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Research Article

Identification of Yield and Yield-Related Quantitative Trait Loci for the Field High Temperature Condition in Backcross Populations of Rice (Oryza sativa L.)

Jeonghwan Seo1, So-Myeong Lee1,2, Jae-Hyuk Han3, Na-Hyun Shin3, Hee-Jong Koh1,*, Joong Hyoun Chin3,*
Plant Breeding and Biotechnology 2019;7(4):415-426.
Published online: December 1, 2019

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* Corresponding author Hee-Jong Koh, heejkoh@snu.ac.kr, Tel: +82-2-880-4541, Fax: +82-2-873-2056
* Corresponding author Joong Hyoun Chin, jhchin@sejong.ac.kr, Tel: +82-2-3408-3897
• Received: November 4, 2019   • Accepted: November 4, 2019

Copyright © 2019 by the Korean Society of Breeding Science

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • The yield related traits are controlled by multiple quantitative trait loci (QTLs) and influenced by environmental change in rice. We analyzed QTLs for 15 yield related traits using two backcross populations, derived from crosses between IR64 as recurrent parent and Koshihikari as donor parent, through two years. A total of 67 backcross inbred lines (BILs) and 40 chromosome segment substitution lines (CSSLs) were genotyped using 183 SNP markers using a high-throughput genotyping system. Some genomic gaps between markers were identified in two populations. For fifteen traits in this study, 36 major QTLs (mQTLs) for 12 traits and 16 digenic epistatic QTLs (EpQTLs) for culm length were detected in BILs. On the other hand, 17 mQTLs were detected for nine traits in CSSLs. Among them, six mQTLs for grain yield traits were collocated on chromosome 10 in both years. For spikelet fertility, six putative QTLs were detected under high temperature conditions in 2018. The QTLs identified in this study could be used for the development of rice varieties conferring inter-subspecific combinations of yield-related traits.
  • Key words: Rice, Yield, QTL, Backcrossed inbred lines, Climate change
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There are two subspecies in rice, indica and japonica. Indica rice is known to be adaptable in tropical conditions, and japonica rice for temperate conditions. To improve the yield potential of japonica rice, inter-subspecific crosses between indica and japonica have been conducted. One of the most historical success made in indica-japonica crosses was the development of Tongil rice (Kim et al. 2014). Likewise, inter-subspecific heterosis will enhance the yield potential of rice (Dwivedi et al. 1998; Kim et al. 2017).
In our study, the population was developed from two famous rice varieties, in tropical and temperate conditions. IR64 is one of the world’s most popular indica rice cultivars developed by the International Rice Research Institute in the Philippines in 1985. It was cultivated on approximately a total of > 10 million ha by the end of the 20th century (Mackill and Khush 2018). IR64 shows high yield, good grain and eating quality compared to other indica cultivars (Khush 1999; Khush and Virk 2005). In addition, IR64 showed moderate heat tolerance on a two-hours exposure to high temperature during anthesis (Jagadish et al. 2008). On the other hand, Koshihikari, Japanese short-grain japonica rice cultivar, is famous for its good palat ability. Koshihikari was developed in 1956 in Japan. Despite its early development, Koshihikari has been the most widely grown cultivar in Japan for more than 35 years. However, Koshihikari has its weakness in abiotic and biotic stress, such as low resistance to lodging and rice blast (Kobayashi et al. 2018).
The yield related traits of rice are generally controlled by multiple quantitative trait loci (QTLs) and influenced by environmental change. The identification of QTLs that control yield is a primary step to improve rice cultivars by integrating favorable QTL alleles into elite genetic backgrounds. Advanced backcross populations are good material for QTL analysis of various agronomic traits, and for the improvement of elite variety (Duan et al. 2013; Nagata et al. 2015; Nonoue et al. 2019). Backcrossed inbred lines (BILs) are developed by two or more times of backcrossing with the parent population. By integrating BILs with small chromosome segments, chromosome substitution lines (CSSLs) can be developed (Doi et al. 1997). Genome-wide functional study utilizing BILs and CSSLs is highly useful, and the identification of major QTLs are effective especially in inter-subspecific and specific crossing populations (Ali et al. 2010). Several major QTLs were identified and reported as major QTLs which showed high level of additive effects in abiotic and biotic stress tolerance, as well as yield (Ali et al. 2010). By help of high-throughput genotyping, based on single nucleotide polymorphic (SNP) markers through whole genome, the inter-subspecific and inter-specific populations can be well-characterized (Seo et al., unpublished).
The effect of high temperature in early growth, flowering, and maturity stage is complicated, and hard to understand. Only a few studies were reported on spikelet degradation, sterility, protein denaturation and enzyme inactivation, and loss of membrane function (Fahad et al. 2019). Only a few studies were reported on the effect and the mechanism of heat tolerance and escape in day- and night-time stress to yield and grain quality (Jagadish et al. 2007; Sreenivasulu et al. 2015; Shi et al. 2017). The effect of all-day warming may cause yield decrease (Yang et al. 2017). They reported that 11-78% of yield reduction by increasing 2℃ air temperature using an apparatus. Although the effect of temperature increase in rice seems clear, it is difficult to dissect the responsible genes due to the complex chromosomal structure and genetic networks. There is a report on the genes coding 1,037 potential transcripts identified within 10 QTLs for heat stress tolerance in the vegetative stage (Kilasi et al. 2018). Furthermore, genes originating from indica/japonica would provide a different additive effect in different temperature condition.
