
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
Over 100 major resistance genes have been used to breed rice varieties with blast resistance against multiple pathogens (Fukuoka
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
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.
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).
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SNP genotyping and genotype calling of 857 breed lines were carried out using KNU Axiom Oryza 580K Genotyping Array (unpublished data; manuscript is being prepared).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
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
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.
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).
Manhattan plots for the markers significantly associated with rice blast resistance are shown in
Table 1 . Locations of QTLs detected in GWAS and previously reported QTLs and candidate QTLs.
QTL | Lead SNP | Chr | ‒log10( | Reported QTL | Reference of previously reported QTLs | |
---|---|---|---|---|---|---|
QTL ID | Related Trait | |||||
27,010,616 | 1 | 6.20 | - | - | - | |
30,002,637 | 4 | 5.39 | Bacterial blight resistance | He | ||
32,005,384 | 4 | 5.39 | Blast field resistance | Miyamoto | ||
27,670,800 | 11 | 7.10 | ||||
11,646,935 | 12 | 6.02 | Field resistance to leaf blast | Bagali |
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
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).
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