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 5^th, 2019. They were scored for leaf blast res-ponse (1-9) at tillering stage on August 20^th, 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 (http://pophelper.com) (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 (www.gramene.org).

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.

QTL	Lead SNP	Chr	‒log10(P)	Reported QTL		Reference of previously reported QTLs
				QTL ID	Related Trait
qRB1	27,010,616	1	6.20	-	-	-
qRB4.1	30,002,637	4	5.39	Xa2	Bacterial blight resistance	He et al. 2006
qRB4.2	32,005,384	4	5.39	qBFR-4-1	Blast field resistance	Miyamoto et al. 2001
qRB11	27,670,800	11	7.10
qRB12	11,646,935	12	6.02	qDLA-12-3	Field resistance to leaf blast	Bagali 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.

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).