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Article

Genome-Wide Association Study of Sheath Blight Resistance within a Core Collection of Rice (Oryza sativa L.)

by
Dong Fu
1,†,
Kaizhen Zhong
2,†,
Zhengzheng Zhong
2,
Guocheng Hu
2,
Peng Zhang
2,* and
Hanhua Tong
2,*
1
State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan 430062, China
2
State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2022, 12(7), 1493; https://doi.org/10.3390/agronomy12071493
Submission received: 17 May 2022 / Revised: 13 June 2022 / Accepted: 17 June 2022 / Published: 22 June 2022
(This article belongs to the Special Issue Discovery and Utilization of Germplasm Resources in Rice)
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Abstract

:
Sheath blight disease (ShB) is considered to be the second most important disease affecting rice, and the genetic mechanism of ShB resistance in rice is great complicated. Uncovering genetic mechanism of ShB resistance and strong resistant varieties in rice are the premise for ShB resistance improvement. A rice ShB genome-wide association study (GWAS) was performed using approximately five million SNPs within Ting’s core collection. “Early pradifice”, one typical japonica, was determined to be the most resistant variety in both 2016 and 2017. A total of 34 and four significant (p ≤ 1.93 × 10−8) SNPs were observed in 2016 and 2017, respectively. Moreover, 23 of 34 and two of four gene-based SNPs not reported in previous studies in 2016 and 2017, respectively, were identified as significantly associated with rice ShB resistance. Furthermore, we performed GO (gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses for the genes located at a region within 10 kb of the significant SNPs. Both in 2016 and 2017, we observed that genes were uniquely enriched in the regulation of transcription and RNA processing in the category of “biological process”, plasma membrane, nucleus, integral component of membrane and cell wall in the category of “cellular component”, and ATP binding in the category of “molecular function”. The results of the present study may establish a foundation for further research investigating these elite genes and utilizing the resistant varieties in Ting’s core collection to improve rice ShB resistance.

1. Introduction

Rice (Oryza sativa L.) is one of the most important crops worldwide, as it feeds more than 50% of the world’s population. Biotic stresses caused by plant pathogens are the primary limiting factors affecting rice production [1]. Sheath blight disease (ShB), which is caused by Rhizoctonia solani AG1-1A [2,3], is considered to be the second most important disease in rice (Oryza sativa L.) and causes yield losses ranging from 20 to 50% [4,5]. ShB is notably difficult to control due to its wide host range and high genetic variability [3]. Reducing the threat of ShB has long been a major issue facing rice pathologists and breeders [3,4,6,7,8,9,10,11,12].
In addition, ShB in rice is thought to be a typical quantitative trait controlled by a number of genes, and more than 50 quantitative trait loci (QTLs) for rice ShB resistance, distributed over all 12 rice chromosomes, have been identified [3,6,8,13,14,15,16,17,18,19,20,21,22,23,24]. Most QTL mapping in rice ShB was performed by using conventional linkage mapping methods in the segregating populations derived from the crossing between typical ShB-tolerant and ShB-sensitive rice varieties that was performed in previous studies [3]. Genome-wide association studies (GWASs) could overcome the limitations (i.e., high cost, poor mapping resolution, and only two alleles that can be studied) of linkage mapping [25] and enables researchers to use the postgenomic data to exploit natural genetic diversity and identify elite genes in the genome [26]. Moreover, GWAS has been used to identify natural variations for ShB resistance in maize [27]. To the best of our knowledge, at least four GWAS have been performed on rice ShB. The first GWAS on rice ShB was performed in 217 sub-core rice entries with 155 markers and demonstrated a significant association of ten marker loci [28]. Moreover, the second rice ShB GWAS detected 11 single-nucleotide polymorphism (SNP) loci significantly associated with ShB resistance using 299 diverse rice varieties and 44,000 SNP markers [29]. The third rice ShB GWAS was performed using 2,977,750 SNPs to analyze 563 rice accessions [30]. Finally, the fourth study used 700,000 SNPs to perform GWAS on the phenotype of 228 rice accessions [31]. The phenotyping of the above four GWASs on rice ShB resistance was performed for only one year. Furthermore, compared with the above four studies, no higher-resolution GWAS on rice ShB resistance within natural populations has been performed.
Ting’s rice collection, which is one of the earliest rice collections in China, consists of 150 varieties constructed from 2262 of 7128 original landraces [32]. Ting’s core collection has been used for association mapping of rice agronomic traits and aluminum tolerance, and abundant genetic diversity has been identified [33,34,35,36]. Therefore, Ting’s core collection may be an appropriate population for GWASs on rice ShB resistance.
In the present study, a GWAS for rice ShB resistance based on lesion length/plant height (LL/PH) was performed using Ting’s core collection with more than 5.1 million high-quality SNPs. Candidate regions identified by GWAS were compared with regions identified as QTLs in previous studies and with ShB-resistant mutants and/or candidate genes. This study provides important information regarding candidate genes for ShB resistance improvement in rice.

