Identification of a novel locus, BPH38(t), conferring resistance to brown planthopper (Nilaparvata lugens Stal.) using early backcross population in rice (Oryza sativa L.)
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Rice is the most important staple food crop, and it feeds more than half of the world population. Brown planthopper (BPH) is a major insect pest of rice that causes 20–80% yield loss through direct and indirect damage. The identification and use of BPH resistance genes can efficiently manage BPH. A molecular marker-based genetic analysis of BPH resistance was carried out using 101 BC1F5 mapping population derived from a cross between a BPH-resistant indica variety Khazar and an elite BPH-susceptible line Huang–Huan–Zhan. The genetic analysis indicated the existence of Mendelian segregation for BPH resistance. A total of 702 high-quality polymorphic single nucleotide polymorphism (SNP) markers, genotypic data, and precisely estimated BPH scores were used for molecular mapping, which resulted in the identification of the BPH38(t) locus on the long arm of chromosome 1 between SNP markers 693,369 and id 10,112,165 of 496.2 kb in size with LOD of 20.53 and phenotypic variation explained of 35.91%. A total of 71 candidate genes were predicted in the detected locus. Among these candidate genes, LOC_Os01g37260 was found to belong to the FBXL class of F-box protein possessing the LRR domain, which is reported to be involved in biotic stress resistance. Furthermore, background analysis and phenotypic selection resulted in the identification of introgression lines (ILs) possessing at least 90% recurrent parent genome recovery and showing superior performance for several agro-morphological traits. The BPH resistance locus and ILs identified in the present study will be useful in marker-assisted BPH resistance breeding programs.
KeywordsBrown planthopper QTL mapping Resistance MAS Early backcross population
Rice is an essential cereal crop that feeds more than 3.5 billion people globally. There is a need to increase rice production by up to 42% by 2025 to feed the ever-increasing population (Seck et al. 2012; Ray et al. 2013). Additionally, about 52% of world rice production is lost annually because of living organisms, of which approximately 21% alone is contributed by the incidence of insect pests (Khush 1979; Sogawa et al. 2003). Among these insect pests, brown planthopper (BPH; Nilaparvata lugens Stal.) is one of the major devastating sap-sucking pests, prevalent throughout the rice-growing countries of Asia (Normile 2008). BPH causes significant damage to the rice crop by sucking sap from the xylem and phloem tissues, which ultimately leads to “hopperburn” and, hence, eventually complete yield loss. Besides the direct damage caused by BPH, it causes indirect damage by transmitting viruses (rice grassy stunt virus and ragged stunt virus) that also results in a yield penalty (Sogawa1982; Cha et al. 2008; Cabauatan et al. 2009).
Different pest management strategies are available to manage the damage caused by insect pests, including chemical control, improving field practices, and developing and cultivating resistant varieties. However, excessive use of chemicals for BPH management is hazardous to human health, cost-ineffective; and unsafe for the environment and natural biodiversity. Additionally, these chemicals would increase pest outbreak incidences as they can kill the natural predators and their competitors, which feed on BPH (Tanaka et al. 2000). Alternatively, the most durable, precise, and environmentally friendly strategy is to develop a sustainable host-plant resistance management system, which makes it possible to reduce pest incidence, maintain ecological fitness, and keep infestation below economic threshold levels (Brar et al. 2009).
