Next Article in Journal
Optimization Design of Spray Cooling Fan Based on CFD Simulation and Field Experiment for Horticultural Crops
Next Article in Special Issue
Genome-Wide Association Study for Detecting Salt-Tolerance Loci and Candidate Genes in Rice
Previous Article in Journal
Pigment Production under Cold Stress in the Green Microalga Chlamydomonas reinhardtii
Previous Article in Special Issue
Transcription Profile Analysis of Chlorophyll Biosynthesis in Leaves of Wild-Type and Chlorophyll b-Deficient Rice (Oryza sativa L.)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agriculture
Volume 11
Issue 6
10.3390/agriculture11060565
agriculture-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

QTL Analysis of Rice Grain Size Using Segregating Populations Derived from the Large Grain Line

by
Ja-Hong Lee
1,2,†,
Jeonghwan Seo
1,3,†,
San Mar Lar
1,
Seong-Gyu Jang
1,
Hongjia Zhang
1,
Ah-Rim Lee
1,
Fang-Yuan Cao
1,
Na-Eun Kim
1,
Joohyun Lee
4 and
Soon-Wook Kwon
1,3,*
1
Department of Plant Bioscience, College of Natural Resources and Life Science, Pusan National University, Miryang 50463, Korea
2
SEEDPIA INC., Suwon 16395, Korea
3
Life and Industry Convergence Research Institute, Pusan National University, Miryang 50463, Korea
4
Department of Crop Science, Konkuk University, Seoul 05029, Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2021, 11(6), 565; https://doi.org/10.3390/agriculture11060565
Submission received: 20 May 2021 / Revised: 17 June 2021 / Accepted: 18 June 2021 / Published: 20 June 2021
(This article belongs to the Special Issue Rice Breeding and Genetics)
Browse Figures
Versions Notes

Abstract

:
Grain size affects the yield and quality of rice. The large grain line (LGL), showing a large grain size and japonica-like genome, was selected in the breeding field. The 94 F2 plants derived from a cross between LGL and Hanareum (a high-yielding tongil-type variety) were used for the quantitative trait loci (QTL) analysis of grain length (GL), grain width (GW), and grain thickness (GT). A linkage map of the F2 population, covering 1312 cM for all 12 chromosomes, was constructed using 123 Fluidigm SNP markers. A total of nine QTLs for the three traits were detected on chromosomes two, three, four, six, and seven. Two QTLs for GL on chromosomes two and six explained 17.3% and 16.2% of the phenotypic variation, respectively. Two QTLs were identified for GW on chromosomes two and three, and explained 24.3% and 23.5% of the phenotypic variation, respectively. The five QTLs for GT detected on chromosomes two, three, five, six and seven, explained 13.2%, 14.5%, 16.6%, 10.9%, and 10.2% of the phenotypic variation, respectively. A novel QTL for GT, qGT2, was validated on the same region of chromosome two in the selected F3 population. The QTLs identified in this study, and LGL, could be applied to the development of large-grain rice varieties.

1. Introduction

Rice is one of the most important grain crops in the world and is also the main source of calories for half of the world’s population. Given the rapid growth of the global population, the world’s population is predicted to reach nine billion by the middle of this century. The grain yield of rice should be increased by 70–100%, relative to the current levels, to feed the increasing global population [1,2]. The grain yield of rice is mainly affected by three components: the number of grains per panicle, the number of panicles per plant, and grain weight. Grain weight is positively associated with grain size. Thus, grain size is an important agricultural trait to improve the yield of rice. In addition, grain size affects not only yield but also quality [3]. Long and slender grains tend to be transparent and of edible quality, and thus highly prized in most rice-consuming regions globally. On the other hand, large grains show a relatively high ratio of chalky grains such as white belly or white core.
Over the past three decades, quantitative trait loci (QTL) mapping has provided a promising way to better understand the genetic regulation of yield traits in rice. More than 400 QTLs associated with rice grain shape traits have been identified, and nearly 30 genes have been cloned and demonstrated to control the traits of grain shape and size in numerous genetic studies. To date, several QTLs directly affecting grain size have been cloned in rice, such as GW2 [4], GS3 [5], qSW5/GW5 [6,7], GS5 [8], GS2 [9], GL3.1/qGL3 [10,11], GL7 [12], GW8 [13], TGW6 [14], TGW2 [15], and etc. The identification and functional characterization of these genes have provided an important theoretical basis for the enrichment of genetic resources and the development of new breeding and cultivation strategies for rice.
An adequate molecular marker system is essential for molecular breeding and genetic analysis. Single nucleotide polymorphisms (SNPs) are a more stable, abundant, fast, and cost effective variation than other DNA markers. The Fluidigm SNP genotyping system has automated polymerase chain reaction (PCR) and integrated fluidic circuit (IFC) technology, which automatically mixes PCR reagents through microfluidic channel networks. The indica-japonica SNP assays, based on the Fluidigm system developed in the previous study [16], have been applied to various genetic analyses and molecular breeding, such as bulked segregant analysis (BSA) [17], genetic diversity analysis [18,19], QTL analysis [20,21,22,23], and background profiling [24,25,26]. This Fluidigm SNP marker set has provided a faster and more cost-effective tool than other high-throughput SNP genotyping systems for primary analysis during molecular breeding using inter-subspecific populations, to date.
In this study, the large grain line (LGL), selected in the breeding field due to its large grain size, was characterized based on morphological traits and the genome-wide indica-japonica SNP set. In addition, nine QTLs associated with grain size traits were identified in an F2 population derived from a cross between LGL and Hanareum, using the indica-japonica SNP set. Furthermore, a novel QTL controlling grain thickness was validated in the selected F2:3 population.