2. Materials and Methods

2.1. Plant Material

Ting’s core collection with 150 accessions of rice landraces was used in the present study. The information for these accessions is shown in Supplementary Table S1. Lemont, which was reported to be susceptible to ShB disease, and Teqing and Jasmine 85, which were proven to be resistant, were selected as the ShB response controls.

2.2. Phenotyping

Ting’s core collection was cultivated at the farm of the China National Rice Research Institute, Hangzhou (30°3′ N, 120°2′ E), during the late season (May–October) in 2016 and 2017. The spaces between rows and between plants were set to 26 and 20 cm, respectively. Twenty-four plants of each variety were grown in four rows with six plants per row. Field management was performed as a common practice in Hangzhou, but fungicides were not utilized. Moreover, pesticides were not utilized before 7 d when inoculated. The inoculation was conducted following the study of Zeng et al. (2017) [37] with minor revisions. Rhizoctonia solani isolate ZJ03 was donated from Dr. Yuxiang Zeng at the China National Rice Research Institute. After ZJ03 was cultured on Petri dishes containing potato dextrose agar medium in the dark at 28 °C, truncated bamboo toothpicks (2.0–2.5 cm long) covered with mycelia were used to penetrate the third leaf sheath, counting from the top at the flowering time when 30% of the individuals of one variety started flowering. Lesion length/plant height was recorded at 30 d after inoculation. Two tillers of each of the three individual plants of each accession located in the middle of the second row were inoculated; thus, six tillers were inoculated for each accession. The method of measuring plant height is identical to that described in detail in our previous study.

2.3. GWAS

Genomic DNA from a single plant was used for sequencing. Re-sequencing was performed by Illumina HiSeqTM 4000 with 6~7 folds of genome coverage. The reads were mapped onto the rice reference genome (IRGSP 1.0) using bwamem with the –M option in BWA software [38], and the mapped reads were realigned by using RealignerTargetCreator in GATK [39]. SNPs were labeled with the −glm BOTH option of UnifiedGenotyper in GATK. After filtering the SNPs with low minor-allele frequency (5%) with vcftools in GATK, a total of 5,173,707 SNPs from 150 varieties were used for GWAS, which is identical to our previous studies, and were employed for identifying the population structure matrix, kinship matrix, and GWAS. Detailed information on the above analysis is described in our previous studies [33,36]. A mixed model was performed using EMMAX software [40]. p ≤ 1.93 × 10−8 (p = 0.1/n, n = total number of markers used [41], which corresponds to −log10(p) = 7.71) was used as the significance threshold in the present study.

