POOLED MAPPING OF QUANTITATIVETRAIT LOCI ASSOCIATED WITH DROUGHT TOLERANCE IN RICE (ORYZA SATIVA L.) AT SEEDLING STAGE
Y. B. Wu1, G. Y. Zhang1, C. Zhang1, W. Q. Tang2, T. Wang1, H. N. Zhang1 and H. K. Wu3, *
1College of Life Sciences, Shangrao Normal University, Shangrao, Jiangxi, China
2Marine and Agricultural Biotechnology Laboratory, Fuzhou Institute of Oceanography Minjiang University, Fuzhou, Fujian, China
3College of Advanced Agricultural Sciences, Zhejiang Agriculture and Forestry University, Hangzhou, Zhejiang, China
*Corresponding Author E-mail: wuhk@zafu.edu.cn
ABSTRACT
Drought is among the foremost constraints influencing global rice productivity. The drought tolerance nature of rice is complicated, depending on multiple components and having low heritability. Thus, breeding drought-tolerant varieties is a fundamental way which can be used to increase rice yield in drought. To investigate the genetic basis of seedling tolerance to drought stress of rice (Oryza sativa L.), we performed QTL mapping on a big F2 population of 2600 participants from a cross between the japonica rice variety Huaidao 5 (HD5) and the indica rice variety 1892S through performing bulked segregant analysis and whole-genome sequencing (BSA-seq). HD5 showed greater tolerance to prolonged drought stress compared with1892Sat seedling stage. By analyzing a pair of opposite DNA pools made from 182 extremely-sensitive seedlings and 182 extremely-tolerant seedlings from the F2 population using the block regression mapping (BRM) method, we mapped a QTL on chromosome 1, of which the additive effect was estimated to explain 2.20% of the phenotypic variance. We named the QTLqSLDT1.1(q represents quantitative trait loci, SL represents seedling leaf, DT represents drought tolerance, 1.1representsthe first one found on chromosome 1), which must be a novel QTL, because no QTLs for rice seedling tolerance to drought stress have been mapped on chromosome 1 before. The information derived from the current research facilitates marker-assisted breeding of drought-resistant lines and positional cloning of the gene conferring drought tolerance in rice.
Key words: Rice, Drought tolerance, QTL mapping, Bulked segregant analysis, Whole-genome sequencing
INTRODUCTION
It is demonstrated that rice (Oryza sativa L.) ranks among the foremost food crops universally, occupying approximately 9% of the arable land on Earth. It offers 21% concentration of the global per capita energy as well as 15% concentration of the per capita protein, supplying nearly 50% of the global population with quality food (Xu et al., 2011). Recently, the rice cultivation worldwide has been challenged by main production constraints, primarily owing to climate change-caused detrimental impacts. Drought is the leading factor of abiotic stress of rice ecosystem, inducing 50% yield loss across the globe (Qin et al., 2011). The occurrences and frequency of such stress is unpredictable and increasing. Around 34 million hectares of rain-fed lowland as well as 8 million hectares of upland rice across Asia frequently suffer drought stress (Venuprasad et al., 2009). Singhal et al. (2016) suggested over half of the agricultural land worldwide is going to be challenged by drought stress until 2050. Therefore, high yielding varieties showing drought tolerance need to be bred for sustaining rice yields.
Under the control of many quantitative trait loci (QTLs), the mechanism of drought tolerance is sophisticated (Fleury et al., 2010). Considerable research works have been conducted on identification of genes or QTLs related to drought stress of rice(Huang et al., 2022; Roy et al., 2023). It is known that drought stress influences varying developmental phases of rice. Most of the studies reported concentrate on the roleof drought stress on the reproductive development in rice(Mishra et al., 2013; Palanog et al., 2014; Prince et al., 2015; Swamy et al., 2017; Bhattarai et al., 2018; Yadav et al., 2019; Baisakh et al., 2020; Satrio et al., 2021). However, the influence of drought stress on rice seedlings is also important and needs attention. Drought stress at seedling stage is critical for crop establishment under direct seeding conditions. In addition, drought stress at seedling stage could possibly be genetically connected with the drought stress at other developmental stages. Therefore, the study on the genetic foundation of drought stress at seedling stage has great importance to the breeding of drought-tolerant varieties of rice. Based on five shoot morphological traits: seedling shoot length, seedling dry weight, seedling leaf rolling, seedling leaf drying and seedling drought recovery, Saikumar et al. (2014) identified 10 QTLs on chromosomes 3, 5 and 8 for drought tolerance at seedling phase with BILs (BC1F6) population of rice. Witha BIL population of 143 BC2F20 lines and leaf drying, leaf rolling, leaf number, dry weight of root, dry weight of shoot, maximum root length as well as maximum shoot length index as indicators, Huang et al. (2022 )mapped13 QTLs on rice chromosomes 1, 2, 4, 5, 7, 8, 10 and 11 conferring drought tolerance at seedling phase.