To date, 37 BPH resistance genes have been reported from different resistance sources (Li et al. 2019; Yang et al. 2019; Yuexiong et al. 2019). Notably, the majority of the BPH resistance genes are mapped on six of the 12 chromosomes (2, 3, 4, 6, 11, and 12) and, reportedly, four clusters of BPH resistance loci are located on three chromosomes. Cluster A is located on the long arm of chromosome12 and contains eight loci. Clusters B and D are located on the short and long arm of chromosome 4 containing 13 and four QTL/gene loci, respectively, including the recently identified BPH34, BPH30, and BPH36 resistance genes located on chromosome 4 (Li et al. 2019; Wang et al. 2018; Kumar et al. 2018). Cluster C is located on the short arm of chromosome 6, which includes six gene loci (Fujita et al. 2013). Out of 34 BPH resistance genes, 20 genes were fine mapped and only eight were cloned and characterized (BPH14, BPH17, BPH18, BPH26, BPH29, BPH9, BPH32, and BPH6) (Du et al. 2009; Tamura et al. 2014; Liu et al. 2015; Wang et al. 2015; Ji et al. 2016; Ren et al. 2016; Zhao et al. 2016; Guo et al. 2018). Wang et al. (2018) mapped BPH30 by using F2:3 populations AC1613/9311 and fine mapped by using backcrossed population on the short arm of the chromosome 4. Later, population derived from Kangwengingzhan/RBPH16 and HHZ/RBPH17 (lines RBHH16 and RBPH17 derived from O. rufipogon accession GX2183) were used to map BPH36 and BPH27 on chromosome 4 [BPH27 already reported by Huang et al. (2013) on Chromosome 4], and BPH36 were fine mapped by using backcross population (Li et al. 2019). Recently, Yang et al. (2019) mapped a novel QTL BPH37 by using F2:3 population derived from KWQZ/IR64 on chromosome 1, between RM 302 and YM35 markers. It is already reported that IR64 is harboring a major resistant gene BPH1 on chromosome 12 and minor QTLs associated with BPH resistance, whereas BPH37 is a second major resistance locus was reported on chromosome 1. Although, unlike blast resistance genes, the cloned BPH resistance genes encode several kinds of proteins such as lectin kinase (BPH17), B3 DNA binding protein (BPH29), SCR domain (BPH32), and exocyst-localized unknown protein (BPH6), three popular BPH resistance genes (BPH26, BPH18, and BPH9) encode a member of coiled-coil–nucleotide-binding site–leucine-rich repeat (CC-NBS-LRR) (Du et al. 2009; Tamura et al. 2014; Liu et al. 2015; Wang et al. 2015; Ji et al. 2016; Ren et al. 2016; Zhao et al. 2016; Guo et al. 2018). This NBS-LRR protein family is involved in the activation of the salicylic acid (SA) dependent and jasmonic acid pathways that mediate sucking inhibition in the phloem sieve element and exhibits a high degree of resistance against different biotypes in India, Korea, and Japan. Hence, these BPH resistance genes encoding NBS-LRR protein family are being used in breeding programs to develop BPH resistant varieties (Suh et al. 2011; Ji et al. 2016; Jena et al. 2017).
Although significant achievements have been made in host-plant resistance to resist the attack of BPH by identifying and introgressing BPH resistance genes, the quick evolution of virulent BPH populations poses a primary concern (Naik et al. 2018). Hence, there is a need to explore more genetic variants from the diverse novel genes for building resistance to defend against the new virulent BPH populations to ultimately, attain durable and broad-spectrum resistance. To address these challenges, our study focused on (1) characterizing the resistance pattern of the parental lines and identifying novel BPH resistance sources (2) detecting the genomic regions conferring BPH resistance, and (3) selecting the introgression lines(ILs) possessing the highest similarity with the recurrent parent and donor genetic segment from a novel breeding strategy.
Materials and methods
Plant materials and development of mapping population
HHZ is a high-yielding indica variety, originated from Guangdong Province of China and used as the recurrent parent in our study (www.ricedata.cn/variety/varis/600877.htm). The elite cultivar, Khazar originated from Gilan Province, Iran, was used as the donor parent. Taichung Native 1 (TN1) and IR62 were used as susceptible and resistant checks, respectively, for BPH phenotypic screening.