2.4. Annotation of Significant Genes within GWAS

To determine whether genes surrounding significant loci are enriched for specific GOs, the genes located at a region of ±10 kb near the significant SNPs were selected as candidates for annotation and pathway analysis. Cluster of orthologous groups (COG) analysis of proteins was performed using the NCBI website (http://www.ncbi.nlm.nih.gov/COG/, accessed on 15 December 2017). We used WEGO 2.0 (http://wego.genomics.org.cn/, accessed on 20 July 2018) [42] to visualize the gene ontology (GO), which was identified as significantly associated with ShB resistance. Moreover, we conducted pathway-based analysis for differentially expressed genes by the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway database using DAVID (https://david.ncifcrf.gov/, accessed on 25 December 2017) [43] and KOBAS 3.0 (http://kobas.cbi.pku.edu.cn/kobas3/genelist/, accessed on 25 December 2017) [44]. TopGO analysis was performed in R package.

3. Results

3.1. Evaluating the ShB Resistance of Ting’s Core Collection

In the present study, lesion length/plant height (LL/PH) was selected as the metric of ShB resistance. Lemont, Teqing, and Jasmine 85, which were selected as the ShB response controls, showed no identical resistance in 2016 and 2017 (Figure 1). A wide range of phenotypic variation in ShB resistance was demonstrated in Ting’s core collection in both 2016 and 2017 (Figure 1 and Table S1). The variety with the largest average LL/PH was a typical japonica variety, “Gui zao bai he”, while the variety “Early pradifice” with the smallest average LL/PH, belonged to the typical japonica subgroup in both years. Moreover, in our previous study, two subgroups were detected in Ting’s core collection, i.e., indica and japonica [33,36]. The median values of LL/PH in japonica were larger than those of indica in 2016 and 2017 (Figure 2).

3.2. GWAS

A total of 5,173,707 SNPs were applied in a GWAS of ShB resistance using the EMMAX method. A total of 34 and four significant (p ≤ 1.93 × 10−8) SNPs were observed for ShB resistance in 2016 and 2017, respectively (Figure 3A,B and Table 1), and our results indicated that the false positives were well controlled in the present study (Figure 3C,D). The most significant SNPs on each chromosome are shown in Table 1. In the present study, 11 and two gene-based SNPs were identified as significantly associated with rice ShB resistance, as reported in previous studies [6,8,13,14,17,21,45,46] in 2016 and 2017, respectively, while there was no identical SNP between the two years (Figure 3A,B, Table 2). However, SNPs (chr09_21,587,781) and SNPs (chr11_26,875,550) located in the major QTLs qShB9-2 [47] and qSBR11-1 [6], respectively, were identified at significant levels (p < 0.00001) in both 2016 and 2017 (Figure 3A,B).

3.3. Effects of Allele Variations

The four most significant SNPs, i.e., 36,918,689 on chromosome 1, 2,779,539 and 19,660,297 on chromosome 8, and 25,034,832 on chromosome 11 in 2016, were selected for allele variation analysis (Table 3). Furthermore, the LL/pHs of varieties with minor alleles for the above four SNPs were greater than those with major alleles (Figure 4).

3.4. Classification of Significant Genes in GWAS

As shown with GO analyses of significant genes in GWAS, in both 2016 and 2017, most of the genes located at a region within 10 kb of the significant SNPs were categorized into metabolic process, cellular process, response to stimulus, biological regulation, localization, and establishment localization for “biological process”. Moreover, most candidate genes were categorized into cell part, cell, organelle, membrane, organelle part, membrane part, and macromolecular complex for “cellular component”. Most candidate genes were categorized into binding and catalytic activity for “molecular function” (Supplementary Figure S1). TopGO analysis was performed to analyze the GO data in depth. Two GO terms, i.e., regulation of transcription (GO:0006355) and RNA processing (GO:0006396), in the category “biological process” might be correlated with ShB resistance in both 2016 and 2017 (Supplementary Figures S2 and S3, and Table 4). There were four GO terms, i.e., plasma membrane (GO:0005886), nucleus (GO:0005634), integral component (GO:0016021), and cell wall (GO:0005618), in the category for “cellular component” observed to correlate with ShB resistance (Supplementary Figures S4 and S5, and Table 4), while only one GO was determined to be associated with ShB resistance, i.e., ATP binding (GO:0005524) in the category for “molecular function” in two years (Supplementary Figures S6 and S7, and Table 4). Cluster of orthologous groups (COG) analysis of proteins was performed for the candidate genes that were used in GO analysis. Some of the candidate genes were classified into such COG functions as amino acid transport and metabolism, and general function prediction was performed in both 2016 and 2017 (Supplementary Figure S8).