Bulked segregant analysis via deep sequencing (BSA-seq) provides the useful means for QTL mapping. BSA-seq in combination with traditional gene mapping method greatly accelerates the fine mapping of QTLs/genes (Liang et al., 2020). In recent years, BSA-seq has been adopted to mining QTLs in rice (Barik et al., 2019, 2020). Huang et al. (2020) proposed a novel statistical approach for BSA-seq, called Block Regression Mapping (BRM). In this study, the BRM method was adopted for mapping QTLs conferring drought tolerance in rice at seedling stage and map a QTL on chromosome 1. The result will facilitate identifying closely linked markers for rice breeding under drought stress, and subsequently fine mapping and cloning of the causal gene in the QTL.
MATERIALS AND METHODS
Mapping population: An F2 population was developed for QTL mapping from a cross between a japonica rice variety Huaidao 5 (HD5) and an indica rice variety 1892S, an elite photoperiodic as well as thermo-sensitive male sterile (P/TGMS) line. The preparative experiment suggested a greater tolerance of HD5 to drought stress compared with 1892S at seedling stage.
Seed sowing and planting: With 54 seeds in each box (9 rows × 6 seeds/row), pre-germinated seeds in the mapping population were sown in clean sand within rectangular (38 × 25 ×10 cm3) plastic turnover boxes. Besides, seedlings grew under the condition of 25°C in the greenhouse. The sand was moistened using tap water each day, and Yoshida nutrient solution (Yoshida et al., 1976) was applied every three days when the seedlings grew to the three-leaf stage. Apart from that, we sowed 100 seeds of every parental line for reference.
Identifying individuals with extreme phenotypes on drought tolerance: Using 25% PEG6000 in the phytotron growth chamber in the cycle of 12 h light (15000 LX) and 12 h dark, seedlings showing uniform growth performance at the three-leaf stage were processed. In addition, seedlings had exposure to 25% PEG6000 for 12 h, and seedlings sowing maximum sensitivity to drought stress (when leaves were wilted at the tip) were identified and collected as the extremely sensitive (ES) individuals. The remaining seedlings were kept in the chamber for 45 h. Meantime, most seedlings were wilted at the leaf tip or even died, while the few seedlings appearing normal were collected as the extremely tolerant (ET) individuals. 50 turnover boxes were chosen in 5 batches, with 10 boxes per batch. For every batch, around 38 ES and 38ET individuals were chosen, each making up of ~7% of total seedlings studied.
Bulking, DNA extraction and sequencing: For bulked segregant analysis, 182 ES individuals and 182 ET individuals were screened for establishing two opposite groups from the 2,600 valid F2 plants. In each group, a segment of equal weight was sliced from every young leave (formerly preserved in the refrigerator at -80°C) of the group and then all segments were mixed to extract DNA by the CTAB method (Doyle and Doyle, 1987) after moderate modifications. Therefore, two DNA pools were formed, namely, the ES pool and the ET pool. It was found that understandard paired-end 150 bp sequencing library construction protocols, DNA samples of the two parents, HD5 and 1892S, and the two pools received whole-genome resequencing on the Illumina HiSeqX Ten platform.