There is a lack of qualitative and quantitative studies on the response of rice production under climate change. The IPCC (Intergovernmental Panel on Climate Change) reported the need for more detailed information on climate change in the local scale (IPCC 2007). In 2018, the highest-ever temperature of 40.7℃ was reported between late July-early August in Korea. The estimated 10-year mean temperature increase in rural area was 0.18℃ in Korea (Park et al. 2017). It is highly difficult to identify QTLs for high temperature conditions in the rice paddy field. There is no study on the QTLs for higher temperatures in the rice fields of Korea. Thus, we have investigated yield and yield-related traits originated from tropical indica (IR64) and temperate japonica (Koshihikari), using two backcrossed populations in two years, which showed different peak temperature in flowering times.
Plant materials
A total of 67 BC1F8 BILs derived from a backcross of IR64/Koshihikari//IR64 were developed and maintained in the Crop Molecular Breeding Lab., Seoul National University. A population of 40 CSSLs with Koshihikari as the donor under IR64 genetic background were kindly provided from Rice Genome Resource Center (RGRC), National Institute of Agrobiological Sciences (NIAS), Japan. The SL2119 line, which showed extremely late heading (late October in Suwon, Korea) among CSSLs, was excluded in phenotyping and QTL analysis. All plants of two populations and two parents were grown by conventional cultural practices at the paddy field within the experimental farm of Seoul National University, Suwon, Korea in 2017 and 2018. All seeds of plants were sown in a plastic-tunnel on 28 April 2017 and 27 April 2018, respectively. All seedlings were transplanted into the paddy field on 3 June 2017 and 2 June 2017, at one seedling per hill at a planting density of 30 × 15 cm. Daily mean, maximum and minimum air temperatures at Suwon during the rice growing season in 2017 and 2018 are shown in Fig. 1. Data were downloaded from the Korea Meteorological Administration (https://data.kma.go.kr/).
Phenotype evaluation
At maturity, six plants from each line were harvested and selected for phenotyping in the field. They were measured and scored for the 15 yield-related traits in 2017 and 2018. Days to heading (DTH) was determined as the number of days from sowing to the date when half of the panicles in each line had emerged. Culm length (CL) and panicle length (PL) were measured in centimeter (cm), from soil surface to panicle neck of the tallest tiller and from the panicle neck to panicle tip, respectively. Panicle number per plant (PN) was counted on field before harvest. Panicle weight (PW) were measured in grams using an electronic scale. Spikelet number per panicle (SN), unfilled spikelet number per panicle (USN) and grain number per panicle (GN) were counted as number of total spikelets, unfilled spikelets and filled grains per panicle, respectively. Spikelet fertility (SF) was calculated as filled grains per total spikelet. 1000 grains weight (TGW) was measured in grams of the weight of 100 fully ripened grains (14% moisture). Grain yield per plant (GY), straw yield per plant (SY) and dry weight per plant (DW, sum of GY and SY) were measured after harvest. Harvest index (HI) and grain straw ration (GSR) were calculated as the ratio of GY to DW and GY to SY, respectively.
DNA extraction and Fluidigm genotyping
Young leaves from each of the plants of the two populations were collected for DNA extraction at the tillering stage. Genomic DNA was extracted using the modified cetyltrimethylammonium bromide (CTAB) method as described by Murray and Thompson (1980). A total 196 SNP markers were used in this study. Two 96-plex indica/ japonica SNP sets were developed based on polymorphism between indica and japonica in the Crop Molecular Breeding Lab., Seoul National University (unpublished). Additionally, specific SNP markers were developed in thus study for four yield related genes, such as GRF4 (Os02g0701300), GIF1 (OS03g0733600), NAL1 (Os04g0615000) and Ghd7 (OS07g0261200) (Supplementary Table S1).
Genotyping was performed using the BioMarkTM HD system (Fluidigm, San Francisco, CA, USA) and 96.96 Dynamic Array IFCs (Fluidigm, San Francisco, CA, USA) according to the manufacturer’s protocol in NICEM (National Instrumentation Center for Environmental Management), Seoul National University (Pyeongchang, Korea). Specific target amplification (STA) was performed prior to SNP genotyping analysis. For genotyping, SNPtype assays were performed using STA products, following the manufacturer’s protocol. The genotyping result was acquired using Fluidigm SNP Genotyping Analysis software. All genotype calling results were manually checked and any obvious errors in homozygous or heterozygous clusters were curated.
Linkage map construction and QTL analysis
Linkage map construction and QTL analysis of BILs were conducted using QTL IciMapping 4.1 software (Meng et al. 2015). First, the BIN functionality (binning of redundant markers) was used to delete redundant markers. The output file generated in the binning step was used for linkage map construction with the MAP functionality (linkage map construction). The Kosambi mapping function was used to calculate genetic distances in centimorgans (cM) (Kosambi 1944). Segregation distortion analysis was conducted using the SDL functionality (segregation distortion locus mapping), using default settings.
QTL mapping of BILs was carried out using the BIP functionality (QTL mapping in biparental populations). The inclusive composite interval mapping of additive (ICIM-ADD) QTL method was used to detect additive QTLs by using default settings. The significant LOD threshold was calculated for each QTL using 1000 permutations at P = 0.05. The inclusive composite interval mapping of digenic epistatic (ICIM-EPI) QTL method was performed to find digenic epistatic QTLs by using default settings. The significant LOD threshold was estimated by 1000 permutations at P = 0.05. QTL mapping of CSSLs was conducted using the CSL functionality (QTL mapping in CSS lines) of QTL IciMapping 4.1 with the default setting. The single marker analysis (SMA) method was carried out with the manual LOD threshold = 2.5.
Evaluation of 15 yield related traits
A total 15 yield related traits were evaluated for BILs and CSSLs in 2017 and 2018. IR64 showed difference from Koshihikari in ten traits in 2017, and seven traits in 2018 (Table 1). For yield-related traits, IR64 showed significantly larger values than Koshikari, except for CL and SF in 2017. This implies that IR64 has a higher yield performance than Koshihikari in Korea. The phenotypic performance of the parental varieties showed a difference between the two years. This suggests that environmental differences of the two years influenced these traits. As shown in Fig. 1, the duration of high temperature in the field was longer in July and August (27 days in 2018 vs. 6 days in 2017 over 34℃). 15 traits, including SF, showed various coefficient variation (CV) for two populations in two years. USN showed the largest CV in the two populations through two years. The DTH of CSSLs showed the smallest variation in two years (Table 1).