3.5. KEGG Pathway Analysis

Furthermore, we conducted pathway-based analysis for the genes located at a region within 10 kb of the significant SNPs by the KEGG pathway database. Three pathways, i.e., KO00020: TCA cycle, KO00630: glyoxylate and dicarboxylate metabolism, and KO00300: lysine biosynthesis pathways, were detected to be related to ShB resistance in 2016 (Supplementary Figures S9–S11), while only one pathway, i.e., KO03040: spliceosome, was identified in 2017 (Supplementary Figure S12). Furthermore, KO1647, which is a citrate synthase (enzyme number 2.3.3.1), was found to be involved in ShB resistance in 2016 (Supplementary Figures S9 and S10, and Table 5). KO1586, which is a diaminopimelate decarboxylase (enzyme number 4.1.1.20), was identified as possibly related to ShB resistance in 2016 (Supplementary Figure S11 and Table 5). K12817, which is a brite named pre-mRNA-splicing factor 18 (brite number KO00001), was detected in the spliceosome pathway in 2017 (Supplementary Figure S12 and Table 5).

4. Discussion

Ting’s core collection may represent a useful reservoir of rice genotypes and a potential source of beneficial alleles for ShB resistance in rice breeding because the abundant genetic variations of agronomic traits and aluminum tolerance in this collection have been reported in our previous studies [32,33,34,35,36]. Although the population size of Ting’s core collection is smaller than that of the other two studies examining ShB resistance GWAS [29,30], the resolution in the present study is higher than that of the above two studies. Moreover, our previous study [36] observed that the phenotypic diversity of several agronomic traits was comparable to that of populations with larger population sizes [48,49,50], or even higher for some agronomic traits. Ting’s core collection consists of rice landraces that are considerably easier to utilize than wild rice in rice breeding because they have more abundant genetic diversity and remain in an intermediate stage between wild rice and cultivars in their domestication histories [37]. Ting’s core collection shows a less complicated population structure, a shorter LD decay distance, and a lower kinship value than other populations utilized in rice GWAS [33]. Thus, Ting’s core collection may be a suitable population for rice ShB resistance. Furthermore, we compared the significant loci between four GWAS and our study in rice ShB resistance. We detected one significant SNP (8,675,283) on chromosome 7 in 2016 which was very close to SAL ID L14 (chr. 7, 8,569,372, Figure 3A) identified in GWAS research about rice ShB resistance [31]. In addition, there was a peak with consecutive loci detected in 2016 which are below the significant threshold (p ≤ 1.93 × 10−8) located in the region of qSB-6 on chromosome 6 (Figure 3A) [29], while there was no identical region associated with ShB resistance between the study of Jia et al. [28] and our study, as well as between the study of Oreiro et al. [31] and our study. The reasons for few loci being identical in different GWAS research might be (i) differential rice population, or (ii) differential traits representing rice ShB resistance.
Approximately 5.1 million SNPs were employed in the present study, which might be the highest resolution in rice GWAS. A mixed linear model was performed by EMMAX software, and this software’s strong points have been described in a previous study [51]. The abovementioned information elucidates and supports the results obtained through ShB GWAS.
