ISSN : 2287-5174(Online)
DOI : https://doi.org/10.9787/KJBS.2012.44.4.462
Fine Mapping of a Quantitative Trait Loci Controlling the Number of Spikelets per Panicle in Rice
Rice (Oryza sativa L.) grain yield is determined by three yield components, panicles per plant, spikelets per panicle and grain weight. Of these grain yield components in rice, spikelets per panicle displayed a largest range of variation among the rice accessions and was the major objective of high yield breeding (Li et al. 1998; Yamagishi et al. 2002). Genetic analysis of spikelets per panicle is difficult because it is a complex trait that is controlled by multiple genes and it is also influenced by the environment. However, the advent of molecular maps in rice (Causse et al. 1994; Cho et al. 1998) and quantitative trait locus (QTL) analysis approaches have facilitated the analysis of quantitative traits.
Many different genes/QTLs for spikelets per panicle have been reported in populations derived from inter-specific crosses (Xiao et al. 1996; Xiong et al. 1999; Thomson et al. 2003; Suh et al. 2005; Li et al. 2006 and Lihn et al. 2008), indica-indica crosses (Lin et al. 1996; Zhuang et al. 1997) indica-japonica inter-subspecific crosses (Yamagishi et al. 2002; Ando et al. 2008). These QTLs detected were distributed throughout all rice chromosomes and created a firm basis to investigate the genetic control of spikelets per panicle.
So far, a few QTLs associated with spikelets per panicle have been isolated, including Gn1a, APO1, DEP1, and OsSPL14. Gn1a controlling grain productivity in rice, was found to encode cytokinin oxidase/dehydrogenase (OsCKX2), an enzyme that degrades the phytohormone cytokinin (Ashikari et al. 2005). Higher expression of OsSPL14 in the reproductive stage promotes panicle branching and higher grain yield in rice (Miura et al. 2010). Another gene, DEP1, which alters panicle architecture, thereby increasing grain yield, has been identified (Huang et al. 2009). Another approach to detect the genes related to panicle architecture is to identify them through the analysis of mutants that have changed the panicle structure. Characterization of rice aberrant panicle organization 1 (apo1) mutant revealed that APO1 positively controls spikelet number by suppressing the precocious conversion of inflorescence meristems to spikelet meristems (Ikeda et al. 2007). Ookawa et al. (2010) also reported that a near isogenic line carrying APO1 showed enhanced culm strength and increased spikelets per panicle.
In previous studies, we developed advanced backcross lines derived from a cross between Ilpumbyeo as a recurrent parent and Moroberekan as a donor parent (Ju et al. 2008). IL28, one of the 117 introgression lines showed significantly higher SPP than the recurrent parent Ilpumbyeo. In the current study, we performed QTL analysis to verify and fine map the QTL for spikelets per panicle responsible for this variation using an F2:3 population derived from a cross between Ilpumbyeo and the near isogenic line, IL28.
MATERIALS AND METHODS
In a previous report (Ju et al. 2008), QTL for spikelets per panicle, qSPP6 was detected near RM3430 and RM3307 on chromosome 6 using 117 BC3F5 lines derived from a cross between ‘Ilpumbyeo’ as a recurrent parent and ‘Moroberekan’ as a donor parent. Among the 117 introgression lines, IL28 showing higher number of spikelets per panicle than the recurrent parent and containing the Moroberekan chromosomal segment at qSPP6 region on chromosome 6 was selected for fine-mapping of qSPP6. IL28 was crossed to ‘Ilpumbyeo’, and the resulting F1 plants were self-pollinated to obtain an F2 population (234 plants in 2009, 1,150 plants in 2010), which was segregating in the target region on chromosome 6. To confirm the qSPP6 QTL, 234 F2 plants were genotyped with five SSR markers and evaluated for four yield-related traits. For substitution mapping of qSPP6, 1,150 F2 plants were genotyped with four additional markers (RM20512, RM20521, RM20652, RM20572). In total, 41 F2 plants with informative breakpoints in the region were selected and selfed to produce F3 population for phenotyping and substitution mapping of qSPP6.