Genetic analysis of BC1F1 mapping population
Number of individuals
χ2 = (O–E)2/E
Bioassay for the BPH resistance study
To conduct the BPH bioassay, a local BPH colony collected from Laguna Province and belonging to biotype 3 (Saxena and Barrion 1985) was used. The pure Laguna BPH colony was developed from the single colony of BPH collected from Laguna Province of the Philippines. These colonies were reared and maintained on susceptible cultivar TN1 in the biotic stress screen house (BSSH) facility at IRRI. The bioassay was conducted by following the modified seedbox method (MSB) as described by Jena et al. (2006) at both BC1F1 and BC1F5 generations. Seedlings of the mapping population, the parental lines, and resistant and susceptible checks at two- to the three-leaf stage (seven days old) were infested with second- or third-instar nymphs at a density of 10–12 nymphs per seedling. A uniform number of seedlings at two- to three-leaf stage were maintained before the infestation to provide a similar opportunity for all the individuals of the test entries. Once the susceptible check (TN1) started wilting (90%), data on seedling survival rate were recorded and finally expressed as a standard evaluation system (SES) score of 0–9 (IRRI 2014). Higher BPH scores indicate susceptibility and lower scores indicate the resistance response of the genotypes. The scoring pattern for BPH reaction is given in Supplementary Table 1 as mentioned in Prahalada et al. (2017).
The Infinium high-density SNP genotyping platform, 6 K SNP chip (Thomson et al. 2017), was used to carry out genotyping of the mapping population. This platform detects SNP alleles by adding fluorescence-labeled, allele-specific nucleotide (allele-specific hybridization) based on single-base extension and detection based on fluorescent color (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5577349/). Genotyping data were generated in a HapMap format. The obtained polymorphic SNP markers were filtered for no calls and unexpected mono- and heteromorphic SNPs between parents (Prahalada et al. 2017), and prepared according to the QTL IciMapping ver.4.0 input file requirement for linkage map construction and QTL analysis.
Linkage map construction and QTL mapping
A total of 702 high-quality SNP data points, filtered out from the 6 K SNP chip, were used to construct the linkage map. The Kosambi mapping function was used to convert recombination fraction into mapping distance to anchor markers to the specified location (Kosambi 1944). The linkage map construction was determined by using the QTL analysis software, QTL IciMapping version 4.0 (Meng et al. 2015). Further, QTL mapping was carried out by using the precisely estimated phenotypic BPH SES scores and genotypic data on SNP markers. Composite interval mapping, which is based on a multiple regression model of maximum likelihood with a 1000 permutation test at P = 0.05, was used for the QTL analysis as it increases the statistical power and precision of the detected QTLs (Doerge and Churchill 1996; Prahalada et al. 2017; Mahender et al. 2019). LOD of 3.00 was considered as the threshold parameter for determining the significant QTLs among the detected QTLs.
In silico post-QTL analysis was performed based on the available information in rice databases: the MSU rice genome annotation project (https://rice.plantbiology.msu.edu/) and RiceXpro (https://ricexpro.dna.affrc.go.jp/). In order to predict the candidate genes associated with a BPH resistance reaction, the candidate genes within the detected locus were listed based on the physical position of the flanking markers. Among the candidate genes, the one that was reported to be involved in biotic stress resistance was considered as a probable candidate gene conferring BPH resistance.
Evaluation of mapping populations for agro-morphological traits
A total of 101 BC1F6 plants derived from 101 BC1F5 mapping population along with its parents were evaluated for the agro-morphological traits: days to 50% flowering (DFF), plant height (PH), tiller number (TN), grain yield (GY), and plot yield (PY). For the evaluation, a randomized complete block design (RCBD) was laid out, and the experiment was carried out with two replications in the 2017 wet season (WS). Each entry was planted in three rows, with 12 plants in each row having a space of 15 × 20 cm between the plants and the rows. DFF was recorded based on the number of days from sowing to 50% flowering in a population on a whole-plot basis. PH was measured from the base of the plant to the tip of the tallest tiller. TN was counted from each plant to express the average TN per plant. The data were recorded from 10 competitive plants from each plot that are chosen at random, and the mean values were computed for different lines. GY per plant and 33 plants per plot were weighed after proper drying (moisture content 12%) as described in Balachiranjeevi et al. (2018).
