Next Article in Journal
Mechanistic Insight into Antimicrobial and Antioxidant Potential of Jasminum Species: A Herbal Approach for Disease Management
Next Article in Special Issue
Morphological Analysis, Protein Profiling and Expression Analysis of Auxin Homeostasis Genes of Roots of Two Contrasting Cultivars of Rice Provide Inputs on Mechanisms Involved in Rice Adaptation towards Salinity Stress
Previous Article in Journal
An Update on Crop ABA Receptors
Previous Article in Special Issue
QMrl-7B Enhances Root System, Biomass, Nitrogen Accumulation and Yield in Bread Wheat
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Genome-Wide Association Mapping of Salinity Tolerance at the Seedling Stage in a Panel of Vietnamese Landraces Reveals New Valuable QTLs for Salinity Stress Tolerance Breeding in Rice

National Key Laboratory for Plant Cell Biotechnology, Agricultural Genetics Institute, LMI RICE-2, Hanoi 00000, Vietnam
UMR DIADE, Université de Montpellier, IRD, 34095 Montpellier, France
Faculty of Agricultural Technology, University of Engineering and Technology, Hanoi 00000, Vietnam
Department of Genetics and Plant Breeding, Faculty of Agronomy, Vietnam National University of Agriculture, Hanoi 00000, Vietnam
Incubation and Support Center for Technology and Science Enterprises, Hanoi 00000, Vietnam
UMR BPMP, Univ Montpellier, CNRS, INRAE, Institut Agro, 34060 Montpellier, France
Department of Molecular Biology, Centre of the Region Haná for Biotechnological and Agricultural Research, Palacký University Olomouc, Šlechtitelů 27, 783 71 Olomouc, Czech Republic
Authors to whom correspondence should be addressed.
Plants 2021, 10(6), 1088;
Submission received: 14 April 2021 / Revised: 18 May 2021 / Accepted: 25 May 2021 / Published: 28 May 2021
(This article belongs to the Special Issue Advances in Cereal Crops Breeding)


Rice tolerance to salinity stress involves diverse and complementary mechanisms, such as the regulation of genome expression, activation of specific ion-transport systems to manage excess sodium at the cell or plant level, and anatomical changes that avoid sodium penetration into the inner tissues of the plant. These complementary mechanisms can act synergistically to improve salinity tolerance in the plant, which is then interesting in breeding programs to pyramidize complementary QTLs (quantitative trait loci), to improve salinity stress tolerance of the plant at different developmental stages and in different environments. This approach presupposes the identification of salinity tolerance QTLs associated with different mechanisms involved in salinity tolerance, which requires the greatest possible genetic diversity to be explored. To contribute to this goal, we screened an original panel of 179 Vietnamese rice landraces genotyped with 21,623 SNP markers for salinity stress tolerance under 100 mM NaCl treatment, at the seedling stage, with the aim of identifying new QTLs involved in the salinity stress tolerance via a genome-wide association study (GWAS). Nine salinity tolerance-related traits, including the salt injury score, chlorophyll and water content, and K+ and Na+ contents were measured in leaves. GWAS analysis allowed the identification of 26 QTLs. Interestingly, ten of them were associated with several different traits, which indicates that these QTLs act pleiotropically to control the different levels of plant responses to salinity stress. Twenty-one identified QTLs colocalized with known QTLs. Several genes within these QTLs have functions related to salinity stress tolerance and are mainly involved in gene regulation, signal transduction or hormone signaling. Our study provides promising QTLs for breeding programs to enhance salinity tolerance and identifies candidate genes that should be further functionally studied to better understand salinity tolerance mechanisms in rice.

Graphical Abstract

1. Introduction

More than one third of cultivated lands are polluted by excess of salt (NaCl) [1]. Sodium is a toxic element for plants and this is particularly true for rice, which is often cultivated in river delta areas where irrigation water is increasingly frequently contaminated by sea water [2]. Rice is the most important food crop, feeding more than three billion people in the world [3]. In Vietnam, rice occupies 85% of the total agricultural area [4]. However, with 3620 km of coastline spreading from north to south, Vietnam has been ranked among the top five countries likely to be most affected by climate change [5]. Vietnam is one of the first rice exporters in the world and consequently plays an important role in food supply security, particularly in Asian countries [6,7]. The Mekong River Delta and Red River Delta are the main areas of rice production in Vietnam; the Mekong River Delta represents 50% of the total rice production area and supplies 90% of the rice exported by the country [7]. The Mekong River Delta is increasingly menaced by an elevation in salinity due to sea water intrusion that results from different climatic and anthropic factors [8]. According to data from the Ministry of Science and Technology of Vietnam, at the end of 2015 and the first months of 2016, saline intrusion in the Mekong River Delta reached the highest level measured during the past 100 years. In addition to global management of the causes leading to increases in salinity, it is important to breed new varieties of rice tolerant to salinity, which necessitates the identification of genetic determinants conferring salinity tolerance [9]. Several salinity tolerance QTLs (quantitative trait loci) have been identified in rice using association genetics approaches, and the mechanisms undelaying rice salinity tolerance start to be well known (for reviews see [10,11]). The mechanisms involved in rice salinity tolerance act at different levels and combine transcriptional and posttranscriptional or posttranslational regulation events that lead to sodium exclusion or compartmentation in specific cell infrastructures, osmolyte production or anatomical changes that avoid sodium penetration into the internal tissues of the plant [10]. These mechanisms act in different complementary ways that synergistically allow salinity tolerance [10]. For these reasons, it is interesting to combine genetic sources with different and complementary salinity tolerance to increase resistance to salinity, which can also buffer the susceptibility of QTL effects to environmental conditions [12,13]. To identify such complementary sources of salinity tolerance, it is necessary to look for them in the widest and most diverse panels of varieties possible.
To contribute to this goal, in this study, we screened a genotyped panel of 179 Vietnamese landrace varieties of indica and japonica rice collected in different agrosystems from North to South Vietnam for salinity tolerance [14]. Vietnamese landrace varieties are often underrepresented in the studied international panels such as the 3K panel developed by the International Rice Research Institute (IRRI), even though, they potentially constitute an original source of valuable alleles [15,16]. We already used this panel to identify valuable QTLs associated with root, leaf or panicle traits and water deficit tolerance by genome wide association study (GWAS) [17,18,19,20]. The plants were screened for salinity stress tolerance at an early developmental stage using a hydroponic culture system in the presence of 100 mM NaCl. The phenotypic standard evaluation score (SES) [21], chlorophyll and relative water content and the concentrations of K+ and Na+ ions were measured in leaves. GWAS revealed 26 QTLs including 10 QTLs associated with several traits. Most of these QTLs contain candidate genes that may explain their effect on salinity tolerance, and the function of the genes are further discussed.

2. Results

2.1. Phenotypic Variation and Heritability of Salinity Tolerance-Related Traits

The phenotyping experiment was conducted for three consecutive years, from 2017 to 2019. The observed salt tolerance diversity in different accessions was reproducible. The data from the last trial were chosen for performing GWAS, for which the screening protocol was improved and standardized for the Vietnamese rice landrace panel and for the parameter measurement as described in the Materials and Methods section. In this trial, on the tenth day after the start of salinization, 25 plots of 19 accessions were monitored to have simultaneously reached a score of 7, which included the susceptible check IR29. A total of 9 salinity tolerance-related traits were evaluated, three of which (leaf water content (WC), chlorophyll a to chlorophyll b ratio in leaves (Chla_b), and ratio of Na+/K+ in leaves (Na_K)) were computed from the directly measured traits. Statistical analysis was conducted for the full panel and the indica and japonica subpanels (Table 1). Within the full panel and the indica subpanel, significant replication and genotypic effects were observed for most of the traits, with the exception of Chla_b. Meanwhile, the genotypic effect for the chlorophyll traits of the japonica subpanel was insignificant (Table 1). The broad-sense heritability (H2) calculated for each trait with a significant genotypic effect was moderate to high, varying from 0.40 to 0.76, while high values were recorded for WC, Score and three ion content traits.
Significant phenotypic variation was observed for all of the traits, with “full name” (CVs) ranging from 15.35% to 70.47% (Table 1). Figure 1 shows statistically significant differences in the mean values of WC, Score and ConcK between the indica and japonica subpanels. Specifically, the indica subpanel displayed a lower WC and higher Score and ConcK than the japonica subpanel (Table 1, Figure 1). Consequently, for the Vietnamese rice landrace panel used in this study, indica accessions were considered less salt-tolerant than japonica accessions.
The correlations among the traits determined the same tendency within the full panel and the two subpanels (Figure S1). However, the correlation coefficients were largely variable between the traits (Table 2). For instance, Score, ConcNa and Na_K were strongly negatively correlated with WC. ConcNa and Na_K were also highly correlated with Score. In contrast, ConcK constituted weak correlations with the other traits, except for a moderate correlation with WC. Overall, higher correlations were observed among WC, Score, ConcNa and Na_K.

2.2. SNP-Trait Associations

GWAS analyses were conducted for the full panel and for the indica and japonica subpanels separately. The GWAS results are presented in the Q-Q and Manhattan plots in Figure 2 and Figure S2. Using the p-value threshold of 1 × 10−4, we identified 64 associations between 58 SNPs and the studied traits, but no associations were detected in the japonica subpanel. These 58 significant SNPs were distributed in 26 QTL regions. Within the detected QTL regions, the number of significant SNPs increased to 119 when the threshold value was set at 1 × 10−3 (Table S1). Among these values, 110 SNPs were found in the full panel, 44 were identified in the indica subpanel, and 35 were common between the full panel and the indica subpanel.
A total of 16 QTLs were associated with Chla_b, 6 with WC, 6 with Score, 4 with ConcNa, 3 with Chl_total, 3 with Chlb, 2 with Chla, 3 with Na_K, and 1 with ConcK (Table 3). Ten of the 26 identified QTLs were associated with multiple traits, including QTL_25 on chromosome 11 associated with five traits (i.e., WC, Score, ConcK, ConcNa, and Na_K); QTL_21 on chromosome 9 associated with 4 traits (i.e., WC, Chla_b, ConcNa, and Na_K); three QTLs (QTL_9, QTL_20, and QTL_23) associated with three traits; and five other QTLs (i.e., QTL_13, QTL_16, QTL_17, QTL_19, and QTL_24) associated with two traits. Most of the individual trait-associated QTLs were detected for chlorophyll traits, except for QTL_1, which was related to Score. The number of significant SNPs within each QTL varied from 1 to 33, whereas QTL_25 was defined by 33 SNPs, QTL_21 by 14 SNPs, QTL_1 and QTL_4 by 8 SNPs, and QTL_16 and QTL_19 by 7 SNPs (Table 3).
Therefore, among the 26 detected QTLs, QTL_25 was supposed to be the major QTL due to being mapped by the highest number (33) of significant SNPs and associated with the greatest number (5) of traits in both the full panel and the indica subpanel (Figure 3). The next was QTL_21, which was common to 4 traits and supported by 14 significant SNPs.

