QTL Detection for Rice Grain Length and Fine Mapping of a Novel Locus qGL6.1

QTL Detection for Rice Grain Length and Fine Mapping of a Novel Locus qGL6.1 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article QTL Detection for Rice Grain Length and Fine Mapping of a Novel Locus qGL6.1 Mingzhu Zhao, Yuanzheng Wang, Na He, Xiu Pang, Lili Wang, Zuobin Ma, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1371305/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Grain length (GL) that is directly associated with appearance quality is a key target of selection in rice breeding. Although abundant quantitative trait locus (QTL) associated with GL have been identified, it was still relatively weak to identify QTL for GL from japonica genetic background, as the shortage of japonica germplasms with long grains. We performed QTLs analysis for GL using a recombinant inbred lines (RILs) population derived from the cross between japonica variety GY8 (short grains) and LX1 (long grains) in four environments. Results A total of 197 RILs were genotyped with 285 polymorphic SNP markers. Three QTLs qGL5.3 , qGL6.1 and qGL11 were detected to control GL by individual environmental analyses and multi-environment joint analysis. Of these, a major-effect and stable QTL qGL6.1 was identified to be a novel QTL, and its LX1 allele had a positive effect on GL. For fine-mapping qGL6.1 , a BC 1 F 2 population consisting of 2,487 individuals was developed from a backcross between GY8 and R176, one line with long grain. Eight key informative recombinants were identified by nine kompetitive allele specific PCR (KASP) markers. By analyzing key recombinants, the qGL6.1 locus was narrowed down to a 20.117 kb genomic interval on chromosome 6. One candidate gene LOC_Os06g43304.1 encoding cytochrome P450 (CYP71D55) was finally selected based on the difference in the transcriptional expression and variations in its upstream and downstream region. Conclusions Three QTLs qGL5.3 , qGL6.1 and qGL11 were identified to control grain length in rice. One novel QTL qGL6.1 was fine mapped within 20.117 kb region, and LOC_Os06g43304.1 encoding cytochrome P450 (CYP71D55) may be its candidate gene. We propose that the further cloning of the qGL6.1 will facilitate improving appearance quality in japonica varieties. Rice Grain length QTLs Fine mapping Candidate genes Figures Figure 1 Figure 2 Figure 3 Introduction Grain length (GL) is the main factor that determines appearance in rice, and affect milling, cooking and eating quality, and is therefore an important agronomic traits in rice ( Oryza sativa L.) breeding. (Fitzgerald et al. 2009). The indica varieties generally exhibit long grains, while the japonica varieties have short grains. However, with the wide popularity of some japonica varieties with long grains, such as Daohuaxiang, among the consumers, increasing GL has become an important target in japonica breeding recently. As rice GL is quantitatively inherited (McKenzie and Rutger 1983 ), it is difficult for breeders to efficiently improve GL using conventional selection methods. Therefore, it should be particularly beneficial to enhance breeding efficiency to use markers closely related to genes or major quantitative trait loci (QTLs) for GL, while the target genotypes could be screened directly in early generations (Wan et al. 2006 ). A lot of QTLs associated with rice GL have been detected in the previous studies, and some major or major-effect QTLs as well as their candidate genes have been identified, including GS3 (Fan et al. 2006 ), GS2 (Hu et al. 2015 ), GL7 (Wang et al. 2015 ), GLW7 (Li et al. 2016), TGW6 (Ishimaru et al. 2013 ), qGL3 (Gao et al. 2019 ), GL6 (Wang et al. 2019 ) and GW6a (Song et al. 2015; Gao et al. 2021 ). Importantly, most of these QTLs or genes were detected from the indica varieties with long grains, such as Minghui63 and Kasalath, which were widely used as the long grain parents to cross with the japonica to develop the mapping populations. Recently only few QTLs for GL, such as qGL3.1 and qGL3.3 , have been identified from japonica rice (Hu et al. 2018 ; Qi et al. 2012 ; Zhang et al. 2012 ; Ying et al. 2018 ; Xia et al. 2018 ). Due to the shortage of japonica germplasms with long grains, however, it was still relatively weak to identify QTLs or genes associated with GL from japonica genetic background. To improve GL in japonica breeding, it was an efficient way by introgression of alleles from indica to japonica via subspecies cross. For example, the major QTL GS3 for grain size was identified from indica (Fan et al. 2006 ), and its long-grain-associated gs3 allele has been introgressed in japonica gene pool by genetic improvement (Sun et al. 2012 ). Whereas, most of dense and erect panicle japonica varieties, released as high-yielding varieties and had extensive production areas in China, did not showed long grains, even though they carried the gs3 allele. The main reason was that the dense and erect panicle varieties carried the high-yield-associated dep1 allele, which showed negative effect on GL (Li et al. 2019 ; Zhao et al. 2019 ). Both GS3 and DEP1 encodes Gγ protein containing a C-terminal cysteine-rich domain (Botella 2012 ). The gs3 allele could enhance the interaction between Gγ subunits DEP1 and Gβ, resulting in a long and slender grain, while the dep1 allele could reduce the interaction between DEP1 and Gβ, resulting in not only dense and erect panicles but also smaller grains (Sun et al. 2018 ). Recently, a major QTL qLGY3 has been identified from American japonica variety L-204, which could simultaneously improve both the grain yield and GL by combining with dep1 and gs3 alleles (Liu et al. 2018 ). These results indicated that discovering novel genes associated with GL in japonica germplasms would be of great importance to improve grain size of erect panicle varieties. In our previous studies, to improve the GL of erect panicle varieties, the japonica variety Shennong9017 with long grains was used as parent to cross with dense and erect panicle variety Liaojing454 to thus develop a japonica super rice variety Liaoxing1 (LX1). In addition to dense and erect panicles and more grains per panicle, one of the most significant morphological features of LX1 is their long grains, which were higher than those of other dense and erect panicle varieties. The objective of present study was (1) to detect the QTLs for GL by using a recombinant inbred lines (RILs) population derived from a cross between Gangyuan8 (GY8) and LX1 in four environments, (2) to localize the major-effect QTL qGL6.1 to a narrow genomic region by using the BC 1 F 2 population, and (3) to identify the candidate gene of qGL6.1 . Our results will provide important information to unearth the QTL for rice GL and help to understand how to achieve high yield and long grains in the LX1. Results Phenotypic variation of grain length in the RIL population There was significant genetic variation in GL among each RIL line and two parents in all environments. LX1 and GY8 gave averaged GL of 7.6 mm and 6.7 mm, respectively (Fig. 1 A). GL of the RILs ranged from 6.55 mm to 8.02 mm in each environment and exhibited approximately normal distributions (Fig. 1 B), indicating that they controlled by multiple genes. Pearson’s correlation coefficients among the four environments and BLUP ranged from 0.596 to 0.906 ( P < 0.01), indicating that the GL was consistent across environments (Table S1). The ANOVAs showed significant phenotypic variation in GL among genotypes, environments and genotype × environment interactions, and the variation among replications within experiments was no significant. More than 79% heritabilities were estimated for GL in four environments (Table S2), indicating that the QTL with large effect for GL could be detected in this study. Qtl Analysis In Individual Environments And Their Blup Values The phenotypic values of GL in four environments and its BLUP values were determined using the ICIM-ADD mapping method in BIP module for QTL mapping. A total of 20 QTLs that controlled GL was detected in the individual environment, which included two QTL in 2018DG, four QTLs in 2019DG, four QTLs in 2019PJ, four QTLs in 2019SY and six QTLs in BLUP values, explaining 30.03%, 32.26%, 36.94%, 38.69% and 50.31% of the phenotypic variation, respectively (Table 1 ). We identified eight single QTL with phenotypic contributions of 10%-20%. Fourteen favorable QTL alleles were from the long grain parent LX1, whereas 6 favorable QTL alleles emerged from GY8. Table 1 QTLs for grain length in rice by single environment analysis QTL Environment Chromosome Marker interval ICIM LOD PVE(%) Add qGL5.1 2018DG 5 AX-153909583 ~ AX-154483228 5.14 7.18 -0.09 qGL5.2 2017DG 5 AX-95927242 ~ AX-95926505 3.40 8.35 -0.09 qGL5.3 2019SY 5 AX-95952779 ~ AX-154380199 4.55 7.22 -0.06 qGL5.3 BLUP 5 AX-95952779 ~ AX-154380199 6.18 7.63 -0.07 qGL5.3 2019PJ 5 AX-154380199 ~ AX-154326915 3.19 4.87 -0.05 qGL6.2 BLUP 6 AX-116842541 ~ AX-95937303 2.53 7.29 -0.07 qGL6.1 2017DG 6 AX-95928617 ~ AX-95962924 7.85 21.68 -0.14 qGL6.1 2018DG 6 AX-95928617 ~ AX-95962924 9.33 14.14 -0.12 qGL6.1 2019SY 6 AX-95928617 ~ AX-95962924 9.35 15.92 -0.09 qGL6.1 2019PJ 6 AX-95928617 ~ AX-95962924 8.48 13.53 -0.09 qGL6.1 BLUP 6 AX-95928617 ~ AX-95962924 11.76 15.56 -0.09 qGL7.2 2018DG 7 AX-95957416 ~ AX-95957100 3.57 4.97 0.08 qGL7.2 BLUP 7 AX-95957416 ~ AX-95957100 3.00 3.53 0.05 qGL9 2018DG 9 AX-153942631 ~ AX-115845259 3.29 5.97 0.08 qGL10 2019SY 10 AX-154280635 ~ AX-115738695 3.18 5.40 0.07 qGL10 2019PJ 10 AX-154280635 ~ AX-115738695 3.92 6.49 0.07 qGL10 BLUP 10 AX-154280635 ~ AX-115738695 3.51 4.74 0.06 qGL11 2019SY 11 AX-115836788 ~ AX-115753113 6.03 10.15 -0.08 qGL11 2019PJ 11 AX-115836788 ~ AX-115753113 6.52 12.05 -0.08 qGL11 BLUP 11 AX-115836788 ~ AX-115753113 7.67 11.56 -0.08 QTL qGL6.1 detected simultaneously by BLUP data and was consistent across four environments, was flanked by AX-95928617 and AX-95962924 on chromosomes 6. Two QTL qGL5.3 and qGL11 detected simultaneously