2. Materials and Methods

2.1. Plant Materials

An LGL showing enlarged grain length, width, and thickness was selected in the breeding field of a rice lab during line selection and was maintained in the rice lab of Pusan National University. Hyowon6 (a high-quality Korean japonica variety [27]) and Hanareum (a Korean high-yielding tongil-type variety [28]) were used for phenotypic comparison with LGL. To map the QTLs, the segregating populations were derived from a cross between LGL as a maternal parent and Hanareum as a paternal parent. A total of 94 F2 and 186 F3 individuals with parents were grown using conventional cultivation methods at the experimental farm of Pusan National University (Miryang, Korea) in 2018 and 2020, respectively.

2.2. Phenotype Evaluation

The phenotypic measurement of three traits related to grain size was performed after harvest. Harvested grain was air-dried and kept at room temperature before measuring grain size traits. 20 randomly selected, fully filled grains from each parent, F2:3, and F3:4, were used to evaluate grain length (GL), grain width (GW), and grain thickness (GT) at 0.01 mm precision, and the values were averaged as the measurements for each plant. The three traits were evaluated using vernier calipers. Statistical analysis was conducted using R software.

2.3. DNA Extraction

At the tillering stage, young leaf tissues from each plant were collected in the field. Total genomic DNA was extracted from leaves using the modified cetyltrimethylammonium bromide (CTAB) method as described by Murray and Thompson [29]. DNA concentration and purity were quantified and qualified using a spectrophotometer (NanoDrop One, Thermo Scientific). DNA samples with absorbance ratios of 260 nm and 280 nm (A260/A280) > 1.8 were diluted to a concentration of 50 ng/μL and used for Fluidigm genotyping.

2.4. Fluidigm Genotyping

A total of 192 indica-japonica Fluidigm markers for SNP genotypes [16], designed based on the genomic difference between two rice subspecies, were used for phylogenetic analysis of LGL and detecting QTLs associated with the three grain size related traits in the F2 population derived from a cross between LGL and Hanareum. For validation of QTLs in the F3 population, 11 indica-japonica Fluidigm SNP markers on target regions and 6 additional Fluidigm SNP markers developed in the previous studies [20,28] were used.
Fluidigm SNP genotyping was performed using the BioMark™ HD system (Fluidigm, San Francisco, CA, USA), 96.96 Dynamic Array IFCs for the F2 population, and 192.24 Dynamic Array IFCs (Fluidigm) for the F3 population, according to the manufacturer’s instructions, at the National Instrumentation Center for Environmental Management (NICEM), Seoul National University (Pyeongchang, Korea). Fluidigm SNP Genotyping Analysis software was used to acquire genotyping results. All base calls were manually checked, and any errors in homozygous or heterozygous clusters were corrected before further analysis.

2.5. Phylogenetic Analysis

A total of 40 rice germplasms, including LGL, Hanareum, Hyowon6, and other germplasms genotyped using 190 indica-japonica SNP markers in a previous study [16], were used for phylogenetic analysis (Table S1). A total of 38 germplasms, not including LGL and Hyowon6, were selected and clearly classified into the specific subgroups of the previous study. A PowerMarker V3.25 [30] was used to calculate the genetic distance based on the CS chord [31] and to construct an unweighted pair group method with an arithmetic mean algorithm (UPGMA), which were visualized in Molecular Evolutionary Genetics Analysis version 7.0 (MEGA7 [32]).

2.6. Linkage Map Construction and QTL Analysis

The QTL IciMapping 4.1 software [33] was used to construct the linkage map and QTL analyses. First, the BIN functionality (binning of redundant markers) was used to remove redundant markers. Markers showing more than 10% missing data were also removed. The output file obtained from the binning step was used for linkage map construction with the MAP functionality. The Kosambi mapping function was used to calculate genetic distances in centimorgans (cM) [34]. QTL mapping was carried out using the BIP functionality (QTL mapping in biparental populations). The inclusive composite interval mapping of additive (ICIM-ADD) QTL method with default option was used to detect additive QTLs. A significant logarithm of the odds (LOD) threshold value was calculated for each QTL using 1000 permutations at p = 0.05, and the LOD threshold for each QTL was ranged from 3.51 to 3.56.

3. Results

3.1. Characterization of LGL

LGL was selected and designated due to its large grain size. LGL showed a wider and longer grain size than Hyowon6 with a round type grain, and Hanareum with a slender type grain. In addition, the panicle and flag leaf size of LGL were also longer and thicker than the other two varieties (Figure 1).
We focused on the grain size of LGL and evaluated three grain size traits: GL, GW, and GT. The average of GLs for Hyowon6, Hanareum, and, LGL were 6.58 mm, 8.02 mm, and 8.15 mm, respectively. The GL of LGL was significantly longer than Hanareum, which has a long grain. For GW, Hyowon6, Hanareum, and LGM presented 3.20 mm, 2.95 mm, and 3.66 mm, respectively. The GW of LGL showed the largest value by a significant amount. The GTs of Hyowon6, Hanareum, and LGL were 2.12 mm, 2.00 mm, and 2.23 mm, respectively. Even for GT, LGL showed to be significantly thicker than the other two varieties (Figure 2). This result showed that LGL has a significantly large grain compared to normal varieties.
LGL was an unstudied line with an unknown pedigree. To identify the genomic relationship of LGL with other varieties, phylogenetic analysis based on 190 indica-japonica SNPs was conducted using a total of 40 germplasms including LGL, Hanareum, and Hyowon6. A total of 40 germplasms were divided into five subgroups, such as indica, aus, aromatic, tropical japonica, and temperate japonica, mirroring previous results [16]. LGL and Hyowon6 were clustered with temperate japonica, but LGL presented the largest genetic distance from Nipponbare, which is used as a reference variety for phylogenetic analysis, in the temperate japonica cluster (Figrue 3A).
LGL showed some indica-like introgression blocks (more than three continuous SNPs showing homozygous alleles different from Nipponbare) on chromosomes 1, 8, 9, and 12. This introgression pattern was unique to LGL (Figure 3B,C). This suggests that LGL is an untypical japonica-type line possessing a japonica-like genome with some indica-like introgression segments.