In BILs, there were significant positive correlations in all 13 paired traits between the two years, except for DTH and PW (Supplementary Table S2). On the other hand, there was no significant correlation between the two years for GY, SY and DW in CSSLs (Supplementary Table S3).
Genotyping and linkage map construction
A total of 183 SNPs (93.4%) out of 196 SNPs in the genotyping system showed polymorphism between IR64 and Koshihikari. Thus, only the polymorphic SNPs were used for scoring. Followed by the binning, the linkage map of BILs using 174 polymorphic was constructed. The length of the linkage map of BILs was 1,603 cM, with 9.9 cM of average genetic distance between each adjacent marker. On the other hand, 26 SNPs by single marker analysis and 10 intervals by interval mapping on chromosome 1, 3, 6, 7, 10, and 12 showed significant segregation distortion. All the markers and the chromosomal regions of segregation distortion favored the IR64 allele (Fig. 2A). The graphical genotype of 40 CSSLs based on 183 SNPs are presented in Fig. 2B.
QTL analysis of yield related traits in BILs
A total of 36 major QTLs (mQTLs) for the twelve yield-related traits were identified in BILs (Table 2). There was no significant additive effect shown in the QTLs for PL, SY and DW traits. However, eleven QTLs for the eight traits, such as CL, USN, GN, SF, TGW, GY, HI, and GSR, were detected in the two years. Notably, QTL for the grain yield traits clustered on chromosome 10 were identified in the both years. The region contains qUSN10.2, qGN10, qGY10, qSF10, qHI10, and qGSR10 (Fig. 3). The Koshihikari allele of the QTLs in this region showed decreased grain number per panicle and spikelet fertility, and consequently decreased grain yield, harvest index and grain straw ratio in the IR64 background. Whereas, 25 mQTLs for nine yield related traits were identified in only one year. They are influenced by the environmental factors to phenotypic difference. 16 digenic epistatic QTLs were detected only for CL (Supplementary Table S4). Six digenic epistatic QTLs (EpQTLs) for CL are linked to the qCL7.
QTL analysis of yield related traits in CSSLs
A total of 17 mQTLs were detected for nine yield-related traits in CSSLs. All the QTLs were identified only in a year, except for CL on chromosome 1 (Table 3).
In the indica-japonica crossing population in rice, a high degree of segregation distortion is observed on many chromosomal regions (Reflinur et al. 2014). Due to the lack of statistical power to calculate the recombination frequency of the population, there are several big gaps in the linkage maps of BILs. In this study, BILs showed significant segregation distortion on 26 regions (14.2% of total polymorphic markers) linked to SNP markers (Fig. 2A). The high frequency of IR64 (indica) alleles in those loci were observed in BILs. It was even more serious in CSSLs. There is no CSSL which contained Koshihikari alleles on 19 SNPs (10.4%) (Fig. 2B). For this reason, the comparison of the effects of indica- and japonica-alleles in the loci were limited. There were five common loci on chromosomes 1, 3, 6, and 7, showing segregation distortion in the two populations. For instance, ah03001094 located on the short arm of chromosome 3 showed significant segregation distortion in BILs. Besides, the S5 gene functional SNP marker (S5-TC) showed significant segregation distortion toward IR64 allele in BILs. Thus, no Koshihikari allele was detected for S5-TC in all the CSSLs. The S5 gene controls the japonica allele embryo sac abortion in indica-japonica F1 hybrids (Chen et al. 2008; Yang et al. 2012). In general, the embryo sac is more viable than pollen in indica-japonica F1 hybrids. Therefore, F1 hybrids have been used as the maternal parent in indica-japonica backcross to achieve more fertile descendants. For this reason, the two populations used in this study have been developed by using F1 plants as maternal parents. In addition, late heading in indica-japonica hybrid was one of the factors for the segregation distortion in BILs. The regions showing segregation distortion in BILs were on Chromosomes 6 and 7 containing Hd1 (Yano et al. 2000) and Ghd7 (Xue et al. 2008), respectively. One CSSL, SL2119, containing the Koshihikari allele of Hd1 on chromosome 6 exhibited extremely late heading. The mQTLs for yield-related traits on chromosome 10 was linked with segregation distortion (Fig. 2A, 3). Thus, the QTLs for spikelet fertility which mainly caused low yielding were identified in the two years.
In this study, a total of 36 mQTLs for the twelve yield-related traits and 16 EpQTLs for CL were detected in BILs (Table 2, Supplementary Table S4). Almost all the QTLs identified in this study were previously reported in several studies (Yonemaru et al. 2010; Ujiie et al. 2015; Nonoue et al. 2019; https://archive.gramene.org/qtl/). For example, qDTH3 in the I-06 line performed similar effect compared to the same chromosomal region in our study (Nonoue et al. 2019). qCL1.2 containing the SD1-GA marker linked with the sd1 gene (Sasaki et al. 2002) performed more than 24% of the phenotypic variation explained (PVE) in two years of this study. However, almost all the QTLs in this study were identified only in one year, which implies the significant effect of the environmental difference between the years.
