditions in IR64-Sub1 background at IRRI, Philippines QTL class QTL 2013WS 2013WS 2014DS 2014DS 2014WS 2015DS 2015DS 2015WS 2015WS NS RS NS RS NS NS RS NS RS F F F F F F F F F 3 3 4 4 5 6 6 7 7 Population size 467 467 194 194 64 64 64 18 18 A Sub1+ qDTY + qDTY +qDTY 4137 ac 3621 cde 7553 bdf 584 g –– – – – 1.1 1.2 12.1 B Sub1+ qDTY + qDTY +qDTY +qDTY 3640 ac 2605 a 7968 bdf 196 abc –– – – – 1.1 1.2 2.2 12.1 C Sub1+ qDTY + qDTY +qDTY 4986 c 2734 ab 5996 abc 377def –– – – – 1.1 1.2 2.2 D Sub1+ qDTY + qDTY 4418 cd 3054 abc 7709 cef 232 abc 3585 ab 5192 a 477 bcd –– 1.1 1.2 E Sub1 + qDTY + qDTY +qDTY 3589 ac 2634 abc – 273 be 3976 a 420 bce –– 1.1 2.2 12.1 F Sub1 + qDTY + qDTY 4953 ac 3169 abe 7637 bdf 367 ceg 3347 ab 5120 a 592 bf 4105 a 188 a 1.1 2.2 G Sub1 + qDTY 4413 ac 2677 ab 8224 cef 410 eg –– – – – 1.1 H Sub1 + qDTY +qDTY 4001 ac 2963 abc 6660 abe 245 be – 5468 a 252 ab –– 1.2 12.1 I Sub1 + qDTY +qDTY + qDTY 5370 cb 3352 abe 8790 bf 259 be –– – – – 1.2 2.2 12.1 J Sub1 + qDTY 4380 cd 2690 abd 6117 ab 189 bc 3066 ab 5125 a 372 abc 3997 a 64 a 12.1 K Sub1 + qDTY + qDTY 4395 cd 3130 bc 6512 ab 308 ae 2592 a 5026 a 459 bc 3762 a 186 a 2.2 12.1 L Sub1 + qDTY 4252 cd 3767 e 7893 cf 223 abc –– – – – 2.2 M Sub1 + qDTY + qDTY 3168 ac 3084 abe 8532 cef 194 be –– – – – 2.3 12.1 N Sub1 + qDTY 3145 ab 2602 a 7080 bde 244 abcd –– – – – 2.3 O Sub1 + qDTY + qDTY 3670 ac 2746 abd 7145 abf 263 bef –– – – – 3.2 12.1 P Sub1 + qDTY + qDTY +qDTY 3109 ac 2728 abd 7798 bdf 197 be –– – – – 3.2 2.2 12.1 Q Sub1 + qDTY + qDTY 3055abd 2526 a 6441 ab 220 abcd 2381 a 4398 a 761 f –– 3.2 2.2 R Sub1 + qDTY + qDTY +qDTY 2845 ac 2931 abc 6469 abc 304 abcd 2293 a 4570 a 719 def 3883 a 275 a 3.2 2.3 12.1 S Sub1 + qDTY + qDTY 1688 a 2891 abe 5319 a 304 bef – 4727 a 255 ab –– 3.2 2.3 T Sub1 + qDTY 3444 ac 3427 be 6230 ad 124 b –– – – – 3.2 X Parent 3620 ac 2305 a 6066 abf 87 abc 3139 ab 5099 a 0a 3849 a 18 a Trial mean 3853 2998 7181 277 3024 4870 862 3943 128 F- value 1.59 2.88 2.92 3.22 2.83 2.26 4.32 1.54 1.53 p-value 0.2956 0.006 0.0006 0.0011 0.0363 0.404 0.0004 0.5566 0.5585 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) −1 Table 3 Mean comparison of QTL classes of grain yield (kg ha ) across BC F to BC F generations under reproductive-stage 1 3 1 8 drought stress and irrigated non-stress control conditions in Samba Mahsuri background at IRRI, Philippines QTL class QTL 2013DS 2013DS 2014WS 2015WS 2015WS 2016DS 2016DS NS RS NS NS RS NS RS BC F BC F BC F BC F BC F BC F BC F 1 3 1 3 1 6 1 7 1 7 1 8 1 8 Population size 42 42 70 20 20 20 20 A qDTY 2020 a 1069 bc 3405 b 3327 b 44 a –– 2.2 † † † † B qDTY 1900 a 894 b 3340 b 4727 d 184 b 5643 b 33 a 4.1 C qDTY +qDTY 2916 b 1296 c 3270 b 4161 c 110 ba 4999 a 216 b 2.2 4.1 X Parent 2742 b 0 a 2137 a 1945 a 15 a 4051 a 39 a Trial Mean 2395 815 3038 3540 88 5198 96 F- value 31.22 46.37 11.18 43.03 2.12 19.98 62.66 p-value 0.0089 < 0.0001 < 0.0001 < 0.0001 0.09 < 0.0001 < 0.0001 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), Mean data of only 2 lines Kumar et al. Rice (2018) 11:35 Page 8 of 16 −1 Table 4 Mean comparison of QTL classes of grain yield (kg ha ) across BC F to BC F generations under reproductive-stage 2 3 2 8 drought stress and irrigated non-stress control conditions in TDK-Sub1 background at IRRI, Philippines QTLclass QTL 2013WS 2014DS 2014WS 2015DS 2015WS 2016DS RS NS RS NS NS RS NS RS NS RS BC F BC F BC F BC F BC F BC F BC F BC F BC F BC F 2 3 2 4 2 4 2 5 2 6 2 6 2 7 2 7 2 8 2 8 Population size 843 231 231 48 48 48 60 60 60 60 A Sub1 + qDTY +qDTY qDTY 1232 gh 6883 bc 2453 c 2763 bc 6252bc 816 f 4356 ab 158 de 4739 ab 298 cd 6.1 6.2 + 3.1 B qDTY +qDTY qDTY 1298 gh 6289 b 2069 b 2629 ac 6174 c 250 bc 4966 cd 122 cd 4871 ab 278 c 6.1 6.2 + 3.1 C Sub1+ qDTY +qDTY 1301 gi 6289 abc 2143 bc 2897 bcd 6475 c 552 de 4797 bd 73.83 abc 4804 b 320 cd 6.1 6.2 D Sub1+ qDTY +qDTY 1091 fde 5707 ab 2120 bc 3476 c 5958 ab 368 bd 4657 bc 75 bc 4780 ab 179 ac 6.1 3.1 E Sub1+ qDTY +qDTY 1178 ge 6061 abc 2112 bc 2576 ac 5157 a 274 bc –– – – 6.2 3.1 F qDTY +qDTY 998 cd 3890 a 2126 bc 2307 ac 4799 a 501 cde –– – – 6.1 6.2 G qDTY +qDTY 1012 ge 5874 ab 1959 b 2704 ac 6775 c 211.97 b 5074 d 73 b 4793 ab 113 ab 6.1 3.1 H qDTY +qDTY 1134 fe –– – – – – – – – 6.2 3.1 I Sub1 + qDTY 1051 ce –– – – – – – – – 6.2 J Sub1+ qDTY 1446 j –– – – – – – – – 6.1 K Sub1 + qDTY 1376 hij –– – – – – – – – 3.1 L qDTY 1416 ij –– – – – – – – – 6.2 M qDTY 1308 gh –– – – – – – – – 6.1 N qDTY 1217 fg –– – – – – – – – 3.1 X Parent 421 a 6091 abc 24 a 2167 a 6135 bc 0 a 3647 a 2 a 4674 a 0 a Trial mean 1165 5886 1863 2715 6091 409 4583 84 4760 198 F- value 34.1 6.6 1.03 3.21 4.99 16.32 6.44 6.0 5.32 5.0 p-value <.0001 0.0012 0.4207 0.0341 0.0105 <.0001 <.0001 0.0001 0.0013 0.0046 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) −1 Table 5 Mean comparison of QTL classes of grain yield (kg ha ) across BC F to BC F generations under reproductive-stage 1 3 1 7 drought stress and irrigated non-stress control conditions in MR219 background at IRRI, Philippines QTL class QTL 2013DS 2014DS 2015DS NS RS NS RS NS RS BC F BC F BC F BC F BC F 1 3 1 3 1 5 1 7 1 7 Population size 214 214 620 620 70 70 A qDTY 6229 a 654 b 6967 b 301 a – – 12.1 B qDTY +qDTY 6633 b 761 bc 7364 ac 598 b 5986 a 540 c 12.1 2.2 C qDTY +qDTY 6652 ac 1072 d 7532 cd 794 e 7111 c 672 d 12.1 3.1 D qDTY 6760 ab 904 cd 7079 ba 669 bc 6957 c 393 b 2.2 E qDTY +qDTY 7158 bc 1112 d 7243 cd 663 c 6843 bc 679 d 2.2 3.1 F qDTY +qDTY +qDTY 6799 ab 642 b 7106 ad 442 b 6674 bc 578 cd 2.2 3.1 12.1 G