Identification of medium‐grain rice based on GS3, a gene linked to rice grain size

Rice grain shape, heading date, plant height and panicle length are the most important factors affecting rice grain quality, yield and adaption area. To detect QTLs controlling grain length (GL), grain width (GW), heading date (HD), plant height (PH), and panicle length (PL), we evaluated two hundred and eighty six F2 individuals derived from the cross between Taiken 2 (TK2) and Taichung Sen 10 (TCS10). Through genotyping all the F2 population, we construct a linkage map with 147 molecular markers including 133 InDel and 14 SSR markers covering all 12 chromosomes in this study. The phenotypic ranges of grain trait in F2 population were between two parents, but the other traits showed transgressive segregation. The correlation between GL and GW are only 0.09. The result of QTL mapping shows that there are 10 and 8 QTLs controlling GL and GW. The PVE of QTLs controlling GL on chromosome 1, 3, 7 are all above 10 %, and GL would increase 0.18 – 0.22 mm by carrying TCS 10 allele. The PVE of QTLs controlling GW on chromosome 2 and 5 are 12% and 44.8%, and GW would increase 0.08 mm and 0.17 mm by carrying TK2 allele. A total 3 QTLs for HD, 5 QTLs for PH and 4 QTLs for PL. The PVE of QTLs on chromosome 3 and 10 controlling HD are around 26%. While these two QTLs carrying TK2 allele, the HD would earlier 6.7 days and delay 6.3 day. The QTL controlling PH on chromosome 1 is the major QTL explained 52.5% of the total variation of PH, and it would increase 14.9 cm with TK2 allele on it. The QTL of PL on chromosome 8 has the largest PVE and would increase 1.5 cm while carrying TK2 allele. Compare all these result with previous study, the QTL of GW on chromosome 5 might be the GW5 gene, and the QTL controlling HD on chromosome 10 would be Ehd1. The QTL at the long arm on chromosome 1 might be the same as sd-1. All these assumption need to be confirm by functional markers or sequence alignment. All these information would be useful to the rice breeder or have practical used through MAS in rice.

Grain size, shape and weight are important factors influencing grain yield and quality of rice. They are mostly determined by grain length (GL) and grain width (GW). A 13.2 Mb interval, RM167–RM287 on chromosome 11 of rice, was previously found to be associated with variations in 1000-grain weight (TGW). In this study, three populations derived from the indica rice cross Teqing/IRBB52 were used to identify and validate quantitative trait loci (QTLs) controlling GL, GW, TGW and the ratio of GL to GW (RLW) in the RM167–RM287 region. First, two QTL clusters associated with these traits were detected using two populations, segregating the RM167–RM287 interval only. One controlled GL, GW and TGW and was designated as qGS11. The other controlled GL and RLW. The allelic directions of the two QTL clusters on GL were opposite. Then, qGS11 was further mapped in a 1.4 Mb interval using near-isogenic lines, showing a small effect on GL and a relatively large effect on TGW, GW and RLW. Meanwhile, a stable and small effect on the heading date was detected. The allelic direction for the heading date was the opposite for TGW and GW but the same for GL and RLW. The results suggest that qGS11 has the potential for application in rice breeding.

