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Genome-Wide Association Study of Blast Resistant in Korean Rice (Oryza sativa L.) Breed Lines
Plant Breed. Biotech. 2022;10:139-144
Published online June 1, 2022
© 2022 Korean Society of Breeding Science.

Tae-Ho Ham1†, Ja-Hong Lee2†, Seong-Gyu Jang2, Muhyun Kim1, Hongjia Zhang2, Na-Eun Kim2, Soon-Wook Kwon2*, Joohyun Lee1*

1Department of Cropscience, Konkuk University, Seoul 05029, Korea
2Department of Plant Bioscience, Pusan National University, Miryang 50463, Korea
Corresponding author: Soon-Wook Kwon,, Tel: +82-55-350-5506, Fax :+82-55-350-5509
Joohyun Lee,, Tel: +82-2-450-3769, Fax: +82-2-455-1044
These authors contributed equally.
Received May 11, 2022; Revised May 25, 2022; Accepted May 25, 2022.
This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
A total of 857 rice breed lines were used to evaluate rice blast resistance. Frequency of leaf spot index was skewed to the right of the 1-9 scale in bar plot, with a score of 7 showing the highest frequency. The average spot index score of 857 breed lines was 5.33. Associations showing higher than the threshold of ‒log10(P) = 5.17 were detected as significant associations. Significantly associated single nucleotide polymorphism (SNP) markers located within ± 250 kb on the lead SNP position was designated to one QTL locus of lead SNP markers. Five association loci were detected. Two associated QTLs detected on Chr. 4 were designated as qRB4.1 and qRB422, explaining 17.8% and 14.3% of total phenotypic variations, respectively. Associated QTLs detected on Chr. 1, 11, and 12 (one each) designated as qRB1, qRB11 and qRB12 explained 44.6%, 9.09%, and 13.7% of total phenotypic variations, respectively. We compared previously reported QTLs. The location of qRB4.2 was overlapped with the previously reported QTL for blast field resistance. The location of qRB12 was also overlapped with the field resistance leaf blast. The other one, qRB4.1, was overlapped with bacterial blight resistance.
Keywords : GWAS, Rice blast, QTL, Korean rice breed lines

Rice is most important staple crop of more than half of all people worldwide (Gnanamanickam 2009). The security of rice supply is threatened by increasing global temperatures, limited water supplies, abiotic and biotic stresses. Among various causes, diseases as biotic stresses are major threats. Rice blast is one of the diseases of rice in tropical and temperate regions of the world (Bonman 1992). Rice blast is caused by fungal pathogen Magnaporthe oryzae. It can lead to 10-30% of yield losses annually with serious economic losses (Valent and Chumley 1991; Talbot 2003; Skamnioti and Gurr 2009). One of the methods to control this pathogen is breeding rice varieties with blast resistance. This method has been used as the most economical and environmentally sustainable way to control rice blast (Hulbert et al. 2001; Sun et al. 2013; Baek et al. 2019).

Over 100 major resistance genes have been used to breed rice varieties with blast resistance against multiple pathogens (Fukuoka et al. 2015; Xiao et al. 2020). Among them, 37 genes have been cloned. Most of them encoded nucleotide-binding site (NBS) and leucine-rich repeat (LRR) proteins (Chen et al. 2006; Zhao et al. 2018; Wang et al. 2019). Many of these resistance genes are located in the rice genome on chromosomes 2, 6, 8, 11, and 12. Donor resistance genes used in molecular breeding studies are mainly alleles or tightly linked resistance genes from three loci, Piz, Pik, and Pi-ta. In these loci, the Piz locus includes Pi2, Pi9, Pi40, Piz-t, and Pigm, the Pik locus includes Pi1 and Pi54, and the Pi-ta locus includes Pi-ta (Xiao et al. 2020). One of the reported genes, Pib, is located on chromosome 2 (Hayashi et al. 2006). Pi2, Pigm, Piz-t, Pi9, and Pi40 are located on chromosome 6 (Jiang et al. 2015; Del et al. 2016; Tian et al. 2016). Pi33 is located on chromosome 8 (Usatov et al. 2016). Pi1, Pi54, and Pik are located on chromosome 11 (Hayashi et al. 2006; S. Vijay Kumar et al. 2018; Vijay Kumar et al. 2019). Pita is located on chromosome 12 (Khanna et al. 2015).

Genome-wide association study (GWAS) based on linkage disequilibrium (LD) and single sequence repeat (SSR) markers or single nucleotide polymorphism (SNP) markers has been widely performed to identify loci significantly associated with many traits in model plant species, including rice. Through a GWAS on blast resistance in rice, Li et al. (2019) have reported 56 loci based on a subset of 277,524 SNPs, three isolates from Hunan Province, and a rice diversity panel 1. Wang et al. (2014) have also examined blast resistance in a GWAS based on genotyping 805,158 SNPs variants across 366 indica diverse accessions and identified 30 associated loci.

