Identification of Stable Quantitative Trait Loci for Sheath Blight Resistance Using Recombinant Inbred Line

Chen Yuan, Zeng Yuxiang, Ji Zhijuan, Liang Yan, Wen Zhihua, Yang Changdeng

Identification of Stable Quantitative Trait Loci for Sheath Blight Resistance Using Recombinant Inbred Line

Chen Yuan#, Zeng Yuxiang#, Ji Zhijuan, Liang Yan, Wen Zhihua, Yang Changdeng

(State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China; These authors contributed equally to this work)

To identify stable quantitative trait loci (QTLs) responsible for sheath blight resistance, a recombinant inbred line mapping population consisting of 219 lines was developed by crossing Lemont and Yangdao 4. Average disease rating, average lesion length, maximum disease rating and maximum lesion length were assayed in six different environments. A total of 128 minor effect QTLs were detected by multiple interval mapping. These QTLs explained less than 11.2% of the phenotypic variations individually, and 106 QTLs were clustered in 20 QTL-rich regions/putative loci. Significant QTL × environment interactions were detected at three putative loci (,and), indicating that these three loci were not stable. The other 17 stable loci (,,,,,,,,,,,,,,,and) provided a foundation for marker-assisted selection in breeding. Analysis of allelic effect on the 20 putative loci identified 7 highly stable loci, including,,,,,and.

rice; sheath blight resistance; quantitative trait locus; recombinant inbred line

Sheath blight disease is caused byKühn and is one of the three major diseases in rice (L.) (Park et al, 2008). In the past decade, sheath blight has become the most serious rice disease in China, with the losses caused by this disease exceeding those caused by rice blast or bacterial blight (Zuo et al, 2008, 2014a; Zeng et al, 2011). In addition, sheath blight is considered to be the most common rice disease in the southern United States (Groth and Bond, 2007), and it reduces rice yield by approximately 20% in India (Ghosh et al, 2016).

Resistance to rice sheath blight is a quantitative trait controlled by quantitative trait loci (QTL). Many QTLs for sheath blight resistance on all the 12 rice chromosomes have been identified (Srinivasachary et al, 2011; Zeng et al, 2015a). Recombinant inbred line (RIL) or doubled-haploid mapping populations have been frequently used for detecting QTLs for sheath blight resistance (Pinson et al, 2005; Liu et al, 2009; Channamallikarjuna et al, 2010; Fu et al, 2011; Xu et al, 2011; Nelson et al, 2012; Liu et al, 2013). Because the phenotypic traits of different genotypes can be repeatedly evaluated in a specific mapping environment, the results obtained using RILs or doubled-haploid populations will be more precise and appropriate than the F2or BC1F1populations. At least two QTLs,qSB-11(Zuo et al, 2013) andqSB-9(Zuo et al, 2014b), have been fine-mapped. Genes with enhanced resistance to sheath blight disease have also been reported. Lin et al (1995) reported that overexpressing the rice chitinase genein transgenic rice plants increases sheath blight resistance. Overexpression of the rice thaumatin-like protein gene results in enhanced resistance to(Datta et al, 1999). Molla et al (2013) reported enhanced resistance to sheath blight disease upon overexpression of the rice() gene. Other genes related to rice sheath blight disease resistance include(Helliwell et al, 2013),(Wang H H et al, 2015),(Tonnessen et al, 2015),(Wang R et al, 2015; Chen et al, 2016),(Xue et al, 2016),(Gao et al, 2018) and(Yuan et al, 2018).

Although many QTLs for sheath blight resistance have been identified using different mapping populations, they have seldom been used for marker-assisted selection because most of the resistance QTLs have only minor effects. Moreover, it is largely unknown whether the minor effect QTLs can be repeatedly detected in different mapping environments. This question is important because QTLs for sheath blight resistance are easily influenced by the environment. Here, we developed an RIL mapping population to map QTLs for sheath blight resistance.

MATERIALS AND METHODS

Mapping population

Lemont, a sheath blight-susceptible Americancultivar, was crossed with Yangdao 4, a Chinesecultivar relatively resistant to sheath blight, to develop a RIL mapping population consisting of 219 lines. The RIL mapping population was sown in six different mapping environments (F6in May 2013, F9in May 2015, F11in May 2016, F13in May 2017, F15in May 2018, and F15in June 2018) to identify QTLs for sheath blight resistance. The mapping population was planted in the farm of the China National Rice Research Institute, Hangzhou (119o95E, 30o07N), China.