qDTY 6488 a 890 c 7374 ac 568 c 6923 bc 537 bcd 3.1 X Parent 5917 ab 13 a 6519 b 0 ab 6148 ab 0 a Trial mean 6705 781 7173 505 6663 486 F- value 2.0 11.76 9.45 19.39 7.76 6.18 p-value 0.05 < 0.0001 < 0.0001 < 0.0001 0.0004 <.0001 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) Kumar et al. Rice (2018) 11:35 Page 9 of 16 Table 6 Comparison of genotyping cost (USD) considering advancement of all QTL classes versus advancement of only higher performing F generation QTL classes Background Generation Number of Population size Cost (USD) Total genotyping cost (USD) Savings QTL classes (USD) Based on all Based on Based on all Based on Based on all Based on classes selected classes classes selected classes classes selected classes Swarna- F 15 754 754 5655 5655 21,420 8978 12,443 Sub1 F 15 754 106 5655 795 F 10 432 106 3240 795 F 10 432 106 3240 795 F 6 432 108 3240 810 F 5 52 17 390 127.50 IR64-Sub1 F 20 467 467 7005 7005 12,105 8385 3720 F 19 194 46 2910 690 F 19 64 18 960 270 F 13 64 18 960 270 F 7 18 10 270 150 Samba BC F 3 42 42 210 210 21,760 6980 14,780 1 3 Mahsuri BC F 3 3000 640 15,000 3200 1 4 BC F 3 1200 640 6000 3200 1 5 BC F 3 70 44 350 220 1 6 BC F 2 20 15 100 75 1 7 BC F 2 20 15 100 75 1 8 TDK1-Sub1 BC F 14 843 843 6323 6323 9225 6954 2272 2 3 BC F 7 231 43 1733 323 2 4 BC F 7 48 14 360 105 2 5 BC F 7 48 14 360 105 2 6 BC F 5 60 13 450 98 2 7 MR219 BC F 7 214 214 1605 1605 11,955 5730 6225 1 3 BC F 7 620 240 4650 1800 1 4 BC F 7 620 240 4650 1800 1 5 BC F 7 70 35 525 262.50 1 6 BC F 7 70 35 525 262.50 1 7 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 each generation (F to F /F ) followed by class analysis to can be screened more precisely in a large plot size 3 7 8 see the performance of each QTL class across generation with more replications. The current cost-effective advancement proved to be an effective approach in identi- high-throughput phenotyping platform (Comar et al. fying best-bet QTL combination classes across five 2012; Andrade-Sanchez et al. 2014; Sharma and Ritchie high-yielding genetic backgrounds. The performance of 2015; Bai et al. 2016) can be used for precise breeding the genotypes in a particular QTL class was consistent and physiological studies considering the small popu- from F to F /F generations in all five studied background lation size. Even at the F level, some heterozygosity 3 7 8 3 in the present study. The advancement of the classes with will be observed when more genes are involved in high mean grain yield performance in the F generation in the introgression program. However, in our study, we addition to the MAB approach involving stepwise pheno- did not observe any change in performance of QTL typing and genotyping screening suggested this as being a classes found superior in F , indicating the F gene- 3 3 cost/labor- and resource-effective breeding strategy. The ration to be suitable to conduct class analysis and lesser number of genotypes in advanced generations reject inferior classes. Kumar et al. Rice (2018) 11:35 Page 10 of 16 Table 7 Comparison of phenotyping cost (USD) considering advancement of all QTL classes versus advancement of only higher performing F generation QTL classes Background Generation Population size Phenotyping cost (USD) Total phenotyping cost (USD) Savings (USD) Based on all Based on selected Based on all Based on selected Based on all Based on selected classes classes classes classes classes classes Swarna- F 754 754 27,280 27,280 103,330 43,307 60,023 Sub1 F 754 106 27,280 3835 F 432 106 15,630 3835 F 432 106 15,630 3835 F 432 108 15,630 3907 F 52 17 1881 615 IR64-Sub1 F 467 467 16,896 16,896 29,197 20,225 8973 F 194 46 7019 1664 F 64 18 2316 651 F 64 18 2316 651 F 18 10 651 362 Samba BC F 42 42 1520 1520 157,455 50,507 106,948 1 3 Mahsuri BC F 3000 640 108,540 23,155 1 4 BC F 1200 640 43,416 23,155 1 5 BC F 70 44 2533 1592 1 6 BC F 20 15 724 543 1 7 BC F 20 15 724 543 1 8 TDK1-Sub1 BC F 843 843 30,500 30,500 44,501 33,539 10,963 2 3 BC F 231 43 8358 1556 2 4 BC F 48 14 1737 507 2 5 BC F 48 14 1737 507 2 6 BC F 60 13 2171 470 2 7 MR219 BC F 214 214 7743 7743 57,671 27,642 30,029 1 3 BC F 620 240 22,432 8683 1 4 BC F 620 240 22,432 8683 1 5 BC F 70 35 2533 1266 1 6 BC F 70 35 2533 1266 1 7 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 (IRRI Standard drought screening costing) Population size and validation of combined breeding across generations (F to F /F ) in five backgrounds in 3 7 8 strategy the present study validates the potential of the suggested In addition to the modern next-generation genotyping combined MAS breeding approach presented in the strategies (Barba et al. 2014; Rius et al. 2015; Dhanapal current study. The integration of accurate phenotyping and Govindaraj 2015) and agricultural system models and the selection of the best class representing the gen- (Antle et al. 2016), several breeding strategies involving etic variability of the whole population in early genera- correlated traits as selection criteria in early generations tions are critical steps for the practical implementation (Senapati et al. 2009), grain yield (Kumar et al. 2014), of this ultimate novel breeding strategy. Keeping a large secondary traits (Mhike et al. 2012), genetic variance, F population size depending upon the number of heritability (Almeida et al. 2013), path coefficient ana- genes/QTLs being introgressed and precise phenotyping lysis, selection tolerance index (Dao et al. 2017), and to exploit the hidden potential of each genotype in each yield index (Raman et al. 2012) have been suggested for QTL class could maximize the potential output of each use in breeding programs. The consistent performance class in early generations. The most logical QTL-class of pyramided lines with specific QTL combinations performance-derived novel breeding strategy could be Kumar et al. Rice (2018) 11:35 Page 11 of 16 Fig. 2 Soil water potential measured by parching water table level in experiments (a) Swarna-Sub1 pyramided lines with qDTY ,qDTY , and 1.1 2.1 qDTY in different generations; b IR64-Sub1 pyramided lines with qDTY ,qDTY ,qDTY ,qDTY ,qDTY , and qDTY in different generations; 3.1 1.1 1.2 2.2 2.3 3.2 12.1 c Samba Mahsuri pyramided lines with qDTY and qDTY in different generations; d TDK1-Sub1 pyramided lines with qDTY qDTY and 2.2 4.1 3.1, 6.1, qDTY in different generations; and (e) MR219 pyramided lines with qDTY ,qDTY and qDTY in different generations using polyvinyl 6.2 2.2 3.1, 12.1 chloride (PVC) pipe adopted to optimize the breeding efficiency of small-to We were able to understand the effectiveness of early moderate-sized breeding programs in rice breeding im- generation selection in the marker-assisted introgression provement programs. Further, the strategy could be program for drought because the breeding program equally useful to other crops in which major genes/ maintained systematic data for both genotyping and QTLs determine the expression of traits and QTL x phenotyping conducted over the past six or more years. QTL or QTL x genetic background interactions have It was only after we successfully identified the best lines been identified. coming from each introgression program after successful Kumar et al. Rice (2018) 11:35 Page 12 of 16 multi-location evaluation that we realized that, as the background are unknown. Such positive/negative inter- breeding program will need to bring in more and more actions affecting grain yield under normal or RS situ- genes for multiple traits to address each of the new ation can be captured through approach that combines emerging climate-related challenges, modifications that selection based on phenotyping and genotyping in the allow plant breeders to make large-scale rejections in the early generations. The current study clearly demon- early generation will become necessary. The effective- strated the success of selection based on combining phe- ness of the combined MAS strategy is evident from the notyping and genotyping in identifying better progenies result that, in none of the five cases were the superior in early generation thereby reducing the number of pro- QTL class combinations identified in F outperformed genies to be advanced. Number of plants to be generated by inferior classes identified in F in any advanced gener- and evaluated in the early generations will depend upon ation under both NS and variable intensities of RS the number of QTLs/genes to be introgressed together, drought stress in different seasons/years across genera- size of introgressed QTLs region as well as availability of tions from F to F /F /F . closely linked markers for each of the QTLs. The QTLs 4 6 7 8 for grain yield under drought have shown undesirable Cost-effectiveness of combined breeding strategy linkages with low yield potential, very early maturity Breeding practices are challenged by being laborious, duration, tall plant height (Vikram et al. 2015). At IRRI, time consuming, and non-economical, requiring large studies were undertaken to break the undesirable link- land space and a large population size (Sandhu and ages of QTLs with tall plant height, very early maturity Kumar 2017), being imprecise, and having unreliable duration and low yield potential (Vikram et al. 2015). phenotyping screening (Bhat et al. 2016); hence, an eco- Such improved lines were used in the MAS introgres- nomical, fast, accurate, and efficient breeding selection sion program. The drought tolerant donors N22, Dular, system is required to increase grain yield potential and Apo, Way Rarem, Kali Aus, Aday Sel that are source of productivity (Khan et al. 2015). The cost-benefit balance identified QTLs do not possess good grain quality. Even (Bhat et al. 2016) must be considered in increasing gen- though, we did not study the linkage of qDTYs with etic gain in the new era of modern science. The use of grain quality, the introgressed lines released as varieties the class analysis approach in the F generation followed in IR64, Swarna backgrounds in India and Nepal did not by advancing only higher performing classes reported a reveal any adverse effect on grain quality. The yield su- genotyping cost savings of 25–68% and phenotyping cost periority of lines with two or more QTLs under both NS savings of 25–68% compared with the traditional mo- and RS drought stress over the five high-yielding back- lecular marker breeding approach (Table 6). Although grounds clearly indicated that qDTY QTLs identified at the cost-benefit of the combined MAS breeding strategy IRRI are free from undesirable linkage drag and can be will always be inversely proportional to the number of successfully used in MAB programs targeting yield superior QTL class combinations identified for advance- improvement under RS drought stress. Further, in ment in F and subsequent generations, the cost savings Swarna-Sub1, IR64-Sub1, and TDK-Sub1, the highest will increase as the number of genes included in the yielding classes identified were the classes possessing introgression program increases because of the rejection