In Korea, we had a hotter July and August in 2018, than in 2017 (Fig. 1). In terms of mean temperature, it was the second hottest months since 1964, in Suwon. Furthermore, the maximum temperature of the 1st of August in 2018 was 39.3℃, which was the highest in Suwon since 1964 (Korea Meteorological Administration, https://data.kma.go.kr/). In rice, temperature increase could be somehow beneficial to the genes originating from the tropical region. However, high temperatures over 39℃ gave negative effects to grain fertility and maturity, etc. (Jagadish et al. 2008; Kilasi et al. 2018; Li et al. 2018). The effect of the high temperature might be different to japonica- and indica-alleles. IR64 showed higher fertility, but Koshihari did not in 2018 (Fig. 4A). Likewise, the average of SF of CSSLs showed higher fertility, because of the high genetic similarity of CSSLs to IR64. On the other hand, we have checked the allelic association with SF on the qSF10 from BILs (Fig. 4B). Interestingly, one CSSL line, SL2133, containing the qSF10 region showed lower SF and GY in 2017, as expected (Fig. 4B, C). However, the CSSL which contains only a single chromosomal segment may not explain the association of the complicated traits if many QTLs are interacting. In 2018, a total of seven QTLs for SF, including qSF10, were reported in BILs. The seven QTLs showed that they might have more complicated interactions under the higher temperature (Table 2, Fig. 5). On the contrary, the SF was controlled by only two mQTLs, qSF1.1 and qSF10, in 2017. Thus, the effect of qSF10 in the corresponding CSSL was more obvious (Fig. 4B).
In conclusion, using the identified QTLs in indica-japonica backcrossed populations, some QTLs for yield and yield-related traits might be improved. Out of 36 major QTLs, six QTLs for yield and yield-related traits in 2018 could be considered for use in higher temperature conditions in the field systems in temperate regions. However, further studies are needed by characterizing each QTL, followed by the molecular physiological studies under the better controlled conditions. The genetic network conferring beneficial effects to the higher temperature will provide the opportunity to develop promising breeding lines for dealing with climate change.
PBB-7-415_Supple.pdf
This study was supported by a grant from the Next-Generation BioGreen 21 Program (No. PJ01319603) of the Rural Development Administration, Korea. We thank to Dr. Hong-Yeol Kim for field management and Mrs. Jeehyoung Shim for assistance in phenotyping.
Fig. 1
Mean (black lines), maximum (red lines) and minimum air temperature (blue lines) in Suwon during rice growing season in 2017 and 2018. The orange and purple broken lines represent 35℃ and 25℃, respectively.
PBB-7-415-f1.jpg
Fig. 2
Molecular linkage map of BILs and graphical genotype of CSSLs. (A) Linkage map and of BILs. The names and genetic positions of SNP markers are on the left side of each chromosome. The LOD graph for segregation distortion based on interval mapping is on the right side of each chromosome. The significantly distorted markers were highlighted yellow. The red horizontal bars on chromosome indicate the segregation distortion loci. (B) The chromosomal location of the introgression blocks from Koshihikari (japonica) in the 40 IR64/Koshihikari CSSLs. The red blocks represent Koshihikari alleles. The SL2119 line which showed very late heading is highlighted by yellow.
PBB-7-415-f2.jpg
Fig. 3
The collocation of the yield-related QTLs on chromosome 10. Dotted line boxes indicate cluster region of the QTLs.
PBB-7-415-f3.jpg
Fig. 4
Comparison of spikelet fertility and grain yield between 2017 and 2018. (A) Comparison of IR64, Koshihikari, CSSLs, and BILs for spikelet fertility. (B) Comparison of BILs possessing the IR64 allele for qSF10 (qSF-IR), BILs possessing Koshihikari allele for qSF10 (qSF10-KO), 38 CSSLs except SL2113, and SL2113 which possesses the Koshihikari allele of qSF10. (C) Comparison of BILs possessing the IR64 allele for qGY10 (qGY10-IR), BILs possessing Koshihikari allele for qGY10 (qGY10-KO), 38 CSSLs except SL2113, and SL2113 which possesses the Koshihikari allele of qGY10. Significance was determined by t-test. *, * and *** indicate significance in 0.05, 0.01 and 0.001 probability levels, respectively. ns represents not significant.
PBB-7-415-f4.jpg
Fig. 5
LOD values of eight QTLs for spikelet fertility in the IR64/Koshihikari BILs.
PBB-7-415-f5.jpg
Table 1
Phenotype performance of IR64, Koshihikari, BILs and CSSLs in 2017 and 2018.
Table 1
Traits Year IR64 Koshihikari BILs CSSL
Mean ± SDz) Mean ± SD Range Mean ± SD CVy) % Range Mean ± SD CV%
DTH 2017 113.2 ± 1.2***x) 100.4 ± 1.8 N/Aw) 105.0-119.0 111.1 ± 2.7 2.4%
2018 112.9 ± 1.5*** 100.1 ± 2.0 99-123.5 108.3 ± 4.9 4.5% 106.0-118.0 111.4 ± 3.0 2.7%
CL (cm) 2017 82.6 ± 3.6*** 88.9 ± 1.9 61-135.3 90.5 ± 16.2 17.9% 62.1-118.7 80.1 ± 10.4 13.0%
2018 78.6 ± 2.9 ns 79.4 ± 5.2 58.3-136.2 86.4 ± 16.2 18.8% 62.0-111.0 74.3 ± 8.6 11.6%
PL (cm) 2017 26.7 ± 1.3*** 19.5 ± 1.1 21.3-32.7 26.3 ± 2.6 9.9% 23.5-29.4 26.5 ± 1.4 5.3%
2018 26.1 ± 1.1*** 20.1 ± 1.7 21.2-32.8 26 ± 2.5 9.6% 23.2-28.5 25.8 ± 1.2 4.5%
PW (g) 2017 3.4 ± 0.4** 3.0 ± 0.3 1.7-5.3 3.5 ± 0.6 18.5% 2.5-4.5 3.2 ± 0.5 14.6%
2018 N/A N/A N/A
PN 2017 12.3 ± 2.4 ns 12.1 ± 2.1 6.3-12 8.9 ± 1.5 17.0% 8.0-15.3 12.0 ± 1.5 12.8%
2018 13.0 ± 2.4*** 9.7 ± 1.9 7.5-16.2 11.1 ± 1.9 17.0% 10.2-18.7 13.0 ± 2.0 15.2%
SN 2017 136.1 ± 16.3* 122.2 ± 17.7 110.9-217.1 156.2 ± 24.6 15.7% 111.2-191.3 135.3 ± 19.2 14.2%
2018 135.7 ± 18.2 ns 135.5 ± 14.0 105.8-224 150 ± 25.7 17.1% 105.7-178.2 138.2 ± 18.2 13.2%
USN 2017 25.4 ± 4.5*** 8.8 ± 6.3 5.1-91.8 26.1 ± 14.2 54.3% 10.0-36.4 23.4 ± 7.0 29.9%
2018 13.8 ± 6.3 ns 15.5 ± 6.8 8.2-122.2 28.8 ± 20.4 71.0% 3.8-36.7 16.2 ± 6.5 40.1%
GN 2017 110.7 ± 12.6 ns 113.4 ± 15.5 63.1-200.4 130.1 ± 26.4 20.3% 78.9-157.4 111.9 ± 18.4 16.5%
2018 122.0 ± 18.1 ns 120.1 ± 12.0 29.3-200.3 121.6 ± 29.7 24.5% 88.3-164.5 122.0 ± 18.5 15.1%
SF (%) 2017 81.4 ± 1.8*** 93.0 ± 4.5 47.9-95.9 83.1 ± 9.4 11.4% 70.7-93.4 82.5 ± 5.1 6.2%
2018 89.8 ± 4.6 ns 88.7 ± 4.5 20.1-94.3 80.8 ± 13.2 16.3% 77.8-97.1 88.2 ± 4.7 5.3%
TGW (g) 2017 28 ± 0.2* 27 ± 0.1 19-30 25 ± 0.3 11.0% 24-29 27 ± 0.1 4.9%
2018 25 ± 0.2 ns 25 ± 0.1 18-27 23 ± 0.2 10.0% 21-26 24 ± 0.1 4.7%
GY (g) 2017 33.7 ± 6.0 ns 28.2 ± 4.8 9.9-35.7 24.3 ± 5.4 22.2% 26.5-41.7 31.8 ± 4.0 12.6%
2018 34.4 ± 6.7*** 24.8 ± 4.7 15.5-38.1 27.6 ± 5.6 20.2% 24.9-46.8 32.3 ± 4.6 14.3%
SY (g) 2017 24.2 ± 7.9 ns 22.1 ± 3.3 9.6-29.8 16.0 ± 4.8 30.1% 17.5-31.1 23.0 ± 3.3 14.3%
2018 23.1 ± 5.2 ns 21.4 ± 4.8 14.4-42.8 22.0 ± 5.5 25.0% 10.4-27.6 21.1 ± 3.3 15.7%
DW (g) 2017 57.9 ± 16.3 ns 50.3 ± 0.8 23.0-56.5 40.4 ± 7.6 18.8% 44.6-68.0 54.8 ± 5.8 10.5%
2018 57.5 ± 11.3*** 46.1 ± 9.0 36.8-63.7 49.6 ± 6.7 13.5% 44.9-74.5 53.8 ± 6.4 11.8%
HI 2017 0.58 ± 0.03* 0.56 ± 0.02 0.27-0.67 0.60 ± 0.09 14.0% 0.51-0.68 0.58 ± 0.04 6.9%
2018 0.60 ± 0.04*** 0.54 ± 0.03 0.29-0.70 0.56 ± 0.09 15.3% 0.55-0.66 0.60 ± 0.03 5.3%
GSR 2017 1.4 ± 0.1* 1.3 ± 0.1 0.4-2.6 1.6 ± 0.5 29.9% 1.0-2.1 1.4 ± 0.2 17.0%
2018 1.5 ± 0.2*** 1.2 ± 0.2 0.4-2.3 1.4 ± 0.4 30.9% 1.2-1.9 1.5 ± 0.1 13.6%