Investigation of reads and variants: The raw reads in sequenced ES and ET pools were rinsed and trimmed with the BBDuk program for BBTools (http://jgi.doe.gov/data-and-tools/bbtools/). Paired reads were mapped to the IRGSP-1.0 reference rice genome (http://rapdb.dna.affrc.go.jp) with Burrows-Wheeler Aligner using the Maximal Exact Matches algorithm (BWA MEM) and the alignments were exposed to treatment with SAMTools (Kawaharaet al., 2013). Freebayes was employed for calling SNPs and InDels based on default parameters (Garrison and Marth, 2012). For obtaining reliable polymorphic markers, variant (SNP or short InDel) filtering was conducted with custom perl scripts.To prevent serious segregation distortion, only the SNPs or short InDels with allele frequency(AF) values of 0.3 - 0.7 in the population were maintained. Such markers were annotated with the use of snpEff (Cingolani et al., 2012).
QTL analysis: The marker set was taken for mapping QTLs. Besides, allele frequency difference (AFD) of each marker between the ES and the ET pools could be measured and smoothed by block regression with the BRM method (Huang et al., 2020). Meanwhile, the block size for regression was determined as 20 kb. To measure the AFD curve threshold at the overall (genome-wise) significance level of 0.05, the theoretical allele frequency assumption (= 0.5) in the F2 population was used. Concerning every significant AFD peak (candidate QTL), the 95% confidence interval was measured. With the peak AFD value, by adopting the Pooled QTL Heritability Estimator method(Tanget al., 2018), the heritability of every QTL was assessed.
RESULTS
In total, 2,600 F2 seedlings from the cross between HD5 and 1892S were detected as having drought tolerance. These F2 seedlings exhibited continuous variation of wilting degree under thedrought stress, suggesting that drought tolerance is a quantitative trait.
Whole-genome sequencing of the ET and ES pools and the parents HD5 and 1892S generated~75.5 to 108.2 million reads of every pool or parental line. Through filtering, 1,984,092 SNPs and 242,848short InDels were found (Table 1). It could be seen that the average densities of SNP as well as short InDel markers were 5.32 SNP/kb (or one SNP every 188 bp) and 0.65 InDel/kb(or one InDel every 1,538 bp), respectively, which werehigh enough for QTL mapping.
Table 1Distributions of SNPs and short InDels identified on different chromosomes.
Chr.
|
Length (bp)
|
SNP
|
Short InDel
|
Number
|
Density (per kb)
|
Number
|
Density (per kb)
|
1
|
43,270,923
|
234,218
|
5.42
|
29,969
|
0.69
|
2
|
35,937,250
|
228,470
|
6.31
|
29,381
|
0.81
|
3
|
36,413,819
|
228,949
|
6.27
|
29,433
|
0.81
|
4
|
35,502,694
|
72,265
|
2.02
|
10,316
|
0.34
|
5
|
29,958,434
|
160,950
|
5.34
|
20,314
|
0.68
|
6
|
31,248,787
|
168,486
|
5.35
|
19,386
|
0.62
|
7
|
29,697,621
|
182,242
|
6.11
|
21,256
|
0.72
|
8
|
28,443,022
|
162,385
|
5.69
|
19,278
|
0.68
|
9
|
23,012,720
|
95,502
|
4.11
|
12,514
|
0.54
|
10
|
23,207,287
|
121,606
|
5.16
|
13,603
|
0.58
|
11
|
29,021,106
|
195,618
|
6.72
|
21,854
|
0.75
|
12
|
27,531,856
|
133,401
|
4.77
|
15,439
|
0.55
|
Average
|
373,245,519
|
1,984,092
|
5.32
|
242,743
|
0.65
|
Using the BRM method, a significant AFD peak was detected on chromosome 1 below the overall (genome-wise) significance level of 0.05 (Figure 1),indicatingthe existence of a QTL. Apart from that, the QTL was named qSLDT1.1. The most probable position of the QTL (the highest point of the peak) was at ~40.50 Mb, with a95% confidence interval ranging 35.11-43.20 Mb. The AFD peak of the QTL was negative, suggesting that the allele from parent DH5 of the QTL acted to increase drought tolerance. The additive effect heritability of the QTL approached 2.20%, butthe dominance effect heritability could not be reasonably estimated due to severe segregation distortion at the QTL.

Figure 1 Block regression mapping of QTLs conferring resistance to drought stress of rice. Obviously, the horizontal orange lines suggest the AFD threshold (±0.135) at the overall (genome-wise) significance level of 0.05. The estimated QTL positions are highlighted in filled triangles. The AFD of a marker was measured through subtractingthe AF of parent 1892S in the ES pool from that in the ET pool.