Eighteen individual plants were planted for each of the 219 lines. The 18 plants per line were arranged in three rows with inter-row and within-row distances of 20 cm and 17 cm, respectively. The plot locations were completely randomized. Field management was performed as per the common practices in Hangzhou, but fungicides were not used. Pesticides were not used from 10 d before inoculation to the end of data recording.

Evaluation of sheath blight resistance in RIL mapping population

The inoculation method was conducted according to Zou et al (2000) with minor modifications. Truncated bamboo-toothpicks (2.0–2.5 cm long) were incubated withisolate ZJ03, which has been used in previous studies (Wen et al, 2015; Zeng et al, 2015b, 2017), on Petri plates containing potato dextrose agar medium in the dark at 28 oC. After 7 d of incubation, toothpicks covered with mycelia were used to penetrate the third leaf sheath, counting from the top, at the late-tillering stage of rice. At this growth stage, the second leaf sheath from the top is no longer elongating, therefore, the toothpick remains stable inside the third sheath (Xue et al, 2016). Sheath blight resistance was recorded at 30 d after inoculation.

Two tillers of each of the three individual plants of each RIL located in the middle of the second row were inoculated, thus, six tillers were inoculated for each line. Four phenotypic traits related to sheath blight resistance were recorded, including maximum lesion length, average lesion length, maximum disease rating and average disease rating. Average lesion length was recorded from the culm of the six inoculated tillers, while maximum lesion length was represented by the most seriously affected tiller in each line. The average disease rating was obtained from the three inoculated plants, while the maximum disease rating was represented by the most seriously affected plant in each line. Disease rating was determined using the 0–9 visual rating system, where ‘0’ indicated that the plant was completely immune to the pathogen, ‘9’ indicated a dead or collapsed plant, and ‘5’ indicated that about 50% of the plant was diseased (Pinson et al, 2005).

Construction of the genetic linkage map

Polymorphic markers between Lemont and Yangdao 4 were screened from 1 047 insertion-deletion (InDel) markers (Zeng et al, 2013) and 548 simple sequence repeat (SSR) markers (http://www.gramene.org). A total of 208 polymorphic markers covering all the 12 rice chromosomes were used to construct a genetic linkage map representing a total of 2 228.0 cM, with an average of 11.4 cM between adjacent markers (Zeng et al, 2019).

QTL analysis

Multiple interval mapping (MIM) method was used to detect QTLs for sheath blight resistance in Windows QTL Cartographer 2.5 (https://brcwebportal.cos.ncsu.edu/qtlcart/ WQTLCart.htm). The MIM model has been described by Zeng et al (2016). The presence of three or more QTLs in the same marker interval was defined as a QTL cluster.

Analysis of allele effect

Each QTL cluster was considered to contain a putative sheath blight resistance QTL. The nearest marker to each putative QTL was used to evaluate its allele effect under the six mapping environments. In each putative QTL, we distinguished two groups of lines within the RIL population, one carrying the Lemont alleles and the other carrying the Yangdao 4 alleles, and compared the average sheath blight phenotypic values between the two groups.

Statistical software

Two-way analysis of variance (ANOVA) was performed using the general linear model (GLM) procedure in SAS 8.01 (SAS Institute, Cary, NC, USA) to examine QTL × environment interaction, using the nearest marker to represent the corresponding QTL. Average lesion length and average disease rating were used in the two-way ANOVA.

RESULTS

Correlation of phenotypic traits related to sheath blight resistance measured in different environments

The maximum lesion length or maximum disease rating were highly correlated with the average lesion length or average disease rating (< 0.01) in each mapping environment (Table 1). Since these four traits were highly correlated with each other and the correlation coefficients (higher than 0.79) were very high in all the mapping environments, it was difficult to judge which trait was the most appropriate one to be used in QTL analysis. Therefore, all the four traits were applied. The distribution of the four phenotypic traits related to sheath blight resistance in the six mapping environments is presented in Supplemental Fig. 1.

Table 1. Correlation coefficients among four sheath blight resistance related traits in recombinant inbred line populations.

ADR, Average disease rating; MDR, Maximum disease rating; ALL, Average lesion length; MLL, Maximum lesion length.

**, Significant at the 0.01 level.

QTLs for sheath blight resistance identified by MIM

Using MIM, a total of 128 QTLs related to sheath blight resistance were detected in the six mapping environments (Table 2). These QTLs were localized on 11 rice chromosomes (almost all except chromosome 6). All the 128 QTLs explained less than 11.2% of the phenotypic variation individually, indicating that the sheath blight resistance was controlled by minor effect QTLs.