both Sub1 and combinations of the drought QTLs. The of a larger proportion of the total population early in the yield superiority of such classes across these three back- F generation. This procedure will save time, labor, re- grounds over all the generations clearly indicated that sources, and space and will allow breeders to focus only tolerance of submergence and drought can be effectively on germplasm with higher value. This will reduce the combined even though they are governed by two differ- population size for phenotypic and genotypic selection ent physiological mechanisms. In the QTL study under- in advanced generations compared with earlier taken at IRRI, qDTY showed a significant mean yield 1.1 marker-assisted breeding strategies (Price 2006; McNally advantage in MTU1010 and IR64 (Sandhu et al. 2015); et al. 2009; Yadaw et al. 2013; Sandhu et al. 2014; Brachi qDTY in Pusa Basmati 1460, MTU1010, and IR64 2.2 et al. 2012; Begum et al. 2015). It will be practical and (Venuprasad et al. 2007; Swamy et al. 2013; Sandhu et realistic only if the phenotyping, genotyping, and class al. 2013; Sandhu et al. 2014); qDTY in Vandana and 2.3 analysis in early generations are accurate. IR64 (Dixit et al. 2012b; Sandhu et al. 2014); qDTY in 3.2 Sabitri (Yadaw et al. 2013); qDTY in IR72 (Venuprasad 6.1 Interactions among QTLs and with background et al. 2009); and qDTY in Vandana (Bernier et al. 12.1 The QTLs for grain yield under drought have shown 2007), Sabitri (Mishra et al. 2013), Kalinga, and Anjali QTL x QTL (Sandhu et al. 2018) as well as QTL x gen- backgrounds. Similar interaction of qDTY and qDTY 2.3 3.2 etic background interactions (Dixit et al. 2012a, b; with qDTY in a Vandana background (Dixit et al. 12.1 Sandhu et al. 2018). Many such interactions that may 2012b); qDTY and qDTY with qDTY in an 2.2 3.1 12.1 occur between QTL x QTL and QTL x genetic MRQ74 background (Shamsudin et al. 2016); and Kumar et al. Rice (2018) 11:35 Page 13 of 16 qDTY + qDTY in an IR64 background (Swamy et al. generation to maintain genetic variability as the number 2.2 4.1 2013) was observed. The interaction of identified QTLs of genes/QTLs increases further. Integration of a with other QTLs in more than two backgrounds sup- cost-effective, efficient, designed, statistics-led early gen- ports the usefulness of such QTL classes in MAS. In all eration superior QTL class selection-based breeding five of these cases, through genotyping and phenotyping strategy with new-era genomics such as genotyping by we were able to identify QTL class combinations with sequencing and genomic selection could be an import- positive interactions and higher yield. As more data are ant breakthrough to build up a scientific next-generation generated across different backgrounds and interactions breeding program. are established, breeders will have the ability to identify and forward only selected classes without phenotyping Methods from F onward. The study was conducted at the International Rice Re- search Institute (IRRI), Philippines, to introgress QTLs Pyramiding of multiple QTLs associated with multiple for grain yield under RS drought stress in the back- traits ground of improved high- yielding widely grown but With the identification of gene-based/closely linked drought-susceptible varieties from India (Swarna, IR64, markers for different biotic stresses (bacterial blight, Samba Mahsuri), Lao PDR (TDK1), and Malaysia blast, brown planthopper, gall midge) and abiotic (MR219). stresses (submergence, drought, phosphorus deficiency, Five sets of introgressed populations were used: cold, anaerobic germination, high temperature), the MAB program is moving forward to introgress more 1. Swarna-Sub1 pyramided lines with qDTY , 1.1 genes/QTLs to develop climate-resilient and better rice qDTY , and qDTY 2.1 3.1 varieties. For effective tolerance to develop a variety 2. IR64-Sub1 pyramided lines with qDTY , qDTY , 1.1 1.2 combining tolerance of biotic and abiotic stresses – bac- qDTY , qDTY , qDTY , and qDTY 2.2 2.3 3.2 12.1 terial leaf blight (three genes – xa5, xa13, Xa21), blast 3. Samba Mahsuri pyramided lines with qDTY and 2.2 (two – pi2, pi9), brown planthopper (two – BPH3, qDTY 4.1 BPH17), gall midge (two – Gm4, Gm8), drought (three – 4. TDK1-Sub1 pyramided lines with qDTY qDTY 3.1, 6.1, qDTY ,qDTY ,qDTY ), and submergence (Sub1) – and qDTY 1.1 2.1 3.1 6.2 researchers will need introgression and the combination 5. MR219 pyramided lines with qDTY , qDTY and 2.2 3.1, of 13–15 genes/QTLs in gene combinations mentioned qDTY 12.1 here or in other combinations depending upon the prevalence of a pathotype/biotype in different regions. Three steps were employed for the development of a The number of genes to be introgressed is likely to in- cost-effective, reliable, and resource-efficient combined crease as exposure of rice to high temperature at the re- MAS breeding strategy: (1) grain yield and genotypic productive stage will probably increase in most data across F ,F ,F ,F ,F and F /fixed lines for all five 3 4 5 6 7, 8 rice-growing regions. The introgression of 10–15 genes sets were compiled; (2) class analysis was carried out to will not only require a larger initial population in F and develop a combined MAS breeding strategy; and (3) the F but will also lead to increased positive/negative inter- performance of the superior classes was monitored actions between genes/QTLs. With capacity develop- across advanced generations to validate the combined ment, as more and more breeding