Thousand-grain weight (TGW) and spikelets per panicle (SPP) directly contributed to rice yield. Heading date (HD) and plant height (PH) also greatly influence the yield. Dissection of genetic bases of yield related traits would provide tools for yield improvement. In this study, SPP, TGW, HD, PH were analyzed for QTL mapping using recombinant inbred lines (RIL) F7:8 derived from a cross between two diverse cultivars, Nanyangzhan and Chuan7. A positive correlation was identified between PH and yield whereas it was opposite between HD and yield. In total, 20 Quantitive trait loci (QTLs) were identified in this study. They were individually located to 11 chromosomes except on chromosome 4. 7 and 5 QTLs were detected in TGW and SPP, respectively. 4 QTLs were identified for both HD and PH. About half of QTLs were detected in both years. Totally, Six QTLs were firstly mapped in this study. Two QTL clusters were identified in the region flanked by RM22065-RM5720 on chromosome 7 and by RM502-RM264 on chromosome 8, respectively. The parent, Nanyangzhan with heavy TGW, carried alleles with increased effects at all 7 detected TGW QTL. In contrast, Chuan7 with more SPP carries positive alleles at all detected SPP QTL except qspp5. Especially the two yield related QTL cluster on chromosome 7 and 8, were worth of further research by map-based cloning strategy and utilization in molecular assistant selection breeding.
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Key word rice; correlation, QTL cluster; yield
Rice is a staple food for most of the people in the world. With the increasing of the world population, Rice yield is still a hot topic in rice breeding despite of the increasing grain yield after green revolution. Grain weight (GW), grain number per panicle (GPP) and panicle number per plant are thought to be three components of it. However, GW and GPP were often focused in the past QTL mapping studies due to their high heritability. Of them, Numerous QTLs were located in last decades (http://www.gramene.org/qtl/index.html). Some major QTLs of them were fine mapped and cloned in last several years. For instances, GS3, GW2, and qSW5/GW5 were cloned on the basis of QTL mapping (Fan et al., 2006; Song et al., 2007; Shomura et al., 2008; Weng et al., 2008). Gn1a, for GPP, was also isolated by map-based cloning strategy (Ashikari et al., 2005). Recently, Huang et al. (2009) reported DEP1 conducts not only erect panicle but also grain number per panicle. On the other hand, Plant height (PH) and heading date (HD) were the two traits, which indirectly affect rice grain yield. The semidwarf gene sd1 bring the first green revolution of grain yield (Spielmeyer et al., 2002). Kojima et al. (2002) found the key HD gene Hd3a that promotes flowering in rice. Besides, it was recently reported that Ghd7 has effects not only on HD and PH but also on rice yield (Xue et al., 2008). Up to date, the above major QTLs were isolated and cloned one after another. In addition, a few yield QTLs were fine mapped, such as gw8.1 and gw9.1 (Xie et al., 2006, 2008), GPP1, gpa7 and SPP3b/TGW3b (Liu et al., 2009; Tian et al., 2006 Liu et al., 2009), and so on. However, they did not completely explain the overall variance of phenotype in rice. Hence, it is necessary and possible to detect a few new yield or yield associated character QTLs/genes which might be covered by the complex genetic background.
It is important to construct a population for QTL mapping. A variety of populations were used for mapping in past years, such as, F2, DH, RIL and BC populations. However, most researchers prefer to use RILs populations for mapping QTLs owing to its repetition in temporal and spatial. Up to date, a larger number of QTLs for grain yield components were mapped using RILs. However, it was used to construct populations that derived from crossing between japonica and indica in past studies, in which most major QTLs were easily identified. However, some rice varieties among intraspecies also showed remarkable difference in grain size/shape and/or grain number, such as two indica cultivable rice, Nanyangzhan with a bigger size grain and less grains per panicle, but Chuan7 with the opposite ones. Hence, in view of limitation of traditional crossing combinations (japonica-indica), it is potential to explore more QTLs using the population that derived from crossing between japonica and japonica or indica and indica, especially there are some big difference between them in either genetic background or/and phenotype. To our present knowledge, it is advantageous to construct a mapping population using two parents which display a much more polymorphism in genome. Thus, more QTLs and/or genes were likely to be discovered attributing to diverse alleles between the parents genome. Hence, it is necessary and feasible to develop some characteristic population in the process of QTL mapping. In this study, a RIL population derived from a cross between two rice cultivars, Nanyangzhan and Chuan7, were used (1) to detect QTL for PH, HD, SPP and GW, (2) to evaluate the power of QTL detection for yield components as compared with other reports, (3) to identify some QTL hotspots for fine mapping and cloning in further study.