Five QTLs showed relatively large effects, which explained more than 10% of the phenotypic variation.detected in May 2018, anddetected in May 2016 explained 11.2% and 10.8% of the average disease rating variations, respectively.detected in May 2017 anddetected in May 2016 explained 11.1% and 10.1% of the average lesion length variations, respectively.detected in May 2016 explained 11.2% of the maximum lesion length variation. These five QTLs were located in three chromosome regions (Table 2).

A total of 106 QTLs were clustered on 20 QTL-rich chromosome regions (Supplemental Fig. 2 and Supplemental Table 1). In this study, we focused on the 20 QTL-clusters consisting of at least three co-located QTLs.

QTL × environment interaction

The 20 QTL-clusters were considered as 20 putative loci. QTL × environment interaction was used to examine the stability of the 20 loci using two-way ANOVA. The nearest markers to each of the 20 putative loci were chosen to represent the 20 putative loci in the two-way ANOVA. Significant QTL × environment interactions were detected at three putative loci on chromosome 11 including(= 2.69,= 0.020),(= 2.61,= 0.023) and(= 2.55,= 0.027). No significant interaction was found among the other 17 putative loci (Supplemental Table 2), suggesting that the 17 putative QTLs were stable across different mapping environments. The 17 stable loci are,,,,,,,,,,,,,,,and

Allele effects at the 20 QTL-clusters/putative loci

Based on the four phenotypic traits, we further tested the allelic effect at the 20 putative loci during the six environments (Supplemental Table 3). Results suggested that allele effects were not stable at some loci. For example, at thelocus, plants carrying the Lemont allele were more resistant than those carrying the Yangdao 4 allele in May 2016, but plants carrying the Yangdao 4 allele were more resistant than those carrying the Lemont allele in May 2013 (Supplemental Table 3). Stable allele effects were only found in 7 of the 20 loci, including,,,,,and, indicating that these 7 loci were highly stable. The sheath blight-resistant alleles were originated from Yangdao 4 in six of the seven highly stable loci, whereas in, the sheath blight-resistant allele was originated from Lemont (Supplemental Table 3).

DISCUSSION

MIM method is more sensitive than composite interval mapping and can improve the precision and power of QTL mapping (Kao et al, 1999). In this study, the MIM detected a total of 128 QTLs, and none of these 128 QTLs explained more than 11.2% of the phenotypic variation, which further confirms previous reports that most of the sheath blight resistance QTLs have only minor effects (Zeng et al, 2015a).

Three major rating methods were used to evaluate sheath blight resistance in previous QTL mapping studies, including disease rating, lesion height/length and percentage of lesion height/length (Zeng et al, 2015a). Disease rating employing the widely used 0–9 rating system is a subjective rating method. Measurement of lesion length/height is an objective rating method, which measures the absolute length/height of sheath blight lesions. The third rating method was not used in this study because it is a mixture of lesion height and plant height. QTLs detected in this study were compared with the previous study (Table 3).is adjacent to thelocus reported by Pinson et al (2005). Five previously reported QTLs,(Pan et al, 1999),(Zou et al, 2000),(Kunihiro et al, 2002),(Liu et al, 2009) and(Liu et al, 2013), are co-localized or overlapped withdetected in this study.is co-localized or overlapped with(Pan et al, 1999),(Zou et al, 2000),(Li et al, 2009) and(Liu et al, 2013).is adjacent to or overlapped with(Pinson et al, 2005; Tan et al, 2005),(Liu et al, 2009),(Nelson et al, 2012),(Liu et al, 2013),(Zuo et al, 2014b) and(Yadav et al, 2015).is co-localized with RG118 (Li et al, 1995),qSB-11(Zuo et al, 2013) and(Wen et al, 2015).is co-localized or overlapped with(Zou et al, 2000),(Channamallikarjuna et al, 2010),(Liu et al, 2013) and(Eizenga et al, 2013).is co-localized or overlapped with(Wang et al, 2012),(Eizenga et al, 2013) and(Wen et al, 2015) (Table 3). The same resistance allele might underline these co-locating loci.

Table 2.Quantitative trait loci (QTLs) detected for sheath blight resistance in the Lemont/Yangdao 4 recombinant inbred line mapping population using multiple interval mapping method.