Plant height is significantly correlated with grain traits, which is a component of wheat yield. The purpose of this study is to investigate the main quantitative trait loci (QTLs) that control plant height and grain-related traits in multiple environments. In this study, we constructed a high-density genetic linkage map using the Wheat50K SNP Array to map QTLs for these traits in 198 recombinant inbred lines (RILs). The two ends of the chromosome were identified as recombination-rich areas in all chromosomes except chromosome 1B. Both the genetic map and the physical map showed a significant correlation, with a correlation coefficient between 0.63 and 0.99. However, there was almost no recombination between 1RS and 1BS. In terms of plant height, 1RS contributed to the reduction of plant height by 3.43 cm. In terms of grain length, 1RS contributed to the elongation of grain by 0.11 mm. A total of 43 QTLs were identified, including eight QTLs for plant height (PH), 11 QTLs for thousand grain weight (TGW), 15 QTLs for grain length (GL), and nine QTLs for grain width (GW), which explained 1.36–33.08% of the phenotypic variation. Seven were environment-stable QTLs, including two loci (Qph.nwafu-4B and Qph.nwafu-4D) that determined plant height. The explanation rates of phenotypic variation were 7.39–12.26% and 20.11–27.08%, respectively. One QTL, Qtgw.nwafu-4B, which influenced TGW, showed an explanation rate of 3.43–6.85% for phenotypic variation. Two co-segregating KASP markers were developed, and the physical locations corresponding to KASP_AX-109316968 and KASP_AX-109519968 were 25.888344 MB and 25.847691 MB, respectively. Qph.nwafu-4B, controlling plant height, and Qtgw.nwafu-4B, controlling TGW, had an obvious linkage relationship, with a distance of 7–8 cM. Breeding is based on molecular markers that control plant height and thousand-grain weight by selecting strains with low plant height and large grain weight. Another QTL, Qgw.nwafu-4D, which determined grain width, had an explanation rate of 3.43–6.85%. Three loci that affected grain length were Qgl.nwafu-5A, Qgl.nwafu-5D.2, and Qgl.nwafu-6B, illustrating the explanation rates of phenotypic variation as 6.72–9.59%, 5.62–7.75%, and 6.68–10.73%, respectively. Two QTL clusters were identified on chromosomes 4B and 4D.

BackgroundGrain weight is a grain yield component, which is an integrated index of grain length, width and thickness. They are controlled by a large number of quantitative trait loci (QTLs). Besides major QTLs, minor QTLs play an essential role. In our previous studies, QTL analysis for grain length and width was performed using a recombinant inbred line population derived from rice cross TQ/IRBB lines. Two major QTLs were detected, which were located in proximity to GS3 and GW5 that have been cloned. In the present study, QTLs for grain weight and shape were identified using rice populations that were homozygous at GS3 and GW5.MethodNine populations derived from the indica rice cross TQ/IRBB52 were used. An F10:11population named W1, consisting of 250 families and covering 16 segregating regions, was developed from one residual heterozygote (RH) in the F7generation of Teqing/IRBB52. Three near isogenic line (NIL)-F2 populations, ZH1, ZH2 and ZH3 that comprised 205, 239 and 234 plants, respectively, were derived from three RHs in F10:11. They segregated the target QTL region in an isogenic background. Two NIL populations, HY2 and HY3, were respectively produced from homozygous progeny of the ZH2 and ZH3 populations. Three other NIL-F2 populations, Z1, Z2 and Z3, were established using three RHs having smaller heterozygous segments. QTL analysis for 1000-grain weight (TGW), grain length (GL), grain width (GW), and length/width ratio (LWR) was conducted using QTL IciMapping and SAS procedure with GLM model.ResultA total of 27 QTLs distributed on 12 chromosomes were identified. One QTL cluster, qTGW2/qGL2/qGW2 located in the terminal region of chromosome 2, were selected for further analysis. Two linked QTLs were separated in region Tw31911−RM266. qGL2 was located in Tw31911−Tw32437 and mainly controlled GL and GW. The effects were larger on GL than on GW and the allelic directions were opposite. qTGW2 was located in Tw35293−RM266 and affected TGW, GL and GW with the same allelic direction. Finally, qTGW2 was delimited within a 103-kb region flanked by Tw35293 and Tw35395.ConclusionqTGW2 with significant effects on TGW, GL and GW was validated and fine-mapped using NIL and NIL-F2 populations. These results provide a basis for map-based cloning of qTGW2 and utilization of qTGW2 in the breeding of high-yielding rice varieties.

The market success of any rice cultivar is exceedingly dependent on its grain appearance, as well as its grain yield, which define its demand by consumers as well as growers. The present study was undertaken to explore the contribution of nine major genes, qPE9~1, GW2, SLG7, GW5, GS3, GS7, GW8, GS5, and GS2, in regulating four size and weight related traits, i.e., grain length (GL), grain width (GW), grain thickness (GT), and thousand grain weight (TGW) in 204 diverse rice germplasms using Insertion/Deletion (InDel) markers. The studied germplasm displayed wide-ranging variability in the four studied traits. Except for three genes, all six genes showed considerable association with these traits with varying strengths. Whole germplasm of 204 genotypes could be categorized into three major clusters with different grain sizes and weights that could be utilized in rice breeding programs where grain appearance and weight are under consideration. The study revealed that TGW was 24.9% influenced by GL, 37.4% influenced by GW, and 49.1% influenced by GT. Hence, assuming the trend of trait selection, i.e., GT > GW > GL, for improving TGW in the rice yield enhancement programs. The InDel markers successfully identified a total of 38 alleles, out of which 27 alleles were major and were found in more than 20 genotypes. GL was associated with four genes (GS3, GS7, GW8, and GS2). GT was also found to be regulated by four different genes (GS3, GS7, GW8, and GS2) out of the nine studied genes. GW was found to be under the control of three studied genes (GW5, GW8, and GS2), whereas TGW was found to be under the influence of four genes (SLG7, GW5, GW8, and GS5) in the germplasm under study. The Unweighted Pair Group Method with Arithmetic means (UPGMA) tree based on the studied InDel marker loci segregated the whole germplasm into three distinct clusters with dissimilar grain sizes and weights. A two-dimensional scatter plot constructed using Principal Coordinate Analysis (PCoA) based on InDel markers further separated the 204 rice germplasms into four sub-populations with prominent demarcations of extra-long, long, medium, and short grain type germplasms that can be utilized in breeding programs accordingly. The present study could help rice breeders to select a suitable InDel marker and in formulation of breeding strategies for improving grain appearance, as well as weight, to develop rice varieties to compete international market demands with higher yield returns. This study also confirms the efficient application of InDel markers in studying diverse types of rice germplasm, allelic frequencies, multiple-gene allele contributions, marker-trait associations, and genetic variations that can be explored further.

Quantitative trait locus (QTL) for grain size traits that include grain length (GL), grain width (GW), grain thickness (GT) as well as thousand grain weight (TGW) were identified using F2 population derived from a cross between a japonica cultivar Nongken58 and its large grain-sized mutant, ‘Dali’, which was selected in SP2 generation of plants from Nongken58 seeds exposed to cosmic radiation upon space-flight, and then advanced it over eight successive generations by bagging the panicles to ensure self pollination. ‘Dali’ had similar GW and GT but 4.8 mm longer in GL, and 18.1 g heavier in TGW than those of Nongken58. Seven main-effect QTLs (M-QTLs) were identified for the grain size and weight traits. Among them, three M-QTLs, QGs3a and QGs3b for both GL and TGW, and QGs5 for GW, GT and TGW, which had strong additive effects on grain shape and grain weight, were validated in the two F2 plant-derived F3 populations. The three M-QTLs were found to be non-allelic to the cloned genes GS3, GL3.1, qSW5 and QGs5 by comparative mapping. However, there was only one pair of digenic epistasis involving QGs3b for TGW detected in this population. Interestingly, homozygous ‘Dali’ alleles at the QGs3a, QGs3b and QGs5 showed significant increase in the grain size and weight, suggesting these novel alleles of ‘Dali’ at the above three loci may be a very useful for marker-assisted improvement of grain quality for japonica cultivars.

Grain width (GW) and grain length (GL) are crucial components affecting grain weight. Dissection of their genetic control is essential for improving yield potential in wheat breeding. Yangmai 12 (YM12) and Yanzhan 1 (YZ1) are two elite cultivars released in the Middle and Lower Yangtze Valleys Wheat Zone (MLYVWZ) and the Yellow-Huai River Valleys Wheat Zone (YRVWZ), respectively. One biparental population derived from YM12/YZ1 cross was employed to perform QTL mapping based on the data from four environments over two years to detect quantitative trait loci (QTL) for GW and GL. A total of eight QTL were identified on chromosomes 1B, 2D, 3B, 4B, 5A, and 6B. Notably, QGW.yz.2D was co-located with QGL.yz.2D, and QGW.yz.4B was co-located with QGL.yz.4B, respectively. QGW.yz.2D and QGL.yz.2D, with the increasing GW/GL allele from YZ1, explained 12.36–18.27% and 13.69–26.53% of the phenotypic variations for GW and GL, respectively. QGW.yz.4B and QGL.yz.4B, with the increasing GW/GL allele from YM12, explained 10.34–11.95% and 10.35–16.04% of the phenotypic variation for GW and GL, respectively. QGL.yz.5A, with the increasing GL allele from YM12, explained 10.04–12.48% of the phenotypic variation for GL. Moreover, the positive alleles of these three QTL regions could significantly increase thousand-grain weight, and QGW.yz.4B/QGL.yz.4B and QGL.yz.5A did not show significant negative effects on grain number per spike. QGL.yz.2D, QGW.yz.4B/QGL.yz.4B, and QGL.yz.5A have not been reported. These three QTL regions were then further validated using Kompetitive Allele-Specific PCR (KASP) markers in 159 wheat cultivars/lines from MLYVWZ and YRVWZ. Combining the positive alleles of the major QTL significantly increased GW and GL. Eleven candidate genes associated with encoding ethylene-responsive transcription factor, oleosin, osmotin protein, and thaumatin protein were identified. Three major QTL and KASP markers reported here will be helpful in developing new wheat cultivars with high and stable yields.

Association mapping of quantitative trait loci for yield-related agronomic traits in rice (Oryza sativa L.)

Key messageThis article used seven characters from the 2D image analysis to dissect the genetic architecture underlying rice grain shape in onejaponica × indicapopulation consisting of 215 recombinant inbred lines.Two-dimensional (2D) digital image analysis is efficient for investigating the rice grain shape characters in large genetic and breeding populations. In this study, we used 2D image analysis to investigate seven characters, i.e., grain length (GL), grain width (GW), length-to-width ratio (LW), grain area (GA), grain circumference (GC), grain diameter (GD), and grain roundness (GR), in one japonica × indica genetic population consisting of 215 recombinant inbred lines. GL and GW can be recorded manually as well, and have been extensively used together with LW (i.e., GL/GW) in genetic studies on grain shape. GC and GA can be hardly measured manually, and have not been used together with GD and GR. Results indicated that the seven characters could be precisely measured by 2D image analysis, genotype by environment interaction was low, and heritability was high. Each character was controlled by a few major stable genes and multiple minor additive genes. A total of 51 QTL were detected for the seven characters across four diverse environments, 22 from GL, GW, and LW, the three traditional characters, and 29 from the other four characters. The 51 QTL were clustered in eighteen marker intervals. Comparing with previous studies and analyzing the stability of identified QTL, we found six non-reported marker intervals, one each on chromosomes 2 and 3, and two each on chromosomes 6 and 8. The newly identified loci and the large-scale phenotyping system would greatly improve our knowledge about the genetic architecture and the future rice breeding on grain shape.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-015-2560-7) contains supplementary material, which is available to authorized users.

High temperature significantly impacts grain appearance quality, yet few studies have focused on identifying new quantitative trait loci (QTLs)/genes related to these traits under heat stress during the flowering stage in rice. In this study, a natural population of 525 rice accessions was used to identify QTLs and candidate genes associated with grain appearance quality using a Genome-Wide Association Study under heat stress. We identified 25 QTLs associated with grain length (GL), grain width (GW), and grain chalkiness (GC) under heat stress across 10 chromosomes in the three rice populations (full, indica, and japonica). Notably, three sets of overlapping QTLs were identified (set 1: qHTT-L3 and qHTT-XL3; set 2: qHTT-C5 and qHTT-XC5; set 3: qHTT-L11.1 and qHTT-GL11), located on chromosomes 3, 5, and 11, respectively. Haplotype analysis indicated that Hap1 is the superior haplotype, and pyramiding more than two superior alleles improved rice grain appearance quality (longer GL, wider GW, and lower GC) in high-temperature environments. Based on RNA-seq, qRT-PCR and functional annotations analysis, LOC_Os05g06920, LOC_Os05g06970, and LOC_Os11g28104 were highly expressed, identifying them as the high-priority candidate genes for QTLs linked to grain appearance quality (GL, GW, and GC) under heat stress. Expression analysis revealed that LOC_Os05g06920, which encodes a relA-SpoT-like protein RSH4, and LOC_Os11g28104, which encodes a protein kinase with a DUF26 domain, were highly expressed in seeds, leaves, and shoots. And LOC_Os05g06970, encoding a peroxidase precursor, exhibited high expression levels in roots. Compared to the wild-type (WT) plants, the mutants of LOC_Os05g06920, LOC_Os05g06970, and LOC_Os11g28104 exhibited increased GL and grain length-to-width ratio, but reduced GW under both natural and heat stress conditions, while the LOC_Os05g06970 and LOC_Os11g28104 mutants significantly increased the chalky grain rate and grain chalkiness degree under natural conditions. Furthermore, the LOC_Os05g06920, LOC_Os05g06970, and LOC_Os11g28104 mutants showed a lower decline in grain appearance quality traits than the WT after high-temperature treatment. These findings suggest that LOC_Os05g06920, LOC_Os05g06970, and LOC_Os11g28104 play crucial roles in regulating both grain development and heat tolerance under heat stress at anthesis, thus affecting grain appearance quality in rice. Our results provide a promising genetic resource for improving rice grain appearance quality under heat stress.

Thirty environmentally stable QTL controlling grain size and/or plant height were identified, among which QTgw.cau-7D was delimited into the physical interval of approximately 4.4Mb. Grain size and plant height (PHT) are important agronomic traits in wheat breeding. To dissect the genetic basis of these traits, we conducted a quantitative trait locus (QTL) analysis using recombinant inbred lines (RILs). In total, 30 environmentally stable QTL for thousand grain weight (TGW), grain length (GL), grain width (GW) and PHT were detected. Notably, one major pleiotropic QTL on chromosome arm 3DS explained the highest phenotypic variance for TGW, GL and PHT, and two stable QTL (QGw.cau-4B and QGw.cau-7D) on chromosome arms 4BS and 7DS contributed greater effects for GW. Furthermore, the stable QTL controlling grain size (QTgw.cau-7D and QGw.cau-7D) were delimited into the physical interval of approximately 4.4Mb harboring 56 annotated genes. The elite NILs of QTgw.cau-7D increased TGW by 12.79-21.75% and GW by 4.10-8.47% across all three environments. Collectively, these results provide further insight into the genetic basis of TGW, GL, GW and PHT, and the fine-mapped QTgw.cau-7D will be an attractive target for positional cloning and marker-assisted selection in wheat breeding programs.

<p>Grain shape is usually characterized by grain length (GL), grain width (GW), grain thickness (GT) and length to width ratio (LWR), and controlled by quantitative trait locus (QTL). In this paper, QTL analysis was performed using an F<sub>2</sub> population and an F<sub>8</sub> recombinant inbred line (RIL) population from a cross Xiang743/Katy. A total of 38 QTLs for grain shape were detected and eight of them were repeatedly identified in both populations. Seven for GL, five for GW, five for GT, and eight for LWR were detected in F<sub>2 </sub>population, explaining totally phenotypic variance of 94.51%, 61.52%, 54.33% and 91.84%, respectively. Five for GL, three for GW, and five for LWR were detected in RILpopulation, explaining totally phenotypic variance of 39.83%, 37.52% and 36.71%, respectively. Many QTLs were located in similar intervals, contributing to complicated trait correlation. A few QTLs were mapped in intervals coincided with previously cloned genes associated with grain size. Two residual heterozygous lines (RHLs) were selected out on the basis of newly identified loci, populations derived from RHLs were constructed for fine mapping QTLs associated with grain shape.</p>

The correlation and path coefficient analysis of some kernel quality traits have been studied for 92 cultivars, breeding lines and landrace varieties of bread wheat (Triticum aestivum L.). Ninety-two genotypes were evaluated in alpha lattice design with two replications. Result of analysis of variance indicated that there were significant differences among genotypes in the most of traits. The correlation analysis showed that there were significant positive correlations among thousand kernel weight (TKW), grain length (GL) and grain width (GW). We also showed TKW, GL and GW had positive correlation with grain protein percentage, gluten weight, and falling number. Grain protein was significantly correlated with several kernel characteristics including: TKW, GL, GW, hardness index, gluten weight, SDS sedimentation, and falling number. On the first and second steps of stepwise regression analysis, protein percentage and falling number were the most effective traits in explaining different trait variations. Path coefficient analysis also showed the direct and significant effects of grain protein percentage and medium direct effect of falling number on SDS sedimentation. This result can be used in wheat breeding programs.

Grain size is an important agronomic trait that influences the yield and end-use quality of bread wheat (Triticum aestivum L.). We conducted quantitative trait loci (QTL) analysis using recombinant inbred lines derived from a cross of a semi-dwarf modern variety with large grains (Zenkoujikomugi: Zen) and a landrace with small grains (Chinese Spring: CS). Grain size was evaluated as grain length (GL), grain width (GW), grain thickness (GT), and thousand-grain weight (TGW) under two environmental conditions. A major QTL (QGl.obu-4B) associated with GL was detected in both environments near the Rht-B1 locus for the semi-dwarf trait on chromosome 4B, despite Zen and CS having the same Rht-B1a allele. No QTL for GW was found in the QGl.obu-4B region, and QTLs for GT and TGW was detected in only one environment. To validate the effect of QGl.obu-4B, we developed two backcrossed populations in the CS and Zen genetic backgrounds. The Zen-derived allele conferred larger GL, GW, and GT, resulting in greater TGW (13.7% increase compared to the CS-derived allele) in the CS background. In contrast, the QGl.obu-4B effect was smaller in the Zen background, and the Zen-derived allele increased TGW by only 7.0%. The QTL was located within the 168-Mbp region tightly linked to the Rht-B1 locus. Further characterization and fine-mapping of QGl.obu-4B will facilitate the use of this allele by marker-assisted selection in wheat breeding programs.

BackgroundThousand grain weight is a key component of grain yield in rice, and a trait closely related to grain length (GL) and grain width (GW) that are important traits for grain quality. Causal genes for 16 quantitative trait loci (QTL) affecting these traits have been cloned, but more QTL remain to be characterized for establishing a genetic regulating network. A QTL controlling grain size in rice, qGS10, was previously mapped in the interval RM6100–RM228 on chromosome 10. This study aimed to delimitate this QTL to a more precise location.MethodA total of 12 populations were used. The ZC9 population comprised 203 S1:2 families derived from a residual heterozygous (RH) plant in the F9 generation of the indica rice cross Teqing (TQ)/IRBB52, segregating the upper region of RM6100–RM228 and three more regions on chromosomes 1, 9, and 11. The Ti52-1 population comprised 171 S1 plants derived from one RH plant in F7 of TQ/IRBB52, segregating a single interval that was in the lower portion of RM6100–RM228. The other ten populations were all derived from Ti52-1, including five S1 populations with sequential segregating regions covering the target region and five near isogenic line (NIL) populations maintaining the same segregating pattern. QTL analysis for 1,000-grain weight, GL, and GW was performed using QTL IciMapping and SAS procedure GLM.ResultThree QTL were separated in the original qGS10 region. The qGL10.1 was located in the upper region RM6704–RM3773, shown to affect GL only. The qGS10.1 was located within a 207.1-kb interval flanked by InDel markers Te20811 and Te21018, having a stable and relatively high effect on all the three traits analyzed. The qGS10.2 was located within a 1.2-Mb interval flanked by simple sequence repeat markers RM3123 and RM6673. This QTL also affected all the three traits but the effect was inconsistent across different experiments. QTL for grain size were also detected in all the other three segregating regions.ConclusionThree QTL for grain size that were tightly linked on the long arm of chromosome 10 of rice were separated using NIL populations with sequential segregating regions. One of them, qGS10.1, had a stable and relatively high effect on grain weight, GL, and GW, providing a good candidate for gene cloning. Another QTL, qGS10.2, had a significant effect on all the three traits but the effect was inconsistent across different experiments, providing an example of genotype-by-environmental interaction.