In the present study, ShB resistance was estimated by one trait, i.e., lesion length/plant height (LL/pH), which had been utilized in previous studies [20,30,37]. It appears that LL/pH is the most applicable method for phenotyping ShB resistance within Ting’s core collection because pH can affect the development of ShB pathogens in rice plants and influence eventual phenotypic outcomes [14]. Moreover, pH was frequently reported to be closely linked with ShB resistance [6,8,21]. Furthermore, genetic diversity in Ting’s core collection was identified as abundant, and pH in this collection varied from 60 cm to 230 cm [34]. LL might not reflect the real resistance to ShB; for instance, a value of 16.7% LL/pH cannot be compared to an 8.8% one for evaluating rice ShB resistance, which does not mean that the ShB resistance of the variety with 60 cm pH is stronger than that with 230 cm pH. Thus, we did not invest time estimating ShB resistance by other traits in the present study.
As hypothesized, there was a wide range of phenotypic variation for ShB resistance in Ting’s core collection in both 2016 and 2017. The varieties in Ting’s core collection, which were identified with small LL/pH values, could be employed as donor parents to introgress ShB resistance in elite rice varieties. ShB resistance was estimated in both the indica and japonica subgroups. Our results showed that the ShB resistance of indica was stronger than that of japonica, which was in keeping with the findings obtained in two previous studies [29,30].
There was no identical significant SNP in 2016 and 2017, but identical SNPs located in previous major QTLs confirmed in many previous studies, such as qShB9-2 and qSBR11-1, could be detected in two years in our study if a lower significant threshold value was set (p < 0.00001, Figure 3). Previous QTL regions were sufficiently wide such that they could be detected in different years and multiple locations in previous studies (QTLs were summarized in a review by Molla et al. (2020)) [3]. It was indicated that the environment had more influence than the genotype on ShB resistance in the field, which might explain the lack of identical significant SNPs found in the two years [37]. Due to the wide range in QTL regions for rice ShB resistance, 13 gene-based SNPs that were significantly related to ShB resistance were supported by previous studies. The above 13 candidate genes could be hypothesized to defend against ShB (Table 2). For instance, LOC_Os02g42310, which encodes the putative serine carboxypeptidase homolog and serine carboxypeptidase, has been reported to be related to disease resistance in oats [51]. LOC_Os11g40780 is annotated as encoding a disease-resistance protein. LOC_Os11g05700 encodes an ABC transporter family protein, while an ABC transporter family protein has been reported to be related to disease resistance in barley [52].
More than 50 QTLs for rice ShB resistance distributed on all of the 12 chromosomes were identified, but no single gene was cloned. The reason for the above finding might be that (i) rice ShB resistance is controlled by a number of QTLs, and there are no major QTLs responsible for ShB resistance, and (ii) rice ShB is influenced more easily by the environment than genotype, which leads to the determination of a gene’s function. It appears that mining the critical genes in possible pathways of ShB resistance is more important than locating or cloning a major QTL. Thus, we performed GO and KEGG analysis for the genes located at a region within 10 kb of the significant SNPs in two different years.
Although no identical SNPs were detected, the same GO terms were found in the two years (Supplementary Figures S2–S7 and Table 4). Identical GO terms were reported to be associated with ShB resistance in previous studies, such as ATP binding [53,54], the nucleus [55,56], and the plasma membrane [57]. Moreover, four KEGG pathways were identified in the present study, and the TCA cycle [58], glyoxylate and dicarboxylate metabolism, and lysine biosynthesis [59,60,61,62,63] were reported to be related to disease resistance in plants in previous studies. The key genes regulating citrate synthase in the TCA cycle and glyoxylate and dicarboxylate pathway, relating to diaminopimelate decarboxylase in the lysine biosynthesis pathway, as well as controlling pre-mRNA-splicing factor 18 in the spliceosome pathway, should be further studied in the future to facilitate the breeding of rice with greater ShB resistance.