2indicates proportion of phenotypic variance explained by the QTL.A positive additive effect indicates that the resistance allele originated from Yangdao 4, while a negative additive effect indicates that the resistance allele originated from Lemont.

Table 3. Comparison of the quantitative trait loci (QTLs) detected in the present and previous studies.

The physical position was determined by using the corresponding marker sequence as a query to BLAST against the rice genome sequence (IRGSP-1.0) in the NCBI website (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastSearch&BLAST_SPEC=OGP_4530_9512).

Using MIM, we uncovered the genetic bases of sheath blight resistance in the Lemont/Yangdao 4 population. All the 128 detected QTLs were minor effect QTLs. We also confirmed that some minor effect QTLs were stable across different mapping environments by using two-way ANOVA and allelic effect analysis.

acknowlegements

This study was financially supported by National Key R&D Program (Grant No. 2016YFD0102102), Zhejiang Provincial Natural Science Foundation (Grant Nos. LY16C060002 and LQ17C130005) and Zhejiang Agricultural Key Breeding Project (Grant No. 2016C02050-4) in China.

SUPPlemental DATA

The following materials are available in the online version of this article at http://www.sciencedirect.com/science/journal/ 16726308; http://www.ricescience.org.

Supplemental Fig. 1. Frequency distributions of sheath blight resistance of the 219 recombinant inbred lines from Lemont/ Yangdao 4.

Supplemental Fig. 2.Genetic linkage map and QTLs for sheath blight resistance detected in Lemont/Yangdao 4 recombinant inbred line mapping population.

Supplemental Table 1. QTLs located as clusters on 20 QTL-rich chromosome regions.

Supplemental Table 2. Two-way ANOVA used to detect QTL by environment interaction at 20 loci.

Supplemental Table 3. Analysis of allele effect at the 20 QTL-clusters/putative loci in six environments.

Channamallikarjuna V, Sonah H, Prasad M, Rao G J N, Chand S, Upreti H C, Singh N K, Sharma T R. 2010. Identification of major quantitative trait locifor sheath blight resistance in rice., 25(1): 155–166.

Chen X J, Chen Y, Zhang L N, Xu B, Zhang J H, Chen Z X, Tong Y H, Zuo S M, Xu J Y. 2016. Overexpression ofenhances rice resistance to sheath blight., 100(2): 388–395.

Chen Z X, Zhang Y F, Feng F, Feng M H, Jiang W, Ma Y Y, Pan C H, Hua H L, Li G S, Pan X B, Zuo S M. 2014. Improvement ofrice resistance to sheath blight by pyramidingqSB-9andqSB-7., 161: 118–127.

Datta K, Velazhahan R, Oliva N, Ona I, Mew T, Khush G S, Muthukrishnan S, Datta S K. 1999. Over-expression of the cloned rice thaumatin-like protein () gene in transgenic rice plants enhances environmental friendly resistance tocausing sheath blight disease., 98: 1138–1145.

Eizenga G C, Prasad B, Jackson A K, Jia M H. 2013. Identification of rice sheath blight and blast quantitative trait loci in two different/advanced backcross populations., 31(4): 889–907.

Fu D, Chen L, Yu G H, Liu Y, Lou Q J, Mei H W, Xiong L, Li M S, Xu X Y, Luo L J. 2011. QTL mapping of sheath blight resistance in a deep-water rice cultivar., 180(2): 209–218.

Gao Y, Zhang C, Han X, Wang Z Y, Ma L, Yuan D P, Wu J N, Zhu X F, Liu J M, Li D P, Hu Y B, Xuan Y H. 2018. Inhibition offunction in mesophyll cells improves resistance of rice to sheath blight disease., 19(9): 2149–2161.

Ghosh P, Sen S, Chakraborty J, Das S. 2016. Monitoring the efficacy of mutatedleaf lectin in transgenic rice against., 16: 24.

Groth D E, Bond J A. 2007. Effects of cultivars and fungicides on rice sheath blight, yield, and quality., 91(12): 1647–1650.

Helliwell E E, Wang Q, Yang Y N. 2013. Transgenic rice with inducible ethylene production exhibits broad-spectrum disease resistance to the fungal pathogensand., 11(1): 33–42.

Jiang S F, Wang C J Z, Shu C W, Zhou E X. 2018. Cloning and expression analysis ofgene inAG-1 IA of rice sheath blight pathogen., 32(2): 111–118. (in Chinese with English abstract)

Kao C H, Zeng Z B, Teasdale R D. 1999. Multiple interval mapping for quantitative trait loci., 152(3): 1203–1216.