Here, we performed GWAS using 857 cultivars to detect QTL(s) associated with blast resistance to expand genomic resources. With the hope to provide genetic information on blast resistance for rice breeders and geneticists, we conducted a GWAS to suggest QTL(s) associated with blast resistance in rice.


Plant Materials and Evaluation of Rice Blast Disease Resistance

A total of 857 genetically uniformed rice breed lines (Supplementary Table 1) were used for estimating rice blast resistance. We carried out field tests of leaf blast using the standard evaluation method (IRRI 2013, Fig. 1A). These 857 rice cultivars were grown in an experimental plot at the Department of the Functional Crop, Miryang, Korea. The planting density was 10 cm × 20 cm. N-P2O5-K2O fertili-zers were applied at the level of 24-8-12 kg/10 a. Seeds were sown on July 5th, 2019. They were scored for leaf blast res-ponse (1-9) at tillering stage on August 20th, 2019 (Fig. 1B).

Figure 1. Blast evaluation of 857 rice breed lines. (A) Index value and the corresponding levels of severity for a leaf spot disease, blast (IRRI 2013). (B) Field test for rice blast.

DNA Extraction and High-Throughput Genotyping

Genomic DNAs of 857 breed lines were extracted from fresh leaves of 14-days-old seedlings using the CTAB method (Murray 1980). The quality of DNA was checked by agarose gel electrophoresis and quantified using a Nanodrop 2000c spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). High-throughput SNP genotyping and genotype calling of 857 breed lines were carried out using KNU Axiom Oryza 580K Genotyping Array (unpublished data; manuscript is being prepared). All equipment and resources required for the Axiom Assay (Thermo Fisher Scientific, Waltham, MA, USA) with automated target preparation were from Affymetrix 2.0 Assay as described in a previous study (Chung et al. 2020).

GWAS analysis

The population structure of 857 breed lines was analyzed using ADMIXTURE 1.3.0 software (Alexander and Lange 2011) based on 260,000 high-quality SNPs using KNU Axiom Oryza 580K Genotyping Array. Results were visualized using Pophelper structure Web App v1.0.10 ( (Francis 2017). For population structure analysis in present populations, we first filtered genotype data with PLINK software (Purcell et al. 2007). High-quality, low-LD SNP set were then collected to undergo subsequent analysis. Trait Analysis by Association, Evolution, and Linkage (TASSEL) package (Bradbury et al. 2007) was used to conduct association analysis of blast resistance of 857 breed lines. A mixed linear model (MLM) was used, in which a kinship (K) matrix as the variance- covariance matrix between individuals was combined with population structure from Principal Component Analysis (PCA). Due to the fact that many SNPs had strong LD in genotype data, thresholds decided by the total number of SNPs were too rigorous for detecting association loci (Zhao et al. 2018). Thus, the genotype was filtered with PLINK software (Purcell et al. 2007). Finally, the threshold was set as ‒log10(P) = 5.17 for identification of association loci. SNP markers located at locus peaks were designed as lead SNPs for the detected loci. Areas 250 kb upstream and downstream of lead SNPs were considered as candidate genomic regions for gene identification. GWAS analysis was used to identify promising candidate genes (QTLs) for blast resistance in rice. We identified candidate genes from 500 kb (upstream and downstream 250 kb each) reference sequence. Detected QTL regions were identified as candi-date regions. Functional annotations of genes within can-didate regions were extracted from the Gramene database (


A total of 857 breed rice lines belonging to three different groups were used to evaluate rice blast. The bar plot of leaf spot index was skewed to the right of the 1-9 scale, with a score of 7 showing the highest frequency (Fig. 2). The average spot index score of 857 breed lines was 5.33, where 211 lines showed an average score of ≤ 3.00. In 857 breed lines, Taebaeg, Hangangchal, Cheongho, and so on showed resistance to rice blast with spot index score of 1. Mihyang Goun, and Cheonma were estimated to have spot index score of 9. Over 46% of all breed lines has mid-resistance (scores of 1-5) to rice blast. Almost 90% of all breed lines has moderated resistance to blast (score > 7). These breed lines were genetically uniform with moderate yield and environment tolerance. Thus, they will be useful genetic resources for rice blast resistance breeding pro-grams.

Figure 2. Frequency distribution of leaf spot score by rice blast in 857 breed lines. Histogram of rice blast spot index. The dotted line is the moving average.

The ADMIXTURE software was used to calculate the genetic composition of all 857 breed lines. Cross-validation (CV) analysis indicated that K = 3 was the optimal popula-tion grouping, which showed the lowest CV error com-pared with other K values (Fig. 3A). These 857 breed lines were divided into three groups. They were mostly distingui-shed by their subspecies (Fig. 3B).