To analyze the extent of variation of the agro-morphological traits among the test lines, descriptive statistics showing the range, standard deviation (SD), and standard error of the mean (SEm) at P = 0.05 were used. The RCBD experimental design was used for conducting the bioassay and the agro-morphological evaluation considering four and two replications, respectively. Analysis of variance (ANOVA) was employed to assess the significance of the bioassay and agro-morphological evaluation at the level of significance P = 0.05. DMRT and Fisher’s t-test were used to determine the significant differences of the individual test entries for BPH resistance. Chi-square goodness of fit was used to analyze the segregation pattern of the markers and the phenotypic SES score. The multiple linear regression model of maximum likelihood was used to localize the genomic regions conferring BPH resistance (Prahalada et al. 2017).
Bioassay and genetic analysis for BPH resistance
Construction of a high-resolution linkage map
Filtration of SNP markers extracted from Infinium 6 K SNP genotyping platform
Total number of initial SNP markers
Total number of SNP markers after filtration
Not amplified markers
Total polymorphic markers extracted
Furthermore, the filtered high-quality polymorphic SNP markers were used to construct the high-resolution linkage map. A total of 702 SNP markers were assigned to different chromosomes depending on the linkage distance. Chromosome 1 was covered by as many as 96 polymorphic markers. The highest number of SNP markers was found on chromosome 1, followed by chromosomes 3, 6, 7, 8, 11, 2, 5, 9, 4, 10 and 12. Apart from identifying the resolution of each chromosome, the percent polymorphism of SNP markers for each chromosome was also studied. The highest percent of polymorphism was attained for chromosome 1, followed by chromosomes 3 and 6 (Supplementary Table 2).
Details of the identified BPH resistance locus, BPH38(t) detected from the 101 BC1F5 mapping population derived from HHZ × Khazar
Background analysis and phenotypic evaluation of the selected BC1F6 plants
Background analyses and BPH resistance score of advanced ILs derived from HHZ × Khazar
Total polymorphic SNP markers
Number of SNP allele
Percentage of recurrent parent genome recovered
BPH SES score
Agronomic performance of selected ILs derived from HHZ × Khazar
Plant height (cm)
Grain yield (g)
Plot yield (Kg)
Days to 50% heading
Candidate gene analysis
In silico candidate gene analysis was performed by using online MSU and RiceXpro databases, and in these 71 candidate genes present within the vicinity of the flanking markers 693,369 and id 10,112,165 were retrieved. Most of them are expressed and retrotransposon proteins (Supplementary Table 3). Among all the candidate genes, LOC_Os01g37260 is found near left SNP marker 693,369 (physical position 20,706,894 Mb) within a physical distance of 98.90 kb. This gene encodes OsFBX14, an F-box protein of FBXL class containing the LRR domain, which is proven to be involved in biotic stress resistance (Jin et al. 2004; Jain et al. 2007). However, research is ongoing to test these candidate genes for their relevance in the phenotypic expression of BPH resistance.
The increased density of BPH populations causes “hopper burn” symptom, which ultimately leads to complete death of rice plants (Prahalada et al. 2017). Although several strategies are available to manage BPH infestation, building BPH resistance in rice plants by identifying and introgressing BPH resistance genes is the most convenient and efficient strategy. To date, 37 BPH resistance genes have been identified from cultivated and wild species of Oryza:BPH1, BPH2, BPH3, BPH4, BPH5, BPH6, BPH7, BPH8, BPH9, BPH10, BPH11(t), BPH12(t),BPH12, BPH13(t), BPH14, BPH15, BPH16(t), BPH17, BPH18, BPH19(t), BPH20, BPH21, BPH22(t), BPH23(t),BPH24(t), BPH25(t), BPH26(t), BPH27, BPH28, BHP29,BPH30, BPH31, BPH32,BPH33, BPH3 