2.3. Colocalizing QTLs and Candidate Genes Underlying the Detected QTLs Involved in Salinity Tolerance

The sites of the QTLs identified in this study were compared with QTLs detected in mapping populations and derived by other GWASs related to salinity tolerance. Consequently, most of our QTLs colocalized with already known QTLs, except for QTL_6, QTL_17 and QTL_22 (Table S2). We found a total of 100 colocalizations, of which 17 were detected by GWAS [22,23,24,25,26], and 83 other colocalizations were mapped in biparental populations [13,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45]. In particular, 8 colocalizations shared similar traits (leaf chlorophyll content, K+ concentration, Na+ concentration, leaf water content). In addition, colocalizations with QTLs identified in previous studies for other traits using the same Vietnamese rice panel and genotyping data were observed (Table S3). For the latter, forty-nine overlapping associations were found that underlie QTL_3, QTL_6, QTL_8, QTL_17, QTL_20, and QTL_26.
In the region of almost all QTLs identified, a number of candidate genes related to the response of plants to salt or abiotic stress were found, with the exception of QTL_7, QTL_11, QTL_14, QTL_15, QTL-17, QTL_18 and QTL_22 (Table 3). These candidate genes encode different kinds of proteins including transcription factors, receptor-like kinases (RLKs), mitogen-activated kinase (MAPK), enzymes and transporters.
Table 3. List of candidate genes located within the identified QTLs.
Table 3. List of candidate genes located within the identified QTLs.
QTL NameChrQTL Position (bp)PanelTraitsNo. of Signif. SNPsCandidate GeneReferences
QTL_1131,557,933–31,695,659FScore8Os01g54890OsERF922Ethylene-responsive transcription factor 2Ethylene response transcription factor, negative regulation of salt resistance[46]
Os01g54930OsVOZ1/EIP8 Vascular one zinc-finger 1/EBR1-interacting protein 8
QTL_2132,165,198–33,076,887FChla_b6Os01g55940OsGH3.2IAA-amido synthetaseModulation of free IAA and ABA homeostasis and drought and cold tolerance[47]
Os01g55974STRIPE 2, ALRDeoxycytidylate deaminaseChloroplast development[48,49]
Os01g56040OsSAP3A20/AN1 zinc-finger protein 3Inducibility to drought and salinity stress[50]
Os01g56070OsRDCP3RING finger protein 5increase tolerance to drought stress in rice[51]
Os01g56400OsABCI6ABC transporter ATP-binding proteinresponse to abiotic stress[52,53]
Os01g56680PsbWPhotosystem II reaction center W proteinphotosynthesis regulation[54]
QTL_3138,515,041–38,722,651F, IChla_b1Os01g66590OsAS2LOB domain proteinRegulation of shoot differentiation and leaf development[55]
Os01g66610 Lectin receptor-like kinase (LecRLK)Regulation of plant growth and developmental processes in response to stress[56]
Os01g66420OsPHD7PHD finger protein (ZF-TF)Up-regulated under drought stress[57]
QTL_426,668,466–6,853,020F, IChla_b8Os02g12750OsTET2Tetraspanin domain containing proteinResponse to heat, salt and water deficit stresses at seedling stage[58]
Os02g12790OsCga1GATA transcription factor (ZF-TF)Regulation of chloroplast development and plant architecture, relating to natural variation in strong stay-green[59,60]
Os02g12794eEF-1B gammaElongation factor 1-gammaSalinity stress adaptation[61]
Os02g12800EF-1gammaElongation factor 1-gammaSalinity stress adaptation[61]
QTL_5232,011,340–32,679,510FChla_b1Os02g52290OsFKBP12Peptidyl-prolyl cis-trans isomeraseSalinity stress response[62,63]
Os02g52650LhCa5Chlorophyll a/b-binding proteinLight-harvesting chlorophyll a/b-binding protein[64]
Os02g52670OsERF#103Ethylene-responsive transcription factorResponsive to drought and salinity stress[65]
Os02g52744 DCL chloroplast precursor
Os02g52780OsbZIP23bZIP transcription factorRegulation of ABA signaling and biosynthesis, salinity and drought tolerance[66,67]
Os02g53030OsRLCK84MAPK kinaseResponse to salinity stress[68]
QTL_63526,748–1,177,466IChla_b1Os03g02010OsDRM2DNA methyltransferaseTissue- and genotype-dependent response to salinity stress[69]
Os03g02280OsS40-4S40-like proteinResponse to leaf senescence and salinity stress[70,71]
Os03g02590OsPEX11-1Peroxisomal biogenesis factor 11Relating to leaf senescence, salt responsive[72,73]
QTL_8329,719,291–29,898,084F, IChla_b5Os03g51970OsGRF6Growth-regulating factorTargeted by osa-miR396 and drought-up sRNA56202 responsive to salt and drought stress[74,75,76]
Os03g52090OsECA2Calcium-transporting ATPase 3P-type Ca2+ ATPase IIA, harboring multiple stress-induced cis-acting elements[77]
QTL_9330,313,283–30,481,199FWC, ConcNa, Na_K1Os03g53060
QTL_10333,128,341–33,501,467FChla_b1Os03g58250OsbZIP33bZIP transcription factorABA-dependent enhancer of drought tolerance, responsive to high salinity, H2O2 and high temperature stress[78]
Os03g58300OsIGLIndole-3-glycerol phosphate lyaseChloroplast precursor
Os03g58390OsSIRP2RING Ub E3 ligaseSalt and osmotic stress tolerance enhancer[79]
Os03g58540TSV3/OsObgC2Obg-like GTPase proteinChloroplast development at the early leaf stage under cold stress[80]
QTL_1144,254,414–4,354,414F, IChla_b1
QTL_12431,433,085–31,558,275F, IChla_b1Os04g52960OsNUC1Nucleolin-like proteinPhotosynthesis adaptation, reduction of oxidative stress and yield loss under salinity stress, enhancement of salt-stress tolerance[81,82]
QTL_13522,437,918–22,840,944F, IWC, Chla_b2Os05g38370OsFKBP20-1aPeptidyl-prolyl cis-trans isomerase FKBP-typeDrought and heat stress-response[83]
Os05g38290OsPP2C49Protein phosphatase 2CRegulation of ABA-mediated signaling pathways[84]
QTL_16723,502,762–23,623,244FWC, Score7Os07g39270OsGGPPS1Geranylgeranyl pyrophosphate synthaseChlorophyll biosynthesis[85]
Os07g39350 Sugar transporterosmo protection
Os07g39360 Sugar transporterosmo protection
QTL_178235,171–472,039F, IScore, Chla_b4
QTL_19817,191,665–17,648,853F, IChla, Chlb7Os08g28710OsRLCK253Receptor-like kinaseImprovement of water-deficit and salinity stress tolerance[86]
QTL_209799,160–1,286,768FChla, Chlb, Chla_b5Os09g02270OsCYL4Protein containing cyclase domainNegative regulation of abiotic stress tolerance in relation to accumulation of ROS[76]
QTL_2194,452,802–5,809,538F, IWC, Chla_b, ConcNa, Na_K14Os09g10600 NADH-dependent enoyl-ACP reductaseChloroplast precursor
QTL_231018,944,166–19,070,983FWC, Score, ConcNa2Os10g35640 Rf1 mitochondrial precursor (Nin-like)Down-regulated salt-responsive, up-regulated cold-responsive[87,88]
Os10g35560OsSFR6Expressed proteinOsmotic stress and chilling tolerance[89]
QTL_241116,335,298–16,441,782F, IScore, Chla_b1
QTL_251118,273,105–18,684,503F, IWC, Score, ConcK, ConcNa, Na_K33Os11g31530OsBDG1BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1, OsBri1Salinity tolerance (upregulated in roots in response to salinity)[90]
Os11g31540OsLRR2BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1, OsBri1Stress tolerance (upregulated in leaves in response to cold and drought stress)[91]
Os11g31550 BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1, OsBri1
Os11g31560 BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1, OsBri1
QTL_261225,841,227–26,215,713FChla_b5Os12g41860OsHox33HDZIP III transcription factorTargeted by a miRNA responsive to salinity stress, control of leaf senescence[75,92]
Os12g41950OsARF6b, OsARF25Auxin response factorCandidate salinity tolerance-related gene at the seedling stage[93]
Os12g42060OsWAK128OsWAK receptor-like kinaseCandidate salinity tolerance-related gene at the seedling stage[93]
Os12g42070OsRLCK375, OsWAK129OsWAK receptor-like kinaseDown-regulated in cold, salt and drought stress conditions at the seedling stage[94]
Os12g42090 37 kDa inner envelope membrane proteinChloroplast precursor, salinity-inducible[93,95]
Os12g42200OsCHX15ATCHX proteinCation H+ antiporter, candidate salinity tolerance-related gene at the seedling stage[93]
Os12g42250OsZFP213, PINE1C2H2 transcription factorInteracting with OsMAPK3 to enhance salinity tolerance by enhancing ROS-scavenging ability, regulating internode elongation and photoperiodic signals[96,97]