z)Standard deviation.

y)Coefficient of variation.

x)*, **, and *** indicate significance at 0.05, 0.01, and 0.001 probability levels, respectively.

w)Not available.

Table 2
Major QTLs for yield related traits detected in BILs.
Table 2
Traits QTL Chr. Positionz) Left marker Right marker LOD 　 PVE (%) 　 Additive effecty)
2017 2018 2017 2018 2017 2018
DTH qDTH3 3 178 id3015453 ah03002520 5.26 15.99 ‒4.01
qDTH10 10 70 ah10001182 id10007384 3.76 19.07 3.47
CL qCL1.1 1 180 qSH1-TG SD1-GA 11.94 23.68 15.01
qCL1.2 1 185 SD1-GA id1024836 14.3 10.73 23.95 44.8 15.18 12.82
qCL7 7 38 cmb0700.1 ud7000187 3.09 7.71 13.51
PW qPW4 4 132 cmb0434.1 id4012434 3.26 13.25 0.27
qPW6 6 60 cmb0625.3 cmb0629.3 5.08 22.16 ‒0.48
qPW10 10 57 wd10003790 ah10001182 3.63 15.35 ‒0.35
PN qPN5 5 64 id5002497 id5004086 3.44 19.83 1.11
SN qSN3 3 166 GIF1 Hd6-AT 5.53 23.62 17.94
qSN4 4 104 id4009823 NAL1 4.89 26.08 17.97
USN qUSN3.1 3 165 ae03006317 GIF1 7.73 3.51 ‒17.68
qUSN3.2 3 177 Hd6-AT id3015453 14.56 8.61 26.51
qUSN10.1 10 46 cmb1016.4 cmb1018.3 14.13 22.82 13.48 18.79 ‒26.45 ‒50.49
qUSN10.2 10 50/49 wd10003790 ah10001182 21.77 28.01 27.3 29.22 32.37 57.95
GN qGN3 3 166 GIF1 Hd6-AT 3.92 12.24 13.53
qGN4.1 4 74 ad04009559 ah04001252 3.67 17.58 19.72
qGN4.2 4 131 cmb0434.1 id4012434 4.43 14.93 12.39
qGN6 6 60 cmb0625.3 cmb0629.3 5.67 19.01 ‒18.77
qGN10 10 57 wd10003790 ah10001182 4.65 4.09 15.43 18.18 ‒14.69 ‒19.39
SF qSF1.1 1 30 cbm0103.4 id1004256 2.59 9.28 ‒7.37
qSF1.2 1 49 ad01003587 id1007185 4.2 6.17 ‒20.16
qSF1.3 1 112 ah01001843 id1015984 3.23 6.8 ‒17.80
qSF7 7 35 cmb0700.1 ud7000187 5.03 6.75 ‒18.48
qSF8.1 8 42 id8001426 wd8001250 2.68 6.24 ‒16.46
qSF8.2 8 99 GW8-AG id8007764 4.31 6.29 ‒19.88
qSF10 10 52 wd10003790 ah10001182 7.12 4.28 21.45 4.09 ‒13.91 ‒12.94
qSF12 12 73 id12007742 cmb1226.0 4.38 6.14 ‒22.40
TGW q100GW3 3 160/166 dd3000535 Hd6-AT 5.09 4.44 25 23.72 ‒1.9 ‒1.6
q100GW4 4 42/40 id4005704 cmb0422.7 4.03 4.35 19.39 23.5 ‒0.15 ‒0.15
GY qGY1 1 137/140 id1015984 id1018870 3.39 3.12 16.58 11.38 ‒2.49 ‒2.11
qGY5 5 42 cmb0500.9 cmb0501.9 6.28 21.08 2.89
qGY6 6 60 cmb0625.3 cmb0629.3 4.23 17.63 ‒3.72
qGY10 10 58/56 wd10003790 id10007384 3.83 5.92 16.09 20.96 ‒3.03 ‒3.73
HI qHI10 10 52/53 wd10003790 ah10001182 6.67 4.77 30.56 24.34 ‒0.08 ‒0.07
GSR qGSR10 10 57/52 wd10003790 ah10001182 5.8 3.71 23.17 21.45 ‒0.35 ‒0.29