and BPH35, BPH36 and BPH37 (Khush et al. 1985; Kabis and Khush 1988; Nemoto et al. 1989; Ishii et al. 1994; Murata et al. 1998; Hirabayashi et al. 1998; Renganayaki et al. 2002; Yanget al. 2002; Sharma et al. 2003; Yang et al. 2004; Hirabayashi et al. 2004; Sun et al. 2005; Chang-Chao et al. 2006; Chen et al. 2006; Jena et al. 2006; Sai Harini et al 2010; Jairin et al. 2007, 2010; Li et al. 2019; Ram et al. 2008; Rahman et al. 2009; Du et al. 2009; Qiu et al. 2010; Deen et al. 2010; Qiu et al. 2012; Myint et al. 2012; Huang et al. 2013; Wu et al. 2014; Wang et al. 2015; Wang et al. 2018; Ren et al. 2016; Prahalada et al. 2017; Kumar et al. 2018; Naik et al. 2018; Yang et al. 2019; Yuexiong et al. 2019). Among these, only eight genes (BPH14, BPH17, BPH18, BPH26, BPH29, BPH9, BPH32, BPH31 and BPH6) were cloned and characterized (Du et al. 2009; Tamura et al. 2014; Liu et al. 2015; Wang et al. 2015; Ji et al. 2016; Ren et al. 2016; Zhao et al. 2016; Guo et al. 2018). However, most of the mapped resistance genes were only effective against a single BPH population/biotype (Horgan et al. 2015). Notably, several BPH resistance genes have been broken down by the evolution of new BPH biotypes, resulting in significant losses of rice production (Liu et al. 2015). Hence, it is imperative to identify new sources of BPH resistance and use them in a crop improvement program. In our study, we carried out high-resolution mapping of BPH resistance derived from Khazar and HHZ and mapped a new BPH resistance locus, BPH38(t), on the long arm of chromosome 1. This locus is located between SNP markers 693,369 and id 10,112,165, within the physical positions of 20.71 Mb and 21.23 Mb and having a size of 496.20 kb. This is the third BPH gene reported on chromosome 1 after BPH33(t), which was reported by Naik et al. (2018) within the physical region of 24.52 Mb and 25.51 Mb, and, recently, Yang et al. (2019) reported a novel QTL BPH35 on chromosome 1 between RM302 and YM35 markers with in a physical distance of 32.98 Mb using IR64 as donor parent derived population. However, the location of the reported BPH38(t) locus in our study is novel as this is the first report on the detected locus from the donor. Early backcross generation BC1F5 plants derived from the novel Green Super Rice breeding strategy were used for the localization of genomic regions conferring BPH resistance. Analogous to our study, several researchers also used early backcross progenies for molecular mapping for locating the genomic regions conferring several beneficial traits. Tamura et al. (1999) detected the Grh1 gene on chromosome 1 by using a backcross population and applying RFLP markers and linkage mapping. Similarly, one of the most used broad-sense bacterial blight (BB) resistance genes, Xa27(t), was also identified using an early backcross generation. Xa27(t) was transferred from tetraploid wild rice Oryza minuta into cultivated O. sativa and is being used in several BB resistance breeding programs (Gu et al. 2005).
The bioassay was conducted using the BPH colony collected from Laguna Province of the Philippines popularly called the Laguna colony. The bioassay revealed that the parent Khazar was the source for BPH resistance with a BPH SES score of 3 and the recurrent parent HHZ exhibited a susceptible reaction with a score of 9. The individuals of the mapping population exhibited a wide range of BPH SES scores ranging from 3 to 9. The frequency distribution of the SES scores of the mapping population showed a bimodal type of segregation, indicating Mendelian segregation of the BPH resistance. Furthermore, BPH resistance scores were tested for the chi-square goodness of fit, which also confirmed the monogenic inheritance of the BPH resistance factor (χ2(0.52) = 1.62; Table 1). This result is also supported by the QTL mapping result, which showed high LOD and PVE for the identified BPH38(t) (Table 3; Fig. 4).