programs adopt MAS breeding strategy. marker-assisted introgression of more genes, the com- The screening of all five population sets was carried bined MAS strategy will be of great help to plant out under NS control and RS drought stress conditions. breeders in reducing the number of plants that they For the NS experiments, 5-cm water depth level was should handle in each generation and make their breed- maintained throughout the rice growing season until ing program cost-effective. physiological maturity. For the screening under RS drought stress, irrigation was stopped at 30 days after Conclusions transplanting (DAT). The last irrigation was provided at The selection of QTL classes with a high mean yield per- 24 DAT and there was no standing water in the field formance and positive interactions among loci and with when drought was initiated at 30 DAT. The stress cycle background in the early generation and consistent per- was continued until severe stress symptoms were ob- formance of QTL classes in subsequent generations served. Monitoring of soil water potential was carried out across five backgrounds supports the effectiveness of a by placing perforated PVC pipes at 100-cm soil depth in combined MAS breeding strategy. The challenge ahead the field in a zig-zag manner. After the initiation of stress, is the appropriate estimation of the precise population the water table level was recorded daily. When approxi- size to be used for QTL class analysis in the early F mately 70% of the lines exhibited severe leaf rolling or 3 Kumar et al. Rice (2018) 11:35 Page 14 of 16 wilting, one life-saving irrigation with a sprinkler system calculate the cost savings for phenotyping. The genotyp- was provided. Then, a second cycle of the stress was initi- ing cost was calculated for the whole population across ated. The water table level was measured from all the successive generations (F to F /F ) and compared with 3 7 8 pipes until the rice crop reached 50% maturity. the genotyping cost (F to F /F ) considering only the 3 7 8 Molecular marker work was carried out following the QTL classes that performed better in F . The genotyping procedure as described in Sandhu et al. (2014). For cost was calculated considering five markers per QTL genotyping, a total of 754, 754, 432, 432, 432, and 52 (one peak/near the peak, two right-hand-side flanking plants were phenotyped and genotyped in F (NS, RS), markers, and two left-hand-side flanking markers) using F (NS), F (NS, RS), F (NS, RS), F (NS), and F (NS, USD 0.50 per data point (Xu et al. 2002;Xu 2010). 4 5 6 7 8 RS) generations, respectively, in a Swarna-Sub1 back- ground. In the IR64-Sub1 background, 467, 194, 64, 64, Statistical analysis and 18 plants were phenotyped and genotyped in F Mean comparison of QTL genotype classes (NS, RS), F (NS, RS), F (NS), F (NS, RS), and F (NS, Hypothesis about no differences among phenotype 4 5 6 7 RS) generations, respectively. In the Samba Mahsuri means of QTL genotype classes for each background background, a total of 42, 3000, 1200, 70, 20 and 20 under NS and RS drought stress in each season was per- plants were phenotyped and genotyped in BC F (NS, formed in SAS v9.2 (SAS Institute Inc. 2009) using the 1 3 RS), BC F (NS, RS), BC F (NS), BC F (NS), BC F following linear model. 1 4 1 5 1 6 1 7 (NS, RS), and BC F (NS, RS) generations respectively. 1 8 In the TDK-1Sub1 background, 843, 231, 48, 48, 60 and y ¼ μ þ r þ brðÞ þ q þgqðÞ þ e k ijkl ijkl kl i ij 60 plants were phenotyped and genotyped in BC F (RS), 2 3 BC F (NS, RS), BC F (NS), BC F (NS, RS), BC F (NS, 2 4 2 5 2 6 2 7 where μ represents the population mean, r represents RS), and BC F (NS, RS) generations, respectively. A total th th 2 8 the effect of the k replicate, b(r) is the effect of the l kl of 214, 620, 620, 70, and 70 plants were phenotyped and th block within the k replicate, q corresponds to the ef- genotyped in BC F (NS, RS), BC F (NS), BC F (NS, th th 1 3 1 4 1 5 fect of the i QTL, g(q) symbolizes the effect of the j ij RS), BC F (NS, RS), and BC F (NS, RS) generations, re- th 1 6 1 7 genotype nested within the i QTL, and e corre- ijkl spectively, in the MR219 background. Data on plant sponds to the error (Knapp 2002). The effects of QTL height, days to 50% flowering, and grain yield were re- class and the genotypes within QTL were considered corded following the procedure of Venuprasad et al. fixed and the replicates and blocks within replicates were (2009). The detailed description on QTLs and markers set to random. used in the present study in each background is presented in Additional file 1: Table S1. The general schematic Additional file scheme followed for QTL introgression and pyramiding program, phenotyping and genotyping screening is shown Additional file 1: Table S1. QTLs and markers information’s in marker in Additional file 1: Figure S1. assisted introgression program in different backgrounds. Figure S1. General schematic scheme for QTL introgression and pyramiding program, phenotyping and genotyping screening. In case of Swarna- Analytical approach to reveal a combined MAS breeding Sub1 and IR64-Sub1 no backcross was attempted. In case of Samba Mah- strategy suri and MR219, one backcross was attempted. In case of TDK1-Sub1 two backcross was attempted. (DOCX 269 kb) The grain yield data from F ,F ,F ,F ,F ,and F genera- 3 4 5 6 7 8 tions across seasons and NS (control) and RS drought Acknowledgements stress conditions for all five sets of pyramided populations We thank Ma. Teresa