by BLUP data and were consistent across two environments, were flanked by AX-95952779 and AX-154326915 on chromosomes 5 and AX-115836788 and AX-115753113 on chromosomes 11, respectively. qGL6.1 , qGL5.3 and qGL11 had positive effect on GL, explaining 13.53–21.68%, 4.87–7.63% and 10.15–12.05% of the total phenotypic variation, respectively, and the positive allele came from the same donor LX1. Joint Analysis In Multiple Environments Three stable QTL qGL5.3 , qGL6.1 and qGL11 detected in individual environment analyses were all mapped to the same marker intervals to those detected by the joint analysis (Table 2 ). By using the ICIM-ADD mapping method in the MET module, the qGL5.3 and qGL11 showed significant additive effect (LOD(A) > 2.5) as well as QTL× environment interaction effects (LOD(AbyE) > 2.5), but these additive contribution rates were higher than those between the additive effect and environment interaction, indicating that these additive effects were the main contributors to phenotypic variation. The qGL6.1 had significant additive effect (LOD(A) > 2.5) rather than additive-environment interaction (LOD(AbyE) < 2.5), which were considered to be expressed independently. These three QTL qGL5.3 , qGL6.1 and qGL11 were detected by the joint analysis with phenotypic contribution of 2.86%, 17.51% and 5.45%, respectively. Table 2 Detection of rice grain length QTLs based on joint analysis in multiple environments QTL Chromosome Marker interval LOD PVE(%) Add A AbyE A AbyE 2017DG 2018DG 2019SY 2019PJ qGL1 1 AX-95941988 ~ AX-95942681 3.23 0.26 1.42 0.09 0.00 0.00 0.01 -0.01 qGL2.1 2 AX-95945787 ~ AX-155279023 2.09 0.77 0.94 0.20 0.01 -0.01 0.01 -0.01 qGL2.2 2 AX-155279023 ~ AX-95962383 2.88 0.34 1.29 0.08 0.00 0.00 0.01 -0.01 qGL3 3 AX-154336714 ~ AX-95922232 2.79 0.09 1.25 0.02 0.00 0.00 0.00 0.01 qGL5.1 5 AX-153909583 ~ AX-154483228 2.03 3.02 0.93 2.30 0.03 -0.06 0.02 0.02 qGL5.3 5 AX-154380199 ~ AX-154326915 4.33 3.59 1.96 0.90 0.03 0.02 -0.02 -0.03 qGL5.4 5 AX-95953387 ~ AX-95954162 2.57 0.12 1.16 0.10 -0.01 0.00 0.01 0.01 qGL6.2 6 AX-116842541 ~ AX-95937303 2.36 0.42 0.95 0.29 0.01 -0.03 0.01 0.00 qGL6.1 6 AX-95928617 ~ AX-95962924 33.91 0.83 17.20 0.31 0.00 -0.02 0.01 0.01 qGL7.1 7 AX-155599243 ~ AX-154017683 1.25 1.73 0.56 0.47 -0.01 -0.03 0.02 0.01 qGL7.2 7 AX-95957416 ~ AX-95957100 7.41 0.10 3.33 0.47 0.00 0.03 -0.02 -0.01 qGL8 8 AX-117362153 ~ AX-115863340 4.06 0.26 1.82 0.11 -0.01 -0.01 0.00 0.01 qGL9 9 AX-153942631 ~ AX-115845259 6.68 0.10 2.89 0.38 0.00 0.02 -0.01 -0.01 qGL10 10 AX-154280635 ~ AX-115738695 5.16 2.46 2.67 0.42 -0.03 -0.01 0.02 0.02 qGL11 11 AX-115836788 ~ AX-115753113 10.31 3.51 4.91 0.54 0.02 0.01 -0.02 -0.02 Fine mapping of qGL6.1 and analyzing candidate genes By individual environmental analyses and multi-environment joint analysis, the major-effect QTL qGL6.1 for GL was detected in an approximate 97.46-kb interval on chromosomes 6 (Fig. 3 A). To obtain a candidate genomic region for the positioning of qGL6.1 , the line R176 with long grains (7.62 mm) to backcrossed with recipient parent GY8, as they carried different allele in qGL6.1 locus but had 68% similar in genetic background by 8 K-SNP chip analysis. A total of 2, 487 BC 1 F 2 plants were detected by nine KASP markers across the qGL6.1 target region to identify eight key informative recombinants (Fig. 3 B). Among them, the recombinants R2, R4, R5, R6 and R7 showed longer grains compared with the others. Finally, the qGL6.1 locus was narrowed down to a 20.117-kb genomic interval with flanking markers K-26011771 and K-26031888 (Fig. 3 C). According to the Rice Annotation Project annotation ( http://rapdb.dna.affrc.go.jp/ ), this region contains two annotated genes, LOC_Os06g43290.1 and LOC_Os06g43304.1 , which encodes putative Ty1-copia subclass retrotransposon protein and cytochrome P450, respectively. By qPCR analysis in the inflorescence meristem, there was significant difference between GY8 and R176 in expression of LOC_Os06g43304.1 rather than LOC_Os06g43290.1 . The expression of LOC_Os06g43304.1 was down-regulated in R176 as compared to GY8 (Fig. 3 D). We also found 10 SNP and 5 InDel in upstream region, 11 SNP and 2 InDel in intron region, as well as 9 SNP and 1 InDel in downstream region, respectively, in the DNA sequence of LOC_Os06g43304.1 between GY8 and LX1 (Fig. 3 E). Through gene prediction and sequencing, the LOC_Os06g43304.1 encoding cytochrome P450 in rice, was identified as a candidate gene of qGL6.1 . Discussion In this study, we sought to identify novel genes that control GL in rice by using the RIL population in four environments to dissect the genetic basis of the long grains japonica variety. As we known, the QTL detected in multiple environments could be more stable than QTL with high effect values detected in a single environment and have a more useful value for MAS breeding (Fulton et al., 1997 ). We simultaneously detected three QTL qGL5.3 , qGL6.1 and qGL11 associated with GL regulation in the japonica variety LX1 by individual environmental analyses and multi-environment joint analysis, while their additive effects had higher phenotypic contributions than QTL× environment interactive effects. Among them, qGL6.1 were identified by BLUP data and were consistent across four environments, so it can be considered as stable QTL for GL. Importantly, the qGL6.1 that explained 13.53–21.68% and 17.51% of the phenotypic variation by individual environment analyses and joint analysis, respectively, could be identified as the major-effect QTL for GL. By using the RIL population, the qGL6.1 was detected in an approximate 97.46 kb interval on chromosome 6 between AX-95928617 and AX-95962924. The qGL6.1 was compared to QTL that had been previously reported for the same or related traits by physical positions of the respective interval markers. The position of qGL6.1 differed slightly from those of known QTL cloned as single genes related to GL on chromosome 6, like TGW6 (Ishimaru et al. 2013 ), GL6 (Wang et al. 2019 ), SDT (Zhao et al. 2015 ), qGL-6 (Zhang et al. 2020 ) and GW6a (Song et al. 2015; Gao et al. 2021 ). Interestingly, the interval of qGL6.1 was within qGN6 and qBNS6.2 , controlling grain number per panicle and number of secondary branches, respectively, which were co-localized in a 0.94 Mb interval on chromosome 6 between AX-95955496 and AX-115755704 in our previous study (Zhao et al. 2020 ). However, it was unknown whether a cluster of QTLs or a true pleiotropy is the cause of multiple phenotypic variations. We suggested that qGL6.1 as a novel QTL could be considered as important candidate loci for fine-mapping to identify the molecular regulatory mechanism for GL as well as other panicle components. The current strategy in QTL fine-mapping is to develop a set of nearly isogenic lines or chromosome segment substitution lines for the target QTL, which were different in the size of the genomic segments that harbor the QTL but had otherwise identical genetic background (Li et al. 2011 ; Tan et al. 2008 ). It is, however, time-consuming to develop a population consisting of idealized nearly isogenic lines or chromosome segment substitution lines (Yang et al. 2012 ). To minimize the influence of ‘noisy’ genetic backgrounds on phenotypic performance as much as possible, in this study, the line R176 was used as parent to develop the BC 1 F 2 population, which had 68% similar in genetic background to GY8. Moreover, KASP as the uniplex SNP genotyping platform offers cost-effective and scalable flexibility in QTL fine mapping that require small to moderate numbers of markers (Semagn et al. 2014 ). By nine KASP markers developed in the target region, eight key informative recombinants were identified in the BC 1 F 2 population. Although these recombinants shared similar genetic backgrounds with heterozygous rather than homozygous genotypes at the QTL region, they also showed significant difference in GL. Thus, the qGL6.1 locus was narrowed down to an interval between KASP markers K-26011771 and K-26031888, covering a 20.117-kb region in the Nipponbare genome. In qGL6.1 interval, only the LOC_Os06g43304.1 encoding a cytochrome P450 (CYP71D55) was significantly differentially expressed in the inflorescence meristem at the stage of primary and secondary rachis branch formation between GY8 and R176, which was most likely caused by the variations in its upstream and downstream region. As we known, the cytochrome P450 monooxygenases catalyze numerous monooxygenation/hydroxylation reactions, play an important role in different biochemical pathways, plant metabolism, cell proliferation, and expansion (Nelson et al. 2008 ; Schuler and Werck-Reichhart 2003). The CYP71D55 as the member of cytochrome P450 family was found to catalyze the successive regio-selective oxidation at the carbon atom C-2 position of premnaspirodiene to yield solavetivone (Banerjee and Hamberger 2018 ). Although the function of CYP71D55 in rice was unknown, it was reported that other homologous members regulated the grain size in Arabidopsis , wheat and rice. In Arabidopsis , several cytochrome P450s, including CYP78A5, CYP78A6, CYP78A7and CYP78A9, have been characterized as the positive regulators of seed size, which acts through a non-cell-autonomous signal to promotes organ growth (Adamski et al. 2009 ; Stransfeld et al. 2010 ; Sotelo-Silveira et al. 2013 ). In wheat, TaCYP78A3 gene encodes cytochrome P450 (CYP78A3), which was specifically expressed in wheat reproductive organs, is responsible for grain size (Ma et al. 2015 ). In rice, the global identification, structural analysis and expression characterization of cytochrome P450 superfamily have been systematically studied, which provided a clue to understanding biological function of cytochrome P450 in development regulation and drought stress response (Wei and Chen 2018 ). The d11 gene, encodes CYP724B1, was implicated in brassinosteroid biosynthesis via the characterization of a rice dwarf mutant, dwarf11 , with reduced grain length in rice (Tanabe et al. 2005 ). The