3.2. Phenotypic Variation in the F2 Population

GL, GW, and GT were evaluated in two parental lines and the F2 population derived from a cross between LGL and Hanareum (Table 1 and Figure 4). All traits showed continuous distribution and a value less than 1.0 for skewness value, and this implies that all traits present approximately normal distribution. In addition, GL and GT showed bidirectional transgressive segregation in the F2 population.
LGL had a similar but significantly longer GL than Hanareum. The range and CV of GL for the F2 population were 6.89 mm to 8.92 mm and 4.26%, respectively. For the GW, LGL was significantly wider than Hanareum, and the F2 population had a range of 2.79 mm to 3.62 mm and a CV of 5.46%. Finally, GT was also significantly thicker in LGL than Hanareum and ranged from 1.85 mm to 2.37 mm in the F2 population, with a CV of 4.75%.
Correlation analysis was carried out to identify the relationship between the three traits in the F2 population. All pairs presented positive correlation. The highest and most significant positive correlation (0.586) was detected between GW and GT. The pair between GL and GT showed the lowest, and a nonsignificant, positive correlation (Table 2). This implies that the three traits, especially GW and GT, could be regulated by the same and/or tightly linked QTLs.

3.3. Linkage Map Construction in the F2 Population

The F2 population, consisting of 94 individuals, was genotyped using 192 Fluidigm indica-japonica SNP markers. A linkage map of the F2 population was constructed using 123 polymorphic and clearly genotyped SNP markers. The linkage map covered all 12 rice chromosomes with at least four markers for each chromosome. Several large genetic gaps showing larger than 30 cM intervals between adjacent markers were identified on eight chromosomes, not including chromosomes 2, 3, 4, and 10. The total length of the linkage map was 1318 cM and the average genetic distance between two adjacent markers was 10.7 cM (Figure 4).

3.4. QTL Analysis in the F2 Population

To map the genomic regions controlling the large grain size of LGL, QTL analysis for three traits—GL, GW, and GT—was carried out using the phenotype and genotype data of the F2 population derived from a cross between LGL and Hanareum. A total of nine QTLs were detected for the three traits on chromosomes two, three, five, six, and seven (Figure 5 and Table 3). Furthermore, we compared the genomic position of the QTLs detected in this study and previously reported QTLs/genes for grain size, based on the physical position of flanking markers for each QTL.

3.4.1. QTLs for GL

For GL, two QTLs were detected on chromosomes two and six, and these QTLs explained 17.26% and 16.15% of the phenotypic variation, respectively. These QTLs were responsible for 33.41% of the total phenotypic variation in GL. Alleles of LGL on qGL2 and qGL6 extended GL by as much as 0.2 mm and 0.28 mm, respectively. An atypical bHLH protein, which is involved in GL regulation and encoded by the PGL2 gene, was reported in the genomic region of qGL2 [35]. Furthermore, qGL6 was overlapped with gw-6, a QTL for grain weight detected from a DH population derived from the inter-subspecific cross [36].

3.4.2. QTLs for GW

Two QTLs for GW were mapped on chromosomes two and three. qGW2 and qGW3 showed 24.27% and 23.53% of PVE, respectively, and LGL alleles on these two QTLs expanded GL by as much as 0.12 mm and 0.11 mm, respectively. GS2 [9], a rare allele of this gene that enhances grain size including GW, was reported in the genomic region of qGW2. qGW3 was overlapped with gw3.1, a QTL for grain weight detected in advanced backcross populations derived from crosses between O. rufipogon and O. sativa.

3.4.3. QTLs for GT

A total of five QTLs for GT were identified on five different chromosomes. The PVE for each QTL ranged from 10.21% to 16.63%. Most of the LGL alleles on these QTLs increased GT. However, the LGL allele of qGT5 showed a negative effect on GT. These QTLs explained a total of 55.43% of the phenotypic variation for GT in the F2 population. The pleiotropic QTL affecting grain size traits, including GT, GL3.1/qGL3, was cloned to OsPPKL1 in the genomic region of qGT3 [10,11]. qGT5 was overlapped with the QTLs for GT region, detected in the doubled haploid population from sake-brewing rice [38]. Furthermore, qGT6 was located on the marker interval identical to qGW6, and overlapped with the previously reported grain weight QTL gw-6 [36]. The grain size and shape regulating gene SRS1/DEP2 was previously identified in the genomic region of qGT7 [39,40]. There has been no QTL/gene related to GT found in the genomic region of qGT2, to date.

