Genetic Mapping of Grain Shape Associated QTL Utilizing Recombinant Inbred Sister Lines in High Yielding Rice ( Oryza sativa L.) Utilizing

: Grain shape is a key factor for yield and quality in rice. To investigate the genetic basis of grain shape in the high-yielding hybrid rice variety Nei2You No.6, a set of recombinant inbred sister lines (RISLs) were used to map quantitative trait loci (QTLs) determining grain length (GL), grain width (GW), and length-width ratio (LWR) in four environments. A total of 91 medium/minor-effect QTL were detected using a high-density genetic map consisting of 3203 Bin markers composed of single nucleotide polymorphisms, among which 64 QTL formed 15 clusters. Twelve of 15 clusters co-localized with QTL previously reported for grain shape/weight. Three new QTL were detected: qGL-7a , qGL-8 , and qGL-11a . A QTL cluster, qLWR-12c/qGW-12 , was detected across all four environments with phenotypic variation explained (PVE) ranging from 3.67% to 11.93%, which was subsequently validated in paired lines of F 17 progeny and tightly linked marker assay in F 10 generation. Subsequently, 17 candidate genes for qLWR-12c/qGW-12 were detected in the 431 Kb interval utilizing bulk segregant analysis (BSA). Among these, OsR498G1222170400 OsR498G1222173400 , and OsR498G1222170500 were the best candidates, which lays the foundation for further cloning and will facilitate high-yield breeding in rice. were collected as libraries for screening RHLs for ﬁne mapping of target QTLs.


Introduction
Rice (Oryza sativa L.) is one of the most important crops in the world, feeding more than 50% of the world's population. In order to meet the increasing consumption demand of the people, high-yield and high-quality rice varieties are becoming more and more popular. Grain shape is a key factor of yield and appearance quality in rice, which is determined by grain length (GL), grain width (GW), and length-width ratio (LWR) [1]. Therefore, paying attention to the quantitative trait loci (QTLs)/genes for grain shape is useful for the improvement of varieties.

Materials and Cultivation
'Super rice' is defined as a variety with at least one elite trait among yield, quality, and resistance [40]. Nei2You No.6 is an indica three-line hybrid variety released by China National Rice Research Institute, which is a 'super rice' with a yield of 591.1 Kg/667m2 (http://www.ricedata.cn/variety/superice.htm (accessed on 5 April 2021)). The population and genetic map are constructed as described before [36]. Consisting of 386 lines (F 15 ) with 3203 Bin markers, the RISL population was developed from the cross between the maintainer Nei2B and the restorer Zhonghui8006 (R8006). For QTLs mapping, two parents and RISLs were cultivated in Fuyang (FY, 119 • 57 E long; 30 • 03 N lat), Zhejiang Province and Lingshui (LS, 110 • 03 E long; 18 • 05 N lat), Hainan Province in 2015 and 2016. For simplicity, we took the abbreviation 15FY, 15LS, 16FY, and 16LS representing the four corresponding environments. For QTLs validation, paired RISLs in F 17 generation were planted in Fuyang in the summer of 2018 and the library of F 10 generation was grown in Lingshui in 2018. For BSA, screened RHL harboring qLWR-12c/qGW-12 from F 10 generation and planted in the summer of 2019 as secondary F 3 population. After the phenotype was identified, selected individual plants with extreme phenotypes form a pool. The workflow of this study was shown in Figure S1.

Phenotypic Analysis
All trials were conducted with a completely randomized block design with two replications. About 25 days after sowing, 16 plants of each RISL were transplanted in two rows with a spacing of 21 × 18 cm. Field management followed the normal agricultural practice [41]. At maturity, effective panicles of six individuals from the inner portion of each line were collected for phenotyping. All seeds sun dried, threshed artificially and then evaluated with an automatic seed examination system (Wanshen Detection Technology Co., Ltd., Hangzhou, China).