3. Discussion

Rice is considered to be very sensitive to salinity [98,99]. Here, to determine the response of the Vietnamese rice landrace panel to salinity, a moderate salinity stress (100 mM NaCl) was applied at the seedling stage. We assessed a total of 9 phenotypic traits, all of which showed high variability within the panel in response to salinity stress. Of these 9 traits, WC, Score and three ion content traits (ConcNa, ConcK and Na_K) exhibited high heritability (0.60–0.76). Additionally, strong correlations (0.59–0.97) were observed among these traits with the exception of ConcK, indicating that WC, Score, ConcNa and Na_K were strongly associated with the response of rice plants to salinity stress, which is consistent with previous studies on rice salinity tolerance evaluation [100,101,102]. WC is a physiological parameter of the plant water status that expresses the response of plants to osmotic stress [103,104], ionic content traits reflect the level of ionic stress (ion homeostasis) [105], and the salt injury score is an indicator of plant damage/survival (growth performance) under salinity stress [100]. Previous studies reported that rice accessions tolerant to salinity stress have the ability to reduce the osmotic stress, prevent the excess accumulation of Na+ and absorb greater K+ to maintain a low shoot Na+/K+ ratio [106,107,108,109].
Correlations among the traits varied in the same direction in the full panel and the two subpanels. However, the japonica subpanel had, on average, greater WC and lower Score than the indica subpanel, indicating that japonica accessions are more salt-tolerant than indica accessions. This finding contradicts the results reported in a previous study [101] that used 4 japonica varieties and 6 indica varieties. This contradiction can be explained by the difference in the number of rice accessions included in the screening. In our study, 112 indica and 64 japonica accessions were evaluated.
In this study, GWAS analyses were applied for the full panel and for the two subpanels. Thus far, we succeeded in identifying 119 significant SNPs assigned to 26 QTLs. Twenty-two QTLs were detected in the full panel and 15 QTLs were detected only in the indica subpanel, but no japonica-specific QTLs were found, although japonica seems to have an average higher salinity tolerance than indica accessions. Similarly, in other studies using the same rice panel and genotyping data screened for water deficit tolerance and leaf traits, no japonica-specific QTLs were detected [17,18], likely because japonica accessions represent only one-third of the total accessions of the panel. In this study, of the 26 identified QTLs, 11 QTLs were detected in both the full panel and the indica subpanel, and 10 QTLs were associated with two or more traits (Table 3). Interestingly, all the QTLs that were detected for WC colocalized with QTLs associated with Score and/or ion content traits, except for QTL_13, which was found for WC and Chla_b. These results suggest that WC, Score and ion content traits have a shared genetic basis related to salinity stress responses. However, there was no overlap between QTLs detected for chlorophyll-related traits and ion content traits.
The QTLs discovered in this study were located on most chromosomes, apart from chromosome 6, and we found 3 QTLs (QTL_1, QTL_2 and QTL3) on chromosome 1, but none of these QTLs colocalized with Saltol, a well-known major QTL for rice salinity tolerance at the seedling stage [110,111]. A large number of QTLs for salinity tolerance detected in this study colocalized with QTLs detected in other studies and populations under conditions of salt stress at vegetative or reproductive stages, which validates our approach (Tables S2 and S3). Interestingly, of 26 QTLs identified in the present study, 3 QTLs (QTL_6, QTL_17 and QTL_22) did not colocalize with previously reported QTLs and thus constituted novel QTLs. They can be of high interest to bring new salinity tolerance sinks into breeding programs.
The major QTL identified in our study, QTL_25 at 18,273.1–18,684.5 kb on chromosome 11 was associated with WC, Score, ConcK, ConcNa and Na_K (Table 3, Figure 3). In particular, QTL_25 was mapped by 33 significant SNPs, and each of them contributed 5.75–14.09% to the phenotypic variation (Table S1). QTL_25 colocalized with previously identified QTLs under conditions of salinity stress using different mapping populations (Table S2), i.e., with 2 QTLs associated with leaf water content [42], with QTL qSHL11.1 for shoot length and QTL qRTL11.1 for root length [39], and with 4 GWAS-derived QTLs for the number of unfilled grains per plant [25] (Table S2), suggesting that this QTL has a pleiotropic effect on plant growth and reproduction under salinity stress and likely acts synergistically with other major salinity tolerance QTLs such as Saltol, in enhancing the salinity tolerance in rice.
Compared to the previous GWAS [14,17,18,20,112], using the same rice panel and genotyping data, we found 28 associations of 6 QTLs identified in this study colocalized with 23 associations of [14,17,20,112], but there was no colocalization between QTLs for salinity tolerance-related traits and for leaf mass traits [18] (Table S3). Remarkably, 9 associations for various drought tolerance-related traits, including relative water content after 2 and 3 weeks of drought stress, slope of relative water content after 2 weeks of drought stress, drought sensitivity score after 2, 3 and 4 weeks of drought stress, and recovery ability, belonging to QTL q9 of [17], were colocalized with all associations in QTL_17 for Chla_b and Score, suggesting that this genomic region contains important genetic determinants for rice adaptation to osmotic stresses.
Underlying 19 out of the 26 QTLs detected in this study, a high number of genes were annotated or functionally associated with salinity tolerance (Table 3).
Most candidate genes encode transcription factors reported to be involved in the rice response to salinity or abiotic stresses. Found in QTL_1 for Score, the OsERF922 gene (ETHYLENE RESPONSE FACTOR 922, Os01g54890) negatively regulates tolerance to salinity stress through an ABA signaling pathway, since rice transgenic plants overexpressing OsERF922 exhibited reduced salinity tolerance with increased shoot Na+/K+ ratio and ABA level, and knockdown of OsERF922 expression reduced the ABA accumulation [46]. Additionally, as a member of the ERF gene family, OsERF922, the expression of OsERF#103 (ETHYLENE RESPONSE FACTOR 103, Os02g52670) (in QTL_5), was reported to be upregulated under drought and salinity stress conditions at the seedling stage [65].
Furthermore, we found two potential genes encoding bZIP transcription factors. In plants, bZIP genes are involved in the response to abiotic stress [66,113]. One of these two genes is OsbZIP23 (b-ZIP TRANSCRIPTION FACTOR 23, Os02g52780) (in QTL_5), which was functionally characterized as being an ABA-dependent enhancer of drought and salinity tolerance [66,67]. On the one hand, OsbZIP23 overexpression significantly enhances tolerance to drought stress, especially to high salinity stress, compared with the wild type [66,67]. On the other hand, the OsbZIP23 mutant displays significantly reduced tolerance to drought and salinity stress [67]. In addition, the SUMO protease OsOSTS1 (OVERLY TOLERANT TO SALT 1), a gene involved in tolerance to high salinity [114], was reported to directly target OsbZIP23, which results in activation of OsbZIP23 and stimulation of OsbZIP23-dependent gene expression, which helps promote tolerance to drought stress [115]. Similar to OsbZIP23, OsbZIP33 (b-ZIP TRANSCRIPTION FACTOR 33, Os03g58250), located in QTL_10, also plays a role as an ABA-dependent enhancer of drought and salinity tolerance. OsbZIP33 is highly upregulated under drought and high salinity stress conditions. OsbZIP33-overexpressing transgenic plants exhibited significantly increased drought tolerance [78].
Three candidate genes belonging to the zinc-finger transcription factors were identified: OsSAP3 (STRESS-ASSOCIATED PROTEIN 3, Os01g56040) in QTL_2, OsPHD7 (PHD FINGER PROTEIN 7, Os01g66420) in QTL_3, and OsCga1 (CYTOKININ GATA TRANSCRIPTION FACTOR 1, Os02g12790) in QTL_4. OsSAP3 and OsPHD7 are related to abiotic stress responses. In particular, the expression of OsSAP3 is induced in response to drought and salinity stress [50], and OsPHD7 is upregulated under drought stress [57]; moreover, OsCga1 is associated with the development of chloroplasts [59] and stay-green [60]. Stay-green refers to the ability to maintain green leaves and photosynthetic capacity and is thus related to plant adaptation to osmotic stress [116]. Overexpression of OsCga1 delays leaf senescence [59].
Underlying QTL_2, OsRDCP3 (RING DOMAIN-CONTAINING PROTEIN 3, Os01g56070) was predicted to be involved in drought stress tolerance [51], and OsABCI6 (ABC TRANSPORTER I FAMILY MEMBER 6, Os01g56400) was supposed to be involved in the response to abiotic stress [52,53]. Similarly, the expression of OsTET2 (TETRASPANIN 2, Os02g12750), an integral membrane protein found in QTL_4, was increased in drought-stress seedlings; in addition, this gene was highly upregulated under heat and salinity stress [58].
Two other candidate transcription factor genes were found in QTL_26 on chromosome 11, including OsHox33 (HOMEOBOX GENE 33, Os12g41860) and OsARF25 (AUXIN RESPONSE FACTOR 25, Os12g41950). OsHox33, encoding an HDZIP transcription factor, is involved in leaf senescence because its knockdown accelerates leaf senescence [92] and is a target of a salinity stress-responsive miRNA [75]. OsARF25 is also a salinity tolerance-related candidate gene discovered by GWAS, as reported by [93].
Another transcription factor gene identified, OsAS2 (ASYMMETRIC LEAVES 2, Os01g66590) in QTL_3 [55], was associated with the development of plants. LhCa5 (PHOTOSYSTEM I LIGHT HARVESTING COMPLEX GENE 5, Os02g52650) in QTL_5 was predicted to function in the photosystem [64].
Within the region of QTL_25, the strongest QTL found in this study, we detected a consecutive set of four BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1 (BAK1), including Os11g31530 (OsBDG1), Os11g31540 (OsLRR2), Os11g31550, and Os11g31560 (Figure 3b). BAK1, encoding a leucine-rich repeat type II receptor-like kinase, functions as a coreceptor of BRI1 in brassinosteroid plant signaling [117]. Perception of brassinosteroids through the BRI1-BAK1 complex can influence the growth and development of rice plants [118], e.g., regulating the leaf angle and grain size [119] and regulating ABA-induced stomatal closure, which is critical for the survival of plants under water stress [120]. Among these four BAK1 genes, OsBDG1 and OsLRR2 are considered to be involved in salt and/or abiotic stress responses [90,91]. Under salinity stress conditions, OsBDG1 is significantly upregulated in roots of the rice-sensitive cultivar IR29, whereas OsLRR2 is upregulated in roots of the rice-tolerant cultivar FL478 [90]. Additionally, the expression of OsLRR2 is highly induced in leaves after cold and drought treatment; thus, OsLRR2 is a supposed candidate gene involved in tolerance to abiotic stress [91]. Interestingly, two significant SNPs identified in this study, Sj11_18426630R and Dj11_18426457R, were located in the sequence of OsBDG1 (Figure 3). Dj11_18426457R is intronic, while Sj11_18426630R is positioned within a coding sequence (i.e., exon 5) that changes the amino acid sequence in the LRR domain of the OsBDG1 protein. Thus, the perspective of a functional characterization of these BAK1 candidate genes is opened.
Three other genes encoding receptor-like kinase (RLK) with enhanced abiotic stress tolerance are Os08g28710 (OsRLCK253) in QTL_19 and Os12g42060 (OsWAK128) and Os12g42070 (OsRLCK375, OsWAK129) in QTL_26. Functionally, OsRLCK253 confers tolerance to salt and water deficits in transgenic Arabidopsis thaliana plants during different growth stages, resulting in yield protection against stress [86]. OsWAK128 and Os12g42070 were candidate genes near a GWAS-derived QTL related to salinity tolerance at the seedling stage [93]. In addition, a mitogen-activated protein kinase (MAPK) encoded by the OsRLCK84 gene (Os02g53030) in QTL_5 was activated in response to salinity stress [68].

4. Materials and Methods

4.1. Plant Materials and Genotyping

This study included 179 Vietnamese rice landraces and 3 control genotypes (Nipponbare, Azucena and IR64). The Vietnamese rice accessions came from diverse locations throughout Vietnam and were originally provided by the Plant Resource Center (21°00′05″N and 105°43′33″E). All 182 accessions were genotyped by 21,623 single nucleotide polymorphism (SNP) markers using genotyping-by-sequencing with a minor allele frequency above 5% [14]. IR29 was used as a susceptibility check for phenotyping experiments. The names of the accessions, provinces of origin and ecosystem are described in Table S4. More detailed information on this panel can be found in [14].

4.2. Phenotyping Experiment

4.2.1. Salt Treatment

The experiment was conducted from August 26, 2019, to September 24, 2019, at the Agriculture Genetics Institute, Hanoi, Vietnam (21°02′55″ N and 105°46′58″ E). The accessions were grown in hydroponics following the IRRI standard protocol with three replicates [100]. Within each replicate, the accessions were randomly distributed in 5.2 individual plastic trays (36 × 31 × 15 cm) fitted with styrofoam float of 35 slots (2 mm diameter) filled with Peters solution composed of 1 g/L Peters water-soluble fertilizer (20-20-20 NPK) and 200 mg/L ferrous sulfate [21]. A total of 16 plastic trays were used.
The experiment was set under greenhouse conditions. After breaking dormancy at 50 °C for five days, seeds were soaked in water for 2–3 days. When germination began, seeds were incubated in a culture room (28 °C, photoperiod 12 h light/12 h dark) for 2 days. Once the primary root emerged well at a length of 2–3 cm, seedlings were cultured in styrofoam floats with a nylon net bottom according to the experimental design. Four seedlings were cultured per slot. Three days after seeding, seedlings were thinned to keep 3 well-developed plants per slot. The pH (5.2) and the level of nutrient solution were adjusted daily. The Peters solution was replaced weekly until the end of the experiment. Salinity stress was applied when plants reached the fourth leaf stage. Salt NaCl was gradually supplemented to the hydroponic medium to avoid osmotic shocks. Each time, 50 mM NaCl was separated by two days to obtain a final concentration of 100 mM NaCl. The experiment was stopped once all the plants exhibited drying in most leaves (average evaluation score of 7).