3.5. qGT2 Validation Using Selected F3 Population

For the validation of qGT2, which is a novel QTL detected in this study, one F2 individual possessing heterozygous alleles on qGT2 and qGT7 and Hanareum homozygous alleles on the other three QTLs was selected to develop the F3 population (Figure 6A). The distribution of GT in the F3 population consisting of 186 individuals showed normal distribution with a range of 1.97 mm to 2.36 mm. The variation of GT in the F3 population was less than the F2 populations (Figure 6B). A total of 17 Fluidigm SNP markers, including six additional markers on chromosome two, were used to construct linkage maps of the segregating target regions of the F3 population (Figure 6C).
The QTL for GT in the F3 population was detected on the marker interval identical to qGT2 and explained 10.52% of the phenotypic variance. In addition, LOD graphs of the two segregating populations presented similar shapes, although several markers were added to that region (Figure 6C,D). This suggests that the QTL detected in the F3 population is identical to qGT2 and stable to the environment and genetic background.
However, no QTL for GT was identified on the qGT7 region in the F3 population. This implies that qGT7 could interact with other genomic regions and/or be affected by external conditions in order to express.

4. Discussion

Large grain size directly affects grain yield and quality, and is often considered a priority trait in specific rice breeding programs such as sake brewing [38,41]. LGL was selected according to its large grain size, but its panicle and leaf length phenotypes are also enlarged. Thus, LGL could be applied to develop whole crop silage rice. There are several reported QTLs/genes showing pleiotropic effects on grain size and plant architecture in the QTL regions identified in this study. For instance, GS2 [9] and SRS1/DEP2 [39], involved in not only grain size but also panicle architecture by regulating cell size and cell number, were located on qGW2 and qGW7. LGL showed positive alleles on these QTLs, and it is considered that a large grain, panicle, and flag leaf could be contributed by these genes. However, we did not collect other phenotypes in the F2 population and further genetic analysis accompanying detailed and repeated phenotype collection using an immortal population is required to elucidate this point. Thus, we have developed RILs derived from the F2 population for the following study.
All the QTLs detected in this study showed the same direction, except qGT5, and at least two QTLs have clustered in similar positions on chromosomes two, three, and six (Figure 5, Table 3). This result could explain the correlation analysis that showed all positive correlation among the three traits. Especially, both QTLs for GW were located in positions less than 10 cM from the two QTLs for GT and GW, with GT showing the highest correlation coefficient by a significant amount (Table 2). This suggests that GW could be used as a selection trait for the large grain size of LGL.
Particularly, a novel QTL for GT, qGT2, was detected in this study. To validate this, one F2 individual containing heterozygous qGT2 was selected, and the F3 population was derived from this individual. A QTL for GT in the F3 population was identified on the same marker interval as qGT2. This result clearly indicated that this interval includes genes responsible for GT. A total of 360 genes were located in the genomic region of qGT2 (Table S2). To clone the genes responsible for qGT2, the fine mapping of qGT2 was performed using advanced segregating populations.
Genome-wide SNP marker sets have supported efficient phylogenetic analysis and polymorphic marker selection in this study. However, there were some genomic regions that markers did not cover due to a lack of polymorphism between LGL and Hanareum (Figure 3C, Figure 4). This phenomenon was caused by the dissimilarity between LGL, Hanareum and the typical japonica and indica. A similar problem occurred using an RILs population derived from TR22183, a Chinese japonica possessing some indica-like introgressions (Figure 3B), and dasanbyeo, a Korean tongil-type variety containing some japonica introgressions [42]. To avoid this problem, identification of the genomic differences between parental lines based on whole-genome resequencing, and the development of additional markers, are required. Despite an incomplete linkage map of the F2 population, the total PVE for each of the three grain size traits was more than 37% (Table 3). This implies that the Fluidigm indica-japonica SNP sets efficiently detected the QTLs for grain size in LGL.
Numerous QTLs related to grain size have been reported in rice, thus, identifying the novelty and validation of the detected QTLs is required before further study. In this study, we found an unstudied line showing large grain traits and carried out a QTL analysis of grain size traits. A relatively small F2 population was used for the primary QTL analysis, and the selected F3 population, which was segregated in the target QTL region, was used to validate the novel QTL detected in the primary analysis. This strategy could provide an efficient tool to profile the QTLs of target traits in unstudied germplasms.

5. Conclusions

The novel large grain line and QTLs related to grain size identified in this study could be applied to breeding programs developing large grain rice. A novel genetic locus for GT was detected and validated in this study, laying the foundation for further fine mapping and positional cloning to discover the mechanism of grain size regulation.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/agriculture11060565/s1, Table S1: Llist of 40 rice germplasms used for phylogenetic analysis; Table S2: List of genes located in the genomic region of qGT2.