Sta. Cruz and Paul Maturan for the management of were compiled and categorized into classes based on the field experiments, Jocelyn Guevarra and RuthErica Carpio for assistance with genotypic QTL information. Class analysis using SAS v9.2 seed preparations. was attempted to see the mean grain yield performance of Funding QTL classes across generation advancement. This study was supported by the Bill & Melinda Gates Foundation (BMGF) and the Generation Challenge Program (GCP). The authors thank BMGF and GCP for financial support for the study. Genotyping and phenotyping cost calculation The phenotyping cost of USD 36.18 per entry (two repli- Availability of data and materials cations, screening under NS and RS drought stress with The relevant supplementary data has been provided with the manuscript. plot size of 1.54 m ) (IRRI Standard drought screening Authors’ contributions costing) including the cost of land preparation, land ren- AK conceived the idea of the study and was involved in critical revision and tal, irrigation, electricity, field layout, seeding, transplant- final approval of the version to be published; NS was involved in conducting the experiments, analysis, interpretation of the data, and drafting the ing, maintenance cost, resource input (fertilizer), manuscript; SD, SY, BPMS, and NAAS were involved in developing pesticides, herbicides, field supplies, harvesting, thresh- populations and conducting the experiments. All authors read and approved ing, drying, data collection, and labor was used to the final manuscript. Kumar et al. Rice (2018) 11:35 Page 15 of 16 Ethics approval and consent to participate Cuthbert JL, Somers DJ, Brûlé-Babel AL, Brown PD, Crow GH (2008) Molecular Not applicable. mapping of quantitative trait loci for yield and yield components in spring wheat (Triticum aestivum L). Theor Appl Genet 117(4):595–608 Dao SJ, Traore EVS, Gracen V, Eric YD (2017) Selection of drought tolerant maize Consent for publication hybrids using path coefficient analysis and selection index. Pakistan J Biol Sci The manuscript has been approved by all authors. 20:132–139 Das G, Rao GJN (2015) Molecular marker assisted gene stacking for biotic and Competing interests abiotic stress resistance genes in an elite rice cultivar. Front Plant Sci 6:698 The authors declare that they have no competing interests. Dhanapal AP, Govindaraj M (2015) Unlimited thirst for genome sequencing data interpretation and database usage in genomic era: the road towards fast- track crop plant improvement. Genet Res Internat 2015:1–15 Publisher’sNote Dixit S, Singh A, Kumar A (2014) Rice breeding for high grain yield under Springer Nature remains neutral with regard to jurisdictional claims in drought: a strategic solution to a complex problem. Int J Agron Article ID published maps and institutional affiliations. 863683:15 Dixit S, Swamy BM, Vikram P, Ahmed HU, Cruz MS, Amante M, Atri D, Leung H, Author details Kumar A (2012a) Fine mapping of QTLs for rice grain yield under drought International Rice Research Institute, DAPO Box 7777, Metro Manila, reveals sub-QTLs conferring a response to variable drought severities. Theor Philippines. Current address: Faculty of Science and Technology, Universiti Appl Genet 125(1):155–169 Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia. Dixit S, Swamy BM, Vikram P, Bernier J, Cruz MS, Amante M, Atri D, Kumar A (2012b) Increased drought tolerance and wider adaptability of qDTY conferred by its 12.1 Received: 27 February 2018 Accepted: 21 May 2018 interaction with qDTY and qDTY . Mol Breed 30:1767–1779 2.3 3.2 Elangovan M, Rai R, Dholakia BB, Lagu MD, Tiwari R, Gupta RK, Rao VS, Röder MS, Gupta VS (2008) Molecular genetic mapping of quantitative trait loci associated with loaf volume in hexaploid wheat (Triticum aestivum). J Cereal References Sci 47(3):587–598 Almeida GD, Makumbi D, Magorokosho C, Nair S, Borém A, Ribaut JM, Bänziger Ghimire KH, Quiatchon LA, Vikram P, Swamy BM, Dixit S, Ahmed H, Hernandez JE, M, Prasanna BM, Crossa J, Babu R (2013) QTL mapping in three tropical Borromeo TH, Kumar A (2012) Identification and mapping of a QTL (qDTY )with 1.1 maize populations reveals a set of constitutive and adaptive genomic a consistent effect on grain yield under drought. Field Crops Res 131:88–96 regions for drought tolerance. Theor Appl Genet 126(3):583–600 Govindaraj M, Vetriventhan M, Srinivasan M (2015) Importance of genetic Andrade-Sanchez P, Gore MA, Heun JT, Thorp KR, Carmo-Silva AE, French AN, diversity assessment in crop plants and its recent advances: an overview of Salvucci ME, White JW (2014) Development and evaluation of a field-based its analytical perspectives. Genet Res Int 2015:1–14. https://doi.org/10.1155/ high-throughput phenotyping platform. Funct Plant Biol 41(1):68–79 2015/431487 Antle JM, Jones JW, Rosenzweig CE (2016) Next generation agricultural system data Heidari B, Sayed-Tabatabaei BE, Saeidi G, Kearsey M, Suenaga K (2011) Mapping models and knowledge products: introduction. Agric Syst AGSY-02173:1–5 QTL for grain yield yield components and spike features in a doubled Baenziger PS, Beecher B, Graybosch RA, Ibrahim AMH, Baltensperger DD, Nelson haploid population of bread wheat. Genome 54(6):517–527 LA (2008) Registration of ‘NEO1643’ wheat. J Plant Registr 2:36–42 Huang X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z, Li M, Fan Bai G, Ge Y, Hussain W, Baenziger PS, Graef G (2016) A multi-sensor system for D (2010) Genome-wide association studies of 14 agronomic traits in