Materials and methods
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Mapping population and field experiment
Mapping populations consist of 185 recombinant inbred lines (RILs) derived from a cross between two indica rice Nanyangzhan and Chuan7 by single-seed descent. The 1000-grain weight is 43.3g in Nanyangzhan but 11.4g in Chuan7. The RIL F7 and F8 were planted in a bird-net-equipped field of the experimental farm of Huazhong Agricultural University in 2006 and 2007 rice growing seasons in Wuhan, China. Field trials were carried out following the randomized complete block design with two replications within each year. One month later from sowing, 10 seedlings of each line were transplanted into one row in the main field and arranged in a 16.5 cm - 26.4 cm lattice design. 8 plants in the middle of each row were harvested individually to score the traits.
The HD was determined from the date of sowing seed to first panicle emergence of plant. After 20 days of complete heading, PH was taken using a long ruler from bottom to the highest panicle of each plant, individually. The plants were individually harvested, whose panicles were packed in small nylon net bags, and dried in the sunlight for one month and collected the phenotype data for panicle number, grain number and grain weight. The Electronic Weighing machine was used to take the grain weight while the panicle number, grain number and no filling spikelet were counted by workers manually.
DNA makers and linkage map construction
Fresh leaves of 185 lines in RIL (F7) were collected individually for DNA extraction. DNA was extracted using a micro-isolation method as described by Cho et al. (1996) with minor modifications. Polymorphic markers between the parents Nanyangzhan and Chuan7 were used for genotyping the population and the genetic linkage map was constructed by using MAPMAKER/EXP version 3.0b (Lander et al., 1987).
QTL analysis in RILs was carried out using composite interval mapping (CIM) performed by the computer program Windows QTL cartographer 2.5 (Wang et al., 2007). Window size was set at 10 cM, stepwise regression analysis was used to detect cofactors. QTL main effects were estimated using the maximum-likelihood estimation method. LOD threshold at the experiment-wise significance level of 0.05 was determined by computing 1, 000 permutations of each morphological character, and the LOD threshold ranged from 2.9-3.3. Heritability for the traits GL, GW and GT was calculated based on the experiments using the formula: , where , and were the estimates of genetic.
Phenotypic variation of the RIL population
A highly significant difference was observed in three of 5 investigated traits except for PH and yield between the two parents Nanyangzhan and Chuan7 (Table 1). Nanyangzhan has a typical large grain size of over 43.3g per thousand grains and small panicle of 91 spikelets per panicle with an earlier heading date. On the contrary, Chuan7 is a variety with extremely small grain size of 11.4g per thousand grains and big panicle of 208 spikelets per panicle with a late heading date; whereas, there were little difference in plant height and yield between them. However, variation range of PH in RILs population was from 63.0 to 189.0 and 84.1 to 170.4 in 2006 and 2007, respectively. The similar situation was happened in yield. Meanwhile, taking two years data into consideration, 4 of five traits exhibited broad distribution in the RILs. Especially, the maximum of SPP was 3 times more than its minimum in both years. Furthermore, yield has the vastest variance in the population, the margin between maximum and minimum of it was more than 27g was observed. In addition, the means of TGW in the RILs were 20.0 and 18.9 in 2006 and 2007, respectively. On the other hand, transgressive segregation was observed for HD, PH, SPP and yield except TGW (Table 1).
Heritability and correlation
HD, PH, SPP and TGW showed higher broad heritability ranged from 79.4% to 90.0%, whereas the heritability of yield was only 33.5% (Table 1). PH was highly significant positive correlation with TGW and yield in two years; however, the TGW, SPP and yield were negative correlation with HD, respectively. Furthermore, SPP and TGW were positive correlation with yield, but a negative correlation was detected between SPP and TGW in 2006 and 2007 (Table 3).
Genetic linkage map
The genetic linkage map consisted of 164 of 262 polymorphic markers, which was obtained by screening a total of 502 SSR markers between the parents Nanyangzhan and Chuan7. 1635.9 cM genome region was harbored by this genetic linkage map and its average distance between adjacent markers was of 9.9 cM (Figure 1).