Field trial and trait evaluation
243 F2 plants, 1150 F2 plants, and 41 F3 families and the parents were grown in a field during the summer of 2009, 2010, and 2011 at the experimental field, Chungnam National University, Daejeon, Korea. In 2009, the 243 plants and the parents were laid in a 30 x 15 cm distance and evaluated for five traits, panicle length, primary branch number, secondary branch number, spikelets per panicle, and fertility. For panicle length, primary branch number, secondary branch number, spikelets per panicle, and fertility, two biggest panicles per plant were measured and the average of the measurements was used as the phenotype of each plant. In 2010, 10 phenotypic traits, culm length (CL), tiller number (TN), panicle length (PL), first node length (FNL), second node length (SNL), first node width (FNW), second node width (SNW), primary branches number (PBN), secondary branch number (SBN), and spikelets per panicle (SPP) for 1150 F2 plants were measured. The experiment using 41 F3 lines derived from 41 F2 plants in 2011 followed a completely randomized block design with two replications, one row per plot, 25 plants per each row. The middle 10 plants from each line were chosen for the evaluation and average number of the measurements was used for the phenotype of each line for the twelve traits: CN, TN, PL, FNL, SNL, FNW, SNW, PBN, SBN, and SPP.
DNA extraction and SSR analysis
DNA was extracted from leaf tissue of the F2 population according to the chloroform-based DNA extraction protocol (Causse et al. 1994). A total volume of 20 uL reaction mixture was composed of the 5.0 uL (5 ng/uL) of template DNA, Taq polymerase 0.1 ul (5 Unit/uL), 0.8 ul dNTP (2.5 mM each), Forward + Reverse primer 1 ul (10pmol each), 2.0 ul 10x PCR buffer (10 mM Tris-HCl PH 8.3, 50 mM KCl, 1.5 mM MgCl2, 0.1% Gelatin), and 11.1 ul triple distilled water. Amplification was achieved using a Thermo Cycler (Bio-Rad) according to the step-cycle program set for denaturation at 94℃ for 5 min, subsequent denaturation was performed at 94℃ for 1 min, annealing at 55℃ for 1 min, extension at 72℃ for 1 min; steps 2 through 4 were repeated for a total of 35 cycles with a final extension step at 72℃ for 5 min. PCR products were run on 4% polyacrylamide denaturing gel for 1- 2 h at 1800-2000 V, and marker bands were revealed by the silver staining (Panaud et al. 1996). The orientations of the SSR markers were based on the SSR maps (McCouch et al. 2002).
Statistical analysis was done using the software Qgene version 2.30 for Macintosh (Nelson 1997) and SAS. Single point analysis (SPA) was performed to determine the effect of each marker on each trait. In SPA, QTL was declared if the phenotype was associated with a marker locus at P < 0.005 or with two adjacent marker loci at P < 0.01. The proportion of the total phenotypic variation explained by each QTL was calculated as an R2 value, from the regressions of each marker/phenotype combination. QTLs were fine mapped by comparing the phenotypic means of genotypic classes of recombinants within the target region by using the SAS statistical software package (SAS Institute).
RESULTS AND DISCUSSION
Characteristics of IL28
A total of 400 simple sequence repeat (SSR) markers of known chromosomal position were used to survey the parents for polymorphism (McCouch et al. 2002). IL28 was scanned using 134 markers showing polymorphisms between Ilpumbyeo and Moroberekan, and two Moroberekan introgression segments were detected on chromosomes 4 and 6 (Fig. 1). Phenotypic evaluation of agronomic traits for the introgression line, IL28 and the recurrent parent was conducted in 2008 and 2009. Comparison of 11 agro nomic traits between IL28 and Ilpumbyeo obtained in this study are shown in Table 1. The results indicated that there were significant differences (P<0.01) in panicle length (PN), secondary branch number (SBN), spikelets per panicle (SPP), first node width (FNW), and second node width (SNW) between IL28 and Ilpumbyeo, while no significant difference in days to heading (DTH), tiller number (TN), and culm length (CL) was observed.
Fig. 1. Graphical genotype of IL28.
Table 1. Comparison of eleven traits between Ilpumbyeo and the introgression line, IL28.
Trait variation of F2 population
Four traits in the 243 F2 plants displayed continuous and normal distributions (Fig. 2). The number of spikelets per panicle of Ilpumbyeo and IL28 were 145 and 188, respectively. In all traits measured, transgressive plants with higher or lower mean values than either parent were observed. This is partly because all traits are affected by the environment and cultivation methods. In addition, the possibility that small Moroberekan introgressed segments affecting these traits might have gone undetected in IL28 cannot be ruled out considering that several genomic regions with a low density of markers appeared as gaps on the map due to low polymorphism between the parents, Ilpumbyeo and Moroberekan (Ju et al. 2008). Correlation between spikelets per panicle and other traits, panicle length (r= 0.7132**), primary (r= 0.7674**) and secondary (r= 0.9250**) branch number was highly significant in the F2 population.