4.2.2. Scoring and Sampling

For each plant, salinity tolerance score was evaluated based on leaf injury symptoms using the modified standard evaluation score (SES) for rice [21], as follows: score 1—normal growth, no leaf symptoms; score 3—near normal growth, but leaf tips or few leaves whitish and rolled; score 5—growth severely retarded, most leaves rolled, only a few elongating; score 7—complete cessation of growth, most leaves dry, some plants dying; score 9—almost all plants dead or dying.
After scoring, the second fully expanded leaves of three plants in each hole were harvested. Quickly cut a 1.5 cm fragment from the leaf base, separately pack the material of each hole in aluminum foil, avoiding folding the leaves, and place on ice for chlorophyll determination. The rest of the cut leaves were immediately put into a small zip plastic bag of known weight for measuring the water content.

4.2.3. Chlorophyll Determination

The chlorophyll content was estimated as described in the protocol of [121] with some modifications. The harvested samples were weighed, put into 2-mL Eppendorf tubes, and ground in liquid nitrogen. The pellet was resuspended in 1.5 mL of 85% acetone solution and centrifuged at 12,000× g at 4 °C for 15 min. One milliliter of the supernatant was collected, and the absorbance was measured at wavelengths of 645 and 663 nm using a 7305 UV/visible spectrophotometer (Jenway, Staffordshire, UK). The chlorophyll content was calculated as follows: total chlorophyll (Chl_total, µg/mL) = 20.2 (A645) + 8.02 (A663), chlorophyll a (Chla, µg/mL) = 12.7 (A663) − 2.69 (A645), chlorophyll b (Chlb, µg/mL) = 22.9 (A645) − 4.68 (A663). The values were then converted to the amount of chlorophyll per milligram of fresh tissue (µg/mg). The ratio of chlorophyll a to chlorophyll b (Chla_b) was also determined.

4.2.4. Water Content Measurement

The bags containing samples were weighed to determine the sample fresh weight (FW). After being dried for 3 days at 70 °C in an oven, the sample dry weight (DW) was measured. The leaf water content of each sampling was calculated using the formula: WC (%) = (FW − DW) × 100/FW.

4.2.5. Ion Content Measurement

The above dried samples with known weight (DW, mg) were used for measurement of Na+ and K+ ion content. The samples were put into 15-mL Falcon tubes, and 10 mL of 0.1 N hydrochloric acid solution was added. After sample ion solubilization at room temperature overnight, 2 mL of sample solution at 200-fold dilution (10 µL of first sample solution + 2 mL 0,1 N hydrochloric acid solution) was used to measure Na+ and K+ concentrations (mg/L) by a SpectrAA 220FS atomic absorption spectrometer (Varian, US). The Na+ and K+ contents (ConcNa and ConcK) were then converted back to the quantity of Na+ and K+ ions per gram of dry weight (mg/gDW) by the following equations: ConcNa = [Na+ measurement (mg/L) × dilution rate (200) × volume of first sample solution (10 mL)]/DW (mg); ConcK = [K+ measurement (mg/L) × dilution rate (200) × volume of first sample solution (10 mL)]/DW (mg). The Na+/K+ ratio (Na_K) was calculated as the proportion of Na+ content to K+ content.

4.3. Statistical Analysis of Phenotypic Data

Statistical analysis of phenotypic data (means, standard deviations, coefficients of variation (CVs), graphs) was carried out in the R software v3.6.2. Analysis of variance (ANOVA) was performed to test the effect of genotype and replication using a linear model of the R function lm(). Broad-sense heritability (H2) was used to estimate the genetic variance based on the variance among phenotypic measurements between three replicates of the panel. H2 was computed using the following formula: H2 = (F-value − 1)/F-value, where the F-value was derived from analysis of ANOVA for the genotype effect [18]. Phenotypic correlations between traits were evaluated by the Pearson method using the corrplot R package. The R function cor.test() was used to test the significance of the correlation coefficients.

4.4. Genome-Wide Association Study

The phenotypic data from the salt test and SNP genotypic data on the full panel and the indica and japonica subpanels were separately used to study the marker-trait associations by incorporating a kinship matrix along with population structure. In the Tassel software v.5.0, the structure matrix was determined with 6 axes on the SNP data of the population by running a principal component analysis (PCA). The kinship matrix was built by the pairwise identity-by-state method, to account for relatedness of individuals among 182 accessions. Q-Q and Manhattan plots of the negative log10-transformed observed p-values for each SNP-trait association were created to visualize the GWAS results. Markers with a p-value ≥ 5 × 10−4 were declared significant.
The number of QTLs from the detected associations was determined based on linkage disequilibrium (LD) between SNPs surrounding the significant markers. The LD heatmaps were plotted by using the LDheatmap R package, and the genomic regions of QTLs were limited by LD blocks with r2 values (squared allele frequency correlation) between SNPs > 0.4. For a low LD block (<50 kb), the interval of QTLs was enlarged by a distance of +/− 50 kb. The qqman package in R software was utilized to highlight the significant markers of strong QTLs in Manhattan plots. The genes in the genomic regions of strong QTLs were scanned in the MSU rice database.

5. Conclusions and Future Prospects

Our approach identified different QTLs characterized by the presence of a high number of genes associated with the response to salinity or abiotic stress. Interestingly, these genes are related to hormone transduction pathways or transcriptional modulation of gene expression in response to stress, suggesting that these QTLs act in complementary ways to control the salinity tolerance, which is of major interest for breeding programs. Pyramiding several favorable QTLs in a variety will ensure a better resilience of the plant to salinity stress under different environmental conditions and then a better sustainability of the variety. Therefore, it will be interesting to conduct introgression of the major QTLs identified in this study such as QTL25 in modern varieties cultivated in the Mekong or Red River Delta areas such as Bac Thom 7 and Khang Dan 18. The function of the four BAK1 genes in QTL25 should be specified by generating single and multiple gene mutations using the CRISPR Cas9 system.

Supplementary Materials

The following are available online at, Figure S1: Correlation plots in the full panel and the indica and japonica subpanels, Figure S2: Manhattan plots and Q-Q plots for GWAS of salinity tolerance-related traits in the indica panel. In the Manhattan plots, significant SNPs are highlighted in red, Table S1: GWAS associations and significant SNPs at p ≤ 1 × 10−3 in the full panel and the indica subpanel, Table S2: Colocalizations of the QTLs identified in this study with previous reports, Table S3: Colocalizations of the QTLs detected in this and previous studies using the same rice panel and genotyping data, Table S4: List of the 183 rice accessions used in the experiment.

Author Contributions

Conceptualization, P.G.; methodology, formal analysis, data curation, G.T.H., A.-A.V. and P.G.; investigation, T.D.L., H.T.V., K.L.N., F.G., L.H.T., H.T.T.V., F.L., X.H.P., A.-A.V., G.T.H.; writing—original draft preparation, G.T.H. and P.G.; writing—review and editing, T.D.L., H.T.V., K.L.N., H.T.T.V., T.X.D., X.H.P., A.-A.V.; visualization, supervision, project administration, G.T.H.; funding acquisition, G.T.H. and P.G. All authors have read and agreed to the published version of the manuscript.


This research was funded by the Ministry of Science and Technology of Vietnam and the French embassy in Vietnam in the frame of project “Application of functional genomics and association genetics to characterize genes involved in abiotic stresses tolerance in rice” (code: NDT.56.FRA/19). This research was also supported by the Global Rice Science Partnership (2011–2016) and by the CGIAR Research Program (CRP) on rice agri-food systems (RICE, 2017–2022).