Author Contributions

Conceptualization, S.-W.K.; methodology, J.-H.L. and S.M.L.; software, J.S.; validation, H.Z. and F.-Y.C.; formal analysis, J.-H.L., J.S. and S.-G.J.; investigation, J.-H.L., J.S., S.M.L., A.-R.L., N.-E.K.; resources, J.L. and S.-W.K.; data curation, J.S., S.-G.J. and H.Z.; writing—original draft preparation, J.-H.L. and J.S.; writing—review and editing, S.-W.K.; visualization, J.L. and J.S.; supervision, project administration, and funding acquisition, S.-W.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by a grant from the Basic Science Research Program (no. 2018R1D1A1B07051390) through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology, South Korea.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zeng, D.; Tian, Z.; Rao, Y.; Dong, G.; Yang, Y.; Huang, L.; Leng, Y.; Xu, J.; Sun, C.; Zhang, G.; et al. Rational design of high-yield and superior-quality rice. Nat. Plants 2017, 3, 17031. [Google Scholar] [CrossRef]
  2. Godfray, H.C.J.; Beddington, J.R.; Crute, I.R.; Haddad, L.; Lawrence, D.; Muir, J.F.; Pretty, J.; Robinson, S.; Thomas, S.M.; Toulmin, C. Food Security: The Challenge of Feeding 9 Billion People. Science 2010, 327, 812–818. [Google Scholar] [CrossRef] [Green Version]
  3. Harberd, N.P. Shaping Taste: The Molecular Discovery of Rice Genes Improving Grain Size, Shape and Quality. J. Genet. Genom. 2015, 42, 597–599. [Google Scholar] [CrossRef]
  4. Song, X.J.; Huang, W.; Shi, M.; Zhu, M.Z.; Lin, H.X. A QTL for rice grain width and weight encodes a previously unknown RING-type E3 ubiquitin ligase. Nat. Genet. 2007, 39, 623–630. [Google Scholar] [CrossRef] [PubMed]
  5. Fan, C.; Xing, Y.; Mao, H.; Lu, T.; Han, B.; Xu, C.; Li, X.; Zhang, Q. GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein. Theor. Appl. Genet. 2006, 112, 1164–1171. [Google Scholar] [CrossRef] [PubMed]
  6. Shomura, A.; Izawa, T.; Ebana, K.; Ebitani, T.; Kanegae, H.; Konishi, S.; Yano, M. Deletion in a gene associated with grain size increased yields during rice domestication. Nat. Genet. 2008, 40, 1023–1028. [Google Scholar] [CrossRef] [PubMed]
  7. Weng, J.; Gu, S.; Wan, X.; Gao, H.; Guo, T.; Su, N.; Lei, C.; Zhang, X.; Cheng, Z.; Guo, X.; et al. Isolation and initial characterization of GW5, a major QTL associated with rice grain width and weight. Cell Res. 2008, 18, 1199–1209. [Google Scholar] [CrossRef]
  8. Li, Y.; Fan, C.; Xing, Y.; Jiang, Y.; Luo, L.; Sun, L.; Shao, D.; Xu, C.; Li, X.; Xiao, J.; et al. Natural variation in GS5 plays an important role in regulating grain size and yield in rice. Nat. Genet. 2011, 43, 1266–1269. [Google Scholar] [CrossRef]
  9. Hu, J.; Wang, Y.; Fang, Y.; Zeng, L.; Xu, J.; Yu, H.; Shi, Z.; Pan, J.; Zhang, D.; Kang, S.; et al. A Rare Allele of GS2 Enhances Grain Size and Grain Yield in Rice. Mol. Plant 2015, 8, 1455–1465. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Qi, P.; Lin, Y.-S.; Song, X.-J.; Shen, J.-B.; Huang, W.; Shan, J.-X.; Zhu, M.-Z.; Jiang, L.; Gao, J.-P.; Lin, H.-X. The novel quantitative trait locus GL3.1 controls rice grain size and yield by regulating Cyclin-T1;3. Cell Res. 2012, 22, 1666–1680. [Google Scholar] [CrossRef] [Green Version]
  11. Zhang, X.; Wang, J.; Huang, J.; Lan, H.; Wang, C.; Yin, C.; Wu, Y.; Tang, H.; Qian, Q.; Li, J.; et al. Rare allele of OsPPKL1 associated with grain length causes extra-large grain and a significant yield increase in rice. Proc. Natl. Acad. Sci. USA 2012, 109, 21534–21539. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Wang, Y.; Xiong, G.; Hu, J.; Jiang, L.; Yu, H.; Xu, J.; Fang, Y.; Zeng, L.; Xu, E.; Xu, J.; et al. Copy number variation at the GL7 locus contributes to grain size diversity in rice. Nat. Genet. 2015, 47, 944–948. [Google Scholar] [CrossRef] [PubMed]
  13. Wang, S.; Wu, K.; Yuan, Q.; Liu, X.; Liu, Z.; Lin, X.; Zeng, R.; Zhu, H.; Dong, G.; Qian, Q.; et al. Control of grain size, shape and quality by OsSPL16 in rice. Nat. Genet. 2012, 44, 950–954. [Google Scholar] [CrossRef]
  14. Ishimaru, K.; Hirotsu, N.; Madoka, Y.; Murakami, N.; Hara, N.; Onodera, H.; Kashiwagi, T.; Ujiie, K.; Shimizu, B.I.; Onishi, A.; et al. Loss of function of the IAA-glucose hydrolase gene TGW6 enhances rice grain weight and increases yield. Nat. Genet. 2013, 45, 707–711. [Google Scholar] [CrossRef]
  15. Ruan, B.; Shang, L.; Zhang, B.; Hu, J.; Wang, Y.; Lin, H.; Zhang, A.; Liu, C.; Peng, Y.; Zhu, L.; et al. Natural variation in the promoter of TGW2 determines grain width and weight in rice. New Phytol. 2020. [Google Scholar] [CrossRef]