BG2 and GL3.2 encodes CYP78A13 are also responsible for grain size and variations in the exon regions of these genes determined the difference in grain yield (Xu et al. 2015 ). By referring the function of its homologous genes, therefore, we suggested that the LOC_Os06g43304.1 encoding CYP71D55 in rice might be the candidate gene for GL in the QTL, qGL6.1 . Conclusions In this study, using the RIL population, three QTL qGL5.3 , qGL6.1 and qGL11 were detected to control GL by individual environmental analyses and multi-environment joint analysis. The positive alleles of these QTLs came from the long grain parent LX1. With the BC1F2 population, a major-effect and stable QTL qGL6.1 was fine mapped within 20.117 kb physical interval on chromosome 6. One candidate gene LOC_Os06g43304.1 encoding cytochrome P450 (CYP71D55) was finally selected based on the difference in the transcriptional expression. The cloning and genetic mechanism study of the qGL6.1 will facilitate improving appearance quality in japonica , especially in erect panicle varieties. Materials And Methods Plant materials The parental lines in this study were the long grains japonica variety LX1 and short grains japonica variety GY8. The mapping population consisted of 197 F 6:9 and F 6:10 RILs developed from a cross between GY8 × LX1. Field trials and trait measurement All 197 RILs and parents were grown at Donggang (39°87′ N, 124°15′ E), Shenyang (41°48′ N, 123°25′ E) and Panjin (41°12′ N, 122°07′ E) in Liaoning Province in 2018 and 2019 cropping seasons (hereafter referred as 2018DG and 2019DG, 2019SY, and 2019PJ) for evaluation of GL. Each RIL and parents were sown in a seedling nursery on April and with one seedling being transplanted per hill on May 24~30. A completely random block design was repeated three times in each environment. Each plot area was 2.5 m 2 , and the seedlings were transplanted at a spacing of 30 cm between rows and 13 cm between plants. Protection lines were set up everywhere, and field management followed local standards of production for rice. At maturity, the panicles of 10 plants of each line, and their parents were harvested from each plot. The distribution of primary branch number per panicle was firstly measured for each plot. Ten panicles with the largest distribution proportion of primary branch number in each plot were selected to collect the fully filled grains. After the grains were dried for 72 h at 50℃, the lengths of twenty randomly chosen grains from each plot were estimated as the lengthwise distance between opposite tips using a vernier caliper, and the values were averaged and used as the measurements for the line. In 2018DG, the GL of only 142 RILs were measured because of serious lodging of some lines, while that of all RILs were measured in the other three environments. Phenotypic data analyses The phenotypic values of RILs in each environment were analyzed. SPSS 19.0 was used for correlation analysis, and AOV function in QTL IciMapping (version 4.2) was used for variance analysis, with default parameters. The information in the ANOVA table was used to calculate broad sense heritability (H2) of each trait: H 2 = σg 2 /(σ g 2 +σ ge 2 /e + σ ε 2 /re), where σ g 2 is (MS f − MS fe )/re, σg e 2 is (MS fe − MS e )/r and σ ε 2 is MS e ; σ g 2 = genetic variance, σ ge 2 = genotype × environment interaction variance, σ ε 2 = error variance, MS f = mean square of genotypes, MS fe = mean square of genotype × environment interaction, MS e = mean square of error, r = number of replications, and e = number of environments. The best linear unbiased prediction (BLUP) of each parameter is calculated in the mixed linear model by using the lmer function in package lme4 of R software (Bates et al. 2012) (http://www.R-project.org/). Genotype, environment, genotype × environment interaction, and nested repeats in the environment are all considered random factors. Genotyping Genomic DNA was extracted from the fresh leaves of each line using the cetyl trimethyl ammonium bromide method (Murray and Thompson 1980). The whole genome resequencing of parents GY8 and LX1 was conducted by Biomarker Technologies. Each RIL (n = 197 genotypes) along with their parents were genotyped using the 8 K-SNP chip by Beijing Golden Marker Biotechnology Co., Ltd (Beijing, China). The SNP markers were screened as follows: ambiguous SNP calling, simplex and poor quality SNP loci with >10% missing values, or minor allele frequencies < 0.05 were removed in further analysis. Linkage mapping and QTL analysis After removing the redundant markers, QTL IciMapping 4.2 software was used to generate an input file for genetic map construction (Meng et al. 2015). In this genetic linkage maps, a total of 559 SNPs covering the 12 chromosomes were polymorphic between Gangyuan8 and Liaoxing1 (Zhao et al. 2020). In an individual environmental analysis, the inclusive composite interval mapping (ICIM) method was conducted using the BIP function to detect the additive QTL for phenotypic value and BLUP value of panicle traits (Li et al. 2007). A 1,000-permutation test was conducted at a 95% confidence level, and an LOD threshold of 2.5 was used. In addition, the multi-environmental joint analysis utilized the ICIM method in the MET functional module to detect the QTL additive and QTL× environment interaction effects (Li et al. 2015). QTL were named using the abbreviated English name of trait with “q” before it, followed by the chromosome number or chromosome number plus an ordered number designating one of multiple QTL in a single chromosome. The QTL detected in this study were compared to the Q-TARO database (Yonemaru et al. 2010). Fine-mapping by using BC 1 F 2 population After phenotypic and genotypic data analysis, one line with long grains, carrying the LX1 allele in major-effect QTL for GL but having similar genetic background to GY8, was selected in RIL population. For QTL fine-mapping, this line was backcrossed with GY8 to generate the BC 1 F 2 population. A total of 2, 487 BC 1 F 2 plants were grown at SY in 2021 cropping seasons for evaluation of GL. DNA was isolated from the two parental lines and the BC 1 F 2 population. According to the SNPs in the QTL region obtained from deep re-sequencing of two parental lines, the informative KASP markers (Table S3) were developed to genotype each plant of the BC 1 F 2 population. KASP assays were conducted in a 1536-well plate format using the protocol of LGC Genomics (LGC, Middlesex, UK). The Synergy H1 fullfunction microplate reader (FLUO star Omega, BMG Labtech, Germany) was used to read the fluorescence signal. Then the linkage relationship between markers and the qGL6.1 locus was analysed for fine-mapping. RNA isolation and qPCR analysis To identify the expression of candidate genes, at the stage of primary and secondary rachis branch formation, the inflorescence meristem of R176 and GY8 were sampled for RNA extraction and qPCR analysis. Total RNA was extracted with TriZol reagent (Invitrogen, Germany) according to the manufacturer’s instructions. The first strand of cDNAs were synthesized from DNaseI-treated total RNA using a Primer Script RT reagent Kit with gDNA Eraser (Takara, Japan). Reverse-transcribed RNA was used as PCR template for gene-specific primers (Table S4). Each reaction contained 3.0 μl of first-strand cDNAs, 2 μl of 200 μM gene-specific primers, and 12.5 μl of 2×SYBR Green Master Mix reagent (Applied Biosystems) in a final volume of 25 μl. The qPCR analysis was performed in a real-time PCR system (BIO-RAD). The qRT-PCR analysis was performed for each cDNA sample with four replications. Relative expression levels were calculated by 2 - △△ CT (Livak and Schmittgen 2001). Abbreviations QTL: Quantitative trait loci; GL: Grain length; RILs: Recombined inbred lines; KASP: Kompetitive Allele Specific PCR; DG, Donggang; SY, Shenyang; PJ, Panjin Declarations Author contributions W. Zheng and J. Liu initiated the research. M. Zhao designed and conducted all the experiments. Y. Wang, N. He and X. Pang analyzed genotype of RILs. L. Wang, Z. Tang, L. Zhang, C. Wang, Z. Ma and H. Gao, and L. Fu conducted the field experiments and measured the phenotype. M. Zhao prepared the manuscript. All authors read and approved the final manuscript. Funding The study was supported by the National Natural Science Foundation of China (31901526), Doctoral Research Foundation of Liaoning Province (2020-BS-299), and China Postdoctoral Science Foundation Grant (2019M651139). Availability of Data and Materials The datasets supporting the conclusions of this article are included within the article. Ethics Approval and Consent to Participate This study complied with the ethical standards of China, where this research work was carried out. Consent for Publication All authors are consent for publication. Competing Interests The authors declare that they have no competing interests. References Adamski NM, Anastasiou E, Eriksson S, O'Neill CM, Lenhard M ( 2009 ) Local maternal control of seed size by KLUH/CYP78A5-dependent growth signaling . P Natl Acad Sci USA 106 : 20115–20120 Banerjee A, Hamberger B ( 2018 ) P450s controlling metabolic bifurcations in plant terpene specialized metabolism . Phytochem Rev 17 : 81–111 Bates D, Maechler M, Bolker B ( 2012 ) lme4: linear mixed-effects models using S4 classes (R package version 0999999-0) , http:// cranr-projectorg/web/packages/lme4/index.html . 