Data Availability Statement

The GBS genotyping dataset supporting the results of this study has been deposited as a downloadable Excel file in TropGeneDB: (accessed on 24 May 2021) tab “studies”, study type “genotype”, study “Vietnamese panel-GBS data”. The seeds of the accessions are available in the National Key Laboratory for Plant Cell Biotechnology of Agricultural Genetics Institute, Hanoi, Vietnam.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Rengasamy, P. Soil Processes Affecting Crop Production in Salt-Affected Soils. Funct. Plant Biol. 2010, 37, 613. [Google Scholar] [CrossRef]
  2. Wassmann, R.; Nelson, G.; Peng, S.; Sumfleth, K.; Jagadish, K.; Hosen, Y.; Rosegrant, M. Rice and global climate change. In Rice in the Global Economy: Strategic Research and Policy Issues for Food Security; IRRI: Los Banos, Philippines, 2010; pp. 411–433. [Google Scholar]
  3. Timmer, C.P.; Block, S.; Dawe, D. Long-run dynamics of rice consumption, 1960–2050. In Rice in the Global Economy: Strategic Research and Policy Issues for Food Security; IRRI: Los Banos, Philippines, 2010; pp. 139–174. [Google Scholar]
  4. Sarah, K.B. System of Rice Intensification in Vietnam: Doing More with Less; 52 Profiles on Agroecology; Oxfam: Food and Agriculture Organisation of the United States: Washington, DC, USA, 2016. [Google Scholar]
  5. Ngoc Huyen Việt Nam chịu ảnh hưởng ra sao bởi biến đổi khí hậu? Báo Đồng Khởi. 2008. Available online: (accessed on 24 May 2021).
  6. Ha, P.V.; Nguyen, H.T.M.; Kompas, T.; Che, T.N.; Trinh, B. Rice Production, Trade and the Poor: Regional Effects of Rice Export Policy on Households in Vietnam. J. Agric. Econ. 2015, 66, 280–307. [Google Scholar] [CrossRef]
  7. Maitah, K.; Smutka, L.; Sahatqija, J.; Maitah, M.; Phuong Anh, N. Rice as a Determinant of Vietnamese Economic Sustainability. Sustainability 2020, 12, 5123. [Google Scholar] [CrossRef]
  8. Loc, H.H.; Van Binh, D.; Park, E.; Shrestha, S.; Dung, T.D.; Son, V.H.; Truc, N.H.T.; Mai, N.P.; Seijger, C. Intensifying Saline Water Intrusion and Drought in the Mekong Delta: From Physical Evidence to Policy Outlooks. Sci. Total Environ. 2021, 757, 143919. [Google Scholar] [CrossRef] [PubMed]
  9. Tin, H.Q.; Loi, N.H.; Labarosa, S.J.E.; McNally, K.L.; McCouch, S.; Kilian, B. Phenotypic Response of Farmer-selected CWR-derived Rice Lines to Salt Stress in the Mekong Delta. Crop Sci. 2021, 61, 201–218. [Google Scholar] [CrossRef]
  10. Ganie, S.A.; Molla, K.A.; Henry, R.J.; Bhat, K.V.; Mondal, T.K. Advances in Understanding Salt Tolerance in Rice. Theor. Appl. Genet. 2019, 132, 851–870. [Google Scholar] [CrossRef]
  11. Negrão, S.; Courtois, B.; Ahmadi, N.; Abreu, I.; Saibo, N.; Oliveira, M.M. Recent Updates on Salinity Stress in Rice: From Physiological to Molecular Responses. Crit. Rev. Plant Sci. 2011, 30, 329–377. [Google Scholar] [CrossRef]
  12. Pang, Y.; Chen, K.; Wang, X.; Wang, W.; Xu, J.; Ali, J.; Li, Z. Simultaneous Improvement and Genetic Dissection of Salt Tolerance of Rice (Oryza Sativa L.) by Designed QTL Pyramiding. Front. Plant Sci. 2017, 8, 1275. [Google Scholar] [CrossRef] [Green Version]
  13. Wang, Z.; Cheng, J.; Chen, Z.; Huang, J.; Bao, Y.; Wang, J.; Zhang, H. Identification of QTLs with Main, Epistatic and QTL × Environment Interaction Effects for Salt Tolerance in Rice Seedlings under Different Salinity Conditions. Theor. Appl. Genet. 2012, 125, 807–815. [Google Scholar] [CrossRef] [PubMed]
  14. Phung, N.T.P.; Mai, C.D.; Mournet, P.; Frouin, J.; Droc, G.; Ta, N.K.; Jouannic, S.; Lê, L.T.; Do, V.N.; Gantet, P.; et al. Characterization of a Panel of Vietnamese Rice Varieties Using DArT and SNP Markers for Association Mapping Purposes. BMC Plant Biol. 2014, 14, 371. [Google Scholar] [CrossRef] [Green Version]
  15. Fukuoka, S.; Alpatyeva, N.V.; Ebana, K.; Luu, N.T.; Nagamine, T. Analysis of Vietnamese Rice Germplasm Provides an Insight into Japonica Rice Differentiation. Plant Breed. 2003, 122, 497–502. [Google Scholar] [CrossRef]
  16. Li, J.-Y.; Wang, J.; Zeigler, R.S. The 3000 Rice Genomes Project: New Opportunities and Challenges for Future Rice Research. GigaScience 2014, 3, 8. [Google Scholar] [CrossRef] [Green Version]
  17. Hoang, G.T.; Van Dinh, L.; Nguyen, T.T.; Ta, N.K.; Gathignol, F.; Mai, C.D.; Jouannic, S.; Tran, K.D.; Khuat, T.H.; Do, V.N.; et al. Genome-Wide Association Study of a Panel of Vietnamese Rice Landraces Reveals New QTLs for Tolerance to Water Deficit during the Vegetative Phase. Rice 2019, 12, 4. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Hoang, G.T.; Gantet, P.; Nguyen, K.H.; Phung, N.T.P.; Ha, L.T.; Nguyen, T.T.; Lebrun, M.; Courtois, B.; Pham, X.H. Genome-Wide Association Mapping of Leaf Mass Traits in a Vietnamese Rice Landrace Panel. PLoS ONE 2019, 14, e0219274. [Google Scholar] [CrossRef] [PubMed]
  19. Phung, N.T.P.; Mai, C.D.; Hoang, G.T.; Truong, H.T.M.; Lavarenne, J.; Gonin, M.; Nguyen, K.L.; Ha, T.T.; Do, V.N.; Gantet, P.; et al. Genome-Wide Association Mapping for Root Traits in a Panel of Rice Accessions from Vietnam. BMC Plant Biol. 2016, 16, 64. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Ta, K.N.; Khong, N.G.; Ha, T.L.; Nguyen, D.T.; Mai, D.C.; Hoang, T.G.; Phung, T.P.N.; Bourrie, I.; Courtois, B.; Tran, T.T.H.; et al. A Genome-Wide Association Study Using a Vietnamese Landrace Panel of Rice (Oryza Sativa) Reveals New QTLs Controlling Panicle Morphological Traits. BMC Plant Biol. 2018, 18, 282. [Google Scholar] [CrossRef]
  21. Egdane, J.; Vispo, N.; Mohammadi, R.; Amas, J.; Katimbang, M.; Platten, J.; Ismail, A.; Gregorio, G. Phenotyping Protocols for Salinity and Other Problem Soils; International Rice Research Institute: Los Banos, Philippines, 2007. [Google Scholar]
  22. Batayeva, D.; Labaco, B.; Ye, C.; Li, X.; Usenbekov, B.; Rysbekova, A.; Dyuskalieva, G.; Vergara, G.; Reinke, R.; Leung, H. Genome-Wide Association Study of Seedling Stage Salinity Tolerance in Temperate Japonica Rice Germplasm. BMC Genet. 2018, 19, 2. [Google Scholar] [CrossRef] [Green Version]
  23. Frouin, J.; Languillaume, A.; Mas, J.; Mieulet, D.; Boisnard, A.; Labeyrie, A.; Bettembourg, M.; Bureau, C.; Lorenzini, E.; Portefaix, M.; et al. Tolerance to Mild Salinity Stress in Japonica Rice: Agenome-Wide Association Mapping Study Highlights Calcium Signaling and Metabolism Genes. PLoS ONE 2018, 13, e0190964. [Google Scholar] [CrossRef] [Green Version]
  24. Kumar, V.; Singh, A.; Mithra, S.V.A.; Krishnamurthy, S.L.; Parida, S.K.; Jain, S.; Tiwari, K.K.; Kumar, P.; Rao, A.R.; Sharma, S.K.; et al. Genome-Wide Association Mapping of Salinity Tolerance in Rice (Oryza Sativa). DNA Res. 2015, 22, 133–145. [Google Scholar] [CrossRef] [Green Version]
  25. Lekklar, C.; Pongpanich, M.; Suriya-arunroj, D.; Chinpongpanich, A.; Tsai, H.; Comai, L.; Chadchawan, S.; Buaboocha, T. Genome-Wide Association Study for Salinity Tolerance at the Flowering Stage in a Panel of Rice Accessions from Thailand. BMC Genom. 2019, 20, 76. [Google Scholar] [CrossRef] [Green Version]
  26. Yuan, J.; Wang, X.; Zhao, Y.; Khan, N.U.; Zhao, Z.; Zhang, Y.; Wen, X.; Tang, F.; Wang, F.; Li, Z. Genetic Basis and Identification of Candidate Genes for Salt Tolerance in Rice by GWAS. Sci. Rep. 2020, 10, 9958. [Google Scholar] [CrossRef]
  27. Ammar, M.H.M.; Pandit, A.; Singh, R.K.; Sameena, S.; Chauhan, M.S.; Singh, A.K.; Sharma, P.C.; Gaikwad, K.; Sharma, T.R.; Mohapatra, T.; et al. Mapping of QTLs Controlling Na+, K+ and CI Ion Concentrations in Salt Tolerant Indica Rice Variety CSR27. J. Plant Biochem. Biotechnol. 2009, 18, 139–150. [Google Scholar] [CrossRef]
  28. Cheng, L.; Wang, Y.; Meng, L.; Hu, X.; Cui, Y.; Sun, Y.; Zhu, L.; Ali, J.; Xu, J.; Li, Z. Identification of Salt-Tolerant QTLs with Strong Genetic Background Effect Using Two Sets of Reciprocal Introgression Lines in Rice. Genome 2012, 55, 45–55. [Google Scholar] [CrossRef] [PubMed]
  29. De Leon, T.B.; Linscombe, S.; Subudhi, P.K. Molecular Dissection of Seedling Salinity Tolerance in Rice (Oryza Sativa L.) Using a High-Density GBS-Based SNP Linkage Map. Rice 2016, 9, 52. [Google Scholar] [CrossRef] [Green Version]
  30. Ghomi, K.; Rabiei, B.; Sabouri, H.; Sabouri, A. Mapping QTLs for Traits Related to Salinity Tolerance at Seedling Stage of Rice (Oryza Sativa L.): An Agrigenomics Study of an Iranian Rice Population. OMICS 2013, 17, 242–251. [Google Scholar] [CrossRef]
  31. Gimhani, D.R.; Gregorio, G.B.; Kottearachchi, N.S.; Samarasinghe, W.L.G. SNP-Based Discovery of Salinity-Tolerant QTLs in a Bi-Parental Population of Rice (Oryza Sativa). Mol. Genet. Genom. 2016, 291, 2081–2099. [Google Scholar] [CrossRef] [PubMed]
  32. Hemamalini, G.S.; Shashidhar, H.E.; Hittalmani, S. Molecular Marker Assisted Tagging of Morphological and Physiological Traits under Two Contrasting Moisture Regimes at Peak Vegetative Stage in Rice (Oryza Sativa L.). Euphytica 2000, 112, 69–78. [Google Scholar] [CrossRef]
  33. Hossain, H.; Rahman, M.A.; Alam, M.S.; Singh, R.K. Mapping of Quantitative Trait Loci Associated with Reproductive-Stage Salt Tolerance in Rice. J. Agron. Crop Sci. 2015, 201, 17–31. [Google Scholar] [CrossRef]
  34. Koyama, M.L.; Levesley, A.; Koebner, R.M.D.; Flowers, T.J.; Yeo, A.R. Quantitative Trait Loci for Component Physiological Traits Determining Salt Tolerance in Rice. Plant Physiol. 2001, 125, 406–422. [Google Scholar] [CrossRef] [Green Version]