Figure 3. Population structure analysis for 857 breed lines. (A) Cross-validation plot. (B) Genetic population structure by K = 2-4.

Manhattan plots for the markers significantly associated with rice blast resistance are shown in Fig. 4. Associations showing higher than the threshold of ‒log10(P) = 5.17 were detected as significant associations. Significantly associated SNP markers located within ± 250 kb on the lead SNP position were designated to one QTL locus of lead SNP markers. Five association loci were detected (Fig. 4, Table 1). Two associated QTLs were detected on Chr. 4. They were designated as qRB4-1 and qRB4-2, explaining 17.8% and 14.3% of total phenotypic variations, respectively. One associated QTL was detected on Chr. 1, 11, and 12 each. They were designated as qRB1, qRB11, and qRB12, explaining 44.6%, 9.09%, and 13.7% of total phenotypic variations, respectively. For segments of the five QTLs, we compared previously reported QTLs (Table 1). The location of qRB4.2 was overlapped with the previously reported QTL for blast field resistance (Miyamoto et al. 2001). qRB12 also was overlapped with field resistance leaf blast (Bagali et al. 1998). qRB4.1 was overlapped with bacterial blight resistance (He et al. 2006).

Figure 4. Manhattan plots and Q-Q plot for rice blast resis-tance of 857 rice breed lines. (A) Manhattan plots, (B) Q-Q plot. The y axis indicates the ‒log10(P) value. The x axis indicates the SNP position of each chromosome. The horizontal red dotted line indicates thresholds (‒log10(P) = 5.17), Red arrow means detected QTLs.

Table 1 . Locations of QTLs detected in GWAS and previously reported QTLs and candidate QTLs.

QTLLead SNPChr‒log10(P)Reported QTLReference of previously reported QTLs
QTL IDRelated Trait
qRB4.130,002,63745.39Xa2Bacterial blight resistanceHe et al. 2006
qRB4.232,005,38445.39qBFR-4-1Blast field resistanceMiyamoto et al. 2001
qRB1211,646,935126.02qDLA-12-3Field resistance to leaf blastBagali et al. 1998

In this study, we evaluated rice blast levels of a collection of 857 rice breed lines. Five blast resistance- associated QTLs were identified by GWAS. As a result of GWAS, QTLs of resistance to field blast were overlapped with our results. The QTL qRB4.1 and qRB4.2 on chromo-some 4 were located closely at 30.00 Mb and 32.00 Mb, respectively. They corresponded well with qBR4-2 (30-34 MB), qBFR4 (31.5 MB), respectively (Fukuoka et al. 2012; Mizobuchi et al. 2014). A chromosomal region derived from wild relative of rice, O. rufipogon, was detected in a similar position (Hirabayashi et al. 2010). Further study could identify genes related to blast resis-tance in these regions. Candidate genes will provide strategies for developing blast resistance elite rice varieties.

Supplemental Materials

This work was supported by 2020 BK21 FOUR Program of Pusan National University and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2017R1E1A1A01075282).