DISCUSSION
Since the beginning of this century, over 310 QTLs for drought tolerance have been recognized from rice (Satrio et al., 2021; Huang et al., 2022; Roy et al., 2023). Most of them are mapped based on the performance during the period of reproductive development. Only 23 QTLs for drought tolerance are mapped at seedling phase. These 23 QTLs are located on chromosomes 1-5, 7, 8 and 10-11 (Saikumar et al., 2014; Huang et al., 2022). However, only two QTLs (qDWR1.1 and qDWS1.1) are located on chromosomes 1, but they are not located within the confidence interval of the qSLDT1.1. So the QTL qSLDT1.1 mapped in this study on chromosome 1 is a novel QTL.
Although only two QTLs for drought tolerance to drought stress at seedling stage in rice have been found on chromosome 1 before, there are a lot of QTLs for drought tolerance at the reproductive phase detected on chromosome 1 (Table 2). Much of the QTLs are located within the confidence interval of the qSLDT1.1. In particular, the QTL QRvd1 for root volume, the QTLqtl1.1 for plant height at maturity, the QTLqDTY1.1 for grain yield, and the QTLqDTF1.1 for days to flowering subject to drought stress are relatively close to the peak tip of the qSLDT1.1. This implies a possibility of common genetic basis between the drought tolerance of seedling stage and that of reproductive phase, although the position of the qSLDT1.1 is not precisely estimated due to the wide confidence interval.
Drought tolerance refers to a comprehensive and sophisticated trait that exhibits plant response to drought stress. Numerous traits can suggest the drought tolerance of rice, such as biomass (Saikumar et al., 2014), leaf area (Sabar et al., 2019), panicle length (Nie et al., 2015; Prince et al., 2015; Roy et al., 2023), maximum root length (Huang et al., 2022), and fresh shoot weight (Verma et al., 2021). However, different indicator traits of drought tolerance may result in very different QTLs (Verma et al., 2021; Huang et al., 2022). Therefore, selection of suitable indicator traits is critical to the mapping of drought tolerance QTLs. The current work used the survival state of seedling under drought stress to indicate drought tolerance. Intuitively, survival state must directly reflect the degree of drought tolerance. However, survival state is difficult to be measured quantitatively. Fortunately, BSA-seq only uses the extremely tolerant and the extremely sensitive seedlings, which can be easily identified without the need of quantitative measurement. Therefore, survival state of seedling came out as a suitable indicator trait for BSA-seq. It is not labor-intensive, time-consuming and costly.
Table 2 Reported QTLs in association with drought stress tolerance on chromosome 1 in rice.
Tolerant/sensitive parents
|
Population
|
Appraisal period
|
QTL name
|
Position (Mb)
|
References
|
Kali Aus/IR64
|
BC1F4
|
Reproductive stage
|
qDTY1.3
|
24.80-36.73
|
Sandhu et al., 2014
|
Kali Aus/MTU1010
|
BC1F4
|
Reproductive stage
|
qDTY1.2
|
7.44-36.73
|
qPH1.1
|
36.73-38.89
|
Cabacu/IR64
|
F6-BILs
|
Reproductive stage
|
qLRS1.1
|
-
|
Trijatmiko et al., 2014
|
qPH1.1
|
-
|
Moroberekan/Swarna
|
BC2F3
|
Reproductive stage
|
qDTH1.1
|
-
|
Dixit et al., 2014
|
Nootripathu/IR20
|
F8/11-RILs
|
Reproductive stage
|
-
|
34.86