rice high throughput field phenotyping in soybean and wheat breeding. Comput landraces. Nat Genet 42(11):961–967 Electron Agric 128:181–192 Jairin J, Teangdeerith S, Leelagud P, Kothcharerk J, Sansen K, Yi M, Vanavichit A, Barba M, Czosnek H, Hadidi A (2014) Historical perspective development and Toojinda T (2009) Development of rice introgression lines with brown applications of next-generation sequencing in plant virology. Viruses 6:106– planthopper resistance and KDML105 grain quality characteristics through 136. https://doi.org/10.3390/v6010106 marker-assisted selection. Field Crops Res 110(3):263–271 Begum H, Spindel JE, Lalusin A, Borromeo T, Gregorio G, Hernandez J (2015) Kahani F, Hittalmani S (2015) Genetic analysis and traits association in F Genome-wide association mapping for yield and other agronomic traits in an intervarietal populations in rice under aerobic condition Rice Res: Open elite breeding population of tropical rice (Oryza sativa). PLoS One 10:e0119873 Access Oct 25, p 9 Bennett D, Reynolds M, Mullan D, Izanloo A, Kuchel H, Langridge P, Schnurbusch Khan MH, Dar ZA, Dar SA (2015) Breeding strategies for improving rice yield: a T (2012) Detection of two major grain yield QTL in bread wheat (Triticum review. Agric Sci 6(5):467 aestivum L) under heat drought and high yield potential environments. Knapp G (2002) Variance estimation in the error components regression model. Theor Appl Genet 125(7):1473–1485 Commun Stat Theor Met 31:1499–1514 Bernier J, Kumar A, Ramaiah V, Spaner D, Atlin G (2007) A large-effect QTL for Kottapalli KR, Narasu ML, Jena KK (2010) Effective strategy for pyramiding three grain yield under reproductive-stage drought stress in upland rice. Crop Sci bacterial blight resistance genes into fine grain rice cultivar Samba Mahsuri 47(2):507–516 using sequence tagged site markers. Biotechnol Lett 32:989–996 Bhat JA, Ali S, Salgotra RK, Mir ZA, Dutta S, Jadon V, Tyagi A, Mushtaq M, Jain N, Kumar A, Dixit S, Ram T, Yadaw RB, Mishra KK, Mandal NP (2014) Breeding high- Singh PK, Singh GP (2016) Genomic selection in the era of next generation yielding drought-tolerant rice: genetic variations and conventional and sequencing for complex traits in plant breeding. Front Genet 7:221 molecular approaches. J Exp Bot 65:6265–6278 Bhatnagar-Mathur P, Vadezm V, Sharmam KK (2008) Transgenic approaches for abiotic McNally KL, Childs KL, Bohnert R, Davidson RM, Zhao K, Ulat VJ, Zeller G, Clark stress tolerance in plants: retrospect and prospects. Plant Cell Rep 27:411–424 RM, Hoen DR, Bureau TE, Stokowski R (2009) Genomewide SNP variation Biscarini F, Cozzi P, Casella L, Riccardi P, Vattari A, Orasen G, Perrini R, Tacconi G, reveals relationships among landraces and modern varieties of rice. Proc Natl Tondelli A, Biselli C, Cattivelli L (2016) Genome-wide association study for Acad Sci U S A 106(30):12273–12278 traits related to plant and grain morphology, and root architecture in Mhike X, Okori P, Magorokosho C, Ndlela T (2012) Validation of the use of temperate rice accessions. PLoS One 11(5):e0155425 secondary traits and selection indices for drought tolerance in tropical maize Brachi B, Aimé C, Glorieux C, Cuguen J, Roux F (2012) Adaptive value of (Zea mays L). African J Plant Sci 6(2):96–102 phenological traits in stressful environments: predictions based on seed Miah G, Rafii MY, Ismail MR, Puteh AB, Rahim HA, Latif MA (2016) Marker-assisted production and laboratory natural selection. PLoS One 7:e32069 introgression of broad-spectrum blast resistance genes into the cultivated Breseghello F, Sorrells ME (2006) Association analysis as a strategy for MR219 rice variety. J Sci Food Agric 97(9):2810–2818 improvement of quantitative traits in plants. Crop Sci 46:1323–1330 Brick MA, Ogg JB, Singh SP, Schwartz HF, Johnson JJ, Pastor-Corrales MA (2008) Mishra KK, Vikram P, Yadaw RB, Swamy BM, Dixit S, Cruz MTS, Maturan P, Marker Registration of drought-tolerant rust-resistant high-yielding pinto bean S, Kumar A (2013) qDTY : a locus with a consistent effect on grain yield 12.1 germplasm line CO46348. J Plant Registr 2:120–134 under drought in rice. BMC Genet 14(1):12 Comar A, Burger P, de Solan B, Baret F, Daumard F, Hanocq JF (2012) A semi- Mondal S, Rutkoski JE, Velu G, Singh PK, Crespo-Herrera LA, Guzmán C, Bhavani S, automatic system for high throughput phenotyping wheat cultivars in-field Lan C, He X, Singh RP (2016) Harnessing diversity in wheat to enhance grain conditions: description and first results. Funct Plant Biol 39(11):914–924 yield climate resilience disease and insect pest resistance and nutrition Kumar et al. Rice (2018) 11:35 Page 16 of 16 through conventional and modern breeding approaches. Front Plant Sci Volante A, Desiderio F, Tondelli A, Perrini R, Orasen G, Biselli C, Riccardi P, Vattari 7(991):1–15 A, Cavalluzzo D, Urso S, Ben Hassen M (2017) Genome-wide analysis of Obert DE, Evans CP, Wesenberg DM, Windes JM, Erickson CA, Jackson EW (2008) japonica rice performance under limited water and permanent flooding Registration of ‘Lenetah’ spring barley. J Plant Registr 2:85–97 conditions. Front Plant Sci 8:1862 Price AH (2006) Believe it or not QTLs are accurate! Trends Plant Sci 11:213–216 Wang Z, Cheng J, Chen Z, Huang J, Bao Y, Wang J, Zhang H (2012) Identification of QTL with main epistatic and QTL×environment interaction effects for salt Raman A, Verulkar S, Mandal N, Variar M, Shukla V, Dwivedi J, Singh B, Singh O, tolerance in rice seedlings under different salinity conditions. Theor Appl