Kunihiro Y, Qian Q, Sato H, Teng S, Zeng D L, Fujimoto K, Zhu L H. 2002. QTL analysis of sheath blight resistance in rice (L.)., 29(1): 50–55. (in Chinese with English abstract)

Li Z K, Pinson S R M, Marchetti M A, Stansel J W, Park W D. 1995. Characterization of quantitative trait loci (QTLs) in cultivated rice contributing to ?eld resistance to sheath blight ()., 91(2): 382–388.

Li F, Cheng L R, Xu M R, Zhou Z, Zhang F, Sun Y, Zhou Y L, Zhu L H, Xu J L, Li Z K. 2009. QTL mining for sheath blight resistance using the backcross selected introgression lines for grain quality in rice., 35(9): 1729–1737. (in Chinese with English abstract)

Lin W, Anuratha C S, Datta K, Potrykus I, Muthukrishnan S, Datta S K. 1995. Genetic engineering of rice for resistance to sheath blight., 13: 686–691.

Liu G, Jia Y, Correa-Victoria F J, Prado G A, Yeater K M, McClung A, Correll J C. 2009. Mapping quantitative trait loci responsible for resistance to sheath blight in rice., 99: 1078–1084.

Liu G, Jia Y, McClung A, Oard J H, Lee F N, Correll J C. 2013. Confirming QTLs and finding additional loci responsible for resistance to rice sheath blight disease., 97(1): 113–117.

Molla K A, Karmakar S, Chanda P K, Ghosh S, Sarkar S N, Datta S K, Datta K. 2013. Rice oxalate oxidase gene driven by green tissue-specific promoter increases tolerance to sheath blight pathogen () in transgenic rice., 14(9): 910–922.

Nelson J C, Oard J H, Groth D, Utomo H S, Jia Y, Liu G, Moldenhauer K A K, Correa-Victoria F J, Fjellstrom R G, Scheffler B, Prado G A. 2012. Sheath-blight resistance QTLs inrice germplasm., 184(1): 23–34.

Pan X B, Zou J H, Chen Z X, Lu J F, Yu H X, Li H T, Wang Z B, Pan X Y, Rush M C, Zhu L H. 1999. Tagging major quantitative trait loci for sheath blight resistance in a rice variety, Jasmine 85., 44(19): 1783–1789.

Park D S, Sayler R J, Hong Y G, Nam M H, Yang Y N. 2008. A method for inoculation and evaluation of rice sheath blight disease., 92: 25–29.

Pinson S R M, Capdevielle F M, Oard J H. 2005. Confirmation QTLs and finding additional loci conditioning sheath blight resistance in rice using recombinant inbred lines., 45(2): 503–510.

Srinivasachary S, Willocquet L, Savary S. 2011. Resistance to rice sheath blight () [(teleomorph:(A.B. Frank) Donk.] disease: Current status and perspectives., 178(1): 1–22.

Tan C X, Ji X M, Yang Y, Pan X Y, Zuo S M, Zhang Y F, Zou J H, Chen Z X, Zhu L H, Pan X B. 2005. Identification and marker- assisted selection of two major quantitative genes controlling rice sheath blight resistance in backcross generations., 32(4): 399–405. (in Chinese with English abstract)

Tonnessen B W, Manosalva P, Lang J M, Baraoidan M, Bordeos A, Mauleon R, Oard J, Hulbert S, Leung H, Leach J E. 2015. Rice phenylalanine ammonialyase geneis associated with broad spectrum disease resistance., 87(3): 273–286.

Wang H H, Meng J, Peng X X, Tang X K, Zhou P L, Xiang J H, Deng X B. 2015. Riceacts as a transcriptional activator mediating defense responses toward, the causing agent of rice sheath blight., 89: 157–171.

Wang R, Lu L X, Pan X B, Hu Z L, Ling F, Yan Y, Liu Y M, Lin Y J. 2015. Functional analysis ofin rice sheath blight resistance., 87: 181–191.

Wang Y, Pinson S R M, Fjellstrom R G, Tabien R E. 2012. Phenotypic gain from introgression of two QTL,and, for rice sheath blight resistance., 30(1): 293–303.

Wen Z H, Zeng Y X, Ji Z J, Yang C D. 2015. Mapping quantitative trait loci for sheath blight disease resistance in Yangdao 4 rice., 14(1): 1636–1649.