z)In case of different positions were detected in same interval through two years, two positions were represented together.

y)Estimated additive effect of Koshihikari allele.

Table 3
Major QTL regions for yield related traits detected in CSSLs.
Table 3
Traits Chr. Marker name / interval LOD PVE (%) Additive effectz)
2017 2018 2017 2018 2017 2018
DTH 8 id8005186 3.16 10.01 2.79
8 ae08007378 2.67 8.70 2.07
8 id8006751 2.62 8.55 2.26
8 id8007764 3.70 11.38 3.59
CL 1 id1010652 2.89 14.35 17.34
1 id1022407-SD1-GA 9.47 11.03 33.70 44.21 19.04 16.36
1 id1024836-id1028304 3.97 5.77 18.62 29.83 19.75 18.76
PL 8 id8006751-cmb0824.7 2.53 25.23 ‒0.95
USN 4 id4005704, cmb0422.7 2.75 16.01 10.57
5 id5010886 2.99 17.19 7.84
GN 8 ae08007378 2.81 14.71 ‒14.32
8 id8006751-cmb0824.7 2.83 14.79 ‒15.83
GY 2 id2016199-cmb0236.6 2.67 26.45 7.43
SY 2 ah02000407-id2004617, id2007512 4.00 36.91 ‒3.74
DW 1 ah01001843 2.89 34.92 6.73
2 id2016199-cmb0236.6 2.84 27.91 10.55
GSR 4 id3010700-ad03013905, ae03006317-id3015453 5.98 49.74 25.14

z)Estimated additive effect of Koshihikari allele.

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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. Biotech.. 2019;7(4):415-426.   Published online December 1, 2019
DOI: https://doi.org/10.9787/PBB.2019.7.4.415
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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. Biotech.. 2019;7(4):415-426.   Published online December 1, 2019
DOI: https://doi.org/10.9787/PBB.2019.7.4.415
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Identification of Yield and Yield-Related Quantitative Trait Loci for the Field High Temperature Condition in Backcross Populations of Rice (Oryza sativa L.)
Image Image Image Image Image
Fig. 1 Mean (black lines), maximum (red lines) and minimum air temperature (blue lines) in Suwon during rice growing season in 2017 and 2018. The orange and purple broken lines represent 35℃ and 25℃, respectively.
Fig. 2 Molecular linkage map of BILs and graphical genotype of CSSLs. (A) Linkage map and of BILs. The names and genetic positions of SNP markers are on the left side of each chromosome. The LOD graph for segregation distortion based on interval mapping is on the right side of each chromosome. The significantly distorted markers were highlighted yellow. The red horizontal bars on chromosome indicate the segregation distortion loci. (B) The chromosomal location of the introgression blocks from Koshihikari (japonica) in the 40 IR64/Koshihikari CSSLs. The red blocks represent Koshihikari alleles. The SL2119 line which showed very late heading is highlighted by yellow.
Fig. 3 The collocation of the yield-related QTLs on chromosome 10. Dotted line boxes indicate cluster region of the QTLs.
Fig. 4 Comparison of spikelet fertility and grain yield between 2017 and 2018. (A) Comparison of IR64, Koshihikari, CSSLs, and BILs for spikelet fertility. (B) Comparison of BILs possessing the IR64 allele for qSF10 (qSF-IR), BILs possessing Koshihikari allele for qSF10 (qSF10-KO), 38 CSSLs except SL2113, and SL2113 which possesses the Koshihikari allele of qSF10. (C) Comparison of BILs possessing the IR64 allele for qGY10 (qGY10-IR), BILs possessing Koshihikari allele for qGY10 (qGY10-KO), 38 CSSLs except SL2113, and SL2113 which possesses the Koshihikari allele of qGY10. Significance was determined by t-test. *, * and *** indicate significance in 0.05, 0.01 and 0.001 probability levels, respectively. ns represents not significant.
Fig. 5 LOD values of eight QTLs for spikelet fertility in the IR64/Koshihikari BILs.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Identification of Yield and Yield-Related Quantitative Trait Loci for the Field High Temperature Condition in Backcross Populations of Rice (Oryza sativa L.)

Phenotype performance of IR64, Koshihikari, BILs and CSSLs in 2017 and 2018.

Traits Year IR64 Koshihikari BILs CSSL
Mean ± SDz) Mean ± SD Range Mean ± SD CVy) % Range Mean ± SD CV%
DTH 2017 113.2 ± 1.2***x) 100.4 ± 1.8 N/Aw) 105.0-119.0 111.1 ± 2.7 2.4%
2018 112.9 ± 1.5*** 100.1 ± 2.0 99-123.5 108.3 ± 4.9 4.5% 106.0-118.0 111.4 ± 3.0 2.7%
CL (cm) 2017 82.6 ± 3.6*** 88.9 ± 1.9 61-135.3 90.5 ± 16.2 17.9% 62.1-118.7 80.1 ± 10.4 13.0%
2018 78.6 ± 2.9 ns 79.4 ± 5.2 58.3-136.2 86.4 ± 16.2 18.8% 62.0-111.0 74.3 ± 8.6 11.6%
PL (cm) 2017 26.7 ± 1.3*** 19.5 ± 1.1 21.3-32.7 26.3 ± 2.6 9.9% 23.5-29.4 26.5 ± 1.4 5.3%
2018 26.1 ± 1.1*** 20.1 ± 1.7 21.2-32.8 26 ± 2.5 9.6% 23.2-28.5 25.8 ± 1.2 4.5%
PW (g) 2017 3.4 ± 0.4** 3.0 ± 0.3 1.7-5.3 3.5 ± 0.6 18.5% 2.5-4.5 3.2 ± 0.5 14.6%
2018 N/A N/A N/A
PN 2017 12.3 ± 2.4 ns 12.1 ± 2.1 6.3-12 8.9 ± 1.5 17.0% 8.0-15.3 12.0 ± 1.5 12.8%
2018 13.0 ± 2.4*** 9.7 ± 1.9 7.5-16.2 11.1 ± 1.9 17.0% 10.2-18.7 13.0 ± 2.0 15.2%
SN 2017 136.1 ± 16.3* 122.2 ± 17.7 110.9-217.1 156.2 ± 24.6 15.7% 111.2-191.3 135.3 ± 19.2 14.2%
2018 135.7 ± 18.2 ns 135.5 ± 14.0 105.8-224 150 ± 25.7 17.1% 105.7-178.2 138.2 ± 18.2 13.2%
USN 2017 25.4 ± 4.5*** 8.8 ± 6.3 5.1-91.8 26.1 ± 14.2 54.3% 10.0-36.4 23.4 ± 7.0 29.9%
2018 13.8 ± 6.3 ns 15.5 ± 6.8 8.2-122.2 28.8 ± 20.4 71.0% 3.8-36.7 16.2 ± 6.5 40.1%
GN 2017 110.7 ± 12.6 ns 113.4 ± 15.5 63.1-200.4 130.1 ± 26.4 20.3% 78.9-157.4 111.9 ± 18.4 16.5%
2018 122.0 ± 18.1 ns 120.1 ± 12.0 29.3-200.3 121.6 ± 29.7 24.5% 88.3-164.5 122.0 ± 18.5 15.1%
SF (%) 2017 81.4 ± 1.8*** 93.0 ± 4.5 47.9-95.9 83.1 ± 9.4 11.4% 70.7-93.4 82.5 ± 5.1 6.2%
2018 89.8 ± 4.6 ns 88.7 ± 4.5 20.1-94.3 80.8 ± 13.2 16.3% 77.8-97.1 88.2 ± 4.7 5.3%
TGW (g) 2017 28 ± 0.2* 27 ± 0.1 19-30 25 ± 0.3 11.0% 24-29 27 ± 0.1 4.9%
2018 25 ± 0.2 ns 25 ± 0.1 18-27 23 ± 0.2 10.0% 21-26 24 ± 0.1 4.7%
GY (g) 2017 33.7 ± 6.0 ns 28.2 ± 4.8 9.9-35.7 24.3 ± 5.4 22.2% 26.5-41.7 31.8 ± 4.0 12.6%
2018 34.4 ± 6.7*** 24.8 ± 4.7 15.5-38.1 27.6 ± 5.6 20.2% 24.9-46.8 32.3 ± 4.6 14.3%
SY (g) 2017 24.2 ± 7.9 ns 22.1 ± 3.3 9.6-29.8 16.0 ± 4.8 30.1% 17.5-31.1 23.0 ± 3.3 14.3%
2018 23.1 ± 5.2 ns 21.4 ± 4.8 14.4-42.8 22.0 ± 5.5 25.0% 10.4-27.6 21.1 ± 3.3 15.7%
DW (g) 2017 57.9 ± 16.3 ns 50.3 ± 0.8 23.0-56.5 40.4 ± 7.6 18.8% 44.6-68.0 54.8 ± 5.8 10.5%
2018 57.5 ± 11.3*** 46.1 ± 9.0 36.8-63.7 49.6 ± 6.7 13.5% 44.9-74.5 53.8 ± 6.4 11.8%
HI 2017 0.58 ± 0.03* 0.56 ± 0.02 0.27-0.67 0.60 ± 0.09 14.0% 0.51-0.68 0.58 ± 0.04 6.9%
2018 0.60 ± 0.04*** 0.54 ± 0.03 0.29-0.70 0.56 ± 0.09 15.3% 0.55-0.66 0.60 ± 0.03 5.3%
GSR 2017 1.4 ± 0.1* 1.3 ± 0.1 0.4-2.6 1.6 ± 0.5 29.9% 1.0-2.1 1.4 ± 0.2 17.0%
2018 1.5 ± 0.2*** 1.2 ± 0.2 0.4-2.3 1.4 ± 0.4 30.9% 1.2-1.9 1.5 ± 0.1 13.6%

Standard deviation.

Coefficient of variation.

*, **, and *** indicate significance at 0.05, 0.01, and 0.001 probability levels, respectively.

Not available.

Major QTLs for yield related traits detected in BILs.