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QTL analysis for four traits
Totally, four HD QTLs were located on chromosome 3, 5, 6, and 11, respectively. Of them, qhd3 and qhd6 were detected repeatedly between 2006 and 2007. In addition, qhd5 and qhd11were identified for one year in 2007 and 2006, respectively. On the other hand, LOD scores and explained phenotypic variance values of four QTLs ranged from 3.7 to 11.2 and 7.6% to 27.7%, respectively. qhd6 was one of the major QTL for HD explaining up to 27% phenotype variance, Chuan7 allele with an increasing trait value. On the contrary, qhd5 and qhd11 showed an increasing effect for HD by Nanyangzhan allele (Figure 1 and Table 2).
A total of four PH QTLs were identified in this mapping population. Two major QTLs, , qph1 and qph8, were detected in both years and located on chromosome 1 and 8, respectively. Whereas, qph6 and qph12 were only identified in 2006, and they have nearly the same additive effect score and explained approximately equal phonotype variance. The LOD scores of the two major QTLs were more than 10 and explained over 24% of the phonotype variance. Compared with them, qph6 and qph12 were two minor QTLs with LOD scores less than 4 and explained phonotype variance below 6% (Table 2).
Spikelets per panicle
Five SPP QTLs were identified in this study on chromosome 3, 5, 7, 8 and 10, respectively. In 2006, only qspp5 was detected and Nanyangzhan allele increased the trait value. On the contrary, the other four QTLs, Chuan7 allele increased trait value, were just detected in 2007. Among them, LOD scores and explained phonotype variance ranged from 3.0 to 5.3 and 6.6% to 14.2%, respectively.
Thousand grain weight
Seven TGW QTLs were totally detected in this QTL mapping. Of them, qtgw3a was a major QTL located on the centromere of chromosome 3 in both years. It's LOD scores was more than 15 and explained over 24% of the phenotype variance. In addition, qtgw2, qtgw7 and qtgw9 were stably identified in two years, although their LOD scores was less than 6.0 and explained below 11.0% of the phenotype variance. On the other hand, qtgw3b, qtgw5 and qtgw8, whose LOD scores and explained phonotype variance ranged from 3.9 to 6.8 and 6.0 to 12.6, respectively, were only identified in 2007. For all the QTLs detected here, Nanyangzhan allele positively affected the trait value.
QTL detection does not depend entirely on the size of its effect
A total of 20 QTLs were identified in this study. Of them, 8 QTLs were commonly detected in both years. Additional three QTLs could also be detected in two years if the LOD threshold decreased to 2.0 (empirical threshold in rice (Vanooijen 1999)). To our noticeable, the major QTL for the three traits were repeatedly detected in both years. Besides, some of minor TGW QTLs, Such as qtgw7 and qtgw9, were also identified in both years, they might be mainly conducted by grain length or/and width which are the high broad heritability traits. To our surprised, 4 minor QTLs for yield were also repeatedly observed with a LOD score ranged from 0.7 to 1.8 (data not shown) in both years. Their loci were overlapped with the qph1, qtgw5, qph8 and qtgw9, respectively. It suggested that these loci have an effect in yield. In contrast, some QTLs were only identified in single year, such as qhd5, qph6, qspp10 and so on. Especially, qtgw5, one of the major TGW QTL, was detected in this study only in 2007, which was possibly affected by the difference of filling rate in grain development process.
Powerful QTL detection compared with previous studies
In all, six of 20 QTLs were firstly detected in this study (Table 2), despite most QTLs overlapped with those of several reports in past. The major HD QTL, qhd6, was at the same position with Hd3a mapped by Monna et al. (2002). In the region of RM3513-RM347 on the long arm end of chromosome 3, qhd3 was corresponding to the locus of Hd6 mapped by Yamamoto et al. (2000) and cloned by Takahashi et al. (2001). It was also reported that Hd6 was involved in photoperiod sensitivity and encodes a α subunit of protein kinase CK2. In the photoperiodic pathway of rice flowering, especially, Hd3a play an important role in transition from vegetative phase to reproductive phase (Tamaki et al., 2007). They were easily detected in different populations, as their conservative functions in rice flowering were easily affected by any mutant happened in rice genome evolution process. On the other hand, interestingly, qhd5 and qhd11 were detected in this population, but they were not found by Yano et al. (2001) using the combination of Nipponbare (japonica) and Kasalath (indica) where 14 HD QTLs were identified. However, qhd5 (RM509-RM430) was like to be qDTH-5 detected from a BC2F2 population (Milyang 23/O. rufipogon//Milyang 23) (Cho et al., 2003). Besides, another similar population was developed and identified dth11.1 in the region RM167-RM209, where was close to the qhd11 (RM209-RM229) detected in this study (Septiningsih et al., 2003). To our noticeable, one of the parents used for mapping qDTH-5 and dth11.1 was wild rice. Hence, it is an apparent approach to identify some specific QTLs using the population derived from a cross between cultivar rice and wild rice. Besides, qph1 was detected on the long arm end of chromosome 1 where was very near to the sd1 locus (Monna et al., 2002; Spielmeyer et al., 2002). It was also identified for many times in different primary mapping populations (Cho et al., 1994; Huang et al., 1996; Zhuang et al., 1997; Xiao et al., 1998; Moncada et al., 2001 and Thomson et al., 2003). qph6 and qph12 were similar to cl6 and cl12 reported by Rahman et al. (2007), respectively. However, qph8 was firstly detected in this population, which had a nearly equivalent effect with qph1. It was reconfirmed that the characteristic populations were costly proposed to mapping more new QTLs. In addition, qtgw3a and qtgw5 were also frequently identified by previous researchers in different populations (Ma et al., 2004; Xing et al., 2002; Cui et al., 2003; Lin and Wu 2003; Thomson et al., 2003). The LOD score of qtgw5 was 6.8 only less than that of qtgw3a and explained up to 12.6% variance of phonotype, although it was only detected in single year 2007. These two QTLs were the same loci of GS3 and GW5 which were the important gene for grain length and grain width in rice, respectively. Especially, it was reported that grain length in different varieties could be classed into two groups by a SNP in GS3 (Fan et al., 2009). That is why qtgw3a was easily identified in most populations which derived from two parents with a different apparent grain size. Taken above together into consideration, it eventually concluded that one population can't map the whole QTLs in rice and different populations have different advantages. Thus, the character population was needed to propose for mapping additional QTL in future.
Correlation analysis among yield and PH and HD
It is acceptable that PH has a positive correlation with yield and its some component factors, as each part of the plant is in proportion to grow up. A positive correlation was observed between PH and yield, TGW and SPP in this study. On the other hand, a negative correlation was detected in two years between the three traits and HD, respectively. The similar results were also reported by Rahman et al. (2007). It suggested that there is enough more time for sink organs to initiate and develop by shortening vegetative phase. In contrast, two pleiotropic QTLs were recently reported, which usually positively controlled PH and panicle size or yield, but give rise to a late heading simultaneously (Zhang et al., 2006; Xue et al., 2008). To our noticeable, the two major HD QTLs identified in this study were probably Hd3a and Hd6, respectively. It is reported that they affect HD in different day length condition, whereas no any yield information was mentioned in those reported (Monna et al., 2002; Takahashi et al., 2001). However, according to present results, it is implied that these two QTLs probably indirectly function in rice yield to some extent.
Genetic dissection of the QTL clusters
In contrast to qph1, the other major PH QTL, qph8, located on long arm end of chromosome 8, whose allele from Nanyangzhan increased the phenotype value. Interestingly, the additive effect score of qph8 was closed to that of qph1 (sd1) but opposite in additive direction, furthermore qph8 still explained more than 24.0% of phenotype variance though qph1 explained over 26.4% (Table 2). On the other hand, there were two minor QTLs qspp8 and qtgw8 in the region between RM502 and RM264. It was apparently questioned weather qph8 has a major effect on PH with a minor effects on SPP and TGW or three QTLs sharing near by locus. In previous report, some QTLs showed a pleiotropic effects, such as cloned gene GS3 which is a major gene for grain length/weight with a minor effect in grain width and thickness, and Ghd7 showed a pleiotropic effects in PH, HD, and SPP (Fan et al., 2006; Xue et al., 2008). In addition, Zhang et al. (2006) reported that one QTL was mapped to the short arm of chromosome 8 that conducted simultaneously four traits (SPP, GPP, HD and PH). In addition, Xie et al (2008) reported that a yield-enhancing QTL cluster was maped to 37.4Kb on chromosome 9, but whether more traits are controlled by single gene or different QTLs is still not clear. Interestingly, in the region RM22065-RM5720 where there also were two QTLs, which were qtgw7 and qspp7 had been located simultaneously on the same region. The NILs for these two new QTLs cluster had already been developed. The detail results will be further reported in future.
In conclusion, novel QTLs identified in this study, especially the two QTLs hotspot located respectively on the long arm end of chromosome 7, 8 respectively and a TWG QTL qtgw9 could be further fine mapped and cloned in future. Besides, the QTLs overlapped with previous reports were again located in the present study could prove their authenticity and dependability. On the other hand, these results made a good genetic basis to utilize characteristic alleles for rice improvement by MAS breeding.