  16. Seo, J.; Lee, G.; Jin, Z.; Kim, B.; Chin, J.H.; Koh, H.-J. Development and application of indica-japonica SNP assays using the Fluidigm platform for rice genetic analysis and molecular breeding. Mol. Breed. 2020, 40, 39. [Google Scholar] [CrossRef] [Green Version]
  17. Lee, G.; Lee, K.-I.; Lee, Y.; Kim, B.; Lee, D.; Seo, J.; Jang, S.; Chin, J.H.; Koh, H.-J. Identification of a novel SPLIT-HULL (SPH) gene associated with hull splitting in rice (Oryza sativa L.). Theor. Appl. Genet. 2018, 131, 1469–1480. [Google Scholar] [CrossRef]
  18. Megersa, A.; Seo, J.; Chin, J.H.; Kim, B.; Koh, H.-J. Characterization of Selected Rice Varieties Adapted in Africa. Plant Breed. Biotechnol. 2016, 4, 297–305. [Google Scholar] [CrossRef] [Green Version]
  19. Kishor, D.S.; Seo, J.; Chin, J.H.; Koh, H.-J. Evaluation of Whole-Genome Sequence, Genetic Diversity, and Agronomic Traits of Basmati Rice (Oryza sativa L.). Front. Genet. 2020, 11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Seo, J.; Lee, S.-M.; Han, J.-H.; Shin, N.-H.; Koh, H.-J.; Chin, J.H. Identification of Yield and Yield-Related Quantitative Trait Loci for the Field High Temperature Condition in Backcross Populations of Rice (Oryza sativa L.). Plant Breed. Biotechnol. 2019, 7, 415–426. [Google Scholar] [CrossRef]
  21. Lee, S.-M.; Kang, J.-W.; Lee, J.-Y.; Seo, J.; Shin, D.; Cho, J.-H.; Jo, S.; Song, Y.-C.; Park, D.-S.; Ko, J.-M.; et al. QTL Analysis for Fe and Zn Concentrations in Rice Grains Using a Doubled Haploid Population Derived from a Cross Between Rice (Oryza sativa) Cultivar 93-11 and Milyang 352. Plant Breed. Biotechnol. 2020, 8, 69–76. [Google Scholar] [CrossRef]
  22. Kwon, Y.; Kabange, N.R.; Lee, J.-Y.; Lee, S.-M.; Cha, J.-K.; Shin, D.; Cho, J.-H.; Kang, J.-W.; Ko, J.-M.; Lee, J.-H. Novel QTL Associated with Shoot Branching Identified in Doubled Haploid Rice (Oryza sativa L.) under Low Nitrogen Cultivation. Genes 2021, 12, 745. [Google Scholar] [CrossRef]
  23. Kang, J.-W.; Kabange, N.R.; Phyo, Z.; Park, S.-Y.; Lee, S.-M.; Lee, J.-Y.; Shin, D.; Cho, J.H.; Park, D.-S.; Ko, J.-M.; et al. Combined Linkage Mapping and Genome-Wide Association Study Identified QTLs Associated with Grain Shape and Weight in Rice (Oryza sativa L.). Agronomy 2020, 10, 1532. [Google Scholar] [CrossRef]
  24. Jang, S.; Shim, S.; Lee, Y.K.; Lee, D.; Koh, H.-J. Major QTLs, qARO1 and qARO9, Additively Regulate Adaxial Leaf Rolling in Rice. Front. Plant Sci. 2021, 12. [Google Scholar] [CrossRef]
  25. Jang, S.; Han, J.-H.; Lee, Y.K.; Shin, N.-H.; Kang, Y.J.; Kim, C.-K.; Chin, J.H. Mapping and Validation of QTLs for the Amino Acid and Total Protein Content in Brown Rice. Front. Genet. 2020, 11. [Google Scholar] [CrossRef]
  26. Han, J.-H.; Shin, N.-H.; Moon, J.-H.; Yi, C.; Yoo, S.-C.; Chin, J.H. Genetic and Phenotypic Characterization of Rice Backcrossed Inbred Sister Lines of Saltol in Temperate Saline Reclaimed Area. Plant Breed. Biotechnol. 2020, 8, 58–68. [Google Scholar] [CrossRef]
  27. Im, D.-E.; San, M.L.; Jang, S.-G.; Park, S.-Y.; Jin, S.-H.; Kim, Y.C.; Ham, T.-H.; Kwon, S.-W. ‘Hyowon6’, a Mid-Late Maturing Rice Variety with High Quality. Korean J. Breed. Sci. 2018, 50, 529–533. [Google Scholar] [CrossRef]
  28. Seo, J.; Lee, S.-M.; Han, J.-H.; Shin, N.-H.; Lee, Y.K.; Kim, B.; Chin, J.H.; Koh, H.-J. Characterization of the Common Japonica-Originated Genomic Regions in the High-Yielding Varieties Developed from Inter-Subspecific Crosses in Temperate Rice (Oryza sativa L.). Genes 2020, 11, 562. [Google Scholar] [CrossRef]
  29. Murray, M.G.; Thompson, W.F. Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res. 1980, 8, 4321–4326. [Google Scholar] [CrossRef] [Green Version]
  30. Liu, K.; Muse, S.V. PowerMarker: An integrated analysis environment for genetic marker analysis. Bioinformatics 2005, 21, 2128–2129. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Cavalli-Sforza, L.L.; Edwards, A.W.F. Phylogenetic analysis. Models and estimation procedures. Am. J. Hum. Genet. 1967, 19, 233–257. [Google Scholar] [PubMed]
  32. Kumar, S.; Stecher, G.; Tamura, K. MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets. Mol. Biol. Evol. 2016, 33, 1870–1874. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Meng, L.; Li, H.; Zhang, L.; Wang, J. QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J. 2015, 3, 269–283. [Google Scholar] [CrossRef] [Green Version]
  34. Kosambi, D.D. The estimate of map distances from recombination values. Ann. Eugen. 1944, 12, 172–175. [Google Scholar] [CrossRef]
