
Rice (
Cold stress can be classified as chilling (0-15°C) or freezing (< 0°C) stress. Cold stress at the seedling stage causes the delayed of growth and defective root growth. Especially, defective roots caused by cold stress cause suppress early-stage growth and tiller growth (Ham
In general, the most efficient method for preventing cold-stress damage is a genetic modification to develop tolerant varieties with the aid of genetic studies, many of which have tried to find QTLs and have identified genes related to cold tolerance. A Genome-wide association study (GWAS) is the analysis that finds the linkage between a single nucleotide polymorphism (SNP) marker and phenotype through the association analysis. Recently, a GWAS has been widely used to discover the genetic control of cold tolerance in rice seedlings (Shakiba
The KRICE_CORE of 136 accessions (Kim
The cold-water treatment for rice seedling were conducted in 2020 at the Chuncheon Substation of the National Institute of Crop Science, RDA (Chunchon, Korea, 127’2’E, 37 16’N, 36 m a.s.l). Rice seedlings were placed in the facility of Chuncheon’s cold-water storage unit which is 50 m long and 8 m wide. This facility can supply 13 ± 1°C of cold water through the cold-water outlet (70 cm gap, 8 mm diameter). Germinated rice seeds were placed in 72-cells trays (each cell size was 3.8 cm × 3.8 cm × 4.5 cm). After two weeks of growing in the normal greenhouse condition, the trays were placed in the cold-water treatment facility and were irrigated 13 ± 1°C cold water for ten days. After the cold treatment, the seedlings recovered for seven days in the greenhouse. The phenotypes of the recovered seedlings were scored. The score standard is ranged from 1 to 9, based on the is evaluated change in leaf color (Fig. 1).
The score of 1 represents symptoms of no change in leaf color, 5 represents a change to light yellow in half of the leaf, and 9 represents an almost entirely dead leaf.
In the first step, we removed SNPs displaying a minimal allele frequency (frequency of the minor allele) lower than 5% were removed. In the GWAS analysis, we used the Mixed Linear Model (MLM) implemented in the GAPIT R package (version 3.0) were used. For MLM with Q-matrix and K-matrix, we computed the kinship (K matrix) and the first three principal components (PCs) of a PCA of genomic data as the Q matrix. The PCA matrix was generated by Plink software (Purcell
We extracted candidate genes ware extracted in the range of ± 250 kb on the lead SNP position (region of QTL) based on RAP-DB (http://rapdb.dna.affrc.go.jp/). We obtained the expression patterns of the candidate genes inside the QTLs from the Rice Expression Profile Database (Rice XPro: https://ricexpro.dna.affrc.go.jp/). We used haplotype analysis for the selected candidate genes, using all SNP markers from the gene, but excluded missing and heterozygote data. Among the groups of haplotypes, we did phenotypic comparisons with a one-way ANOVA followed by LSD through SAS 9.4.
We used six rice subgroups consisting of 136 rice accessions were used for evaluating cold tolerance under the cold-water irrigating. The six subgroups were
The average score of the 136 lines was 5.8, where 39 accessions showed a cold-tolerance score of 9. We compared the variation between the subspecies groups (Fig. 2B). The average score of
The Manhattan plots for the markers significantly associated with cold tolerance at the seedling stage are shown in Fig. 3
. Associations showing higher than the threshold of ‒log10(P) = 4.6 were detected as a significant association. The significantly associated SNP makers located within ± 250 kb on the lead SNP position was designated to one QTL locus of the lead SNP markers. Seven association loci were detected (Fig. 3, Table 1)
Table 1 . The locations of QTL of detected in GWAS and previously reported QTL.
QTL | Lead SNP | Chr | ‒log10(P) | PVE(%) | Reported QTL | Reference of previously reported QTLs |
---|---|---|---|---|---|---|
Related trait | ||||||
7,239,225 | 1 | 4.63 | 8.63 | Drought tolerance | Wan | |
24,297,420 | 1 | 4.78 | 8.96 | Drought tolerance | Li | |
1,094,354 | 3 | 4.6 | 8.55 | Drought tolerance | Hemamalini | |
24,641,646 | 6 | 4.74 | 8.87 | Drought tolerance | Zhang | |
20,640,777 | 7 | 4.51 | 8.34 | Cold tolerance | Hou | |
18,189,402 | 10 | 4.92 | 9.29 | Drought tolerance | Ali | |
22,021,391 | 12 | 5.02 | 9.51 | Blast resistance | Bagali |
. Two associated QTLs were detected on chromosome 1, designated as
To identify candidate genes responsible for cold tolerance under the cold-water irrigating, we extracted all annotated genes located within 500 kb of the QTL regions were extracted based on the RAP-DB (IRGSP 1.0). There were 966 genes were in the seven QTL regions where 286 genes were located in chromosome 1, 176 genes in chromosome 3, 132 genes in chromosome 6, 160 genes in chromosome 7, 139 genes in chromosome 10, and 73 genes in chromosome 12. We looked for the expression patterns of the 966 genes in the previously reported RNA-seq database (Kawahara
We used only the SNPs in exon regions for analyzing haplotypes and haplotype variations, where heterozygous SNPs and SNPs with missing data were filtered. The candidate gene of Os01g0228600 encodes a cytosolic hydroxypyruvate reductase. It contained six non-synonymous SNPs (C→T, Chr1_7100465, A→V substitution; G→C, Chr1_7100466, A→P substitution; C→T, Chr1_7100621, A→V sub-stitution; G→A, Chr1_7102701, D→N substitution; T→G, Chr1_7102745, L→R substitution; T→C, Chr1_7102844, L→P substitution) that formed five haplotypes (Fig. 4). The Hap5 of Os01g0357800 was the superior genotype in cold tolerance. The candidate gene of Os03g0115000 encodes a cupredoxin domain containing protein. It contained two non-synonymous SNPs SNPs (G→T, Chr3_851240, A→S substitution; T→C, Chr3_851346, L→P substitution) that formed three haplotypes (Fig. 5). The cold-tolerance score of Hap1 differed from that of Hap2 and Hap3.
The candidate gene of Os06g0612800 encodes a stress associated protein (SAP) gene family. It contained a non-synonymous SNPs SNPs (C→T, Chr6_851240, M→T substitution) that formed two haplotypes (Fig. 6). The cold tolerance score of Hap1 differed significantly from that of Hap2. The candidate gene of Os10g0482900 encodes a thioredoxin fold domain containing protein. It contained three non-synonymous SNPs (T→G, Chr10_18232294, S→R substitution; A→G, Chr10_18232294, K→E sub-stitution; C→T, Chr10_18232851, A→V substitution) that formed three haplotypes (Fig. 7). The cold tolerance score of Hap1 differed from that of Hap2 and Hap3. The candidate gene of Os12g0552500 encode a universal stress protein domain containing protein. It contained two non-synonymous SNPs (T→G, Chr12_22430675, S→A substitution; G→T, Chr12_22431727, A→S substitution) that formed two haplotypes (Fig. 8). The Hap2 differed significantly from that of Hap1.
In general, rice cold tolerance in seedling rice has been evaluated in the temperature-controlled growth chamber, where the rice shoot is mainly affected by cold air. In this study, we evaluated the phenotypic variations as a cold-tolerantce score for rice seedling under cold- water irrigating, where the irrigated cold water continuously affected mainly the roots and basal shoots. The treated seedlings recovered from the cold treatment by replacing them in the greenhouse for seven days. Therefore, the cold-tolerance score represented both the damage from the cold treatment and the recovering ability. The overall distribution of cold-tolerance scores was skewed to the right (cold sensitive), implyingd that the cold treatment in this study was effective.
Among the detected seven QTLs, five were overlapped with previously reported QTLs for unfavorite environ-mental conditions. Interestingly, the location of
Based on the gene expression patterns, haplotype analysis, and previously known function of the genes, we suggested five candidate genes as for cold tolerance. Os01g0228600 encodes a cytosolic hydroxypyruvate reductase (HPR) which is responsible for the conversion of hydroxypyruvate into glycerate in the photorespiration cycle (Shi
From previously reported studies and the known functions of these five candidate genes, the direct and indirect evidence suggested their possible role in cold tolerance in rice; so it is highly worthwhile to conduct future evaluation of the molecular mechanisms of these genes in stress response in cold tolerance and other abiotic stress as well.
This research was funded by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through Agri-Food Export Business Model Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (320105-3).
This research was funded by Rural Development Administration, grant number PJ01579403.
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