Swain P, Mall A, Robin S (2012) Drought yield index to select high yielding Genet 125:807–815 rice lines under different drought stress severities. Rice 5(1):31 Xinglai P, Sangang X, Qiannying P, Yinhong S (2006) Registration of ‘Jinmai 50’ Reif JC, Hallauer AR, Melchinger AE (2005) Heterosis and heterotic patterns in wheat. Crop Sci 46:983–995 maize. Maydica 50:215–223 Xu Y (2010) Molecular plant breeding CABI Publishing Rius M, Bourne S, Hornsby HG, Chapman MA (2015) Applications of next- Xu Y, Crouch JH (2008) Marker-assisted selection in plant breeding: from generation sequencing to the study of biological invasions. Current Zool publications to practice. Crop Sci 48:391–407 61(3):488–504 Xu Y, Lobos KB, Clare KM (2002) Development of SSR markers for rice molecular Sandhu N, Dixit S, Swamy BM, Vikram P, Venkateshwarlu C, Catolos M, Kumar A breeding In: Proceedings of Twenty-Ninth Rice Technical Working Group (2018) Positive interactions of major-effect QTLs with genetic background Meeting 24–27 February 2002 Little Rock Arkansas Rice Technical Working that enhances rice yield under drought. Sci Rep 8(1):1626 Group Little Rock Arkansas, p 49 Sandhu N, Jain S, Kumar A, Mehla BS, Jain R (2013) Genetic variation linkage Xue D, Huang Y, Zhang X, Wei K, Westcott S, Li C, Chen M, Zhang G, Lance R mapping of QTL and correlation studies for yield root and agronomic traits (2009) Identification of QTLs associated with salinity tolerance at late growth for aerobic adaptation. BMC Genet 14:104–119 stage in barley. Euphytica 169(2):187–196 Sandhu N, Kumar A (2017) Bridging the rice yield gaps under drought: QTLs Yadaw RB, Dixit S, Raman A, Mishra KK, Vikram P, Swamy BM, Cruz MTS, Maturan genes and their use in breeding programs. Agronomy 7(2):27 PT, Pandey M, Kumar A (2013) A QTL for high grain yield under lowland Sandhu N, Singh A, Dixit S, Cruz MTS, Maturan PC, Jain RK, Kumar A (2014) drought in the background of popular rice variety Sabitri from Nepal. Field Identification and mapping of stable QTL with main and epistasis effect on Crops Res 144:281–287 rice grain yield under upland drought stress. BMC Genet 15(1):63 Yang S, Vanderbeld B, Wan J, Huang Y (2010) Narrowing down the targets: Sandhu N, Torres RO, Cruz MTS, Maturan PC, Jain R, Kumar A, Henry A (2015) towards successful genetic engineering of drought tolerant crops. Mol Plant Traits and QTLs for development of dry direct-seeded rainfed rice varieties. 3:469–490 J Exp Bot 66(1):225–244 Senapati BK, Pal S, Roy S, De DK, Pal S (2009) Selection criteria for high yield in early segregating generation of rice (Oryza sativa L) crosses. J Crop Weed 5:36–38 Septiningsih EM, Pamplona AM, Sanchez DL, Neeraja CN, Vergara GV, Heuer S, Ismail AM, Mackill DJ (2009) Development of submergence-tolerant rice cultivars: the Sub1 locus and beyond. Ann Bot 103(2):151–160 Shamsudin NAA, Swamy BM, Ratnam W, Cruz MTS, Sandhu N, Raman AK, Kumar A (2016) Pyramiding of drought yield QTLs into a high quality Malaysian rice cultivar MRQ74 improves yield under reproductive stage drought. Rice 9:21. https://doi.org/10.1186/s12284-016-0093-6 Sharma B, Ritchie GL (2015) High-throughput phenotyping of cotton in multiple irrigation environments. Crop Sci 55(2):958–969 Shull GH (1948) What is “heterosis”? Genetics 33:439–446 Singh AK, Gopalakrishnan S, Singh VP (2011) Marker assisted selection: a paradigm shift in basmati breeding. Indian J Genet 71(2):1–9 Singh RP, Rajaram S, Miranda A, Huerta-Espino J, Autrique E (1998) Comparison of two crossing and four selection schemes for yield traits and slow rusting resistance to leaf rust in wheat. Euphytica 100:35–43. https://doi.org/10.1023/ A:1018391519757 Singh S, Sidhu JS, Huang N, Vikal Y, Li Z, Brar DS (2001) Pyramiding three bacterial blight resistance genes (xa5 xa13 and Xa21) using marker-assisted selection into indica rice cultivar PR106. Theor Appl Genet 102:1011–1015 Swamy BPM, Ahmed HU, Henry A, Mauleon R, Dixit S, Vikram P, Tilatto R, Verulkar SB, Perraju P, Mandal NP, Variar M, Robin S (2013) Genetic physiological and gene expression analyses reveal that multiple QTL enhance yield of rice mega-variety IR64 under drought. PLoS One 8(5):e62795 Venuprasad R, Bool ME, Quiatchon L, Sta Cruz MT, Amante M, Atlin GN (2012) A large effect QTL for rice grain yield under upland drought stress on chromosome 1. Mol Breed 30:535–547 Venuprasad R, Dalid CO, Del Valle M, Zhao D, Espiritu M, Cruz MTS, Amante M, Kumar A, Atlin GN (2009) Identification and characterization of large-effect quantitative trait loci for grain yield under lowland drought stress in rice using bulk-segregant analysis. Theor Appl Genet 120(1):177–190 Venuprasad R, Lafitte HR, Atlin GN (2007) Response to direct selection for grain yield under drought stress in rice. Crop Sci 47:285–293 Vikram P, Swamy BM, Dixit S, Ahmed HU, Cruz MTS, Singh AK, Kumar A (2011) qDTY a major QTL for rice grain yield under reproductive-stage drought 1.1 stress with a consistent effect in multiple elite genetic backgrounds. BMC Genet 12(1):89 Vikram P, Swamy BM, Dixit S, Trinidad J, Cruz MTS, Maturan PC, Amante M, Kumar A (2016) Linkages and interactions analysis of major effect drought grain yield QTLs in rice. PLoS One 11(3):e0151532 Vikram P, Swamy BPM, Dixit S, Singh R, Singh BP, Miro B, Kohli A, Henry A, Singh NK, Kumar A (2015) qDTY drought susceptibility of modern rice varieties: an effect 1.1 of linkage of drought tolerance with undesirable traits. Nat Sci Rep 5:14799
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