  35. Lafitte, R.H.; Courtois, B. Genetic Variation in Performance under Reproductive-Stage Water Deficit in a Doubled Haploid Rice Population in Upland Fields. In Proceedings of the Molecular Approaches for the Genetic Improvement of Cereals for Stable Production in Water-Limited Environments, CIMMYT, El Batan, Mexico, 21 June 1999. [Google Scholar]
  36. Mohammadi, R.; Mendioro, M.S.; Diaz, G.Q.; Gregorio, G.B.; Singh, R.K. Mapping Quantitative Trait Loci Associated with Yield and Yield Components under Reproductive Stage Salinity Stress in Rice (Oryza Sativa L.). J. Genet. 2013, 92, 433–443. [Google Scholar] [CrossRef]
  37. Prasad, S.R.; Bagali, P.G.; Hittalmani, S.; Shashidhar, H.E. Molecular Mapping of Quantitative Trait Loci Associated with Seedling Tolerance to Salt Stress in Rice (Oryza Sativa L.). Curr. Sci. 2000, 78, 162–164. [Google Scholar]
  38. Price, A.H.; Steele, K.A.; Moore, B.J.; Jones, R.G.W. Upland Rice Grown in Soil-Filled Chambers and Exposed to Contrasting Water-Deficit Regimes II. Mapping Quantitative Trait Loci for Root Morphology and Distribution. Field Crops Res. 2002, 76, 25–43. [Google Scholar] [CrossRef]
  39. Puram, V.R.R.; Ontoy, J.; Linscombe, S.; Subudhi, P.K. Genetic Dissection of Seedling Stage Salinity Tolerance in Rice Using Introgression Lines of a Salt Tolerant Landrace Nona Bokra. J. Hered. 2017, 108, 658–670. [Google Scholar] [CrossRef] [PubMed]
  40. Sabouri, H.; Rezai, A.M.; Moumeni, A.; Kavousi, A.; Katouzi, M.; Sabouri, A. QTLs Mapping of Physiological Traits Related to Salt Tolerance in Young Rice Seedlings. Biol. Plant. 2009, 53, 657–662. [Google Scholar] [CrossRef]
  41. Thomson, M.J.; de Ocampo, M.; Egdane, J.; Rahman, M.A.; Sajise, A.G.; Adorada, D.L.; Tumimbang-Raiz, E.; Blumwald, E.; Seraj, Z.I.; Singh, R.K.; et al. Characterizing the Saltol Quantitative Trait Locus for Salinity Tolerance in Rice. Rice 2010, 3, 148–160. [Google Scholar] [CrossRef] [Green Version]
  42. Ul Haq, T.; Gorham, J.; Akhtar, J.; Akhtar, N.; Steele, K.A. Dynamic Quantitative Trait Loci for Salt Stress Components on Chromosome 1 of Rice. Funct. Plant Biol. 2010, 37, 634–645. [Google Scholar] [CrossRef]
  43. Wang, Z.; Chen, Z.; Cheng, J.; Lai, Y.; Wang, J.; Bao, Y.; Huang, J.; Zhang, H. QTL Analysis of Na+ and K+ Concentrations in Roots and Shoots under Different Levels of NaCl Stress in Rice (Oryza Sativa L.). PLoS ONE 2012, 7, e51202. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Wang, Z.; Wang, J.; Bao, Y.; Wu, Y.; Zhang, H. Quantitative Trait Loci Controlling Rice Seed Germination under Salt Stress. Euphytica 2011, 178, 297–307. [Google Scholar] [CrossRef]
  45. Yue, B.; Xue, W.; Xiong, L.; Yu, X.; Luo, L.; Cui, K.; Jin, D.; Xing, Y.; Zhang, Q. Genetic Basis of Drought Resistance at Reproductive Stage in Rice: Separation of Drought Tolerance from Drought Avoidance. Genetics 2006, 172, 1213–1228. [Google Scholar] [CrossRef] [Green Version]
  46. Liu, D.; Chen, X.; Liu, J.; Guo, Z. The Rice ERF Transcription Factor OsERF922 Negatively Regulates Resistance to Magnaporthe Oryzae and Salt Tolerance. J. Exp. Bot. 2012, 63. [Google Scholar] [CrossRef] [Green Version]
  47. Du, H.; Wu, N.; Fu, J.; Wang, S.; Li, X.; Xiao, J.; Xiong, L. A GH3 Family Member, OsGH3-2, Modulates Auxin and Abscisic Acid Levels and Differentially Affects Drought and Cold Tolerance in Rice. J. Exp. Bot. 2012, 63, 6467–6480. [Google Scholar] [CrossRef] [Green Version]
  48. Niu, M.; Wang, Y.; Wang, C.; Lyu, J.; Wang, Y.; Dong, H.; Long, W.; Wang, D.; Kong, W.; Wang, L.; et al. ALR Encoding DCMP Deaminase Is Critical for DNA Damage Repair, Cell Cycle Progression and Plant Development in Rice. J. Exp. Bot. 2017, 68, 5773–5786. [Google Scholar] [CrossRef] [PubMed]
  49. Xu, J.; Deng, Y.; Li, Q.; Zhu, X.; He, Z. STRIPE2 Encodes a Putative DCMP Deaminase That Plays an Important Role in Chloroplast Development in Rice. J. Genet. Genom. 2014, 41, 539–548. [Google Scholar] [CrossRef] [PubMed]
  50. Vij, S.; Tyagi, A.K. Genome-Wide Analysis of the Stress Associated Protein (SAP) Gene Family Containing A20/AN1 Zinc-Finger(s) in Rice and Their Phylogenetic Relationship with Arabidopsis. Mol. Genet. Genom. 2006, 276, 565–575. [Google Scholar] [CrossRef]
  51. Bae, H.; Kim, S.K.; Cho, S.K.; Kang, B.G.; Kim, W.T. Overexpression of OsRDCP1, a Rice RING Domain-Containing E3 Ubiquitin Ligase, Increased Tolerance to Drought Stress in Rice (Oryza Sativa L.). Plant Sci. 2011, 180, 775–782. [Google Scholar] [CrossRef] [PubMed]
  52. Nguyen, V.N.T.; Moon, S.; Jung, K.-H. Genome-Wide Expression Analysis of Rice ABC Transporter Family across Spatio-Temporal Samples and in Response to Abiotic Stresses. J. Plant Physiol. 2014, 171, 1276–1288. [Google Scholar] [CrossRef]
  53. Saha, J.; Sengupta, A.; Gupta, K.; Gupta, B. Molecular Phylogenetic Study and Expression Analysis of ATP-Binding Cassette Transporter Gene Family in Oryza Sativa in Response to Salt Stress. Comput. Biol. Chem. 2015, 54, 18–32. [Google Scholar] [CrossRef]
  54. Wang, Y.; Jiang, Q.; Liu, J.; Zeng, W.; Zeng, Y.; Li, R.; Luo, J. Comparative Transcriptome Profiling of Chilling Tolerant Rice Chromosome Segment Substitution Line in Response to Early Chilling Stress. Genes Genom. 2017, 39, 127–141. [Google Scholar] [CrossRef]
  55. Ma, Y.; Wang, F.; Guo, J.; Zhang, X.S. Rice OsAS2 Gene, a Member of LOB Domain Family, Functions in the Regulation of Shoot Differentiation and Leaf Development. J. Plant Biol. 2009, 52, 374–381. [Google Scholar] [CrossRef]
  56. Passricha, N.; Saifi, S.; Ansari, M.W.; Tuteja, N. Prediction and Validation of Cis-Regulatory Elements in 5′ Upstream Regulatory Regions of Lectin Receptor-like Kinase Gene Family in Rice. Protoplasma 2017, 254, 669–684. [Google Scholar] [CrossRef] [PubMed]
  57. Sun, M.; Jia, B.; Yang, J.; Cui, N.; Zhu, Y.; Sun, X. Genome-Wide Identification of the PHD-Finger Family Genes and Their Responses to Environmental Stresses in Oryza Sativa L. Int. J. Mol. Sci. 2017, 18, 2005. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Mani, B.; Agarwal, M.; Katiyar-Agarwal, S. Comprehensive Expression Profiling of Rice Tetraspanin Genes Reveals Diverse Roles during Development and Abiotic Stress. Front. Plant Sci. 2015, 6. [Google Scholar] [CrossRef] [Green Version]
  59. Hudson, D.; Guevara, D.R.; Hand, A.J.; Xu, Z.; Hao, L.; Chen, X.; Zhu, T.; Bi, Y.-M.; Rothstein, S.J. Rice Cytokinin GATA Transcription Factor1 Regulates Chloroplast Development and Plant Architecture. Plant Physiol. 2013, 162, 132–144. [Google Scholar] [CrossRef] [Green Version]
  60. Zhao, Y.; Qiang, C.; Wang, X.; Chen, Y.; Deng, J.; Jiang, C.; Sun, X.; Chen, H.; Li, J.; Piao, W.; et al. New Alleles for Chlorophyll Content and Stay-Green Traits Revealed by a Genome Wide Association Study in Rice (Oryza Sativa). Sci. Rep. 2019, 9, 2541. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Hossain, M.R.; Bassel, G.W.; Pritchard, J.; Sharma, G.P.; Ford-Lloyd, B.V. Trait Specific Expression Profiling of Salt Stress Responsive Genes in Diverse Rice Genotypes as Determined by Modified Significance Analysis of Microarrays. Front. Plant Sci. 2016, 7. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Lakra, N.; Kaur, C.; Singla-Pareek, S.L.; Pareek, A. Mapping the ‘Early Salinity Response’ Triggered Proteome Adaptation in Contrasting Rice Genotypes Using ITRAQ Approach. Rice 2019, 12, 3. [Google Scholar] [CrossRef] [PubMed]
  63. Lim, S.D.; Jung, C.G.; Park, Y.C.; Lee, S.C.; Lee, C.; Lim, C.W.; Kim, D.S.; Jang, C.S. Molecular Dissection of a Rice Microtubule-Associated RING Finger Protein and Its Potential Role in Salt Tolerance in Arabidopsis. Plant Mol. Biol. 2015, 89, 365–384. [Google Scholar] [CrossRef] [PubMed]
  64. Umate, P. Genome-Wide Analysis of the Family of Light-Harvesting Chlorophyll a/b-Binding Proteins in Arabidopsis and Rice. Plant Signal. Behav. 2010, 5, 1537–1542. [Google Scholar] [CrossRef] [Green Version]
  65. Mishra, M.; Kanwar, P.; Singh, A.; Pandey, A.; Kapoor, S.; Pandey, G.K. Plant Omics: Genome-Wide Analysis of ABA Repressor1 (ABR1) Related Genes in Rice during Abiotic Stress and Development. OMICS 2013, 17, 439–450. [Google Scholar] [CrossRef]
  66. Park, S.-H.; Jeong, J.S.; Lee, K.H.; Kim, Y.S.; Do Choi, Y.; Kim, J.-K. OsbZIP23 and OsbZIP45, Members of the Rice Basic Leucine Zipper Transcription Factor Family, Are Involved in Drought Tolerance. Plant Biotechnol. Rep. 2015, 9, 89–96. [Google Scholar] [CrossRef]
  67. Xiang, Y.; Tang, N.; Du, H.; Ye, H.; Xiong, L. Characterization of OsbZIP23 as a Key Player of the Basic Leucine Zipper Transcription Factor Family for Conferring Abscisic Acid Sensitivity and Salinity and Drought Tolerance in Rice. Plant Physiol. 2008, 148, 1938–1952. [Google Scholar] [CrossRef] [Green Version]
  68. Mirdar Mansuri, R.; Shobbar, Z.-S.; Babaeian Jelodar, N.; Ghaffari, M.R.; Nematzadeh, G.-A.; Asari, S. Dissecting Molecular Mechanisms Underlying Salt Tolerance in Rice: A Comparative Transcriptional Profiling of the Contrasting Genotypes. Rice 2019, 12, 13. [Google Scholar] [CrossRef]