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Plant Cell Enviro 38 : 800–811 Yang Q, Zhang D, Xu M ( 2012 ) a sequential quantitative trait locus fine-mapping strategy using recombinant-derived progeny . J Integr Plant Biol 54 : 228–237 Ying JZ, Ma M, Bai C, Huang XH, Liu JL, Fan YY, Song XJ ( 2018 ) TGW3, a major QTL that negatively modulates grain length and weight in rice . Mol Plant 11 : 750–753 Yonemaru JI, Yamamoto T, Fukuoka S, Uga Y, Hori K, Yano M ( 2010 ) Q-taro: QTL annotation rice online database. Rice 3: 194 ± 203 Zhang T, Wang S, Sun S, Zhang Y, Li J, You J, Su T, Chen W, Ling Y, He G, Zhao F ( 2020 ) Analysis of QTL for grain size in a rice chromosome segment substitution line Z1392 with long grains and fine mapping of qGL-6. Rice 13: 1–11 Zhang X, Wang J, Huang J, Lan, H, Wang C, Yin C, Wu, Y, Tang H, Qian Q, Li J, Zhang H ( 2012 ) Rare allele of OsPPKL1 associated with grain length causes extra-large grain and a significant yield increase in rice . P Natl Acad Sci USA 109 : 21534–21539 Zhao M, Ma Z, Wang L, Tang Z, Mao T, Liang C, Gao H, Zhang L, He N, Fu L, Wang C, Sui G, Zheng W ( 2020 ) SNP–based QTL mapping for panicle traits in the japonica super rice cultivar Liaoxing 1 .Crop J 8 : 769–780 Zhao M, Liu B, Wu K, Ye Y, Huang S, Wang S, Wang Y, Han R, Liu Q, Fu X, Wu Y ( 2015 ) Regulation of OsmiR156h through alternative polyadenylation improves grain yield in rice . PloS ONE 10 : e0126154 Zhao M, Zhao M, Gu S, Sun J, Ma Z, Wang L, Zheng W, Xu Z ( 2019 ) DEP1 is involved in regulating the carbon-nitrogen metabolic balance to affect grain yield and quality in rice (Oriza sativa L. ) PloS ONE 14 : e0213504 Additional Declarations No competing interests reported. Supplementary Files SupportingInformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 29 May, 2022 Reviews received at journal 29 May, 2022 Reviews received at journal 25 May, 2022 Reviewers agreed at journal 16 May, 2022 Reviewers invited by journal 15 May, 2022 Submission checks completed at journal 20 Feb, 2022 Editor assigned by journal 20 Feb, 2022 First submitted to journal 17 Feb, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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02:44:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1371305/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1371305/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":18524405,"identity":"760eb651-9ea9-4e1d-ba83-f150c2299618","added_by":"auto","created_at":"2022-02-23 15:24:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40632,"visible":true,"origin":"","legend":"\u003cp\u003eDifference of grain length between Gangyuan8 and Liaoxing1 (\u003cstrong\u003ea\u003c/strong\u003e) and the frequency distributions of grain length (\u003cstrong\u003eb\u003c/strong\u003e) in the RIL population in four environments. DG, Donggang; SY, Shenyang; PJ, Panjin.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1371305/v1/f149fb14883d4849983a64cf.jpg"},{"id":18524713,"identity":"21baf935-cfbb-4d61-9cba-5d8929481241","added_by":"auto","created_at":"2022-02-23 15:27:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":133079,"visible":true,"origin":"","legend":"\u003cp\u003ePhysical map showing the QTL identified for grain length in RIL population by individual environmental analyses. Red font, known genes; black font, the markers and QTL detected in this study.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1371305/v1/963200c4e12fa6ab4178ca3d.jpg"},{"id":18524406,"identity":"e2721827-6463-4482-b257-fc68de23afa9","added_by":"auto","created_at":"2022-02-23 15:24:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":677517,"visible":true,"origin":"","legend":"\u003cp\u003eFine mapping of \u003cem\u003eqGL6.1\u003c/em\u003e and and analyzing candidate genes. \u003cstrong\u003ea\u003c/strong\u003e The physical map of \u003cem\u003eqGL6.1\u003c/em\u003e region on chromosome 6. \u003cstrong\u003eb\u003c/strong\u003e Fine mapping of \u003cem\u003eqGL6.1\u003c/em\u003e with eight recombinants. The black bar with markers denotes the recombinant sites. The white, black and gray boxes stand for genotypes of Gangyuan8, R176 and heterozygote, respectively. Vertical dotted lines denote the target region. \u003cstrong\u003ec\u003c/strong\u003e Two candidate genes in the fine mapping region. Arrows indicate direction of gene orientation. \u003cstrong\u003ed\u003c/strong\u003e The expression level of two candidate genes by qPCR analysis. \u003cstrong\u003ee\u003c/strong\u003e Variations in the upstream, intron and downstream region of \u003cem\u003eLOC_Os06g43304.1\u003c/em\u003e among Nipponbare, Gangyuan8 (GY8) and Liaoxing1 (LX1).\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1371305/v1/4a266a1a8cd71db57af9fb11.jpg"},{"id":18524714,"identity":"78de60cf-8c60-4322-b3e0-85650af5874f","added_by":"auto","created_at":"2022-02-23 15:27:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":679085,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1371305/v1/4589e37f-1565-4cfe-b24b-9bde0050ac3a.pdf"},{"id":18524407,"identity":"31fb0051-b0a9-4273-8c33-6e40d9b976ae","added_by":"auto","created_at":"2022-02-23 15:24:30","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":16971,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-1371305/v1/ab2b55ac80a588ab709b43e9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eQTL Detection for Rice Grain Length and Fine Mapping of a Novel Locus qGL6.1\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGrain length (GL) is the main factor that determines appearance in rice, and affect milling, cooking and eating quality, and is therefore an important agronomic traits in rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) breeding. (Fitzgerald et al. 2009). The \u003cem\u003eindica\u003c/em\u003e varieties generally exhibit long grains, while the \u003cem\u003ejaponica\u003c/em\u003e varieties have short grains. However, with the wide popularity of some \u003cem\u003ejaponica\u003c/em\u003e varieties with long grains, such as Daohuaxiang, among the consumers, increasing GL has become an important target in \u003cem\u003ejaponica\u003c/em\u003e breeding recently.\u003c/p\u003e \u003cp\u003eAs rice GL is quantitatively inherited (McKenzie and Rutger \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), it is difficult for breeders to efficiently improve GL using conventional selection methods. Therefore, it should be particularly beneficial to enhance breeding efficiency to use markers closely related to genes or major quantitative trait loci (QTLs) for GL, while the target genotypes could be screened directly in early generations (Wan et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). A lot of QTLs associated with rice GL have been detected in the previous studies, and some major or major-effect QTLs as well as their candidate genes have been identified, including \u003cem\u003eGS3\u003c/em\u003e (Fan et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), \u003cem\u003eGS2\u003c/em\u003e (Hu et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), \u003cem\u003eGL7\u003c/em\u003e (Wang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), \u003cem\u003eGLW7\u003c/em\u003e (Li et al. 2016), \u003cem\u003eTGW6\u003c/em\u003e (Ishimaru et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), \u003cem\u003eqGL3\u003c/em\u003e (Gao et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), \u003cem\u003eGL6\u003c/em\u003e (Wang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and \u003cem\u003eGW6a\u003c/em\u003e (Song et al. 2015; Gao et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Importantly, most of these QTLs or genes were detected from the \u003cem\u003eindica\u003c/em\u003e varieties with long grains, such as Minghui63 and Kasalath, which were widely used as the long grain parents to cross with the \u003cem\u003ejaponica\u003c/em\u003e to develop the mapping populations. Recently only few QTLs for GL, such as \u003cem\u003eqGL3.1\u003c/em\u003e and \u003cem\u003eqGL3.3\u003c/em\u003e, have been identified from \u003cem\u003ejaponica\u003c/em\u003e rice (Hu et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Qi et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ying et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xia et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Due to the shortage of \u003cem\u003ejaponica\u003c/em\u003e germplasms with long grains, however, it was still relatively weak to identify QTLs or genes associated with GL from \u003cem\u003ejaponica\u003c/em\u003e genetic background.\u003c/p\u003e \u003cp\u003eTo improve GL in \u003cem\u003ejaponica\u003c/em\u003e breeding, it was an efficient way by introgression of alleles from \u003cem\u003eindica\u003c/em\u003e to \u003cem\u003ejaponica via\u003c/em\u003e subspecies cross. For example, the major QTL \u003cem\u003eGS3\u003c/em\u003e for grain size was identified from \u003cem\u003eindica\u003c/em\u003e (Fan et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and its long-grain-associated \u003cem\u003egs3\u003c/em\u003e allele has been introgressed in \u003cem\u003ejaponica\u003c/em\u003e gene pool by genetic improvement (Sun et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Whereas, most of dense and erect panicle \u003cem\u003ejaponica\u003c/em\u003e varieties, released as high-yielding varieties and had extensive production areas in China, did not showed long grains, even though they carried the \u003cem\u003egs3\u003c/em\u003e allele. The main reason was that the dense and erect panicle varieties carried the high-yield-associated \u003cem\u003edep1\u003c/em\u003e allele, which showed negative effect on GL (Li et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Both \u003cem\u003eGS3\u003c/em\u003e and \u003cem\u003eDEP1\u003c/em\u003e encodes Gγ protein containing a C-terminal cysteine-rich domain (Botella \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The \u003cem\u003egs3\u003c/em\u003e allele could enhance the interaction between Gγ subunits \u003cem\u003eDEP1\u003c/em\u003e and Gβ, resulting in a long and slender grain, while the \u003cem\u003edep1\u003c/em\u003e allele could reduce the interaction between \u003cem\u003eDEP1\u003c/em\u003e and Gβ, resulting in not only dense and erect panicles but also smaller grains (Sun et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recently, a major QTL \u003cem\u003eqLGY3\u003c/em\u003e has been identified from American \u003cem\u003ejaponica\u003c/em\u003e variety L-204, which could simultaneously improve both the grain yield and GL by combining with \u003cem\u003edep1\u003c/em\u003e and \u003cem\u003egs3\u003c/em\u003e alleles (Liu et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These results indicated that discovering novel genes associated with GL in \u003cem\u003ejaponica\u003c/em\u003e germplasms would be of great importance to improve grain size of erect panicle varieties.