|
Prince et al., 2015
|
-
|
33.05
|
-
|
23.32
|
-
|
36.47
|
N-22/Cocodrie
|
F7-RILs
|
Reproductive stage
|
qPH1.07
|
7.05-7.08
|
Bhattarai et al., 2018
|
qPH1.38
|
38.28-38.61
|
qLRS1.37
|
37.27-37.37
|
qGY1.42
|
42.92-42.97
|
qYI1.42
|
42.98-43.07
|
qHI1.37
|
37.56-37.74
|
Vandana/Cocodrie
|
F2:3
|
Reproductive stage
|
qGYD1.1
|
12.90-15.15
|
Solis et al., 2018
|
qGYD1.2
|
30.70-32.13
|
qGYD1.3
|
-
|
JG88/IR66897B JG88/MR77
JG88/MR167
JG88/SN265
|
BC2F2
|
Reproductive stage
|
QDT1.3
|
9.86
|
Cui et al., 2018
|
QDT1.4
|
15.12
|
-
|
-
|
Vegetative stage
|
q1
|
22.79-23.32
|
Hoang et al., 2019
|
Dular/Swarn
|
BC1F3
|
Reproductive stage
|
qDTY1.1
|
40.01-40.08
|
Yadav et al., 2019
|
qDTY1.3
|
5.57-5.62
|
qDTF1.2
|
42.65-42.89
|
qPH1.2
|
4.48-4.95
|
qPH1.3
|
38.28-38.61
|
AUS 196/IR11N121
|
BC1F3
|
Reproductive stage
|
qDTY1.1
|
41.76-42.91
|
qDTY1.4
|
25.58-27.77
|
qDTF1.1
|
39.50-41.22
|
qPH1.1
|
38.75-39.38
|
IR55419-04/Basmati
|
F2
|
Vegetative stage
|
qHt1
|
33.05-35.19
|
Sabar et al., 2019
|
N22/Cocodrie
|
F2:3
|
Reproductive stage
|
qPN1.1
|
-
|
Baisakh et al., 2020
|
qGY1.1
|
-
|
CR 143–2-2/
Krishnahamsa
|
F7-RILs
|
Reproductive stage
|
qRCC1.1
|
34.51-36.47
|
Barik et al., 2020
|
qCHLa1.1
|
0.21-34.51
|
B6144F-MR-6/MH63
|
ILs
|
Reproductive stage
|
qAER1
|
30.55-32.78
|
Xu et al., 2020
|
-
|
-
|
Tiller stage
|
-
|
33.05
|
Verma et al., 2021
|
HB/ IR64
|
F9-RILs
|
Vegetative stage
|
qCW.D-1
|
20.6-21.1
|
Satrio et al., 2021
|
qRSR.RDI-1
|
25.2-25.7
|
qBB.DRI-1
|
33.2-33.3
|
O. longistaminata/9311
|
BC2F20-BILs
|
Seedling stage
|
qDWR1.1
|
161.0-162.0
|
Huang et al., 2022
|
qDWS1.1
|
234.0-235.0
|
Banglami/Ranjit
|
RILs
|
Reproductive stage
|
qPL_1.1
|
3.0-34.5
|
Roy et al., 2023
|
qNOC_1.2
|
6.0-6.2
|
Only one QTL with a relatively small additive effect heritability (2.20%) was detected, although a large F2 population was used. The result suggested that although there was significant difference between the two parental varieties on seedling tolerance to drought stress, the difference was probably controlled by many minor genes of very small effects only. As a very stringent selection intensity (~7%) was used for BSA-seq in this study, the statistical power was not high enough to detect the minor QTLs. To detect these minor QTLs, a larger selection proportion (e.g. 15–20%) of the pools may be required (Magwene et al., 2011).
Conclusion: In this study, we identified a novel drought-tolerant QTL at seedling stage in rice using RBM method based on an F2 population.
Acknowledgements: The study was sponsored by the Science and Technology Project of the Education Department in Jiangxi Province (grants GJJ180879, GJJ211724, GJJ2201806 and GJJ2201826), the Open Project of the Key Laboratory of Crop Design and Breeding of Fujian Province (grants 2018FJCBD01) and National Science Foundation of China (grant 32160457).
Compliance With Ethical Standards: The writers state that there exists no conflict of interest. No study on animals or human is involved in the article.
Authors’ Contribution: YW made the experiments and wrote the first draft; WT performed genotyping and processed the data; GZ, CZ, TW and HZ involved in cultivation of rice seedlings and treatment; HW provided the seed samples, created the F2 population and revised the manuscript.