  35. Heang, D.; Sassa, H. An atypical bHLH protein encoded by POSITIVE REGULATOR OF GRAIN LENGTH 2 is involved in controlling grain length and weight of rice through interaction with a typical bHLH protein APG. Breed Sci. 2012, 62, 133–141. [Google Scholar] [CrossRef] [Green Version]
  36. Lu, C.; Shen, L.; Tan, Z.; Xu, Y.; He, P.; Chen, Y.; Zhu, L. Comparative mapping of QTLs for agronomic traits of rice across environments using a doubled haploid population. Theor. Appl. Genet. 1996, 93, 1211–1217. [Google Scholar] [CrossRef]
  37. Thomson, M.; Tai, T.; McClung, A.; Lai, X.; Hinga, M.; Lobos, K.; Xu, Y.; Martinez, C.; McCouch, S. Mapping quantitative trait loci for yield, yield components and morphological traits in an advanced backcross population between Oryza rufipogon and the Oryza sativa cultivar Jefferson. Theor. Appl. Genet. 2003, 107, 479–493. [Google Scholar] [CrossRef] [PubMed]
  38. Yoshida, S.; Ikegami, M.; Kuze, J.; Sawada, K.; Hashimoto, Z.; Ishii, T.; Nakamura, C.; Kamijima, O. QTL Analysis for Plant and Grain Characters of Sake-brewing Rice Using a Doubled Haploid Population. Breed. Sci. 2002, 52, 309–317. [Google Scholar] [CrossRef] [Green Version]
  39. Abe, Y.; Mieda, K.; Ando, T.; Kono, I.; Yano, M.; Kitano, H.; Iwasaki, Y. The SMALL AND ROUND SEED1 (SRS1/DEP2) gene is involved in the regulation of seed size in rice. Genes Genet. Syst. 2010, 85, 327–339. [Google Scholar] [CrossRef] [Green Version]
  40. Seo, J.; Bordiya, Y.; Lee, C.; Koh, H.-J. Fine Mapping and Candidate Gene Analysis of Small Round Grain Mutant in Rice. Plant Breed. Biotech. 2017, 5, 354–362. [Google Scholar] [CrossRef] [Green Version]
  41. Okada, S.; Iijima, K.; Hori, K.; Yamasaki, M. Genetic and epistatic effects for grain quality and yield of three grain-size QTLs identified in brewing rice (Oryza sativa L.). Mol. Breed. 2020, 40. [Google Scholar] [CrossRef]
  42. Navea, I.P.; Dwiyanti, M.S.; Park, J.; Kim, B.; Lee, S.; Huang, X.; Koh, H.-J.; Chin, J.H. Identification of quantitative trait loci for panicle length and yield related traits under different water and P application conditions in tropical region in rice (Oryza sativa L.). Euphytica 2017, 213, 37. [Google Scholar] [CrossRef]
Agriculture 11 00565 g001 550
Figure 1. Morphological comparison of grain (A), panicle (B), and flag leaf (C) of Hyowon6, Hanareum, and LGL. Scale bar lengths are 10 mm, 10 cm, and 10 cm in (A–C), respectively.
Figure 1. Morphological comparison of grain (A), panicle (B), and flag leaf (C) of Hyowon6, Hanareum, and LGL. Scale bar lengths are 10 mm, 10 cm, and 10 cm in (A–C), respectively.
Agriculture 11 00565 g001
Agriculture 11 00565 g002 550
Figure 2. Phenotypic comparison of GL, GT, and GW. Duncan’s least significant ranges (LSR) was used to identify significance. Significant differences at 5% level were presented by different letters such as a, b, and c.
Figure 2. Phenotypic comparison of GL, GT, and GW. Duncan’s least significant ranges (LSR) was used to identify significance. Significant differences at 5% level were presented by different letters such as a, b, and c.
Agriculture 11 00565 g002
Agriculture 11 00565 g003 550
Figure 3. Phylogenetic analysis and whole-genome profiling of LGL using 190 indica-japonica SNPs: (A) UPGMA dendrogram of 40 germplasms; (B) allele distribution of 190 SNPs. Homozygous alleles identical to Nipponbare are shown in red and those different from Nipponbare are shown in green. Heterozygous alleles are shown in blue. Gray indicates missing genotype; (C) direct genotype comparison of Hanareum, LGL, and Hyowon6. Hanareum, LGL, and Hyowon6 were highlighted by yellow color.
Figure 3. Phylogenetic analysis and whole-genome profiling of LGL using 190 indica-japonica SNPs: (A) UPGMA dendrogram of 40 germplasms; (B) allele distribution of 190 SNPs. Homozygous alleles identical to Nipponbare are shown in red and those different from Nipponbare are shown in green. Heterozygous alleles are shown in blue. Gray indicates missing genotype; (C) direct genotype comparison of Hanareum, LGL, and Hyowon6. Hanareum, LGL, and Hyowon6 were highlighted by yellow color.
Agriculture 11 00565 g003
Agriculture 11 00565 g004 550
Figure 4. Frequency distribution for GL (A), GW (B), and GT (C) in the F2 population derived from a cross between LGL and Hanareum.
Figure 4. Frequency distribution for GL (A), GW (B), and GT (C) in the F2 population derived from a cross between LGL and Hanareum.
Agriculture 11 00565 g004
Agriculture 11 00565 g005 550
Figure 5. Linkage map and chromosomal locations of QTLs identified in the F2 population. Chromosomes are numbered at the top and markers are listed on the right side of each chromosome. On the map, the leftmost sides indicate the scale of genetic distance (cM). Three types of rectangles located on the left side of chromosomes indicate the location marker interval of QTLs for three traits.