  69. Ferreira, L.J.; Azevedo, V.; Maroco, J.; Oliveira, M.M.; Santos, A.P. Salt Tolerant and Sensitive Rice Varieties Display Differential Methylome Flexibility under Salt Stress. PLoS ONE 2015, 10, e0124060. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  70. Jehanzeb, M.; Zheng, X.; Miao, Y. The Role of the S40 Gene Family in Leaf Senescence. Int. J. Mol. Sci. 2017, 18, 2152. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Zheng, X.; Jehanzeb, M.; Habiba; Zhang, Y.; Li, L.; Miao, Y. Characterization of S40-like Proteins and Their Roles in Response to Environmental Cues and Leaf Senescence in Rice. BMC Plant Biol. 2019, 19, 174. [Google Scholar] [CrossRef] [PubMed]
  72. Liu, L.; Zhou, Y.; Zhou, G.; Ye, R.; Zhao, L.; Li, X.; Lin, Y. Identification of Early Senescence-Associated Genes in Rice Flag Leaves. Plant Mol. Biol. 2008, 67, 37–55. [Google Scholar] [CrossRef] [PubMed]
  73. Molla, K.A.; Debnath, A.B.; Ganie, S.A.; Mondal, T.K. Identification and Analysis of Novel Salt Responsive Candidate Gene Based SSRs (CgSSRs) from Rice (Oryza Sativa L.). BMC Plant Biol. 2015, 15, 122. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Gao, P.; Bai, X.; Yang, L.; Lv, D.; Li, Y.; Cai, H.; Ji, W.; Guo, D.; Zhu, Y. Over-Expression of Osa-MIR396c Decreases Salt and Alkali Stress Tolerance. Planta 2010, 231, 991–1001. [Google Scholar] [CrossRef]
  75. Huang, X.; Feng, J.; Wang, R.; Zhang, H.; Huang, J. Comparative Analysis of MicroRNAs and Their Targets in the Roots of Two Cultivars with Contrasting Salt Tolerance in Rice (Oryza Sativa L.). Plant Growth Regul. 2019, 87, 139–148. [Google Scholar] [CrossRef]
  76. Qin, J.; Ma, X.; Tang, Z.; Meng, Y. Construction of Regulatory Networks Mediated by Small RNAs Responsive to Abiotic Stresses in Rice (Oryza Sativa). Comput. Biol. Chem. 2015, 58, 69–80. [Google Scholar] [CrossRef]
  77. Huda, K.K.; Yadav, S.; Banu, M.S.A.; Trivedi, D.K.; Tuteja, N. Genome-Wide Analysis of Plant-Type II Ca2+ ATPases Gene Family from Rice and Arabidopsis: Potential Role in Abiotic Stresses. Plant Physiol. Biochem. 2013, 65, 32–47. [Google Scholar] [CrossRef]
  78. Chen, H.; Dai, X.J.; Gu, Z.Y. OsbZIP33 Is an ABA-Dependent Enhancer of Drought Tolerance in Rice. Crop Sci. 2015, 55, 1673–1685. [Google Scholar] [CrossRef]
  79. Chapagain, S.; Park, Y.C.; Kim, J.H.; Jang, C.S. Oryza Sativa Salt-Induced RING E3 Ligase 2 (OsSIRP2) Acts as a Positive Regulator of Transketolase in Plant Response to Salinity and Osmotic Stress. Planta 2018, 247, 925–939. [Google Scholar] [CrossRef] [PubMed]
  80. Lin, D.; Jiang, Q.; Ma, X.; Zheng, K.; Gong, X.; Teng, S.; Xu, J.; Dong, Y. Rice TSV3 Encoding Obg-Like GTPase Protein Is Essential for Chloroplast Development during the Early Leaf Stage under Cold Stress. Genes Genomes Genet. 2018, 8, 253–263. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  81. Boonchai, C.; Udomchalothorn, T.; Sripinyowanich, S.; Comai, L.; Buaboocha, T.; Chadchawan, S. Rice Overexpressing OsNUC1-S Reveals Differential Gene Expression Leading to Yield Loss Reduction after Salt Stress at the Booting Stage. Int. J. Mol. Sci. 2018, 19, 3936. [Google Scholar] [CrossRef] [Green Version]
  82. Sripinyowanich, S.; Chamnanmanoontham, N.; Udomchalothorn, T.; Maneeprasopsuk, S.; Santawee, P.; Buaboocha, T.; Qu, L.-J.; Gu, H.; Chadchawan, S. Overexpression of a Partial Fragment of the Salt-Responsive Gene OsNUC1 Enhances Salt Adaptation in Transgenic Arabidopsis Thaliana and Rice (Oryza Sativa L.) during Salt Stress. Plant Sci. 2013, 213, 67–78. [Google Scholar] [CrossRef]
  83. Ahn, J.C.; Kim, D.-W.; You, Y.N.; Seok, M.S.; Park, J.M.; Hwang, H.; Kim, B.-G.; Luan, S.; Park, H.-S.; Cho, H.S. Classification of Rice (Oryza Sativa l. Japonica Nipponbare) Immunophilins (Fkbps, Cyps) and Expression Patterns under Water Stress. BMC Plant Biol. 2010, 10, 253. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Xue, T.; Wang, D.; Zhang, S.; Ehlting, J.; Ni, F.; Jakab, S.; Zheng, C.; Zhong, Y. Genome-Wide and Expression Analysis of Protein Phosphatase 2C in Rice and Arabidopsis. BMC Genomics 2008, 9, 550. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Zhou, F.; Wang, C.-Y.; Gutensohn, M.; Jiang, L.; Zhang, P.; Zhang, D.; Dudareva, N.; Lu, S. A Recruiting Protein of Geranylgeranyl Diphosphate Synthase Controls Metabolic Flux toward Chlorophyll Biosynthesis in Rice. Proc. Natl. Acad. Sci. USA 2017, 114, 6866–6871. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  86. Giri, J.; Vij, S.; Dansana, P.K.; Tyagi, A.K. Rice A20/AN1 Zinc-Finger Containing Stress-Associated Proteins (SAP1/11) and a Receptor-like Cytoplasmic Kinase (OsRLCK253) Interact via A20 Zinc-Finger and Confer Abiotic Stress Tolerance in Transgenic Arabidopsis Plants. New Phytol. 2011, 191, 721–732. [Google Scholar] [CrossRef] [PubMed]
  87. Goswami, K.; Tripathi, A.; Sanan-Mishra, N. Comparative MiRomics of Salt-Tolerant and Salt-Sensitive Rice. J. Integr. Bioinform. 2017, 14. [Google Scholar] [CrossRef] [PubMed]
  88. Pradhan, S.K.; Pandit, E.; Nayak, D.K.; Behera, L.; Mohapatra, T. Genes, Pathways and Transcription Factors Involved in Seedling Stage Chilling Stress Tolerance in Indica Rice through RNA-Seq Analysis. BMC Plant Biol. 2019, 19, 352. [Google Scholar] [CrossRef] [Green Version]
  89. Lakmini, W.G. The Role of SENSITIVE TO FREEZING6 (SFR6) in Plant Tolerance to Stress. In Durham Theses; Durham University: Durham, UK, 2010. [Google Scholar]
  90. Senadheera, P.; Maathuis, F.J.M. Differentially Regulated Kinases and Phosphatases in Roots May Contribute to Inter-Cultivar Difference in Rice Salinity Tolerance. Plant Signal. Behav. 2009, 4, 1163–1165. [Google Scholar] [CrossRef]
  91. Liao, Y.; Hu, C.; Zhang, X.; Cao, X.; Xu, Z.; Gao, X.; Li, L.; Zhu, J.; Chen, R. Isolation of a Novel Leucine-Rich Repeat Receptor-like Kinase (OsLRR2) Gene from Rice and Analysis of Its Relation to Abiotic Stress Responses. Biotechnol. Biotechnol. Equip. 2017, 31, 51–57. [Google Scholar] [CrossRef] [Green Version]
  92. Luan, W.; Shen, A.; Jin, Z.; Song, S.; Li, Z.; Sha, A. Knockdown of OsHox33, a Member of the Class III Homeodomain-Leucine Zipper Gene Family, Accelerates Leaf Senescence in Rice. Sci. China Life Sci. 2013, 56, 1113–1123. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  93. Yu, J.; Zao, W.; He, Q.; Kim, T.-S.; Park, Y.-J. Genome-Wide Association Study and Gene Set Analysis for Understanding Candidate Genes Involved in Salt Tolerance at the Rice Seedling Stage. Mol. Genet. Genom. 2017, 292, 1391–1403. [Google Scholar] [CrossRef] [PubMed]
  94. Vij, S.; Giri, J.; Dansana, P.K.; Kapoor, S.; Tyagi, A.K. The Receptor-like Cytoplasmic Kinase (OsRLCK) Gene Family in Rice: Organization, Phylogenetic Relationship, and Expression during Development and Stress. Mol. Plant 2008, 1, 732–750. [Google Scholar] [CrossRef]
  95. Hedayati, P.; Monfard, H.H.; Isa, N.M.; Hwang, D.J.; Zain, C.R.C.M.; Uddin, M.I.; Zuraida, A.R.; Ismail, I.; Zainal, Z. Construction and Analysis of an Oryza Sativa (Cv. MR219) Salinity-Related CDNA Library. Acta Physiol. Plant. 2015, 37, 91. [Google Scholar] [CrossRef]
  96. Gómez-Ariza, J.; Brambilla, V.; Vicentini, G.; Landini, M.; Cerise, M.; Carrera, E.; Shrestha, R.; Chiozzotto, R.; Galbiati, F.; Caporali, E.; et al. A Transcription Factor Coordinating Internode Elongation and Photoperiodic Signals in Rice. Nat. Plants 2019, 5, 358–362. [Google Scholar] [CrossRef]
  97. Zhang, Z.; Liu, H.; Sun, C.; Ma, Q.; Bu, H.; Chong, K.; Xu, Y. A C2H2 Zinc-Finger Protein OsZFP213 Interacts with OsMAPK3 to Enhance Salt Tolerance in Rice. J. Plant Physiol. 2018, 229, 100–110. [Google Scholar] [CrossRef]
  98. Flowers, T.J.; Yeo, A.R. Variability in the Resistance of Sodium Chloride Salinity within Rice (Oryza Sativa L.) Varieties. New Phytol. 1981, 88, 363–373. [Google Scholar] [CrossRef]
  99. Lutts, S.; Kinet, J.M.; Bouharmont, J. Changes in plant response to NaCl during development of rice (Oryza sativa L.) varieties differing in salinity resistance. J. Exp. Bot. 1995, 46, 1843–1852. [Google Scholar] [CrossRef]
  100. Gregorio, G.B.; Senadhira, D.; Mendoza, R.D. Screening Rice for Salinity Tolerance; IRRl Discussion Paper Series; IRRI: Los Banos, Philippines, 1997; pp. 2–23. [Google Scholar]
  101. Lee, K.-S.; Choi, W.-Y.; Ko, J.-C.; Kim, T.-S.; Gregorio, G.B. Salinity Tolerance of Japonica and Indica Rice (Oryza Sativa L.) at the Seedling Stage. Planta 2003, 216, 1043–1046. [Google Scholar] [CrossRef] [PubMed]
  102. Neang, S.; de Ocampo, M.; Egdane, J.A.; Platten, J.D.; Ismail, A.M.; Seki, M.; Suzuki, Y.; Skoulding, N.S.; Kano-Nakata, M.; Yamauchi, A.; et al. A GWAS Approach to Find SNPs Associated with Salt Removal in Rice Leaf Sheath. Ann. Bot. 2020, 126, 1193–1202. [Google Scholar] [CrossRef] [PubMed]
  103. Negrão, S.; Schmöckel, S.M.; Tester, M. Evaluating Physiological Responses of Plants to Salinity Stress. Ann. Bot. 2017, 119, 1–11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  104. Verslues, P.E.; Agarwal, M.; Katiyar-Agarwal, S.; Zhu, J.; Zhu, J.-K. Methods and Concepts in Quantifying Resistance to Drought, Salt and Freezing, Abiotic Stresses That Affect Plant Water Status. Plant J. 2006, 45, 523–539. [Google Scholar] [CrossRef]
  105. Zhu, J.-K. Regulation of Ion Homeostasis under Salt Stress. Curr. Opin. Plant Biol. 2003, 6, 441–445. [Google Scholar] [CrossRef]
  106. Chakraborty, K.; Chattaopadhyay, K.; Nayak, L.; Ray, S.; Yeasmin, L.; Jena, P.; Gupta, S.; Mohanty, S.K.; Swain, P.; Sarkar, R.K. Ionic Selectivity and Coordinated Transport of Na+ and K+ in Flag Leaves Render Differential Salt Tolerance in Rice at the Reproductive Stage. Planta 2019, 250, 1637–1653. [Google Scholar] [CrossRef]