REFERENCES
- Baisakh, N., J. Yabes, A. Gutierrez, V. Mangu, P. Ma, A. Famoso and A. Pereira (2020). Genetic mapping identifies consistent quantitative trait loci for yield traits of rice under greenhouse drought conditions. Genes. 11(1):62. DOI: 10.3390/genes11010062
- Barik, S.R., E. Pandit, S.P. Mohanty, D.K. Nayak and S.K. Pradhan (2020). Genetic mapping of physiological traits associated with terminal stage drought tolerance in rice. BMC Genet. 21(1):76. DOI: 10.1186/s12863-020-00883-x
- Barik, S.R., E. Pandit, S.K. Pradhan, S,P. Mohanty and T. Mohapatra (2019). Genetic mapping of morpho-physiological traits involved during reproductive stage drought tolerance in rice. PLoS One. 14(12):e0214979. DOI: 10.1371/journal.pone.0214979
- Bhattarai, U. and P.K. Subudhi (2018). Genetic analysis of yield and agronomic traits under reproductive-stage drought stress in rice using a high-resolution linkage map. Gene. 669:69–DOI: 10.1016/j.gene.2018.05.086
- Cingolani, P., A. Platts, L.L. Wang, M. Coon, Nguyen, L. Wang, S.J. Land, X. Lu and D.M. Ruden (2012). A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 6(2):80–92. DOI: 10.4161/fly.19695
- Cui, Y., W. Zhang, X. Lin, S. Xu, J. Xu and Z. Li (2018). Simultaneous improvement and genetic dissection of drought tolerance using selected breeding populations of rice. Front Plant Sci. 9:320. DOI:3389/fpls.2018.00320
- Dixit, S., B.E. Huang, M.T. Sta Cruz, P.T. Maturan, J.C. Ontoy and A. Kumar (2014). QTLs for tolerance of drought and breeding for tolerance of abiotic and biotic stress: an integrated approach. PLoS One. 9(10):e109574. DOI: 1371/journal.pone.0109574
- Doyle, J. and J. Doyle (1987). A rapid procedure for DNA purification from small quantities of fresh leaf tissue. Phytochemical Bulletin. 19:11–
- Fleury, D., S. Jeferies, H. Kuchel and P. Langridge (2010). Genetic and genomic tools to improve drought tolerance in wheat. Exp. Bot.61(12):3211–3222. DOI: 10.1093/jxb/erq152
- Garrison, E. and G. Marth (2012). Haplotype-based variant detection from short-read sequencing. Quant Biol. arXiv:1207.3907. DOI: 48550/arXiv.1207.3907
- Hoang, G.T., L. Van Dinh, T.T. Nguyen, N.K. Ta, F. Gathignol, C.D. Mai, S. Jouannic, K.D. Tran, T.H. Khuat, V.N. Do, M. Lebrun, B. Courtois and P. Gantet (2019). Genome-wide association study of a panel of vietnamese rice landraces reveals new QTLs for tolerance to water deficit during the vegetative phase. Rice. 12(1):4. DOI: 1186/s12284-018-0258-6
- Huang, L., W. Tang, S. Bu and W. Wu (2020). BRM: a statistical method for QTL mapping based on bulked segregant analysis by deep sequencing. Bioinformatics. 36(7): 2150– DOI: 10.1093/bioinformatics/btz861
- Huang, S., M. Liu, G. Chen, F. Si, F. Fan, Y. Guo, L. Yuan, F. Yang, and Li (2022). Favorable QTLs from Oryza longistaminata improve rice drought resistance. BMC Plant Biol. 22(1):136. DOI: 10.1186/s12870-022-03516-w
- Kaur, V., S.K.Yadav, D.P. Wankhede, Pulivendula, A. Kumar and V. Chinnusamy (2020). Cloning and characterization of a gene encoding MIZ1, a domain of unknown function protein and its role in salt and drought stress in rice. Protoplasma. 257(2):475–487. DOI: 10.1007/s00709-019-01452-5