QTLs Mapping and Statistical Analysis
QTLs analysis was performed with the composite interval mapping (CIM) function in WinQTLCart 2.5 software (https://statgen.ncsu.edu/qtlcart/WQTLCart.htm (accessed on 5 April 2021)). One thousand permutations were conducted with 1 cM as a search step, and the LOD threshold of 2.5 was set to declare a QTL. We obtained the percentage of phenotypic variation explained (PVE) and the value of additive effects of each QTL. The nomenclature of QTLs followed the rules reported by McCouch [42].
Statistical analysis was conducted using Microsoft Excel and GraphPad Prism 8 software for parents and RISLs. Frequency distribution was carried out in Microsoft Excel and drawn in Prism 8 software. Using R3.6.3 software, package "corrplot" was used to calculate pearson correlation coefficient and show the correlation among GL, GW, LWR, and 1000-grain weight (TGW). Also, using pakage "PCA" and "ggplot2" to conduct the principal component analysis. For qLWR-12c/qGW-12 validation, clustering analysis of two groups (Nei2B-type and R8006-type) divided by linked marker 12-26 (F: 5 -ACATTGCTTCACGGGATTTGG-3 , R: 5 -CGGCAATAACCACAACGCCT-3 ) was carried out in F 10 progeny. Data of the traits for paired line and two separated groups were compared using Student's t-tests.

Bulk Segregant Analysis and Re-Sequencing
We selected 30 individuals with extremely high values of LWR in secondary F 3 to form a pool. Genomic DNA of individuals and paired line (Q42 and Q43) was extracted using a CTAB method [43]. Re-sequencing of the mixed pool and paired lines were carried out on the Illumina HiSeq X Ten platform in Berry Genomics (Beijing Berry Genomics Biotechnology Co. Ltd., Beijing, China). For SNP calling, clean data was compared with R498 reference genome sequence (http://www.mbkbase.org/R498/ (accessed on 5 April 2021)) by the Burrows-Wheeler Aligner (BWA) 0.7.5a-r405 with default parameters [44].
SNPs and Insertion and Deletions (InDels) were detected by genome analysis toolkit 3.8 (GATK 3.8) and annotated by Annovar software [45]. SNP-index and InDel-index were calculated according to a previous report [46]. SNP-index and InDel-index were merged into All-index and the distribution of All-index on chromosome was plotted. At the 90% confidence level, the sites with All-index greater than 0.9 were selected as candidate SNPs/InDels.

Phenotypes of Parents and RISLs
Considerable variations were observed between the two parental lines, specifically, Nei2B exhibited decreased GL and LWR but increased GW compared to R8006 (Figure 1). In the RISL population, all three traits showed continuous distribution and transgressive segregation across the four environments, indicating their quantitative inheritance. And the value of kurtosis and skewness of each trait were all between −1 and 1 except the kurtosis of GL in 15YF and 16LS (Table S1). These results indicated that GL, GW, and LWR were controlled by multiple genes in hybrid Nei2You No.6, meeting the requirements of QTLs analysis. out on the Illumina HiSeq X Ten platform in Berry Genomics (Beijing Berry Genomics Biotechnology Co. Ltd., Beijing, China). For SNP calling, clean data was compared with R498 reference genome sequence (http://www.mbkbase.org/R498/ (accessed on 5 April 2021)) by the Burrows-Wheeler Aligner (BWA) 0.7.5a-r405 with default parameters [44]. SNPs and Insertion and Deletions (InDels) were detected by genome analysis toolkit 3.8 (GATK 3.8) and annotated by Annovar software [45]. SNP-index and InDel-index were calculated according to a previous report [46]. SNP-index and InDel-index were merged into All-index and the distribution of All-index on chromosome was plotted. At the 90% confidence level, the sites with All-index greater than 0.9 were selected as candidate SNPs/InDels.

Phenotypes of Parents and RISLs
Considerable variations were observed between the two parental lines, specifically, Nei2B exhibited decreased GL and LWR but increased GW compared to R8006 (Figure 1). In the RISL population, all three traits showed continuous distribution and transgressive segregation across the four environments, indicating their quantitative inheritance. And the value of kurtosis and skewness of each trait were all between −1 and 1 except the kurtosis of GL in 15YF and 16LS (Table S1). These results indicated that GL, GW, and LWR were controlled by multiple genes in hybrid Nei2You No.6, meeting the requirements of QTLs analysis. Correlation analysis between TGW and three grain shape traits were shown in Figure  S2. TGW, GL, and GW positively correlated with each other across the four environments. LWR was positively correlated with GL and negatively correlated with GW and TGW. Notably, LWR and GW had a supreme negative correlation in each environment.

QTLs Analysis for GL, GW, and LWR
To discover the genetic mechanism underlying the grain shape of Nei2You No.6, we principally focused on three traits for grain shape (GL, GW, and LWR). A total of 91 additive QTLs were detected in RISLs in four environments, totally explaining 11.27-51.35% Correlation analysis between TGW and three grain shape traits were shown in Figure  S2. TGW, GL, and GW positively correlated with each other across the four environments. LWR was positively correlated with GL and negatively correlated with GW and TGW. Notably, LWR and GW had a supreme negative correlation in each environment.

Verification of qLWR-12c/qGW-12
Since qLWR-12c and qGW-12 co-localized in the interval of Bin3241-Bin3273, forming a stable QTL cluster, we used it as the subject for subsequent research. A pair of lines with a similar genetic background, Q146 and Q147, was chosen to validate qLWR-12c/qGW-12 ( Figure S3A). The allele of qLWR-12c/qGW-12 from the GW of Q147 increased by 1.90% while the LWR enhanced by 3.44% than Q146 ( Figure S3B,C,E,F), indicating the role of qGW-12 for GW and LWR. These results were consistent with the result of the highest correlation between the LWR and GW in RISLs. Variance in grain length between Q146 and Q147 was observed, which was probably caused by qGL-2b and qGL-7a ( Figure S3A,D).
Also, 291 Nei2B-type (Type I) and 348 R8006-type (Type II) individuals were screened from F 10 progeny with marker 12-26, which is tightly linked with qLWR-12c/qGW-12. GW and LWR showed significant differences between Type I and Type II, while there was no significant difference in GL ( Figure 3A-D). However, Type I and Type II could not be divided into two completely independent groups for all grain shape related traits ( Figure 3E), indicating qLWR-12c/qGW-12 partially determined the grain shape, specifically, the grain width.
OsR498G1222185300 SNP (G 21857552 -A) was within 1 Kb upstream of OsR498G1221877800 and OsR498G1221 877900, so they were both considered as candidate genes for the SNP. Similarly, SNP (G 26280090 -A) exists within 1Kb upstream of OsR498G1222173400 and within 1 Kb downstream of OsR498G1222173600, SNP (G 26446003 -A) exists within 1 Kb upstream of OsR498G1 222185500 and within 1 Kb downstream of OsR498G1222185300. Therefore, we obtained a total of 17 candidate genes, among which amino acid changes occurred in 5 candidates: OsR498G1222170400, OsR498G1222171900, OsR498G1222173400, and OsR498G1222185100 had non-synonymous substitutions, OsR498G1222170500 had a frameshifting insertion and non-synonymous substitutions.

Discussion
Compared with genetic maps constructed by traditional molecular markers, highdensity genetic maps have the advantages of high throughput, and are time-saving and labor-saving, which provide effective means for the mining and identifying of QTLs for important agronomic traits. For instance, Zhu et al. used Guanghui 998 (R998) and Francis to construct recombinant inbred lines and detected 26 yield-related QTLs through the high-density genetic map with 3061 Bin makers [31]. Zhu et al. utilized a recombinant inbred line constructed with indica rice 'Yuzhenxiang' and japonica rice '02428 to construct a high-density genetic map containing 2771 Bin markers to map 14 heading date QTLs [53]. In this study, 91 QTLs were detected using RISLs through a high-density genetic map consisting of 3203 Bin markers. With sufficient Bin markers, the reliability of our results is guaranteed. Compared with other mapping groups, RISLs showed advantages of greater time-saving and labor-saving without marker-assisted selection. As well as QTLs mapping, the RISLs are efficient in QTLs validating. As a supplement of RISLs, 1700 F 11 , 2780 F 12 , and 2464 F 13 lines were collected as libraries for screening RHLs for fine mapping of target QTLs.
Previous studies have shown that the QTLs detected by the same mapping population under different environmental conditions are not necessarily the same [54]. This situation also appeared in our research. For instance, in the interval between Bin326 and Bin387 on chromosome 1, the QTL for grain length was detected in 15LS, the QTL for grain width was detected in 16FY, and the QTL for both grain length and grain width was detected at 15FY. There probably is a QTL cluster formed in the interval of markers Bin326 and Bin387.
Our study revealed that the grain shape in three-line hybrid Nei2You No.6 is controlled by medium/minor QTLs. In total, some of the 91 QTLs detected clustered in 15 loci in multiple environments. Compared with previous studies, 3 of 15 stable loci (qGL-7a, qGL-8, and qGL-11a) are novel, and the rest overlap with reported QTLs (Table 2), indicating the accuracy of our mapping results. For instance, we detected qGW-2b and qLWR-2c in the interval between 31.05 and 36.25 Mb on chromosome 2, which co-located with the major QTL TGW2 and qGW2 [5]. On chromosome 7, we detected qGW-7 in the interval of 18.85-20.25 Mb, in which GLW7 was cloned in previous research [12]. We detected qLWR-9a in the 7.75-18.35 Mb interval on chromosome 9, where GS9 was located and cloned [11]. In the present study, the qGW-12/qLWR-12c were repeatedly detected in the interval of Bin3241-Bin3273 across four environments, which overlapped the interval of mapped qTGW-12 for 1000-grain weight in the same RISLs in our previous study [36]. In addition, all 91 QTLs explain 2.17-11.93% of the phenotypic variation, indicating that the grain shape of Nei2You No.6 was coordinated by multiple medium and minor QTLs. Among the 17 candidate genes for qLWR-12c/qGW-12, there were five genes with exonic differences between two parents (Table 4). OsR498G1222170400 encodes a GNS1/SUR4 membrane family protein and its homolog in Arabidopsis, ELO4/HOS3, participates in the synthesis of very long chain fatty acids [55]. Containing a putative ankyrin repeat (ANK) domain, OsR498G1222171900 may be involved in protein-protein interaction and organism development [56]. OsR498G1222173400 encodes an α/β-hydrolase (ABH) with multiple functions in cell differentiation and development, which is the basic mechanism in the formation of grain size [57]. Encoding a reverse transcriptional transposon protein, OsR498G1222170500 harbors the DNase I-like domain, including magnesium-dependent endonuclease and phosphatase involving in intracellular signal transduction [58]. However, OsR498G1222185100 has no annotation information in Nipponbare or the R498 genome. Overall, although OsR498G1222171900 and OsR498G1222173400 are more likely the causal genes of qLWR-12c/qGW-12, we still can't exclude the other genes. In the future, we will identify the candidate gene for qLWR-12c/qGW-12 through two strategies. Firstly, further fine mapping will be conducted to narrow the target interval of qLWR-12c/qGW-12 using the segregating heterozygous line. Secondly, the expression level of candidate genes will be measured between two parents utilizing Quantitative Real-time PCR.

Conclusions
In total, we identified 91 medium/minor-effect QTLs for grain shape in RISLs with a high-density genetic map. Among 15 clusters harboring 64 QTLs, qGL-7a, qGL-8, and qGL-11a were the three firstly reported loci associated with grain size in rice. For further mapping of the stable qLWR-12c/qGW-12, 17 candidate genes were preliminarily obtained by BSA. Bioinformatics analysis shows that one of OsR498G1222185100, OsR498G1222170400, OsR498G1222171900, OsR498G1222173400, and OsR498G1222170500 was probably responsible for qLWR-12c/qGW-12, providing a potential candidate gene for further cloning. In conclusion, the revealed genetic basis of grain shape in Nei2You No.6 and the genetically dissected qLWR-12c/qGW-12 will provide useful information for breeders in developing hybrid rice varieties.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/agronomy11040705/s1, Figure S1: The workflow of this study. Figure S2: Correlation analysis of grain shape associated traits. Figure S3: Validation of identified qLWR-12c/qGW-12 using a pair of RISL. Figure S4: Distribution of grain shape associated traits of 171 individuals in secondary F 3 . Table S1: Means of grain shape related traits for the parental lines and RISL population. Table S2: QTLs for grain shape identified in the RISL population in one environment. Table S3 Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.
Data Availability Statement: Data sets analysed during the current study are available from the current author on reasonable request.