5. Conclusions

In the present study, Ting’s core collection showed abundant genetic variation for rice ShB resistance and was proven to be a suitable natural population for studying ShB resistance. “Early pradifice”, one typical japonica which was determined to be the most resistant variety in both years, might be an important donor of rice ShB elite resistant genes. Furthermore, the results of the study may establish a foundation for further research investigating these elite genes in Ting’s core collection to improve rice ShB resistance. The novel candidate genes and the reported loci identified for rice SNP ShB resistance in 2016 in the present study are especially worth studying in future.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/agronomy12071493/s1, Figure S1: Gene ontology (GO) plot of the genes locating at a region of ±10 kb near to the significant s. A. 2016; B. 2017, Figure S2: TopGO analysis of the genes locating at a region of ±10 kb near to the significant SNPs for biological process in 2016, Figure S3: TopGO analysis of the genes locating at a region of ±10 kb near to the significant SNPs for biological process in 2017, Figure S4: TopGO analysis of the genes locating at a region of ±10 kb near to the significant SNPs for cellular component in 2016, Figure S5: TopGO analysis of the genes locating at a region of ±10 kb near to the significant SNPs for cellular component in 2017, Figure S6: TopGO analysis of the genes locating at a region of ±10 kb near to the significant SNPs for molecular function in 2016, Figure S7: TopGO analysis of the genes locating at a region of ±10 kb near to the significant SNPs for molecular function in 2017, Figure S8: Cluster of orthologous groups of proteins (COG) function classification of the genes locating at a region of ±10 kb near to the significant SNPs. A. 2016; B. 2017, Figure S9: TCA cycle pathway for the genes locating at a region of ±10 kb near to the significant SNPs by the KEGG pathway database in 2016, Figure S10: Lysine biosynthesis pathway for the genes locating at a region of ±10 kb near to the significant SNPs by the KEGG pathway database in 2016, Figure S11: Glyoxylate and dicarboxylate metabolism pathway for the genes locating at a region of ±10 kb near to the significant SNPs by the KEGG pathway database in 2016, Figure S12: Spliceosome pathway for the genes locating at a region of ±10 kb near to the significant SNPs by the KEGG pathway database in 2017, Table S1: Accessions, variety names, origin, germplasm types and ShB resistance of 150 rice varieties in Ting’s core collection.

Author Contributions

D.F., K.Z., H.T. and P.Z. conceived and designed the experiments. D.F. and K.Z. performed the experiments. D.F., K.Z., Z.Z., G.H., H.T. and P.Z. analyzed the data. P.Z. and H.T. contributed reagents/materials/analysis tools. K.Z., P.Z. and H.T. wrote the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by a fund of Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City which provided funding (2021JJLH0041), a fund of the National Natural Science Foundation of China grant 31701401, and a fund of Nanfan special project, Chinese Academy of Agricultural Sciences (YBXM06).

Data Availability Statement

Not applicable.

Acknowledgments

We are grateful to Yuxiang Zeng from China National Rice Research Institute, for providing Rhizoctonia solani isolate ZJ03, and the anonymous reviewers.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

ShBSheath blight resistance
QTLQuantitative trait locus
DNADeoxyribonucleic acid
RNARibonucleic acid
FpKMFragments per kilobase of exon model per million mapped reads
GOGene ontology
KEGGKyoto Encyclopedia of Genes and Genomics