- Kawahara, Y., M. Bastide, J.P. Hamilton, H. Kanamori, W.R. McCombie, S. Ouyang, D.C. Schwartz, T. Tanaka, J. Wu, S. Zhou, K.L. Childs, R.M. Davidson, H. Lin, L. Quesada-Ocampo, B. Vaillancourt, H. Sakai, S.S. Lee, J. Kim, H. Numa, T. Itoh, C.R. Buell and T. Matsumoto (2013). Improvement of the Oryza sativa nipponbare reference genome using next generation sequence and optical map data. Rice. 6(1):4. DOI: 1186/1939-8433-6-4
- Liang, T., W. Chi, L. Huang, M. Qu, S. Zhang, Z.Q. Chen, Z.J. Chen, D. Tian, Y. Gui, X. Chen, Z. Wang, W. Tang and S. Chen (2020). Bulked segregant analysis coupled with whole-genome sequencing (BSA-seq) mapping identifies a novel pi21 haplotype conferring basal resistance to rice blast disease. Int J Mol Sci. 21(6):2162. DOI: 3390/ijms21062162
- Magwene, P.M., J.H. Willis and J.K. Kelly (2011). The statistics of bulk segregant analysis using next generation sequencing.PLoS Comput Biol. 7(11):e1002255. DOI: 1371/journal.pcbi.1002255
- Mishra, K.K., P. Vikram, R.B. Yadaw, B.P. Swamy, S. Dixit, M.T. Cruz, P. Maturan, S. Marker and A. Kumar (2013). qDTY1: a locus with a consistent effect on grain yield under drought in rice. BMC Genet. 14:12. DOI: 10.1186/1471-2156-14-12
- Nie, Y.Y., L. Zhang, Y.H. Wu, H.J. Liu, W.W. Mao, J. Du, H.L. Xiu, X.Y. Wu, X. Li, Y.W. Yan, G.L. Liu, H.Y. Liu and S.P. Hu (2015). Screening of candidate genes and fine mapping of drought tolerance quantitative trait loci on chromosome 4 in rice (Oryza sativa) under drought stress. Ecol. Evol., 5(21):5007–5015. DOI: 10.1002/ece3.1786
- Palanog, A.D., B.P.M. Swamy, N.A.A. Shamsudin, S. Dixit, J.E. Hernandez, T.H. Boromeo and P.C.S. Cruz (2014). Grain yield QTLs with consistent-effect under reproductive-stage drought stress in rice. Crop Res. 161:46–54. DOI: 10.1016/j.fcr.2014.01.004
- Prince, S.J., R. Beena, S.M. Gomez, S. Senthivel and R.C. Babu (2015). Mapping consistent rice (Oryza sativa) yield QTLs underdrought stress in target rainfed environments. Rice. 8:25. DOI: 10.1186/s12284-015-0053-6
- Qin, F., K. Shinozaki and K. Yamaguchi-Shinozaki (2011). Achievements and challenges in understanding plant abiotic stress responses and tolerance. Plant Cell Physiol. 52(9):1569– DOI: 10.1093/pcp/pcr106
- Roy, N., R.K. Verma, S.K. Chetia, V, Sharma, P. Sen, M.K. Modi (2023). Molecular mapping of drought-responsive QTLs during the reproductive stage of rice using a GBS (genotyping-by-sequencing) based SNP linkage map. Biol. Rep. 50(1):65–76. DOI: 10.1007/s11033-022-08002-y
- Sabar, M., G. Shabir, S.M. Shah, K. Aslam, A. Naveed and M. Arif (2019). Identification and mapping of QTLs associated with drought tolerance traits in rice by a cross between Super Basmati and IR55419-04. Breed Sci. 69(1):169–178. DOI: 10.1270/jsbbs.18068
- Saikumar, S., P.K. Gouda, A.Saiharini, C.M.K. Varma, O. Vineesha, G. Padmavathi and V.V. Shenoy (2014). Major QTL forenhancing rice grain yield under lowland reproductive drought stress identified using an sativa/O. glaberrima introgressionline. F. Crop Res. 163:119–131. DOI: 10.1016/j.fcr.2014.03.011
- Sandhu, N., A. Singh, S. Dixit, M.T. Sta Cruz, P.C. Maturan, R.K. Jain and A. Kumar (2014). Identification and mapping of stable QTL with main and epistasis effect on rice grain yield under upland drought stress. BMC Genet. 15:63. DOI: 1186/1471-2156-15-63