  1. Baek MK, Cho YC, Park HS, Jeong JM, Kim WJ, Nam JK, et al. 2019. Molecular mapping of the blast resistance loci in the durable resistance japonica rice cultivar, palgong. Korean J. Breed Sci. 51(4): 395-403.
  2. Bonman JM. 1992. Durable resistance to rice blast disease - environmental-influences. Euphytica 63: 115-123.
  3. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. 2007. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23(19): 633-2635.
    Pubmed CrossRef
  4. Chen XW, Shang JJ, Chen DX, Lei CL, Zou Y, Zhai WX, et al. 2006. A B-lectin receptor kinase gene conferring rice blast resistance. Plant J. 46(5): 794-804.
    Pubmed CrossRef
  5. Chung IM, Ham TH, Cho GW, Kwon SW, Lee Y, Seo J, et al. 2020. Study of quantitative trait loci (QTLs) associated with allelopathic trait in rice. Genes 11(5): 470.
    Pubmed KoreaMed CrossRef
  6. Del MM, Man S, Vinarao RB, Surek H, Jena KK. 2016. Marker-assisted introgression of a broad-spectrum resistance gene, Pi40 improved blast resistance of two elite rice (Oryza sativa L.) cultivars of Turkey. Mol. Plant Breed. 7
  7. Fukuoka S, Saka N, Mizukami Y, Koga H, Yamanouchi U, Yoshioka Y, et al. 2015. Gene pyramiding enhances durable blast disease resistance in rice. Sci. Rep. 5(1): 1-7.
    Pubmed KoreaMed CrossRef
  8. Gnanamanickam SS. 2009. Rice and its importance to human life. In: Biological Control of Rice Diseases. Springer Netherlands, Dordrecht, pp 1-11.
  9. Hayashi K, Yoshida H, Ashikawa I. 2006. Development of PCR-based allele-specific and InDel marker sets for nine rice blast resistance genes. Theor. Appl. Genet. 113(2): 251-260.
    Pubmed CrossRef
  10. Hulbert SH, Webb CA, Smith SM, Sun Q. 2001. Resistance gene complexes: Evolution and utilization. Annu. Rev. Phytopathol. 39(1): 285-312.
    Pubmed CrossRef
  11. IRRI. 2013. Standard Evaluation System (SES). 5th edition: pp 18-19
  12. Jiang JF, Mou TM, Yu HH, Zhou FS. 2015. Molecular breeding of thermo-sensitive genic male sterile (TGMS) lines of rice for blast resistance using Pi2 gene. Rice 8(1): 1-10.
    Pubmed KoreaMed CrossRef
  13. Khanna A, Sharma V, Ellur RK, Shikari AB, Krishnan SG, Singh UD, et al. 2015. Marker assisted pyramiding of major blast resistance genes Pi9 and Pita in the genetic background of an elite Basmati rice variety, Pusa Basmati 1. Indian J. Genet. 75(4): 417-425.
  14. Li CG, Wang D, Peng SS, Chen Y, Su P, Chen JB, et al. 2019. Genome-wide association mapping of resistance against rice blast strains in South China and identification of a new Pik allele. Rice. 12(1): 1-9.
    Pubmed KoreaMed CrossRef
  15. Kumar SV, Rambabu R, Bhaskar B, Madhavi KR, Srikanth S, Prakasam V, et al. 2018. Introgression of durable blast resistance gene Pi-54 into indica rice cv. samba mahsuri, through marker assisted backcross breeding. Electron. J. Plant Breed. 9(2): 705-715.
  16. Kumar SV, Srinivas Prasad M, Rambabu R, Madhavi KR, Bhaskar B, Abhilash Kumar V, et al. 2019. Marker- Assisted introgression of Pi-1 gene conferring resistance to rice blast pathogen pyricularia oryzae in the background of samba mahsuri. Int. J. Curr. Microbiol. App. Sci. 8(1): 2133-2146.
  17. Skamnioti P, Gurr SJ. 2009. Against the grain: safeguarding rice from rice blast disease. Trends Biotechnol. 27(3): 141-150.
    Pubmed CrossRef
  18. Sun PY, Liu JL, Wang Y, Jiang N, Wang SH, Dai YS, et al. 2013. Molecular mapping of the blast resistance gene Pi49 in the durably resistant rice cultivar Mowanggu. Euphytica 192(1): 45-54.
  19. Talbot NJ. 2003. On the trail of a cereal killer: Exploring the biology of Magnaporthe grisea. Annu. Rev. Microbiol. 57(1): 177-202.
    Pubmed CrossRef
  20. Tian H, Cheng H, Hu J, Lei C, Zhu X, Qian Q. 2016. Effect of introgressed Pigm gene on rice blast resistance and yield traits of Japonica rice in cold area. Journal of Shenyang Agricultural University 47(5): 520-526
  21. Usatov AV, Kostylev PI, Azarin KV, Markin NV, Makarenko MS, Khachumov VA, et al. 2016. Introgression of the rice blast resistance genes Pi1, Pi2 and Pi33 into Russian rice varieties by marker-assisted selection. Indian J. Genet Plant Breed. 76(1).
  22. Valent B, Chumley FG. 1991. Molecular genetic-analysis of the rice blast fungus, magnaporthe-grisea. Annu. Rev. Phytopathol. 29(1): 443-467.
    Pubmed CrossRef
  23. Wang CH, Yang YL, Yuan XP, Xu Q, Feng Y, Yu HY, et al. 2014. Genome-wide association study of blast resistance in indica rice. BMC Plant Biol. 14(1): 1-11.
    Pubmed KoreaMed CrossRef
  24. Wang JC, Liu X, Zhang A, Ren YL, Wu FQ, Wang G, et al. 2019. A cyclic nucleotide-gated channel mediates cytoplasmic calcium elevation and disease resistance in rice. Cell Res. 29(10): 820-831.
    Pubmed KoreaMed CrossRef
  25. Xiao N, Wu YY, Li AL. 2020. Strategy for use of rice blast resistance genes in rice molecular breeding. Rice Sci. 27(4): 263-277.
  26. Xiao N, Wu YY, Pan CH, Yu L, Chen Y, Liu GQ, et al. 2017. Improving of rice blast resistances in Japonica by pyramiding major R genes. Front. Plant Sci. 7: 1918.
  27. Zhao HJ, Wang XY, Jia YL, Minkenberg B, Wheatley M, Fan JB, et al. 2018. The rice blast resistance gene Ptr encodes an atypical protein required for broad-spectrum disease resistance. Nat. Commun. 9(1): 1-12.
    Pubmed KoreaMed CrossRef

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