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Figure 1. Distribution of ShB resistance (lesion length/plant height) in the Ting’s core collection in 2016 and 2017.
Figure 1. Distribution of ShB resistance (lesion length/plant height) in the Ting’s core collection in 2016 and 2017.
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Figure 2. Evaluation of ShB resistance (lesion length/plant height) in indica and japonica in two years. (A) Lesion length/plant height in 2016; (B) lesion length/plant height in 2017.
Figure 2. Evaluation of ShB resistance (lesion length/plant height) in indica and japonica in two years. (A) Lesion length/plant height in 2016; (B) lesion length/plant height in 2017.
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Figure 3. Manhattan plots of EMMAX for ShB resistance in genome-wide association studies. Negative log10(p) values from a genome-wide scan are plotted against position on each of 12 chromosomes. (A) Manhattan plots of EMMAX for ShB resistance in 2016. Red and blue horizontal dashed line indicates the genome wide significant threshold p ≤ 1.93 × 10−8 and p < 0.00001, respectively. Red arrows represent the significant SNPs which are located in the regions of qShB9-2 and qSBR11-1. Green arrows and black arrows represent the novel loci in the present study and the loci which were reported by previous studies. (B) Manhattan plots of EMMAX for ShB resistance in 2017. (C) Plots of observed versus expected p-values in 2016. (D) Plots of observed versus expected p-values in 2017. Red symbol represents expected p-values, and blue symbol represents observed p-values.
Figure 3. Manhattan plots of EMMAX for ShB resistance in genome-wide association studies. Negative log10(p) values from a genome-wide scan are plotted against position on each of 12 chromosomes. (A) Manhattan plots of EMMAX for ShB resistance in 2016. Red and blue horizontal dashed line indicates the genome wide significant threshold p ≤ 1.93 × 10−8 and p < 0.00001, respectively. Red arrows represent the significant SNPs which are located in the regions of qShB9-2 and qSBR11-1. Green arrows and black arrows represent the novel loci in the present study and the loci which were reported by previous studies. (B) Manhattan plots of EMMAX for ShB resistance in 2017. (C) Plots of observed versus expected p-values in 2016. (D) Plots of observed versus expected p-values in 2017. Red symbol represents expected p-values, and blue symbol represents observed p-values.
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Figure 4. Effect analysis of allelic variations of top four significant SNPs on ShB resistance (lesion length/plant height) in 2016. (A) SNP position on Chro. 01 36,918,689. (B) SNP position on Chro. 08 2,779,539. (C) SNP position on Chro. 08 19,660,297. (D) SNP position on Chro. 11 25,034,832. Lesion length/plant height with reference and alternative allele in Ting’s core collection are shown with red box and green box in this figure, respectively. “*” represents the significant difference in p < 0.05 level (t test).
Figure 4. Effect analysis of allelic variations of top four significant SNPs on ShB resistance (lesion length/plant height) in 2016. (A) SNP position on Chro. 01 36,918,689. (B) SNP position on Chro. 08 2,779,539. (C) SNP position on Chro. 08 19,660,297. (D) SNP position on Chro. 11 25,034,832. Lesion length/plant height with reference and alternative allele in Ting’s core collection are shown with red box and green box in this figure, respectively. “*” represents the significant difference in p < 0.05 level (t test).
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Table
Table 1. Summary of association mapping results for sheath blast resistance using EMMAX.
Table 1. Summary of association mapping results for sheath blast resistance using EMMAX.
Chr.Number of Significant Loci (−log10(p) > 7.71)SNP Position with the Highest −log10(p) (−log10(p) > 7.71) on Each Chromosome (IRGSP-1.0)
2016201720162017
11036,186,689 (−log10(p) = 9.17)-
23025,444,072 (−log10(p) = 7.78)-
301s-28,137,771 (−log10(p) = 8.83)
4102,837,224 (−log10(p) = 8.30)-
500--
6201,526,864 (−log10(p) = 7.97)-
7508,675,285 (−log10(p) = 8.17)-