Several studies describe the mechanism of BPH resistance by cloning and characterizing several BPH resistance genes. These studies describe the BPH resistance influenced by candidate genes and their association with the chemical composition of the phloem sap, especially primary and secondary dietary metabolites. Salicylic acid (SA) is one of the metabolites reportedly involved in the BPH resistance mechanism (Du et al. 2009). The first cloned BPH14 resistance gene and later cloned, BPH26, BPH29, BPH9, and BPH18 were found to be involved in triggering the induction of SA through the signaling pathway and callous deposition in the phloem tissues coupled with trypsin secretion, which causes inhibition of sucking in the phloem and, hence, resistance (Du et al. 2009; Liu et al. 2015; Wang et al. 2015; Zhao et al. 2016; Ji et al. 2016; Prahalada et al. 2017). Similar to these researches, we have also explored the detected BPH resistance locus to identify the probable candidate gene involved in BPH resistance by applying recent genomics and bioinformatics tools. A genomic segment of BPH38(t) of 496.20 kb in size mapped in this study contained a minimum of 71 candidate genes. Most of the candidate genes within the locus were reported to be an encoding group of retrotransposon proteins followed by expressed proteins and other different kinds of proteins that were not associated with any biotic resistance (Supplementary Table 3). Interestingly, in our current study, we have also found a candidate gene LOC_Os01g37260, which is ~ 90.61 kb near the detected BPH38(t) resistance locus that encodes for F-box protein. F-box proteins constitute a large family in eukaryotes and are characterized by a conserved F-box motif (approximately 40 amino acids) at their N terminus that interacts with Skp1p-cullin-F-box (SCF) protein, which is a major class of plant E3 ligases (Jain et al. 2007). The name F-box was given because it was identified first in the N-terminal region of cycline F (Bai et al. 1994). The C terminus of F-box proteins generally contains one or several highly variable protein–protein interaction domains, including leucine-rich repeat (LRR) (Jin et al. 2004). Furthermore, there are 687 F-box proteins, and they have ten different domains, out of which FBLD and FBXL are the F-box protein subfamilies that possess the LRR domain in nine different categories (Jain et al. 2007). The candidate gene LOC_Os01g37260 is one among those F-box proteins (https://rice.plantbiology.msu.edu/cgi-bin/ORF_infopage.cgi?orf=LOC_Os01g37260.1) that possesses the LRR domain at the interaction with the C terminus, which is reported to be involved in the salicylic acid signaling pathway to attain high BPH resistance which consequently acts as a defense against BPH attack (Yan et al. 2009; Ji et al. 2016). However, more observations and further research are needed to confirm the involvement of the candidate gene LOC_Os01g37260 in the expression of BPH resistance.
The recurrent parent HHZ is a commercially released mega-variety, originating from Guangdong Province of China that is susceptible to BPH. The ILs were generated by introgressing the BPH38(t) locus and initially subjected to background analysis to select the lines with maximum recovery of the recurrent parent genome, and the same set of lines was used for the bioassay to study the effectiveness of the BPH38(t) locus. The ILs LBS-013 and LBS-004 showed 91.91% and 91.85% recurrent parent genome, respectively, and were found to possess the BPH38(t) locus. Notably, positive ILs possessing BPH38(t) exhibited a high level of BPH resistance compared with that of the background line HHZ. On the contrary, ILs without BPH38(t) showed a similar level of BPH reaction (susceptibility) as HHZ confirmed the efficacy of the BPH38(t) locus (Table 4; Fig. 5). These lines also showed superior yield and yield-associated traits, especially LBS-011 and LBS-009, which showed the highest yield of 5.85 and 5.05 t ha−1, respectively. On the other hand, the IL LBS-019 showed the highest recurrent parent genome recovery (92.33%) without exhibiting any resistance against BPH as it did not carry the BPH38(t) locus and was also not superior in agro-morphological characters in comparison to ILs carrying the BPH38(t) locus (Table 5). One backcross was made and was followed by six generations of selfing, which led to the higher recurrent parent genome recovery levels (85.16–91.91%) (Table 4; Hasan et al. 2015). Thus, our study not only detected a locus conferring BPH resistance but could also able to identify ILs that were ready to be used as breeding products. Hence, we strongly believe that the achievements of our study can help manage the BPH insect pests to a great extent.
The authors acknowledge that this study was carried out by using Green Super Rice Project Funds (ID OPP1130530) for Zhikang Li, CAAS.
Compliance with ethical standards
Conflict of interest
The authors declare no conflict of interest.
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