Figure 5. Linkage map and chromosomal locations of QTLs identified in the F2 population. Chromosomes are numbered at the top and markers are listed on the right side of each chromosome. On the map, the leftmost sides indicate the scale of genetic distance (cM). Three types of rectangles located on the left side of chromosomes indicate the location marker interval of QTLs for three traits.
Agriculture 11 00565 g005
Agriculture 11 00565 g006 550
Figure 6. Validation of qGT2 using the F3 population derived from a selected F2 individual: (A) schematic genotype and alleles for five GT QTLs of a selected F2 individual; (B) frequency distribution of GT in the F3 population derived from selected F2 individual; (C) linkage maps along with LOD graph of GT for chromosome 2 and 7. Blue dash line link same markers between linkage map of the F2 and F3 populations. Markers highlighted by yellow are flanking markers of qGT2 and qGT7; (D) comparison of result of QTL analysis for GT in the F2 and F3 populations.
Figure 6. Validation of qGT2 using the F3 population derived from a selected F2 individual: (A) schematic genotype and alleles for five GT QTLs of a selected F2 individual; (B) frequency distribution of GT in the F3 population derived from selected F2 individual; (C) linkage maps along with LOD graph of GT for chromosome 2 and 7. Blue dash line link same markers between linkage map of the F2 and F3 populations. Markers highlighted by yellow are flanking markers of qGT2 and qGT7; (D) comparison of result of QTL analysis for GT in the F2 and F3 populations.
Agriculture 11 00565 g006
Table
Table 1. Three traits related to the grain size of two parental lines and the F2 population evaluated in the 2018 field.
Table 1. Three traits related to the grain size of two parental lines and the F2 population evaluated in the 2018 field.
TraitLGLHanareumF2 Population
AverageSDAverageSDAverageSDCV (%)RangeSkewnessKurtosis
GL (mm)8.15 0.148.020.128.040.344.266.89–8.92−0.251.01
GW (mm)3.780.082.950.073.210.185.462.79–3.62−0.04−0.44
GT (ww)2.230.052.000.042.110.14.751.85–2.370.10.24
Abbreviations are as follows: SD = standard deviation; CV = coefficient of variation.
Table
Table 2. Correlation coefficients among the GL, GW, and GT in the F2 population.
Table 2. Correlation coefficients among the GL, GW, and GT in the F2 population.
GLGW
GW0.208 *
GT0.1620.586 **
* and ** indicate significant at p = 0.05 and p = 0.01 level, respectively.
Table
Table 3. QTL for grain size related traits detected in the F2 population derived from a cross between LGL and Hanareum.
Table 3. QTL for grain size related traits detected in the F2 population derived from a cross between LGL and Hanareum.
TraitQTLChr.Position (cM)Left MarkerRight MarkerLODPVE (%)AddReported Gene/QTLReference
GLqGL22130id2012773id20145754.517.260.2PGL2[35]
qGL6666cmb0610.0id60081184.216.150.28gw-6[36]
GWqGW22111ad02011845id20127736.2224.270.12GS2[9]
qGW3394id3010700ad030139055.9723.530.11gw3.1[37]
GTqGT22102ah02001499id20098895.8813.240.05-This study
qGT3398ad03013905ae030063175.8914.50.05GL3.1/qGL3[10,11]
qGT5579id5008218ad050084457.416.63−0.06N/A[38]
qGT6660cmb0610.0id60081185.0110.860.07gw-6[36]
qGT77108id7004645cmb0727.04.7310.210.04SRS1/DEP2[39,40]
Abbreviations are as follows: LOD = logarithm of the odds; PVE = phenotypic variation explained by each QTL; Add = additive effect of LGL allele; N/A = not available.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Lee, J.-H.; Seo, J.; Lar, S.M.; Jang, S.-G.; Zhang, H.; Lee, A.-R.; Cao, F.-Y.; Kim, N.-E.; Lee, J.; Kwon, S.-W. QTL Analysis of Rice Grain Size Using Segregating Populations Derived from the Large Grain Line. Agriculture 2021, 11, 565. https://doi.org/10.3390/agriculture11060565

AMA Style

Lee J-H, Seo J, Lar SM, Jang S-G, Zhang H, Lee A-R, Cao F-Y, Kim N-E, Lee J, Kwon S-W. QTL Analysis of Rice Grain Size Using Segregating Populations Derived from the Large Grain Line. Agriculture. 2021; 11(6):565. https://doi.org/10.3390/agriculture11060565

Chicago/Turabian Style

Lee, Ja-Hong, Jeonghwan Seo, San Mar Lar, Seong-Gyu Jang, Hongjia Zhang, Ah-Rim Lee, Fang-Yuan Cao, Na-Eun Kim, Joohyun Lee, and Soon-Wook Kwon. 2021. "QTL Analysis of Rice Grain Size Using Segregating Populations Derived from the Large Grain Line" Agriculture 11, no. 6: 565. https://doi.org/10.3390/agriculture11060565

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop