Dissection and Fine-Mapping of Two QTL Controlling Grain Size Linked in a 515.6-kb Region on Chromosome 10 of Rice

Grain size is a primary determinant of grain weight, which is one of the three essential components of rice grain yield. Mining the genes that control grain size plays an important role in analyzing the regulation mechanism of grain size and improving grain appearance quality. In this study, two closely linked quantitative trait loci (QTL) controlling grain size, were dissected and fine-mapped in a 515.6-kb region on the long arm of chromosome 10 by using six near isogenic line populations. One of them, qGS10.2, which controlled 1000 grain weight (TGW) and grain width (GW), was delimited into a 68.1-kb region containing 14 annotated genes. The Teqing allele increased TGW and GW by 0.17 g and 0.011 mm with the R2 of 12.7% and 11.8%, respectively. The other one, qGL10.2, which controlled grain length (GL), was delimited into a 137.3-kb region containing 22 annotated genes. The IRBB52 allele increased GL by 0.018 mm with the R2 of 6.8%. Identification of these two QTL provides candidate regions for cloning of grain size genes.


Introduction
Grain size is a primary determinant of grain weight, which is one of the three essential components of rice grain yield.Additionally, grain size is also a trait for grain appearance quality that rice breeders pay attention to, because the rice consumers in many countries prefer slender rice.Mining the genes that control grain size plays an important role in analyzing the regulation mechanism of grain size and improving grain appearance quality.
Although there were many QTL cloned for grain size, their proportion was still very low compared to the number of QTL that have been primary mapped.According to statistics, a total of 568 QTL for grain size were collected in Gramene database (https: //archive.gramene.org/qtl/,accessed on 15 April 2024), and only 4.9% of them were cloned.The reason was that most of these cloned QTL show major effects and were easy to be isolated and cloned.However, the genetic effects of most QTL for grain size were small, and the phenotype was easily disturbed by environment and background genes, which made it difficult for fine-mapping and gene functional complementation verification.Nonetheless, based on quantitative genetics theory and modern molecular mapping results, minor-effect QTL also played important roles in regulating important agronomic traits in rice, whether in mechanism analysis or breeding applications [40], these QTL cannot be ignored.
In our previous study, a minor-effect QTL for controlling TGW and GW, qGS10.2, was located in the region RM3123-RM6673 on the long arm of chromosome 10 by using five near isogenic line (NIL) populations derived from the cross between indica rice varieties Teqing (TQ) and IRBB52 [41].In this study, the genetic effect of qGS10.2 on grain size was further validated by using six NIL populations.Finally, two closely linked QTL controlling grain size, were dissected and fine-mapped in a 515.6-kbregion.qGS10.2,which controlled TGW and GW, was delimited into a 68.1-kb region.qGL10.2,which controlled GL, was delimited into a 137.3-kb region.

Validation of qGS10.2
One F 12:13 NIL population carrying the heterozygous region Te21873-Te22365, W1, was firstly used to validate the genetic effect of qGS10.2.As illustrated in Figure 1, it consisted of 28 NIL-TQ homozygous lines and 28 NIL-IRBB52 homozygous lines in the segregating region, derived from an F 11 plant of the rice cross TQ/IRBB52.At maturity, three traits, TGW, GL and GW, were measured.
Two genotypic groups were used as two series to plot the frequency distributions of the three traits.For TGW and GW, the NIL-TQ lines concentrated in the high-value area, and the NIL-IRBB52 lines concentrated in the low-value area (Figure 2A,C).These results suggested that QTL for TGW and GW was segregated in W1 with the enhancing allele derived from TQ.
Two-way analysis of variance (ANOVA) was performed to test the phenotypic differences in W1 population.The analysis was performed using the statistical analysis software SAS [42].A mixed model GENOTYPE + LINE (GENOTYPE) + REP + GENOTYPE*REP was applied, in which LINE (GENOTYPE) was defined as a random effect and used as the error term to test GENOTYPE differences.When significant differences were detected (p < 0.05), the additive effect was estimated by (IRBB52-TQ)/2.Positive values indicate that the increasing allele from IRBB52, and negative values indicate that the increasing allele from TQ.As shown in Table 1, highly significant effects were detected for TGW and GW.The TQ allele increased TGW and GW by 0.14 g and 0.011 mm with the R 2 of 6.6% and 8.1%, respectively.The results indicated that qGS10.2 was segregated in the region flanked by markers Te21852 and Te22367 (Figure 3A), corresponding to a 531.6-kbregion in the Nipponbare genome.
by markers Te21852 and Te22367 (Figure 3A), corresponding to a 531.6-kbregion in the Nipponbare genome.
Frequency distributions of the three traits in each population were exhibited in Figure 2D-R.In K2 and K3, the NIL-TQ and NIL-IRBB52 lines concentrated in the high-and low-value areas of TGW and GW, respectively (Figure 2G-L).In K4 and K5, the NIL-IRBB52 and NIL-TQ lines concentrated in the high-and low-value areas of GL, respectively (Figure 2M-R).These results suggested that there may be two QTL segregating in the region of qGS10.2,one controlled TGW and GW, and the other controlled GL.
Results of the two-way ANOVA on the three traits in these five NIL populations were shown in Table 1.No significant effect was detected in K1.Highly significant effects were detected for TGW and GW in K2 and K3.Additive effects estimated in these two NIL populations were similar.The TQ allele increased TGW and GW by 0.17 and 0.13 g, and 0.011 and 0.007 mm with the R 2 of 12.7% and 10.0%, 11.8% and 10.7%, respectively.In addition, highly significant effects were also detected for GL in K4 and K5.The additive effects estimated in these two populations were also similar.The IRBB52 allele increased GL by 0.018 and 0.017 mm with the R 2 of 6.8% and 6.7%, respectively.
Frequency distributions of the three traits in each population were exhibited in Figure 2D-R.In K2 and K3, the NIL-TQ and NIL-IRBB52 lines concentrated in the high-and low-value areas of TGW and GW, respectively (Figure 2G-L).In K4 and K5, the NIL-IRBB52 and NIL-TQ lines concentrated in the high-and low-value areas of GL, respectively (Figure 2M-R).These results suggested that there may be two QTL segregating in the region of qGS10.2,one controlled TGW and GW, and the other controlled GL.
Results of the two-way ANOVA on the three traits in these five NIL populations were shown in Table 1.No significant effect was detected in K1.Highly significant effects were detected for TGW and GW in K2 and K3.Additive effects estimated in these two NIL populations were similar.The TQ allele increased TGW and GW by 0.17 and 0.13 g, and 0.011 and 0.007 mm with the R 2 of 12.7% and 10.0%, 11.8% and 10.7%, respectively.In addition, highly significant effects were also detected for GL in K4 and K5.The additive effects estimated in these two populations were also similar.The IRBB52 allele increased GL by 0.018 and 0.017 mm with the R 2 of 6.8% and 6.7%, respectively.
As shown in Figure 3B, since the segregating regions of K2 and K5 were totally separated from each other, this suggested that each of these two segregating regions contained one QTL.The additive effects of TGW and GW detected in K2 and K3 were similar to those of qGS10.2,indicating that qGS10.2 was located in the common segregating region of K2 and K3.This region was flanked by markers Te21927 and Te21995, corresponding to a 68.1-kb region in the Nipponbare genome.In addition, the other QTL detected in K4 and Plants 2024, 13, 2054 6 of 11 K5, suggesting that this QTL was located in the common segregating regions of K4 and K5.This region was flanked by markers Te22077 and Te22215, corresponding to 137.7-kb region in the Nipponbare genome.Since this QTL only controlled GL, it is named qGL10.2.
In summary, two QTL closely linked in a 515.6-kbregion were separated.The qGS10.2 controlling TGW and GW was delimited into a 68.1-kb region.The other QTL, qGL10.2controlling GL, was located within a 137.3-kb region.

Candidate Genes for qGS10.2 and qGL10.2
According to the Rice Genome Annotation Project (http://rice.uga.edu/,accessed on 30 April 2024), there are fourteen annotated genes in the 68.1-kb region of qGS10.2.As shown in Table 2, three annotated genes encode retrotransposon protein, two encode expressed protein, one encodes a hypothetical protein, and the remaining eight encode protein with known functional domains.Three of the eight, Os10g40880, Os10g40900 and Os10g40934, all encode flavonol synthases.The other five, Os10g40810, Os10g40830, Os10g40859, Os10g40920 and Os10g40950, encode GATA zinc finger domain containing protein, metalloendoproteinase 1 precursor, matrixin family protein, pentatricopeptide and polyol transporter 5, respectively.For the qGL10.2,there are 22 annotated genes in the 137.3-kb region (Table 2).One of them encodes a hypothetical protein, six of them encode expressed protein, and the remaining 15 encode protein with known functional domains.Four of the fifteen, Os10g41130, Os10g41200, Os10g41260 and Os10g41330, encode AP2 domain containing protein and MYB family transcription factor, which are similar to the proteins encoded by the cloned grain size genes OsLG3 [25] and SG3 [26].In addition, the coding products of the other 11 annotation genes are different from those of the cloned grain size genes.More studies will continue for map-based cloning of qGS10.2 and qGL10.2.