\u003c/p\u003e \u003cp\u003eIn our previous studies, to improve the GL of erect panicle varieties, the \u003cem\u003ejaponica\u003c/em\u003e variety Shennong9017 with long grains was used as parent to cross with dense and erect panicle variety Liaojing454 to thus develop a \u003cem\u003ejaponica\u003c/em\u003e super rice variety Liaoxing1 (LX1). In addition to dense and erect panicles and more grains per panicle, one of the most significant morphological features of LX1 is their long grains, which were higher than those of other dense and erect panicle varieties. The objective of present study was (1) to detect the QTLs for GL by using a recombinant inbred lines (RILs) population derived from a cross between Gangyuan8 (GY8) and LX1 in four environments, (2) to localize the major-effect QTL \u003cem\u003eqGL6.1\u003c/em\u003e to a narrow genomic region by using the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e population, and (3) to identify the candidate gene of \u003cem\u003eqGL6.1\u003c/em\u003e. Our results will provide important information to unearth the QTL for rice GL and help to understand how to achieve high yield and long grains in the LX1.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic variation of grain length in the RIL population\u003c/h2\u003e \u003cp\u003eThere was significant genetic variation in GL among each RIL line and two parents in all environments. LX1 and GY8 gave averaged GL of 7.6 mm and 6.7 mm, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). GL of the RILs ranged from 6.55 mm to 8.02 mm in each environment and exhibited approximately normal distributions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), indicating that they controlled by multiple genes. Pearson\u0026rsquo;s correlation coefficients among the four environments and BLUP ranged from 0.596 to 0.906 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that the GL was consistent across environments (Table S1). The ANOVAs showed significant phenotypic variation in GL among genotypes, environments and genotype \u0026times; environment interactions, and the variation among replications within experiments was no significant. More than 79% heritabilities were estimated for GL in four environments (Table S2), indicating that the QTL with large effect for GL could be detected in this study.\u003c/p\u003e\u003c/div\u003e\n\u003ch2\u003eQtl Analysis In Individual Environments And Their Blup Values\u003c/h2\u003e\n\u003cp\u003eThe phenotypic values of GL in four environments and its BLUP values were determined using the ICIM-ADD mapping method in BIP module for QTL mapping. A total of 20 QTLs that controlled GL was detected in the individual environment, which included two QTL in 2018DG, four QTLs in 2019DG, four QTLs in 2019PJ, four QTLs in 2019SY and six QTLs in BLUP values, explaining 30.03%, 32.26%, 36.94%, 38.69% and 50.31% of the phenotypic variation, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We identified eight single QTL with phenotypic contributions of 10%-20%. Fourteen favorable QTL alleles were from the long grain parent LX1, whereas 6 favorable QTL alleles emerged from GY8.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQTLs for grain length in rice by single environment analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQTL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEnvironment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMarker interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eICIM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLOD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePVE(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdd\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL5.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018DG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-153909583\u0026thinsp;~\u0026thinsp;AX-154483228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL5.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2017DG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-95927242\u0026thinsp;~\u0026thinsp;AX-95926505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL5.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019SY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-95952779\u0026thinsp;~\u0026thinsp;AX-154380199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL5.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-95952779\u0026thinsp;~\u0026thinsp;AX-154380199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL5.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019PJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-154380199\u0026thinsp;~\u0026thinsp;AX-154326915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL6.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-116842541\u0026thinsp;~\u0026thinsp;AX-95937303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL6.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2017DG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-95928617\u0026thinsp;~\u0026thinsp;AX-95962924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL6.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018DG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-95928617\u0026thinsp;~\u0026thinsp;AX-95962924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL6.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019SY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-95928617\u0026thinsp;~\u0026thinsp;AX-95962924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL6.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019PJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-95928617\u0026thinsp;~\u0026thinsp;AX-95962924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL6.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-95928617\u0026thinsp;~\u0026thinsp;AX-95962924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL7.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018DG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-95957416\u0026thinsp;~\u0026thinsp;AX-95957100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL7.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-95957416\u0026thinsp;~\u0026thinsp;AX-95957100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018DG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-153942631\u0026thinsp;~\u0026thinsp;AX-115845259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019SY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-154280635\u0026thinsp;~\u0026thinsp;AX-115738695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019PJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-154280635\u0026thinsp;~\u0026thinsp;AX-115738695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-154280635\u0026thinsp;~\u0026thinsp;AX-115738695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019SY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-115836788\u0026thinsp;~\u0026thinsp;AX-115753113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019PJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-115836788\u0026thinsp;~\u0026thinsp;AX-115753113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAX-115836788\u0026thinsp;~\u0026thinsp;AX-115753113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eQTL \u003cem\u003eqGL6.1\u003c/em\u003e detected simultaneously by BLUP data and was consistent across four environments, was flanked by AX-95928617 and AX-95962924 on chromosomes 6. Two QTL \u003cem\u003eqGL5.3\u003c/em\u003e and \u003cem\u003eqGL11\u003c/em\u003e detected simultaneously by BLUP data and were consistent across two environments, were flanked by AX-95952779 and AX-154326915 on chromosomes 5 and AX-115836788 and AX-115753113 on chromosomes 11, respectively. \u003cem\u003eqGL6.1\u003c/em\u003e, \u003cem\u003eqGL5.3\u003c/em\u003e and \u003cem\u003eqGL11\u003c/em\u003e had positive effect on GL, explaining 13.53\u0026ndash;21.68%, 4.87\u0026ndash;7.63% and 10.15\u0026ndash;12.05% of the total phenotypic variation, respectively, and the positive allele came from the same donor LX1.\u003c/p\u003e\n\u003ch2\u003eJoint Analysis In Multiple Environments\u003c/h2\u003e\n\u003cp\u003eThree stable QTL \u003cem\u003eqGL5.3\u003c/em\u003e, \u003cem\u003eqGL6.1\u003c/em\u003e and \u003cem\u003eqGL11\u003c/em\u003e detected in individual environment analyses were all mapped to the same marker intervals to those detected by the joint analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). By using the ICIM-ADD mapping method in the MET module, the \u003cem\u003eqGL5.3\u003c/em\u003e and \u003cem\u003eqGL11\u003c/em\u003e showed significant additive effect (LOD(A)\u0026thinsp;\u0026gt;\u0026thinsp;2.5) as well as QTL\u0026times; environment interaction effects (LOD(AbyE)\u0026thinsp;\u0026gt;\u0026thinsp;2.5), but these additive contribution rates were higher than those between the additive effect and environment interaction, indicating that these additive effects were the main contributors to phenotypic variation. The \u003cem\u003eqGL6.1\u003c/em\u003e had significant additive effect (LOD(A)\u0026thinsp;\u0026gt;\u0026thinsp;2.5) rather than additive-environment interaction (LOD(AbyE)\u0026thinsp;\u0026lt;\u0026thinsp;2.5), which were considered to be expressed independently. These three QTL \u003cem\u003eqGL5.3\u003c/em\u003e, \u003cem\u003eqGL6.1\u003c/em\u003e and \u003cem\u003eqGL11\u003c/em\u003e were detected by the joint analysis with phenotypic contribution of 2.86%, 17.51% and 5.45%, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetection of rice grain length QTLs based on joint analysis in multiple environments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQTL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMarker interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLOD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ePVE(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eAdd\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbyE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbyE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2017DG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2018DG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2019SY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2019PJ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAX-95941988\u0026thinsp;~\u0026thinsp;AX-95942681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL2.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAX-95945787\u0026thinsp;~\u0026thinsp;AX-155279023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL2.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAX-155279023\u0026thinsp;~\u0026thinsp;AX-95962383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAX-154336714\u0026thinsp;~\u0026thinsp;AX-95922232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eqGL5.1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAX-153909583\u0026thinsp;~\u0026thinsp;AX-154483228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eqGL5.3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAX-154380199\u0026thinsp;~\u0026thinsp;AX-154326915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL5.4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAX-95953387\u0026thinsp;~\u0026thinsp;AX-95954162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eqGL6.2\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAX-116842541\u0026thinsp;~\u0026thinsp;AX-95937303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eqGL6.1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAX-95928617\u0026thinsp;~\u0026thinsp;AX-95962924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL7.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAX-155599243\u0026thinsp;~\u0026thinsp;AX-154017683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eqGL7.2\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAX-95957416\u0026thinsp;~\u0026thinsp;AX-95957100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqGL8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAX-117362153\u0026thinsp;~\u0026thinsp;AX-115863340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eqGL9\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAX-153942631\u0026thinsp;~\u0026thinsp;AX-115845259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eqGL10\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAX-154280635\u0026thinsp;~\u0026thinsp;AX-115738695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eqGL11\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAX-115836788\u0026thinsp;~\u0026thinsp;AX-115753113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFine mapping of\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eqGL6.1\u003c/span\u003e \u003cb\u003eand analyzing candidate genes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBy individual environmental analyses and multi-environment joint analysis, the major-effect QTL \u003cem\u003eqGL6.1\u003c/em\u003e for GL was detected in an approximate 97.46-kb interval on chromosomes 6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). To obtain a candidate genomic region for the positioning of \u003cem\u003eqGL6.1\u003c/em\u003e, the line R176 with long grains (7.62 mm) to backcrossed with recipient parent GY8, as they carried different allele in \u003cem\u003eqGL6.1\u003c/em\u003e locus but had 68% similar in genetic background by 8 K-SNP chip analysis. A total of 2, 487 BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants were detected by nine KASP markers across the \u003cem\u003eqGL6.1\u003c/em\u003e target region to identify eight key informative recombinants (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Among them, the recombinants R2, R4, R5, R6 and R7 showed longer grains compared with the others. Finally, the \u003cem\u003eqGL6.1\u003c/em\u003e locus was narrowed down to a 20.117-kb genomic interval with flanking markers K-26011771 and K-26031888 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). According to the Rice Annotation Project annotation (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rapdb.dna.affrc.go.jp/\u003c/span\u003e\u003c/span\u003e), this region contains two annotated genes, \u003cem\u003eLOC_Os06g43290.1\u003c/em\u003e and \u003cem\u003eLOC_Os06g43304.1\u003c/em\u003e, which encodes putative Ty1-copia subclass retrotransposon protein and cytochrome P450, respectively.\u003c/p\u003e\u003cp\u003eBy qPCR analysis in the inflorescence meristem, there was significant difference between GY8 and R176 in expression of \u003cem\u003eLOC_Os06g43304.1\u003c/em\u003e rather than \u003cem\u003eLOC_Os06g43290.1\u003c/em\u003e. The expression of \u003cem\u003eLOC_Os06g43304.1\u003c/em\u003e was down-regulated in R176 as compared to GY8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). We also found 10 SNP and 5 InDel in upstream region, 11 SNP and 2 InDel in intron region, as well as 9 SNP and 1 InDel in downstream region, respectively, in the DNA sequence of \u003cem\u003eLOC_Os06g43304.1\u003c/em\u003e between GY8 and LX1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Through gene prediction and sequencing, the \u003cem\u003eLOC_Os06g43304.1\u003c/em\u003e encoding cytochrome P450 in rice, was identified as a candidate gene of \u003cem\u003eqGL6.1\u003c/em\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we sought to identify novel genes that control GL in rice by using the RIL population in four environments to dissect the genetic basis of the long grains \u003cem\u003ejaponica\u003c/em\u003e variety. As we known, the QTL detected in multiple environments could be more stable than QTL with high effect values detected in a single environment and have a more useful value for MAS breeding (Fulton et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). We simultaneously detected three QTL \u003cem\u003eqGL5.3\u003c/em\u003e, \u003cem\u003eqGL6.1\u003c/em\u003e and \u003cem\u003eqGL11\u003c/em\u003e associated with GL regulation in the \u003cem\u003ejaponica\u003c/em\u003e variety LX1 by individual environmental analyses and multi-environment joint analysis, while their additive effects had higher phenotypic contributions than QTL\u0026times; environment interactive effects. Among them, \u003cem\u003eqGL6.1\u003c/em\u003e were identified by BLUP data and were consistent across four environments, so it can be considered as stable QTL for GL. Importantly, the \u003cem\u003eqGL6.1\u003c/em\u003e that explained 13.53\u0026ndash;21.68% and 17.51% of the phenotypic variation by individual environment analyses and joint analysis, respectively, could be identified as the major-effect QTL for GL.\u003c/p\u003e \u003cp\u003eBy using the RIL population, the \u003cem\u003eqGL6.1\u003c/em\u003e was detected in an approximate 97.46 kb interval on chromosome 6 between AX-95928617 and AX-95962924. The \u003cem\u003eqGL6.1\u003c/em\u003e was compared to QTL that had been previously reported for the same or related traits by physical positions of the respective interval markers. The position of \u003cem\u003eqGL6.1\u003c/em\u003e differed slightly from those of known QTL cloned as single genes related to GL on chromosome 6, like \u003cem\u003eTGW6\u003c/em\u003e (Ishimaru et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), \u003cem\u003eGL6\u003c/em\u003e (Wang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), \u003cem\u003eSDT\u003c/em\u003e (Zhao et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), \u003cem\u003eqGL-6\u003c/em\u003e (Zhang et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and \u003cem\u003eGW6a\u003c/em\u003e (Song et al. 2015; Gao et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Interestingly, the interval of \u003cem\u003eqGL6.1\u003c/em\u003e was within \u003cem\u003eqGN6\u003c/em\u003e and \u003cem\u003eqBNS6.2\u003c/em\u003e, controlling grain number per panicle and number of secondary branches, respectively, which were co-localized in a 0.94 Mb interval on chromosome 6 between AX-95955496 and AX-115755704 in our previous study (Zhao et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, it was unknown whether a cluster of QTLs or a true pleiotropy is the cause of multiple phenotypic variations. We suggested that \u003cem\u003eqGL6.1\u003c/em\u003e as a novel QTL could be considered as important candidate loci for fine-mapping to identify the molecular regulatory mechanism for GL as well as other panicle components.\u003c/p\u003e \u003cp\u003eThe current strategy in QTL fine-mapping is to develop a set of nearly isogenic lines or chromosome segment substitution lines for the target QTL, which were different in the size of the genomic segments that harbor the QTL but had otherwise identical genetic background (Li et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tan et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). It is, however, time-consuming to develop a population consisting of idealized nearly isogenic lines or chromosome segment substitution lines (Yang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). To minimize the influence of \u0026lsquo;noisy\u0026rsquo; genetic backgrounds on phenotypic performance as much as possible, in this study, the line R176 was used as parent to develop the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e population, which had 68% similar in genetic background to GY8. Moreover, KASP as the uniplex SNP genotyping platform offers cost-effective and scalable flexibility in QTL fine mapping that require small to moderate numbers of markers (Semagn et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). By nine KASP markers developed in the target region, eight key informative recombinants were identified in the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e population. Although these recombinants shared similar genetic backgrounds with heterozygous rather than homozygous genotypes at the QTL region, they also showed significant difference in GL. Thus, the \u003cem\u003eqGL6.1\u003c/em\u003e locus was narrowed down to an interval between KASP markers K-26011771 and K-26031888, covering a 20.117-kb region in the Nipponbare genome.\u003c/p\u003e \u003cp\u003eIn \u003cem\u003eqGL6.1\u003c/em\u003e interval, only the \u003cem\u003eLOC_Os06g43304.1\u003c/em\u003e encoding a cytochrome P450 (CYP71D55) was significantly differentially expressed in the inflorescence meristem at the stage of primary and secondary rachis branch formation between GY8 and R176, which was most likely caused by the variations in its upstream and downstream region. As we known, the cytochrome P450 monooxygenases catalyze numerous monooxygenation/hydroxylation reactions, play an important role in different biochemical pathways, plant metabolism, cell proliferation, and expansion (Nelson et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Schuler and Werck-Reichhart 2003). The CYP71D55 as the member of cytochrome P450 family was found to catalyze the successive regio-selective oxidation at the carbon atom C-2 position of premnaspirodiene to yield solavetivone (Banerjee and Hamberger \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although the function of CYP71D55 in rice was unknown, it was reported that other homologous members regulated the grain size in \u003cem\u003eArabidopsis\u003c/em\u003e, wheat and rice. In \u003cem\u003eArabidopsis\u003c/em\u003e, several cytochrome P450s, including CYP78A5, CYP78A6, CYP78A7and CYP78A9, have been characterized as the positive regulators of seed size, which acts through a non-cell-autonomous signal to promotes organ growth (Adamski et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Stransfeld et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sotelo-Silveira et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In wheat, \u003cem\u003eTaCYP78A3\u003c/em\u003e gene encodes cytochrome P450 (CYP78A3), which was specifically expressed in wheat reproductive organs, is responsible for grain size (Ma et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In rice, the global identification, structural analysis and expression characterization of cytochrome P450 superfamily have been systematically studied, which provided a clue to understanding biological function of cytochrome P450 in development regulation and drought stress response (Wei and Chen \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The \u003cem\u003ed11\u003c/em\u003e gene, encodes CYP724B1, was implicated in brassinosteroid biosynthesis via the characterization of a rice dwarf mutant, \u003cem\u003edwarf11\u003c/em\u003e, with reduced grain length in rice (Tanabe et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The \u003cem\u003eBG2\u003c/em\u003e and \u003cem\u003eGL3.2\u003c/em\u003e encodes CYP78A13 are also responsible for grain size and variations in the exon regions of these genes determined the difference in grain yield (Xu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). By referring the function of its homologous genes, therefore, we suggested that the \u003cem\u003eLOC_Os06g43304.1\u003c/em\u003e encoding CYP71D55 in rice might be the candidate gene for GL in the QTL, \u003cem\u003eqGL6.1\u003c/em\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, using the RIL population, three QTL \u003cem\u003eqGL5.3\u003c/em\u003e, \u003cem\u003eqGL6.1\u003c/em\u003e and \u003cem\u003eqGL11\u003c/em\u003e were detected to control GL by individual environmental analyses and multi-environment joint analysis. The positive alleles of these QTLs came from the long grain parent LX1. With the BC1F2 population, a major-effect and stable QTL \u003cem\u003eqGL6.1\u003c/em\u003e was fine mapped within 20.117 kb physical interval on chromosome 6. One candidate gene \u003cem\u003eLOC_Os06g43304.1\u003c/em\u003e encoding cytochrome P450 (CYP71D55) was finally selected based on the difference in the transcriptional expression. The cloning and genetic mechanism study of the \u003cem\u003eqGL6.1\u003c/em\u003e will facilitate improving appearance quality in \u003cem\u003ejaponica\u003c/em\u003e, especially in erect panicle varieties.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003ch2\u003ePlant materials\u003c/h2\u003e\n\u003cp\u003eThe parental lines in this study were the long grains \u003cem\u003ejaponica\u003c/em\u003e variety LX1 and short grains \u003cem\u003ejaponica\u003c/em\u003e variety GY8. The mapping population consisted of 197 F\u003csub\u003e6:9\u0026nbsp;\u003c/sub\u003eand F\u003csub\u003e6:10\u0026nbsp;\u003c/sub\u003eRILs developed from a cross between GY8 \u0026times; LX1.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eField trials and trait measurement\u003c/h2\u003e\n\u003cp\u003eAll 197 RILs and parents were grown at\u0026nbsp;Donggang (39\u0026deg;87\u0026prime; N, 124\u0026deg;15\u0026prime; E), Shenyang (41\u0026deg;48\u0026prime; N, 123\u0026deg;25\u0026prime; E) and Panjin (41\u0026deg;12\u0026prime; N, 122\u0026deg;07\u0026prime; E) in Liaoning Province in 2018 and 2019\u0026nbsp;cropping seasons (hereafter referred as 2018DG and 2019DG, 2019SY, and 2019PJ) for evaluation of GL. Each RIL and parents were sown in a seedling nursery on April and with one seedling being transplanted per hill on May 24~30. A completely random block design was repeated three times in each environment. Each plot area was 2.5 m\u003csup\u003e2\u003c/sup\u003e, and the seedlings were transplanted at a spacing of 30 cm between rows and 13 cm between plants.\u0026nbsp;Protection lines were set up everywhere, and field management followed local standards of production for rice.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt maturity, the panicles of 10 plants of each line, and their parents were harvested from each plot. The distribution of primary branch number per panicle was firstly measured for each plot. Ten panicles with the largest distribution proportion of primary branch number in each plot were selected to collect the fully filled grains. After the grains were dried for 72 h at 50℃, the lengths of twenty randomly chosen grains from each plot were estimated as the lengthwise distance between opposite tips using a\u0026nbsp;vernier caliper, and the values were averaged and used as the measurements for the line. In 2018DG, the GL of only 142 RILs were measured because of serious lodging of some lines, while that of all RILs were measured in the other three environments.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003ePhenotypic data analyses\u003c/h2\u003e\n\u003cp\u003eThe phenotypic values of RILs in each environment were analyzed. SPSS 19.0 was used for correlation analysis, and AOV function in QTL IciMapping (version 4.2) was used for variance analysis, with default parameters. The information in the ANOVA table was used to calculate broad sense heritability (H2) of each trait: H\u003csup\u003e2\u003c/sup\u003e = \u0026sigma;g\u003csup\u003e2\u003c/sup\u003e/(\u0026sigma;\u003csub\u003eg\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e +\u0026sigma;\u003csub\u003ege\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e /e + \u0026sigma;\u003csub\u003e\u0026epsilon;\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e/re), where \u0026sigma;\u003csub\u003eg\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e is (MS\u003csub\u003ef\u003c/sub\u003e \u0026minus; MS\u003csub\u003efe\u003c/sub\u003e)/re, \u0026sigma;g\u003csub\u003ee\u003c/sub\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eis (MS\u003csub\u003efe\u003c/sub\u003e \u0026minus; MS\u003csub\u003ee\u003c/sub\u003e)/r and \u0026sigma;\u003csub\u003e\u0026epsilon;\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e is MS\u003csub\u003ee\u003c/sub\u003e; \u0026sigma;\u003csub\u003eg\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e = genetic variance, \u0026sigma;\u003csub\u003ege\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e = genotype \u0026times; environment interaction variance, \u0026sigma;\u003csub\u003e\u0026epsilon;\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e= error variance, MS\u003csub\u003ef\u0026nbsp;\u003c/sub\u003e= mean square of genotypes, MS\u003csub\u003efe\u003c/sub\u003e = mean square of genotype \u0026times; environment interaction, MS\u003csub\u003ee\u003c/sub\u003e = mean square of error, r = number of replications, and e = number of environments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe best linear unbiased prediction (BLUP) of each parameter is calculated in the mixed linear model by using the lmer function in package lme4 of R software (Bates et al. 2012)\u0026nbsp;(http://www.R-project.org/). Genotype, environment, genotype \u0026times; environment interaction, and nested repeats in the environment are all considered random factors.\u003c/p\u003e\n\u003ch2\u003eGenotyping\u003c/h2\u003e\n\u003cp\u003eGenomic DNA was extracted from the fresh leaves of each line using the cetyl trimethyl ammonium bromide method (Murray and Thompson 1980). The whole genome resequencing of parents GY8 and LX1 was conducted by Biomarker Technologies. Each RIL (n = 197 genotypes) along with their parents were genotyped using the 8 K-SNP chip by Beijing Golden Marker Biotechnology Co., Ltd (Beijing, China). The SNP markers were screened as follows: ambiguous SNP calling, simplex and poor quality SNP loci with \u0026gt;10% missing values, or minor allele frequencies \u0026lt; 0.05 were removed in further analysis.\u003c/p\u003e\n\u003ch2\u003eLinkage mapping and QTL analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAfter removing the redundant markers, QTL IciMapping 4.2 software was used to generate an input file for genetic map construction\u0026nbsp;(Meng et al. 2015). In this genetic linkage maps, a total of 559 SNPs covering the 12 chromosomes were polymorphic between Gangyuan8 and Liaoxing1\u0026nbsp;(Zhao et al. 2020).\u0026nbsp;In an individual environmental analysis, the inclusive composite interval mapping (ICIM) method was conducted using the BIP function to detect the additive QTL for phenotypic value and BLUP value of panicle traits (Li et al. 2007). A 1,000-permutation test was conducted at a 95% confidence level, and an LOD threshold of 2.5 was used. In addition, the multi-environmental joint analysis utilized the ICIM method in the MET functional module to detect the QTL additive and QTL\u0026times; environment interaction effects\u0026nbsp;(Li et al. 2015).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQTL were named using the abbreviated English name of trait with \u0026ldquo;q\u0026rdquo; before it, followed by the chromosome number or chromosome number plus an ordered number designating one of multiple QTL in a single chromosome. The QTL detected in this study were compared to the Q-TARO database\u0026nbsp;(Yonemaru et al. 2010).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFine-mapping by using BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e population\u003c/h2\u003e\n\u003cp\u003eAfter phenotypic and genotypic data analysis, one line with long grains, carrying the LX1 allele in major-effect QTL\u003cem\u003e\u0026nbsp;\u003c/em\u003efor GL but having similar genetic background to GY8, was selected in RIL population. For QTL fine-mapping, this line was backcrossed with GY8 to generate the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e population. A total of 2, 487 BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eplants were grown at SY in 2021 cropping seasons for evaluation of GL. DNA was isolated from the two parental lines and the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e population. According to the SNPs in the QTL region obtained from deep re-sequencing of two parental lines, the informative KASP markers (Table S3)\u0026nbsp;were developed to genotype each plant of the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e population. KASP assays were conducted in a 1536-well plate format using the protocol of LGC Genomics (LGC, Middlesex, UK). The Synergy H1 fullfunction microplate reader (FLUO star Omega, BMG Labtech, Germany) was used to read the fluorescence signal. Then the linkage relationship between markers and the \u003cem\u003eqGL6.1\u003c/em\u003e locus was analysed for fine-mapping.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eRNA isolation and qPCR analysis\u003c/h2\u003e\n\u003cp\u003eTo identify the expression of candidate genes, at the stage of primary and secondary rachis branch formation, the inflorescence meristem of R176 and GY8 were sampled for RNA extraction and qPCR analysis. Total RNA was extracted with TriZol reagent (Invitrogen, Germany) according to the manufacturer\u0026rsquo;s instructions. The first strand of cDNAs were synthesized from DNaseI-treated total RNA using a Primer Script RT reagent Kit with gDNA Eraser (Takara, Japan). Reverse-transcribed RNA was used as PCR template for gene-specific primers (Table S4). Each reaction contained 3.0\u0026nbsp;\u0026mu;l of first-strand cDNAs, 2\u0026nbsp;\u0026mu;l of 200\u0026nbsp;\u0026mu;M\u0026nbsp;gene-specific primers, and 12.5\u0026nbsp;\u0026mu;l of 2\u0026times;SYBR Green Master Mix reagent (Applied Biosystems) in a final volume of 25\u0026nbsp;\u0026mu;l. The qPCR analysis was performed in a real-time PCR system (BIO-RAD). The qRT-PCR analysis was performed for each cDNA sample with four replications. Relative expression levels were calculated by 2\u003csup\u003e-\u003c/sup\u003e\u003csup\u003e△△\u003c/sup\u003e\u003csup\u003eCT\u0026nbsp;\u003c/sup\u003e(Livak and Schmittgen 2001).\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eQTL: Quantitative trait loci; GL: Grain length; RILs: Recombined inbred lines; KASP: Kompetitive Allele Specific PCR; DG, Donggang; SY, Shenyang; PJ, Panjin\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW. Zheng and J. Liu initiated the research. M. Zhao designed and conducted all the experiments. Y. Wang, N. He and X. Pang analyzed genotype of RILs. L. Wang, Z. Tang, L. Zhang, C. Wang, Z. Ma and H. Gao, and L. Fu conducted the field experiments and measured the phenotype. M. Zhao prepared the manuscript. \u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the National Natural Science Foundation of China (31901526), Doctoral Research Foundation of Liaoning Province (2020-BS-299), and China Postdoctoral Science Foundation Grant (2019M651139).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are included within the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study complied with the ethical standards of China, where this research work was carried out.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors are consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdamski NM, Anastasiou E, Eriksson S, O'Neill CM, Lenhard M \u003cem\u003e(\u003c/em\u003e2009\u003cem\u003e) Local maternal control of seed size by KLUH/CYP78A5-dependent growth signaling\u003c/em\u003e. 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PloS ONE \u003cem\u003e10\u003c/em\u003e: e0126154\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao M, Zhao M, Gu S, Sun J, Ma Z, Wang L, Zheng W, Xu Z \u003cem\u003e(\u003c/em\u003e2019\u003cem\u003e) DEP1 is involved in regulating the carbon-nitrogen metabolic balance to affect grain yield and quality in rice (Oriza sativa L.\u003c/em\u003e) PloS ONE \u003cem\u003e14\u003c/em\u003e: e0213504\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"rice","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rice","sideBox":"Learn more about [Rice](http://thericejournal.springeropen.com)","snPcode":"12284","submissionUrl":"https://submission.nature.com/new-submission/12284/3","title":"Rice","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rice, Grain length, QTLs, Fine mapping, Candidate genes","lastPublishedDoi":"10.21203/rs.3.rs-1371305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1371305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGrain length (GL) that is directly associated with appearance quality is a key target of selection in rice breeding. Although abundant quantitative trait locus (QTL) associated with GL have been identified, it was still relatively weak to identify QTL for GL from \u003cem\u003ejaponica\u003c/em\u003e genetic background, as the shortage of \u003cem\u003ejaponica\u003c/em\u003e germplasms with long grains. We performed QTLs analysis for GL using a recombinant inbred lines (RILs) population derived from the cross between \u003cem\u003ejaponica\u003c/em\u003e variety GY8 (short grains) and LX1 (long grains) in four environments.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 197 RILs were genotyped with 285 polymorphic SNP markers. Three QTLs \u003cem\u003eqGL5.3\u003c/em\u003e, \u003cem\u003eqGL6.1\u003c/em\u003e and \u003cem\u003eqGL11\u003c/em\u003e were detected to control GL by individual environmental analyses and multi-environment joint analysis. Of these, a major-effect and stable QTL \u003cem\u003eqGL6.1\u003c/em\u003e was identified to be a novel QTL, and its LX1 allele had a positive effect on GL. For fine-mapping \u003cem\u003eqGL6.1\u003c/em\u003e, a BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e population consisting of 2,487 individuals was developed from a backcross between GY8 and R176, one line with long grain. Eight key informative recombinants were identified by nine kompetitive allele specific PCR (KASP) markers. By analyzing key recombinants, the \u003cem\u003eqGL6.1\u003c/em\u003e locus was narrowed down to a 20.117 kb genomic interval on chromosome 6. One candidate gene \u003cem\u003eLOC_Os06g43304.1\u003c/em\u003e encoding cytochrome P450 (CYP71D55) was finally selected based on the difference in the transcriptional expression and variations in its upstream and downstream region.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThree QTLs \u003cem\u003eqGL5.3\u003c/em\u003e, \u003cem\u003eqGL6.1\u003c/em\u003e and \u003cem\u003eqGL11\u003c/em\u003e were identified to control grain length in rice. One novel QTL \u003cem\u003eqGL6.1\u003c/em\u003e was fine mapped within 20.117 kb region, and \u003cem\u003eLOC_Os06g43304.1\u003c/em\u003e encoding cytochrome P450 (CYP71D55) may be its candidate gene. We propose that the further cloning of the \u003cem\u003eqGL6.1\u003c/em\u003e will facilitate improving appearance quality in \u003cem\u003ejaponica\u003c/em\u003e varieties.\u003c/p\u003e","manuscriptTitle":"QTL Detection for Rice Grain Length and Fine Mapping of a Novel Locus qGL6.1","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-02-23 15:24:28","doi":"10.21203/rs.3.rs-1371305/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-05-29T10:13:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-05-29T10:08:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-05-25T23:52:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"edecf884-4cdd-43d0-9ed0-9d60ffbccfd9","date":"2022-05-16T08:53:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-05-15T10:05:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-02-21T03:07:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-02-21T03:07:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Rice","date":"2022-02-18T02:40:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"rice","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rice","sideBox":"Learn more about [Rice](http://thericejournal.springeropen.com)","snPcode":"12284","submissionUrl":"https://submission.nature.com/new-submission/12284/3","title":"Rice","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"db82b47d-a148-40ab-9939-906555f95b79","owner":[],"postedDate":"February 23rd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-11-21T05:44:16+00:00","versionOfRecord":[],"versionCreatedAt":"2022-02-23 15:24:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1371305","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1371305","identity":"rs-1371305","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}