Traits QTL Chr. Positionz) Left marker Right marker LOD 　 PVE (%) 　 Additive effecty)
2017 2018 2017 2018 2017 2018
DTH qDTH3 3 178 id3015453 ah03002520 5.26 15.99 ‒4.01
qDTH10 10 70 ah10001182 id10007384 3.76 19.07 3.47
CL qCL1.1 1 180 qSH1-TG SD1-GA 11.94 23.68 15.01
qCL1.2 1 185 SD1-GA id1024836 14.3 10.73 23.95 44.8 15.18 12.82
qCL7 7 38 cmb0700.1 ud7000187 3.09 7.71 13.51
PW qPW4 4 132 cmb0434.1 id4012434 3.26 13.25 0.27
qPW6 6 60 cmb0625.3 cmb0629.3 5.08 22.16 ‒0.48
qPW10 10 57 wd10003790 ah10001182 3.63 15.35 ‒0.35
PN qPN5 5 64 id5002497 id5004086 3.44 19.83 1.11
SN qSN3 3 166 GIF1 Hd6-AT 5.53 23.62 17.94
qSN4 4 104 id4009823 NAL1 4.89 26.08 17.97
USN qUSN3.1 3 165 ae03006317 GIF1 7.73 3.51 ‒17.68
qUSN3.2 3 177 Hd6-AT id3015453 14.56 8.61 26.51
qUSN10.1 10 46 cmb1016.4 cmb1018.3 14.13 22.82 13.48 18.79 ‒26.45 ‒50.49
qUSN10.2 10 50/49 wd10003790 ah10001182 21.77 28.01 27.3 29.22 32.37 57.95
GN qGN3 3 166 GIF1 Hd6-AT 3.92 12.24 13.53
qGN4.1 4 74 ad04009559 ah04001252 3.67 17.58 19.72
qGN4.2 4 131 cmb0434.1 id4012434 4.43 14.93 12.39
qGN6 6 60 cmb0625.3 cmb0629.3 5.67 19.01 ‒18.77
qGN10 10 57 wd10003790 ah10001182 4.65 4.09 15.43 18.18 ‒14.69 ‒19.39
SF qSF1.1 1 30 cbm0103.4 id1004256 2.59 9.28 ‒7.37
qSF1.2 1 49 ad01003587 id1007185 4.2 6.17 ‒20.16
qSF1.3 1 112 ah01001843 id1015984 3.23 6.8 ‒17.80
qSF7 7 35 cmb0700.1 ud7000187 5.03 6.75 ‒18.48
qSF8.1 8 42 id8001426 wd8001250 2.68 6.24 ‒16.46
qSF8.2 8 99 GW8-AG id8007764 4.31 6.29 ‒19.88
qSF10 10 52 wd10003790 ah10001182 7.12 4.28 21.45 4.09 ‒13.91 ‒12.94
qSF12 12 73 id12007742 cmb1226.0 4.38 6.14 ‒22.40
TGW q100GW3 3 160/166 dd3000535 Hd6-AT 5.09 4.44 25 23.72 ‒1.9 ‒1.6
q100GW4 4 42/40 id4005704 cmb0422.7 4.03 4.35 19.39 23.5 ‒0.15 ‒0.15
GY qGY1 1 137/140 id1015984 id1018870 3.39 3.12 16.58 11.38 ‒2.49 ‒2.11
qGY5 5 42 cmb0500.9 cmb0501.9 6.28 21.08 2.89
qGY6 6 60 cmb0625.3 cmb0629.3 4.23 17.63 ‒3.72
qGY10 10 58/56 wd10003790 id10007384 3.83 5.92 16.09 20.96 ‒3.03 ‒3.73
HI qHI10 10 52/53 wd10003790 ah10001182 6.67 4.77 30.56 24.34 ‒0.08 ‒0.07
GSR qGSR10 10 57/52 wd10003790 ah10001182 5.8 3.71 23.17 21.45 ‒0.35 ‒0.29

In case of different positions were detected in same interval through two years, two positions were represented together.

Estimated additive effect of Koshihikari allele.

Major QTL regions for yield related traits detected in CSSLs.

Traits Chr. Marker name / interval LOD PVE (%) Additive effectz)
2017 2018 2017 2018 2017 2018
DTH 8 id8005186 3.16 10.01 2.79
8 ae08007378 2.67 8.70 2.07
8 id8006751 2.62 8.55 2.26
8 id8007764 3.70 11.38 3.59
CL 1 id1010652 2.89 14.35 17.34
1 id1022407-SD1-GA 9.47 11.03 33.70 44.21 19.04 16.36
1 id1024836-id1028304 3.97 5.77 18.62 29.83 19.75 18.76
PL 8 id8006751-cmb0824.7 2.53 25.23 ‒0.95
USN 4 id4005704, cmb0422.7 2.75 16.01 10.57
5 id5010886 2.99 17.19 7.84
GN 8 ae08007378 2.81 14.71 ‒14.32
8 id8006751-cmb0824.7 2.83 14.79 ‒15.83
GY 2 id2016199-cmb0236.6 2.67 26.45 7.43
SY 2 ah02000407-id2004617, id2007512 4.00 36.91 ‒3.74
DW 1 ah01001843 2.89 34.92 6.73
2 id2016199-cmb0236.6 2.84 27.91 10.55
GSR 4 id3010700-ad03013905, ae03006317-id3015453 5.98 49.74 25.14

Estimated additive effect of Koshihikari allele.

Table 1 Phenotype performance of IR64, Koshihikari, BILs and CSSLs in 2017 and 2018.

Standard deviation.

Coefficient of variation.

*, **, and *** indicate significance at 0.05, 0.01, and 0.001 probability levels, respectively.

Not available.

Table 2 Major QTLs for yield related traits detected in BILs.

In case of different positions were detected in same interval through two years, two positions were represented together.

Estimated additive effect of Koshihikari allele.

Table 3 Major QTL regions for yield related traits detected in CSSLs.

Estimated additive effect of Koshihikari allele.

Table 1

Table 2

Table 3

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