  107. Gregorio, G.B.; Senadhira, D. Genetic Analysis of Salinity Tolerance in Rice (Oryza Sativa L.). Theoret. Appl. Genet. 1993, 86, 333–338. [Google Scholar] [CrossRef] [PubMed]
  108. Moradi, F.; Ismail, A.M. Responses of Photosynthesis, Chlorophyll Fluorescence and ROS-Scavenging Systems to Salt Stress during Seedling and Reproductive Stages in Rice. Ann. Bot. 2007, 99, 1161–1173. [Google Scholar] [CrossRef] [Green Version]
  109. Yeo, A.R.; Yeo, M.E.; Flowers, S.A.; Flowers, T.J. Screening of Rice (Oryza Sativa L.) Genotypes for Physiological Characters Contributing to Salinity Resistance, and Their Relationship to Overall Performance. Theoret. Appl. Genet. 1990, 79, 377–384. [Google Scholar] [CrossRef] [PubMed]
  110. Bonilla, P.; Dvorak, J.; Mackill, D.; Deal, K.; Gregorio, G. RFLP and SSLP Mapping of Salinity Tolerance Genes in Chromosome 1 of Rice (Oryza Sativa L.) Using Recombinant Inbred Lines. Philipp. Agric. Sci. 2002, 85, 68–76. [Google Scholar]
  111. Gregorio, G.B. Tagging Salinity Tolerance Genes in Rice Using Amplified Fragment Length Polymorphism (AFLP); University of the Philippines: Los Baños, Philippines, 1997. [Google Scholar]
  112. To, H.T.M.; Nguyen, H.T.; Dang, N.T.M.; Nguyen, N.H.; Bui, T.X.; Lavarenne, J.; Phung, N.T.P.; Gantet, P.; Lebrun, M.; Bellafiore, S.; et al. Unraveling the Genetic Elements Involved in Shoot and Root Growth Regulation by Jasmonate in Rice Using a Genome-Wide Association Study. Rice 2019, 12, 69. [Google Scholar] [CrossRef] [Green Version]
  113. Baillo, E.H.; Kimotho, R.N.; Zhang, Z.; Xu, P. Transcription Factors Associated with Abiotic and Biotic Stress Tolerance and Their Potential for Crops Improvement. Genes 2019, 10, 771. [Google Scholar] [CrossRef] [Green Version]
  114. Srivastava, A.K.; Zhang, C.; Yates, G.; Bailey, M.; Brown, A.; Sadanandom, A. SUMO Is a Critical Regulator of Salt Stress Responses in Rice. Plant Physiol. 2016, 170, 2378–2391. [Google Scholar] [CrossRef] [Green Version]
  115. Srivastava, A.K.; Zhang, C.; Caine, R.S.; Gray, J.; Sadanandom, A. Rice SUMO Protease Overly Tolerant to Salt 1 Targets the Transcription Factor, OsbZIP23 to Promote Drought Tolerance in Rice. Plant J. 2017, 92, 1031–1043. [Google Scholar] [CrossRef] [Green Version]
  116. Kamal; Alnor Gorafi; Abdelrahman; Abdellatef; Tsujimoto Stay-Green Trait: A Prospective Approach for Yield Potential, and Drought and Heat Stress Adaptation in Globally Important Cereals. IJMS 2019, 20, 5837. [CrossRef] [Green Version]
  117. Li, J.; Wen, J.; Lease, K.A.; Doke, J.T.; Tax, F.E.; Walker, J.C. BAK1, an Arabidopsis LRR Receptor-like Protein Kinase, Interacts with BRI1 and Modulates Brassinosteroid Signaling. Cell 2002, 110, 213–222. [Google Scholar] [CrossRef] [Green Version]
  118. Wang, X. Brassinosteroids Regulate Dissociation of BKI1, a Negative Regulator of BRI1 Signaling, from the Plasma Membrane. Science 2006, 313, 1118–1122. [Google Scholar] [CrossRef]
  119. Jang, S.; Li, H.-Y. Oryza Sativa BRASSINOSTEROID UPREGULATED1 LIKE1 Induces the Expression of a Gene Encoding a Small Leucine-Rich-Repeat Protein to Positively Regulate Lamina Inclination and Grain Size in Rice. Front. Plant Sci. 2017, 8, 1253. [Google Scholar] [CrossRef]
  120. Shang, Y.; Dai, C.; Lee, M.M.; Kwak, J.M.; Nam, K.H. BRI1-Associated Receptor Kinase 1 Regulates Guard Cell ABA Signaling Mediated by Open Stomata 1 in Arabidopsis. Mol. Plant 2016, 9, 447–460. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  121. Arnon, D.I. Copper Enzymes in Isolated Chloroplasts. Polyphenoloxidase in Beta Vulgaris. Plant Physiol. 1949, 24, 1–15. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Figure 1. Boxplots of the distribution of salinity tolerance-related traits. Indica subpanel in red; japonica subpanel in blue; statistical significance (ANOVA p-values) between the two subpanels is indicated; (a) leaf water content (WC); (b) score of visual salt injury (Score); (c) total chlorophyll content in leaves (Chl_total); (d) chlorophyll a content in leaves (Chla); (e) chlorophyll b content in leaves (Chlb); (f) chlorophyll a to chlorophyll b ratio in leaves (Chla_b); (g) K+ concentration in leaves (ConcK); (h) Na+ concentration in leaves (ConcNa); (i) ratio of Na+/K+ in leaves (Na_K).
Figure 1. Boxplots of the distribution of salinity tolerance-related traits. Indica subpanel in red; japonica subpanel in blue; statistical significance (ANOVA p-values) between the two subpanels is indicated; (a) leaf water content (WC); (b) score of visual salt injury (Score); (c) total chlorophyll content in leaves (Chl_total); (d) chlorophyll a content in leaves (Chla); (e) chlorophyll b content in leaves (Chlb); (f) chlorophyll a to chlorophyll b ratio in leaves (Chla_b); (g) K+ concentration in leaves (ConcK); (h) Na+ concentration in leaves (ConcNa); (i) ratio of Na+/K+ in leaves (Na_K).
Plants 10 01088 g001
Figure 2. Manhattan plots and Q-Q plots for GWAS of salinity tolerance-related traits in the full panel. (a) Leaf water content (WC); (b) score of visual salt injury (Score); (c) total chlorophyll content in leaves (Chl_total); (d) chlorophyll a content in leaves (Chla); (e) chlorophyll b content in leaves (Chlb); (f) chlorophyll a to chlorophyll b ratio in leaves (Chla_b); (g) K+ concentration in leaves (ConcK); (h) Na+ concentration in leaves (ConcNa); (i) ratio of Na+/K+ in leaves (Na_K). In the Manhattan plots, significant SNPs are highlighted in red.
Figure 2. Manhattan plots and Q-Q plots for GWAS of salinity tolerance-related traits in the full panel. (a) Leaf water content (WC); (b) score of visual salt injury (Score); (c) total chlorophyll content in leaves (Chl_total); (d) chlorophyll a content in leaves (Chla); (e) chlorophyll b content in leaves (Chlb); (f) chlorophyll a to chlorophyll b ratio in leaves (Chla_b); (g) K+ concentration in leaves (ConcK); (h) Na+ concentration in leaves (ConcNa); (i) ratio of Na+/K+ in leaves (Na_K). In the Manhattan plots, significant SNPs are highlighted in red.
Plants 10 01088 g002
Figure 3. QTL_25. (a) Manhattan plot for K+, Na+ and water content in leaves in the full panel; (b) linkage disequilibrium (LD) heatmap. In the Manhattan plots, significant SNPs are highlighted in red, and genes of interest are mentioned. The genomic region of QTL_25 is specified in the boundary area in the LD heatmap.
Figure 3. QTL_25. (a) Manhattan plot for K+, Na+ and water content in leaves in the full panel; (b) linkage disequilibrium (LD) heatmap. In the Manhattan plots, significant SNPs are highlighted in red, and genes of interest are mentioned. The genomic region of QTL_25 is specified in the boundary area in the LD heatmap.
Plants 10 01088 g003
Table 1. Phenotypic variation and broad-sense trait heritability for the three panels.
Table 1. Phenotypic variation and broad-sense trait heritability for the three panels.
TraitsnMeanSDCVReplication EffectGenotype Effect
Full panel
Indica subpanel
Japonica subpanel
n: number of accessions; Rep: replication; CV: coefficient of variations; H2: broad-sense heritability; WC: leaf water content; Score: score of visual salt injury; Chl_total: total chlorophyll content in leaves; Chla: chlorophyll a content in leaves; Chlb: chlorophyll b content in leaves; Chla_b: chlorophyll a to chlorophyll b ratio in leaves; ConcK: K+ concentration in leaves; ConcNa: Na+ concentration in leaves; Na_K: ratio of Na+/K+ in leaves.
Table 2. Correlation coefficients between traits in the three panels (below the diagonal). Probabilities above the diagonal.
Table 2. Correlation coefficients between traits in the three panels (below the diagonal). Probabilities above the diagonal.
F: full panel; I: indica subpanel; J: japonica subpanel; WC: leaf water content; Score: score of visual salt injury; Chl_total: total chlorophyll content in leaves; Chla: chlorophyll a content in leaves; Chlb: chlorophyll b content in leaves; Chla_b: chlorophyll a to chlorophyll b ratio in leaves; ConcK: K+ concentration in leaves; ConcNa: Na+ concentration in leaves; Na_K: ratio of Na+/K+ in leaves.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Le, T.D.; Gathignol, F.; Vu, H.T.; Nguyen, K.L.; Tran, L.H.; Vu, H.T.T.; Dinh, T.X.; Lazennec, F.; Pham, X.H.; Véry, A.-A.; et al. Genome-Wide Association Mapping of Salinity Tolerance at the Seedling Stage in a Panel of Vietnamese Landraces Reveals New Valuable QTLs for Salinity Stress Tolerance Breeding in Rice. Plants 2021, 10, 1088.

AMA Style

Le TD, Gathignol F, Vu HT, Nguyen KL, Tran LH, Vu HTT, Dinh TX, Lazennec F, Pham XH, Véry A-A, et al. Genome-Wide Association Mapping of Salinity Tolerance at the Seedling Stage in a Panel of Vietnamese Landraces Reveals New Valuable QTLs for Salinity Stress Tolerance Breeding in Rice. Plants. 2021; 10(6):1088.

Chicago/Turabian Style

Le, Thao Duc, Floran Gathignol, Huong Thi Vu, Khanh Le Nguyen, Linh Hien Tran, Hien Thi Thu Vu, Tu Xuan Dinh, Françoise Lazennec, Xuan Hoi Pham, Anne-Aliénor Véry, and et al. 2021. "Genome-Wide Association Mapping of Salinity Tolerance at the Seedling Stage in a Panel of Vietnamese Landraces Reveals New Valuable QTLs for Salinity Stress Tolerance Breeding in Rice" Plants 10, no. 6: 1088.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop