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Detection of Whole-Genome Resequencing-Based QTLs Associated with Pre-Harvest Sprouting in Rice (Oryza sativa L.)
Plant Breed. Biotech. 2020;8:396-404
Published online December 1, 2020
© 2020 Korean Society of Breeding Science.

Seong-Gyu Jang1, San Mar Lar1, Hongjia Zhang1, Ah-Rim Lee1, Ja-Hong Lee1, Na-Eun Kim1, So-Yeon Park2, Joohyun Lee4, Tae-Ho Ham4, Soon-Wook Kwon1,3*

1Department of Plant Bioscience, College of Natural Resources and Life Science, Pusan National University, Miryang 50463, Korea
2National Institute of Crop Science, Rural Development Administration, Miryang 50463, Korea
3Life and Industry Convergence Research Institute, Pusan National University, Miryang 50463, Korea
4Department of Crop Science, Konkuk University, Seoul 05029, Korea
Corresponding author: Soon-Wook Kwon,, Tel: +82-55-350-5506, Fax: +82-55-350-5509
Received October 7, 2020; Revised November 7, 2020; Accepted November 7, 2020.
This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Pre-harvest sprouting (PHS) is one of the important traits that not only cause serious economic issues but also lead to reduction in grain quality and yield in rice (Oryza sativa L.). To analyze the quantitative trait loci (QTLs) for PHS tolerance, we evaluated PHS, seed dormancy (SD), and low-temperature germination (LTG) of 88 F2:3 populations and their parental lines. Genotypic analysis was performed by using 441 single nucleotide polymorphisms (SNPs) detected from re-sequencing data. Seed dormancy (SD) and low-temperature germination (LTG) were identified to exhibit a positive correlation with PHS. Under the field condition, two major QTLs for PHS, qPHS1-1FC and qPHS1-2FC were identified on chromosome 1. Under the growth chamber condition, qPHS1-1GC and qPHS1-2GC had the same regions on chromosome 1. QTLs of SD and LTG (qSD1-1, qSD1-2, qLTG1-1, and qLTG1-2) had the same regions; these results suggested that candidate QTLs demonstrate pleiotropy about PHS, SD, and LTG. The major QTLs detected in this study are hypothesized to provide an important resource for molecular breeding and gain a better understanding of the genetics of traits in rice.
Keywords : Pre-harvest sprouting, Whole genome resequencing, Recombinant inbred lines, Rice, QTL

Pre-harvest sprouting (PHS) of rice (Oryza sativa L.) is one of the most important traits because of serious economic issues that have led to a significant reduction in grain quality and grain yield. Seed dormancy (SD) prevents seedling emergence in the wrong season or place in wild species, in which the lack of dormancy results in PHS, the phenomenon of seed germination of the rice panicle or cereal grains before harvest. When matured rice panicles are not harvested in time or harvested rice grains are not dried immediately after harvesting, and rice cultivars with high PHS are subjected to germination by frequent rainfall and high temperatures (Gubler et al. 2005; Chono et al. 2006; Wan et al. 2006; Tejakhod and Ellis 2018). The weather is changing in Korea due to typhoons and unexpectedly high temperatures and heavy rains are more frequent during the rice maturing stage that causes an increase in PHS (Lee et al. 2016).

Seed germination is very important for the success of crop production (Almansouri et al. 2001). Among the associate traits about seed germination, the effects of PHS, SD, and low-temperature germination (LTG) are extremely complex, involving various physical and biochemical quality. These traits are expressed as quantitative traits under complex genetic control. The germination associated genes were reported to be related to abscisic acid (ABA) and gibberellic acid (GA) that are major signaling molecules involved in germination induction. ABA has demonstrated that endospermic sugars act as an essential energy source for seed germination and determine seed dormancy and germination by affecting ABA signaling (Du et al. 2018). GA promotes germination, which requires not only initiating the activity of the embryo, but also breaking the physical barrier of the seed coat surrounding the embryo (Debeaujon and Koornneef 2000). Several studies have considered SD as the major component of PHS (Li and Foley 1997; Gu et al. 2003; Chen et al. 2008; Fang et al. 2008; Gu et al. 2011; Graeber et al. 2012; Ye et al. 2015).

Many quantitative trait loci (QTL) with these traits were reported in different subspecies and ecotypes (Cai and Morishima 2000; Gu et al. 2003; Gu et al. 2004; Gao et al. 2008; Hori et al. 2010; Li et al. 2011). A QTL, qPHS-11 was identified between field and greenhouse environments utilizing genome re-sequencing (Cheon et al. 2020). Also, qSDR9.1 and qSDR9.2 were identified to use chromosome segment substitution lines (CSSLs) and simple sequence repeat (SSR) markers (Mizuno et al. 2018). Only a few genes (SD1-2 and Sdr4) have been identified in map-based cloning (Sugimoto et al. 2010; Ye et al. 2015).

Improving PHS tolerance for rice is one of the major breeding targets (Zhang et al. 2014). To overcome PHS in rice under unpredictable weather changes, many genes and alleles that are associated with PHS must be identified. In an attempt to face the challenge to incorporate PHS tolerance into commercial rice cultivars, herein, we conducted QTL analysis with the F2:3 population derived from the cross between high-quality japonica varieties. It is hypo-thesized that the QTLs detected in this study can be used directly in commercial rice breeding programs.


Plant materials

PHS tolerance line (PHS-T) and PHS susceptible line (PHS-S) were selected from the RILs derived from a cross between ‘Jinsang’, a parent with high-quality rice (Hong et al. 2014) and ‘Gopum’ with high-quality rice but susceptible to PHS (Kang et al. 2018). For QTL analysis, the 88 F2 population was developed from a cross between PHS-T and PHS-S. PHS-T, PHS-S, and 88 F2 plants were grown in the experimental field at Pusan National University in 2018 (Fig. 1).

Figure 1. Process of development of the plant materials for the current study.

Evaluation of preharvest sprouting tolerance

On 45 days after heading, three panicles from parent and 88 F2 population were evaluated for PHS tolerance under the chamber condition. The harvested panicles of each line were wrapped in paper towels and placed in water at 100% relative humidity in the chamber at 20℃ for 7 days (Gao et al. 2008). The three panicles of each plant were evaluated for PHS tolerance under field condition at 52 days after heading (Supplementary Table S1). Each panicle from chamber and field conditions were evaluated for PHS by checking the germination rate. All statistical analyses were conducted using the Statistical Analysis System (SAS) software version 9.4 for Windows.

Evaluation of seed dormancy and low-temperature germination

To evaluate SD and LTG, high-quality seeds were selected by the removal of shriveled and unfilled seeds. Fifty seeds with two times from each plant harvested at 45 days after heading were immediately placed in a petri dish (diameter 9 cm) with two sheets of moistened filter paper. The seeds were germinated under two conditions for the germination test in a growth chamber at 13℃ for 15 days and 28℃ for 7 days, respectively. Seed dormancy was evaluated from the germination rate at 28℃. The germi-nation rate of each selected individual was scored as a percentage of germinated seeds in the total numbers of seeds (Lin et al. 1998; Gu et al. 2003; Cruz and Milach 2004; Gu et al. 2011; Hyun et al. 2017).

DNA extraction and genotype analysis

Total genomic DNA was extracted from young leaves of PHS-T, PHS-S, and 88 F2 lines, using modified cetyl trimethyl ammonium bromide (CTAB) protocol (Porebski et al. 1997). Whole-genome re-sequencing of the materials was performed on an MGISEQ-2000 platform (MGI, Shenzhen, China). The raw SNPs were filtered out by alignment with the Nipponbare reference genome (IRGSP-1.0) by BWA and GATK tools. To ensure high-confidence SNP markers for genetic map construction, all raw SNPs identified between the parental lines were filtered as follows: depth at each SNP in both the parental lines > 10 and mapping quality of reads > 20. We further selected SNPs which were 100% homozygous alleles in each parental line. The final candidate SNPs were used for the geno-typing of the F2 population. To ensure high quality, reads of the F2 population were filtered as follows: read depth > 10 and mapping quality of reads > 20. SNPs that had > 50% missing or ambiguous data were excluded. Subsequently, selected SNPs were filtered such that the distance between SNPs was 100K bp; the remaining high-quality data were used in the mapping.

Linkage map construction and QTL analysis

Linkage maps were constructed from the genotype data by using version 4.1 of QTL IciMapping (Meng et al. 2015). Genetic distances were estimated by using the software’s Kosambi map function (Kosambi 1944). QTL IciMapping was also used with the phenotype data for 4 traits for QTL analysis. The inclusive composite interval mapping for additive QTL (ICIM-ADD) and inclusive composite interval mapping for epistatic QTL (ICIM-EPI), along with other default parameters, were used. The LOD threshold for ICIM-ADD and ICIM-EPI were set by using permutation 1000 and manual LOD value 5, respectively.


Phenotypic analysis of preharvest sprouting

The PHS rate of PHS-T, PHS-S, and F2:3 populations were surveyed by involving three panicles under growth chamber and field conditions. F2:3 population showed obvious segregation of tolerance and susceptibility, while agricultural traits such as plant height, heading date, culm length, and panicle length demonstrated uniformity in F2 and F3 populations. The PHS rate of PHS-T displayed lower than 10% and PHS-S displayed 70.2% and 36.1% under growth chamber and field conditions, respectively (Fig. 2). PHS rate of the F3 population had a mean of 16.9% with a range from 0.6% to 79.8% under chamber condition, which had a mean of 13.7% with a range from 0% to 77.9% under field condition (Fig. 3).

Figure 2. Experimental results of PHS-T and PHS-S under chamber and field conditions. (A) Pre-harvest sprouting under growth chamber condition. (B) Pre-harvest sprouting under field condition.
Figure 3. Frequency distributions of germination rate at 28℃ (A) and low-temperature germination rate (B) in parental cultivars (PHS-T, PHS-S) and F3 population.

Phenotypic analysis of seed dormancy and low-temperature germination

The germination rate at 28℃ of two parents, and F2:3 populations were surveyed by fifty seeds per panicle. The germination rate at 28℃ of PHS-T and PHS-S were 41.44% and 98.61%, respectively. The germination rate of the F3 population had a mean of 62.49% with a range from 9.17% to 100%. The LTG rate of PHS-T indicated 25.67% and PHS-S resulted in 69.46%. LTG rate of the F3 population had a mean of 38.01% with a range from 7.49% to 95.19% (Fig. 4).

Figure 4. Frequency distributions of preharvest sprouting rates in parental cultivars (PHS-T, PHS-S), and F3 population under the growth chamber (A) and field conditions (B).

Traits correlations

A high variability was observed among the F2:3 populations for PHS, SD, and LTG traits (Table 1). There was significant correlation between each PHS and SD (r = 0.64, r = 0.57, P-value < 0.01). Also, there was a significant correlation between each PHS and LTG (r = 0.77, r = 0.77, P-value < 0.01). SD trait showed a significant correlation with LTG trait (r = 0.70, P-value < 0.01).

Table 1 . Pearson’s correlation of preharvest sprouting under the field and growth chamber conditions, seed dormancy, and low-temperature germination using 88 F3 population.


PHS (Field)z)PHS (Chamber)SD
PHS (Chamber)0.82**

z)PHS: preharvest sprouting, SD: seed dormancy, LTG: low-temperature germination.

SNP identification in re-sequencing and construction of linkage map

A total of 90 accessions (PHS-T, PHS-S, and 88 F2:3) were sequenced using MGISEQ-2000 to identify polymorphic SNP position between PHS-T and PHS-S; 20,568 SNPs showing polymorphism were identified (Supplementary Fig. S1). To construct a linkage map, these SNPs were filtered so that the distance between SNPs was 100 kb, and 567 SNPs were selected from total SNPs. 376 distinct positions of 567 SNPs were mapped on 17 linkage groups on 12 chromosomes. The linkage map was 991.8 cM in size with an average interval of 2.9 cM (Fig. 5).

Figure 5. Linkage maps for preharvest sprouting tolerance, seed dormancy, and low-temperature germination in 88 F2 population. Markers are indicated on the right and genetic distances (cM).

QTLs for preharvest sprouting tolerance

1 QTL was identified in the same SNP markers position under each condition. Under the field condition, 1 QTL (qPHS1FC) was detected on chromosome 1 with LOD score of 35.8, thereby accounting for PVE of 89.0%. Under the chamber condition also, 1 QTL (qPHS1GC) was detected at identical positions with LOD scores of 24.0, thereby accounting for PVE of 86.1% (Table 2).

Table 2 . QTLs of preharvest sprouting identified using the F3 population under field and growth chamber conditions.

TraitQTLsLinkage groupMarker intervalQTL peak location (cM)LODz)PVEy) (%)Additive effectx)
PHS (Field)qPHS1FC1ch01-0.040/ch01-0.05325.035.889.031.0
PHS (Chamber)qPHS1GC1ch01-0.040/ch01-0.05325.

z)LOD: logarithm of odds numbers, y)PVE: phenotypic variance explained, x)Additive effect: additive effect of allele from PHS-T.

QTLs for seed dormancy and low-temperature germination

The QTLs of SD and LTG were detected in the F2:3 populations, 1 QTLs of each trait was also identified in the same SNP markers position as PHS QTLs. In the SD trait, 1 QTL (qSD1) was detected on chromosome 1 with LOD score of 8.6, thereby accounting for PVE of 35.5%. In the LTG trait, 1 QTL (qLTG1) was detected at identical positions with LOD score of 13.7, thereby accounting for PVE of 49.6% (Table 3).

Table 3 . QTLs of seed dormancy and low-temperature germination were identified using the F3 population under growth chamber condition.

TraitQTLsLinkage groupMarker intervalQTL peak location (cM)LODz)PVEy) (%)Additive effectx)
Seed dormancyqSD11ch01-0.040/ch01-0.05325.08.635.517.9
Low temperature germinationqLTG11ch01-0.040/ch01-0.05325.013.749.620.6

z)LOD: logarithm of odds numbers, y)PVE: phenotypic variance explained, x)Additive effect: additive effect of allele from PHS-T.

Epistatic QTL analysis

Total 45 and 17 digenic epistatic QTLs for PHS were identified under the field condition and the chamber condition. Among those QTLs, 2 and 7 epistatic QTLs under field condition and chamber condition contained qPHS1FC and qPHS1GC, respectively. For SD and LTG, 3 and 4 epistatic interactions were detected, respectively. The 1 epistatic interaction of LTG contains qLTG1, however qSD1 was not involved in any epistatic interaction for SD (Supplementary Table S2 and Fig. S2).


PHS is one of the essential traits for yield and quality of the grain, which is closely associated with seed dormancy, inhibits seed germination under prevailing high humidity and temperature conditions (Bewley et al. 2013; Rodríguez et al. 2015). Seed dormancy is a complex agronomic trait that is influenced not only environmental factors such as temperature and humidity but also endogenous hormones such as ABA, and GA (Du et al. 2018). Therefore, to identify QTLs highly related to the genetic control of PHS tolerance, we performed evaluation under field and chamber conditions using the selected two inbred lines (PHS-T and PHS-S) in F7 RIL and F2:3 populations.

For the construction of the linkage map, after a series of filtering of 20,568 SNPs, a total of 567 SNPs was found suitable to build the genetic map. 567 SNPs were mapped to 376 distinct positions, but the patterns of classification revealed genetic diversity on chromosome 9. The presence of wide structural gaps was identified between the patterns (Supplementary Fig. S1). In the previous study, regions of pattern with wide structural gaps on chromosome 9 were reported to have low levels of polymorphism in the Japonica group (Santos et al. 2020).

The reported QTLs have been detected for PHS tolerance on chromosome 1. qSD-1, Sdr6, qSD1, and qDEG1 are located on the short arm of chromosome 1, and qSD1.1 and qSnd-1 are co-localized on the long arm of chromosome 1 (Miura et al. 2002; Gu et al. 2006; Li et al. 2011; Lu et al. 2011; Marzougui et al. 2012; Wang et al. 2014). The qPHS1FC and qPHS1GC were detected in this study at the marker interval ch01-0.040-ch01-0.053 (400 kb to 530 kb) on chromosome 1, qSD1 and qLTG1 region overlapped on the same position. The Sdr6, the closest reported QTL compare to QTLs identified in this study, was detected in the interval between RM7278 and RM3425 (1,791 kb to 5,114 kb) on chromosome 1 using forty-four chromosome segment substitution lines (CSSLs) derived from a cross between Koshihikari and Nona Bokra (Marzougui et al. 2012). The PHS9 on chromosome 1 in rice regulated pre-harvesting sprouting by controlling the reactive oxygen species (ROS) signaling and ABA signaling through interaction with OsGAP (Xu et al. 2019). To clone the PHS9, a mapping population was generated by crossing phs9-D with Kasalath, and 53 bp deletions were detected as causal variation in LOC_Os01g13950. However, there was no difference between PHS-T and PHS-S in this variation (data not shown). The region of QTLs detected in this study was not reported for PHS traits yet. In addition, all QTLs identified in this study showed large additive effect and PVE. This implies this QTLs are suitable to apply in marker assist breeding program.

PHS leads to a reduction in grain quality and grain yield in rice. Thus, developing a cultivar with PHS tolerance is one of the important goals in rice breeding. In this study, we used the F2 and F3 populations that were developed from selected PHS-T line and PHS-S line in the RILs to detect QTL of PHS-related phenotypes. We identified one novel major QTL for PHS tolerance under the two environments. It is hypothesized that the results of this study can be used as important resources for molecular breeding and to gain a better understanding of the genetics of the genetic traits in rice.

PBB-8-396_SuppleF1.pdf PBB-8-396_SuppleT1.xlsx

This work was supported for 2 years by Pusan National University.

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