
Rice (
In rice breeding, lodging resistance is an essential factor that affects the environment in order to achieve high grain yield (Setter
Many important agronomical traits are controlled by multiple genetic factors referred to as quantitative trait loci (QTLs) and affected by wide phenotype variation, including different environments. Analysis of QTLs can reveal the genetic basis of relationships between morphological and physiological traits (Kashiwagi
Molecular markers have been developed for map construction, QTL analysis, and genome-wide association studies. Previous rice studies reported QTL mapping based on restriction fragment length polymorphisms (RFLPs), randomly-amplified polymorphic DNA (RAPD), amplified fragment length polymorphisms (AFLPs), simple sequence repeats (SSRs), and cleaved amplified polymorphic sequence (CAPS) (Bernado 2008; Varshney
The advent of next-generation sequencing (NGS) technologies has enabled high-throughput genotyping and analysis for defining genome structure in various plant cultivars using fast and cost-effective platforms (Huang
In our previous study, we reported the detection of a major QTL within a large area related to stem diameter in the 160 recombinant inbred lines derived from a cross between ‘Milyang23’ and ‘Giho’ (MGRILs) (Lee
Two different rice types [‘Milyang23’ (M; a three-way cross of
The population of 160 RILs was derived from a cross between two parental lines generated over F25 and maintained at the National Institute of Agricultural Sciences (Jeonju, Republic of Korea) (Lee
Rice seeds were sown in a pot, as described in our previous study (Lee
A genetic map for the MGRIL population was constructed by resequencing data and comprised 3,563 SNP markers from our previous study (Lee
To narrow down a target QTL region, two additional InDel markers were amplified from the genomic DNA (gDNA) of MGRILs (Supplementary Table S1). Primers were designed using CLC Genomic Workbench (v.6.0; CLC Bio, Aarhus, Denmark) according to InDel regions from two different rice varieties (‘Milyang23’ and ‘Giho’). InDel sequences were designed between 500 bp of both the left and right flanking sequences for the forward and reverse primers. Polymerase chain reactions (PCR) was conducted in a total volume of 10 µL and contained 10 ng of gDNA, 0.2 mM of forward and reverse primers, and the Ex-Taq polymerase (Takara, Shiga, Japan) and under the following cycling conditions: initial denaturation at 95°C for 3 minutes, followed by 32 cycles of denaturation at 95°C for 30 seconds, annealing at 61°C for 30 seconds, extension at 72°C for 30 seconds and a final extension at 72°C for 2 minutes. The amplified products were visu-alized on a 1.5% (w/v) agarose gel.
The genotype from recombinant plants was selected between flanking markers involving the interval at 95% probability of QTL region. For one-way analysis of variance (ANOVA), the observed data from plant materials was used to test the phenotypic differences between three RILs and two parental lines using R software (R Core Team, 2013). Tukey’s tests for multiple comparisons were performed for the genetic analysis. Statistical analysis for mean comparison was performed at the 0.05 significance level.
To predict candidate genes from the target QTL region, we used the genome browser from Rice Annotation Project Database (RAP-DB; http://rapdb.dna.go.jp/) to annotate genes from the ‘Nipponbare’ reference genome (IRGSP- 1.0). Candidate gene detection and sequence variances identification were analyzed between ‘Milyang23’ and ‘Giho’. A previous study aligned resequencing data to the ‘Nipponbare’ reference sequence, which identified SNPs and InDels within this population (Lee
We identified phenotypic variations in nine stem traits in two parental lines and their 160 RILs (Table 1). The 160 RILs exhibited substantial variations, which resulted in all traits displaying transgressive segregation. ‘Milyang23’ (tongil rice) has a shorter culm and thicker diameter than ‘Giho’ (
Table 1 . Descriptive statistics of phenotypic variations for stem traits in 160 RILs derived from a cross between ‘Milyang23’ and ‘Giho’.
Traitz) | Parenty) | RIL population (n = 160)x) | |||||
---|---|---|---|---|---|---|---|
‘Milyang23’ | ‘Giho’ | Range | Mean ± SD | Skewness | Kurtosis | CV (%) | |
1IL | 27.1 | 30.5 | 15.17-40.57 | 27.76 ± 4.69 | 0.29 | 0.05 | 16.88 |
2IL | 13.3 | 15.4 | 1.63-23.60 | 15.64 ± 3.48 | ‒0.41 | 0.62 | 22.26 |
3IL | 7 | 10 | 3.97-20.80 | 12.55 ± 3.90 | ‒0.12 | ‒0.89 | 31.07 |
4IL | 4.5 | 1.9 | 0.00-15.27 | 8.37 ± 3.32 | ‒0.03 | ‒0.66 | 39.65 |
1ID | 2.38 | 1.28 | 1.28-2.80 | 1.80 ± 0.29 | 0.71 | 0.49 | 16.15 |
2ID | 3.92 | 2.61 | 2.23-4.42 | 3.11 ± 0.42 | 0.59 | 0.56 | 13.52 |
3ID | 4.39 | 3.01 | 2.90-5.79 | 3.81 ± 0.49 | 0.79 | 1.56 | 12.95 |
4ID | 4.88 | 3.27 | 1.85-6.79 | 4.36 ± 0.60 | 0.2 | 2.62 | 13.69 |
CLw) | 54 | 57.8 | 37.67-101.87 | 66.46 ± 13.66 | 0 | ‒0.84 | 20.56 |
z)First internode length (1IL, cm), second internode length (2IL, cm), third internode length (3IL, cm), fourth internode length (4IL, cm), first internode diameter (1ID, mm), second internode diameter (2ID, mm), third internode diameter (3ID, mm), fourth internode diameter (4ID, mm) and culm length (CL, cm).
y)‘Milyang23’ (tongil rice), ‘Giho’ (
x)Population size n = 160; SD, standard deviation; CV, coefficient of variation.
w)Phenotypic data was used from Lee
Table 2 . Correlation coefficient of stem traits in MGRIL population.
Traitz) | 1IL | 2IL | 3IL | 4IL | 1ID | 2ID | 3ID | 4ID |
---|---|---|---|---|---|---|---|---|
2IL | 0.590*** | |||||||
3IL | 0.402*** | 0.725*** | ||||||
4IL | 0.334*** | 0.570*** | 0.764*** | |||||
1ID | 0.361*** | 0.11 | 0.047 | ‒0.096 | ||||
2ID | 0.348*** | 0.257** | 0.106 | ‒0.040 | 0.749*** | |||
3ID | 0.294*** | 0.227** | 0.195* | 0.06 | 0.694*** | 0.815*** | ||
4ID | 0.219*** | 0.188* | 0.226** | 0.133* | 0.563*** | 0.716*** | 0.865** | |
CLy) | 0.741*** | 0.845*** | 0.850*** | 0.809*** | 0.151* | 0.201* | 0.230** | 0.221** |
z)First internode length (1IL, cm), second internode length (2IL, cm), third internode length (3IL, cm), fourth internode length (4IL, cm), first internode diameter (1ID, mm), second internode diameter (2ID, mm), third internode diameter (3ID, mm), fourth internode diameter (4ID, mm) and culm length (CL, cm).
y)Phenotypic data was used from Lee et al. (2020).
*, **, *** indicate the least significant at the level of 0.05, 0.01 and 0.001 respectively.
We integrated the genetic map generated by Lee
Table 3 . Detection of stem trait QTLs based on a high-resolution genetic map by Lee
Traitz) | Chr | QTL name | Position (cM) | Genetic interval (cM) | LOD | PVEy) | Additive effectx) |
---|---|---|---|---|---|---|---|
1IL | 1 | 163.3 | 162.5-164.1 | 13.14 | 20.65 | ‒2.16 | |
5 | 81.2 | 78.5-82.0 | 12.68 | 19.63 | 2.19 | ||
9 | 45.3 | 44.4-46.5 | 45.31 | 4.64 | ‒1.02 | ||
2IL | 1 | 162.3 | 162.0-163.0 | 26.24 | 51.1 | ‒2.60 | |
1 | 168.8 | 167.5-169.3 | 23.14 | 46.88 | ‒2.51 | ||
8 | 8.2 | 5.7-9.4 | 3.53 | 4.17 | 0.74 | ||
3IL | 1 | 163.3 | 163.0-163.9 | 30.07 | 49.92 | ‒2.78 | |
5 | 186.3 | 170.6-196.3 | 5.48 | 12.35 | 1.41 | ||
4IL | 1 | 154.5 | 154.1-154.8 | 8.43 | 15.74 | ‒1.40 | |
1 | 164.2 | 163.4-167.4 | 28.81 | 41.18 | ‒2.19 | ||
10 | 54 | 53.1-55.4 | 3.75 | 3.67 | ‒0.66 | ||
1ID | 1 | 25.1 | 24.5-26.3 | 5.01 | 8.93 | 0.09 | |
5 | 72.1 | 70.9-72.8 | 7.66 | 15.45 | 0.12 | ||
5 | 77.8 | 77.5-78.5 | 11.02 | 21.24 | 0.14 | ||
6 | 5.2 | 1.4-8.4 | 4.54 | 8.5 | 0.09 | ||
2ID | 1 | 25.1 | 24.4-25.4 | 3.83 | 7.33 | 0.12 | |
4 | 344.5 | 343.9-345.7 | 3.95 | 7.43 | 0.12 | ||
5 | 62.4 | 62.0-63.0 | 4 | 7.87 | 0.12 | ||
5 | 72.1 | 70.6-72.8 | 5.47 | 10.53 | 0.14 | ||
3ID | 5 | 73.1 | 71.9-73.4 | 5.77 | 10.86 | 0.16 | |
8 | 91.9 | 90.9-94.6 | 4.61 | 8.57 | 0.15 | ||
10 | 84.2 | 82.3-100.4 | 3.36 | 6.3 | ‒0.12 | ||
4ID | 1 | 40.9 | 39.6-41.3 | 4.13 | 8.08 | 0.16 | |
1 | 49.7 | 48.2-51.7 | 4.94 | 9.56 | 0.18 | ||
4 | 293.1 | 292.3-295.2 | 4.82 | 9.36 | ‒0.18 | ||
4 | 305.6 | 303.4-306.9 | 3.39 | 6.72 | ‒0.15 | ||
CL | 1 | 163.3 | 162.6-163.9 | 40.88 | 57.5 | ‒10.71 | |
5 | 72.8 | 72.1-73.3 | 7.97 | 7.34 | 4 | ||
5 | 80.9 | 80.4-81.2 | 11.15 | 9.84 | 4.97 | ||
6 | 0 | 0.0-0.9 | 5.68 | 4.62 | 3.04 |
z)First internode length (1IL, cm), second internode length (2IL, cm), third internode length (3IL, cm), fourth internode length (4IL, cm), first internode diameter (1ID, mm), second internode diameter (2ID, mm), third internode diameter (3ID, mm), fourth internode diameter (4ID, mm) and culm length (CL, cm).
y)Percentage of phenotypic variation explained by the QTL.
x)Additive effect, negativeand positive values of the additive effect indicated alleles from ‘Milyang23’ and ‘Giho’, with increasing trait score, respectively.
A total of 15 QTLs were identified among four IDs and showed PVEs and LOD values ranging from 6.30% to 21.24% and 3.36 to 11.02, respectively. Among these, four QTLs in the 1ID trait (
For the CL, a total of four QTLs were located at chromosomes 1, 5 and 6, with LOD values and PVEs ranging from 5.68 to 40.88 and 4.62% to 57.50%, respectively. Among all QTLs,
These findings showed that two regions in chromosome 1, containing four QTLs (
Stem is a complex trait, and epistatic effects of QTLs might exist between their different QTL interactions. Therefore, we analyzed the effects of epistatic QTLs for ILs, IDs and the CL in the MGRIL population (Supple-mentary Table S2). A total of seven epistatic QTLs (LOD ≥ 4.0) were detected and located in chromosomes 1, 2, 4, 5, 6 and 7, including four ILs, two IDs and one CL in each trait. These QTLs accounted for PVEs ranging from 5.34% to 23.69%. Three QTL interactions in chromosomes 1 and 5 affected by 1IL, 2IL and CL involved same flanking markers between Chr1_4303 and Chr1_4317 in chromo-some 1; however, we identified no QTL interactions in the 3IL, 1ID and 3ID traits in MGRIL populations.
We focused on the
According to RAP-DB, a 140 kb
(Table 4). Among these genes, nine were identified with specific functions, including a zinc finger and indeterminate domain (IDD) family transcription factor (
Table 4 . List of candidate genes located in
Gene locus ID | Description of function | Start position (bp) | End position (bp) |
---|---|---|---|
Os01g0195000 | Zinc finger and indeterminate domain (IDD) family transcription factor, regulation of secondary cell wall formation, INDETERMINATE DOMAIN 2 | 5,099,555 | 5,102,080 |
Os01g0195066 | Non-protein coding transcript | 5,102,129 | 5,102,343 |
Os01g0195100 | Non-protein coding transcript | 5,103,843 | 5,108,907 |
Os01g0195200 | Similar to serine/threonine-protein kinase PBS1 (EC 2.7.1.37) (AvrPphB susceptible protein 1) | 5,109,949 | 5,112,457 |
Os01g0195300 | Hypothetical conserved gene | 5,128,137 | 5,128,696 |
Os01g0195400 | Harpin-induced 1 domain containing protein | 5,131,629 | 5,132,915 |
Os01g0195500 | Translation initiation factor SUI1 domain containing protein | 5,138,518 | 5,140,925 |
Os01g0195700 | Hypothetical conserved gene | 5,148,879 | 5,149,201 |
Os01g0195801 | Hypothetical conserved gene | 5,149,335 | 5,150,341 |
Os01g0196133 | Similar to H0315A08.1 protein | 5,196,418 | 5,197,056 |
Os01g0196300 | Basic helix-loop-helix (bHLH) transcription factor, diterpenoid phytoalexin factor, biosynthesis of diterpenoid phytoalexins, stress response, DITERPENIOD PHYTOALEXIN FACTOR | 5,201,862 | 5,203,996 |
Os01g0196500 | Prenylated rab acceptor PRA1 family protein | 5,213,979 | 5,216,701 |
Os01g0196600 | Similar to 260-kDa major acidic fibroblast growth factor-stimulated phosphoprotein | 5,218,573 | 5,220,335 |
Os01g0196800 | Hypothetical protein | 5,227,044 | 5,229,966 |
Os01g0197100 | Cytochrome P450, brassinosteroids biosynthesis, regulation of plant architecture, DWARF EBISU | 5,236,623 | 5,244,520 |
The development of semi-dwarf varieties in rice harboring the
Genetic analysis using populations is useful for identifying genetic loci and genes associated with target traits. In rice, stem diameter represents a major trait for improving lodging resistance, as it increases yield. Although many studies reported genetic mechanisms associated with lodging, additional efforts to explore stem diameter are still required to identify new genes for enabling the creation of high-yielding rice (Sasaki
High-resolution genetic maps are widely used for QTL mapping and candidate gene identification in relation to the complex traits of plants. Previous studies using RFLP, RAPD, AFLP, SSR and CAPS markers have been undertaken on biparental populations of rice (Cho
The efficiency of QTL mapping largely depends on marker density and population size (Chen
In fine-mapping, we found three recombinant plants between flanking markers in the
This work was supported by a grant from the National Institute of Agricultural Sciences (Project No. PJ013442), Rural Development Administration, Korea.
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