Xu Q, Yuan X P, Yu H Y, Wang Y P, Tang S X, Wei X H. 2011. Mapping quantitative trait loci for sheath blight resistance in rice using double haploid population., 130(3): 404–406.

Xue X, Cao Z X, Zhang X T, Wang Y, Zhang Y F, Chen Z X, Pan X B, Zuo S M. 2016. Overexpression ofenhances resistance to rice sheath blight., 100(8): 1634–1642.

Yadav S, Anuradha G, Kumar R R, Vemireddy L R, Sudhakar R, Donempudi K, Venkata D, Jabeen F, Narasimhan Y K, Marathi B, Siddiq E A. 2015. Identification of QTLs and possible candidate genes conferring sheath blight resistance in rice (L.)., 4: 175.

Yuan D P, Zhang C, Wang Z Y, Zhu X F, Xuan Y H. 2018.activates brassinosteroids and ethylene signaling to modulate response to sheath blight disease in rice., 108(9): 1104–1113.

Zeng Y X, Ji Z J, Ma L Y, Li X M, Yang C D. 2011. Advances in mapping loci conferring resistance to rice sheath blight and miningresistant resources., 18(1): 56–66.

Zeng Y X, Wen Z H, Ma L Y, Ji Z J, Li X M, Yang C D. 2013. Development of 1047 insertion-deletion markers for rice genetic studies and breeding., 12(4): 5226–5235.

Zeng Y X, Ji Z J, Yang C D. 2015a. The way to a more precise sheath blight resistance QTL in rice., 203(1): 33–45.

Zeng Y X, Xia L Z, Wen Z H, Ji Z J, Zeng D L, Qian Q, Yang C D. 2015b. Mapping resistant QTLs for rice sheath blight disease with a doubled haploid population., 14(5): 801–810.

Zeng Y X, Ji Z J, Wen Z H, Liang Y, Yang C D. 2016. Combination of eight alleles at four quantitative trait loci determines grain length in rice., 11(3): e0150832.

Zeng Y X, Shi J S, Ji Z J, Wen Z H, Liang Y, Yang C D. 2017. Genotype by environment interaction: The greatest obstacle in precise determination of rice sheath blight resistance in the field., 101: 1795–1801.

Zeng Y X, Chen Y, Ji Z J, Liang Y, Yang C D. 2019. Twenty-four alleles at twelve quantitative trait loci act additively to control tiller angle in cultivated rice., 88(2): 195–203.

Zhang Q X, Zhang Y, He L L, Chen X J, Tong Y H, Ji Z L. 2018. Identification of strain 7-5, antagonistic to rice sheath blight, and preliminary study of its biocontrol mechanism., 32(3): 277–284. (in Chinese with English abstract)

Zou J H, Pan X B, Chen Z X, Xu J Y, Lu J F, Zhai W X, Zhu L H. 2000. Mapping quantitative trait loci controlling sheath blight resistance in two rice cultivars (L.)., 101(4): 569–573.

Zuo S M, Zhang L, Wang H, Yin Y J, Zhang Y F, Chen Z X, Ma Y Y, Pan X B. 2008. Prospect of the QTLqSB-9utilized in molecular breeding program ofrice against sheath blight., 35(8): 499–505.

Zuo S M, Yin Y J, Pan C H, Chen Z X, Zhang Y F, Gu S L, Zhu L H, Pan X B. 2013. Fine mapping ofqSB-11, the QTL that confers partial resistance to rice sheath blight., 126(5): 1257–1272.

Zuo S M, Zhu Y J, Yin Y J, Wang H, Zhang Y F, Chen Z X, Gu S L, Pan X B. 2014a. Comparison and confirmation of quantitative trait loci conferring partial resistance to rice sheath blight on chromosome 9., 98(7): 957–964.

Zuo S M, Zhang Y F, Yin Y J, Li G Z, Zhang G W, Wang H, Chen Z X, Pan X B. 2014b. Fine-mapping ofqSB-9, a gene conferring major quantitative resistance to rice sheath blight., 34(4): 2191–2203.

28 December 2018;

26 April 2019

Yang Changdeng (yangchangdeng@126.com)

Copyright ? 2019, China National Rice Research Institute. Hosting by Elsevier B V

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Peer review under responsibility of China National Rice Research Institute

http://dx.doi.org/10.1016/j.rsci.2019.08.007

(Managing Editor: Li Guan)