819019,660,297 (−log10(p) = 9.13)-
900--
1000--
113325,034,832 (−log10(p) = 9.47)2,598,775 (−log10(p) = 8.00)
1200--
Total344--
Table
Table 2. List of significant gene-based SNPs for sheath blast resistance using EMMAX.
Table 2. List of significant gene-based SNPs for sheath blast resistance using EMMAX.
YearChromosomeSNP Position (bp)p ValueGene IDAnnotationPrevious QTL
2016136,918,6896.75 × 10−10LOC_Os01g63660retrotransposon protein, putative, Ty3-gypsy subclassqSBD-1 [21], QRh1 [18,46], and qSBR1-2 [13]
225,444,0726.16 × 10−8LOC_Os02g42310OsSCP8 - Putative Serine Carboxypeptidase homologue, expressedqSB-2 [14]
225,444,1891.77 × 10−8LOC_Os02g42310OsSCP8 - Putative Serine Carboxypeptidase homologue, expressedqSB-2 [14]
61,526,8641.06 × 10−8LOC_Os06g03810expressed proteinqLH6 [13] and qShB6 [46]
78,675,2856.71 × 10−9LOC_Os07g15120transposon protein, putative, unclassifiedSAL ID L14 [30], qSB-7 [22], Rh-7 [15] and qShB7 [8]
710,552,0321.02 × 10−8LOC_Os07g17840transposon protein, putative, unclassified, expressedqSB-7 [22], Rh-7 [15] and qShB7 [8]
718,194,4511.84 × 10−8LOC_Os07g30740retrotransposon protein, putative, Ty3-gypsy subclassqSBR7-1 [6]
819,639,2774.48 × 10−9LOC_Os08g31680retrotransposon protein, putative, unclassified, expressedqLH8, qRLH8 and qLL8 [13]
819,668,1751.01 × 10−8LOC_Os08g31740carboxyl-terminal proteinase, putative, expressedqLH8, qRLH8 and qLL8 [13]
819,696,9781.69 × 10−8LOC_Os08g31769expressed proteinqLH8, qRLH8 and qLL8 [13]
1124,388,3931.91 × 10−8LOC_Os11g40780disease resistance protein, putative, expressedQDs11a [46]
2017112,598,7759.85 × 10−8LOC_Os11g05690amino acid permease family protein, putative, expressedqShB11 [8]
112,610,0511.09 × 10−8LOC_Os11g05700ABC transporter family protein, putative, expressedqShB11 [8]
Table
Table 3. Top highest significant association signals for sheath blast resistance using EMMAX.
Table 3. Top highest significant association signals for sheath blast resistance using EMMAX.
Chr.Position (IRGSP-1.0)Minor AlleleMajor AlleleMinor Allele Frequency−log10(p)
136,918,689TC0.209.17
82,779,539TC0.098.84
819,660,297TC0.149.13
1125,034,832TC0.079.47
Table
Table 4. GO terms of candidate genes through topGO both in 2016 and 2017 (p < 0.05).
Table 4. GO terms of candidate genes through topGO both in 2016 and 2017 (p < 0.05).
GO CategoryGO IDGO Term AnnotationSignificant Genes Numberp Value
2016201720162017
Biological processGO:0006355regulation of transcription, DNA-templated221.00 × 10−31.00 × 10−3
GO:0006396RNA processing235.00 × 10−37.00 × 10−3
Cellular componentGO:0005886plasma membrane241.50 × 10−136.20 × 10−14
GO:0005634nucleus672.20 × 10−111.90 × 10−11
GO:0016021integral component of membrane341.40 × 10−98.10 × 10−10
GO:0005618cell wall437.70 × 10−86.50 × 10−8
Molecular functionGO:0005524ATP binding611.60 × 10−62.30 × 10−6
Table
Table 5. KEGG terms of candidate genes both in 2016 and 2017.
Table 5. KEGG terms of candidate genes both in 2016 and 2017.
KEGG PathwayKEGG OrthologyKEGG DefinitionKEGG Enzyme or Brite NumberYear
KO00020 TCA cycleKO1647citrate synthase2.3.3.12016
KO00630 Glyoxylate and dicarboxylate metabolism
KO00300 Lysine biosynthesisKO1586diaminopimelate decarboxylase4.1.1.202016
KO03040 SpliceosomeK12817pre-mRNA-splicing factor 18KO000012017
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Fu, D.; Zhong, K.; Zhong, Z.; Hu, G.; Zhang, P.; Tong, H. Genome-Wide Association Study of Sheath Blight Resistance within a Core Collection of Rice (Oryza sativa L.). Agronomy 2022, 12, 1493. https://doi.org/10.3390/agronomy12071493

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Fu D, Zhong K, Zhong Z, Hu G, Zhang P, Tong H. Genome-Wide Association Study of Sheath Blight Resistance within a Core Collection of Rice (Oryza sativa L.). Agronomy. 2022; 12(7):1493. https://doi.org/10.3390/agronomy12071493

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Fu, Dong, Kaizhen Zhong, Zhengzheng Zhong, Guocheng Hu, Peng Zhang, and Hanhua Tong. 2022. "Genome-Wide Association Study of Sheath Blight Resistance within a Core Collection of Rice (Oryza sativa L.)" Agronomy 12, no. 7: 1493. https://doi.org/10.3390/agronomy12071493

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