Fig. 2. Frequency distribution of four traits in the F2 population (P1: Ilpumbyeo, P2 : IL28).
The seven SSR markers around the target region were used to genotype the 234 F2 plants (Fig. 3). Three significant QTLs for panicle length, secondary branch number, and spikelets per panicle were detected and these QTLs were in the same region near the SSR marker RM3430 on chromosome 6. The additive effect of the Moroberekan alleles at this locus was 1.8 cm panicle length, 3.5 secondary branch number, and 11.5 spikelets per panicle (Table 2). QTLs for panicle length, secondary branch number, and spikelets per panicle explained 27.6%, 18.2%, and 18.0% of the phenotypic variance, respectively. The Moroberekan alleles increased spikelets per panicle, panicle length, and the number of secondary branch at this locus.
Fig. 3. A linkage map of chromosomes 6 showing introgression from Moroberekan and location of qSPP6 on chromosome 6 in IL28. Black region indicates introgressed segments, and an arrow shows the position of peak LOD score.
Table 2. QTLs detected for three traits on single-point analysis in the F2 population.
Substitution mapping of qSPP6
To confirm and narrow down the target region containing qSPP6, 1,120 F2 plants derived from a cross between Ilpumbye and IL28 were genotyped with four additional markers near the target region (RM20512, RM20521, RM20652, and RM20572). 41 plants with different recombination breakpoints in the target region were selected and selfed to produce F3 population. According to the marker genotypes, 41 F3 lines were divided into ten groups and used for substitution mapping of qSPP6 (Fig. 4). The mean phenotypic values of each trait for each F3 lines were compared to those of Ilpumbyeo and IL28 at the P<0.01 level. As indicated in the table in Fig. 4, SPP of group D was significantly different from that of groups C and J, suggesting that the qSPP6 allele was located between RM20521 and RM20572. Group D, E, F, G, H, and I with common introgression between RM3430–RM20562 showed significantly higher value than Ilpumbyeo in spikelets per panicle, whereas no significant differences were detected between Ilpumbyeo and group A, B, C, and J without introgression between RM3430-RM20562 in spikelets per panicle. This increase in spikelets per panicle by qspp6 was caused mainly by the increase in the panicle length and secondary branch number. These results suggest that qSPP6 was related to panicle structure and located between RM20521 and RM20572, which are 680-kb apart. The original target of this study was the SPP QTL, qSPP6, mapped on the long arm of chromosome 6. In the process of fine-mapping qSPP6, QTLs for four additional traits were consistently detected in the same region. In addition to qSPP6, QTL for PL, SBN, FNW and SNW were mapped in same region. A significant difference between six groups (D, E, F, G, H, and I) and four groups (A, B, C, and J) with/without the target region was observed in the phenotypic means of PL, SBN, FNW, and SNW. The colocalization of QTLs for SPP and node width in the same region suggests the pleiotropy of the gene and is consistent with that of the study by Ookawa et al. (2010) in which SCM2 not only enhanced culm strength but also increased the number of spikelets per panicle because of the pleiotropic effect of the gene. Terao et al. (2010) also reported that APO1 allele from ‘Habataki’ increased both the number of primary rachis branches and the number of grains per panicle leading to increase in grain yield. qSPP6 appears to be identical to APO1 or SCM2 based on the map position and the function, because node width is functionally related with culm strength. Additional experiments are underway to know the allelic relationship between qSPP6 and SCM2.
Fig. 4. Graphical representation of F3 lines and a fine scale map of the target region on chromosome 6. White and black portions of the graph are homozygous Ilpumbyeo, homozygous Moroberekan, respectively and dotted regions are where crossing-over occurred. The table to the right of the graphical genotypes indicates mean values of traits. Numbers followed by the same letter in each column are not significantly different at P=0.05 based on Duncan’s multiple range test. & No. in ( ) indicate the number of F3 lines. P1: Ilpumbyeo, P2: IL28.
In this research, Moroberekan alleles in the target region on chromosome 6 had a favorable effect on yield and lodging tolerance by increasing the number of spikelets per panicle (data not shown) whereas it had no negative effect on heading date and plant height. Thus, the qSPP6 gene would be valuable for improvement in rice yield.
In conclusion, we developed a high-density map of the qSPP6 QTL by using NILs from the Ilpumbyeo/ Moroberekan cross. Our data support the concept of using NILs for validating and dissecting QTLs into a single Mendelian factor and lay the foundation for map-based cloning of qSPP6.
This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD, Basic Research Promotion Fund) (KRF- 2010-0024118).
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