Discussion
In this study, we identified two closely linked QTL controlling grain size from a 515.6-kbregion on the long arm of chromosome 10.One QTL controlling TGW and GW, qGS10.2, was delimited into a 68.1-kb region, which contained 14 annotated genes.One of them, Os10g40810 encoding a GATA zinc finger domain containing protein, functions in controlling rice plant architecture and panicle/grain development.The knock-down lines in a japonica variety Wuyunjing 7 background have ideal architecture, better grain shape, and enhanced grain yield [43].These results implied that Os10g40810 was the most likely candidate gene for qGS10.2.The other one controlling GL, qGL10.2, was delimited into a 137.3-kb region, which contained 22 annotated genes.In addition, we also noticed that the GL of NIL-TQ lines was longer than that of NIL-IRBB52 lines in K2 and K3 populations separated by qGS10.2(Table 1).This result showed that TQ allele may have an effect of increasing GL in qGS10.2segregation region, but it was not statistically significant.This may be the reason why the genetic effect of qGL10.2 was not detected in W1 population, that is, TQ allele increased the GL in qGS10.2region, but decreased the GL in qGL10.2region.In the meantime, it was found that K1 and K3 differed from the remaining three populations in terms of the GL and GW (Table 1), which was not unexpected since K2, K4 and K5 were derived from the same F 12 recombinant plant, whereas K1 and K3 were derived from another F 12 recombinant plant.This phenomenon also happened in our previous study [44].Identification of these two QTL provides candidate regions for cloning of grain size genes.
In our previous study, three QTL controlling grain size, qGL10, qGS10.1 and qGS10.2,were identified in a 4.2-Mb region on the long arm of chromosome 10 [41].Up to now, GW10 controlling grain size and number [24] and qTGW10-20.8/qGL10/GL10[35][36][37] controlling TGW and GL have been cloned in the region of qGL10 and qGS10.1,respectively.In this study, we not only fine-mapped qGS10.2,but also identified a new QTL, qGL10.2,controlling grain length.For the region of qGS10.2,one annotated gene, Os10g40810, was determined to be related to rice grain development by reverse genetics [43].It suggested that there were at least three genes controlling grain size in the 4.2-Mb region.Similarly, the phenomenon of tightly linked genes simultaneously controlling grain size also occurs on the other chromosomes.OsLG3 and OsLG3b for grain length were cloned in a 1.7-Mb region on chromosome 3 [25,27,28], GS5 and GSE5 for grain width in a 1.9-Mb region on chromosome 5 [16,17,23], and TGW6 and GW6a for grain weight in a 0.6-Mb region on chromosome 6 [8,29].These results implied that the genes controlling quantitative traits are often distributed in clusters.
In recent years, our group has cloned several minor-effect QTL controlling grain size, such as qTGW1.2b,qGS1-35.5 and qTGW10-20.8[4,35,38].In the process of map-based cloning of these QTL, a genetic resource, residual heterozygote (RH), was used for the construction of NIL populations.RH means that a single plant was heterozygous in the region covering the target QTL, while in other regions, it was homozygous for the parental alleles.The advantage was that the NIL populations derived from a RH can effectively eliminate the interference of background genes.Therefore, although the genetic effects of the target QTL were small, the effects remain stable in different NIL populations.For example, the additive effects of qGS10.2 and qGL10.2here were similar in K2 and K3, K4 and K5 populations respectively (Table 1).Based on this advantage, more and more studies began to apply RH to map-based cloning of QTL for yield-related traits in rice [45,46].
Generally, the QTL genetic effects are determined by the phenotypic differences between different alleles in a genetical population.For example, if the two parents carry functional and non-functional alleles respectively at the target gene locus in a bi-parental mapping population, the genetic effect is generally large.Conversely, if the two parents both carry functional alleles, the effect may be small.qTGW10-20.8/OsMADS56 is a minoreffect QTL for controlling grain weight and grain length, which was previously cloned from the cross between indica rice varieties TQ and IRBB52 by our group [35].Since both TQ and IRBB52 carrying functional alleles, genetic effect analysis reveals qTGW10-20.8as a minor effect QTL, with TQ allele increasing TGW and GL by only 0.22 g and 0.026 mm, respectively.In addition, this gene was also isolated from two other indica/japonica crosses, Huajingxian 74 (HJX74) / Lemont and Zhai-Ye-Xi (ZYX) / 02428 [36,37].Compared with the genome sequence of HJX74, Lemont has a 1019-bp deletion in the 5 ′ UTR and exon 1 region, resulting in the loss of transcriptional activity of OsMADS56 [36].In another cross, five haplotypes were detected from 15 SNPs in the OsMADS56 promoter region.The Hap3 was associated with higher values of TGW and GL and was distributed mainly in the japonica varieties [37].Due to the genetic effects of OsMADS56 representing the difference between the indicaand the japonica-allele in these two mapping populations, it is manifested as a major effect gene.These results suggested that strong functional allele can be discovered from germplasm resources through minor-effect gene cloning and haplotype analysis.

Rice Materials
Six NIL populations segregating in an isogenic background were used in this study.As shown in Figure 1, one F 11 plant of TQ/IRBB52, heterozygous in the region RM3123-RM6673, was firstly selected.Then, 11 new polymorphic markers were developed in this region and used to test genotypes of the F 11 plant.The heterozygous region was updated to Te21873-Te22365.In the resultant F 12 population consisting of 192 plants, homozygous non-recombinants were identified and selfed to develop homozygous lines.One NIL population in F 12:13 named W1 was constructed and was used for validation of qGS10.2.

Field Experiments and Phenotyping
All the NIL populations were planted in the paddy field of the China National Rice Research Institute in Hangzhou, Zhejiang province, China.For all trails, a randomized complete block design with two replications was performed.In each replication, each line was planted in a single row of 10 plants, with 26.7 cm between rows and 16.7 cm between plants.Field management followed local agricultural practice.
At maturity, four of the middle 8 plants in each row were harvested.Fully filled grains were selected and measured for TGW, GW and GL following the method reported by Zhang et al. [47].

DNA Marker Analysis
A total of 15 markers were used in this study, including 11 InDel and 4 simple sequence repeat (SSR) markers (Table S1).The InDels were developed based on the variance between TQ and IRBB52 as defined by whole-genome resequencing, while the SSRs were chosen from the Gramene database.DNA extraction and PCR amplification were followed Zheng Plants 2024, 13, 2054 9 of 11 et al. [48] and Chen et al. [49], respectively.The PCR products were visualized on 6.0% non-denaturing polyacrylamide gels using silver staining.

Data Analysis
For the six NIL populations, two-way ANOVA was used to analyze phenotypic differences between the two homozygous genotypic groups in each population.The analysis was performed using SAS procedure GLM (general linear model).When significant differences were detected (p < 0.05), the genetic effects of the QTL, including additive effect (A) and the proportions of phenotypic variance explained (R 2 ) were estimated using the same model.

Conclusions
In this study, two closely linked QTL controlling grain size, were dissected and finemapped in a 515.6-kbregion on the long arm of chromosome 10.One of them, qGS10.2,which controlled TGW and GW, was delimited into a 68.1-kb region.The other was qGL10.2, which controlled GL.It was delimited into a 137.3-kb region.Identification of these two QTL provides candidate regions for cloning of grain size genes.

Supplementary Materials:
The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/plants13152054/s1,Table S1: Polymorphic markers developed and used in this study.

Figure 1 .
Figure 1.Development of the rice populations used in this study.NIL, near isogenic line.

a
TGW, 1000-grain weight (g); GL, Grain length (mm); GW, Grain width (mm).b A, additive effect of replacing a Teqing allele with a IRBB52 allele.c R 2 , proportion of the phenotypic variance explained by the QTL.

Figure 3 .
Figure 3. Segregating regions of the six near isogenic line populations.(A) W1 was used to validate the genetic effect of qGS10.2.The underlined molecular markers were used in previous study.(B) Five populations were used for fine-mapping of qGS10.2.

Author Contributions:
Conceptualization, J.Z., B.S. and Z.S.; methodology, Z.Z. and Y.F.; investigation, Y.S. and D.H.; data curation, Y.S. and D.H.; writing-original draft preparation, Y.S., D.H. and Y.Z.; writing-review and editing, B.S. and Y.Z.All authors have read and agreed to the published version of the manuscript.Funding: This research was funded by the Key R&D Program of Jiangxi Province (20232BBF60001); the Agricultural Science and Technology Innovation Program (ASTIP), the Central Public-interest Scientific Institution Basal Research Fund (CPSIBRF-CNRRI-202112).

Table 1 .
Validation and fine-mapping of qGS10.2 using six NIL populations.
Figure 1.Development of the rice populations used in this study.NIL, near isogenic line.

Table 1 .
Validation and fine-mapping of qGS10.2 using six NIL populations.
a TGW, 1000-grain weight (g); GL, Grain length (mm); GW, Grain width (mm).b A, additive effect of replacing a Teqing allele with a IRBB52 allele.c R 2 , proportion of the phenotypic variance explained by the QTL.