- Satrio, R.D., M.H. Fendiyanto, E.D.J. Supena, S. Suharsono, and Miftahudin (2021). Genome-wide SNP discovery, linkage mapping, and analysis of QTL for morpho-physiological traits in rice during vegetative stage under drought stress. Physiol Mol Biol Plants. 27(11):2635–2650. DOI: 10.1007/s12298-021-01095-y
- Singhal, P., A.T. Jan, M. Azam and Q.M.R. Haq (2016). Plant abiotic stress: A prospective strategy of exploiting promoters as alternative to overcome the escalating burden. Life Sci. 9(1):52–63. DOI: 10.1080/21553769.2015.1077478
- Solis, J., A. Gutierrez, V. Mangu, E. Sanchez, R. Bedre, S. Linscombe and N. Baisakh (2018). Genetic mapping of quantitative trait loci for grain yield under drought in rice under controlled greenhouse c Front Chem. 5:129. DOI: 10.3389/fchem.2017.00129
- Swamy, B.P., H.U. Ahmed, A. Henry, R. Mauleon, S. Dixit, P. Vikram, R. Tilatto, S.B. Verulkar, P. Perraju, N.P. Mandal, M. Variar, S. Robin, R. Chandrababu, O.N. Singh, J.L. Dwivedi, S.P. Das, K.K. Mishra, R.B. Yadaw, T.L. Aditya, B. Karmakar, K. Satoh, A. Moumeni, S. Kikuchi, H. Leung and A. Kumar (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. DOI: 1371/journal.pone.0062795
- Tang, W., L. Huang, S. Bu, X. Zhang and Wu (2018). Estimation of QTL heritability based on pooled sequencingdata.Bioinformatics. 34(6):978–984. DOI: 10.1093/bioinformatics/btx703
- Tang, Y., X. Bao, Y. Zhi, Q. Wu, Y. Guo, X. Yin, L. Zeng, J. Li, J. Zhang, W. He, W. Liu, Q. Wang, C. Jia, Z. Li and K. Liu (2019). Overexpression of a MYB family gene, OsMYB6, increases drought and salinity stress tolerance in transgenic rice. Front Plant Sci. 10:168. DOI: 3389/fpls.2019.00168
- Trijatmiko, K.R., Supriyanta, J. Prasetiyono, M.J. Thomson, C.M. Vera Cruz, S. Moeljopawiro and A. Pereira (2014). Meta-analysis of quantitative trait loci for grain yield and component traits under reproductive-stage drought stress in an upland rice population. Breed. 34(2):283–295. DOI: 10.1007/s11032-013-0012-0
- Uga, Y., K. Sugimoto, S. Ogawa, J. Rane, M. Ishitani, N. Hara, Y. Kitomi, Y. Inukai, K. Ono, N. Kanno, H. Inoue, H. Takehisa, R. Motoyama, Y. Nagamura, J. Wu, T. Matsumoto, T. Takai, K. Okuno and M. Yano (2013). Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions. Genet., 45(9):1097–1102. DOI: 10.1038/ng.2725
- Venuprasad, R., C.O. Dalid, M.D. Valle, D. Zhao, M. Espiritu, M.T. Sta Cruz, M. Amante, A. Kumar and G.N. Atlin (2009). Identification and characterization of large-effect quantitative trait loci for grain yield under lowland drought stress in rice using bulk-segregant analysis. Appl. Genet. 120(1):177–190. DOI: 10.1007/s00122-009-1168-1
- Verma, H. and R.N. Sarma (2021). Identification of markers for root traits related to drought tolerance using traditional rice germplasm. Biotechnol. 63(12):1280–1292. DOI: 10.1007/s12033-021-00380-1
- Xu, P., J. Yang, Z. Ma, D. Yu, J. Zhou, D. Tao and Z. Li (2020). Identification and validation of aerobic adaptation QTLs in upland rice. Life. 10(5):65. DOI: 3390/life10050065
- Yadav, S., N. Sandhu, V.K. Singh, M. Catolos and A. Kumar (2019). Genotyping-by-sequencing based QTL mapping for rice grain yield under reproductive stage drought stress tolerance. Rep., 9(1):14326. DOI: 10.1038/s41598-019-50880-z
- Yoshida, S., D.A. Forno, J.H. Cock and A. Gomez (1976). Laboratory manual for physiological studies of rice. International Rice Research Institute Philippines. 61–66.
|